From c28c407fcd86613a68ac781b2ffda0cfebe33021 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 28 Feb 2024 18:41:35 +0000 Subject: [PATCH 01/43] first commit, lots of WIP needed --- README.md | 113 +- act/.gitignore | 140 + act/LICENSE | 21 + act/README.md | 89 + act/assets/bimanual_viperx_ee_insertion.xml | 59 + .../bimanual_viperx_ee_transfer_cube.xml | 48 + act/assets/bimanual_viperx_insertion.xml | 53 + act/assets/bimanual_viperx_transfer_cube.xml | 42 + act/assets/scene.xml | 38 + act/assets/tabletop.stl | Bin 0 -> 684 bytes act/assets/vx300s_10_custom_finger_left.stl | Bin 0 -> 83384 bytes act/assets/vx300s_10_custom_finger_right.stl | Bin 0 -> 83384 bytes act/assets/vx300s_10_gripper_finger.stl | Bin 0 -> 42884 bytes act/assets/vx300s_11_ar_tag.stl | Bin 0 -> 3884 bytes act/assets/vx300s_1_base.stl | Bin 0 -> 99984 bytes act/assets/vx300s_2_shoulder.stl | Bin 0 -> 63884 bytes act/assets/vx300s_3_upper_arm.stl | Bin 0 -> 102984 bytes 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+ .../scripts/config_gen/act_gen_no_define.py | 266 ++ robomimic/scripts/config_gen/bc_xfmr_gen.py | 174 +- robomimic/scripts/config_gen/diffusion_gen.py | 480 ++++ .../config_gen/diffusion_gen_no_define.py | 399 +++ robomimic/scripts/config_gen/helper.py | 617 +++- .../scripts/config_gen/r2d2_runs_bcxfmr.py | 456 +++ .../r2d2_runs_language_conditioned.py | 531 ++++ .../r2d2_runs_language_conditioned_rlds.py | 416 +++ robomimic/scripts/config_gen/voltron.py | 593 ++++ .../conversion/add_lang_to_converted_data.py | 97 + robomimic/scripts/conversion/convert_r2d2.py | 283 +- .../scripts/conversion/convert_robosuite.py | 54 +- .../scripts/conversion/extract_action_dict.py | 81 + .../conversion/get_eval_lang_annotations.py | 51 + .../get_lang_annotations_in_manifest.py | 48 + robomimic/scripts/conversion/oxe_to_h5.py | 136 + .../robosuite_add_absolute_actions.py | 298 ++ .../scripts/conversion/set_dataset_attr.py | 98 + robomimic/scripts/dataset_states_to_obs.py | 27 +- robomimic/scripts/filter_dataset_size.py | 81 + robomimic/scripts/playback_dataset.py | 6 +- robomimic/scripts/plot_model_predictions.py | 213 ++ robomimic/scripts/run_trained_agent.py | 3 +- robomimic/scripts/train.py | 282 +- robomimic/utils/action_utils.py | 51 + robomimic/utils/camera_utils.py | 2471 +++++++++++++++++ robomimic/utils/dataset.py | 507 +++- robomimic/utils/env_utils.py | 2 +- robomimic/utils/file_utils.py | 114 +- robomimic/utils/hyperparam_utils.py | 25 +- robomimic/utils/log_utils.py | 7 +- robomimic/utils/obs_utils.py | 105 +- robomimic/utils/rlds_utils.py | 89 + robomimic/utils/script_utils.py | 15 + robomimic/utils/tensor_utils.py | 7 +- robomimic/utils/torch_utils.py | 412 +++ robomimic/utils/train_utils.py | 83 +- robomimic/utils/vis_utils.py | 92 + runallruns.sh | 89 + setup.py | 3 + 111 files changed, 16304 insertions(+), 621 deletions(-) create mode 100644 act/.gitignore create mode 100644 act/LICENSE create mode 100644 act/README.md create mode 100644 act/assets/bimanual_viperx_ee_insertion.xml create mode 100644 act/assets/bimanual_viperx_ee_transfer_cube.xml create mode 100644 act/assets/bimanual_viperx_insertion.xml create mode 100644 act/assets/bimanual_viperx_transfer_cube.xml create mode 100644 act/assets/scene.xml create mode 100644 act/assets/tabletop.stl create mode 100644 act/assets/vx300s_10_custom_finger_left.stl create mode 100644 act/assets/vx300s_10_custom_finger_right.stl create mode 100644 act/assets/vx300s_10_gripper_finger.stl create mode 100644 act/assets/vx300s_11_ar_tag.stl create mode 100644 act/assets/vx300s_1_base.stl create mode 100644 act/assets/vx300s_2_shoulder.stl create mode 100644 act/assets/vx300s_3_upper_arm.stl create mode 100644 act/assets/vx300s_4_upper_forearm.stl create mode 100644 act/assets/vx300s_5_lower_forearm.stl create mode 100644 act/assets/vx300s_6_wrist.stl create mode 100644 act/assets/vx300s_7_gripper.stl create mode 100644 act/assets/vx300s_8_gripper_prop.stl create mode 100644 act/assets/vx300s_9_gripper_bar.stl create mode 100644 act/assets/vx300s_dependencies.xml create mode 100644 act/assets/vx300s_left.xml create mode 100644 act/assets/vx300s_right.xml create mode 100644 act/conda_env.yaml create mode 100644 act/constants.py create mode 100644 act/detr/LICENSE create mode 100644 act/detr/README.md create mode 100644 act/detr/main.py create mode 100644 act/detr/models/__init__.py create mode 100644 act/detr/models/backbone.py create mode 100644 act/detr/models/detr_vae.py create mode 100644 act/detr/models/position_encoding.py create mode 100644 act/detr/models/transformer.py create mode 100644 act/detr/setup.py create mode 100644 act/detr/util/__init__.py create mode 100644 act/detr/util/box_ops.py create mode 100644 act/detr/util/misc.py create mode 100644 act/detr/util/plot_utils.py create mode 100644 act/ee_sim_env.py create mode 100644 act/imitate_episodes.py create mode 100644 act/policy.py create mode 100644 act/record_sim_episodes.py create mode 100644 act/scripted_policy.py create mode 100644 act/sim_env.py create mode 100644 act/utils.py create mode 100644 act/visualize_episodes.py create mode 100644 oxe_conversion.sh create mode 100644 robomimic/algo/act.py create mode 100644 robomimic/algo/diffusion_policy.py create mode 100644 robomimic/config/act_config.py create mode 100644 robomimic/config/diffusion_policy_config.py create mode 100644 robomimic/exps/templates/act.json create mode 100644 robomimic/exps/templates/diffusion_policy.json create mode 100644 robomimic/scripts/config_gen/act_gen.py create mode 100644 robomimic/scripts/config_gen/act_gen_no_define.py create mode 100644 robomimic/scripts/config_gen/diffusion_gen.py create mode 100644 robomimic/scripts/config_gen/diffusion_gen_no_define.py create mode 100644 robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py create mode 100644 robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py create mode 100644 robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py create mode 100644 robomimic/scripts/config_gen/voltron.py create mode 100644 robomimic/scripts/conversion/add_lang_to_converted_data.py create mode 100644 robomimic/scripts/conversion/extract_action_dict.py create mode 100644 robomimic/scripts/conversion/get_eval_lang_annotations.py create mode 100644 robomimic/scripts/conversion/get_lang_annotations_in_manifest.py create mode 100644 robomimic/scripts/conversion/oxe_to_h5.py create mode 100644 robomimic/scripts/conversion/robosuite_add_absolute_actions.py create mode 100644 robomimic/scripts/conversion/set_dataset_attr.py create mode 100644 robomimic/scripts/filter_dataset_size.py create mode 100644 robomimic/scripts/plot_model_predictions.py create mode 100644 robomimic/utils/action_utils.py create mode 100644 robomimic/utils/camera_utils.py create mode 100644 robomimic/utils/rlds_utils.py create mode 100644 robomimic/utils/script_utils.py create mode 100644 runallruns.sh diff --git a/README.md b/README.md index c8e1b540..88a0a951 100644 --- a/README.md +++ b/README.md @@ -1,88 +1,59 @@ # robomimic -

- - - - - - - - -

- [**[Homepage]**](https://robomimic.github.io/)   [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html)   [**[Study Paper]**](https://arxiv.org/abs/2108.03298)   [**[Study Website]**](https://robomimic.github.io/study/)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative) ------- -## Latest Updates -- [05/23/2022] **v0.2.1**: Updated website and documentation to feature more tutorials :notebook_with_decorative_cover: -- [12/16/2021] **v0.2.0**: Modular observation modalities and encoders :wrench:, support for [MOMART](https://sites.google.com/view/il-for-mm/home) datasets :open_file_folder: [[release notes]](https://github.com/ARISE-Initiative/robomimic/releases/tag/v0.2.0) [[documentation]](https://robomimic.github.io/docs/v0.2/introduction/overview.html) -- [08/09/2021] **v0.1.0**: Initial code and paper release +## Installation +1. Clone the repo with the `--recurse-submodules` flag. +2. (if applicable) switch to `r2d2` branch +3. Run `pip install -e .` in `robomimic` +4. Run `pip install -e .` in `robomimic/act/detr` ------- +## Pre-processing datasets +Before training, you need to pre-process your datasets to ensure they're the correct format. +### r2d2 datasets +Convert the raw r2d2 data using this simple script: +``` +python robomimic/scripts/convertion/convert_r2d2.py --folder +``` +### robosuite datasets +1. Convert the raw robosuite dataset to robomimic format +``` +python robomimic/scripts/conversion/convert_robosuite.py --dataset --filter_num_demos +``` +`--filter_num_demos` corresponds to the number of demos to filter by. It's a list, eg. `10 30 50 100 200 500 1000` -## Colab quickstart -Get started with a quick colab notebook demo of robomimic with installing anything locally. - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1b62r_km9pP40fKF0cBdpdTO2P_2eIbC6?usp=sharing) +This script will extract absolute actions, extract the action dict, and add filter keys. +2. Extract image observations from robomimic dataset +``` +python robomimic/scripts/dataset_states_to_obs.py --camera_names robot0_agentview_left robot0_agentview_right robot0_eye_in_hand --compress --exclude-next-obs --dataset +``` +This script will generate a new dataset with the suffix `_im84.hdf5` in the same directory as `--dataset` ------- +## Training +There are a number of algorithms to choose from: Diffusion Policy, ACT, BC-Transformer, etc. -**robomimic** is a framework for robot learning from demonstration. -It offers a broad set of demonstration datasets collected on robot manipulation domains and offline learning algorithms to learn from these datasets. -**robomimic** aims to make robot learning broadly *accessible* and *reproducible*, allowing researchers and practitioners to benchmark tasks and algorithms fairly and to develop the next generation of robot learning algorithms. - -## Core Features - -

- -

- - - - -## Reproducing benchmarks - -The robomimic framework also makes reproducing the results from different benchmarks and datasets easy. See the [datasets page](https://robomimic.github.io/docs/datasets/overview.html) for more information on downloading datasets and reproducing experiments. - -## Troubleshooting +Each algorithm has its own config generator script. For example for diffusion policy run: +``` +python robomimic/scripts/config_gen/diffusion_gen.py --name +``` +You can add `--debug` to generate small runs for testing. Running this script will generate training run commands. You can use this script for generating a single run or multiple (for comparing settings and hyperparameter tuning). +After running this script you just need to run the command(s) outputted. -Please see the [troubleshooting](https://robomimic.github.io/docs/miscellaneous/troubleshooting.html) section for common fixes, or [submit an issue](https://github.com/ARISE-Initiative/robomimic/issues) on our github page. +Want to learn how to set your own config values and sweep them? Read this short [tutorial section](https://robomimic.github.io/docs/tutorials/hyperparam_scan.html#step-3-set-hyperparameter-values). -## Contributing to robomimic -This project is part of the broader [Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative](https://github.com/ARISE-Initiative), with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics. -The project originally began development in late 2018 by researchers in the [Stanford Vision and Learning Lab](http://svl.stanford.edu/) (SVL). -Now it is actively maintained and used for robotics research projects across multiple labs. -We welcome community contributions to this project. -For details please check our [contributing guidelines](https://robomimic.github.io/docs/miscellaneous/contributing.html). +### Loading model checkpoint weights +Want to intialize your model with weights from a previous model checkpoint? Set the checkpoint path under `experiment.ckpt_path` in the config. -## Citation +------- +## Logging and viewing results +Read this short [tutorial page](https://robomimic.github.io/docs/tutorials/viewing_results.html). -Please cite [this paper](https://arxiv.org/abs/2108.03298) if you use this framework in your work: +------- +## Real robot evaluation (r2d2 only) +Use this forked r2d2: https://github.com/snasiriany/r2d2/tree/robomimic-eval. Note that the branch is `robomimic-eval`. -```bibtex -@inproceedings{robomimic2021, - title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation}, - author={Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese and Yuke Zhu and Roberto Mart\'{i}n-Mart\'{i}n}, - booktitle={Conference on Robot Learning (CoRL)}, - year={2021} -} -``` +Run this script: https://github.com/snasiriany/r2d2/blob/robomimic-eval/scripts/evaluation/evaluate_policy.py. Before doing so, make sure to fill out `CKPT_PATH` with the path to the saved robomimic checkpoint you wish to evaluate. diff --git a/act/.gitignore b/act/.gitignore new file mode 100644 index 00000000..d1e4c693 --- /dev/null +++ b/act/.gitignore @@ -0,0 +1,140 @@ +bin +logs +wandb +outputs +data +data_local +.vscode +_wandb + +**/.DS_Store + +fuse.cfg + +*.ai + +# Generation results +results/ + +ray/auth.json + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ \ No newline at end of file diff --git a/act/LICENSE b/act/LICENSE new file mode 100644 index 00000000..35e5f5e2 --- /dev/null +++ b/act/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Tony Z. Zhao + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/act/README.md b/act/README.md new file mode 100644 index 00000000..2a345157 --- /dev/null +++ b/act/README.md @@ -0,0 +1,89 @@ +# ACT: Action Chunking with Transformers + +### *New*: [ACT tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) +TL;DR: if your ACT policy is jerky or pauses in the middle of an episode, just train for longer! Success rate and smoothness can improve way after loss plateaus. + +#### Project Website: https://tonyzhaozh.github.io/aloha/ + +This repo contains the implementation of ACT, together with 2 simulated environments: +Transfer Cube and Bimanual Insertion. You can train and evaluate ACT in sim or real. +For real, you would also need to install [ALOHA](https://github.com/tonyzhaozh/aloha). + +### Updates: +You can find all scripted/human demo for simulated environments [here](https://drive.google.com/drive/folders/1gPR03v05S1xiInoVJn7G7VJ9pDCnxq9O?usp=share_link). + + +### Repo Structure +- ``imitate_episodes.py`` Train and Evaluate ACT +- ``policy.py`` An adaptor for ACT policy +- ``detr`` Model definitions of ACT, modified from DETR +- ``sim_env.py`` Mujoco + DM_Control environments with joint space control +- ``ee_sim_env.py`` Mujoco + DM_Control environments with EE space control +- ``scripted_policy.py`` Scripted policies for sim environments +- ``constants.py`` Constants shared across files +- ``utils.py`` Utils such as data loading and helper functions +- ``visualize_episodes.py`` Save videos from a .hdf5 dataset + + +### Installation + + conda create -n aloha python=3.8 + conda activate aloha + pip install torchvision + pip install torch + pip install pyquaternion + pip install pyyaml + pip install rospkg + pip install pexpect + pip install mujoco + pip install dm_control + pip install opencv-python + pip install matplotlib + pip install einops + pip install packaging + pip install h5py + pip install ipython + cd act/detr && pip install -e . + +### Example Usages + +To set up a new terminal, run: + + conda activate aloha + cd + +### Simulated experiments + +We use ``sim_transfer_cube_scripted`` task in the examples below. Another option is ``sim_insertion_scripted``. +To generated 50 episodes of scripted data, run: + + python3 record_sim_episodes.py \ + --task_name sim_transfer_cube_scripted \ + --dataset_dir \ + --num_episodes 50 + +To can add the flag ``--onscreen_render`` to see real-time rendering. +To visualize the episode after it is collected, run + + python3 visualize_episodes.py --dataset_dir --episode_idx 0 + +To train ACT: + + # Transfer Cube task + python3 imitate_episodes.py \ + --task_name sim_transfer_cube_scripted \ + --ckpt_dir \ + --policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \ + --num_epochs 2000 --lr 1e-5 \ + --seed 0 + + +To evaluate the policy, run the same command but add ``--eval``. This loads the best validation checkpoint. +The success rate should be around 90% for transfer cube, and around 50% for insertion. +To enable temporal ensembling, add flag ``--temporal_agg``. +Videos will be saved to ```` for each rollout. +You can also add ``--onscreen_render`` to see real-time rendering during evaluation. + +For real-world data where things can be harder to model, train for at least 5000 epochs or 3-4 times the length after the loss has plateaued. +Please refer to [tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) for more info. + diff --git a/act/assets/bimanual_viperx_ee_insertion.xml b/act/assets/bimanual_viperx_ee_insertion.xml new file mode 100644 index 00000000..700aaac5 --- /dev/null +++ b/act/assets/bimanual_viperx_ee_insertion.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/bimanual_viperx_ee_transfer_cube.xml b/act/assets/bimanual_viperx_ee_transfer_cube.xml new file mode 100644 index 00000000..25893842 --- /dev/null +++ b/act/assets/bimanual_viperx_ee_transfer_cube.xml @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/bimanual_viperx_insertion.xml b/act/assets/bimanual_viperx_insertion.xml new file mode 100644 index 00000000..f701d70a --- /dev/null +++ b/act/assets/bimanual_viperx_insertion.xml @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/bimanual_viperx_transfer_cube.xml b/act/assets/bimanual_viperx_transfer_cube.xml new file mode 100644 index 00000000..bdc9e644 --- /dev/null +++ b/act/assets/bimanual_viperx_transfer_cube.xml @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + \ No newline at end of file diff --git a/act/assets/vx300s_right.xml b/act/assets/vx300s_right.xml new file mode 100644 index 00000000..2c6f007c --- /dev/null +++ b/act/assets/vx300s_right.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/conda_env.yaml b/act/conda_env.yaml new file mode 100644 index 00000000..0f44d6b0 --- /dev/null +++ b/act/conda_env.yaml @@ -0,0 +1,23 @@ +name: aloha +channels: + - pytorch + - nvidia + - conda-forge +dependencies: + - python=3.9 + - pip=23.0.1 + - pytorch=2.0.0 + - torchvision=0.15.0 + - pytorch-cuda=11.8 + - pyquaternion=0.9.9 + - pyyaml=6.0 + - rospkg=1.5.0 + - pexpect=4.8.0 + - mujoco=2.3.3 + - dm_control=1.0.9 + - py-opencv=4.7.0 + - matplotlib=3.7.1 + - einops=0.6.0 + - packaging=23.0 + - h5py=3.8.0 + - ipython=8.12.0 diff --git a/act/constants.py b/act/constants.py new file mode 100644 index 00000000..f445350a --- /dev/null +++ b/act/constants.py @@ -0,0 +1,76 @@ +import pathlib + +### Task parameters +DATA_DIR = '' +SIM_TASK_CONFIGS = { + 'sim_transfer_cube_scripted':{ + 'dataset_dir': DATA_DIR + '/sim_transfer_cube_scripted', + 'num_episodes': 50, + 'episode_len': 400, + 'camera_names': ['top'] + }, + + 'sim_transfer_cube_human':{ + 'dataset_dir': DATA_DIR + '/sim_transfer_cube_human', + 'num_episodes': 50, + 'episode_len': 400, + 'camera_names': ['top'] + }, + + 'sim_insertion_scripted': { + 'dataset_dir': DATA_DIR + '/sim_insertion_scripted', + 'num_episodes': 50, + 'episode_len': 400, + 'camera_names': ['top'] + }, + + 'sim_insertion_human': { + 'dataset_dir': DATA_DIR + '/sim_insertion_human', + 'num_episodes': 50, + 'episode_len': 500, + 'camera_names': ['top'] + }, +} + +### Simulation envs fixed constants +DT = 0.02 +JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] +START_ARM_POSE = [0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239, 0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239] + +XML_DIR = str(pathlib.Path(__file__).parent.resolve()) + '/assets/' # note: absolute path + +# Left finger position limits (qpos[7]), right_finger = -1 * left_finger +MASTER_GRIPPER_POSITION_OPEN = 0.02417 +MASTER_GRIPPER_POSITION_CLOSE = 0.01244 +PUPPET_GRIPPER_POSITION_OPEN = 0.05800 +PUPPET_GRIPPER_POSITION_CLOSE = 0.01844 + +# Gripper joint limits (qpos[6]) +MASTER_GRIPPER_JOINT_OPEN = 0.3083 +MASTER_GRIPPER_JOINT_CLOSE = -0.6842 +PUPPET_GRIPPER_JOINT_OPEN = 1.4910 +PUPPET_GRIPPER_JOINT_CLOSE = -0.6213 + +############################ Helper functions ############################ + +MASTER_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_POSITION_CLOSE) / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) +PUPPET_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_POSITION_CLOSE) / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) +MASTER_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) + MASTER_GRIPPER_POSITION_CLOSE +PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + PUPPET_GRIPPER_POSITION_CLOSE +MASTER2PUPPET_POSITION_FN = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(MASTER_GRIPPER_POSITION_NORMALIZE_FN(x)) + +MASTER_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) +PUPPET_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) +MASTER_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE +PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE +MASTER2PUPPET_JOINT_FN = lambda x: PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN(MASTER_GRIPPER_JOINT_NORMALIZE_FN(x)) + +MASTER_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) +PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + +MASTER_POS2JOINT = lambda x: MASTER_GRIPPER_POSITION_NORMALIZE_FN(x) * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE +MASTER_JOINT2POS = lambda x: MASTER_GRIPPER_POSITION_UNNORMALIZE_FN((x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)) +PUPPET_POS2JOINT = lambda x: PUPPET_GRIPPER_POSITION_NORMALIZE_FN(x) * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE +PUPPET_JOINT2POS = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN((x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)) + +MASTER_GRIPPER_JOINT_MID = (MASTER_GRIPPER_JOINT_OPEN + MASTER_GRIPPER_JOINT_CLOSE)/2 diff --git a/act/detr/LICENSE b/act/detr/LICENSE new file mode 100644 index 00000000..b1395e94 --- /dev/null +++ b/act/detr/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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All Rights Reserved +import argparse +from pathlib import Path + +import numpy as np +import torch +from .models import build_ACT_model, build_CNNMLP_model + +import IPython +e = IPython.embed + +def get_args_parser(): + parser = argparse.ArgumentParser('Set transformer detector', add_help=False) + parser.add_argument('--lr', default=1e-4, type=float) # will be overridden + parser.add_argument('--lr_backbone', default=1e-5, type=float) # will be overridden + parser.add_argument('--batch_size', default=2, type=int) # not used + parser.add_argument('--weight_decay', default=1e-4, type=float) + parser.add_argument('--epochs', default=300, type=int) # not used + parser.add_argument('--lr_drop', default=200, type=int) # not used + parser.add_argument('--clip_max_norm', default=0.1, type=float, # not used + help='gradient clipping max norm') + + # Model parameters + # * Backbone + parser.add_argument('--backbone', default='resnet18', type=str, # will be overridden + help="Name of the convolutional backbone to use") + parser.add_argument('--dilation', action='store_true', + help="If true, we replace stride with dilation in the last convolutional block (DC5)") + parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'), + help="Type of positional embedding to use on top of the image features") + parser.add_argument('--camera_names', default=[], type=list, # will be overridden + help="A list of camera names") + + # * Transformer + parser.add_argument('--enc_layers', default=4, type=int, # will be overridden + help="Number of encoding layers in the transformer") + parser.add_argument('--dec_layers', default=6, type=int, # will be overridden + help="Number of decoding layers in the transformer") + parser.add_argument('--dim_feedforward', default=2048, type=int, # will be overridden + help="Intermediate size of the feedforward layers in the transformer blocks") + parser.add_argument('--hidden_dim', default=256, type=int, # will be overridden + help="Size of the embeddings (dimension of the transformer)") + parser.add_argument('--dropout', default=0.1, type=float, + help="Dropout applied in the transformer") + parser.add_argument('--nheads', default=8, type=int, # will be overridden + help="Number of attention heads inside the transformer's attentions") + parser.add_argument('--num_queries', default=400, type=int, # will be overridden + help="Number of query slots") + parser.add_argument('--pre_norm', action='store_true') + + # * Segmentation + parser.add_argument('--masks', action='store_true', + help="Train segmentation head if the flag is provided") + + parser.add_argument('--a_dim', default=-1, type=float, + help="Action dim") + parser.add_argument('--latent_dim', default=-1, type=float, + help="Latent dim") + parser.add_argument('--state_dim', default=-1, type=float, + help="State dim") + + return parser + + +def build_ACT_model_and_optimizer(args_override): + parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) + args, _ = parser.parse_known_args() + + for k, v in args_override.items(): + setattr(args, k, v) + + model = build_ACT_model(args) + model.cuda() + + param_dicts = [ + {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, + { + "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], + "lr": args.lr_backbone, + }, + ] + optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, + weight_decay=args.weight_decay) + + return model, optimizer + + +def build_CNNMLP_model_and_optimizer(args_override): + parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) + args = parser.parse_args() + + for k, v in args_override.items(): + setattr(args, k, v) + + model = build_CNNMLP_model(args) + model.cuda() + + param_dicts = [ + {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, + { + "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], + "lr": args.lr_backbone, + }, + ] + optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, + weight_decay=args.weight_decay) + + return model, optimizer + diff --git a/act/detr/models/__init__.py b/act/detr/models/__init__.py new file mode 100644 index 00000000..cc78db10 --- /dev/null +++ b/act/detr/models/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .detr_vae import build as build_vae +from .detr_vae import build_cnnmlp as build_cnnmlp + +def build_ACT_model(args): + return build_vae(args) + +def build_CNNMLP_model(args): + return build_cnnmlp(args) \ No newline at end of file diff --git a/act/detr/models/backbone.py b/act/detr/models/backbone.py new file mode 100644 index 00000000..f28637ea --- /dev/null +++ b/act/detr/models/backbone.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Backbone modules. +""" +from collections import OrderedDict + +import torch +import torch.nn.functional as F +import torchvision +from torch import nn +from torchvision.models._utils import IntermediateLayerGetter +from typing import Dict, List + +from util.misc import NestedTensor, is_main_process + +from .position_encoding import build_position_encoding + +import IPython +e = IPython.embed + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + num_batches_tracked_key = prefix + 'num_batches_tracked' + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + eps = 1e-5 + scale = w * (rv + eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + +class BackboneBase(nn.Module): + + def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): + super().__init__() + # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this? + # if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: + # parameter.requires_grad_(False) + if return_interm_layers: + return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} + else: + return_layers = {'layer4': "0"} + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.num_channels = num_channels + + def forward(self, tensor): + xs = self.body(tensor) + return xs + # out: Dict[str, NestedTensor] = {} + # for name, x in xs.items(): + # m = tensor_list.mask + # assert m is not None + # mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + # out[name] = NestedTensor(x, mask) + # return out + + +class Backbone(BackboneBase): + """ResNet backbone with frozen BatchNorm.""" + def __init__(self, name: str, + train_backbone: bool, + return_interm_layers: bool, + dilation: bool): + backbone = getattr(torchvision.models, name)( + replace_stride_with_dilation=[False, False, dilation], + pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) # pretrained # TODO do we want frozen batch_norm?? + num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 + super().__init__(backbone, train_backbone, num_channels, return_interm_layers) + + +class Joiner(nn.Sequential): + def __init__(self, backbone, position_embedding): + super().__init__(backbone, position_embedding) + + def forward(self, tensor_list: NestedTensor): + xs = self[0](tensor_list) + out: List[NestedTensor] = [] + pos = [] + for name, x in xs.items(): + out.append(x) + # position encoding + pos.append(self[1](x).to(x.dtype)) + + return out, pos + + +def build_backbone(args): + position_embedding = build_position_encoding(args) + train_backbone = args.lr_backbone > 0 + return_interm_layers = args.masks + backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation) + model = Joiner(backbone, position_embedding) + model.num_channels = backbone.num_channels + return model diff --git a/act/detr/models/detr_vae.py b/act/detr/models/detr_vae.py new file mode 100644 index 00000000..dbde581e --- /dev/null +++ b/act/detr/models/detr_vae.py @@ -0,0 +1,284 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DETR model and criterion classes. +""" +import torch +from torch import nn +from torch.autograd import Variable +from .backbone import build_backbone +from .transformer import build_transformer, TransformerEncoder, TransformerEncoderLayer + +import numpy as np + +import IPython +e = IPython.embed + + +def reparametrize(mu, logvar): + std = logvar.div(2).exp() + eps = Variable(std.data.new(std.size()).normal_()) + return mu + std * eps + + +def get_sinusoid_encoding_table(n_position, d_hid): + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.FloatTensor(sinusoid_table).unsqueeze(0) + + +class DETRVAE(nn.Module): + """ This is the DETR module that performs object detection """ + def __init__(self, backbones, transformer, encoder, latent_dim, a_dim, state_dim, num_queries, camera_names): + """ Initializes the model. + Parameters: + backbones: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + state_dim: robot state dimension of the environment + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + DETR can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + """ + super().__init__() + self.action_dim = a_dim + self.latent_dim = latent_dim + self.state_dim = state_dim + + self.num_queries = num_queries + self.camera_names = camera_names + self.transformer = transformer + self.encoder = encoder + hidden_dim = transformer.d_model + self.action_head = nn.Linear(hidden_dim, self.action_dim) + self.is_pad_head = nn.Linear(hidden_dim, 1) + self.query_embed = nn.Embedding(num_queries, hidden_dim) + if backbones is not None: + self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1) + self.backbones = nn.ModuleList(backbones) + self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) + else: + # input_dim = 14 + 7 # robot_state + env_state + self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) + self.input_proj_env_state = nn.Linear(10, hidden_dim) # TODO not used in robomimic + self.pos = torch.nn.Embedding(2, hidden_dim) + self.backbones = None + + # encoder extra parameters + self.latent_dim = self.latent_dim # final size of latent z # TODO tune + self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding + self.encoder_action_proj = nn.Linear(self.action_dim, hidden_dim) # project action to embedding + self.encoder_joint_proj = nn.Linear(state_dim, hidden_dim) # project qpos to embedding + self.latent_proj = nn.Linear(hidden_dim, self.latent_dim*2) # project hidden state to latent std, var + self.register_buffer('pos_table', get_sinusoid_encoding_table(1+1+num_queries, hidden_dim)) # [CLS], qpos, a_seq + + # decoder extra parameters + self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim) # project latent sample to embedding + self.additional_pos_embed = nn.Embedding(2, hidden_dim) # learned position embedding for proprio and latent + + def forward(self, qpos, image, env_state, actions=None, is_pad=None): + """ + qpos: batch, qpos_dim + image: batch, num_cam, channel, height, width + env_state: None + actions: batch, seq, action_dim + """ + is_training = actions is not None # train or val + bs, _ = qpos.shape + ### Obtain latent z from action sequence + if is_training: + # project action sequence to embedding dim, and concat with a CLS token + action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim) + qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim) + qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim) + cls_embed = self.cls_embed.weight # (1, hidden_dim) + cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim) + encoder_input = torch.cat([cls_embed, qpos_embed, action_embed], axis=1) # (bs, seq+2, hidden_dim) + encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim) + # do not mask cls token + cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding + is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1) + # obtain position embedding + pos_embed = self.pos_table.clone().detach() + pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim) + # query model + encoder_output = self.encoder(encoder_input, pos=pos_embed, src_key_padding_mask=is_pad) + encoder_output = encoder_output[0] # take cls output only + latent_info = self.latent_proj(encoder_output) + mu = latent_info[:, :self.latent_dim] + logvar = latent_info[:, self.latent_dim:] + latent_sample = reparametrize(mu, logvar) + latent_input = self.latent_out_proj(latent_sample) + else: + mu = logvar = None + latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device) + latent_input = self.latent_out_proj(latent_sample) + + if self.backbones is not None: + # Image observation features and position embeddings + all_cam_features = [] + all_cam_pos = [] + for cam_id, cam_name in enumerate(self.camera_names): + features, pos = self.backbones[cam_id](image[:, cam_id]) + features = features[0] # take the last layer feature + pos = pos[0] + all_cam_features.append(self.input_proj(features)) + all_cam_pos.append(pos) + # proprioception features + proprio_input = self.input_proj_robot_state(qpos) + # fold camera dimension into width dimension + src = torch.cat(all_cam_features, axis=3) + pos = torch.cat(all_cam_pos, axis=3) + hs = self.transformer(src, None, self.query_embed.weight, pos, latent_input, proprio_input, self.additional_pos_embed.weight)[0] + else: + qpos = self.input_proj_robot_state(qpos).unsqueeze(dim=1) + env_state = self.input_proj_env_state(env_state).unsqueeze(dim=1) + transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2 + hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0] + a_hat = self.action_head(hs) + is_pad_hat = self.is_pad_head(hs) + return a_hat, is_pad_hat, [mu, logvar] + + + +class CNNMLP(nn.Module): + def __init__(self, backbones, state_dim, camera_names): + """ Initializes the model. + Parameters: + backbones: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + state_dim: robot state dimension of the environment + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + DETR can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + """ + super().__init__() + self.camera_names = camera_names + self.action_head = nn.Linear(1000, state_dim) # TODO add more + if backbones is not None: + self.backbones = nn.ModuleList(backbones) + backbone_down_projs = [] + for backbone in backbones: + down_proj = nn.Sequential( + nn.Conv2d(backbone.num_channels, 128, kernel_size=5), + nn.Conv2d(128, 64, kernel_size=5), + nn.Conv2d(64, 32, kernel_size=5) + ) + backbone_down_projs.append(down_proj) + self.backbone_down_projs = nn.ModuleList(backbone_down_projs) + + mlp_in_dim = 768 * len(backbones) + 14 + self.mlp = mlp(input_dim=mlp_in_dim, hidden_dim=1024, output_dim=14, hidden_depth=2) + else: + raise NotImplementedError + + def forward(self, qpos, image, env_state, actions=None): + """ + qpos: batch, qpos_dim + image: batch, num_cam, channel, height, width + env_state: None + actions: batch, seq, action_dim + """ + is_training = actions is not None # train or val + bs, _ = qpos.shape + # Image observation features and position embeddings + all_cam_features = [] + for cam_id, cam_name in enumerate(self.camera_names): + features, pos = self.backbones[cam_id](image[:, cam_id]) + features = features[0] # take the last layer feature + pos = pos[0] # not used + all_cam_features.append(self.backbone_down_projs[cam_id](features)) + # flatten everything + flattened_features = [] + for cam_feature in all_cam_features: + flattened_features.append(cam_feature.reshape([bs, -1])) + flattened_features = torch.cat(flattened_features, axis=1) # 768 each + features = torch.cat([flattened_features, qpos], axis=1) # qpos: 14 + a_hat = self.mlp(features) + return a_hat + + +def mlp(input_dim, hidden_dim, output_dim, hidden_depth): + if hidden_depth == 0: + mods = [nn.Linear(input_dim, output_dim)] + else: + mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)] + for i in range(hidden_depth - 1): + mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)] + mods.append(nn.Linear(hidden_dim, output_dim)) + trunk = nn.Sequential(*mods) + return trunk + + +def build_encoder(args): + d_model = args.hidden_dim # 256 + dropout = args.dropout # 0.1 + nhead = args.nheads # 8 + dim_feedforward = args.dim_feedforward # 2048 + num_encoder_layers = args.enc_layers # 4 # TODO shared with VAE decoder + normalize_before = args.pre_norm # False + activation = "relu" + + encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + return encoder + + +def build(args): + + # From state + # backbone = None # from state for now, no need for conv nets + # From image + backbones = [] + for _ in args.camera_names: + backbone = build_backbone(args) + backbones.append(backbone) + + transformer = build_transformer(args) + + encoder = build_encoder(args) + + model = DETRVAE( + backbones, + transformer, + encoder, + latent_dim=args.latent_dim, + a_dim=args.a_dim, + state_dim=args.state_dim, + num_queries=args.num_queries, + camera_names=args.camera_names, + ) + + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print("number of parameters: %.2fM" % (n_parameters/1e6,)) + + return model + +def build_cnnmlp(args): + state_dim = 14 # TODO hardcode + + # From state + # backbone = None # from state for now, no need for conv nets + # From image + backbones = [] + for _ in args.camera_names: + backbone = build_backbone(args) + backbones.append(backbone) + + model = CNNMLP( + backbones, + state_dim=state_dim, + camera_names=args.camera_names, + ) + + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print("number of parameters: %.2fM" % (n_parameters/1e6,)) + + return model + diff --git a/act/detr/models/position_encoding.py b/act/detr/models/position_encoding.py new file mode 100644 index 00000000..209d9171 --- /dev/null +++ b/act/detr/models/position_encoding.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Various positional encodings for the transformer. +""" +import math +import torch +from torch import nn + +from util.misc import NestedTensor + +import IPython +e = IPython.embed + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor): + x = tensor + # mask = tensor_list.mask + # assert mask is not None + # not_mask = ~mask + + not_mask = torch.ones_like(x[0, [0]]) + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + def __init__(self, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(50, num_pos_feats) + self.col_embed = nn.Embedding(50, num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = torch.cat([ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) + return pos + + +def build_position_encoding(args): + N_steps = args.hidden_dim // 2 + if args.position_embedding in ('v2', 'sine'): + # TODO find a better way of exposing other arguments + position_embedding = PositionEmbeddingSine(N_steps, normalize=True) + elif args.position_embedding in ('v3', 'learned'): + position_embedding = PositionEmbeddingLearned(N_steps) + else: + raise ValueError(f"not supported {args.position_embedding}") + + return position_embedding diff --git a/act/detr/models/transformer.py b/act/detr/models/transformer.py new file mode 100644 index 00000000..f38afd0e --- /dev/null +++ b/act/detr/models/transformer.py @@ -0,0 +1,314 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DETR Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +import copy +from typing import Optional, List + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +import IPython +e = IPython.embed + +class Transformer(nn.Module): + + def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, + num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False, + return_intermediate_dec=False): + super().__init__() + + encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, + return_intermediate=return_intermediate_dec) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, query_embed, pos_embed, latent_input=None, proprio_input=None, additional_pos_embed=None): + # TODO flatten only when input has H and W + if len(src.shape) == 4: # has H and W + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + # mask = mask.flatten(1) + + additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1) # seq, bs, dim + pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0) + + addition_input = torch.stack([latent_input, proprio_input], axis=0) + src = torch.cat([addition_input, src], axis=0) + else: + assert len(src.shape) == 3 + # flatten NxHWxC to HWxNxC + bs, hw, c = src.shape + src = src.permute(1, 0, 2) + pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + + tgt = torch.zeros_like(query_embed) + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, + pos=pos_embed, query_pos=query_embed) + hs = hs.transpose(1, 2) + return hs + +class TransformerEncoder(nn.Module): + + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward(self, src, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + output = src + + for layer in self.layers: + output = layer(output, src_mask=mask, + src_key_padding_mask=src_key_padding_mask, pos=pos) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class TransformerDecoder(nn.Module): + + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + output = tgt + + intermediate = [] + + for layer in self.layers: + output = layer(output, memory, tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, query_pos=query_pos) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre(self, src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward(self, src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def build_transformer(args): + return Transformer( + d_model=args.hidden_dim, + dropout=args.dropout, + nhead=args.nheads, + dim_feedforward=args.dim_feedforward, + num_encoder_layers=args.enc_layers, + num_decoder_layers=args.dec_layers, + normalize_before=args.pre_norm, + return_intermediate_dec=True, + ) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/act/detr/setup.py b/act/detr/setup.py new file mode 100644 index 00000000..55d18c0d --- /dev/null +++ b/act/detr/setup.py @@ -0,0 +1,10 @@ +from distutils.core import setup +from setuptools import find_packages + +setup( + name='detr', + version='0.0.0', + packages=find_packages(), + license='MIT License', + long_description=open('README.md').read(), +) \ No newline at end of file diff --git a/act/detr/util/__init__.py b/act/detr/util/__init__.py new file mode 100644 index 00000000..168f9979 --- /dev/null +++ b/act/detr/util/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/act/detr/util/box_ops.py b/act/detr/util/box_ops.py new file mode 100644 index 00000000..9c088e5b --- /dev/null +++ b/act/detr/util/box_ops.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities for bounding box manipulation and GIoU. +""" +import torch +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), + (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, + (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / area + + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = (masks * x.unsqueeze(0)) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = (masks * y.unsqueeze(0)) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/act/detr/util/misc.py b/act/detr/util/misc.py new file mode 100644 index 00000000..dfa9fb5b --- /dev/null +++ b/act/detr/util/misc.py @@ -0,0 +1,468 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import os +import subprocess +import time +from collections import defaultdict, deque +import datetime +import pickle +from packaging import version +from typing import Optional, List + +import torch +import torch.distributed as dist +from torch import Tensor + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision +if version.parse(torchvision.__version__) < version.parse('0.7'): + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.4f}') + data_time = SmoothedValue(fmt='{avg:.4f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.4f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], :img.shape[2]] = False + else: + raise ValueError('not supported') + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + else: + print('Not using distributed mode') + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = 'nccl' + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if version.parse(torchvision.__version__) < version.parse('0.7'): + if input.numel() > 0: + return torch.nn.functional.interpolate( + input, size, scale_factor, mode, align_corners + ) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/act/detr/util/plot_utils.py b/act/detr/util/plot_utils.py new file mode 100644 index 00000000..0f24bed0 --- /dev/null +++ b/act/detr/util/plot_utils.py @@ -0,0 +1,107 @@ +""" +Plotting utilities to visualize training logs. +""" +import torch +import pandas as pd +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +from pathlib import Path, PurePath + + +def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): + ''' + Function to plot specific fields from training log(s). Plots both training and test results. + + :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file + - fields = which results to plot from each log file - plots both training and test for each field. + - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots + - log_name = optional, name of log file if different than default 'log.txt'. + + :: Outputs - matplotlib plots of results in fields, color coded for each log file. + - solid lines are training results, dashed lines are test results. + + ''' + func_name = "plot_utils.py::plot_logs" + + # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, + # convert single Path to list to avoid 'not iterable' error + + if not isinstance(logs, list): + if isinstance(logs, PurePath): + logs = [logs] + print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") + else: + raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ + Expect list[Path] or single Path obj, received {type(logs)}") + + # Quality checks - verify valid dir(s), that every item in list is Path object, and that log_name exists in each dir + for i, dir in enumerate(logs): + if not isinstance(dir, PurePath): + raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") + if not dir.exists(): + raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") + # verify log_name exists + fn = Path(dir / log_name) + if not fn.exists(): + print(f"-> missing {log_name}. Have you gotten to Epoch 1 in training?") + print(f"--> full path of missing log file: {fn}") + return + + # load log file(s) and plot + dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] + + fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) + + for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): + for j, field in enumerate(fields): + if field == 'mAP': + coco_eval = pd.DataFrame( + np.stack(df.test_coco_eval_bbox.dropna().values)[:, 1] + ).ewm(com=ewm_col).mean() + axs[j].plot(coco_eval, c=color) + else: + df.interpolate().ewm(com=ewm_col).mean().plot( + y=[f'train_{field}', f'test_{field}'], + ax=axs[j], + color=[color] * 2, + style=['-', '--'] + ) + for ax, field in zip(axs, fields): + ax.legend([Path(p).name for p in logs]) + ax.set_title(field) + + +def plot_precision_recall(files, naming_scheme='iter'): + if naming_scheme == 'exp_id': + # name becomes exp_id + names = [f.parts[-3] for f in files] + elif naming_scheme == 'iter': + names = [f.stem for f in files] + else: + raise ValueError(f'not supported {naming_scheme}') + fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) + for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): + data = torch.load(f) + # precision is n_iou, n_points, n_cat, n_area, max_det + precision = data['precision'] + recall = data['params'].recThrs + scores = data['scores'] + # take precision for all classes, all areas and 100 detections + precision = precision[0, :, :, 0, -1].mean(1) + scores = scores[0, :, :, 0, -1].mean(1) + prec = precision.mean() + rec = data['recall'][0, :, 0, -1].mean() + print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + + f'score={scores.mean():0.3f}, ' + + f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' + ) + axs[0].plot(recall, precision, c=color) + axs[1].plot(recall, scores, c=color) + + axs[0].set_title('Precision / Recall') + axs[0].legend(names) + axs[1].set_title('Scores / Recall') + axs[1].legend(names) + return fig, axs diff --git a/act/ee_sim_env.py b/act/ee_sim_env.py new file mode 100644 index 00000000..01df2337 --- /dev/null +++ b/act/ee_sim_env.py @@ -0,0 +1,267 @@ +import numpy as np +import collections +import os + +from constants import DT, XML_DIR, START_ARM_POSE +from constants import PUPPET_GRIPPER_POSITION_CLOSE +from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN +from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN +from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN + +from utils import sample_box_pose, sample_insertion_pose +from dm_control import mujoco +from dm_control.rl import control +from dm_control.suite import base + +import IPython +e = IPython.embed + + +def make_ee_sim_env(task_name): + """ + Environment for simulated robot bi-manual manipulation, with end-effector control. + Action space: [left_arm_pose (7), # position and quaternion for end effector + left_gripper_positions (1), # normalized gripper position (0: close, 1: open) + right_arm_pose (7), # position and quaternion for end effector + right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) + + Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position + left_gripper_position (1), # normalized gripper position (0: close, 1: open) + right_arm_qpos (6), # absolute joint position + right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) + "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) + left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) + right_arm_qvel (6), # absolute joint velocity (rad) + right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) + "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' + """ + if 'sim_transfer_cube' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_transfer_cube.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = TransferCubeEETask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + elif 'sim_insertion' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_insertion.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = InsertionEETask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + else: + raise NotImplementedError + return env + +class BimanualViperXEETask(base.Task): + def __init__(self, random=None): + super().__init__(random=random) + + def before_step(self, action, physics): + a_len = len(action) // 2 + action_left = action[:a_len] + action_right = action[a_len:] + + # set mocap position and quat + # left + np.copyto(physics.data.mocap_pos[0], action_left[:3]) + np.copyto(physics.data.mocap_quat[0], action_left[3:7]) + # right + np.copyto(physics.data.mocap_pos[1], action_right[:3]) + np.copyto(physics.data.mocap_quat[1], action_right[3:7]) + + # set gripper + g_left_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_left[7]) + g_right_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_right[7]) + np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl])) + + def initialize_robots(self, physics): + # reset joint position + physics.named.data.qpos[:16] = START_ARM_POSE + + # reset mocap to align with end effector + # to obtain these numbers: + # (1) make an ee_sim env and reset to the same start_pose + # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link'] + # get env._physics.named.data.xquat['vx300s_left/gripper_link'] + # repeat the same for right side + np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084]) + np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0]) + # right + np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084])) + np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0]) + + # reset gripper control + close_gripper_control = np.array([ + PUPPET_GRIPPER_POSITION_CLOSE, + -PUPPET_GRIPPER_POSITION_CLOSE, + PUPPET_GRIPPER_POSITION_CLOSE, + -PUPPET_GRIPPER_POSITION_CLOSE, + ]) + np.copyto(physics.data.ctrl, close_gripper_control) + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + super().initialize_episode(physics) + + @staticmethod + def get_qpos(physics): + qpos_raw = physics.data.qpos.copy() + left_qpos_raw = qpos_raw[:8] + right_qpos_raw = qpos_raw[8:16] + left_arm_qpos = left_qpos_raw[:6] + right_arm_qpos = right_qpos_raw[:6] + left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] + right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] + return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) + + @staticmethod + def get_qvel(physics): + qvel_raw = physics.data.qvel.copy() + left_qvel_raw = qvel_raw[:8] + right_qvel_raw = qvel_raw[8:16] + left_arm_qvel = left_qvel_raw[:6] + right_arm_qvel = right_qvel_raw[:6] + left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] + right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] + return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) + + @staticmethod + def get_env_state(physics): + raise NotImplementedError + + def get_observation(self, physics): + # note: it is important to do .copy() + obs = collections.OrderedDict() + obs['qpos'] = self.get_qpos(physics) + obs['qvel'] = self.get_qvel(physics) + obs['env_state'] = self.get_env_state(physics) + obs['images'] = dict() + obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') + obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') + obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') + # used in scripted policy to obtain starting pose + obs['mocap_pose_left'] = np.concatenate([physics.data.mocap_pos[0], physics.data.mocap_quat[0]]).copy() + obs['mocap_pose_right'] = np.concatenate([physics.data.mocap_pos[1], physics.data.mocap_quat[1]]).copy() + + # used when replaying joint trajectory + obs['gripper_ctrl'] = physics.data.ctrl.copy() + return obs + + def get_reward(self, physics): + raise NotImplementedError + + +class TransferCubeEETask(BimanualViperXEETask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + self.initialize_robots(physics) + # randomize box position + cube_pose = sample_box_pose() + box_start_idx = physics.model.name2id('red_box_joint', 'joint') + np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose) + # print(f"randomized cube position to {cube_position}") + + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether left gripper is holding the box + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_table = ("red_box", "table") in all_contact_pairs + + reward = 0 + if touch_right_gripper: + reward = 1 + if touch_right_gripper and not touch_table: # lifted + reward = 2 + if touch_left_gripper: # attempted transfer + reward = 3 + if touch_left_gripper and not touch_table: # successful transfer + reward = 4 + return reward + + +class InsertionEETask(BimanualViperXEETask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + self.initialize_robots(physics) + # randomize peg and socket position + peg_pose, socket_pose = sample_insertion_pose() + id2index = lambda j_id: 16 + (j_id - 16) * 7 # first 16 is robot qpos, 7 is pose dim # hacky + + peg_start_id = physics.model.name2id('red_peg_joint', 'joint') + peg_start_idx = id2index(peg_start_id) + np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose) + # print(f"randomized cube position to {cube_position}") + + socket_start_id = physics.model.name2id('blue_socket_joint', 'joint') + socket_start_idx = id2index(socket_start_id) + np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose) + # print(f"randomized cube position to {cube_position}") + + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether peg touches the pin + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + + peg_touch_table = ("red_peg", "table") in all_contact_pairs + socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ + ("socket-2", "table") in all_contact_pairs or \ + ("socket-3", "table") in all_contact_pairs or \ + ("socket-4", "table") in all_contact_pairs + peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ + ("red_peg", "socket-2") in all_contact_pairs or \ + ("red_peg", "socket-3") in all_contact_pairs or \ + ("red_peg", "socket-4") in all_contact_pairs + pin_touched = ("red_peg", "pin") in all_contact_pairs + + reward = 0 + if touch_left_gripper and touch_right_gripper: # touch both + reward = 1 + if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both + reward = 2 + if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching + reward = 3 + if pin_touched: # successful insertion + reward = 4 + return reward diff --git a/act/imitate_episodes.py b/act/imitate_episodes.py new file mode 100644 index 00000000..34f9a372 --- /dev/null +++ b/act/imitate_episodes.py @@ -0,0 +1,435 @@ +import torch +import numpy as np +import os +import pickle +import argparse +import matplotlib.pyplot as plt +from copy import deepcopy +from tqdm import tqdm +from einops import rearrange + +from constants import DT +from constants import PUPPET_GRIPPER_JOINT_OPEN +from utils import load_data # data functions +from utils import sample_box_pose, sample_insertion_pose # robot functions +from utils import compute_dict_mean, set_seed, detach_dict # helper functions +from policy import ACTPolicy, CNNMLPPolicy +from visualize_episodes import save_videos + +from sim_env import BOX_POSE + +import IPython +e = IPython.embed + +def main(args): + set_seed(1) + # command line parameters + is_eval = args['eval'] + ckpt_dir = args['ckpt_dir'] + policy_class = args['policy_class'] + onscreen_render = args['onscreen_render'] + task_name = args['task_name'] + batch_size_train = args['batch_size'] + batch_size_val = args['batch_size'] + num_epochs = args['num_epochs'] + + # get task parameters + is_sim = task_name[:4] == 'sim_' + if is_sim: + from constants import SIM_TASK_CONFIGS + task_config = SIM_TASK_CONFIGS[task_name] + else: + from aloha_scripts.constants import TASK_CONFIGS + task_config = TASK_CONFIGS[task_name] + dataset_dir = task_config['dataset_dir'] + num_episodes = task_config['num_episodes'] + episode_len = task_config['episode_len'] + camera_names = task_config['camera_names'] + + # fixed parameters + state_dim = 14 + lr_backbone = 1e-5 + backbone = 'resnet18' + if policy_class == 'ACT': + enc_layers = 4 + dec_layers = 7 + nheads = 8 + policy_config = {'lr': args['lr'], + 'num_queries': args['chunk_size'], + 'kl_weight': args['kl_weight'], + 'hidden_dim': args['hidden_dim'], + 'dim_feedforward': args['dim_feedforward'], + 'lr_backbone': lr_backbone, + 'backbone': backbone, + 'enc_layers': enc_layers, + 'dec_layers': dec_layers, + 'nheads': nheads, + 'camera_names': camera_names, + } + elif policy_class == 'CNNMLP': + policy_config = {'lr': args['lr'], 'lr_backbone': lr_backbone, 'backbone' : backbone, 'num_queries': 1, + 'camera_names': camera_names,} + else: + raise NotImplementedError + + config = { + 'num_epochs': num_epochs, + 'ckpt_dir': ckpt_dir, + 'episode_len': episode_len, + 'state_dim': state_dim, + 'lr': args['lr'], + 'policy_class': policy_class, + 'onscreen_render': onscreen_render, + 'policy_config': policy_config, + 'task_name': task_name, + 'seed': args['seed'], + 'temporal_agg': args['temporal_agg'], + 'camera_names': camera_names, + 'real_robot': not is_sim + } + + if is_eval: + ckpt_names = [f'policy_best.ckpt'] + results = [] + for ckpt_name in ckpt_names: + success_rate, avg_return = eval_bc(config, ckpt_name, save_episode=True) + results.append([ckpt_name, success_rate, avg_return]) + + for ckpt_name, success_rate, avg_return in results: + print(f'{ckpt_name}: {success_rate=} {avg_return=}') + print() + exit() + + train_dataloader, val_dataloader, stats, _ = load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val) + + # save dataset stats + if not os.path.isdir(ckpt_dir): + os.makedirs(ckpt_dir) + stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') + with open(stats_path, 'wb') as f: + pickle.dump(stats, f) + + best_ckpt_info = train_bc(train_dataloader, val_dataloader, config) + best_epoch, min_val_loss, best_state_dict = best_ckpt_info + + # save best checkpoint + ckpt_path = os.path.join(ckpt_dir, f'policy_best.ckpt') + torch.save(best_state_dict, ckpt_path) + print(f'Best ckpt, val loss {min_val_loss:.6f} @ epoch{best_epoch}') + + +def make_policy(policy_class, policy_config): + if policy_class == 'ACT': + policy = ACTPolicy(policy_config) + elif policy_class == 'CNNMLP': + policy = CNNMLPPolicy(policy_config) + else: + raise NotImplementedError + return policy + + +def make_optimizer(policy_class, policy): + if policy_class == 'ACT': + optimizer = policy.configure_optimizers() + elif policy_class == 'CNNMLP': + optimizer = policy.configure_optimizers() + else: + raise NotImplementedError + return optimizer + + +def get_image(ts, camera_names): + curr_images = [] + for cam_name in camera_names: + curr_image = rearrange(ts.observation['images'][cam_name], 'h w c -> c h w') + curr_images.append(curr_image) + curr_image = np.stack(curr_images, axis=0) + curr_image = torch.from_numpy(curr_image / 255.0).float().cuda().unsqueeze(0) + return curr_image + + +def eval_bc(config, ckpt_name, save_episode=True): + set_seed(1000) + ckpt_dir = config['ckpt_dir'] + state_dim = config['state_dim'] + real_robot = config['real_robot'] + policy_class = config['policy_class'] + onscreen_render = config['onscreen_render'] + policy_config = config['policy_config'] + camera_names = config['camera_names'] + max_timesteps = config['episode_len'] + task_name = config['task_name'] + temporal_agg = config['temporal_agg'] + onscreen_cam = 'angle' + + # load policy and stats + ckpt_path = os.path.join(ckpt_dir, ckpt_name) + policy = make_policy(policy_class, policy_config) + loading_status = policy.load_state_dict(torch.load(ckpt_path)) + print(loading_status) + policy.cuda() + policy.eval() + print(f'Loaded: {ckpt_path}') + stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') + with open(stats_path, 'rb') as f: + stats = pickle.load(f) + + pre_process = lambda s_qpos: (s_qpos - stats['qpos_mean']) / stats['qpos_std'] + post_process = lambda a: a * stats['action_std'] + stats['action_mean'] + + # load environment + if real_robot: + from aloha_scripts.robot_utils import move_grippers # requires aloha + from aloha_scripts.real_env import make_real_env # requires aloha + env = make_real_env(init_node=True) + env_max_reward = 0 + else: + from sim_env import make_sim_env + env = make_sim_env(task_name) + env_max_reward = env.task.max_reward + + query_frequency = policy_config['num_queries'] + if temporal_agg: + query_frequency = 1 + num_queries = policy_config['num_queries'] + + max_timesteps = int(max_timesteps * 1) # may increase for real-world tasks + + num_rollouts = 50 + episode_returns = [] + highest_rewards = [] + for rollout_id in range(num_rollouts): + rollout_id += 0 + ### set task + if 'sim_transfer_cube' in task_name: + BOX_POSE[0] = sample_box_pose() # used in sim reset + elif 'sim_insertion' in task_name: + BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset + + ts = env.reset() + + ### onscreen render + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(env._physics.render(height=480, width=640, camera_id=onscreen_cam)) + plt.ion() + + ### evaluation loop + if temporal_agg: + all_time_actions = torch.zeros([max_timesteps, max_timesteps+num_queries, state_dim]).cuda() + + qpos_history = torch.zeros((1, max_timesteps, state_dim)).cuda() + image_list = [] # for visualization + qpos_list = [] + target_qpos_list = [] + rewards = [] + with torch.inference_mode(): + for t in range(max_timesteps): + ### update onscreen render and wait for DT + if onscreen_render: + image = env._physics.render(height=480, width=640, camera_id=onscreen_cam) + plt_img.set_data(image) + plt.pause(DT) + + ### process previous timestep to get qpos and image_list + obs = ts.observation + if 'images' in obs: + image_list.append(obs['images']) + else: + image_list.append({'main': obs['image']}) + qpos_numpy = np.array(obs['qpos']) + qpos = pre_process(qpos_numpy) + qpos = torch.from_numpy(qpos).float().cuda().unsqueeze(0) + qpos_history[:, t] = qpos + curr_image = get_image(ts, camera_names) + + ### query policy + if config['policy_class'] == "ACT": + if t % query_frequency == 0: + all_actions = policy(qpos, curr_image) + if temporal_agg: + all_time_actions[[t], t:t+num_queries] = all_actions + actions_for_curr_step = all_time_actions[:, t] + actions_populated = torch.all(actions_for_curr_step != 0, axis=1) + actions_for_curr_step = actions_for_curr_step[actions_populated] + k = 0.01 + exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step))) + exp_weights = exp_weights / exp_weights.sum() + exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1) + raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True) + else: + raw_action = all_actions[:, t % query_frequency] + elif config['policy_class'] == "CNNMLP": + raw_action = policy(qpos, curr_image) + else: + raise NotImplementedError + + ### post-process actions + raw_action = raw_action.squeeze(0).cpu().numpy() + action = post_process(raw_action) + target_qpos = action + + ### step the environment + ts = env.step(target_qpos) + + ### for visualization + qpos_list.append(qpos_numpy) + target_qpos_list.append(target_qpos) + rewards.append(ts.reward) + + plt.close() + if real_robot: + move_grippers([env.puppet_bot_left, env.puppet_bot_right], [PUPPET_GRIPPER_JOINT_OPEN] * 2, move_time=0.5) # open + pass + + rewards = np.array(rewards) + episode_return = np.sum(rewards[rewards!=None]) + episode_returns.append(episode_return) + episode_highest_reward = np.max(rewards) + highest_rewards.append(episode_highest_reward) + print(f'Rollout {rollout_id}\n{episode_return=}, {episode_highest_reward=}, {env_max_reward=}, Success: {episode_highest_reward==env_max_reward}') + + if save_episode: + save_videos(image_list, DT, video_path=os.path.join(ckpt_dir, f'video{rollout_id}.mp4')) + + success_rate = np.mean(np.array(highest_rewards) == env_max_reward) + avg_return = np.mean(episode_returns) + summary_str = f'\nSuccess rate: {success_rate}\nAverage return: {avg_return}\n\n' + for r in range(env_max_reward+1): + more_or_equal_r = (np.array(highest_rewards) >= r).sum() + more_or_equal_r_rate = more_or_equal_r / num_rollouts + summary_str += f'Reward >= {r}: {more_or_equal_r}/{num_rollouts} = {more_or_equal_r_rate*100}%\n' + + print(summary_str) + + # save success rate to txt + result_file_name = 'result_' + ckpt_name.split('.')[0] + '.txt' + with open(os.path.join(ckpt_dir, result_file_name), 'w') as f: + f.write(summary_str) + f.write(repr(episode_returns)) + f.write('\n\n') + f.write(repr(highest_rewards)) + + return success_rate, avg_return + + +def forward_pass(data, policy): + image_data, qpos_data, action_data, is_pad = data + image_data, qpos_data, action_data, is_pad = image_data.cuda(), qpos_data.cuda(), action_data.cuda(), is_pad.cuda() + return policy(qpos_data, image_data, action_data, is_pad) # TODO remove None + + +def train_bc(train_dataloader, val_dataloader, config): + num_epochs = config['num_epochs'] + ckpt_dir = config['ckpt_dir'] + seed = config['seed'] + policy_class = config['policy_class'] + policy_config = config['policy_config'] + + set_seed(seed) + + policy = make_policy(policy_class, policy_config) + policy.cuda() + optimizer = make_optimizer(policy_class, policy) + + train_history = [] + validation_history = [] + min_val_loss = np.inf + best_ckpt_info = None + for epoch in tqdm(range(num_epochs)): + print(f'\nEpoch {epoch}') + # validation + with torch.inference_mode(): + policy.eval() + epoch_dicts = [] + for batch_idx, data in enumerate(val_dataloader): + forward_dict = forward_pass(data, policy) + epoch_dicts.append(forward_dict) + epoch_summary = compute_dict_mean(epoch_dicts) + validation_history.append(epoch_summary) + + epoch_val_loss = epoch_summary['loss'] + if epoch_val_loss < min_val_loss: + min_val_loss = epoch_val_loss + best_ckpt_info = (epoch, min_val_loss, deepcopy(policy.state_dict())) + print(f'Val loss: {epoch_val_loss:.5f}') + summary_string = '' + for k, v in epoch_summary.items(): + summary_string += f'{k}: {v.item():.3f} ' + print(summary_string) + + # training + policy.train() + optimizer.zero_grad() + for batch_idx, data in enumerate(train_dataloader): + forward_dict = forward_pass(data, policy) + # backward + loss = forward_dict['loss'] + loss.backward() + optimizer.step() + optimizer.zero_grad() + train_history.append(detach_dict(forward_dict)) + epoch_summary = compute_dict_mean(train_history[(batch_idx+1)*epoch:(batch_idx+1)*(epoch+1)]) + epoch_train_loss = epoch_summary['loss'] + print(f'Train loss: {epoch_train_loss:.5f}') + summary_string = '' + for k, v in epoch_summary.items(): + summary_string += f'{k}: {v.item():.3f} ' + print(summary_string) + + if epoch % 100 == 0: + ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{epoch}_seed_{seed}.ckpt') + torch.save(policy.state_dict(), ckpt_path) + plot_history(train_history, validation_history, epoch, ckpt_dir, seed) + + ckpt_path = os.path.join(ckpt_dir, f'policy_last.ckpt') + torch.save(policy.state_dict(), ckpt_path) + + best_epoch, min_val_loss, best_state_dict = best_ckpt_info + ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{best_epoch}_seed_{seed}.ckpt') + torch.save(best_state_dict, ckpt_path) + print(f'Training finished:\nSeed {seed}, val loss {min_val_loss:.6f} at epoch {best_epoch}') + + # save training curves + plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed) + + return best_ckpt_info + + +def plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed): + # save training curves + for key in train_history[0]: + plot_path = os.path.join(ckpt_dir, f'train_val_{key}_seed_{seed}.png') + plt.figure() + train_values = [summary[key].item() for summary in train_history] + val_values = [summary[key].item() for summary in validation_history] + plt.plot(np.linspace(0, num_epochs-1, len(train_history)), train_values, label='train') + plt.plot(np.linspace(0, num_epochs-1, len(validation_history)), val_values, label='validation') + # plt.ylim([-0.1, 1]) + plt.tight_layout() + plt.legend() + plt.title(key) + plt.savefig(plot_path) + print(f'Saved plots to {ckpt_dir}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--eval', action='store_true') + parser.add_argument('--onscreen_render', action='store_true') + parser.add_argument('--ckpt_dir', action='store', type=str, help='ckpt_dir', required=True) + parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True) + parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) + parser.add_argument('--batch_size', action='store', type=int, help='batch_size', required=True) + parser.add_argument('--seed', action='store', type=int, help='seed', required=True) + parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True) + parser.add_argument('--lr', action='store', type=float, help='lr', required=True) + + # for ACT + parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False) + parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False) + parser.add_argument('--hidden_dim', action='store', type=int, help='hidden_dim', required=False) + parser.add_argument('--dim_feedforward', action='store', type=int, help='dim_feedforward', required=False) + parser.add_argument('--temporal_agg', action='store_true') + + main(vars(parser.parse_args())) diff --git a/act/policy.py b/act/policy.py new file mode 100644 index 00000000..7b091e5e --- /dev/null +++ b/act/policy.py @@ -0,0 +1,84 @@ +import torch.nn as nn +from torch.nn import functional as F +import torchvision.transforms as transforms + +from detr.main import build_ACT_model_and_optimizer, build_CNNMLP_model_and_optimizer +import IPython +e = IPython.embed + +class ACTPolicy(nn.Module): + def __init__(self, args_override): + super().__init__() + model, optimizer = build_ACT_model_and_optimizer(args_override) + self.model = model # CVAE decoder + self.optimizer = optimizer + self.kl_weight = args_override['kl_weight'] + print(f'KL Weight {self.kl_weight}') + + def __call__(self, qpos, image, actions=None, is_pad=None): + env_state = None + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + image = normalize(image) + if actions is not None: # training time + actions = actions[:, :self.model.num_queries] + is_pad = is_pad[:, :self.model.num_queries] + + a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad) + total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar) + loss_dict = dict() + all_l1 = F.l1_loss(actions, a_hat, reduction='none') + l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() + loss_dict['l1'] = l1 + loss_dict['kl'] = total_kld[0] + loss_dict['loss'] = loss_dict['l1'] + loss_dict['kl'] * self.kl_weight + return loss_dict + else: # inference time + a_hat, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior + return a_hat + + def configure_optimizers(self): + return self.optimizer + + +class CNNMLPPolicy(nn.Module): + def __init__(self, args_override): + super().__init__() + model, optimizer = build_CNNMLP_model_and_optimizer(args_override) + self.model = model # decoder + self.optimizer = optimizer + + def __call__(self, qpos, image, actions=None, is_pad=None): + env_state = None # TODO + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + image = normalize(image) + if actions is not None: # training time + actions = actions[:, 0] + a_hat = self.model(qpos, image, env_state, actions) + mse = F.mse_loss(actions, a_hat) + loss_dict = dict() + loss_dict['mse'] = mse + loss_dict['loss'] = loss_dict['mse'] + return loss_dict + else: # inference time + a_hat = self.model(qpos, image, env_state) # no action, sample from prior + return a_hat + + def configure_optimizers(self): + return self.optimizer + +def kl_divergence(mu, logvar): + batch_size = mu.size(0) + assert batch_size != 0 + if mu.data.ndimension() == 4: + mu = mu.view(mu.size(0), mu.size(1)) + if logvar.data.ndimension() == 4: + logvar = logvar.view(logvar.size(0), logvar.size(1)) + + klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) + total_kld = klds.sum(1).mean(0, True) + dimension_wise_kld = klds.mean(0) + mean_kld = klds.mean(1).mean(0, True) + + return total_kld, dimension_wise_kld, mean_kld diff --git a/act/record_sim_episodes.py b/act/record_sim_episodes.py new file mode 100644 index 00000000..253fdea1 --- /dev/null +++ b/act/record_sim_episodes.py @@ -0,0 +1,189 @@ +import time +import os +import numpy as np +import argparse +import matplotlib.pyplot as plt +import h5py + +from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN, SIM_TASK_CONFIGS +from ee_sim_env import make_ee_sim_env +from sim_env import make_sim_env, BOX_POSE +from scripted_policy import PickAndTransferPolicy, InsertionPolicy + +import IPython +e = IPython.embed + + +def main(args): + """ + Generate demonstration data in simulation. + First rollout the policy (defined in ee space) in ee_sim_env. Obtain the joint trajectory. + Replace the gripper joint positions with the commanded joint position. + Replay this joint trajectory (as action sequence) in sim_env, and record all observations. + Save this episode of data, and continue to next episode of data collection. + """ + + task_name = args['task_name'] + dataset_dir = args['dataset_dir'] + num_episodes = args['num_episodes'] + onscreen_render = args['onscreen_render'] + inject_noise = False + render_cam_name = 'angle' + + if not os.path.isdir(dataset_dir): + os.makedirs(dataset_dir, exist_ok=True) + + episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] + camera_names = SIM_TASK_CONFIGS[task_name]['camera_names'] + if task_name == 'sim_transfer_cube_scripted': + policy_cls = PickAndTransferPolicy + elif task_name == 'sim_insertion_scripted': + policy_cls = InsertionPolicy + else: + raise NotImplementedError + + success = [] + for episode_idx in range(num_episodes): + print(f'{episode_idx=}') + print('Rollout out EE space scripted policy') + # setup the environment + env = make_ee_sim_env(task_name) + ts = env.reset() + episode = [ts] + policy = policy_cls(inject_noise) + # setup plotting + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images'][render_cam_name]) + plt.ion() + for step in range(episode_len): + action = policy(ts) + ts = env.step(action) + episode.append(ts) + if onscreen_render: + plt_img.set_data(ts.observation['images'][render_cam_name]) + plt.pause(0.002) + plt.close() + + episode_return = np.sum([ts.reward for ts in episode[1:]]) + episode_max_reward = np.max([ts.reward for ts in episode[1:]]) + if episode_max_reward == env.task.max_reward: + print(f"{episode_idx=} Successful, {episode_return=}") + else: + print(f"{episode_idx=} Failed") + + joint_traj = [ts.observation['qpos'] for ts in episode] + # replace gripper pose with gripper control + gripper_ctrl_traj = [ts.observation['gripper_ctrl'] for ts in episode] + for joint, ctrl in zip(joint_traj, gripper_ctrl_traj): + left_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[0]) + right_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[2]) + joint[6] = left_ctrl + joint[6+7] = right_ctrl + + subtask_info = episode[0].observation['env_state'].copy() # box pose at step 0 + + # clear unused variables + del env + del episode + del policy + + # setup the environment + print('Replaying joint commands') + env = make_sim_env(task_name) + BOX_POSE[0] = subtask_info # make sure the sim_env has the same object configurations as ee_sim_env + ts = env.reset() + + episode_replay = [ts] + # setup plotting + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images'][render_cam_name]) + plt.ion() + for t in range(len(joint_traj)): # note: this will increase episode length by 1 + action = joint_traj[t] + ts = env.step(action) + episode_replay.append(ts) + if onscreen_render: + plt_img.set_data(ts.observation['images'][render_cam_name]) + plt.pause(0.02) + + episode_return = np.sum([ts.reward for ts in episode_replay[1:]]) + episode_max_reward = np.max([ts.reward for ts in episode_replay[1:]]) + if episode_max_reward == env.task.max_reward: + success.append(1) + print(f"{episode_idx=} Successful, {episode_return=}") + else: + success.append(0) + print(f"{episode_idx=} Failed") + + plt.close() + + """ + For each timestep: + observations + - images + - each_cam_name (480, 640, 3) 'uint8' + - qpos (14,) 'float64' + - qvel (14,) 'float64' + + action (14,) 'float64' + """ + + data_dict = { + '/observations/qpos': [], + '/observations/qvel': [], + '/action': [], + } + for cam_name in camera_names: + data_dict[f'/observations/images/{cam_name}'] = [] + + # because the replaying, there will be eps_len + 1 actions and eps_len + 2 timesteps + # truncate here to be consistent + joint_traj = joint_traj[:-1] + episode_replay = episode_replay[:-1] + + # len(joint_traj) i.e. actions: max_timesteps + # len(episode_replay) i.e. time steps: max_timesteps + 1 + max_timesteps = len(joint_traj) + while joint_traj: + action = joint_traj.pop(0) + ts = episode_replay.pop(0) + data_dict['/observations/qpos'].append(ts.observation['qpos']) + data_dict['/observations/qvel'].append(ts.observation['qvel']) + data_dict['/action'].append(action) + for cam_name in camera_names: + data_dict[f'/observations/images/{cam_name}'].append(ts.observation['images'][cam_name]) + + # HDF5 + t0 = time.time() + dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}') + with h5py.File(dataset_path + '.hdf5', 'w', rdcc_nbytes=1024 ** 2 * 2) as root: + root.attrs['sim'] = True + obs = root.create_group('observations') + image = obs.create_group('images') + for cam_name in camera_names: + _ = image.create_dataset(cam_name, (max_timesteps, 480, 640, 3), dtype='uint8', + chunks=(1, 480, 640, 3), ) + # compression='gzip',compression_opts=2,) + # compression=32001, compression_opts=(0, 0, 0, 0, 9, 1, 1), shuffle=False) + qpos = obs.create_dataset('qpos', (max_timesteps, 14)) + qvel = obs.create_dataset('qvel', (max_timesteps, 14)) + action = root.create_dataset('action', (max_timesteps, 14)) + + for name, array in data_dict.items(): + root[name][...] = array + print(f'Saving: {time.time() - t0:.1f} secs\n') + + print(f'Saved to {dataset_dir}') + print(f'Success: {np.sum(success)} / {len(success)}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) + parser.add_argument('--dataset_dir', action='store', type=str, help='dataset saving dir', required=True) + parser.add_argument('--num_episodes', action='store', type=int, help='num_episodes', required=False) + parser.add_argument('--onscreen_render', action='store_true') + + main(vars(parser.parse_args())) + diff --git a/act/scripted_policy.py b/act/scripted_policy.py new file mode 100644 index 00000000..4fd8f000 --- /dev/null +++ b/act/scripted_policy.py @@ -0,0 +1,194 @@ +import numpy as np +import matplotlib.pyplot as plt +from pyquaternion import Quaternion + +from constants import SIM_TASK_CONFIGS +from ee_sim_env import make_ee_sim_env + +import IPython +e = IPython.embed + + +class BasePolicy: + def __init__(self, inject_noise=False): + self.inject_noise = inject_noise + self.step_count = 0 + self.left_trajectory = None + self.right_trajectory = None + + def generate_trajectory(self, ts_first): + raise NotImplementedError + + @staticmethod + def interpolate(curr_waypoint, next_waypoint, t): + t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"]) + curr_xyz = curr_waypoint['xyz'] + curr_quat = curr_waypoint['quat'] + curr_grip = curr_waypoint['gripper'] + next_xyz = next_waypoint['xyz'] + next_quat = next_waypoint['quat'] + next_grip = next_waypoint['gripper'] + xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac + quat = curr_quat + (next_quat - curr_quat) * t_frac + gripper = curr_grip + (next_grip - curr_grip) * t_frac + return xyz, quat, gripper + + def __call__(self, ts): + # generate trajectory at first timestep, then open-loop execution + if self.step_count == 0: + self.generate_trajectory(ts) + + # obtain left and right waypoints + if self.left_trajectory[0]['t'] == self.step_count: + self.curr_left_waypoint = self.left_trajectory.pop(0) + next_left_waypoint = self.left_trajectory[0] + + if self.right_trajectory[0]['t'] == self.step_count: + self.curr_right_waypoint = self.right_trajectory.pop(0) + next_right_waypoint = self.right_trajectory[0] + + # interpolate between waypoints to obtain current pose and gripper command + left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count) + right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count) + + # Inject noise + if self.inject_noise: + scale = 0.01 + left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape) + right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape) + + action_left = np.concatenate([left_xyz, left_quat, [left_gripper]]) + action_right = np.concatenate([right_xyz, right_quat, [right_gripper]]) + + self.step_count += 1 + return np.concatenate([action_left, action_right]) + + +class PickAndTransferPolicy(BasePolicy): + + def generate_trajectory(self, ts_first): + init_mocap_pose_right = ts_first.observation['mocap_pose_right'] + init_mocap_pose_left = ts_first.observation['mocap_pose_left'] + + box_info = np.array(ts_first.observation['env_state']) + box_xyz = box_info[:3] + box_quat = box_info[3:] + # print(f"Generate trajectory for {box_xyz=}") + + gripper_pick_quat = Quaternion(init_mocap_pose_right[3:]) + gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) + + meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90) + + meet_xyz = np.array([0, 0.5, 0.25]) + + self.left_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep + {"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position + {"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position + {"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper + {"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left + {"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay + ] + + self.right_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep + {"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube + {"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down + {"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper + {"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position + {"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position + {"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper + {"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right + {"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay + ] + + +class InsertionPolicy(BasePolicy): + + def generate_trajectory(self, ts_first): + init_mocap_pose_right = ts_first.observation['mocap_pose_right'] + init_mocap_pose_left = ts_first.observation['mocap_pose_left'] + + peg_info = np.array(ts_first.observation['env_state'])[:7] + peg_xyz = peg_info[:3] + peg_quat = peg_info[3:] + + socket_info = np.array(ts_first.observation['env_state'])[7:] + socket_xyz = socket_info[:3] + socket_quat = socket_info[3:] + + gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:]) + gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) + + gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:]) + gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60) + + meet_xyz = np.array([0, 0.5, 0.15]) + lift_right = 0.00715 + + self.left_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep + {"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube + {"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down + {"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper + {"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position + {"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0}, # insertion + {"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # insertion + ] + + self.right_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep + {"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube + {"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down + {"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper + {"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position + {"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion + {"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion + + ] + + +def test_policy(task_name): + # example rolling out pick_and_transfer policy + onscreen_render = True + inject_noise = False + + # setup the environment + episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] + if 'sim_transfer_cube' in task_name: + env = make_ee_sim_env('sim_transfer_cube') + elif 'sim_insertion' in task_name: + env = make_ee_sim_env('sim_insertion') + else: + raise NotImplementedError + + for episode_idx in range(2): + ts = env.reset() + episode = [ts] + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images']['angle']) + plt.ion() + + policy = PickAndTransferPolicy(inject_noise) + for step in range(episode_len): + action = policy(ts) + ts = env.step(action) + episode.append(ts) + if onscreen_render: + plt_img.set_data(ts.observation['images']['angle']) + plt.pause(0.02) + plt.close() + + episode_return = np.sum([ts.reward for ts in episode[1:]]) + if episode_return > 0: + print(f"{episode_idx=} Successful, {episode_return=}") + else: + print(f"{episode_idx=} Failed") + + +if __name__ == '__main__': + test_task_name = 'sim_transfer_cube_scripted' + test_policy(test_task_name) + diff --git a/act/sim_env.py b/act/sim_env.py new file mode 100644 index 00000000..b79b935b --- /dev/null +++ b/act/sim_env.py @@ -0,0 +1,278 @@ +import numpy as np +import os +import collections +import matplotlib.pyplot as plt +from dm_control import mujoco +from dm_control.rl import control +from dm_control.suite import base + +from constants import DT, XML_DIR, START_ARM_POSE +from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN +from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN +from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN +from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN + +import IPython +e = IPython.embed + +BOX_POSE = [None] # to be changed from outside + +def make_sim_env(task_name): + """ + Environment for simulated robot bi-manual manipulation, with joint position control + Action space: [left_arm_qpos (6), # absolute joint position + left_gripper_positions (1), # normalized gripper position (0: close, 1: open) + right_arm_qpos (6), # absolute joint position + right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) + + Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position + left_gripper_position (1), # normalized gripper position (0: close, 1: open) + right_arm_qpos (6), # absolute joint position + right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) + "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) + left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) + right_arm_qvel (6), # absolute joint velocity (rad) + right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) + "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' + """ + if 'sim_transfer_cube' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_transfer_cube.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = TransferCubeTask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + elif 'sim_insertion' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_insertion.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = InsertionTask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + else: + raise NotImplementedError + return env + +class BimanualViperXTask(base.Task): + def __init__(self, random=None): + super().__init__(random=random) + + def before_step(self, action, physics): + left_arm_action = action[:6] + right_arm_action = action[7:7+6] + normalized_left_gripper_action = action[6] + normalized_right_gripper_action = action[7+6] + + left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_left_gripper_action) + right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_right_gripper_action) + + full_left_gripper_action = [left_gripper_action, -left_gripper_action] + full_right_gripper_action = [right_gripper_action, -right_gripper_action] + + env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action]) + super().before_step(env_action, physics) + return + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + super().initialize_episode(physics) + + @staticmethod + def get_qpos(physics): + qpos_raw = physics.data.qpos.copy() + left_qpos_raw = qpos_raw[:8] + right_qpos_raw = qpos_raw[8:16] + left_arm_qpos = left_qpos_raw[:6] + right_arm_qpos = right_qpos_raw[:6] + left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] + right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] + return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) + + @staticmethod + def get_qvel(physics): + qvel_raw = physics.data.qvel.copy() + left_qvel_raw = qvel_raw[:8] + right_qvel_raw = qvel_raw[8:16] + left_arm_qvel = left_qvel_raw[:6] + right_arm_qvel = right_qvel_raw[:6] + left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] + right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] + return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) + + @staticmethod + def get_env_state(physics): + raise NotImplementedError + + def get_observation(self, physics): + obs = collections.OrderedDict() + obs['qpos'] = self.get_qpos(physics) + obs['qvel'] = self.get_qvel(physics) + obs['env_state'] = self.get_env_state(physics) + obs['images'] = dict() + obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') + obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') + obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') + + return obs + + def get_reward(self, physics): + # return whether left gripper is holding the box + raise NotImplementedError + + +class TransferCubeTask(BimanualViperXTask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside + # reset qpos, control and box position + with physics.reset_context(): + physics.named.data.qpos[:16] = START_ARM_POSE + np.copyto(physics.data.ctrl, START_ARM_POSE) + assert BOX_POSE[0] is not None + physics.named.data.qpos[-7:] = BOX_POSE[0] + # print(f"{BOX_POSE=}") + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether left gripper is holding the box + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_table = ("red_box", "table") in all_contact_pairs + + reward = 0 + if touch_right_gripper: + reward = 1 + if touch_right_gripper and not touch_table: # lifted + reward = 2 + if touch_left_gripper: # attempted transfer + reward = 3 + if touch_left_gripper and not touch_table: # successful transfer + reward = 4 + return reward + + +class InsertionTask(BimanualViperXTask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside + # reset qpos, control and box position + with physics.reset_context(): + physics.named.data.qpos[:16] = START_ARM_POSE + np.copyto(physics.data.ctrl, START_ARM_POSE) + assert BOX_POSE[0] is not None + physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects + # print(f"{BOX_POSE=}") + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether peg touches the pin + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + + peg_touch_table = ("red_peg", "table") in all_contact_pairs + socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ + ("socket-2", "table") in all_contact_pairs or \ + ("socket-3", "table") in all_contact_pairs or \ + ("socket-4", "table") in all_contact_pairs + peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ + ("red_peg", "socket-2") in all_contact_pairs or \ + ("red_peg", "socket-3") in all_contact_pairs or \ + ("red_peg", "socket-4") in all_contact_pairs + pin_touched = ("red_peg", "pin") in all_contact_pairs + + reward = 0 + if touch_left_gripper and touch_right_gripper: # touch both + reward = 1 + if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both + reward = 2 + if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching + reward = 3 + if pin_touched: # successful insertion + reward = 4 + return reward + + +def get_action(master_bot_left, master_bot_right): + action = np.zeros(14) + # arm action + action[:6] = master_bot_left.dxl.joint_states.position[:6] + action[7:7+6] = master_bot_right.dxl.joint_states.position[:6] + # gripper action + left_gripper_pos = master_bot_left.dxl.joint_states.position[7] + right_gripper_pos = master_bot_right.dxl.joint_states.position[7] + normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos) + normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos) + action[6] = normalized_left_pos + action[7+6] = normalized_right_pos + return action + +def test_sim_teleop(): + """ Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """ + from interbotix_xs_modules.arm import InterbotixManipulatorXS + + BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0] + + # source of data + master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", + robot_name=f'master_left', init_node=True) + master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", + robot_name=f'master_right', init_node=False) + + # setup the environment + env = make_sim_env('sim_transfer_cube') + ts = env.reset() + episode = [ts] + # setup plotting + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images']['angle']) + plt.ion() + + for t in range(1000): + action = get_action(master_bot_left, master_bot_right) + ts = env.step(action) + episode.append(ts) + + plt_img.set_data(ts.observation['images']['angle']) + plt.pause(0.02) + + +if __name__ == '__main__': + test_sim_teleop() + diff --git a/act/utils.py b/act/utils.py new file mode 100644 index 00000000..673cbb10 --- /dev/null +++ b/act/utils.py @@ -0,0 +1,189 @@ +import numpy as np +import torch +import os +import h5py +from torch.utils.data import TensorDataset, DataLoader + +import IPython +e = IPython.embed + +class EpisodicDataset(torch.utils.data.Dataset): + def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats): + super(EpisodicDataset).__init__() + self.episode_ids = episode_ids + self.dataset_dir = dataset_dir + self.camera_names = camera_names + self.norm_stats = norm_stats + self.is_sim = None + self.__getitem__(0) # initialize self.is_sim + + def __len__(self): + return len(self.episode_ids) + + def __getitem__(self, index): + sample_full_episode = False # hardcode + + episode_id = self.episode_ids[index] + dataset_path = os.path.join(self.dataset_dir, f'episode_{episode_id}.hdf5') + with h5py.File(dataset_path, 'r') as root: + is_sim = root.attrs['sim'] + original_action_shape = root['/action'].shape + episode_len = original_action_shape[0] + if sample_full_episode: + start_ts = 0 + else: + start_ts = np.random.choice(episode_len) + # get observation at start_ts only + qpos = root['/observations/qpos'][start_ts] + qvel = root['/observations/qvel'][start_ts] + image_dict = dict() + for cam_name in self.camera_names: + image_dict[cam_name] = root[f'/observations/images/{cam_name}'][start_ts] + # get all actions after and including start_ts + if is_sim: + action = root['/action'][start_ts:] + action_len = episode_len - start_ts + else: + action = root['/action'][max(0, start_ts - 1):] # hack, to make timesteps more aligned + action_len = episode_len - max(0, start_ts - 1) # hack, to make timesteps more aligned + + self.is_sim = is_sim + padded_action = np.zeros(original_action_shape, dtype=np.float32) + padded_action[:action_len] = action + is_pad = np.zeros(episode_len) + is_pad[action_len:] = 1 + + # new axis for different cameras + all_cam_images = [] + for cam_name in self.camera_names: + all_cam_images.append(image_dict[cam_name]) + all_cam_images = np.stack(all_cam_images, axis=0) + + # construct observations + image_data = torch.from_numpy(all_cam_images) + qpos_data = torch.from_numpy(qpos).float() + action_data = torch.from_numpy(padded_action).float() + is_pad = torch.from_numpy(is_pad).bool() + + # channel last + image_data = torch.einsum('k h w c -> k c h w', image_data) + + # normalize image and change dtype to float + image_data = image_data / 255.0 + action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"] + qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"] + + return image_data, qpos_data, action_data, is_pad + + +def get_norm_stats(dataset_dir, num_episodes): + all_qpos_data = [] + all_action_data = [] + for episode_idx in range(num_episodes): + dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5') + with h5py.File(dataset_path, 'r') as root: + qpos = root['/observations/qpos'][()] + qvel = root['/observations/qvel'][()] + action = root['/action'][()] + all_qpos_data.append(torch.from_numpy(qpos)) + all_action_data.append(torch.from_numpy(action)) + all_qpos_data = torch.stack(all_qpos_data) + all_action_data = torch.stack(all_action_data) + all_action_data = all_action_data + + # normalize action data + action_mean = all_action_data.mean(dim=[0, 1], keepdim=True) + action_std = all_action_data.std(dim=[0, 1], keepdim=True) + action_std = torch.clip(action_std, 1e-2, 10) # clipping + + # normalize qpos data + qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True) + qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True) + qpos_std = torch.clip(qpos_std, 1e-2, 10) # clipping + + stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(), + "qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(), + "example_qpos": qpos} + + return stats + + +def load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val): + print(f'\nData from: {dataset_dir}\n') + # obtain train test split + train_ratio = 0.8 + shuffled_indices = np.random.permutation(num_episodes) + train_indices = shuffled_indices[:int(train_ratio * num_episodes)] + val_indices = shuffled_indices[int(train_ratio * num_episodes):] + + # obtain normalization stats for qpos and action + norm_stats = get_norm_stats(dataset_dir, num_episodes) + + # construct dataset and dataloader + train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats) + val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats) + train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) + val_dataloader = DataLoader(val_dataset, batch_size=batch_size_val, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) + + return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim + + +### env utils + +def sample_box_pose(): + x_range = [0.0, 0.2] + y_range = [0.4, 0.6] + z_range = [0.05, 0.05] + + ranges = np.vstack([x_range, y_range, z_range]) + cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) + + cube_quat = np.array([1, 0, 0, 0]) + return np.concatenate([cube_position, cube_quat]) + +def sample_insertion_pose(): + # Peg + x_range = [0.1, 0.2] + y_range = [0.4, 0.6] + z_range = [0.05, 0.05] + + ranges = np.vstack([x_range, y_range, z_range]) + peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) + + peg_quat = np.array([1, 0, 0, 0]) + peg_pose = np.concatenate([peg_position, peg_quat]) + + # Socket + x_range = [-0.2, -0.1] + y_range = [0.4, 0.6] + z_range = [0.05, 0.05] + + ranges = np.vstack([x_range, y_range, z_range]) + socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) + + socket_quat = np.array([1, 0, 0, 0]) + socket_pose = np.concatenate([socket_position, socket_quat]) + + return peg_pose, socket_pose + +### helper functions + +def compute_dict_mean(epoch_dicts): + result = {k: None for k in epoch_dicts[0]} + num_items = len(epoch_dicts) + for k in result: + value_sum = 0 + for epoch_dict in epoch_dicts: + value_sum += epoch_dict[k] + result[k] = value_sum / num_items + return result + +def detach_dict(d): + new_d = dict() + for k, v in d.items(): + new_d[k] = v.detach() + return new_d + +def set_seed(seed): + torch.manual_seed(seed) + np.random.seed(seed) diff --git a/act/visualize_episodes.py b/act/visualize_episodes.py new file mode 100644 index 00000000..4e55e471 --- /dev/null +++ b/act/visualize_episodes.py @@ -0,0 +1,147 @@ +import os +import numpy as np +import cv2 +import h5py +import argparse + +import matplotlib.pyplot as plt +from constants import DT + +import IPython +e = IPython.embed + +JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] +STATE_NAMES = JOINT_NAMES + ["gripper"] + +def load_hdf5(dataset_dir, dataset_name): + dataset_path = os.path.join(dataset_dir, dataset_name + '.hdf5') + if not os.path.isfile(dataset_path): + print(f'Dataset does not exist at \n{dataset_path}\n') + exit() + + with h5py.File(dataset_path, 'r') as root: + is_sim = root.attrs['sim'] + qpos = root['/observations/qpos'][()] + qvel = root['/observations/qvel'][()] + action = root['/action'][()] + image_dict = dict() + for cam_name in root[f'/observations/images/'].keys(): + image_dict[cam_name] = root[f'/observations/images/{cam_name}'][()] + + return qpos, qvel, action, image_dict + +def main(args): + dataset_dir = args['dataset_dir'] + episode_idx = args['episode_idx'] + dataset_name = f'episode_{episode_idx}' + + qpos, qvel, action, image_dict = load_hdf5(dataset_dir, dataset_name) + save_videos(image_dict, DT, video_path=os.path.join(dataset_dir, dataset_name + '_video.mp4')) + visualize_joints(qpos, action, plot_path=os.path.join(dataset_dir, dataset_name + '_qpos.png')) + # visualize_timestamp(t_list, dataset_path) # TODO addn timestamp back + + +def save_videos(video, dt, video_path=None): + if isinstance(video, list): + cam_names = list(video[0].keys()) + h, w, _ = video[0][cam_names[0]].shape + w = w * len(cam_names) + fps = int(1/dt) + out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + for ts, image_dict in enumerate(video): + images = [] + for cam_name in cam_names: + image = image_dict[cam_name] + image = image[:, :, [2, 1, 0]] # swap B and R channel + images.append(image) + images = np.concatenate(images, axis=1) + out.write(images) + out.release() + print(f'Saved video to: {video_path}') + elif isinstance(video, dict): + cam_names = list(video.keys()) + all_cam_videos = [] + for cam_name in cam_names: + all_cam_videos.append(video[cam_name]) + all_cam_videos = np.concatenate(all_cam_videos, axis=2) # width dimension + + n_frames, h, w, _ = all_cam_videos.shape + fps = int(1 / dt) + out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + for t in range(n_frames): + image = all_cam_videos[t] + image = image[:, :, [2, 1, 0]] # swap B and R channel + out.write(image) + out.release() + print(f'Saved video to: {video_path}') + + +def visualize_joints(qpos_list, command_list, plot_path=None, ylim=None, label_overwrite=None): + if label_overwrite: + label1, label2 = label_overwrite + else: + label1, label2 = 'State', 'Command' + + qpos = np.array(qpos_list) # ts, dim + command = np.array(command_list) + num_ts, num_dim = qpos.shape + h, w = 2, num_dim + num_figs = num_dim + fig, axs = plt.subplots(num_figs, 1, figsize=(w, h * num_figs)) + + # plot joint state + all_names = [name + '_left' for name in STATE_NAMES] + [name + '_right' for name in STATE_NAMES] + for dim_idx in range(num_dim): + ax = axs[dim_idx] + ax.plot(qpos[:, dim_idx], label=label1) + ax.set_title(f'Joint {dim_idx}: {all_names[dim_idx]}') + ax.legend() + + # plot arm command + for dim_idx in range(num_dim): + ax = axs[dim_idx] + ax.plot(command[:, dim_idx], label=label2) + ax.legend() + + if ylim: + for dim_idx in range(num_dim): + ax = axs[dim_idx] + ax.set_ylim(ylim) + + plt.tight_layout() + plt.savefig(plot_path) + print(f'Saved qpos plot to: {plot_path}') + plt.close() + +def visualize_timestamp(t_list, dataset_path): + plot_path = dataset_path.replace('.pkl', '_timestamp.png') + h, w = 4, 10 + fig, axs = plt.subplots(2, 1, figsize=(w, h*2)) + # process t_list + t_float = [] + for secs, nsecs in t_list: + t_float.append(secs + nsecs * 10E-10) + t_float = np.array(t_float) + + ax = axs[0] + ax.plot(np.arange(len(t_float)), t_float) + ax.set_title(f'Camera frame timestamps') + ax.set_xlabel('timestep') + ax.set_ylabel('time (sec)') + + ax = axs[1] + ax.plot(np.arange(len(t_float)-1), t_float[:-1] - t_float[1:]) + ax.set_title(f'dt') + ax.set_xlabel('timestep') + ax.set_ylabel('time (sec)') + + plt.tight_layout() + plt.savefig(plot_path) + print(f'Saved timestamp plot to: {plot_path}') + plt.close() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_dir', action='store', type=str, help='Dataset dir.', required=True) + parser.add_argument('--episode_idx', action='store', type=int, help='Episode index.', required=False) + main(vars(parser.parse_args())) diff --git a/oxe_conversion.sh b/oxe_conversion.sh new file mode 100644 index 00000000..86b50bb5 --- /dev/null +++ b/oxe_conversion.sh @@ -0,0 +1,38 @@ +#!/bin/bash +datasets=( + "austin_buds_dataset_converted_externally_to_rlds" + "jaco_play" + "austin_sailor_dataset_converted_externally_to_rlds" + "kuka" + "austin_sirius_dataset_converted_externally_to_rlds" + # "language_table" + "bc_z" + "nyu_door_opening_surprising_effectiveness" + "berkeley_autolab_ur5" + "nyu_franka_play_dataset_converted_externally_to_rlds" + "berkeley_cable_routing" + "ppgm" + "berkeley_fanuc_manipulation" + "roboturk" + "bridge_dataset" + "stanford_hydra_dataset_converted_externally_to_rlds" + # "bridge_orig" + "taco_play" + "cmu_stretch" + "toto" + "dlr_edan_shared_control_converted_externally_to_rlds" + "ucsd_kitchen_dataset_converted_externally_to_rlds" + "fractal20220817_data" + "utaustin_mutex" + "furniture_bench_dataset_converted_externally_to_rlds" + "viola" + "iamlab_cmu_pickup_insert_converted_externally_to_rlds" +) + +for ((i=0; i<${#datasets[@]}; i++)); do + # Get the current command + d="${datasets[$i]}" + + /mnt/fsx/surajnair/mambaforge/envs/octo/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/oxe_to_h5.py --out_dir=/mnt/fsx/surajnair/datasets/oxe_hdf5/$d --dataset=$d; + done +done diff --git a/requirements.txt b/requirements.txt index 6f64af97..c451769d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,3 +11,5 @@ matplotlib egl_probe>=1.0.1 torch torchvision +diffusers==0.11.1 +pytorch3d==0.7.3 diff --git a/robomimic/algo/__init__.py b/robomimic/algo/__init__.py index 68d70a57..5fd1d53f 100644 --- a/robomimic/algo/__init__.py +++ b/robomimic/algo/__init__.py @@ -9,3 +9,5 @@ from robomimic.algo.hbc import HBC from robomimic.algo.iris import IRIS from robomimic.algo.td3_bc import TD3_BC +from robomimic.algo.diffusion_policy import DiffusionPolicyUNet +from robomimic.algo.act import ACT diff --git a/robomimic/algo/act.py b/robomimic/algo/act.py new file mode 100644 index 00000000..d0c0024d --- /dev/null +++ b/robomimic/algo/act.py @@ -0,0 +1,265 @@ +""" +Implementation of Action Chunking with Transformers (ACT). +""" +from collections import OrderedDict +from functools import reduce + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms + +import robomimic.utils.tensor_utils as TensorUtils + +from robomimic.algo import register_algo_factory_func, PolicyAlgo +from robomimic.algo.bc import BC_VAE + + +@register_algo_factory_func("act") +def algo_config_to_class(algo_config): + """ + Maps algo config to the BC algo class to instantiate, along with additional algo kwargs. + + Args: + algo_config (Config instance): algo config + + Returns: + algo_class: subclass of Algo + algo_kwargs (dict): dictionary of additional kwargs to pass to algorithm + """ + algo_class, algo_kwargs = ACT, {} + + return algo_class, algo_kwargs + + +class ACT(BC_VAE): + """ + BC training with a VAE policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + + self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + self.nets = nn.ModuleDict() + self.chunk_size = self.global_config["train"]["seq_length"] + self.camera_keys = self.obs_config['modalities']['obs']['rgb'].copy() + self.proprio_keys = self.obs_config['modalities']['obs']['low_dim'].copy() + self.obs_keys = self.proprio_keys + self.camera_keys + + self.proprio_dim = 0 + for k in self.proprio_keys: + self.proprio_dim += reduce(lambda x, y: x*y, self.obs_key_shapes[k]) + + from act.detr.main import build_ACT_model_and_optimizer + policy_config = {'num_queries': self.chunk_size, + 'hidden_dim': self.algo_config.act.hidden_dim, + 'dim_feedforward': self.algo_config.act.dim_feedforward, + 'backbone': self.algo_config.act.backbone, + 'enc_layers': self.algo_config.act.enc_layers, + 'dec_layers': self.algo_config.act.dec_layers, + 'nheads': self.algo_config.act.nheads, + 'latent_dim': self.algo_config.act.latent_dim, + 'a_dim': self.ac_dim, + 'state_dim': self.proprio_dim, + 'camera_names': self.camera_keys + } + self.kl_weight = self.algo_config.act.kl_weight + model, optimizer = build_ACT_model_and_optimizer(policy_config) + self.nets["policy"] = torch.nn.parallel.DataParallel(model, device_ids=list(range(0,torch.cuda.device_count()))) + self.nets = self.nets.float().to(self.device) + + self.temporal_agg = False + self.query_frequency = self.chunk_size # TODO maybe tune + + self._step_counter = 0 + self.a_hat_store = None + + + def process_batch_for_training(self, batch): + """ + Processes input batch from a data loader to filter out + relevant information and prepare the batch for training. + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader + Returns: + input_batch (dict): processed and filtered batch that + will be used for training + """ + + input_batch = dict() + input_batch["obs"] = {k: batch["obs"][k][:, 0, :] for k in batch["obs"] if k != 'pad_mask'} + input_batch["obs"]['pad_mask'] = batch["obs"]['pad_mask'] + input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present + # For the goal, only keep the camera observations + if input_batch["goal_obs"] is not None: + input_batch["goal_obs"] = {key: value for key, value in batch["goal_obs"].items() if key.startswith('camera')} + input_batch["actions"] = batch["actions"][:, :, :] + # we move to device first before float conversion because image observation modalities will be uint8 - + # this minimizes the amount of data transferred to GPU + return TensorUtils.to_float(TensorUtils.to_device(input_batch, self.device)) + + def train_on_batch(self, batch, epoch, validate=False): + """ + Update from superclass to set categorical temperature, for categorcal VAEs. + """ + + return super(BC_VAE, self).train_on_batch(batch, epoch, validate=validate) + + def _forward_training(self, batch): + """ + Internal helper function for BC algo class. Compute forward pass + and return network outputs in @predictions dict. + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + Returns: + predictions (dict): dictionary containing network outputs + """ + # TODO (Ashwin): remove this once we add in the robomimic observation encoders + for k in self.proprio_keys: + batch["obs"][k] = batch["obs"][k].reshape((batch["obs"][k].shape[0], -1)) + proprio = [batch["obs"][k] for k in self.proprio_keys] + proprio = torch.cat(proprio, axis=1) + qpos = proprio + + images = [] + goal_images = [] + for cam_name in self.camera_keys: + image = batch['obs'][cam_name] + image = self.normalize(image) + image = image.unsqueeze(axis=1) + images.append(image) + goal_image = batch['goal_obs'][cam_name] + goal_image = self.normalize(goal_image) + goal_images.append(goal_image) + + images = torch.cat(images, axis=1) + goal_images = torch.cat(goal_images, axis=1) + + # Concatenate images and goal images channel-wise + images = torch.cat((images, goal_images), axis=1) + + env_state = torch.zeros([qpos.shape[0], 10]).cuda() # this is not used + + actions = batch['actions'] + is_pad = batch['obs']['pad_mask'] == 0 # from 1.0 or 0 to False and True + is_pad = is_pad.squeeze(dim=-1) + + a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state, actions, is_pad) + total_kld, dim_wise_kld, mean_kld = self.kl_divergence(mu, logvar) + loss_dict = dict() + all_l1 = F.l1_loss(actions, a_hat, reduction='none') + l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() + loss_dict['l1'] = l1 + loss_dict['kl'] = total_kld[0] + + + predictions = OrderedDict( + actions=actions, + kl_loss=loss_dict['kl'], + reconstruction_loss=loss_dict['l1'], + ) + + return predictions + + def get_action(self, obs_dict, goal_dict=None): + """ + Get policy action outputs. + Args: + obs_dict (dict): current observation + goal_dict (dict): (optional) goal + Returns: + action (torch.Tensor): action tensor + """ + assert not self.nets.training + + # TODO (Ashwin): remove this once we add in the robomimic observation encoders + for k in self.proprio_keys: + obs_dict[k] = obs_dict[k].reshape((obs_dict[k].shape[0], -1)) + proprio = [obs_dict[k] for k in self.proprio_keys] + proprio = torch.cat(proprio, axis=1) + qpos = proprio + + images = [] + for cam_name in self.camera_keys: + image = obs_dict[cam_name] + image = self.normalize(image) + image = image.unsqueeze(axis=1) + images.append(image) + images = torch.cat(images, axis=1) + + env_state = torch.zeros([qpos.shape[0], 10]).cuda() # not used + + if self._step_counter % self.query_frequency == 0: + a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state) + self.a_hat_store = a_hat + + action = self.a_hat_store[:, self._step_counter % self.query_frequency, :] + self._step_counter += 1 + return action + + + def reset(self): + """ + Reset algo state to prepare for environment rollouts. + """ + self._step_counter = 0 + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + Returns: + losses (dict): dictionary of losses computed over the batch + """ + + # total loss is sum of reconstruction and KL, weighted by beta + kl_loss = predictions["kl_loss"] + recons_loss = predictions["reconstruction_loss"] + action_loss = recons_loss + self.kl_weight * kl_loss + return OrderedDict( + recons_loss=recons_loss, + kl_loss=kl_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + Args: + info (dict): dictionary of info + Returns: + loss_log (dict): name -> summary statistic + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["KL_Loss"] = info["losses"]["kl_loss"].item() + log["Reconstruction_Loss"] = info["losses"]["recons_loss"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + def kl_divergence(self, mu, logvar): + batch_size = mu.size(0) + assert batch_size != 0 + if mu.data.ndimension() == 4: + mu = mu.view(mu.size(0), mu.size(1)) + if logvar.data.ndimension() == 4: + logvar = logvar.view(logvar.size(0), logvar.size(1)) + + klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) + total_kld = klds.sum(1).mean(0, True) + dimension_wise_kld = klds.mean(0) + mean_kld = klds.mean(1).mean(0, True) + + return total_kld, dimension_wise_kld, mean_kld + diff --git a/robomimic/algo/algo.py b/robomimic/algo/algo.py index 9e508174..1e1e93e6 100644 --- a/robomimic/algo/algo.py +++ b/robomimic/algo/algo.py @@ -10,13 +10,21 @@ import textwrap from copy import deepcopy from collections import OrderedDict +import random import torch.nn as nn +import torch +import os +import numpy as np +import imageio import robomimic.utils.tensor_utils as TensorUtils import robomimic.utils.torch_utils as TorchUtils import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.action_utils as AcUtils +import robomimic.utils.vis_utils as VisUtils +from torch.utils.data import DataLoader # mapping from algo name to factory functions that map algo configs to algo class names REGISTERED_ALGO_FACTORY_FUNCS = OrderedDict() @@ -225,7 +233,7 @@ def postprocess_batch_for_training(self, batch, obs_normalization_stats): if k in batch and batch[k] is not None: batch[k] = ObsUtils.process_obs_dict(batch[k]) if obs_normalization_stats is not None: - batch[k] = ObsUtils.normalize_obs(batch[k], obs_normalization_stats=obs_normalization_stats) + batch[k] = ObsUtils.normalize_dict(batch[k], obs_normalization_stats=obs_normalization_stats) return batch def train_on_batch(self, batch, epoch, validate=False): @@ -337,6 +345,176 @@ def get_action(self, obs_dict, goal_dict=None): """ raise NotImplementedError + def compute_traj_pred_actual_actions(self, traj, return_images=False): + """ + traj is an R2D2Dataset object representing one trajectory + This function is slow (>1s per trajectory) because there is no batching + and instead loops through all timesteps one by one + TODO: documentation + """ + if return_images: + image_keys = [item for item in traj.__getitem__(0)['obs'].keys() if "image" in item] + images = {key: [] for key in image_keys} + else: + images = None + + dataloader = DataLoader( + dataset=traj, + sampler=None, + batch_size=1, + shuffle=False, + num_workers=1, + drop_last=True, + ) + + self.reset() + actual_actions = [] + predicted_actions = [] + + # loop through each timestep + for batch in iter(dataloader): + batch = self.process_batch_for_training(batch) + + if return_images: + for image_key in image_keys: + im = batch["obs"][image_key][0][-1] + im = TensorUtils.to_numpy(im).astype(np.uint32) + images[image_key].append(im) + + batch = self.postprocess_batch_for_training(batch, obs_normalization_stats=None) # ignore obs_normalization for now + + model_output = self.get_action(batch["obs"]) + + actual_action = TensorUtils.to_numpy( + batch["actions"][0][0] + ) + predicted_action = TensorUtils.to_numpy( + model_output[0] + ) + + actual_actions.append(actual_action) + predicted_actions.append(predicted_action) + + actual_actions = np.array(actual_actions) + predicted_actions = np.array(predicted_actions) + return actual_actions, predicted_actions, images + + def compute_batch_visualize(self, batch, num_samples, savedir=None): + visualize = savedir is not None + + varied_cam_1_images = batch["obs"]['camera/image/varied_camera_1_left_image'][:num_samples][:, 0, :, :, :] + varied_cam_2_images = batch["obs"]['camera/image/varied_camera_2_left_image'][:num_samples][:, 0, :, :, :] + images = { + "varied_camera_1_image": varied_cam_1_images, + "varied_camera_2_image": varied_cam_2_images + } + + if visualize: + print("Saving batch visualization plots to {}".format(savedir)) + + vis_log = {} + if visualize: + save_path = os.path.join(savedir, "batch_images.png") + VisUtils.make_batch_vis_plot( + save_path=save_path, + images=images, + ) + try: + vis_log[traj_key] = imageio.imread(save_path) + except: + pass + + return vis_log + + def compute_mse_visualize(self, trainset, validset, num_samples, savedir=None): + """If savedir is not None, then also visualize the model predictions and save them to savedir""" + visualize = savedir is not None + + # set model into eval mode + self.set_eval() + eval_data = [d for d in trainset.datasets if "eval" in d.hdf5_path] + broad_data = [d for d in trainset.datasets if "eval" not in d.hdf5_path] + if len(eval_data) < int(num_samples / 2): + training_sampled_data = random.sample(broad_data, int(num_samples)) + elif len(broad_data) < int(num_samples / 2): + training_sampled_data = random.sample(eval_data, int(num_samples)) + else: + training_sampled_data = random.sample(eval_data, int(num_samples / 2)) + random.sample(broad_data, int(num_samples / 2)) + + + if validset is not None: + random_state = np.random.RandomState(0) + valid_indices = random_state.choice( + len(validset.datasets), + min(len(validset.datasets), num_samples) + ).astype(int) + validation_sampled_data = [validset.datasets[idx] for idx in valid_indices] + + inference_datasets_mapping = {"Train": training_sampled_data, "Valid": validation_sampled_data} + else: + inference_datasets_mapping = {"Train": training_sampled_data} + + # extract action name for visualization + action_keys = self.global_config.train.action_keys + training_sample=training_sampled_data[0][0] + modified_action_keys = [element.replace("action/", "") for element in action_keys] + action_names = [] + + for i, action_key in enumerate(action_keys): + if isinstance(training_sample[action_key][0], np.ndarray): + action_names.extend([f'{modified_action_keys[i]}_{j+1}' for j in range(len(training_sample[action_key][0]))]) + else: + action_names.append(modified_action_keys[i]) + + if visualize: + print("Saving model prediction plots to {}".format(savedir)) + + mse_log = {} + vis_log = {} + # loop through training and validation sets + for inference_key in inference_datasets_mapping: + actual_actions_all_traj = [] # (NxT, D) + predicted_actions_all_traj = [] # (NxT, D) + + # loop through each trajectory + traj_num = 1 + for d in inference_datasets_mapping[inference_key]: + actual_actions, predicted_actions, images = self.compute_traj_pred_actual_actions(d, return_images=visualize) + actual_actions_all_traj.append(actual_actions) + predicted_actions_all_traj.append(predicted_actions) + if visualize: + traj_key = "{}_traj_{}".format(inference_key.lower(), traj_num) + save_path = os.path.join(savedir, traj_key + ".png") + VisUtils.make_model_prediction_plot( + hdf5_path=d.hdf5_path, + save_path=save_path, + images=images, + action_names=action_names, + actual_actions=actual_actions, + predicted_actions=predicted_actions, + ) + try: + vis_log[traj_key] = imageio.imread(save_path) + except: + pass + traj_num += 1 + + actual_actions_all_traj = np.concatenate(actual_actions_all_traj, axis=0) + predicted_actions_all_traj = np.concatenate(predicted_actions_all_traj, axis=0) + accuracy_thresholds = np.logspace(-3,-5, num=3).tolist() + mse = torch.nn.functional.mse_loss( + torch.tensor(predicted_actions_all_traj), + torch.tensor(actual_actions_all_traj), + reduction='none' + ) # (NxT, D) + mse_log[f'{inference_key}/action_mse_error'] = mse.mean().item() # average MSE across all timesteps averaged across all action dimensions (D,) + + # compute percentage of timesteps that have MSE less than the accuracy thresholds + for accuracy_threshold in accuracy_thresholds: + mse_log[f'{inference_key}/action_accuracy@{accuracy_threshold}'] = (torch.less(mse,accuracy_threshold).float().mean().item()) + + return mse_log, vis_log + class ValueAlgo(Algo): """ @@ -448,7 +626,7 @@ class RolloutPolicy(object): """ Wraps @Algo object to make it easy to run policies in a rollout loop. """ - def __init__(self, policy, obs_normalization_stats=None): + def __init__(self, policy, obs_normalization_stats=None, action_normalization_stats=None): """ Args: policy (Algo instance): @Algo object to wrap to prepare for rollouts @@ -460,6 +638,7 @@ def __init__(self, policy, obs_normalization_stats=None): """ self.policy = policy self.obs_normalization_stats = obs_normalization_stats + self.action_normalization_stats = action_normalization_stats def start_episode(self): """ @@ -477,7 +656,7 @@ def _prepare_observation(self, ob): and np.array values for each key) """ if self.obs_normalization_stats is not None: - ob = ObsUtils.normalize_obs(ob, obs_normalization_stats=self.obs_normalization_stats) + ob = ObsUtils.normalize_dict(ob, obs_normalization_stats=self.obs_normalization_stats) ob = TensorUtils.to_tensor(ob) ob = TensorUtils.to_batch(ob) ob = TensorUtils.to_device(ob, self.policy.device) @@ -500,5 +679,25 @@ def __call__(self, ob, goal=None): ob = self._prepare_observation(ob) if goal is not None: goal = self._prepare_observation(goal) - ac = self.policy.get_action(obs_dict=ob, goal_dict=goal) - return TensorUtils.to_numpy(ac[0]) + ac = self.policy.get_action(obs_dict=ob, goal_mode=self.goal_mode, eval_mode=self.eval_mode) + ac = TensorUtils.to_numpy(ac[0]) + if self.action_normalization_stats is not None: + action_keys = self.policy.global_config.train.action_keys + action_shapes = {k: self.action_normalization_stats[k]["offset"].shape[1:] for k in self.action_normalization_stats} + ac_dict = AcUtils.vector_to_action_dict(ac, action_shapes=action_shapes, action_keys=action_keys) + ac_dict = ObsUtils.unnormalize_dict(ac_dict, normalization_stats=self.action_normalization_stats) + action_config = self.policy.global_config.train.action_config + for key, value in ac_dict.items(): + this_format = action_config[key].get("format", None) + if this_format == "rot_6d": + rot_6d = torch.from_numpy(value).unsqueeze(0) + conversion_format = action_config[key].get("convert_at_runtime", "rot_axis_angle") + if conversion_format == "rot_axis_angle": + rot = TorchUtils.rot_6d_to_axis_angle(rot_6d=rot_6d).squeeze().numpy() + elif conversion_format == "rot_euler": + rot = TorchUtils.rot_6d_to_euler_angles(rot_6d=rot_6d, convention="XYZ").squeeze().numpy() + else: + raise ValueError + ac_dict[key] = rot + ac = AcUtils.action_dict_to_vector(ac_dict, action_keys=action_keys) + return ac diff --git a/robomimic/algo/bc.py b/robomimic/algo/bc.py index c5674f05..e8a6e955 100644 --- a/robomimic/algo/bc.py +++ b/robomimic/algo/bc.py @@ -207,6 +207,7 @@ def _train_step(self, losses): net=self.nets["policy"], optim=self.optimizers["policy"], loss=losses["action_loss"], + max_grad_norm=self.global_config.train.max_grad_norm, ) info["policy_grad_norms"] = policy_grad_norms return info @@ -683,13 +684,14 @@ def _create_networks(self): assert self.algo_config.transformer.enabled self.nets = nn.ModuleDict() - self.nets["policy"] = PolicyNets.TransformerActorNetwork( - obs_shapes=self.obs_shapes, - goal_shapes=self.goal_shapes, - ac_dim=self.ac_dim, - encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), - **BaseNets.transformer_args_from_config(self.algo_config.transformer), - ) + self.nets["policy"] = torch.nn.parallel.DataParallel( + PolicyNets.TransformerActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.transformer_args_from_config(self.algo_config.transformer), + ), device_ids=list(range(0,torch.cuda.device_count()))) self._set_params_from_config() self.nets = self.nets.float().to(self.device) @@ -700,6 +702,9 @@ def _set_params_from_config(self): """ self.context_length = self.algo_config.transformer.context_length self.supervise_all_steps = self.algo_config.transformer.supervise_all_steps + self.pred_future_acs = self.algo_config.transformer.pred_future_acs + if self.pred_future_acs: + assert self.supervise_all_steps is True def process_batch_for_training(self, batch): """ @@ -719,11 +724,18 @@ def process_batch_for_training(self, batch): if self.supervise_all_steps: # supervision on entire sequence (instead of just current timestep) - input_batch["actions"] = batch["actions"][:, :h, :] + if self.pred_future_acs: + ac_start = h - 1 + else: + ac_start = 0 + input_batch["actions"] = batch["actions"][:, ac_start:ac_start+h, :] else: # just use current timestep input_batch["actions"] = batch["actions"][:, h-1, :] + if self.pred_future_acs: + assert input_batch["actions"].shape[1] == h + input_batch = TensorUtils.to_device(TensorUtils.to_float(input_batch), self.device) return input_batch @@ -765,8 +777,19 @@ def get_action(self, obs_dict, goal_dict=None): """ assert not self.nets.training - return self.nets["policy"](obs_dict, actions=None, goal_dict=goal_dict)[:, -1, :] + output = self.nets["policy"](obs_dict=obs_dict, actions=None, goal_dict=goal_dict) + + if self.supervise_all_steps: + if self.algo_config.transformer.pred_future_acs: + output = output[:, 0, :] + else: + output = output[:, -1, :] + else: + output = output[:, -1, :] + + return output + class BC_Transformer_GMM(BC_Transformer): """ diff --git a/robomimic/algo/diffusion_policy.py b/robomimic/algo/diffusion_policy.py new file mode 100644 index 00000000..a38e8c8b --- /dev/null +++ b/robomimic/algo/diffusion_policy.py @@ -0,0 +1,786 @@ +""" +Implementation of Diffusion Policy https://diffusion-policy.cs.columbia.edu/ by Cheng Chi +""" +from typing import Callable, Union +import math +from collections import OrderedDict, deque +from packaging.version import parse as parse_version +import random +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +# requires diffusers==0.11.1 +from diffusers.schedulers.scheduling_ddpm import DDPMScheduler +from diffusers.schedulers.scheduling_ddim import DDIMScheduler +from diffusers.training_utils import EMAModel + +import robomimic.models.obs_nets as ObsNets +import robomimic.utils.tensor_utils as TensorUtils +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.obs_utils as ObsUtils + +from robomimic.algo import register_algo_factory_func, PolicyAlgo + +import random +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.tensor_utils as TensorUtils +import robomimic.utils.obs_utils as ObsUtils +import os + + +from transformers import AutoTokenizer, AutoModel +tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') +lang_model = AutoModel.from_pretrained("distilbert-base-uncased", torch_dtype=torch.float16) +lang_model.to('cuda') + + +# import torch.distributed as dist +# from torch.nn.parallel import DistributedDataParallel as DDP + +import cv2 +import copy + + +@register_algo_factory_func("diffusion_policy") +def algo_config_to_class(algo_config): + """ + Maps algo config to the BC algo class to instantiate, along with additional algo kwargs. + + Args: + algo_config (Config instance): algo config + + Returns: + algo_class: subclass of Algo + algo_kwargs (dict): dictionary of additional kwargs to pass to algorithm + """ + + if algo_config.unet.enabled: + return DiffusionPolicyUNet, {} + elif algo_config.transformer.enabled: + raise NotImplementedError() + else: + raise RuntimeError() + +class DiffusionPolicyUNet(PolicyAlgo): + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + # set up different observation groups for @MIMO_MLP + observation_group_shapes = OrderedDict() + observation_group_shapes["obs"] = OrderedDict(self.obs_shapes) + encoder_kwargs = ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder) + + obs_encoder = ObsNets.ObservationGroupEncoder( + observation_group_shapes=observation_group_shapes, + encoder_kwargs=encoder_kwargs, + ) + + + # IMPORTANT! + # replace all BatchNorm with GroupNorm to work with EMA + # performance will tank if you forget to do this! + obs_encoder = replace_bn_with_gn(obs_encoder) + + obs_dim = obs_encoder.output_shape()[0] + + # create network object + noise_pred_net = ConditionalUnet1D( + input_dim=self.ac_dim, + global_cond_dim=obs_dim*self.algo_config.horizon.observation_horizon + ) + + # the final arch has 2 parts + nets = nn.ModuleDict({ + 'policy': nn.ModuleDict({ + 'obs_encoder': torch.nn.parallel.DataParallel(obs_encoder, device_ids=list(range(0,torch.cuda.device_count()))), + 'noise_pred_net': torch.nn.parallel.DataParallel(noise_pred_net, device_ids=list(range(0,torch.cuda.device_count()))) + }) + }) + + nets = nets.float().to(self.device) + + # setup noise scheduler + noise_scheduler = None + if self.algo_config.ddpm.enabled: + noise_scheduler = DDPMScheduler( + num_train_timesteps=self.algo_config.ddpm.num_train_timesteps, + beta_schedule=self.algo_config.ddpm.beta_schedule, + clip_sample=self.algo_config.ddpm.clip_sample, + prediction_type=self.algo_config.ddpm.prediction_type + ) + elif self.algo_config.ddim.enabled: + noise_scheduler = DDIMScheduler( + num_train_timesteps=self.algo_config.ddim.num_train_timesteps, + beta_schedule=self.algo_config.ddim.beta_schedule, + clip_sample=self.algo_config.ddim.clip_sample, + set_alpha_to_one=self.algo_config.ddim.set_alpha_to_one, + steps_offset=self.algo_config.ddim.steps_offset, + prediction_type=self.algo_config.ddim.prediction_type + ) + else: + raise RuntimeError() + + # setup EMA + ema = None + if self.algo_config.ema.enabled: + ema = EMAModel(model=nets, power=self.algo_config.ema.power) + + # set attrs + self.nets = nets + self.noise_scheduler = noise_scheduler + self.ema = ema + self.action_check_done = False + self.obs_queue = None + self.action_queue = None + + def process_batch_for_training(self, batch): + """ + Processes input batch from a data loader to filter out + relevant information and prepare the batch for training. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader + + Returns: + input_batch (dict): processed and filtered batch that + will be used for training + """ + To = self.algo_config.horizon.observation_horizon + Ta = self.algo_config.horizon.action_horizon + Tp = self.algo_config.horizon.prediction_horizon + + input_batch = dict() + + ## Semi-hacky fix which does the filtering for raw language which is just a list of lists of strings + input_batch["obs"] = {k: batch["obs"][k][:, :To, :] for k in batch["obs"] if "raw" not in k } + if "lang_fixed/language_raw" in batch["obs"].keys(): + str_ls = list(batch['obs']['lang_fixed/language_raw'][0]) + input_batch["obs"]["lang_fixed/language_raw"] = [str_ls] * To + + with torch.no_grad(): + if "raw_language" in batch["obs"].keys(): + raw_lang_strings = [byte_string.decode('utf-8') for byte_string in batch["obs"]['raw_language']] + encoded_input = tokenizer(raw_lang_strings, padding=True, truncation=True, return_tensors='pt').to('cuda') + outputs = lang_model(**encoded_input) + encoded_lang = outputs.last_hidden_state.sum(1).squeeze().unsqueeze(1).repeat(1, To, 1) + input_batch["obs"]["lang_fixed/language_distilbert"] = encoded_lang.type(torch.float32) + + input_batch["actions"] = batch["actions"][:, :Tp, :] + + # check if actions are normalized to [-1,1] + if not self.action_check_done: + actions = input_batch["actions"] + in_range = (-1 <= actions) & (actions <= 1) + all_in_range = torch.all(in_range).item() + if not all_in_range: + raise ValueError('"actions" must be in range [-1,1] for Diffusion Policy! Check if hdf5_normalize_action is enabled.') + self.action_check_done = True + + ## LOGGING HOW MANY NANs there are + # bz = input_batch["actions"].shape[0] + # nanamt = torch.BoolTensor([False] * bz) + # for key in input_batch["obs"]: + # if key == "pad_mask": + # continue + # nanamt = torch.logical_or(nanamt, torch.isnan(input_batch["obs"][key].reshape(bz, -1).mean(1))) + # print(nanamt.float().mean()) + + for key in input_batch["obs"]: + input_batch["obs"][key] = torch.nan_to_num(input_batch["obs"][key]) + input_batch["actions"] = torch.nan_to_num(input_batch["actions"]) + + return TensorUtils.to_device(TensorUtils.to_float(input_batch), self.device) + + def train_on_batch(self, batch, epoch, validate=False): + """ + Training on a single batch of data. + + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + + epoch (int): epoch number - required by some Algos that need + to perform staged training and early stopping + + validate (bool): if True, don't perform any learning updates. + + Returns: + info (dict): dictionary of relevant inputs, outputs, and losses + that might be relevant for logging + """ + To = self.algo_config.horizon.observation_horizon + Ta = self.algo_config.horizon.action_horizon + Tp = self.algo_config.horizon.prediction_horizon + action_dim = self.ac_dim + B = batch['actions'].shape[0] + + + with TorchUtils.maybe_no_grad(no_grad=validate): + info = super(DiffusionPolicyUNet, self).train_on_batch(batch, epoch, validate=validate) + actions = batch['actions'] + + # encode obs + inputs = { + 'obs': batch["obs"], + } + for k in self.obs_shapes: + ## Shape assertion does not apply to list of strings for raw language + if "raw" in k: + continue + # first two dimensions should be [B, T] for inputs + assert inputs['obs'][k].ndim - 2 == len(self.obs_shapes[k]) + + obs_features = TensorUtils.time_distributed({"obs":inputs["obs"]}, self.nets['policy']['obs_encoder'], inputs_as_kwargs=True) + assert obs_features.ndim == 3 # [B, T, D] + obs_cond = obs_features.flatten(start_dim=1) + + num_noise_samples = self.algo_config.noise_samples + + # sample noise to add to actions + noise = torch.randn([num_noise_samples] + list(actions.shape), device=self.device) + + # sample a diffusion iteration for each data point + timesteps = torch.randint( + 0, self.noise_scheduler.config.num_train_timesteps, + (B,), device=self.device + ).long() + + # add noise to the clean actions according to the noise magnitude at each diffusion iteration + # (this is the forward diffusion process) + noisy_actions = torch.cat([self.noise_scheduler.add_noise( + actions, noise[i], timesteps) + for i in range(len(noise))], dim=0) + + obs_cond = obs_cond.repeat(num_noise_samples, 1) + timesteps = timesteps.repeat(num_noise_samples) + + # predict the noise residual + noise_pred = self.nets['policy']['noise_pred_net']( + noisy_actions, timesteps, global_cond=obs_cond) + + # L2 loss + noise = noise.view(noise.size(0) * noise.size(1), *noise.size()[2:]) + loss = F.mse_loss(noise_pred, noise) + + # logging + losses = { + 'l2_loss': loss + } + info["losses"] = TensorUtils.detach(losses) + + if not validate: + # gradient step + policy_grad_norms = TorchUtils.backprop_for_loss( + net=self.nets, + optim=self.optimizers["policy"], + loss=loss, + ) + + # update Exponential Moving Average of the model weights + if self.ema is not None: + self.ema.step(self.nets) + + step_info = { + 'policy_grad_norms': policy_grad_norms + } + info.update(step_info) + + return info + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + + Args: + info (dict): dictionary of info + + Returns: + loss_log (dict): name -> summary statistic + """ + log = super(DiffusionPolicyUNet, self).log_info(info) + log["Loss"] = info["losses"]["l2_loss"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + def reset(self): + """ + Reset algo state to prepare for environment rollouts. + """ + # setup inference queues + To = self.algo_config.horizon.observation_horizon + Ta = self.algo_config.horizon.action_horizon + obs_queue = deque(maxlen=To) + action_queue = deque(maxlen=Ta) + self.obs_queue = obs_queue + self.action_queue = action_queue + + def get_action(self, obs_dict, goal_mode=None, eval_mode=False): + """ + Get policy action outputs. + + Args: + obs_dict (dict): current observation [1, Do] + goal_dict (dict): (optional) goal + + Returns: + action (torch.Tensor): action tensor [1, Da] + """ + + # obs_dict: key: [1,D] + To = self.algo_config.horizon.observation_horizon + Ta = self.algo_config.horizon.action_horizon + + if eval_mode: + from r2d2.misc.parameters import hand_camera_id, varied_camera_1_id, varied_camera_2_id + root_path = os.path.join(os. getcwd(), "eval_params") + + if goal_mode is not None: + # Read in goal images + goal_hand_camera_left_image = torch.FloatTensor((cv2.cvtColor(cv2.imread(os.path.join(root_path, f"{hand_camera_id}_left.png")), cv2.COLOR_BGR2RGB) / 255.0)).cuda().permute(2, 0, 1).unsqueeze(0).repeat([1, 1, 1, 1]).unsqueeze(0) + goal_hand_camera_right_image = torch.FloatTensor((cv2.cvtColor(cv2.imread(os.path.join(root_path, f"{hand_camera_id}_right.png")), cv2.COLOR_BGR2RGB) / 255.0)).cuda().permute(2, 0, 1).unsqueeze(0).repeat([1, 1, 1, 1]).unsqueeze(0) + goal_varied_camera_1_left_image = torch.FloatTensor((cv2.cvtColor(cv2.imread(os.path.join(root_path, f"{varied_camera_1_id}_left.png")), cv2.COLOR_BGR2RGB) / 255.0)).cuda().permute(2, 0, 1).unsqueeze(0).repeat([1, 1, 1, 1]).unsqueeze(0) + goal_varied_camera_1_right_image = torch.FloatTensor((cv2.cvtColor(cv2.imread(os.path.join(root_path, f"{varied_camera_1_id}_right.png")), cv2.COLOR_BGR2RGB) / 255.0)).cuda().permute(2, 0, 1).unsqueeze(0).repeat([1, 1, 1, 1]).unsqueeze(0) + goal_varied_camera_2_left_image = torch.FloatTensor((cv2.cvtColor(cv2.imread(os.path.join(root_path, f"{varied_camera_2_id}_left.png")), cv2.COLOR_BGR2RGB) / 255.0)).cuda().permute(2, 0, 1).unsqueeze(0).repeat([1, 1, 1, 1]).unsqueeze(0) + goal_varied_camera_2_right_image = torch.FloatTensor((cv2.cvtColor(cv2.imread(os.path.join(root_path, f"{varied_camera_2_id}_right.png")), cv2.COLOR_BGR2RGB) / 255.0)).cuda().permute(2, 0, 1).unsqueeze(0).repeat([1, 1, 1, 1]).unsqueeze(0) + + obs_dict['camera/image/hand_camera_left_image'] = torch.cat([obs_dict['camera/image/hand_camera_left_image'], goal_hand_camera_left_image.repeat(1, To, 1, 1, 1)], dim=2) + obs_dict['camera/image/hand_camera_right_image'] = torch.cat([obs_dict['camera/image/hand_camera_right_image'], goal_hand_camera_right_image.repeat(1, To, 1, 1, 1)], dim=2) + obs_dict['camera/image/varied_camera_1_left_image'] = torch.cat([obs_dict['camera/image/varied_camera_1_left_image'], goal_varied_camera_1_left_image.repeat(1, To, 1, 1, 1)], dim=2) + obs_dict['camera/image/varied_camera_1_right_image'] = torch.cat([obs_dict['camera/image/varied_camera_1_right_image'] , goal_varied_camera_1_right_image.repeat(1, To, 1, 1, 1)], dim=2) + obs_dict['camera/image/varied_camera_2_left_image'] = torch.cat([obs_dict['camera/image/varied_camera_2_left_image'] , goal_varied_camera_2_left_image.repeat(1, To, 1, 1, 1)], dim=2) + obs_dict['camera/image/varied_camera_2_right_image'] = torch.cat([obs_dict['camera/image/varied_camera_2_right_image'], goal_varied_camera_2_right_image.repeat(1, To, 1, 1, 1)], dim=2) + # Note: currently assumes that you are never doing both goal and language conditioning + else: + # Reads in current language instruction from file and fills the appropriate obs key, only will + # actually use it if the policy uses language instructions + with open(os.path.join(root_path, "lang_command.txt"), 'r') as file: + raw_lang = file.read() + + encoded_input = tokenizer(raw_lang, return_tensors='pt').to('cuda') + outputs = lang_model(**encoded_input) + encoded_lang = outputs.last_hidden_state.sum(1).squeeze().unsqueeze(0).repeat(To, 1).unsqueeze(0) + obs_dict["lang_fixed/language_distilbert"] = encoded_lang.type(torch.float32) + + ############################### + + # TODO: obs_queue already handled by frame_stack + # make sure we have at least To observations in obs_queue + # if not enough, repeat + # if already full, append one to the obs_queue + # n_repeats = max(To - len(self.obs_queue), 1) + # self.obs_queue.extend([obs_dict] * n_repeats) + + if len(self.action_queue) == 0: + # no actions left, run inference + # turn obs_queue into dict of tensors (concat at T dim) + # import pdb; pdb.set_trace() + # obs_dict_list = TensorUtils.list_of_flat_dict_to_dict_of_list(list(self.obs_queue)) + # obs_dict_tensor = dict((k, torch.cat(v, dim=0).unsqueeze(0)) for k,v in obs_dict_list.items()) + + # run inference + # [1,T,Da] + action_sequence = self._get_action_trajectory(obs_dict=obs_dict) + + # put actions into the queue + self.action_queue.extend(action_sequence[0]) + + # has action, execute from left to right + # [Da] + action = self.action_queue.popleft() + + # [1,Da] + action = action.unsqueeze(0) + return action + + def _get_action_trajectory(self, obs_dict): + assert not self.nets.training + To = self.algo_config.horizon.observation_horizon + Ta = self.algo_config.horizon.action_horizon + Tp = self.algo_config.horizon.prediction_horizon + action_dim = self.ac_dim + if self.algo_config.ddpm.enabled is True: + num_inference_timesteps = self.algo_config.ddpm.num_inference_timesteps + elif self.algo_config.ddim.enabled is True: + num_inference_timesteps = self.algo_config.ddim.num_inference_timesteps + else: + raise ValueError + + # select network + nets = self.nets + if self.ema is not None: + nets = self.ema.averaged_model + + # encode obs + inputs = { + 'obs': obs_dict, + } + for k in self.obs_shapes: + ## Shape assertion does not apply to list of strings for raw language + if "raw" in k: + continue + # first two dimensions should be [B, T] for inputs + assert inputs['obs'][k].ndim - 2 == len(self.obs_shapes[k]) + obs_features = TensorUtils.time_distributed({"obs":inputs["obs"]}, nets['policy']['obs_encoder'].module, inputs_as_kwargs=True) + assert obs_features.ndim == 3 # [B, T, D] + B = obs_features.shape[0] + + # reshape observation to (B,obs_horizon*obs_dim) + obs_cond = obs_features.flatten(start_dim=1) + + + # initialize action from Guassian noise + noisy_action = torch.randn( + (B, Tp, action_dim), device=self.device) + naction = noisy_action + + # init scheduler + self.noise_scheduler.set_timesteps(num_inference_timesteps) + + for k in self.noise_scheduler.timesteps: + # predict noise + noise_pred = nets['policy']['noise_pred_net'].module( + sample=naction, + timestep=k, + global_cond=obs_cond + ) + + # inverse diffusion step (remove noise) + naction = self.noise_scheduler.step( + model_output=noise_pred, + timestep=k, + sample=naction + ).prev_sample + + # process action using Ta + start = To - 1 + end = start + Ta + action = naction[:,start:end] + return action + + def serialize(self): + """ + Get dictionary of current model parameters. + """ + return { + "nets": self.nets.state_dict(), + "ema": self.ema.averaged_model.state_dict() if self.ema is not None else None, + } + + def deserialize(self, model_dict): + """ + Load model from a checkpoint. + + Args: + model_dict (dict): a dictionary saved by self.serialize() that contains + the same keys as @self.network_classes + """ + self.nets.load_state_dict(model_dict["nets"]) + if model_dict.get("ema", None) is not None: + self.ema.averaged_model.load_state_dict(model_dict["ema"]) + + + + + +# =================== Vision Encoder Utils ===================== +def replace_submodules( + root_module: nn.Module, + predicate: Callable[[nn.Module], bool], + func: Callable[[nn.Module], nn.Module]) -> nn.Module: + """ + Replace all submodules selected by the predicate with + the output of func. + + predicate: Return true if the module is to be replaced. + func: Return new module to use. + """ + if predicate(root_module): + return func(root_module) + + if parse_version(torch.__version__) < parse_version('1.9.0'): + raise ImportError('This function requires pytorch >= 1.9.0') + + bn_list = [k.split('.') for k, m + in root_module.named_modules(remove_duplicate=True) + if predicate(m)] + for *parent, k in bn_list: + parent_module = root_module + if len(parent) > 0: + parent_module = root_module.get_submodule('.'.join(parent)) + if isinstance(parent_module, nn.Sequential): + src_module = parent_module[int(k)] + else: + src_module = getattr(parent_module, k) + tgt_module = func(src_module) + if isinstance(parent_module, nn.Sequential): + parent_module[int(k)] = tgt_module + else: + setattr(parent_module, k, tgt_module) + # verify that all modules are replaced + bn_list = [k.split('.') for k, m + in root_module.named_modules(remove_duplicate=True) + if predicate(m)] + assert len(bn_list) == 0 + return root_module + +def replace_bn_with_gn( + root_module: nn.Module, + features_per_group: int=16) -> nn.Module: + """ + Relace all BatchNorm layers with GroupNorm. + """ + replace_submodules( + root_module=root_module, + predicate=lambda x: isinstance(x, nn.BatchNorm2d), + func=lambda x: nn.GroupNorm( + num_groups=x.num_features//features_per_group, + num_channels=x.num_features) + ) + return root_module + +# =================== UNet for Diffusion ============== + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class Downsample1d(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = nn.Conv1d(dim, dim, 3, 2, 1) + + def forward(self, x): + return self.conv(x) + +class Upsample1d(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = nn.ConvTranspose1d(dim, dim, 4, 2, 1) + + def forward(self, x): + return self.conv(x) + + +class Conv1dBlock(nn.Module): + ''' + Conv1d --> GroupNorm --> Mish + ''' + + def __init__(self, inp_channels, out_channels, kernel_size, n_groups=8): + super().__init__() + + self.block = nn.Sequential( + nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2), + nn.GroupNorm(n_groups, out_channels), + nn.Mish(), + ) + + def forward(self, x): + return self.block(x) + + +class ConditionalResidualBlock1D(nn.Module): + def __init__(self, + in_channels, + out_channels, + cond_dim, + kernel_size=3, + n_groups=8): + super().__init__() + + self.blocks = nn.ModuleList([ + Conv1dBlock(in_channels, out_channels, kernel_size, n_groups=n_groups), + Conv1dBlock(out_channels, out_channels, kernel_size, n_groups=n_groups), + ]) + + # FiLM modulation https://arxiv.org/abs/1709.07871 + # predicts per-channel scale and bias + cond_channels = out_channels * 2 + self.out_channels = out_channels + self.cond_encoder = nn.Sequential( + nn.Mish(), + nn.Linear(cond_dim, cond_channels), + nn.Unflatten(-1, (-1, 1)) + ) + + # make sure dimensions compatible + self.residual_conv = nn.Conv1d(in_channels, out_channels, 1) \ + if in_channels != out_channels else nn.Identity() + + def forward(self, x, cond): + ''' + x : [ batch_size x in_channels x horizon ] + cond : [ batch_size x cond_dim] + + returns: + out : [ batch_size x out_channels x horizon ] + ''' + out = self.blocks[0](x) + embed = self.cond_encoder(cond) + + embed = embed.reshape( + embed.shape[0], 2, self.out_channels, 1) + scale = embed[:,0,...] + bias = embed[:,1,...] + out = scale * out + bias + + out = self.blocks[1](out) + out = out + self.residual_conv(x) + return out + + +class ConditionalUnet1D(nn.Module): + def __init__(self, + input_dim, + global_cond_dim, + diffusion_step_embed_dim=256, + down_dims=[256,512,1024], + kernel_size=5, + n_groups=8 + ): + """ + input_dim: Dim of actions. + global_cond_dim: Dim of global conditioning applied with FiLM + in addition to diffusion step embedding. This is usually obs_horizon * obs_dim + diffusion_step_embed_dim: Size of positional encoding for diffusion iteration k + down_dims: Channel size for each UNet level. + The length of this array determines numebr of levels. + kernel_size: Conv kernel size + n_groups: Number of groups for GroupNorm + """ + + super().__init__() + all_dims = [input_dim] + list(down_dims) + start_dim = down_dims[0] + + dsed = diffusion_step_embed_dim + diffusion_step_encoder = nn.Sequential( + SinusoidalPosEmb(dsed), + nn.Linear(dsed, dsed * 4), + nn.Mish(), + nn.Linear(dsed * 4, dsed), + ) + cond_dim = dsed + global_cond_dim + + in_out = list(zip(all_dims[:-1], all_dims[1:])) + mid_dim = all_dims[-1] + self.mid_modules = nn.ModuleList([ + ConditionalResidualBlock1D( + mid_dim, mid_dim, cond_dim=cond_dim, + kernel_size=kernel_size, n_groups=n_groups + ), + ConditionalResidualBlock1D( + mid_dim, mid_dim, cond_dim=cond_dim, + kernel_size=kernel_size, n_groups=n_groups + ), + ]) + + down_modules = nn.ModuleList([]) + for ind, (dim_in, dim_out) in enumerate(in_out): + is_last = ind >= (len(in_out) - 1) + down_modules.append(nn.ModuleList([ + ConditionalResidualBlock1D( + dim_in, dim_out, cond_dim=cond_dim, + kernel_size=kernel_size, n_groups=n_groups), + ConditionalResidualBlock1D( + dim_out, dim_out, cond_dim=cond_dim, + kernel_size=kernel_size, n_groups=n_groups), + Downsample1d(dim_out) if not is_last else nn.Identity() + ])) + + up_modules = nn.ModuleList([]) + for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])): + is_last = ind >= (len(in_out) - 1) + up_modules.append(nn.ModuleList([ + ConditionalResidualBlock1D( + dim_out*2, dim_in, cond_dim=cond_dim, + kernel_size=kernel_size, n_groups=n_groups), + ConditionalResidualBlock1D( + dim_in, dim_in, cond_dim=cond_dim, + kernel_size=kernel_size, n_groups=n_groups), + Upsample1d(dim_in) if not is_last else nn.Identity() + ])) + + final_conv = nn.Sequential( + Conv1dBlock(start_dim, start_dim, kernel_size=kernel_size), + nn.Conv1d(start_dim, input_dim, 1), + ) + + self.diffusion_step_encoder = diffusion_step_encoder + self.up_modules = up_modules + self.down_modules = down_modules + self.final_conv = final_conv + + print("number of parameters: {:e}".format( + sum(p.numel() for p in self.parameters())) + ) + + def forward(self, + sample: torch.Tensor, + timestep: Union[torch.Tensor, float, int], + global_cond=None): + """ + x: (B,T,input_dim) + timestep: (B,) or int, diffusion step + global_cond: (B,global_cond_dim) + output: (B,T,input_dim) + """ + # (B,T,C) + sample = sample.moveaxis(-1,-2) + # (B,C,T) + + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) + elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + global_feature = self.diffusion_step_encoder(timesteps) + + if global_cond is not None: + global_feature = torch.cat([ + global_feature, global_cond + ], axis=-1) + + x = sample + h = [] + for idx, (resnet, resnet2, downsample) in enumerate(self.down_modules): + x = resnet(x, global_feature) + x = resnet2(x, global_feature) + h.append(x) + x = downsample(x) + + for mid_module in self.mid_modules: + x = mid_module(x, global_feature) + + for idx, (resnet, resnet2, upsample) in enumerate(self.up_modules): + x = torch.cat((x, h.pop()), dim=1) + x = resnet(x, global_feature) + x = resnet2(x, global_feature) + x = upsample(x) + + x = self.final_conv(x) + + # (B,C,T) + x = x.moveaxis(-1,-2) + # (B,T,C) + return x diff --git a/robomimic/config/__init__.py b/robomimic/config/__init__.py index fa60a2f5..b8b9da73 100644 --- a/robomimic/config/__init__.py +++ b/robomimic/config/__init__.py @@ -9,4 +9,6 @@ from robomimic.config.gl_config import GLConfig from robomimic.config.hbc_config import HBCConfig from robomimic.config.iris_config import IRISConfig -from robomimic.config.td3_bc_config import TD3_BCConfig \ No newline at end of file +from robomimic.config.td3_bc_config import TD3_BCConfig +from robomimic.config.diffusion_policy_config import DiffusionPolicyConfig +from robomimic.config.act_config import ACTConfig diff --git a/robomimic/config/act_config.py b/robomimic/config/act_config.py new file mode 100644 index 00000000..9be3926b --- /dev/null +++ b/robomimic/config/act_config.py @@ -0,0 +1,47 @@ +""" +Config for BC algorithm. +""" + +from robomimic.config.base_config import BaseConfig + + +class ACTConfig(BaseConfig): + ALGO_NAME = "act" + + def train_config(self): + """ + BC algorithms don't need "next_obs" from hdf5 - so save on storage and compute by disabling it. + """ + super(ACTConfig, self).train_config() + self.train.hdf5_load_next_obs = False + + def algo_config(self): + """ + This function populates the `config.algo` attribute of the config, and is given to the + `Algo` subclass (see `algo/algo.py`) for each algorithm through the `algo_config` + argument to the constructor. Any parameter that an algorithm needs to determine its + training and test-time behavior should be populated here. + """ + + # optimization parameters + self.algo.optim_params.policy.optimizer_type = "adamw" + self.algo.optim_params.policy.learning_rate.initial = 5e-5 # policy learning rate + self.algo.optim_params.policy.learning_rate.decay_factor = 1 # factor to decay LR by (if epoch schedule non-empty) + self.algo.optim_params.policy.learning_rate.epoch_schedule = [] # epochs where LR decay occurs + self.algo.optim_params.policy.learning_rate.scheduler_type = "linear" # learning rate scheduler ("multistep", "linear", etc) + self.algo.optim_params.policy.regularization.L2 = 0.0001 # L2 regularization strength + + # loss weights + self.algo.loss.l2_weight = 0.0 # L2 loss weight + self.algo.loss.l1_weight = 1.0 # L1 loss weight + self.algo.loss.cos_weight = 0.0 # cosine loss weight + + # ACT policy settings + self.algo.act.hidden_dim = 512 # length of (s, a) seqeunces to feed to transformer - should usually match train.frame_stack + self.algo.act.dim_feedforward = 3200 # dimension for embeddings used by transformer + self.algo.act.backbone = "resnet18" # number of transformer blocks to stack + self.algo.act.enc_layers = 4 # number of attention heads for each transformer block (should divide embed_dim evenly) + self.algo.act.dec_layers = 7 # dropout probability for embedding inputs in transformer + self.algo.act.nheads = 8 # dropout probability for attention outputs for each transformer block + self.algo.act.latent_dim = 32 # latent dim of VAE + self.algo.act.kl_weight = 20 # KL weight of VAE diff --git a/robomimic/config/base_config.py b/robomimic/config/base_config.py index 3e7164de..5137b5e9 100644 --- a/robomimic/config/base_config.py +++ b/robomimic/config/base_config.py @@ -88,7 +88,13 @@ def experiment_config(self): self.experiment.logging.log_wandb = False # enable wandb logging self.experiment.logging.wandb_proj_name = "debug" # project name if using wandb - + # log model prediction MSE + self.experiment.mse.enabled = False # whether to log model prediction MSE + self.experiment.mse.every_n_epochs = 50 # log model prediction MSE every n epochs + self.experiment.mse.on_save_ckpt = True # log model prediction MSE on model checkpoint + self.experiment.mse.num_samples = 20 # number of datapoints to use for MSE prediction + self.experiment.mse.visualize = True # save model prediction visualizations + ## save config - if and when to save model checkpoints ## self.experiment.save.enabled = True # whether model saving should be enabled or disabled self.experiment.save.every_n_seconds = None # save model every n seconds (set to None to disable) @@ -122,6 +128,13 @@ def experiment_config(self): self.experiment.rollout.warmstart = 0 # number of epochs to wait before starting rollouts self.experiment.rollout.terminate_on_success = True # end rollout early after task success + # for updating the evaluation env meta data + self.experiment.env_meta_update_dict = Config() + self.experiment.env_meta_update_dict.do_not_lock_keys() + + # whether to load in a previously trained model checkpoint + self.experiment.ckpt_path = None + def train_config(self): """ This function populates the `config.train` attribute of the config, which @@ -187,9 +200,30 @@ class has a default implementation that usually doesn't need to be overriden. ) self.train.action_keys = ["actions"] - - # one of [None, "last"] - set to "last" to include goal observations in each batch + self.train.action_shapes = [(1, 1)] + + # specifing each action keys to load and their corresponding normalization/conversion requirement + # e.g. for dataset keys "action/eef_pos" and "action/eef_rot" + # the desired value of self.train.action_config is: + # { + # "action/eef_pos": { + # "normalization": "min_max", + # "rot_conversion: None + # }, + # "action/eef_rot": { + # "normalization": None, + # "rot_conversion: "axis_angle_to_6d" + # } + # } + # self.train.action_config.actions.normalization = None # "min_max" + # self.train.action_config.actions.rot_conversion = None # "axis_angle_to_6d" + self.train.action_config = {} + # self.train.action_config.do_not_lock_keys() + + # one of [None, "last", "geom"] - set to "last" or "geom" to include goal observations in each batch self.train.goal_mode = None + # Used only if using geometric goal sampling + self.train.truncated_geom_factor = None ## learning config ## @@ -198,8 +232,21 @@ class has a default implementation that usually doesn't need to be overriden. self.train.num_epochs = 2000 # number of training epochs self.train.seed = 1 # seed for training (for reproducibility) + self.train.max_grad_norm = None # clip gradient norms (see `backprop_for_loss` function in torch_utils.py) + self.train.data_format = "robomimic" # either "robomimic" or "r2d2" + # list of observation keys to shuffle randomly in the dataset. + # must be list of tuples pairs, with each pair representing + # the corresponding observation key groups to shuffle + self.train.shuffled_obs_key_groups = None + + # RLDS only + self.train.data_path = "" + self.train.shuffle_buffer_size = 100000 + self.train.sample_weights = [1, 1] + self.train.dataset_names = ["", ""] + def algo_config(self): """ This function populates the `config.algo` attribute of the config, and is given to the @@ -221,7 +268,7 @@ def observation_config(self): configs may choose to, in order to have seperate configs for different networks in the algorithm. """ - + self.observation.image_dim = [] # For RLDS # observation modalities self.observation.modalities.obs.low_dim = [ # specify low-dim observations for agent "robot0_eef_pos", @@ -253,9 +300,13 @@ def observation_config(self): self.observation.encoder.low_dim.obs_randomizer_kwargs.do_not_lock_keys() # =============== RGB default encoder (ResNet backbone + linear layer output) =============== + self.observation.encoder.rgb.fuser = None # How to combine the outputs of multi-camera vision encoders self.observation.encoder.rgb.core_class = "VisualCore" # Default VisualCore class combines backbone (like ResNet-18) with pooling operation (like spatial softmax) self.observation.encoder.rgb.core_kwargs = Config() # See models/obs_core.py for important kwargs to set and defaults used self.observation.encoder.rgb.core_kwargs.do_not_lock_keys() + # input keys for the encoder + self.observation.encoder.rgb.input_maps = Config() # mapping each obs key to encoder input map + self.observation.encoder.rgb.input_maps.do_not_lock_keys() # RGB: Obs Randomizer settings self.observation.encoder.rgb.obs_randomizer_class = None # Can set to 'CropRandomizer' to use crop randomization diff --git a/robomimic/config/bc_config.py b/robomimic/config/bc_config.py index 1f701c68..22376a66 100644 --- a/robomimic/config/bc_config.py +++ b/robomimic/config/bc_config.py @@ -104,3 +104,5 @@ def algo_config(self): self.algo.transformer.activation = "gelu" # activation function for MLP in Transformer Block self.algo.transformer.supervise_all_steps = False # if true, supervise all intermediate actions, otherwise only final one self.algo.transformer.nn_parameter_for_timesteps = True # if true, use nn.Parameter otherwise use nn.Embedding + self.algo.transformer.pred_future_acs = False # shift action prediction forward to predict future actions instead of past actions + self.algo.transformer.causal = True # whether the transformer is causal diff --git a/robomimic/config/diffusion_policy_config.py b/robomimic/config/diffusion_policy_config.py new file mode 100644 index 00000000..70a0f24e --- /dev/null +++ b/robomimic/config/diffusion_policy_config.py @@ -0,0 +1,58 @@ +""" +Config for Diffusion Policy algorithm. +""" + +from robomimic.config.base_config import BaseConfig + +class DiffusionPolicyConfig(BaseConfig): + ALGO_NAME = "diffusion_policy" + + def algo_config(self): + """ + This function populates the `config.algo` attribute of the config, and is given to the + `Algo` subclass (see `algo/algo.py`) for each algorithm through the `algo_config` + argument to the constructor. Any parameter that an algorithm needs to determine its + training and test-time behavior should be populated here. + """ + + # optimization parameters + self.algo.optim_params.policy.learning_rate.initial = 1e-4 # policy learning rate + self.algo.optim_params.policy.learning_rate.decay_factor = 0.1 # factor to decay LR by (if epoch schedule non-empty) + self.algo.optim_params.policy.learning_rate.epoch_schedule = [] # epochs where LR decay occurs + self.algo.optim_params.policy.regularization.L2 = 0.00 # L2 regularization strength + + # horizon parameters + self.algo.horizon.observation_horizon = 2 + self.algo.horizon.action_horizon = 8 + self.algo.horizon.prediction_horizon = 16 + + # UNet parameters + self.algo.unet.enabled = True + self.algo.unet.diffusion_step_embed_dim = 256 + self.algo.unet.down_dims = [256,512,1024] + self.algo.unet.kernel_size = 5 + self.algo.unet.n_groups = 8 + + # EMA parameters + self.algo.ema.enabled = True + self.algo.ema.power = 0.75 + + # Noise Scheduler + ## DDPM + self.algo.ddpm.enabled = True + self.algo.ddpm.num_train_timesteps = 100 + self.algo.ddpm.num_inference_timesteps = 100 + self.algo.ddpm.beta_schedule = 'squaredcos_cap_v2' + self.algo.ddpm.clip_sample = True + self.algo.ddpm.prediction_type = 'epsilon' + self.algo.noise_samples = 1 + + ## DDIM + self.algo.ddim.enabled = False + self.algo.ddim.num_train_timesteps = 50 #100 + self.algo.ddim.num_inference_timesteps = 10 + self.algo.ddim.beta_schedule = 'squaredcos_cap_v2' + self.algo.ddim.clip_sample = True + self.algo.ddim.set_alpha_to_one = True + self.algo.ddim.steps_offset = 0 + self.algo.ddim.prediction_type = 'epsilon' diff --git a/robomimic/envs/env_robosuite.py b/robomimic/envs/env_robosuite.py index dc66390a..2792de4f 100644 --- a/robomimic/envs/env_robosuite.py +++ b/robomimic/envs/env_robosuite.py @@ -7,7 +7,6 @@ import numpy as np from copy import deepcopy -import mujoco_py import robosuite import robomimic.utils.obs_utils as ObsUtils @@ -80,6 +79,7 @@ def __init__( self._env_name = env_name self._init_kwargs = deepcopy(kwargs) self.env = robosuite.make(self._env_name, **kwargs) + self.base_env = self.env # for mimicgen if self._is_v1: # Make sure joint position observations and eef vel observations are active @@ -200,7 +200,8 @@ def get_observation(self, di=None): # ensures that we don't accidentally add robot wrist images a second time pf = robot.robot_model.naming_prefix for k in di: - if k.startswith(pf) and (k not in ret) and (not k.endswith("proprio-state")): + if k.startswith(pf) and (k not in ret) and \ + (not k.endswith("proprio-state")): ret[k] = np.array(di[k]) else: # minimal proprioception for older versions of robosuite @@ -374,7 +375,7 @@ def rollout_exceptions(self): that the entire training run doesn't crash because of a bad policy that causes unstable simulation computations. """ - return (mujoco_py.builder.MujocoException) + return (Exception) def __repr__(self): """ diff --git a/robomimic/exps/templates/act.json b/robomimic/exps/templates/act.json new file mode 100644 index 00000000..4512ecdf --- /dev/null +++ b/robomimic/exps/templates/act.json @@ -0,0 +1,160 @@ +{ + "algo_name": "act", + "experiment": { + "name": "test", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": false, + "wandb_proj_name": "debug" + }, + "mse":{}, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 40, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": true, + "n": 50, + "horizon": 400, + "rate": 40, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir":"../act_trained_models", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 128, + "num_epochs": 10000, + "seed": 1 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adamw", + "learning_rate": { + "initial": 0.00005, + "decay_factor": 1, + "epoch_schedule": [ + 100 + ], + "scheduler_type": "linear" + }, + "regularization": { + "L2": 0.0001 + } + } + }, + "loss": { + "l2_weight": 0.0, + "l1_weight": 1.0, + "cos_weight": 0.0 + }, + "act": { + "hidden_dim": 512, + "dim_feedforward": 3200, + "backbone": "resnet18", + "enc_layers": 4, + "dec_layers": 7, + "nheads": 8, + "latent_dim": 32, + "kl_weight": 20 + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "robot0_eef_pos", + "robot0_eef_quat", + "robot0_gripper_qpos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0 + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} \ No newline at end of file diff --git a/robomimic/exps/templates/bc.json b/robomimic/exps/templates/bc.json index 82ad783f..a2c18105 100644 --- a/robomimic/exps/templates/bc.json +++ b/robomimic/exps/templates/bc.json @@ -9,6 +9,7 @@ "log_wandb": false, "wandb_proj_name": "debug" }, + "mse":{}, "save": { "enabled": true, "every_n_seconds": null, diff --git a/robomimic/exps/templates/bc_transformer.json b/robomimic/exps/templates/bc_transformer.json index c28696cb..0bb5c3d9 100644 --- a/robomimic/exps/templates/bc_transformer.json +++ b/robomimic/exps/templates/bc_transformer.json @@ -9,6 +9,7 @@ "log_wandb": false, "wandb_proj_name": "debug" }, + "mse":{}, "save": { "enabled": true, "every_n_seconds": null, @@ -64,7 +65,7 @@ "learning_rate": { "initial": 0.0001, "decay_factor": 0.1, - "epoch_schedule": [100], + "epoch_schedule": [], "scheduler_type": "linear" }, "regularization": { diff --git a/robomimic/exps/templates/diffusion_policy.json b/robomimic/exps/templates/diffusion_policy.json new file mode 100644 index 00000000..315a3907 --- /dev/null +++ b/robomimic/exps/templates/diffusion_policy.json @@ -0,0 +1,176 @@ +{ + "algo_name": "diffusion_policy", + "experiment": { + "name": "test", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": false, + "wandb_proj_name": "debug" + }, + "mse":{}, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": true, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir":"../diffusion_policy_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "seq_length": 15, + "pad_seq_length": true, + "frame_stack": 2, + "pad_frame_stack": true, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "truncated_geom_factor": null, + "cuda": true, + "batch_size": 256, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "optim_params": { + "policy": { + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [] + }, + "regularization": { + "L2": 0.0 + } + } + }, + "horizon": { + "observation_horizon": 2, + "action_horizon": 8, + "prediction_horizon": 16 + }, + "unet": { + "enabled": true, + "diffusion_step_embed_dim": 256, + "down_dims": [256,512,1024], + "kernel_size": 5, + "n_groups": 8 + }, + "ema": { + "enabled": true, + "power": 0.75 + }, + "ddpm": { + "enabled": true, + "num_train_timesteps": 100, + "num_inference_timesteps": 100, + "beta_schedule": "squaredcos_cap_v2", + "clip_sample": true, + "prediction_type": "epsilon" + }, + "ddim": { + "enabled": false, + "num_train_timesteps": 100, + "num_inference_timesteps": 10, + "beta_schedule": "squaredcos_cap_v2", + "clip_sample": true, + "set_alpha_to_one": true, + "steps_offset": 0, + "prediction_type": "epsilon" + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "robot0_eef_pos", + "robot0_eef_quat", + "robot0_gripper_qpos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0 + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} \ No newline at end of file diff --git a/robomimic/macros.py b/robomimic/macros.py index 3b6c0503..9d54a4cc 100644 --- a/robomimic/macros.py +++ b/robomimic/macros.py @@ -9,11 +9,11 @@ VISUALIZE_RANDOMIZER = False # wandb entity (eg. username or team name) -WANDB_ENTITY = None +WANDB_ENTITY = "tri" # wandb api key (obtain from https://wandb.ai/authorize) # alternatively, set up wandb from terminal with `wandb login` -WANDB_API_KEY = None +WANDB_API_KEY = "10eb447cdaa881ddb7748e1ebb4814706c2be903" try: from robomimic.macros_private import * diff --git a/robomimic/models/base_nets.py b/robomimic/models/base_nets.py index e81b241c..6c5d9b5c 100644 --- a/robomimic/models/base_nets.py +++ b/robomimic/models/base_nets.py @@ -530,6 +530,63 @@ def __repr__(self): return header + '(input_channel={}, input_coord_conv={})'.format(self._input_channel, self._input_coord_conv) +class ResNet50Conv(ConvBase): + """ + A ResNet50 block that can be used to process input images. + """ + def __init__( + self, + input_channel=3, + pretrained=False, + input_coord_conv=False, + ): + """ + Args: + input_channel (int): number of input channels for input images to the network. + If not equal to 3, modifies first conv layer in ResNet to handle the number + of input channels. + pretrained (bool): if True, load pretrained weights for all ResNet layers. + input_coord_conv (bool): if True, use a coordinate convolution for the first layer + (a convolution where input channels are modified to encode spatial pixel location) + """ + super(ResNet50Conv, self).__init__() + net = vision_models.resnet50(pretrained=pretrained) + + if input_coord_conv: + # copied from ResNet18Conv. TODO: check if sizes need to be changed + net.conv1 = CoordConv2d(input_channel, 64, kernel_size=7, stride=2, padding=3, bias=False) + elif input_channel != 3: + # copied from ResNet18Conv. TODO: check if sizes need to be changed + net.conv1 = nn.Conv2d(input_channel, 64, kernel_size=7, stride=2, padding=3, bias=False) + + # cut the last fc layer + self._input_coord_conv = input_coord_conv + self._input_channel = input_channel + self.nets = torch.nn.Sequential(*(list(net.children())[:-2])) + + def output_shape(self, input_shape): + """ + Function to compute output shape from inputs to this module. + + Args: + input_shape (iterable of int): shape of input. Does not include batch dimension. + Some modules may not need this argument, if their output does not depend + on the size of the input, or if they assume fixed size input. + + Returns: + out_shape ([int]): list of integers corresponding to output shape + """ + assert(len(input_shape) == 3) + out_h = int(math.ceil(input_shape[1] / 32.)) + out_w = int(math.ceil(input_shape[2] / 32.)) + return [2048, out_h, out_w] + + def __repr__(self): + """Pretty print network.""" + header = '{}'.format(str(self.__class__.__name__)) + return header + '(input_channel={}, input_coord_conv={})'.format(self._input_channel, self._input_coord_conv) + + class R3MConv(ConvBase): """ Base class for ConvNets pretrained with R3M (https://arxiv.org/abs/2203.12601) diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index 315dd2b5..4c9d35c9 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -7,6 +7,7 @@ import abc import numpy as np import textwrap +import random import torch import torch.nn as nn @@ -18,9 +19,13 @@ # NOTE: this is required for the backbone classes to be found by the `eval` call in the core networks from robomimic.models.base_nets import * +from robomimic.utils.camera_utils import DeFiNeImageCamEncoder, VoltronImageCamEncoder, R3MImageCamEncoder, VC1ImageCamEncoder +from robomimic.utils.camera_utils import ResNetFiLMEncoder from robomimic.utils.vis_utils import visualize_image_randomizer from robomimic.macros import VISUALIZE_RANDOMIZER +import torchvision.transforms.functional as TVF +from torchvision.transforms import Lambda, Compose """ ================================================ @@ -181,6 +186,418 @@ def __repr__(self): return msg +class PretrainedVisualCore(EncoderCore, BaseNets.ConvBase): + """ + Extension of VisualCore that uses a pretrained model + """ + def __init__( + self, + input_shape, + backbone_class="ResNet18Conv", + pool_class="SpatialSoftmax", + backbone_kwargs=None, + pool_kwargs=None, + flatten=True, + feature_dimension=64, + ): + """ + Args: + input_shape (tuple): shape of input (not including batch dimension) + backbone_class (str): class name for the visual backbone network. Defaults + to "ResNet18Conv". + pool_class (str): class name for the visual feature pooler (optional) + Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to + "SpatialSoftmax". + backbone_kwargs (dict): kwargs for the visual backbone network (optional) + pool_kwargs (dict): kwargs for the visual feature pooler (optional) + flatten (bool): whether to flatten the visual features + feature_dimension (int): if not None, add a Linear layer to + project output into a desired feature dimension + """ + super(PretrainedVisualCore, self).__init__(input_shape=input_shape) + self.flatten = flatten + + self.nets = nn.ModuleDict() + + if backbone_kwargs is None: + backbone_kwargs = dict() + + # add input channel dimension to visual core inputs + backbone_kwargs["input_channel"] = input_shape[0] + + # extract only relevant kwargs for this specific backbone + backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) + + # visual backbone + assert isinstance(backbone_class, str) + self.backbone = eval(backbone_class)(**backbone_kwargs) + + feat_shape = self.backbone.output_shape(input_shape) + self.feat_shape = feat_shape + # net_list = [self.backbone] + self.nets["backbone"] = self.backbone + + post_proc_list = [] + + # maybe make pool net + if pool_class is not None: + assert isinstance(pool_class, str) + # feed output shape of backbone to pool net + if pool_kwargs is None: + pool_kwargs = dict() + # extract only relevant kwargs for this specific backbone + pool_kwargs["input_shape"] = feat_shape + pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) + self.pool = eval(pool_class)(**pool_kwargs) + assert isinstance(self.pool, BaseNets.Module) + + feat_shape = self.pool.output_shape(feat_shape) + post_proc_list.append(self.pool) + else: + self.pool = None + + # flatten layer + if self.flatten: + post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) + + # maybe linear layer + self.feature_dimension = feature_dimension + if feature_dimension is not None: + assert self.flatten + post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) + + self.nets["post_proc"] = nn.Sequential(*post_proc_list) + + def output_shape(self, input_shape): + """ + Function to compute output shape from inputs to this module. + + Args: + input_shape (iterable of int): shape of input. Does not include batch dimension. + Some modules may not need this argument, if their output does not depend + on the size of the input, or if they assume fixed size input. + + Returns: + out_shape ([int]): list of integers corresponding to output shape + """ + if self.feature_dimension is not None: + # linear output + return [self.feature_dimension] + feat_shape = self.backbone.output_shape(input_shape) + if self.pool is not None: + # pool output + feat_shape = self.pool.output_shape(feat_shape) + # backbone + flat output + if self.flatten: + return [np.prod(feat_shape)] + else: + return feat_shape + + def forward(self, inputs): + """ + Forward pass through visual core. + """ + ndim = len(self.input_shape) + assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) + image = inputs["image"] + # Sometimes will be language conditioned image representation + lang_emb = inputs.get("lang_emb", None) + + x = self.nets["backbone"].forward(image=image, lang_emb=lang_emb) + # x = self.nets["backbone"].forward(inputs["image"]) + x = self.nets["post_proc"](x) + + return x + + def __repr__(self): + """Pretty print network.""" + header = '{}'.format(str(self.__class__.__name__)) + msg = '' + indent = ' ' * 2 + msg += textwrap.indent( + "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) + msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) + msg += textwrap.indent("\npool_net={}".format(self.pool), indent) + msg = header + '(' + msg + '\n)' + return msg + +class DeFiNeVisualCore(EncoderCore, BaseNets.ConvBase): + """ + Extension of VisualCore that takes in camera information + """ + def __init__( + self, + input_shape, + backbone_class="ResNet18Conv", + pool_class="SpatialSoftmax", + backbone_kwargs=None, + pool_kwargs=None, + flatten=True, + feature_dimension=64, + ): + """ + Args: + input_shape (tuple): shape of input (not including batch dimension) + backbone_class (str): class name for the visual backbone network. Defaults + to "ResNet18Conv". + pool_class (str): class name for the visual feature pooler (optional) + Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to + "SpatialSoftmax". + backbone_kwargs (dict): kwargs for the visual backbone network (optional) + pool_kwargs (dict): kwargs for the visual feature pooler (optional) + flatten (bool): whether to flatten the visual features + feature_dimension (int): if not None, add a Linear layer to + project output into a desired feature dimension + """ + super(DeFiNeVisualCore, self).__init__(input_shape=input_shape) + self.flatten = flatten + + self.nets = nn.ModuleDict() + + if backbone_kwargs is None: + backbone_kwargs = dict() + + # add input channel dimension to visual core inputs + backbone_kwargs["input_channel"] = input_shape[0] + + # extract only relevant kwargs for this specific backbone + backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) + + # visual backbone + assert isinstance(backbone_class, str) + self.backbone = eval(backbone_class)(**backbone_kwargs) + + feat_shape = self.backbone.output_shape(input_shape) + self.feat_shape = feat_shape + # net_list = [self.backbone] + self.nets["backbone"] = self.backbone + + post_proc_list = [] + + # maybe make pool net + if pool_class is not None: + assert isinstance(pool_class, str) + # feed output shape of backbone to pool net + if pool_kwargs is None: + pool_kwargs = dict() + # extract only relevant kwargs for this specific backbone + pool_kwargs["input_shape"] = feat_shape + pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) + self.pool = eval(pool_class)(**pool_kwargs) + assert isinstance(self.pool, BaseNets.Module) + + feat_shape = self.pool.output_shape(feat_shape) + post_proc_list.append(self.pool) + else: + self.pool = None + + # flatten layer + if self.flatten: + post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) + + # maybe linear layer + self.feature_dimension = feature_dimension + if feature_dimension is not None: + assert self.flatten + post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) + + self.nets["post_proc"] = nn.Sequential(*post_proc_list) + + def output_shape(self, input_shape): + """ + Function to compute output shape from inputs to this module. + + Args: + input_shape (iterable of int): shape of input. Does not include batch dimension. + Some modules may not need this argument, if their output does not depend + on the size of the input, or if they assume fixed size input. + + Returns: + out_shape ([int]): list of integers corresponding to output shape + """ + if self.feature_dimension is not None: + # linear output + return [self.feature_dimension] + feat_shape = self.backbone.output_shape(input_shape) + if self.pool is not None: + # pool output + feat_shape = self.pool.output_shape(feat_shape) + # backbone + flat output + if self.flatten: + return [np.prod(feat_shape)] + else: + return feat_shape + + def forward(self, inputs): + """ + Forward pass through visual core. + """ + ndim = len(self.input_shape) + assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) + image = inputs["image"] + intrinsics = inputs.get("intrinsics", None) + extrinsics = inputs.get("extrinsics", None) + lang_emb = inputs.get("lang_emb", None) + + # # TODO: replace with actual extrinsics + # extrinsics = TensorUtils.to_torch(np.array([[ + # [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], + # ]] * image.shape[0]), image.device) + + x = self.nets["backbone"].forward(image=image, intrinsics=intrinsics, extrinsics=extrinsics, lang_emb=lang_emb) + # x = self.nets["backbone"].forward(inputs["image"]) + x = self.nets["post_proc"](x) + + return x + + def __repr__(self): + """Pretty print network.""" + header = '{}'.format(str(self.__class__.__name__)) + msg = '' + indent = ' ' * 2 + msg += textwrap.indent( + "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) + msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) + msg += textwrap.indent("\npool_net={}".format(self.pool), indent) + msg = header + '(' + msg + '\n)' + return msg + + +class FiLMVisualCore(EncoderCore, BaseNets.ConvBase): + """ + Extension of VisualCore that takes in camera information + """ + def __init__( + self, + input_shape, + backbone_class="ResNet18Conv", + pool_class="SpatialSoftmax", + backbone_kwargs=None, + pool_kwargs=None, + flatten=True, + feature_dimension=64, + ): + """ + Args: + input_shape (tuple): shape of input (not including batch dimension) + backbone_class (str): class name for the visual backbone network. Defaults + to "ResNet18Conv". + pool_class (str): class name for the visual feature pooler (optional) + Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to + "SpatialSoftmax". + backbone_kwargs (dict): kwargs for the visual backbone network (optional) + pool_kwargs (dict): kwargs for the visual feature pooler (optional) + flatten (bool): whether to flatten the visual features + feature_dimension (int): if not None, add a Linear layer to + project output into a desired feature dimension + """ + super(FiLMVisualCore, self).__init__(input_shape=input_shape) + self.flatten = flatten + + self.nets = nn.ModuleDict() + + if backbone_kwargs is None: + backbone_kwargs = dict() + + # add input channel dimension to visual core inputs + backbone_kwargs["input_channel"] = input_shape[0] + + # extract only relevant kwargs for this specific backbone + backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) + + # visual backbone + assert isinstance(backbone_class, str) + self.backbone = eval(backbone_class)(**backbone_kwargs) + + feat_shape = self.backbone.output_shape(input_shape) + self.feat_shape = feat_shape + # net_list = [self.backbone] + self.nets["backbone"] = self.backbone + + post_proc_list = [] + + # maybe make pool net + if pool_class is not None: + assert isinstance(pool_class, str) + # feed output shape of backbone to pool net + if pool_kwargs is None: + pool_kwargs = dict() + # extract only relevant kwargs for this specific backbone + pool_kwargs["input_shape"] = feat_shape + pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) + self.pool = eval(pool_class)(**pool_kwargs) + assert isinstance(self.pool, BaseNets.Module) + + feat_shape = self.pool.output_shape(feat_shape) + post_proc_list.append(self.pool) + else: + self.pool = None + + # flatten layer + if self.flatten: + post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) + + # maybe linear layer + self.feature_dimension = feature_dimension + if feature_dimension is not None: + assert self.flatten + post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) + + self.nets["post_proc"] = nn.Sequential(*post_proc_list) + + def output_shape(self, input_shape): + """ + Function to compute output shape from inputs to this module. + + Args: + input_shape (iterable of int): shape of input. Does not include batch dimension. + Some modules may not need this argument, if their output does not depend + on the size of the input, or if they assume fixed size input. + + Returns: + out_shape ([int]): list of integers corresponding to output shape + """ + if self.feature_dimension is not None: + # linear output + return [self.feature_dimension] + feat_shape = self.backbone.output_shape(input_shape) + if self.pool is not None: + # pool output + feat_shape = self.pool.output_shape(feat_shape) + # backbone + flat output + if self.flatten: + return [np.prod(feat_shape)] + else: + return feat_shape + + def forward(self, inputs): + """ + Forward pass through visual core. + """ + ndim = len(self.input_shape) + assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) + image = inputs["image"] + lang_emb = inputs.get("lang_emb", None) + + x = self.nets["backbone"].forward(image=image, lang_emb=lang_emb) + # x = self.nets["backbone"].forward(inputs["image"]) + x = self.nets["post_proc"](x) + + return x + + def __repr__(self): + """Pretty print network.""" + header = '{}'.format(str(self.__class__.__name__)) + msg = '' + indent = ' ' * 2 + msg += textwrap.indent( + "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) + msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) + msg += textwrap.indent("\npool_net={}".format(self.pool), indent) + msg = header + '(' + msg + '\n)' + return msg + + """ ================================================ Scan Core Networks (Conv1D Sequential + Pool) @@ -663,7 +1080,10 @@ def get_batch_transform(self, N): each sub-set of samples along batch dimension, assumed to be the FIRST dimension in the inputted tensor Note: This function will MULTIPLY the first dimension by N """ - return Lambda(lambda x: torch.stack([self.get_transform()(x_) for x_ in x for _ in range(N)])) + return Lambda(lambda x: + torch.stack([ + self.get_transform()(x_) for x_ in x for _ in range(N)]) + ) def output_shape_in(self, input_shape=None): # outputs are same shape as inputs @@ -690,7 +1110,16 @@ def _forward_in(self, inputs): # Create lambda to aggregate all color randomizings at once transform = self.get_batch_transform(N=self.num_samples) - return transform(inputs) + ## Special case when image is frame stacked + ## Apply processsing to each image individually + if inputs.shape[1] == 6: + im1 = transform(inputs[:, :3]) + im2 = transform(inputs[:, 3:]) + out = torch.cat([im1, im2], 1) + else: + out = transform(inputs) + + return out def _forward_out(self, inputs): """ diff --git a/robomimic/models/obs_nets.py b/robomimic/models/obs_nets.py index b3284185..270c4d26 100644 --- a/robomimic/models/obs_nets.py +++ b/robomimic/models/obs_nets.py @@ -59,37 +59,68 @@ def obs_encoder_factory( obs_modality2: dict ... """ - enc = ObservationEncoder(feature_activation=feature_activation) + enc = ObservationEncoder(feature_activation=feature_activation, fuser = encoder_kwargs["rgb"]["fuser"]) for k, obs_shape in obs_shapes.items(): obs_modality = ObsUtils.OBS_KEYS_TO_MODALITIES[k] enc_kwargs = deepcopy(ObsUtils.DEFAULT_ENCODER_KWARGS[obs_modality]) if encoder_kwargs is None else \ deepcopy(encoder_kwargs[obs_modality]) + + # Sanity check for kwargs in case they don't exist / are None + if enc_kwargs.get("core_kwargs", None) is None: + enc_kwargs["core_kwargs"] = {} + # Add in input shape info + enc_kwargs["core_kwargs"]["input_shape"] = obs_shape + # If group class is specified, then make sure corresponding kwargs only contain relevant kwargs + if enc_kwargs["core_class"] is not None: + enc_kwargs["core_kwargs"] = extract_class_init_kwargs_from_dict( + cls=ObsUtils.OBS_ENCODER_CORES[enc_kwargs["core_class"]], + dic=enc_kwargs["core_kwargs"], + copy=False, + ) - for obs_module, cls_mapping in zip(("core", "obs_randomizer"), - (ObsUtils.OBS_ENCODER_CORES, ObsUtils.OBS_RANDOMIZERS)): - # Sanity check for kwargs in case they don't exist / are None - if enc_kwargs.get(f"{obs_module}_kwargs", None) is None: - enc_kwargs[f"{obs_module}_kwargs"] = {} - # Add in input shape info - enc_kwargs[f"{obs_module}_kwargs"]["input_shape"] = obs_shape - # If group class is specified, then make sure corresponding kwargs only contain relevant kwargs - if enc_kwargs[f"{obs_module}_class"] is not None: - enc_kwargs[f"{obs_module}_kwargs"] = extract_class_init_kwargs_from_dict( - cls=cls_mapping[enc_kwargs[f"{obs_module}_class"]], - dic=enc_kwargs[f"{obs_module}_kwargs"], + # Add in input shape info + randomizers = [] + obs_randomizer_class_list = enc_kwargs["obs_randomizer_class"] + obs_randomizer_kwargs_list = enc_kwargs["obs_randomizer_kwargs"] + + if not isinstance(obs_randomizer_class_list, list): + obs_randomizer_class_list = [obs_randomizer_class_list] + + if not isinstance(obs_randomizer_kwargs_list, list): + obs_randomizer_kwargs_list = [obs_randomizer_kwargs_list] + + for rand_class, rand_kwargs in zip(obs_randomizer_class_list, obs_randomizer_kwargs_list): + rand = None + if rand_class is not None: + rand_kwargs["input_shape"] = obs_shape + rand_kwargs = extract_class_init_kwargs_from_dict( + cls=ObsUtils.OBS_RANDOMIZERS[rand_class], + dic=rand_kwargs, copy=False, ) + rand = ObsUtils.OBS_RANDOMIZERS[rand_class](**rand_kwargs) + randomizers.append(rand) - # Add in input shape info - randomizer = None if enc_kwargs["obs_randomizer_class"] is None else \ - ObsUtils.OBS_RANDOMIZERS[enc_kwargs["obs_randomizer_class"]](**enc_kwargs["obs_randomizer_kwargs"]) + input_maps = enc_kwargs.get("input_maps", {}) + + if any("camera/image/varied_camera" in s for s in enc.obs_shapes.keys()) and ("camera/image/varied_camera" in k): + existing_varied_cam = [a for a in enc.obs_shapes.keys() if "camera/image/varied_camera" in a][0] + share = existing_varied_cam + net_class = None + net_kwargs = None + else: + share = None + net_class = enc_kwargs["core_class"] + net_kwargs = enc_kwargs["core_kwargs"] enc.register_obs_key( name=k, shape=obs_shape, - net_class=enc_kwargs["core_class"], - net_kwargs=enc_kwargs["core_kwargs"], - randomizer=randomizer, + input_map=input_maps.get(k, None), + net_class=net_class, + net_kwargs=net_kwargs, + randomizers=randomizers, + share_net_from=share, ) enc.make() @@ -103,7 +134,7 @@ class ObservationEncoder(Module): Call @register_obs_key to register observation keys with the encoder and then finally call @make to create the encoder networks. """ - def __init__(self, feature_activation=nn.ReLU): + def __init__(self, feature_activation=nn.ReLU, fuser = None): """ Args: feature_activation: non-linearity to apply after each obs net - defaults to ReLU. Pass @@ -111,22 +142,26 @@ def __init__(self, feature_activation=nn.ReLU): """ super(ObservationEncoder, self).__init__() self.obs_shapes = OrderedDict() + self.obs_input_maps = OrderedDict() self.obs_nets_classes = OrderedDict() self.obs_nets_kwargs = OrderedDict() self.obs_share_mods = OrderedDict() self.obs_nets = nn.ModuleDict() self.obs_randomizers = nn.ModuleDict() self.feature_activation = feature_activation + self.fuser = fuser + self.num_images = 0 self._locked = False def register_obs_key( self, name, - shape, + shape, + input_map=None, net_class=None, net_kwargs=None, net=None, - randomizer=None, + randomizers=None, share_net_from=None, ): """ @@ -149,6 +184,8 @@ def register_obs_key( """ assert not self._locked, "ObservationEncoder: @register_obs_key called after @make" assert name not in self.obs_shapes, "ObservationEncoder: modality {} already exists".format(name) + if "image" in name: + self.num_images += 1 if net is not None: assert isinstance(net, Module), "ObservationEncoder: @net must be instance of Module class" @@ -161,17 +198,19 @@ def register_obs_key( assert share_net_from in self.obs_shapes net_kwargs = deepcopy(net_kwargs) if net_kwargs is not None else {} - if randomizer is not None: - assert isinstance(randomizer, Randomizer) - if net_kwargs is not None: - # update input shape to visual core - net_kwargs["input_shape"] = randomizer.output_shape_in(shape) + for rand in randomizers: + if rand is not None: + assert isinstance(rand, Randomizer) + if net_kwargs is not None: + # update input shape to visual core + net_kwargs["input_shape"] = rand.output_shape_in(shape) self.obs_shapes[name] = shape + self.obs_input_maps[name] = input_map self.obs_nets_classes[name] = net_class self.obs_nets_kwargs[name] = net_kwargs self.obs_nets[name] = net - self.obs_randomizers[name] = randomizer + self.obs_randomizers[name] = nn.ModuleList(randomizers) self.obs_share_mods[name] = share_net_from def make(self): @@ -200,6 +239,76 @@ def _create_layers(self): if self.feature_activation is not None: self.activation = self.feature_activation() + if self.fuser == "transformer": + ## Define a fuser which takes multiple camera features as [B, sequence of pixels, features] + ## and encodes them with a transformer + input_features = self.obs_nets["camera/image/hand_camera_left_image"].feat_shape + # First scales down number of features on each pixel + self.c1 = torch.nn.Conv1d(in_channels = input_features[1], out_channels=512, kernel_size=1) + self.c2 = torch.nn.Conv1d(in_channels = 512, out_channels=512, kernel_size=1) + layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first = True) + self.fusernetwork = nn.TransformerEncoder(layer, num_layers=6) + # Finally flatten and linear layer before concatenated with other low dim features + self.l1 = torch.nn.Linear(input_features[0] * self.num_images * 512, 2048) + elif self.fuser == "perceiver": + from transformers import PerceiverModel, PerceiverConfig + ## Define a fuser which takes multiple camera features as [B, sequence of pixels, features] + ## and encodes them with a transformer + input_features = self.obs_nets["camera/image/hand_camera_left_image"].feat_shape + # Copied all values from here: https://github.com/TRI-ML/vidar/blob/main/vidar/arch/networks/perceiver/DeFiNeNet.py#L102 + self.percveiver_config = PerceiverConfig( + d_latents=512, + d_model=input_features[1], + num_latents=512, + hidden_act='gelu', + hidden_dropout_prob=0.25, + initializer_range=0.02, + layer_norm_eps=1e-12, + num_blocks=1, + num_cross_attention_heads=1, + num_self_attends_per_block=8, + num_self_attention_heads=8, + qk_channels=None, + v_channels=None, + ) + self.fusernetwork = PerceiverModel(self.percveiver_config) + # Finally flatten and linear layer before concatenated with other low dim features + self.l1 = torch.nn.Linear(512 * 512, 2048) + + def final_collation(self, feats): + if self.fuser is None: + ## Extremely hacky fix to handle the expected shape of raw language + featsnew = [TensorUtils.flatten(x, begin_axis=1) for k, x in feats.items() if "raw" not in k] + if 'lang_fixed/language_raw' in feats.keys(): + featsnew += [torch.zeros(featsnew[0].shape[0], 1).to(featsnew[0].device)] + a = torch.cat(featsnew, dim=-1) + return a + + keys_with_images = [a for a in feats.keys() if "image" in a] + keys_without_images = [a for a in feats.keys() if "image" not in a] + non_image_feats = [TensorUtils.flatten(feats[k], begin_axis=1) for k in keys_without_images] + + all_image_feats = [] + for k in keys_with_images: + all_image_feats.append(feats[k]) + + if self.fuser == "transformer": + all_image_feats = torch.cat(all_image_feats, dim = 1) + all_image_feats_postconv = self.c2(F.relu(self.c1(all_image_feats.permute(0, 2, 1)))).permute(0, 2, 1) + all_image_feats_posttrans = self.fusernetwork(all_image_feats_postconv) + output = self.l1(TensorUtils.flatten(all_image_feats_posttrans, begin_axis=1)) + return torch.cat(non_image_feats + [output], -1) + elif self.fuser == "perceiver": + # Concatenate all embeddings before applying Perceiver block like here: + # https://github.com/TRI-ML/vidar/blob/main/vidar/arch/networks/perceiver/DeFiNeNet.py#L357 + all_image_feats = torch.cat(all_image_feats, dim = 1) + all_image_feats_posttrans = self.fusernetwork(all_image_feats).last_hidden_state + output = self.l1(TensorUtils.flatten(all_image_feats_posttrans, begin_axis=1)) + # TODO (Ashwin): Ideally should include low-dim features in perceiver input too + return torch.cat(non_image_feats + [output], -1) + else: + raise NotImplementedError("Unsupported fuser") + def forward(self, obs_dict): """ Processes modalities according to the ordering in @self.obs_shapes. For each @@ -224,26 +333,48 @@ def forward(self, obs_dict): ) # process modalities by order given by @self.obs_shapes - feats = [] + feats = {} for k in self.obs_shapes: - x = obs_dict[k] + if self.obs_input_maps[k] is not None: + x = dict() + for input_name, input_obs_key in self.obs_input_maps[k].items(): + x[input_name] = obs_dict[input_obs_key] + else: + x = obs_dict[k] # maybe process encoder input with randomizer - if self.obs_randomizers[k] is not None: - x = self.obs_randomizers[k].forward_in(x) + if isinstance(x, dict): + for input_name, input_obs_key in self.obs_input_maps[k].items(): + randomizers = self.obs_randomizers[input_obs_key] + for rand in randomizers: + if rand is not None: + x[input_name] = rand.forward_in(x[input_name]) + else: + for rand in self.obs_randomizers[k]: + if rand is not None: + x = rand.forward_in(x) # maybe process with obs net if self.obs_nets[k] is not None: x = self.obs_nets[k](x) if self.activation is not None: x = self.activation(x) # maybe process encoder output with randomizer - if self.obs_randomizers[k] is not None: - x = self.obs_randomizers[k].forward_out(x) + if isinstance(x, dict): + for input_name, input_obs_key in self.obs_input_maps[k].items(): + randomizers = self.obs_randomizers[input_obs_key] + for rand in randomizers: + if rand is not None: + x[input_name] = rand.forward_out(x[input_name]) + else: + for rand in self.obs_randomizers[k]: + if rand is not None: + x = rand.forward_out(x) # flatten to [B, D] - x = TensorUtils.flatten(x, begin_axis=1) - feats.append(x) + # x = TensorUtils.flatten(x, begin_axis=1) + feats[k] = (x) + output = self.final_collation(feats) # concatenate all features together - return torch.cat(feats, dim=-1) + return output def output_shape(self, input_shape=None): """ @@ -251,14 +382,21 @@ def output_shape(self, input_shape=None): """ feat_dim = 0 for k in self.obs_shapes: + # If the fuser is not None for image features, don't naively concatenate flattened shapes + if (self.fuser is not None) and ("image" in k): + continue feat_shape = self.obs_shapes[k] - if self.obs_randomizers[k] is not None: - feat_shape = self.obs_randomizers[k].output_shape_in(feat_shape) + for rand in self.obs_randomizers[k]: + if rand is not None: + feat_shape = rand.output_shape_in(feat_shape) if self.obs_nets[k] is not None: feat_shape = self.obs_nets[k].output_shape(feat_shape) - if self.obs_randomizers[k] is not None: - feat_shape = self.obs_randomizers[k].output_shape_out(feat_shape) + for rand in self.obs_randomizers[k]: + if rand is not None: + feat_shape = rand.output_shape_out(feat_shape) feat_dim += int(np.prod(feat_shape)) + if self.fuser is not None: + feat_dim += 2048 return [feat_dim] def __repr__(self): @@ -415,6 +553,15 @@ def __init__( encoder_kwargs=encoder_kwargs, ) + self.out_size = 512 + self.combine = nn.Sequential( + nn.Linear(self.combo_output_shape(), 1024), + nn.ReLU(), + nn.Linear(1024, 512), + nn.ReLU(), + nn.Linear(512, self.out_size) + ) + def forward(self, **inputs): """ Process each set of inputs in its own observation group. @@ -445,9 +592,11 @@ def forward(self, **inputs): self.nets[obs_group].forward(inputs[obs_group]) ) - return torch.cat(outputs, dim=-1) + combo = torch.cat(outputs, dim=-1) + out = self.combine(combo) + return out - def output_shape(self): + def combo_output_shape(self): """ Compute the output shape of this encoder. """ @@ -455,7 +604,13 @@ def output_shape(self): for obs_group in self.observation_group_shapes: # get feature dimension of these keys feat_dim += self.nets[obs_group].output_shape()[0] - return [feat_dim] + return feat_dim + + def output_shape(self): + """ + Compute the output shape of this encoder. + """ + return [self.out_size] def __repr__(self): """Pretty print network.""" diff --git a/robomimic/scripts/config_gen/act_gen.py b/robomimic/scripts/config_gen/act_gen.py new file mode 100644 index 00000000..8962941d --- /dev/null +++ b/robomimic/scripts/config_gen/act_gen.py @@ -0,0 +1,131 @@ +from robomimic.scripts.config_gen.helper import * + +def make_generator_helper(args): + algo_name_short = "act" + generator = get_generator( + algo_name="act", + config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[64], + ) + + generator.add_param( + key="train.max_grad_norm", + name="", + group=-1, + values=[100.0], + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [{"path": p} for p in scan_datasets("~/Downloads/example_pen_in_cup", postfix="trajectory_im128.h5")], + ], + value_names=[ + "pen-in-cup", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + ) + elif args.env == "kitchen": + raise NotImplementedError + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "TODO.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "~/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/act_gen_no_define.py b/robomimic/scripts/config_gen/act_gen_no_define.py new file mode 100644 index 00000000..51658b16 --- /dev/null +++ b/robomimic/scripts/config_gen/act_gen_no_define.py @@ -0,0 +1,266 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# evaldataset = evaldataset[:200] +# broaddataset = broaddataset[:2000] +random.shuffle(evaldataset) +# random.shuffle(broaddataset) +N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +def make_generator_helper(args): + algo_name_short = "act" + generator = get_generator( + algo_name="act", + config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[2048], # 4096 works for wrist + ) + + generator.add_param( + key="train.max_grad_norm", + name="", + group=-1, + values=[100.0], + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # broaddataset + evaldataset, + # [{"path": p["path"], "weight": (0.5 / N_EVAL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset], + # broaddataset, + evaldataset, + ], + value_names=[ + # "mixed_unbalanced", + # "mixed_balanced", + # "broad", + "eval", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + # None, # Change this to "geom" to do goal conditioning + "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + # 0.5, + 0.3 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=2498, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + ] + ], + value_names=[ + # "proprio", + "proprio-cam", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="backbone", + group=1234, + values=[ + # "ResNet18Conv", + "ResNet50Conv", + ], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + # 64, + 512, + ], + ) + elif args.env == "kitchen": + raise NotImplementedError + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "TODO.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "~/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/bc_xfmr_gen.py b/robomimic/scripts/config_gen/bc_xfmr_gen.py index e67856f3..713085de 100644 --- a/robomimic/scripts/config_gen/bc_xfmr_gen.py +++ b/robomimic/scripts/config_gen/bc_xfmr_gen.py @@ -13,101 +13,125 @@ def make_generator_helper(args): if args.ckpt_mode is None: args.ckpt_mode = "off" - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[4], - ) - generator.add_param( - key="experiment.save.every_n_epochs", - name="", - group=-1, - values=[ - 100 - ], - ) - - # run rollouts at epoch 0 only - generator.add_param( - key="experiment.rollout.warmstart", - name="", - group=-1, - values=[ - -1, - ], - ) - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[40], - ) - generator.add_param( - key="experiment.rollout.rate", - name="", - group=-1, - values=[10], - ) - if args.env == "r2d2": generator.add_param( key="train.data", name="ds", group=2, values=[ - [{"path": p} for p in scan_datasets("~/code/r2d2/data/success/2023-05-23_t2c-cans", postfix="trajectory_im84.h5")], - [{"path": p} for p in scan_datasets("~/code/r2d2/data/success/2023-05-23_t2c-cans", postfix="trajectory_im128.h5")], + [{"path": p} for p in scan_datasets("~/Downloads/example_pen_in_cup", postfix="trajectory_im128.h5")], ], value_names=[ - "pnp-t2c-cans-84", - "pnp-t2c-cans-128", + "pen-in-cup", ], ) generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs.crop_height", - name="", - group=2, + key="observation.modalities.obs.rgb", + name="cams", + group=130, values=[ - 76, - 116 + # ["camera/image/hand_camera_left_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], ], + value_names=[ + # "wrist", + "3cams", + ] ) + elif args.env == "kitchen": generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs.crop_width", - name="", + key="train.data", + name="ds", group=2, values=[ - 76, - 116 + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], ], - ) - else: - raise ValueError - - if "experiment.ckpt_path" in generator.parameters: - generator.add_param( - key="algo.optim_params.policy.learning_rate.initial", - name="lrinit", - group=110, - values=[ - 1e-5, + value_names=[ + "bowls-human-100", + # "human-100", + # "mg-100", + # "mg-1000", ], - hidename=True, ) + elif args.env == "square": generator.add_param( - key="algo.optim_params.policy.learning_rate.lr_scheduler_type", - name="lrsch", - group=111, + key="train.data", + name="ds", + group=2, values=[ - # "linear", - None, + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], ], value_names=[ - "none" + "square", ], - hidename=True, ) + else: + raise ValueError + + # change default settings: predict 10 steps into future + generator.add_param( + key="algo.transformer.pred_future_acs", + name="predfuture", + group=1, + values=[ + True, + # False, + ], + # hidename=True, + ) + generator.add_param( + key="algo.transformer.supervise_all_steps", + name="supallsteps", + group=1, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.transformer.causal", + name="causal", + group=1, + values=[ + False, + # True, + ], + hidename=True, + ) + generator.add_param( + key="train.seq_length", + name="", + group=-1, + values=[10], + hidename=True, + ) + + generator.add_param( + key="algo.gmm.min_std", + name="mindstd", + group=271314, + values=[ + 0.03, + #0.0001, + ], + hidename=True, + ) + generator.add_param( + key="train.max_grad_norm", + name="maxgradnorm", + group=18371, + values=[ + # None, + 100.0, + ], + hidename=True, + ) generator.add_param( key="train.output_dir", @@ -122,20 +146,10 @@ def make_generator_helper(args): ], ) - generator.add_param( - key="experiment.rollout.enabled", - name="", - group=-1, - values=[ - True - ], - hidename=False, - ) - return generator if __name__ == "__main__": parser = get_argparser() args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file + make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/diffusion_gen.py b/robomimic/scripts/config_gen/diffusion_gen.py new file mode 100644 index 00000000..7c1bba00 --- /dev/null +++ b/robomimic/scripts/config_gen/diffusion_gen.py @@ -0,0 +1,480 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = broaddataset[:2000] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] +N_EVAL_MULTI = len(evaldataset_multitask) +print(N_BROAD, N_EVAL_MULTI) + + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[8], + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # broaddataset + evaldataset, + [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], + # broaddataset, + evaldataset_multitask, + ], + value_names=[ + # "mixed_unbalanced", + "mixed_balanced", + # "broad", + "eval_multi", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + None, # Change this to "geom" to do goal conditioning + # "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + # 0.5 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + "proprio-cam", + "proprio-cam-lang", + ], + hidename=False, + ) + ## All of the following are needed for DeFiNe style encoding + generator.add_param( + key="observation.encoder.rgb.input_maps", + name="", + group=24986, + values=[ + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + }, + ], + value_names=[ + "define", + "define-lang", + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["DeFiNeVisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="", + group=-1, + values=["DeFiNeImageCamEncoder"], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + True, + # True, + # False, + ], + hidename=False, + ) + + + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.backbone_class", + # name="backbone", + # group=1234, + # values=[ + # # "ResNet18Conv", + # "ResNet50Conv", + # ], + # ) + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.feature_dimension", + # name="visdim", + # group=1234, + # values=[ + # # 64, + # 512, + # ], + # ) + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/diffusion_gen_no_define.py b/robomimic/scripts/config_gen/diffusion_gen_no_define.py new file mode 100644 index 00000000..c022b77f --- /dev/null +++ b/robomimic/scripts/config_gen/diffusion_gen_no_define.py @@ -0,0 +1,399 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = broaddataset[:2000] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] +N_EVAL_MULTI = len(evaldataset_multitask) +print(N_BROAD, N_EVAL_MULTI) + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[16], + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # broaddataset + evaldataset, + [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], + # broaddataset, + evaldataset_multitask, + ], + value_names=[ + # "mixed_unbalanced", + "mixed_balanced", + # "broad", + "eval_multi", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + None, # Change this to "geom" to do goal conditioning + # "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + # 0.5, + 0.3 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24987, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + "proprio-cam", + "proprio-cam-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.input_maps", + name="", + group=24987, + values=[ + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "lang_emb" : "lang_fixed/language_distilbert" + }, + }, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["FiLMVisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="backbone", + group=1234, + values=[ + # "ResNet18Conv", + # "ResNet50Conv", + "ResNetFiLMEncoder" + ], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + # 64, + 512, + ], + ) + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/helper.py b/robomimic/scripts/config_gen/helper.py index 1cc8e0ce..6cc009e0 100644 --- a/robomimic/scripts/config_gen/helper.py +++ b/robomimic/scripts/config_gen/helper.py @@ -32,7 +32,7 @@ def get_generator(algo_name, config_file, args, algo_name_short=None, pt=False): else: curr_time = datetime.datetime.fromtimestamp(time.time()).strftime('%m-%d-%y-%H-%M-%S') generated_config_dir=os.path.join( - '~/', 'tmp/autogen_configs/ril', algo_name, args.env, args.mod, args.name, curr_time, "json", + '/mnt/fsx/surajnair/', 'tmp/autogen_configs/ril', algo_name, args.env, args.mod, args.name, curr_time, "json", ) generator = HyperparamUtils.ConfigGenerator( @@ -59,6 +59,42 @@ def set_env_settings(generator, args): False ], ) + generator.add_param( + key="experiment.save.every_n_epochs", + name="", + group=-1, + values=[50], + ) + generator.add_param( + key="experiment.mse.enabled", + name="", + group=-1, + values=[True], + ), + generator.add_param( + key="experiment.mse.every_n_epochs", + name="", + group=-1, + values=[10], + ), + generator.add_param( + key="experiment.mse.on_save_ckpt", + name="", + group=-1, + values=[True], + ), + generator.add_param( + key="experiment.mse.num_samples", + name="", + group=-1, + values=[6], + ), + generator.add_param( + key="experiment.mse.visualize", + name="", + group=-1, + values=[True], + ), if "observation.modalities.obs.low_dim" not in generator.parameters: generator.add_param( key="observation.modalities.obs.low_dim", @@ -74,10 +110,14 @@ def set_env_settings(generator, args): name="", group=-1, values=[ - ["camera/image/hand_camera_image", "camera/image/varied_camera_left_image", "camera/image/varied_camera_right_image"] + [ + "camera/image/hand_camera_left_image", + "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image" # uncomment to use all 3 cameras + ] ], ) - if "observation.encoder.rgb.obs_randomizer_kwargs.crop_height" not in generator.parameters: + if ("observation.encoder.rgb.obs_randomizer_kwargs" not in generator.parameters) and \ + ("observation.encoder.rgb.obs_randomizer_kwargs.crop_height" not in generator.parameters): generator.add_param( key="observation.encoder.rgb.obs_randomizer_kwargs.crop_height", name="", @@ -94,122 +134,170 @@ def set_env_settings(generator, args): 116 ], ) + # remove spatial softmax by default for r2d2 dataset generator.add_param( - key="train.data_format", + key="observation.encoder.rgb.core_kwargs.pool_class", name="", group=-1, values=[ - "r2d2" + None ], ) generator.add_param( - key="train.action_keys", + key="observation.encoder.rgb.core_kwargs.pool_kwargs", name="", group=-1, values=[ - ["action/cartesian_velocity", "action/gripper_velocity"], + None ], ) - # generator.add_param( - # key="train.hdf5_cache_mode", - # name="", - # group=-1, - # values=[None], - # ) - elif args.env == 'calvin': - if "observation.modalities.obs.low_dim" not in generator.parameters: - if args.mod == 'ld': - generator.add_param( - key="observation.modalities.obs.low_dim", - name="lowdimkeys", - group=-1, - values=[ - ["scene_obs", "env_id", "robot0_eef_pos", "robot0_eef_euler", "robot0_gripper_qpos"], - ], - value_names=[ - "scene_proprio", - ], - hidename=True, - ) - else: - generator.add_param( - key="observation.modalities.obs.low_dim", - name="lowdimkeys", - group=-1, - values=[ - ["robot0_eef_pos", "robot0_eef_euler", "robot0_gripper_qpos"], - ], - value_names=[ - "proprio", - ], - hidename=True, - ) - - if args.mod == 'im': - generator.add_param( - key="observation.modalities.obs.rgb", - name="", - group=-1, - values=[["rgb_static"]], - ) - generator.add_param( - key="observation.modalities.obs.rgb2", - name="", - group=-1, - values=[["rgb_gripper"]], - ) - - if "experiment.rollout.horizon" not in generator.parameters: - generator.add_param( - key="experiment.rollout.horizon", - name="", - group=-1, - values=[1000], - ) - - # no batching yet for calvin + + # here, we list how each action key should be treated (normalized etc) generator.add_param( - key="experiment.rollout.batched", + key="train.action_config", name="", group=-1, - values=[False], + values=[ + { + "action/cartesian_position":{ + "normalization": "min_max", + }, + "action/abs_pos":{ + "normalization": "min_max", + }, + "action/abs_rot_6d":{ + "normalization": "min_max", + "format": "rot_6d", + "convert_at_runtime": "rot_euler", + }, + "action/abs_rot_euler":{ + "normalization": "min_max", + "format": "rot_euler", + }, + "action/gripper_position":{ + "normalization": "min_max", + }, + "action/cartesian_velocity":{ + "normalization": None, + }, + "action/rel_pos":{ + "normalization": None, + }, + "action/rel_rot_6d":{ + "format": "rot_6d", + "normalization": None, + "convert_at_runtime": "rot_euler", + }, + "action/rel_rot_euler":{ + "format": "rot_euler", + "normalization": None, + }, + "action/gripper_velocity":{ + "normalization": None, + }, + } + ], ) - - elif args.env == 'franka_kitchen': generator.add_param( - key="observation.modalities.obs.low_dim", + key="train.dataset_keys", name="", group=-1, - values=[ - ["flat"] - ], + values=[[]], ) - if args.mod == 'im': + if "train.action_keys" not in generator.parameters: generator.add_param( - key="observation.modalities.obs.low_dim", - name="", + key="train.action_keys", + name="ac_keys", group=-1, values=[ - ["robot_joints"] + [ + "action/rel_pos", + "action/rel_rot_euler", + "action/gripper_velocity", + ], + ], + value_names=[ + "rel", ], ) + if "train.action_shapes" not in generator.parameters: generator.add_param( - key="observation.modalities.obs.rgb", - name="", + key="train.action_shapes", + name="ac_shapes", group=-1, values=[ - ["agentview_image", "eye_in_hand_image"] + [ + (1, 3), + (1, 6), + (1, 1), + ], + ], + value_names=[ + "ac_shapes", ], ) - - if "experiment.rollout.horizon" not in generator.parameters: - generator.add_param( - key="experiment.rollout.horizon", - name="", - group=-1, - values=[280], - ) - elif args.env in ["kitchen", "kitchen_mm"]: + # observation key groups to swap + generator.add_param( + key="train.shuffled_obs_key_groups", + name="", + group=-1, + values=[[[ + ( + "camera/image/varied_camera_1_left_image", + "camera/image/varied_camera_1_right_image", + "camera/extrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", + ), + ( + "camera/image/varied_camera_2_left_image", + "camera/image/varied_camera_2_right_image", + "camera/extrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", + ), + ]]], + ) + elif args.env == "kitchen": + generator.add_param( + key="train.action_config", + name="", + group=-1, + values=[ + { + "actions":{ + "normalization": None, + }, + "action_dict/abs_pos": { + "normalization": "min_max" + }, + "action_dict/abs_rot_axis_angle": { + "normalization": "min_max", + "format": "rot_axis_angle" + }, + "action_dict/abs_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/rel_pos": { + "normalization": None, + }, + "action_dict/rel_rot_axis_angle": { + "normalization": None, + "format": "rot_axis_angle" + }, + "action_dict/rel_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/gripper": { + "normalization": None, + }, + "action_dict/base_mode": { + "normalization": None, + } + } + ], + ) + if args.mod == 'im': generator.add_param( key="observation.modalities.obs.low_dim", @@ -250,6 +338,44 @@ def set_env_settings(generator, args): # # set videos off # args.no_video = True + generator.add_param( + key="train.action_config", + name="", + group=-1, + values=[ + { + "actions":{ + "normalization": None, + }, + "action_dict/abs_pos": { + "normalization": "min_max" + }, + "action_dict/abs_rot_axis_angle": { + "normalization": "min_max", + "format": "rot_axis_angle" + }, + "action_dict/abs_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/rel_pos": { + "normalization": None, + }, + "action_dict/rel_rot_axis_angle": { + "normalization": None, + "format": "rot_axis_angle" + }, + "action_dict/rel_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/gripper": { + "normalization": None, + } + } + ], + ) + if args.mod == 'im': generator.add_param( key="observation.modalities.obs.low_dim", @@ -286,6 +412,45 @@ def set_env_settings(generator, args): # set videos off args.no_video = True + # TODO: fix 2 robot case + generator.add_param( + key="train.action_config", + name="", + group=-1, + values=[ + { + "actions":{ + "normalization": None, + }, + "action_dict/abs_pos": { + "normalization": "min_max" + }, + "action_dict/abs_rot_axis_angle": { + "normalization": "min_max", + "format": "rot_axis_angle" + }, + "action_dict/abs_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/rel_pos": { + "normalization": None, + }, + "action_dict/rel_rot_axis_angle": { + "normalization": None, + "format": "rot_axis_angle" + }, + "action_dict/rel_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/gripper": { + "normalization": None, + } + } + ], + ) + if args.mod == 'im': generator.add_param( key="observation.modalities.obs.low_dim", @@ -337,6 +502,44 @@ def set_env_settings(generator, args): # set videos off args.no_video = True + generator.add_param( + key="train.action_config", + name="", + group=-1, + values=[ + { + "actions":{ + "normalization": None, + }, + "action_dict/abs_pos": { + "normalization": "min_max" + }, + "action_dict/abs_rot_axis_angle": { + "normalization": "min_max", + "format": "rot_axis_angle" + }, + "action_dict/abs_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/rel_pos": { + "normalization": None, + }, + "action_dict/rel_rot_axis_angle": { + "normalization": None, + "format": "rot_axis_angle" + }, + "action_dict/rel_rot_6d": { + "normalization": None, + "format": "rot_6d" + }, + "action_dict/gripper": { + "normalization": None, + } + } + ], + ) + if args.mod == 'im': generator.add_param( key="observation.modalities.obs.low_dim", @@ -408,42 +611,205 @@ def set_env_settings(generator, args): group=-1, values=[700], ) - elif args.env in ['real_breakfast', 'real_lift', 'real_cook']: - assert args.mod == "im" + else: + raise ValueError + + +def set_mod_settings(generator, args): + if args.mod == 'ld': + if "experiment.save.epochs" not in generator.parameters: + generator.add_param( + key="experiment.save.epochs", + name="", + group=-1, + values=[ + [2000] + ], + ) + elif args.mod == 'im': + if "experiment.save.every_n_epochs" not in generator.parameters: + generator.add_param( + key="experiment.save.every_n_epochs", + name="", + group=-1, + values=[40], + ) + generator.add_param( - key="experiment.save.enabled", + key="experiment.epoch_every_n_steps", name="", group=-1, - values=[ - True - ], + values=[500], ) + if "train.num_data_workers" not in generator.parameters: + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[4], + ) generator.add_param( - key="experiment.rollout.enabled", + key="train.hdf5_cache_mode", name="", group=-1, - values=[ - False - ], + values=[None], ) + if "train.batch_size" not in generator.parameters: + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[16], + ) + if "train.data_format" not in generator.parameters: + generator.add_param( + key="train.data_format", + name="", + group=-1, + values=[ + "r2d2" + ], + ) + if "train.num_epochs" not in generator.parameters: + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[600], + ) + if "experiment.rollout.rate" not in generator.parameters: + generator.add_param( + key="experiment.rollout.rate", + name="", + group=-1, + values=[40], + ) + + +def set_debug_mode(generator, args): + if not args.debug: + return + + generator.add_param( + key="experiment.mse.every_n_epochs", + name="", + group=-1, + values=[2], + value_names=[""], + ) + generator.add_param( + key="experiment.mse.visualize", + name="", + group=-1, + values=[True], + value_names=[""], + ) + generator.add_param( + key="experiment.rollout.n", + name="", + group=-1, + values=[2], + value_names=[""], + ) + generator.add_param( + key="experiment.rollout.horizon", + name="", + group=-1, + values=[30], + value_names=[""], + ) + generator.add_param( + key="experiment.rollout.rate", + name="", + group=-1, + values=[2], + value_names=[""], + ) + generator.add_param( + key="experiment.epoch_every_n_steps", + name="", + group=-1, + values=[2], + value_names=[""], + ) + generator.add_param( + key="experiment.save.every_n_epochs", + name="", + group=-1, + values=[2], + value_names=[""], + ) + generator.add_param( + key="experiment.validation_epoch_every_n_steps", + name="", + group=-1, + values=[2], + value_names=[""], + ) + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[2], + value_names=[""], + ) + if args.name is None: generator.add_param( - key="observation.modalities.obs.low_dim", + key="experiment.name", name="", group=-1, - values=[ - ["ee_pos", "ee_quat", "gripper_states"] - ], + values=["debug"], + value_names=[""], ) + generator.add_param( + key="experiment.save.enabled", + name="", + group=-1, + values=[False], + value_names=[""], + ) + generator.add_param( + key="train.hdf5_cache_mode", + name="", + group=-1, + values=["low_dim"], + value_names=[""], + ) + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[3], + ) + + +def set_output_dir(generator, args): + assert args.name is not None + + vals = generator.parameters["train.output_dir"].values + + for i in range(len(vals)): + vals[i] = os.path.join(vals[i], args.name) + + +def set_wandb_mode(generator, args): + generator.add_param( + key="experiment.logging.log_wandb", + name="", + group=-1, + values=[not args.no_wandb], + ) + + +def set_num_seeds(generator, args): + if args.n_seeds is not None and "train.seed" not in generator.parameters: generator.add_param( - key="observation.modalities.obs.rgb", - name="", - group=-1, - values=[ - ["agentview_rgb", "eye_in_hand_rgb"] - ], + key="train.seed", + name="seed", + group=-10, + values=[i + 1 for i in range(args.n_seeds)], + prepend=True, ) - else: - raise ValueError def get_argparser(): @@ -512,26 +878,6 @@ def get_argparser(): action="store_true", ) - parser.add_argument( - "--no_cuda", - action="store_true", - ) - - parser.add_argument( - "--no_rollout", - action="store_true", - ) - - parser.add_argument( - "--r3m", - action="store_true", - ) - - parser.add_argument( - "--pl", - action="store_true", - ) - parser.add_argument( "--n_seeds", type=int, @@ -579,10 +925,13 @@ def make_generator(args, make_generator_helper): args.ckpt_mode = "best_only" set_env_settings(generator, args) - # set_mod_settings(generator, args) + set_mod_settings(generator, args) + set_output_dir(generator, args) + set_num_seeds(generator, args) + set_wandb_mode(generator, args) # set the debug settings last, to override previous setting changes - # set_debug_mode(generator, args) + set_debug_mode(generator, args) """ misc settings """ generator.add_param( @@ -595,4 +944,4 @@ def make_generator(args, make_generator_helper): ) # generate jsons and script - generator.generate() \ No newline at end of file + generator.generate(override_base_name=True) diff --git a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py new file mode 100644 index 00000000..e09f172d --- /dev/null +++ b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py @@ -0,0 +1,456 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD_FULL = len(broaddataset_full) + +chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] +N_CHIPS = len(chips_singletask) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang_filtered.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +# evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl' in l['path']] +# N_EVAL_MULTI = len(evaldataset_multitask) + +# apple_singletask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl/apple' in l['path']] +# N_APPLE = len(apple_singletask) + +## Getting single task eval data (food in bowl) +# evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] +# evaldataset_foodinbowl = evaldataset_foodinbowl[:20] +# N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) + +## Getting single task eval data (open microwave) +# evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] +# N_EVAL_OPENMICRO = len(evaldataset_openmicro) + +## Getting single task eval data (close microwave) +# evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] +# N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) + +print(f"Broad Datapoints: {N_BROAD, N_BROAD_FULL} \ + \nSingle Task CHIPS Datapoints: {N_CHIPS} ") + # \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ + # \nSingle Task APPLE Datapoints: {N_APPLE}") + # # \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") + + +def make_generator_helper(args): + algo_name_short = "bc_xfmr" + + generator = get_generator( + algo_name="bc", + config_file=os.path.join(base_path, 'robomimic/exps/templates/bc_transformer.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[100000], + ) + + generator.add_param( + key="train.batch_size", + name="bz", + group=1212111, + values=[128], + hidename=False, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + "wrist", + # "wrist-stereo", + "2cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=130, + values=[ + {}, # jitter only + [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + ### CONDITIONING + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=24986, + values = [ + "geom", + None, # Change this to "geom" to do goal conditioning + + ] + ) + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + ] + ) + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + "proprio", + "proprio-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + False, + # True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["VisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="", + group=-1, + values=["ResNet50Conv"], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + False, + ], + hidename=False, + ) + + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + chips_singletask, + [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], + [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], + ], + value_names=[ + "eval_chips", + "balanced_chips", + "full_balanced_chips", + ], + ) + + generator.add_param( + key="algo.transformer.supervise_all_steps", + name="supallsteps", + group=1, + values=[ + True, + ], + hidename=True, + ) + generator.add_param( + key="algo.transformer.pred_future_acs", + name="futureacs", + group=1, + values=[ + False, + ], + hidename=True, + ) + generator.add_param( + key="algo.transformer.causal", + name="causal", + group=1, + values=[ + False, + ], + hidename=True, + ) + generator.add_param( + key="train.frame_stack", + name="", + group=-1, + values=[2], + hidename=True, + ) + generator.add_param( + key="train.seq_length", + name="H", + group=-1, + values=[2], + hidename=False, + ) + generator.add_param( + key="algo.transformer.context_length", + name="", + group=-1, + values=[2], + hidename=True, + ) + generator.add_param( + key="algo.gmm.enabled", + name="", + group=-1, + values=[False], + hidename=True, + ) + + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py new file mode 100644 index 00000000..7d5708ba --- /dev/null +++ b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py @@ -0,0 +1,531 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json +import numpy as np + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all OXE +with open("/mnt/fsx/surajnair/datasets/oxe_hdf5/manifest_oxe.json", 'r') as file: + oxe = json.load(file) +N_OXE = len(oxe) + +## Getting all stanford data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang_stanford_eval.json", 'r') as file: + langs = json.load(file) +stanford_singletask_eraser = [{'path': l['path']} for l in langs if 'stanford_eval_0125' in l['path']] +N_STANFORD_ERASER = len(stanford_singletask_eraser) + +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_stanford_evgr_laundry.json", 'r') as file: + stanford_singletask_laundry = json.load(file) +N_STANFORD_LAUNDRY = len(stanford_singletask_laundry) + +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_stanford_evgr_cooking.json", 'r') as file: + stanford_singletask_cooking = json.load(file) +N_STANFORD_COOKING = len(stanford_singletask_cooking) + + +## Getting all TRI data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_tri_chips.json", 'r') as file: + langs = json.load(file) +chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] +N_CHIPS = len(chips_singletask) + +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_tri_frenchpress.json", 'r') as file: + tri_frenchpress = json.load(file) +N_FRENCHPRESS = len(tri_frenchpress) + +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_tri_pot.json", 'r') as file: + tri_pot = json.load(file) +N_POT = len(tri_pot) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD_FULL = len(broaddataset_full) + +with open("/mnt/fsx/surajnair/datasets/r2d2-data/filter_traj_20scene_10k_episodes_success.json", 'r') as file: + subset = json.load(file) + +broaddataset_filtered = [{'path': l['path']} for l in broaddataset_full if "/".join(l['path'].split("/")[7:-1])+"/" in subset] +N_BROAD_FILTERED = len(broaddataset_filtered) + +broaddataset_filtered_random = np.random.choice(broaddataset_full, N_BROAD_FILTERED).tolist() +N_BROAD_FILTERED_RANDOM = len(broaddataset_filtered_random) + +cmutoast_singletask = [{'path': l['path']} for l in langs if 'r2_d2_toaster3_cmu_rgb' in l['path']] +N_CMU_TOASTER = len(cmutoast_singletask) + +cmu_multi = [{'path': l['path']} for l in langs if 'cmu_rgb' in l['path']] +N_CMU_MULTI = len(cmu_multi) + + +print(f"Broad Datapoints: R2D2 {N_BROAD_FULL, N_BROAD_FILTERED, N_BROAD_FILTERED_RANDOM} OXE {N_OXE} \ + \nSingle Task TRI Datapoints: CHIPS {N_CHIPS} POT {N_POT} FRENCHPRESS {N_FRENCHPRESS} \ + \nStanford Datapoints: Eraser {N_STANFORD_ERASER} Laundry {N_STANFORD_LAUNDRY} Cooking {N_STANFORD_COOKING} \ + \nCMU Datapoints: {N_CMU_MULTI, N_CMU_TOASTER} ") + # \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ + # \nSingle Task APPLE Datapoints: {N_APPLE}") + # # \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") + + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[100000], + ) + + generator.add_param( + key="train.batch_size", + name="bz", + group=1212111, + values=[128], + hidename=False, + ) + + generator.add_param( + key="algo.noise_samples", + name="noise_samples", + group=1010101, + values=[8], + value_names=["8"] + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "2cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=130, + values=[ + # {}, # jitter only + [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + ### CONDITIONING + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=24986, + values = [ + # "geom", + None, # Change this to "geom" to do goal conditioning + + ] + ) + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + # 0.5 + ] + ) + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + # "proprio", + "proprio-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + False, + # True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["VisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="", + group=-1, + values=["ResNet50Conv"], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + False, + ], + hidename=False, + ) + + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # stanford_singletask_cooking, + # stanford_singletask_laundry, + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + stanford_singletask_cooking], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + stanford_singletask_laundry], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + stanford_singletask_cooking], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + stanford_singletask_laundry], + # stanford_singletask_eraser, + # cmutoast_singletask, + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + stanford_singletask_eraser], + # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + cmutoast_singletask], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + stanford_singletask_eraser], + # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + cmutoast_singletask], + # stanford_singletask_cooking, + # stanford_singletask_laundry, + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + stanford_singletask_cooking], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + stanford_singletask_laundry], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_cooking], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_laundry], + # tri_pot, + # tri_frenchpress, + # [{"path": p["path"], "weight": (0.5 / N_POT if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + tri_pot], + # [{"path": p["path"], "weight": (0.5 / N_FRENCHPRESS if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + tri_frenchpress], + # [{"path": p["path"], "weight": (0.5 / N_POT if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + tri_pot], + # [{"path": p["path"], "weight": (0.5 / N_FRENCHPRESS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + tri_frenchpress], + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + stanford_singletask_eraser], + # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + chips_singletask], + # [{"path": p["path"], "weight": (0.5 / N_CMU_MULTI if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + cmu_multi], + # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + cmutoast_singletask], + # stanford_singletask_eraser, + # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_eraser], + # cmu_multi, + # cmutoast_singletask, + # [{"path": p["path"], "weight": (0.5 / N_CMU_MULTI if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + cmu_multi], + # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + cmutoast_singletask], + broaddataset_full, + # chips_singletask, + # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], + # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], + ], + value_names=[ + # "stanford_cooking", + # "stanford_laundry", + # "filteredr2d2_stanford_cooking", + # "filteredr2d2_stanford_laundry", + # "filteredrandomr2d2_stanford_cooking", + # "filteredrandomr2d2_stanford_laundry", + # "stanford_eraser", + # "cmu_toast", + # "filteredr2d2_stanford_eraser", + # "filteredr2d2_cmu_toast", + # "filteredrandomr2d2_stanford_eraser", + # "filteredrandomr2d2_cmu_toast", + # "stanford_cooking", + # "stanford_laundry", + # "oxe_balanced_stanford_cooking", + # "oxe_balanced_stanford_laundry", + # "fullr2d2_balanced_stanford_cooking", + # "fullr2d2_balanced_stanford_laundry", + # "tri_pot", + # "tri_frenchpress", + # "oxe_balanced_tri_pot", + # "oxe_balanced_tri_frenchpress", + # "fullr2d2_balanced_tri_pot", + # "fullr2d2_balanced_tri_frenchpress", + # "oxe_balanced_stanford_eraser", + # "oxe_balanced_chips", + # "oxe_balanced_cmu_multi", + # "oxe_balanced_cmu_toast", + # "stanford_eraser", + # "fullr2d2_balanced_stanford_eraser" + # "cmu_multi", + # "cmu_toast", + # "fullr2d2_balanced_cmu_multi", + # "fullr2d2_balanced_cmu_toast", + "broad", + # "eval_chips", + # "balanced_chips", + # "full_balanced_chips", + ], + ) + + generator.add_param( + key="train.hdf5_cache_mode", + name="", + group=-1, + values=[None], + value_names=[""], + ) + + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py new file mode 100644 index 00000000..41cb37e8 --- /dev/null +++ b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py @@ -0,0 +1,416 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json +import numpy as np + +DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" + + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.data_format", + name="", + group=-1, + values=[ + "r2d2_rlds" + ], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[100000], + ) + + generator.add_param( + key="train.data_path", + name="", + group=-1, + values=["/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2"], + ) + + generator.add_param( + key="train.shuffle_buffer_size", + name="", + group=-1, + values=[50000], + ) + + generator.add_param( + key="train.batch_size", + name="bz", + group=1212111, + values=[128], + hidename=False, + ) + + generator.add_param( + key="algo.noise_samples", + name="noise_samples", + group=1010101, + values=[8], + value_names=["8"] + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.sample_weights", + name="sample_weights", + group=-1, + values=[ + [1, 1], + ], + ) + generator.add_param( + key="train.dataset_names", + name="dataset_names", + group=24988, + values=[ + ["r2_d2", "r2_d2_cmu_waffle"], + ["r2_d2", "r2_d2_cmu_toaster"], + ], + value_names=[ + "r2_d2__r2_d2_cmu_waffle_fix_norm_fix_obs", + "r2_d2__r2_d2_cmu_toaster_fix_norm_fix_obs", + ] + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="train.action_shapes", + name="ac_shapes", + group=-1, + values=[ + [ + (1, 3), + (1, 6), + (1, 1), + ], + ], + value_names=[ + "ac_shapes", + ], + hidename=True, + ) + generator.add_param( + key="observation.image_dim", + name="", + group=-1, + values=[ + [128, 128], + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "2cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=130, + values=[ + # {}, # jitter only + [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + ### CONDITIONING + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=24986, + values = [ + # "geom", + None, # Change this to "geom" to do goal conditioning + + ] + ) + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + # 0.5 + ] + ) + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + ["robot_state/cartesian_position", "robot_state/gripper_position"], + ], + value_names=[ + "proprio-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + False, + # True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["VisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="", + group=-1, + values=["ResNet50Conv"], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + False, + ], + hidename=False, + ) + + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/voltron.py b/robomimic/scripts/config_gen/voltron.py new file mode 100644 index 00000000..3d1f29b0 --- /dev/null +++ b/robomimic/scripts/config_gen/voltron.py @@ -0,0 +1,593 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] +N_EVAL_MULTI = len(evaldataset_multitask) + +## Getting single task eval data (food in bowl) +evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] +N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) + +## Getting single task eval data (open microwave) +evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] +N_EVAL_OPENMICRO = len(evaldataset_openmicro) + +## Getting single task eval data (close microwave) +evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] +N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) + +print(f"Broad Datapoints: {N_BROAD} \ + \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ + \nSingle Task Food in Bowl Datapoints: {N_EVAL_FOODINBOWL} \ + \nSingle Task Microwave Open Datapoints: {N_EVAL_OPENMICRO} \ + \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") + + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[100000], + ) + + generator.add_param( + key="train.batch_size", + name="bs", + group=-1, + values=[32], + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + None, # Change this to "geom" to do goal conditioning + # "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + # 0.5 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=138, + values=[ + "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + # ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + # [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + # [ + # "robot_state/cartesian_position", "robot_state/gripper_position", + # "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + # "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + # "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + # "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + # "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + # "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + # ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_raw" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + ], + value_names=[ + # "proprio", + "proprio-cam-lang-bert2", + "proprio-cam-lang-bert", + "proprio-cam-lang-raw", + "proprio-cam-lang-bert3", + "proprio-cam-lang-bert4", + ], + hidename=False, + ) + ## All of the following are needed for DeFiNe style encoding + generator.add_param( + key="observation.encoder.rgb.input_maps", + name="", + group=24986, + values=[ + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "lang_emb" : "lang_fixed/language_raw" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "lang_emb" : "lang_fixed/language_raw" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "lang_emb" : "lang_fixed/language_raw" + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + }, + }, + ], + value_names=[ + "r3m-no-lang", + "vc1-no-lang", + "voltron-lang", + "define-film-lang", + "define-nofilm-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=24986, + values=["PretrainedVisualCore", "PretrainedVisualCore", "PretrainedVisualCore", "DeFiNeVisualCore", "DeFiNeVisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="enc", + group=24986, + values=["R3MImageCamEncoder", "VC1ImageCamEncoder", "VoltronImageCamEncoder", "DeFiNeImageCamEncoder", "DeFiNeImageCamEncoder"], + hidename=False, + ) + + + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + + + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + True, + # True, + # False, + ], + hidename=False, + ) + + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + broaddataset, + # evaldataset_multitask, + # evaldataset_foodinbowl, + # evaldataset_openmicro, + # evaldataset_closemicro, + # [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], + # [{"path": p["path"], "weight": (0.5 / N_EVAL_FOODINBOWL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_foodinbowl], + # [{"path": p["path"], "weight": (0.5 / N_EVAL_OPENMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_openmicro], + # [{"path": p["path"], "weight": (0.5 / N_EVAL_CLOSEMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_closemicro], + ], + value_names=[ + "broad", + # "eval_multi", + # "eval_foodinbowl", + # "eval_openmicro", + # "eval_closemicro", + # "balanced_broad_eval_multi", + # "balanced_broad_eval_foodinbowl", + # "balanced_broad_eval_openmicro", + # "balanced_broad_eval_closemicro", + ], + ) + + + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.backbone_class", + # name="backbone", + # group=1234, + # values=[ + # # "ResNet18Conv", + # "ResNet50Conv", + # ], + # ) + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.feature_dimension", + # name="visdim", + # group=1234, + # values=[ + # # 64, + # 512, + # ], + # ) + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/conversion/add_lang_to_converted_data.py b/robomimic/scripts/conversion/add_lang_to_converted_data.py new file mode 100644 index 00000000..f98a0b6d --- /dev/null +++ b/robomimic/scripts/conversion/add_lang_to_converted_data.py @@ -0,0 +1,97 @@ +""" +Add image information to existing r2d2 hdf5 file +""" +import h5py +import os +import numpy as np +import glob +from tqdm import tqdm +import argparse +import shutil +import torch +import random +import json +import cv2 + +from transformers import AutoTokenizer, AutoModel +tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') +model = AutoModel.from_pretrained("distilbert-base-uncased", torch_dtype=torch.float16) +model.to('cuda') + +""" +Follow instructions here to setup zed: +https://www.stereolabs.com/docs/installation/linux/ +""" +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.tensor_utils as TensorUtils + +def add_lang(path, raw_lang, args): + output_path = os.path.join(os.path.dirname(path), "trajectory_im{}.h5".format(args.imsize)) + f = h5py.File(output_path, "a") + print(f.keys()) + + # Extract language data + if "lang_fixed" not in f["observation"]: + f["observation"].create_group("lang_fixed") + lang_grp = f["observation/lang_fixed"] + + H = f["observation/robot_state/cartesian_position"].shape[0] + encoded_input = tokenizer(raw_lang, return_tensors='pt').to('cuda') + outputs = model(**encoded_input) + encoded_lang = outputs.last_hidden_state.sum(1).squeeze().unsqueeze(0).repeat(H, 1) + if "language_raw" not in f["observation/lang_fixed"]: + lang_grp.create_dataset("language_raw", data=[raw_lang]*H) + lang_grp.create_dataset("language_distilbert", data=encoded_lang.cpu().detach().numpy()) + else: + f["observation/lang_fixed/language_raw"][...] = [raw_lang]*H + f["observation/lang_fixed/language_distilbert"][...] = encoded_lang.cpu().detach().numpy() + f.close() + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--folder", + type=str, + help="folder containing hdf5's to add camera images to", + default="~/datasets/r2d2/success" + ) + + parser.add_argument( + "--imsize", + type=int, + default=128, + help="image size (w and h)", + ) + + args = parser.parse_args() + + with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + langs = [l for l in langs if "TRI_chips_only_1_21" in l["path"]] + datasets = langs + print(datasets) + + # datasets = [] + # for root, dirs, files in os.walk(os.path.expanduser(args.folder)): + # for f in files: + # if f == f"trajectory_im{args.imsize}.h5": + # datasets.append(os.path.join(root, f)) + + print("adding lang to datasets...") + random.shuffle(datasets) + for item in tqdm(datasets): + d, l = item['path'], item['lang'] + d = os.path.expanduser(d) + # try: + add_lang(d, l, args) + # except: + # print(d, "Failed") + + + +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/AUTOLab/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/CLVR/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/GuptaLab/success --imsize 128 + +# python robomimic/scripts/conversion/add_lang_to_converted_data.py --folder /mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval/TRI_1_10_multitask_food_in_bowl/ --imsize 256 \ No newline at end of file diff --git a/robomimic/scripts/conversion/convert_r2d2.py b/robomimic/scripts/conversion/convert_r2d2.py index 54f7bbb5..e3436223 100644 --- a/robomimic/scripts/conversion/convert_r2d2.py +++ b/robomimic/scripts/conversion/convert_r2d2.py @@ -8,26 +8,62 @@ from tqdm import tqdm import argparse import shutil +import torch +import random +import traceback +import json +import cv2 + +""" +Follow instructions here to setup zed: +https://www.stereolabs.com/docs/installation/linux/ +""" +import pyzed.sl as sl + +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.tensor_utils as TensorUtils from r2d2.camera_utils.wrappers.recorded_multi_camera_wrapper import RecordedMultiCameraWrapper from r2d2.trajectory_utils.trajectory_reader import TrajectoryReader +from r2d2.camera_utils.info import camera_type_to_string_dict -def convert_dataset(path, args): - recording_folderpath = os.path.join(os.path.dirname(path), "recordings", "MP4") - camera_kwargs = dict( - hand_camera=dict(image=True, concatenate_images=False, resolution=(args.imsize, args.imsize), resize_func="cv2"), - varied_camera=dict(image=True, concatenate_images=False, resolution=(args.imsize, args.imsize), resize_func="cv2"), - ) - camera_reader = RecordedMultiCameraWrapper(recording_folderpath, camera_kwargs) +from r2d2.camera_utils.camera_readers.zed_camera import ZedCamera, standard_params + + +DISNEY_IMAGE_NAME_TO_CAM_KEY_MAPPING = {'hand_camera_left_image': '16522755_left', 'hand_camera_right_image': '16522755_right', 'varied_camera_1_left_image': '29431508_left', 'varied_camera_1_right_image': '29431508_right', 'varied_camera_2_left_image': '29513368_left', 'varied_camera_2_right_image': '29513368_right'} +DISNEY_CAM_ID_TO_TYPE = {'16522755': 'hand_camera', '29431508': 'varied_camera', '29513368': 'varied_camera'} + + +def get_cam_instrinsics(svo_path): + """ + utility function to get camera intrinsics + """ + intrinsics = {} + return intrinsics + +def convert_dataset(path, args): output_path = os.path.join(os.path.dirname(path), "trajectory_im{}.h5".format(args.imsize)) + recording_folderpath = os.path.join(os.path.dirname(path), "recordings", "SVO") if os.path.exists(output_path): # dataset already exists, skip f = h5py.File(output_path) - if "observation/camera/image/hand_camera_image" in f.keys(): + if "observation/camera/image/hand_camera_left_image" in f.keys(): + # print("Skipping finished") + if os.path.exists(recording_folderpath): + shutil.rmtree(recording_folderpath) return f.close() + + num_svo_files = len([f for f in os.listdir(recording_folderpath) if os.path.isfile(os.path.join(recording_folderpath, f))]) + assert(num_svo_files == 3), "Didnt find 3 svos!" + camera_kwargs = dict( + hand_camera=dict(image=True, concatenate_images=False, resolution=(args.imsize, args.imsize), resize_func="cv2"), + varied_camera=dict(image=True, concatenate_images=False, resolution=(args.imsize, args.imsize), resize_func="cv2"), + ) + camera_reader = RecordedMultiCameraWrapper(recording_folderpath, camera_kwargs) + shutil.copyfile(path, output_path) f = h5py.File(output_path, "a") @@ -37,23 +73,58 @@ def convert_dataset(path, args): f["observation"].create_group("camera").create_group("image") image_grp = f["observation/camera/image"] - cam_data = dict( - hand_camera_image=[], - varied_camera_left_image=[], - varied_camera_right_image=[], - ) - + """ + Extract camera type and keys. Examples of what they should look like: camera_type_dict = { '17225336': 'hand_camera', '24013089': 'varied_camera', '25047636': 'varied_camera' } CAM_NAME_TO_KEY_MAPPING = { - "hand_camera_image": "17225336_left", - "varied_camera_left_image": "25047636_right", - "varied_camera_right_image": "24013089_left" + "hand_camera_left_image": "17225336_left", + "hand_camera_right_image": "17225336_right", + "varied_camera_1_left_image": "24013089_left", + "varied_camera_1_right_image": "24013089_right", + "varied_camera_2_left_image": "25047636_left", + "varied_camera_2_right_image": "25047636_right", } + """ + + CAM_ID_TO_TYPE = {} + hand_cam_ids = [] + varied_cam_ids = [] + for k in f["observation"]["camera_type"]: + cam_type = camera_type_to_string_dict[f["observation"]["camera_type"][k][0]] + CAM_ID_TO_TYPE[k] = cam_type + if cam_type == "hand_camera": + hand_cam_ids.append(k) + elif cam_type == "varied_camera": + varied_cam_ids.append(k) + else: + raise ValueError + + if "TRI_" in path: + CAM_ID_TO_TYPE = DISNEY_CAM_ID_TO_TYPE + + # sort the camera ids: important to maintain consistency of cams between train and eval! + hand_cam_ids = sorted(hand_cam_ids) + varied_cam_ids = sorted(varied_cam_ids) + IMAGE_NAME_TO_CAM_KEY_MAPPING = {} + IMAGE_NAME_TO_CAM_KEY_MAPPING["hand_camera_left_image"] = "{}_left".format(hand_cam_ids[0]) + IMAGE_NAME_TO_CAM_KEY_MAPPING["hand_camera_right_image"] = "{}_right".format(hand_cam_ids[0]) + + # set up mapping for varied cameras + for i in range(len(varied_cam_ids)): + for side in ["left", "right"]: + cam_name = "varied_camera_{}_{}_image".format(i+1, side) + cam_key = "{}_{}".format(varied_cam_ids[i], side) + IMAGE_NAME_TO_CAM_KEY_MAPPING[cam_name] = cam_key + + if "TRI_" in path: + IMAGE_NAME_TO_CAM_KEY_MAPPING = DISNEY_IMAGE_NAME_TO_CAM_KEY_MAPPING + + cam_data = {cam_name: [] for cam_name in IMAGE_NAME_TO_CAM_KEY_MAPPING.keys()} traj_reader = TrajectoryReader(path, read_images=False) for index in range(demo_len): @@ -63,14 +134,15 @@ def convert_dataset(path, args): timestamp_dict = {} camera_obs = camera_reader.read_cameras( - index=index, camera_type_dict=camera_type_dict, timestamp_dict=timestamp_dict + index=index, camera_type_dict=CAM_ID_TO_TYPE, timestamp_dict=timestamp_dict ) - for cam_name in ["hand_camera_image", "varied_camera_left_image", "varied_camera_right_image"]: + for cam_name in IMAGE_NAME_TO_CAM_KEY_MAPPING.keys(): if camera_obs is None: im = np.zeros((args.imsize, args.imsize, 3)) else: - im_key = CAM_NAME_TO_KEY_MAPPING[cam_name] + im_key = IMAGE_NAME_TO_CAM_KEY_MAPPING[cam_name] im = camera_obs["image"][im_key] + im = cv2.cvtColor(im, cv2.COLOR_BGRA2BGR) # perform bgr_to_rgb operation im = im[:,:,::-1] @@ -83,7 +155,134 @@ def convert_dataset(path, args): del image_grp[cam_name] image_grp.create_dataset(cam_name, data=cam_data[cam_name], compression="gzip") + # extract camera extrinsics data + if "extrinsics" not in f["observation/camera"]: + f["observation/camera"].create_group("extrinsics") + extrinsics_grp = f["observation/camera/extrinsics"] + for (im_name, cam_key) in IMAGE_NAME_TO_CAM_KEY_MAPPING.items(): + raw_data = f["observation/camera_extrinsics"][cam_key][:] + raw_data = torch.from_numpy(raw_data) + pos = raw_data[:,0:3] + rot_mat = TorchUtils.euler_angles_to_matrix(raw_data[:,3:6], convention="XYZ") + extrinsics = np.zeros((len(pos), 4, 4)) + extrinsics[:,:3,:3] = TensorUtils.to_numpy(rot_mat) + extrinsics[:,:3,3] = TensorUtils.to_numpy(pos) + extrinsics[:,3,3] = 1.0 + # invert the matrix to represent standard definition of extrinsics: from world to cam + extrinsics = np.linalg.inv(extrinsics) + extr_name = "_".join(im_name.split("_")[:-1]) + extrinsics_grp.create_dataset(extr_name, data=extrinsics) + + svo_path = os.path.join(os.path.dirname(path), "recordings", "SVO") + cam_reader_svo = camera_reader #RecordedMultiCameraWrapper(svo_path, camera_kwargs) + if "intrinsics" not in f["observation/camera"]: + f["observation/camera"].create_group("intrinsics") + intrinsics_grp = f["observation/camera/intrinsics"] + for cam_id, svo_reader in cam_reader_svo.camera_dict.items(): + cam = svo_reader._cam + calib_params = cam.get_camera_information().camera_configuration.calibration_parameters + for (posftix, params)in zip( + ["_left", "_right"], + [calib_params.left_cam, calib_params.right_cam] + ): + # get name to store intrinsics under + cam_key = cam_id + posftix + # reverse search for image name + im_name = None + for (k, v) in IMAGE_NAME_TO_CAM_KEY_MAPPING.items(): + if v == cam_key: + im_name = k + break + if im_name is None: # sometimes the raw_key doesn't correspond to any camera we have images for + continue + intr_name = "_".join(im_name.split("_")[:-1]) + + # if intr_name not in intrinsics_grp: + # intrinsics_grp.create_group(intr_name) + # cam_intr_grp = intrinsics_grp[intr_name] + + # these lines are copied from _process_intrinsics function in svo_reader.py + cam_intrinsics = np.array([[params.fx, 0, params.cx], [0, params.fy, params.cy], [0, 0, 1]]) + data = np.repeat(cam_intrinsics[None], demo_len, axis=0) + intrinsics_grp.create_dataset(intr_name, data=data) + # { + # "camera_matrix": np.array([[params.fx, 0, params.cx], [0, params.fy, params.cy], [0, 0, 1]]), + # "dist_coeffs": np.array(list(params.disto)), + # } + # # batchify across trajectory + # for k in cam_intrinsics: + # data = np.repeat(cam_intrinsics[k][None], demo_len, axis=0) + # cam_intr_grp.create_dataset(k, data=data) + + # extract action key data + action_dict_group = f["action"] + for in_ac_key in ["cartesian_position", "cartesian_velocity"]: + in_action = action_dict_group[in_ac_key][:] + in_pos = in_action[:,:3].astype(np.float64) + in_rot = in_action[:,3:6].astype(np.float64) # in euler format + rot_ = torch.from_numpy(in_rot) + rot_6d = TorchUtils.euler_angles_to_rot_6d( + rot_, convention="XYZ", + ) + rot_6d = rot_6d.numpy().astype(np.float64) + + if in_ac_key == "cartesian_position": + prefix = "abs_" + elif in_ac_key == "cartesian_velocity": + prefix = "rel_" + else: + raise ValueError + + this_action_dict = { + prefix + 'pos': in_pos, + prefix + 'rot_euler': in_rot, + prefix + 'rot_6d': rot_6d, + } + for key, data in this_action_dict.items(): + if key in action_dict_group: + del action_dict_group[key] + action_dict_group.create_dataset(key, data=data) + + # ensure all action keys are batched (ie., are not 0-dimensional) + for k in action_dict_group: + if isinstance(action_dict_group[k], h5py.Dataset) and len(action_dict_group[k].shape) == 1: + reshaped_values = np.reshape(action_dict_group[k][:], (-1, 1)) + del action_dict_group[k] + action_dict_group.create_dataset(k, data=reshaped_values) + + # post-processing: remove timesteps where robot movement is disabled + movement_enabled = f["observation/controller_info/movement_enabled"][:] + timesteps_to_remove = np.where(movement_enabled == False)[0] + + if not args.keep_idle_timesteps: + remove_timesteps(f, timesteps_to_remove) + f.close() + camera_reader.disable_cameras() + del camera_reader + # print("Deleting", recording_folderpath) + # shutil.rmtree(recording_folderpath) + +def remove_timesteps(f, timesteps_to_remove): + total_timesteps = f["action/cartesian_position"].shape[0] + + def remove_timesteps_for_group(g): + for k in g: + if isinstance(g[k], h5py._hl.dataset.Dataset): + if g[k].shape[0] != total_timesteps: + print("skipping {}".format(k)) + continue + new_dataset = np.delete(g[k], timesteps_to_remove, axis=0) + del g[k] + g.create_dataset(k, data=new_dataset) + elif isinstance(g[k], h5py._hl.group.Group): + remove_timesteps_for_group(g[k]) + else: + raise NotImplementedError + + for k in f: + remove_timesteps_for_group(f[k]) + if __name__ == "__main__": parser = argparse.ArgumentParser() @@ -98,19 +297,57 @@ def convert_dataset(path, args): parser.add_argument( "--imsize", type=int, - default=84, + default=128, help="image size (w and h)", ) + + parser.add_argument( + "--keep_idle_timesteps", + action="store_true", + help="override the default behavior of truncating idle timesteps", + ) args = parser.parse_args() + # langs = json.load("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json") + # with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest.json", 'r') as file: + # files = json.load(file) + # # files = [os.path.join(os.path.dirname(l["path"]), "trajectory.h5") for l in langs if ("eval" in l["path"])] + # datasets = files + datasets = [] - for root, dirs, files in os.walk(os.path.expanduser(args.folder)): + j = os.walk(os.path.expanduser(args.folder)) + import pdb; pdb.set_trace() + for root, dirs, files in j: for f in files: if f == "trajectory.h5": + # if "success" in root: datasets.append(os.path.join(root, f)) + print(len(datasets)) + + print("converting datasets...") + random.shuffle(datasets) + failed = 0 for d in tqdm(datasets): d = os.path.expanduser(d) - convert_dataset(d, args) + try: + convert_dataset(d, args) + except Exception as e: + print(d, "Failed", failed, e) + traceback.print_exc() + failed += 1 + # try: + # output_path = os.path.join(os.path.dirname(d), "trajectory_im{}.h5".format(args.imsize)) + # os.remove(output_path) + # except: + # pass + + +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/AUTOLab/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/CLVR/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/GuptaLab/success --imsize 128 + +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --imsize 256 +# /mnt/fsx/surajnair/mambaforge/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --imsize 128 \ No newline at end of file diff --git a/robomimic/scripts/conversion/convert_robosuite.py b/robomimic/scripts/conversion/convert_robosuite.py index 88258698..d3c748ee 100644 --- a/robomimic/scripts/conversion/convert_robosuite.py +++ b/robomimic/scripts/conversion/convert_robosuite.py @@ -22,6 +22,9 @@ import robomimic.envs.env_base as EB from robomimic.scripts.split_train_val import split_train_val_from_hdf5 +from robomimic.scripts.conversion.robosuite_add_absolute_actions import add_absolute_actions_to_dataset +from robomimic.scripts.conversion.extract_action_dict import extract_action_dict +from robomimic.scripts.filter_dataset_size import filter_dataset_size if __name__ == "__main__": @@ -31,22 +34,33 @@ type=str, help="path to input hdf5 dataset", ) + parser.add_argument( + "--filter_num_demos", + type=int, + nargs='+', + help="Num demos to filter by (can be list)", + ) args = parser.parse_args() f = h5py.File(args.dataset, "a") # edit mode # store env meta - env_name = f["data"].attrs["env"] - env_info = json.loads(f["data"].attrs["env_info"]) - env_meta = dict( - type=EB.EnvType.ROBOSUITE_TYPE, - env_name=env_name, - env_version=f["data"].attrs["repository_version"], - env_kwargs=env_info, - ) - if "env_args" in f["data"].attrs: - del f["data"].attrs["env_args"] - f["data"].attrs["env_args"] = json.dumps(env_meta, indent=4) + env_name = f["data"].attrs.get("env", None) + if "env_info" in f["data"].attrs: + env_info = json.loads(f["data"].attrs["env_info"]) + if env_name is not None and env_info is not None: + env_meta = dict( + type=EB.EnvType.ROBOSUITE_TYPE, + env_name=env_name, + env_version=f["data"].attrs["repository_version"], + env_kwargs=env_info, + ) + if "env_args" in f["data"].attrs: + del f["data"].attrs["env_args"] + f["data"].attrs["env_args"] = json.dumps(env_meta, indent=4) + else: + # assume env_args already present + assert "env_args" in f["data"].attrs print("====== Stored env meta ======") print(f["data"].attrs["env_args"]) @@ -73,3 +87,21 @@ # create 90-10 train-validation split in the dataset split_train_val_from_hdf5(hdf5_path=args.dataset, val_ratio=0.1) + + # add absolute actions to dataset + add_absolute_actions_to_dataset( + dataset=args.dataset, + eval_dir=None, + num_workers=10, + ) + + # extract corresponding action keys into action_dict + extract_action_dict(dataset=args.dataset) + + # create filter keys according to number of demos + if args.filter_num_demos is not None: + for n in args.filter_num_demos: + filter_dataset_size( + args.dataset, + num_demos=n, + ) \ No newline at end of file diff --git a/robomimic/scripts/conversion/extract_action_dict.py b/robomimic/scripts/conversion/extract_action_dict.py new file mode 100644 index 00000000..2d3a50e3 --- /dev/null +++ b/robomimic/scripts/conversion/extract_action_dict.py @@ -0,0 +1,81 @@ +import argparse +import pathlib +import sys +import tqdm +import h5py +import numpy as np +import torch +import os + +import robomimic.utils.torch_utils as TorchUtils + +def extract_action_dict(dataset): + # find files + f = h5py.File(os.path.expanduser(dataset), mode="r+") + + SPECS = [ + dict( + key="actions", + is_absolute=False, + ), + dict( + key="actions_abs", + is_absolute=True, + ) + ] + + # execute + for spec in SPECS: + input_action_key = spec["key"] + is_absolute = spec["is_absolute"] + + if is_absolute: + prefix = "abs_" + else: + prefix = "rel_" + + for demo in f["data"].values(): + in_action = demo[str(input_action_key)][:] + in_pos = in_action[:,:3].astype(np.float32) + in_rot = in_action[:,3:6].astype(np.float32) + in_grip = in_action[:,6:7].astype(np.float32) + + rot_6d = TorchUtils.axis_angle_to_rot_6d( + axis_angle=torch.from_numpy(in_rot) + ) + rot_6d = rot_6d.numpy().astype(np.float32) # convert to numpy + + this_action_dict = { + prefix + "pos": in_pos, + prefix + "rot_axis_angle": in_rot, + prefix + "rot_6d": rot_6d, + "gripper": in_grip + } + + # special case: 8 dim actions mean there is a mobile base mode in the action space + if in_action.shape[1] == 8: + this_action_dict["base_mode"] = in_action[:,7:8].astype(np.float32) + + action_dict_group = demo.require_group("action_dict") + for key, data in this_action_dict.items(): + if key in action_dict_group: + del action_dict_group[key] + action_dict_group.create_dataset(key, data=data) + + f.close() + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--dataset", + type=str, + required=True + ) + + args = parser.parse_args() + extract_action_dict(args.dataset) + +if __name__ == "__main__": + main() diff --git a/robomimic/scripts/conversion/get_eval_lang_annotations.py b/robomimic/scripts/conversion/get_eval_lang_annotations.py new file mode 100644 index 00000000..562c79b2 --- /dev/null +++ b/robomimic/scripts/conversion/get_eval_lang_annotations.py @@ -0,0 +1,51 @@ +import random +import json +import numpy as np +import os +import csv + +EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval/" +RAW_ANNOTATIONS = os.path.join(EVAL_DATA_ROOT_DIR, "tri-multi-task-eval-lang-annotations.csv") +METADATA_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval-json/" + +def read_csv(file_path): + data_list = [] + with open(file_path, 'r') as csvfile: + csv_reader = csv.reader(csvfile) + for row in csv_reader: + data_list.append(row) + return data_list + +# Update CSV to have dummy language annotations for single task data +single_task_evals = { + "TRI_food_in_bowl": "Put the food item in the bowl", + "TRI_microwave_close": "Close the microwave", + "TRI_microwave_open": "Open the microwave"} + +with open(RAW_ANNOTATIONS, 'a', newline='') as csvfile: + # Create a CSV writer object + csv_writer = csv.writer(csvfile) + + for task in single_task_evals.keys(): + for traj_folder in os.listdir(os.path.join(EVAL_DATA_ROOT_DIR, task)): + new_line = [os.path.join(task, traj_folder, "recordings"), single_task_evals[task]] + csv_writer.writerow(new_line) + +csv_data = read_csv(RAW_ANNOTATIONS) + +aggregated_annotations_eval = {} +for _, annotation_info in enumerate(csv_data): + path = annotation_info[0].split("/recordings")[0] + lang = annotation_info[1] + hdf5_path = os.path.join(path, "trajectory.h5") + uuid = path.replace("/", "-") + uuid_annotations = f"pilot/{uuid}.mp4" + aggregated_annotations_eval[uuid_annotations] = { + "language_instruction1": lang + } + metadata_path = os.path.join(METADATA_DIR, f"metadata_{uuid}.json") + with open(metadata_path, 'w') as metadata_path: + json.dump({"uuid": uuid, "hdf5_path": hdf5_path, "lab": ""}, metadata_path) + +with open(os.path.join(METADATA_DIR, "aggregated_annotations_eval.json"), 'w') as annotations_path: + json.dump(aggregated_annotations_eval, annotations_path, indent=4) \ No newline at end of file diff --git a/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py b/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py new file mode 100644 index 00000000..e6f2d073 --- /dev/null +++ b/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py @@ -0,0 +1,48 @@ +import random +import json +import numpy as np +import os + +DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/" +ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-json/" +AGGREGATED_ANNOTATIONS_PATH = os.path.join(ANNOTATIONS_DIR, "aggregated-annotations.json") + +EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval/" +EVAL_ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval-json/" +AGGREGATED_ANNOTATIONS_EVAL_PATH = os.path.join(EVAL_ANNOTATIONS_DIR, "aggregated_annotations_eval.json") + +with open(AGGREGATED_ANNOTATIONS_PATH, "r") as a_file: + ANNOTATIONS = json.load(a_file) + +with open(AGGREGATED_ANNOTATIONS_EVAL_PATH, "r") as a_file: + ANNOTATIONS_EVAL = json.load(a_file) + +def fill_hdf5_to_lang_map(broaddataset_lang, annotations, data_root, annotations_dir): + print("ANNOTATIONS DIR: ", annotations_dir) + for i, uuid in enumerate(list(annotations.keys())): + if i % 1000 == 0: + print("I: ", i) + ann = annotations[uuid] + lang_keys = [l_key for l_key in ann.keys() if "language_instruction" in l_key] + lang_annotations = [ann[l_key] for l_key in lang_keys] + # Select random annotation + lang_annotation = np.random.choice(lang_annotations) + # Get metadata file + uuid = uuid.split("/")[-1].split(".")[0] + metadata_path = os.path.join(annotations_dir, f"metadata_{uuid}.json") + if not os.path.exists(metadata_path) or lang_annotation in [""]: + continue + with open(metadata_path, "r") as m_file: + metadata = json.load(m_file) + hdf5_path = os.path.join(data_root, metadata["lab"], metadata["hdf5_path"]).replace('trajectory', 'trajectory_im128') + broaddataset_lang.append({"path": hdf5_path, "lang": lang_annotation}) + +broaddataset_lang = [] + +# Get hdf5 to lang for broad data +fill_hdf5_to_lang_map(broaddataset_lang, ANNOTATIONS, DATA_ROOT_DIR, ANNOTATIONS_DIR) +# Get hdf5 to lang for eval data +fill_hdf5_to_lang_map(broaddataset_lang, ANNOTATIONS_EVAL, EVAL_DATA_ROOT_DIR, EVAL_ANNOTATIONS_DIR) + +# Save broaddataset_lang to a new manifest file called manifest_lang.json +json.dump(broaddataset_lang, open(os.path.join(DATA_ROOT_DIR.split("lab-uploads")[0], "manifest_lang.json"), "w"), indent=4) \ No newline at end of file diff --git a/robomimic/scripts/conversion/oxe_to_h5.py b/robomimic/scripts/conversion/oxe_to_h5.py new file mode 100644 index 00000000..c05cfa1d --- /dev/null +++ b/robomimic/scripts/conversion/oxe_to_h5.py @@ -0,0 +1,136 @@ +from absl import app, flags, logging +import dlimp as dl +import h5py +import os +import tqdm +import tensorflow as tf +from functools import partial +from typing import Callable +import torch + +from octo.data.oxe import make_oxe_dataset_kwargs +from octo.data.dataset import make_dataset_from_rlds, apply_frame_transforms +from octo.data import obs_transforms +from octo.data.utils.data_utils import NormalizationType + +from transformers import AutoTokenizer, AutoModel +tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') +model = AutoModel.from_pretrained("distilbert-base-uncased", torch_dtype=torch.float16) +model.to('cuda') + + +FLAGS = flags.FLAGS +flags.DEFINE_string("out_dir", None, "Path of output directory.") +flags.DEFINE_string("dataset", None, "Name of dataset.") + +DATA_DIR = "/mnt/fsx/surajnair/datasets/openx_processed" + + +import tensorflow_graphics.geometry.transformation as tfg + +def euler_to_rmat(euler): + return tfg.rotation_matrix_3d.from_euler(euler) + +def quat_to_rmat(quat): + return tfg.rotation_matrix_3d.from_quaternion(quat) + +def mat_to_rot6d(mat): + r6 = mat[..., :2, :] + r6_0, r6_1 = r6[..., 0, :], r6[..., 1, :] + r6_flat = tf.concat([r6_0, r6_1], axis=-1) + return r6_flat + +def main(_): + + if FLAGS.dataset == "bridge_orig": + ds = "bridge_dataset" + # get dataset specific config + dataset_kwargs = make_oxe_dataset_kwargs( + FLAGS.dataset, + DATA_DIR, + load_camera_views=("primary", "secondary", "wrist"), + load_proprio=True, + load_language=True, + action_proprio_normalization_type=NormalizationType.BOUNDS, + ) + + # create RLDS dataset + ds, _ = make_dataset_from_rlds( + **dataset_kwargs, + train=True, + ) + + def apply_obs_transform(fn: Callable[[dict], dict], frame: dict) -> dict: + # observation is chunked -- apply fn along first axis + frame["observation"] = fn(frame["observation"]) + return frame + + def apply_6d_transform(frame: dict) -> dict: + # observation is chunked -- apply fn along first axis + if frame["observation"]["proprio"][6] == 0: + euler_rot = frame["observation"]["proprio"][3:6][:] + r6 = mat_to_rot6d(euler_to_rmat(euler_rot)) + else: + quat_rot = frame["observation"]["proprio"][3:7][:] + mat = quat_to_rmat(quat_rot) + r6 = mat_to_rot6d(mat) + euler_rot = tfg.euler.from_rotation_matrix(mat) + + frame["observation"]["proprio_rot_6d"] = r6 + frame["observation"]["proprio"] = tf.concat([frame["observation"]["proprio"][:3], euler_rot, tf.zeros_like(euler_rot)[:1], frame["observation"]["proprio"][7:]], axis=-1) + return frame + + + # decode + resize images (and depth images) + ds = ds.frame_map( + partial( + apply_obs_transform, + partial( + obs_transforms.decode_and_resize, + resize_size={ + "primary": (128, 128), + "secondary": (128, 128), + "wrist": (128, 128), + }, + depth_resize_size=None, + ), + ), + tf.data.AUTOTUNE, + ) + + ds = ds.frame_map( + partial( + apply_6d_transform, + ), + tf.data.AUTOTUNE, + ) + + + # iterate over dataset and dump to h5: + os.makedirs(FLAGS.out_dir, exist_ok=True) + print(f"Converting {FLAGS.dataset}...") + for i, episode in enumerate(tqdm.tqdm(ds.iterator())): + with h5py.File(os.path.join(FLAGS.out_dir, f"episode_{i}.h5"), "w") as F: + F["observation/camera/image/varied_camera_1_left_image"] = episode["observation"]["image_primary"][:-1] + F["observation/camera/image/varied_camera_2_left_image"] = episode["observation"]["image_secondary"][:-1] + F["observation/camera/image/hand_camera_left_image"] = episode["observation"]["image_wrist"][:-1] + + F["action/abs_pos"] = episode["observation"]["proprio"][1:, :3] + # F["action/abs_rot_euler"] = episode["observation"]["proprio"][1:, 3:6] + F["action/gripper_position"] = episode["observation"]["proprio"][1:, 7:] + F["action/abs_rot_6d"] = episode["observation"]["proprio_rot_6d"][1:, :] + + F["observation/robot_state/cartesian_position"] = episode["observation"]["proprio"][:-1, :6] + F["observation/robot_state/gripper_position"] = episode["observation"]["proprio"][:-1, 7] + + H = F["observation/robot_state/gripper_position"].shape[0] + l = episode["task"]["language_instruction"][0:1].tolist()[0].decode("utf-8") + encoded_input = tokenizer([l], return_tensors='pt').to('cuda') + outputs = model(**encoded_input) + encoded_lang = outputs.last_hidden_state.sum(1).squeeze().unsqueeze(0).repeat(H, 1) + F["observation/lang_fixed/language_raw"] = [l] * H + F["observation/lang_fixed/language_distilbert"] = encoded_lang.cpu().detach().numpy() + + +if __name__ == "__main__": + app.run(main) diff --git a/robomimic/scripts/conversion/robosuite_add_absolute_actions.py b/robomimic/scripts/conversion/robosuite_add_absolute_actions.py new file mode 100644 index 00000000..8d634f20 --- /dev/null +++ b/robomimic/scripts/conversion/robosuite_add_absolute_actions.py @@ -0,0 +1,298 @@ +import multiprocessing +import os +import pathlib +import h5py +from tqdm import tqdm +import collections +import pickle +import argparse +import numpy as np +import copy + +import h5py +import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.env_utils as EnvUtils +from scipy.spatial.transform import Rotation + +from robomimic.config import config_factory + +""" +copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/common/robomimic_util.py +""" +class RobomimicAbsoluteActionConverter: + def __init__(self, dataset_path, algo_name='bc'): + # default BC config + config = config_factory(algo_name=algo_name) + + # read config to set up metadata for observation modalities (e.g. detecting rgb observations) + # must ran before create dataset + ObsUtils.initialize_obs_utils_with_config(config) + + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path) + abs_env_meta = copy.deepcopy(env_meta) + abs_env_meta['env_kwargs']['controller_configs']['control_delta'] = False + + env = EnvUtils.create_env_from_metadata( + env_meta=env_meta, + render=False, + render_offscreen=False, + use_image_obs=False, + ) + assert len(env.env.robots) in (1, 2) + + abs_env = EnvUtils.create_env_from_metadata( + env_meta=abs_env_meta, + render=False, + render_offscreen=False, + use_image_obs=False, + ) + assert not abs_env.env.robots[0].controller.use_delta + + self.env = env + self.abs_env = abs_env + self.file = h5py.File(dataset_path, 'r') + + def get_demo_keys(self): + return list(self.file['data'].keys()) + + def convert_actions(self, + states: np.ndarray, + actions: np.ndarray) -> np.ndarray: + """ + Given state and delta action sequence + generate equivalent goal position and orientation for each step + keep the original gripper action intact. + """ + env = self.env + d_a = len(env.env.robots[0].action_limits[0]) + + # in case of multi robot + # reshape (N,14) to (N,2,7) + # or (N,7) to (N,1,7) + stacked_actions = actions.reshape(*actions.shape[:-1], -1, d_a) + + # generate abs actions + action_goal_pos = np.zeros( + stacked_actions.shape[:-1]+(3,), + dtype=stacked_actions.dtype) + action_goal_ori = np.zeros( + stacked_actions.shape[:-1]+(3,), + dtype=stacked_actions.dtype) + action_remainder = stacked_actions[...,6:] + for i in range(len(states)): + _ = env.reset_to({'states': states[i]}) + + # taken from robot_env.py L#454 + for idx, robot in enumerate(env.env.robots): + # run controller goal generator + robot.control(stacked_actions[i,idx], policy_step=True) + + # read pos and ori from robots + controller = robot.controller + action_goal_pos[i,idx] = controller.goal_pos + action_goal_ori[i,idx] = Rotation.from_matrix( + controller.goal_ori).as_rotvec() + + stacked_abs_actions = np.concatenate([ + action_goal_pos, + action_goal_ori, + action_remainder + ], axis=-1) + abs_actions = stacked_abs_actions.reshape(actions.shape) + return abs_actions + + def convert_demo(self, demo_key): + file = self.file + demo = file["data/{}".format(demo_key)] + # input + states = demo['states'][:] + actions = demo['actions'][:] + + # generate abs actions + abs_actions = self.convert_actions(states, actions) + return abs_actions + + def convert_and_eval_demo(self, demo_key): + env = self.env + abs_env = self.abs_env + file = self.file + # first step have high error for some reason, not representative + eval_skip_steps = 1 + + demo = file["data/{}".format(demo_key)] + # input + states = demo['states'][:] + actions = demo['actions'][:] + + # generate abs actions + abs_actions = self.convert_actions(states, actions) + + # verify + robot0_eef_pos = demo['obs']['robot0_eef_pos'][:] + robot0_eef_quat = demo['obs']['robot0_eef_quat'][:] + + delta_error_info = self.evaluate_rollout_error( + env, states, actions, robot0_eef_pos, robot0_eef_quat, + metric_skip_steps=eval_skip_steps) + abs_error_info = self.evaluate_rollout_error( + abs_env, states, abs_actions, robot0_eef_pos, robot0_eef_quat, + metric_skip_steps=eval_skip_steps) + + info = { + 'delta_max_error': delta_error_info, + 'abs_max_error': abs_error_info + } + return abs_actions, info + + @staticmethod + def evaluate_rollout_error(env, + states, actions, + robot0_eef_pos, + robot0_eef_quat, + metric_skip_steps=1): + # first step have high error for some reason, not representative + + # evaluate abs actions + rollout_next_states = list() + rollout_next_eef_pos = list() + rollout_next_eef_quat = list() + obs = env.reset_to({'states': states[0]}) + for i in range(len(states)): + obs = env.reset_to({'states': states[i]}) + obs, reward, done, info = env.step(actions[i]) + obs = env.get_observation() + rollout_next_states.append(env.get_state()['states']) + rollout_next_eef_pos.append(obs['robot0_eef_pos']) + rollout_next_eef_quat.append(obs['robot0_eef_quat']) + rollout_next_states = np.array(rollout_next_states) + rollout_next_eef_pos = np.array(rollout_next_eef_pos) + rollout_next_eef_quat = np.array(rollout_next_eef_quat) + + next_state_diff = states[1:] - rollout_next_states[:-1] + max_next_state_diff = np.max(np.abs(next_state_diff[metric_skip_steps:])) + + next_eef_pos_diff = robot0_eef_pos[1:] - rollout_next_eef_pos[:-1] + next_eef_pos_dist = np.linalg.norm(next_eef_pos_diff, axis=-1) + max_next_eef_pos_dist = next_eef_pos_dist[metric_skip_steps:].max() + + next_eef_rot_diff = Rotation.from_quat(robot0_eef_quat[1:]) \ + * Rotation.from_quat(rollout_next_eef_quat[:-1]).inv() + next_eef_rot_dist = next_eef_rot_diff.magnitude() + max_next_eef_rot_dist = next_eef_rot_dist[metric_skip_steps:].max() + + info = { + 'state': max_next_state_diff, + 'pos': max_next_eef_pos_dist, + 'rot': max_next_eef_rot_dist + } + return info + +""" +copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/scripts/robomimic_dataset_conversion.py +""" +def worker(x): + path, demo_key, do_eval = x + converter = RobomimicAbsoluteActionConverter(path) + if do_eval: + abs_actions, info = converter.convert_and_eval_demo(demo_key) + else: + abs_actions = converter.convert_demo(demo_key) + info = dict() + return abs_actions, info + + +def add_absolute_actions_to_dataset(dataset, eval_dir, num_workers): + # process inputs + dataset = pathlib.Path(dataset).expanduser() + assert dataset.is_file() + + do_eval = False + if eval_dir is not None: + eval_dir = pathlib.Path(eval_dir).expanduser() + assert eval_dir.parent.exists() + do_eval = True + + converter = RobomimicAbsoluteActionConverter(dataset) + demo_keys = converter.get_demo_keys() + del converter + + # run + with multiprocessing.Pool(num_workers) as pool: + results = pool.map(worker, [(dataset, demo_key, do_eval) for demo_key in demo_keys]) + + # modify action + with h5py.File(dataset, 'r+') as out_file: + for i in tqdm(range(len(results)), desc="Writing to output"): + abs_actions, info = results[i] + demo = out_file["data/{}".format(demo_keys[i])] + if "actions_abs" not in demo: + demo.create_dataset("actions_abs", data=np.array(abs_actions)) + else: + demo['actions_abs'][:] = abs_actions + + # save eval + if do_eval: + eval_dir.mkdir(parents=False, exist_ok=True) + + print("Writing error_stats.pkl") + infos = [info for _, info in results] + pickle.dump(infos, eval_dir.joinpath('error_stats.pkl').open('wb')) + + print("Generating visualization") + metrics = ['pos', 'rot'] + metrics_dicts = dict() + for m in metrics: + metrics_dicts[m] = collections.defaultdict(list) + + for i in range(len(infos)): + info = infos[i] + for k, v in info.items(): + for m in metrics: + metrics_dicts[m][k].append(v[m]) + + from matplotlib import pyplot as plt + plt.switch_backend('PDF') + + fig, ax = plt.subplots(1, len(metrics)) + for i in range(len(metrics)): + axis = ax[i] + data = metrics_dicts[metrics[i]] + for key, value in data.items(): + axis.plot(value, label=key) + axis.legend() + axis.set_title(metrics[i]) + fig.set_size_inches(10,4) + fig.savefig(str(eval_dir.joinpath('error_stats.pdf'))) + fig.savefig(str(eval_dir.joinpath('error_stats.png'))) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--dataset", + type=str, + required=True + ) + + parser.add_argument( + "--eval_dir", + type=str, + help="directory to output evaluation metrics", + ) + + parser.add_argument( + "--num_workers", + type=int, + default=10, + ) + + args = parser.parse_args() + + + add_absolute_actions_to_dataset( + dataset=args.dataset, + eval_dir=args.eval_dir, + num_workers=args.num_workers, + ) \ No newline at end of file diff --git a/robomimic/scripts/conversion/set_dataset_attr.py b/robomimic/scripts/conversion/set_dataset_attr.py new file mode 100644 index 00000000..4f148d08 --- /dev/null +++ b/robomimic/scripts/conversion/set_dataset_attr.py @@ -0,0 +1,98 @@ +""" +Example: +python robomimic/scripts/set_dataset_attr.py --glob 'datasets/**/*_abs.hdf5' --env_args env_kwargs.controller_configs.control_delta=false absolute_actions=true +""" +import argparse +import pathlib +import json +import sys +import tqdm +import h5py + +def update_env_args_dict(env_args_dict: dict, key: tuple, value): + if key is None: + return env_args_dict + elif len(key) == 0: + return env_args_dict + elif len(key) == 1: + env_args_dict[key[0]] = value + return env_args_dict + else: + this_key = key[0] + if this_key not in env_args_dict: + env_args_dict[this_key] = dict() + update_env_args_dict(env_args_dict[this_key], key[1:], value) + return env_args_dict + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--glob", + type=str, + required=True + ) + + parser.add_argument( + "--env_args", + type=str, + default=None + ) + + parser.add_argument( + 'attrs', + nargs='*' + ) + + args = parser.parse_args() + + # parse attrs to set + # format: key=value + # values are parsed with json + attrs_dict = dict() + for attr_arg in args.attrs: + key, svalue = attr_arg.split("=") + value = json.loads(svalue) + attrs_dict[key] = value + + # parse env_args update + env_args_key = None + env_args_value = None + if args.env_args is not None: + key, svalue = args.env_args.split('=') + env_args_key = key.split('.') + env_args_value = json.loads(svalue) + + # find files + file_paths = list(pathlib.Path.cwd().glob(args.glob)) + + # confirm with the user + print("Found matching files:") + for f in file_paths: + print(f) + print("Are you sure to modify these files with the following attributes:") + print(json.dumps(attrs_dict, indent=2)) + if env_args_key is not None: + print("env_args."+'.'.join(env_args_key)+'='+str(env_args_value)) + result = input("[y/n]?") + if 'y' not in result: + sys.exit(0) + + # execute + for file_path in tqdm.tqdm(file_paths): + with h5py.File(str(file_path), mode='r+') as file: + # update env_args + if env_args_key is not None: + env_args = file['data'].attrs['env_args'] + env_args_dict = json.loads(env_args) + env_args_dict = update_env_args_dict( + env_args_dict=env_args_dict, + key=env_args_key, value=env_args_value) + env_args = json.dumps(env_args_dict) + file['data'].attrs['env_args'] = env_args + + # update other attrs + file['data'].attrs.update(attrs_dict) + +if __name__ == "__main__": + main() diff --git a/robomimic/scripts/dataset_states_to_obs.py b/robomimic/scripts/dataset_states_to_obs.py index 6665ab17..3550e9f4 100644 --- a/robomimic/scripts/dataset_states_to_obs.py +++ b/robomimic/scripts/dataset_states_to_obs.py @@ -61,6 +61,7 @@ def extract_trajectory( initial_state, states, actions, + actions_abs, done_mode, ): """ @@ -92,6 +93,9 @@ def extract_trajectory( states=np.array(states), initial_state_dict=initial_state, ) + if actions_abs is not None: + traj["actions_abs"] = np.array(actions_abs) + traj_len = states.shape[0] # iteration variable @t is over "next obs" indices for t in range(1, traj_len + 1): @@ -174,7 +178,14 @@ def dataset_states_to_obs(args): demos = demos[:args.n] # output file in same directory as input file - output_path = os.path.join(os.path.dirname(args.dataset), args.output_name) + output_name = args.output_name + if output_name is None: + if len(args.camera_names) == 0: + output_name = os.path.basename(args.dataset)[:-5] + "_ld.hdf5" + else: + output_name = os.path.basename(args.dataset)[:-5] + "_im{}.hdf5".format(args.camera_width) + + output_path = os.path.join(os.path.dirname(args.dataset), output_name) f_out = h5py.File(output_path, "w") data_grp = f_out.create_group("data") print("input file: {}".format(args.dataset)) @@ -192,11 +203,16 @@ def dataset_states_to_obs(args): # extract obs, rewards, dones actions = f["data/{}/actions".format(ep)][()] + if "data/{}/actions_abs".format(ep) in f: + actions_abs = f["data/{}/actions_abs".format(ep)][()] + else: + actions_abs = None traj = extract_trajectory( env=env, initial_state=initial_state, states=states, actions=actions, + actions_abs=actions_abs, done_mode=args.done_mode, ) @@ -215,6 +231,8 @@ def dataset_states_to_obs(args): ep_data_grp.create_dataset("states", data=np.array(traj["states"])) ep_data_grp.create_dataset("rewards", data=np.array(traj["rewards"])) ep_data_grp.create_dataset("dones", data=np.array(traj["dones"])) + if "actions_abs" in traj: + ep_data_grp.create_dataset("actions_abs", data=np.array(traj["actions_abs"])) for k in traj["obs"]: if args.compress: ep_data_grp.create_dataset("obs/{}".format(k), data=np.array(traj["obs"][k]), compression="gzip") @@ -226,6 +244,12 @@ def dataset_states_to_obs(args): else: ep_data_grp.create_dataset("next_obs/{}".format(k), data=np.array(traj["next_obs"][k])) + # copy action dict (if applicable) + if "data/{}/action_dict".format(ep) in f: + action_dict = f["data/{}/action_dict".format(ep)] + for k in action_dict: + ep_data_grp.create_dataset("action_dict/{}".format(k), data=np.array(action_dict[k][()])) + # episode metadata if is_robosuite_env: ep_data_grp.attrs["model_file"] = traj["initial_state_dict"]["model"] # model xml for this episode @@ -259,7 +283,6 @@ def dataset_states_to_obs(args): parser.add_argument( "--output_name", type=str, - required=True, help="name of output hdf5 dataset", ) diff --git a/robomimic/scripts/filter_dataset_size.py b/robomimic/scripts/filter_dataset_size.py new file mode 100644 index 00000000..a1509dd1 --- /dev/null +++ b/robomimic/scripts/filter_dataset_size.py @@ -0,0 +1,81 @@ +import argparse +import h5py +import numpy as np + +from robomimic.utils.file_utils import create_hdf5_filter_key + + +def filter_dataset_size(hdf5_path, num_demos, input_filter_key=None, output_filter_key=None): + # retrieve number of demos + f = h5py.File(hdf5_path, "r") + if input_filter_key is not None: + print("using filter key: {}".format(input_filter_key)) + demos = sorted([elem.decode("utf-8") for elem in np.array(f["mask/{}".format(input_filter_key)])]) + else: + demos = sorted(list(f["data"].keys())) + f.close() + + # get random split + total_num_demos = len(demos) + mask = np.zeros(total_num_demos) + mask[:num_demos] = 1. + np.random.shuffle(mask) + mask = mask.astype(int) + subset_inds = mask.nonzero()[0] + subset_keys = [demos[i] for i in subset_inds] + + # pass mask to generate split + if output_filter_key is not None: + name = output_filter_key + else: + name = "{}_demos".format(num_demos) + + if input_filter_key is not None: + name = "{}_{}".format(input_filter_key, name) + + subset_lengths = create_hdf5_filter_key(hdf5_path=hdf5_path, demo_keys=subset_keys, key_name=name) + + print("Total number of subset samples: {}".format(np.sum(subset_lengths))) + print("Average number of subset samples {}".format(np.mean(subset_lengths))) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--dataset", + type=str, + required=True, + help="path to hdf5 dataset", + ) + parser.add_argument( + "--input_filter_key", + type=str, + default=None, + help="if provided, split the subset of trajectories in the file that correspond to\ + this filter key into a training and validation set of trajectories, instead of\ + splitting the full set of trajectories", + ) + parser.add_argument( + "--num_demos", + type=int, + nargs='+', + required=True, + ) + parser.add_argument( + "--output_filter_key", + type=str, + required=False, + help="(optional) use custom name for output filter key name" + ) + args = parser.parse_args() + + # seed to make sure results are consistent + np.random.seed(0) + + for n in args.num_demos: + filter_dataset_size( + args.dataset, + input_filter_key=args.input_filter_key, + num_demos=n, + output_filter_key=args.output_filter_key, + ) \ No newline at end of file diff --git a/robomimic/scripts/playback_dataset.py b/robomimic/scripts/playback_dataset.py index 94799140..d214d57a 100644 --- a/robomimic/scripts/playback_dataset.py +++ b/robomimic/scripts/playback_dataset.py @@ -61,6 +61,7 @@ import argparse import imageio import numpy as np +import random import robomimic import robomimic.utils.obs_utils as ObsUtils @@ -233,9 +234,12 @@ def playback_dataset(args): demos = list(f["data"].keys()) inds = np.argsort([int(elem[5:]) for elem in demos]) demos = [demos[i] for i in inds] - + # maybe reduce the number of demonstrations to playback if args.n is not None: + # if not args.dont_shuffle_demos: + # random.shuffle(demos) + random.shuffle(demos) demos = demos[:args.n] # maybe dump video diff --git a/robomimic/scripts/plot_model_predictions.py b/robomimic/scripts/plot_model_predictions.py new file mode 100644 index 00000000..403f2756 --- /dev/null +++ b/robomimic/scripts/plot_model_predictions.py @@ -0,0 +1,213 @@ +import json +import os +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.gridspec as gridspec +from copy import deepcopy +import random +from sklearn.metrics import mean_squared_error +import re +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.torch_utils as TorchUtils +import robomimic.utils.tensor_utils as TensorUtils +import robomimic.utils.train_utils as TrainUtils +from robomimic.config import config_factory +import robomimic.utils.obs_utils as ObsUtils +import torch +from torch.utils.data import DataLoader + +""" +TODO: track rotation magnitude seperately (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.magnitude.html) +""" + +# the configs of the models to be plotted +model_config_mapping = { + # "bottle_less_obs": { + # "model":"/home/zehan/expdata/r2d2/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/models/model_epoch_60.pth", + # 'folder':"/home/zehan/expdata/r2d2/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/test_inference_figures/", + # # "action_names": ['x', 'y', 'z', 'roll', 'pitch', 'yaw', "gripper_action" , 'terminate'], + # "action_names": None, + # "trajectory_name_regex": r'(\d+_trajectory_im\d+)' + # }, + "r2d2_wire": { + # "model": "/home/soroushn/expdata/r2d2/im/diffusion_policy/debug/ds_pen-in-cup_cams_3cams/20230830160945/models/model_epoch_2.pth", + "model": "/home/soroushn/expdata/r2d2/im/bc_xfmr/debug/ds_pen-in-cup_cams_3cams_predfuture_True_ac_keys_rel/20230830161631/models/model_epoch_2.pth", + "folder": "/home/soroushn/tmp/model_predictions", + # "action_names": ['x', 'y', 'z', 'r', 'p', 'y', "gripper_pos"], # use custom names + "action_names": None, # use default names, see line 71 + "trajectory_name_regex": r'(\w+_\w+_\d{2}_\d{2}:\d{2}:\d{2}_\d{4})' # the name of the figure files need to be custom defined (the part of the names of the trajectories that uniquely identifies them) + } +} + +NUM_SAMPLES = 2 + +# loop through each model +for model_name in model_config_mapping: + ckpt_path = model_config_mapping[model_name]['model'] + saving_folder = model_config_mapping[model_name]['folder'] + # can custom-define or using default action_names + action_names = model_config_mapping[model_name]['action_names'] + trajectory_name_regex = model_config_mapping[model_name]['trajectory_name_regex'] + accuracy_thresholds = np.logspace(-3,-5, num=3).tolist() + + + + device = TorchUtils.get_torch_device(try_to_use_cuda=True) + + ckpt_dict = FileUtils.maybe_dict_from_checkpoint(ckpt_path=ckpt_path) + config = json.loads(ckpt_dict["config"]) + config["train"]["shuffled_obs_key_groups"] = None + ckpt_dict["config"] = json.dumps(config) + policy, _ = FileUtils.policy_from_checkpoint(ckpt_dict=ckpt_dict, device=device, verbose=True) + shape_meta = ckpt_dict['shape_metadata'] + ext_cfg = json.loads(ckpt_dict["config"]) + config = config_factory(ext_cfg["algo_name"]) + with config.values_unlocked(): + config.update(ext_cfg) + + + frame_stack = config.train.frame_stack + device = TorchUtils.get_torch_device(try_to_use_cuda=config.train.cuda) + + trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) + # trainset.datasets is a list + # the trajectories to plot is randomly sampled from the training and validation sets + training_sampled_data = random.sample(trainset.datasets, NUM_SAMPLES) + # validation_sampled_data = random.sample(validset.datasets, NUM_SAMPLES) + + inference_datasets_mapping = {"training": training_sampled_data} #, "validation": validation_sampled_data} + + + if action_names == None: + # TODO + action_keys = config.train.action_keys # Need to adjust. For Robomimic datasets, there is no `action_keys`, it is config.train.dataset_keys + modified_action_keys = [element.replace('action/', '') for element in action_keys] + action_names = [] + for i, action_key in enumerate(action_keys): + if isinstance(training_sampled_data[0].__getitem__(0)[action_key][frame_stack-1], np.ndarray): + action_names.extend([f'{modified_action_keys[i]}_{j+1}' for j in range(len(training_sampled_data[0].__getitem__(0)[action_key][frame_stack-1]))]) + else: + action_names.append(modified_action_keys[i]) + + # loop through training and validation sets + for inference_key in inference_datasets_mapping: + mse_training_per_traj = [] + data_name = [] + actual_actions_all_traj = [] # (NxT, D) + predicted_actions_all_traj = [] # (NxT, D) + + # loop through each trajectory + for d in inference_datasets_mapping[inference_key]: + hdf5_path = d.hdf5_path + mse_for_one_traj = [] + traj_length = len(d) + action_dim = len(action_names) + actual_actions = [[] for _ in range(action_dim)] # (T, D) + predicted_actions = [[] for _ in range(action_dim)] # (T, D) + + image_keys = [item for item in d.__getitem__(0)['obs'].keys() if "image" in item] + images = {key: [] for key in image_keys} + + dataloader = DataLoader( + dataset=d, + sampler=None, + batch_size=1, + shuffle=False, + num_workers=1, + drop_last=True, + ) + + model = policy.policy + + model.reset() + + # loop through each timestep + for batch in iter(dataloader): + batch = model.process_batch_for_training(batch) + + for image_key in image_keys: + im = batch["obs"][image_key][0][-1] + im = TensorUtils.to_numpy(im).astype(np.uint32) + images[image_key].append(im) + + batch = model.postprocess_batch_for_training(batch, obs_normalization_stats=None) # ignore obs_normalization for now + # model_output = model.nets["policy"](batch["obs"]) + + model_output = model.get_action(batch["obs"]) + + actual_action = TensorUtils.to_numpy( + batch["actions"][0][0] + ) + predicted_action = TensorUtils.to_numpy( + model_output[0] + ) + + actual_actions_all_traj.append(actual_action) + predicted_actions_all_traj.append(predicted_action) + + for dim in range(action_dim): + actual_actions[dim].append(actual_action[dim]) + predicted_actions[dim].append(predicted_action[dim]) + + # Plot + fig, axs = plt.subplots(len(images) + action_dim, 1, figsize=(30, (len(images) + action_dim) * 3)) + for i, image_key in enumerate(image_keys): + interval = int(traj_length/15) # plot `5` images + images[image_key] = images[image_key][::interval] + combined_images = np.concatenate(images[image_key], axis=1) + axs[i].imshow(combined_images) + if i == 0: + axs[i].set_title(hdf5_path + '\n' + image_key, fontsize=30) + else: + axs[i].set_title(image_key, fontsize=30) + axs[i].axis("off") + for dim in range(action_dim): + mse = mean_squared_error(actual_actions[dim], predicted_actions[dim]) + mse_for_one_traj.append(mse) + axs[len(images)+dim].plot(range(traj_length), actual_actions[dim], label='Actual Action', color='blue') + axs[len(images)+dim].plot(range(traj_length), predicted_actions[dim], label='Predicted Action', color='red') + # axs[len(images)+dim].set_xlabel('Timestep') + # axs[len(images)+dim].set_ylabel('Action Dimension {}'.format(dim + 1)) + axs[len(images)+dim].set_title(action_names[dim], fontsize=30) + axs[len(images)+dim].xaxis.set_tick_params(labelsize=24) + axs[len(images)+dim].yaxis.set_tick_params(labelsize=24) + axs[len(images)+dim].legend(fontsize=20) + plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3, hspace=0.6) + + # Save inference figures + save_path = saving_folder + inference_key+"/" #remember to add / at the end + # data_content = re.search(trajectory_name_regex, hdf5_path).group(1) + data_content = "test" + filename = "comparison_figure_"+data_content +".png" + if not os.path.exists(save_path): + os.makedirs(save_path) + print(save_path + filename) + # Save the figure with the specified path and filename + plt.savefig(save_path + filename) + mse_training_per_traj.append(mse_for_one_traj) + data_name.append(hdf5_path) + + # log MSE information + accuracy_thresholds = np.logspace(-3,-5, num=3).tolist() + mse = torch.nn.functional.mse_loss(torch.tensor(predicted_actions_all_traj), torch.tensor(actual_actions_all_traj), reduction='none') # (NxT, D) + step_log = {} + step_log[f'{inference_key}_action_mse_error'] = mse.mean().item() # average MSE across all timesteps averaged across all action dimensions (D,) + + # compute percentage of timesteps that have MSE less than the accuracy thresholds + for accuracy_threshold in accuracy_thresholds: + step_log[f'{inference_key}_action_accuracy@{accuracy_threshold}'] = (torch.less(mse,accuracy_threshold).float().mean().item()) + + + average_mse_per_dimension = np.mean(mse_training_per_traj, axis=0) # (D,) + txt_path = saving_folder+inference_key+"/" +"output.txt" + list_str = '\n'.join(['{} {}'.format(desc, ' '.join(map(str, sublist))) for desc, sublist in zip(data_name, mse_training_per_traj)]) + + # save MSE information + with open(txt_path, "w+") as txt_file: + txt_file.write(f"MSE per trajectory:\n{list_str}\n") + txt_file.write("\n") + txt_file.write(f"Average MSE across trajectories per dimension: {average_mse_per_dimension}\n") + txt_file.write("\n") + txt_file.write(f"MSE log: {step_log}\n") + + diff --git a/robomimic/scripts/run_trained_agent.py b/robomimic/scripts/run_trained_agent.py index 95bddb21..2ec09dfe 100644 --- a/robomimic/scripts/run_trained_agent.py +++ b/robomimic/scripts/run_trained_agent.py @@ -66,6 +66,7 @@ import robomimic.utils.tensor_utils as TensorUtils import robomimic.utils.obs_utils as ObsUtils from robomimic.envs.env_base import EnvBase +from robomimic.envs.wrappers import EnvWrapper from robomimic.algo import RolloutPolicy @@ -91,7 +92,7 @@ def rollout(policy, env, horizon, render=False, video_writer=None, video_skip=5, stats (dict): some statistics for the rollout - such as return, horizon, and task success traj (dict): dictionary that corresponds to the rollout trajectory """ - assert isinstance(env, EnvBase) + assert isinstance(env, EnvBase) or isinstance(env, EnvWrapper) assert isinstance(policy, RolloutPolicy) assert not (render and (video_writer is not None)) diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index 9b37a233..2787bc06 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -25,21 +25,28 @@ import sys import socket import traceback +import tqdm from collections import OrderedDict import torch from torch.utils.data import DataLoader +import tensorflow as tf import robomimic import robomimic.utils.train_utils as TrainUtils import robomimic.utils.torch_utils as TorchUtils import robomimic.utils.obs_utils as ObsUtils import robomimic.utils.env_utils as EnvUtils +import robomimic.utils.action_utils as ActionUtils import robomimic.utils.file_utils as FileUtils +from robomimic.utils.dataset import action_stats_to_normalization_stats from robomimic.config import config_factory from robomimic.algo import algo_factory, RolloutPolicy from robomimic.utils.log_utils import PrintLogger, DataLogger, flush_warnings +from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, R2D2_TO_RLDS_OBS_KEY_MAP, R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset + +from octo.data.dataset import make_interleaved_dataset def train(config, device): @@ -57,7 +64,7 @@ def train(config, device): print("\n============= New Training Run with Config =============") print(config) print("") - log_dir, ckpt_dir, video_dir = TrainUtils.get_exp_dir(config) + log_dir, ckpt_dir, video_dir, vis_dir = TrainUtils.get_exp_dir(config) if config.experiment.logging.terminal_output_to_txt: # log stdout and stderr to a text file @@ -68,22 +75,151 @@ def train(config, device): # read config to set up metadata for observation modalities (e.g. detecting rgb observations) ObsUtils.initialize_obs_utils_with_config(config) - # make sure the dataset exists - eval_dataset_cfg = config.train.data[0] - dataset_path = os.path.expanduser(eval_dataset_cfg["path"]) ds_format = config.train.data_format - if not os.path.exists(dataset_path): - raise Exception("Dataset at provided path {} not found!".format(dataset_path)) - - # load basic metadata from training file - print("\n============= Loaded Environment Metadata =============") - env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=dataset_path, ds_format=ds_format) - shape_meta = FileUtils.get_shape_metadata_from_dataset( - dataset_path=dataset_path, - all_obs_keys=config.all_obs_keys, - ds_format=ds_format, - verbose=True - ) + + if ds_format == "r2d2_rlds": + # # load basic metadata from training file + # print("\n============= Loaded Environment Metadata =============") + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=None, ds_format=ds_format) + # TODO (Ashwin): make sure setting to None here is OK, are observations pre-normalized the way we want? + obs_normalization_stats = None + + # FOR RLDS + tf.config.set_visible_devices([], "GPU") + + obs_modalities = config.observation.modalities.obs.rgb + # NOTE(Ashwin): must be 2 cam for now, can clean this up later + assert(len(obs_modalities) == 2) + + BASE_DATASET_KWARGS = { + "data_dir": config.train.data_path, + "image_obs_keys": {"primary": R2D2_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": R2D2_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, + "state_obs_keys": [R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], + "language_key": "language_instruction", + "action_proprio_normalization_type": "bounds", + "absolute_action_mask": [True] * 10, + "action_normalization_mask": [True] * 10, + "standardize_fn": droid_dataset_transform, + } + + # you can add more datasets here & the sampling weights below if you want to mix + dataset_names = config.train.dataset_names + dataset_kwargs_list = [ + {"name": d_name, **BASE_DATASET_KWARGS} for d_name in dataset_names + ] + + # TODO(Ashwin): Wrap more of the parameters below in the robomimic configs + dataset = make_interleaved_dataset( + dataset_kwargs_list, + config.train.sample_weights, + train=True, + shuffle_buffer_size=config.train.shuffle_buffer_size, + batch_size=None, # batching will be handles in PyTorch Dataloader object + balance_weights=True, + traj_transform_kwargs=dict( + # NOTE(Ashwin): window_size and future_action_window_size may break if + # not using diffusion policy + window_size=config.algo.horizon.observation_horizon, + future_action_window_size=config.algo.horizon.prediction_horizon-1, + subsample_length=100, + skip_unlabeled=True, # skip all trajectories without language + ), + frame_transform_kwargs=dict( + image_augment_kwargs=dict( + ), + resize_size=dict( + primary=config.observation.image_dim, + secondary=config.observation.image_dim, + ), + num_parallel_calls=200, + ), + traj_transform_threads=48, + traj_read_threads=48, + ) + # TODO(Ashwin): this assumes that you are doing co-training and that the co-training dataset + # that corresponds to the eval setting is last + rlds_dataset_stats = dataset.dataset_statistics[-1]["action"] + action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats, config.train.action_keys, config.train.action_shapes) + action_config = config.train.action_config + action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) + dataset = dataset.map(robomimic_transform, num_parallel_calls=48) + + pytorch_dataset = TorchRLDSDataset(dataset) + train_loader = DataLoader( + pytorch_dataset, + batch_size=config.train.batch_size, + num_workers=0, # important to keep this to 0 so PyTorch does not mess with the parallelism + ) + + + # For RLDS, get batch from train loader to compute shapes + data_loader_iter = iter(train_loader) + rlds_batch = next(data_loader_iter) + + shape_meta = FileUtils.get_shape_metadata_from_dataset( + dataset_path=None, + batch=rlds_batch, + action_keys=config.train.action_keys, + all_obs_keys=config.all_obs_keys, + ds_format=ds_format, + verbose=True, + config = config + ) + else: + # make sure the dataset exists + eval_dataset_cfg = config.train.data[0] + dataset_path = os.path.expanduser(eval_dataset_cfg["path"]) + + if not os.path.exists(dataset_path): + raise Exception("Dataset at provided path {} not found!".format(dataset_path)) + + # # load basic metadata from training file + print("\n============= Loaded Environment Metadata =============") + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=dataset_path, ds_format=ds_format) + + # update env meta if applicable + from robomimic.utils.script_utils import deep_update + deep_update(env_meta, config.experiment.env_meta_update_dict) + + + shape_meta = FileUtils.get_shape_metadata_from_dataset( + dataset_path=dataset_path, + batch=None, + action_keys=config.train.action_keys, + all_obs_keys=config.all_obs_keys, + ds_format=ds_format, + verbose=True, + config = config + ) + # load training data + trainset, validset = TrainUtils.load_data_for_training( + config, obs_keys=shape_meta["all_obs_keys"]) + train_sampler = trainset.get_dataset_sampler() + print("\n============= Training Dataset =============") + print(trainset) + print("") + if validset is not None: + print("\n============= Validation Dataset =============") + print(validset) + print("") + + # # maybe retreve statistics for normalizing observations + obs_normalization_stats = None + if config.train.hdf5_normalize_obs: + obs_normalization_stats = trainset.get_obs_normalization_stats() + + # maybe retreve statistics for normalizing actions + action_normalization_stats = trainset.get_action_normalization_stats() + + # initialize data loaders + train_loader = DataLoader( + dataset=trainset, + sampler=train_sampler, + batch_size=config.train.batch_size, + shuffle=(train_sampler is None), + num_workers=config.train.num_data_workers, + drop_last=True + ) if config.experiment.env is not None: env_meta["env_name"] = config.experiment.env @@ -132,38 +268,22 @@ def train(config, device): with open(os.path.join(log_dir, '..', 'config.json'), 'w') as outfile: json.dump(config, outfile, indent=4) + # if checkpoint is specified, load in model weights + ckpt_path = config.experiment.ckpt_path + if ckpt_path is not None: + print("LOADING MODEL WEIGHTS FROM " + ckpt_path) + from robomimic.utils.file_utils import maybe_dict_from_checkpoint + ckpt_dict = maybe_dict_from_checkpoint(ckpt_path=ckpt_path) + model.deserialize(ckpt_dict["model"]) + print("\n============= Model Summary =============") print(model) # print model summary print("") - # load training data - trainset, validset = TrainUtils.load_data_for_training( - config, obs_keys=shape_meta["all_obs_keys"]) - train_sampler = trainset.get_dataset_sampler() - print("\n============= Training Dataset =============") - print(trainset) - print("") - if validset is not None: - print("\n============= Validation Dataset =============") - print(validset) - print("") - - # maybe retreve statistics for normalizing observations - obs_normalization_stats = None - if config.train.hdf5_normalize_obs: - obs_normalization_stats = trainset.get_obs_normalization_stats() - - # initialize data loaders - train_loader = DataLoader( - dataset=trainset, - sampler=train_sampler, - batch_size=config.train.batch_size, - shuffle=(train_sampler is None), - num_workers=config.train.num_data_workers, - drop_last=True - ) + ##### ------------------------------------------------------------------------------------ ###### - if config.experiment.validate: + # TODO(Ashwin): Support loading validation splits for RLDS + if ds_format != "r2d2_rlds" and config.experiment.validate: # cap num workers for validation dataset at 1 num_workers = min(config.train.num_data_workers, 1) valid_sampler = validset.get_dataset_sampler() @@ -195,13 +315,15 @@ def train(config, device): train_num_steps = config.experiment.epoch_every_n_steps valid_num_steps = config.experiment.validation_epoch_every_n_steps + data_loader_iter = iter(train_loader) for epoch in range(1, config.train.num_epochs + 1): # epoch numbers start at 1 - step_log = TrainUtils.run_epoch( + step_log, data_loader_iter = TrainUtils.run_epoch( model=model, data_loader=train_loader, epoch=epoch, num_steps=train_num_steps, obs_normalization_stats=obs_normalization_stats, + data_loader_iter = data_loader_iter ) model.on_epoch_end(epoch) @@ -260,7 +382,11 @@ def train(config, device): if config.experiment.rollout.enabled and (epoch > config.experiment.rollout.warmstart) and rollout_check: # wrap model as a RolloutPolicy to prepare for rollouts - rollout_model = RolloutPolicy(model, obs_normalization_stats=obs_normalization_stats) + rollout_model = RolloutPolicy( + model, + obs_normalization_stats=obs_normalization_stats, + action_normalization_stats=action_normalization_stats, + ) num_episodes = config.experiment.rollout.n all_rollout_logs, video_paths = TrainUtils.rollout_with_stats( @@ -305,6 +431,62 @@ def train(config, device): if updated_stats["ckpt_reason"] is not None: ckpt_reason = updated_stats["ckpt_reason"] + # check if we need to save model MSE + #TODO(Ashwin): support MSE Logging with RLDS dataloading + if ds_format != "r2d2_rlds": + should_save_mse = False + if config.experiment.mse.enabled: + if config.experiment.mse.every_n_epochs is not None and epoch % config.experiment.mse.every_n_epochs == 0: + should_save_mse = True + if config.experiment.mse.on_save_ckpt and should_save_ckpt: + should_save_mse = True + if should_save_mse: + print("Computing MSE ...") + if config.experiment.mse.visualize: + save_vis_dir = os.path.join(vis_dir, epoch_ckpt_name) + else: + save_vis_dir = None + mse_log, vis_log = model.compute_mse_visualize( + trainset, + validset, + num_samples=config.experiment.mse.num_samples, + savedir=save_vis_dir, + ) + for k, v in mse_log.items(): + data_logger.record("{}".format(k), v, epoch) + + for k, v in vis_log.items(): + data_logger.record("{}".format(k), v, epoch, data_type='image') + + + print("MSE Log Epoch {}".format(epoch)) + print(json.dumps(mse_log, sort_keys=True, indent=4)) + else: + should_save_batch_samples = False + # TODO(Ashwin): eventually clean up to use different config parameters for + # batch visualization vs. mse visualization + if config.experiment.mse.enabled: + if config.experiment.mse.every_n_epochs is not None and epoch % config.experiment.mse.every_n_epochs == 0: + should_save_batch_samples = True + if config.experiment.mse.on_save_ckpt and should_save_ckpt: + should_save_batch_samples = True + if should_save_batch_samples: + print("Computing Batch Visualization ...") + if config.experiment.mse.visualize: + save_vis_dir = os.path.join(vis_dir, epoch_ckpt_name) + else: + save_vis_dir = None + vis_log = model.compute_batch_visualize( + batch=rlds_batch, + num_samples=config.experiment.mse.num_samples, + savedir=save_vis_dir, + ) + + for k, v in vis_log.items(): + data_logger.record("{}".format(k), v, epoch, data_type='image') + + print("Batch Log Epoch {}".format(epoch)) + # Only keep saved videos if the ckpt should be saved (but not because of validation score) should_save_video = (should_save_ckpt and (ckpt_reason != "valid")) or config.experiment.keep_all_videos if video_paths is not None and not should_save_video: @@ -312,7 +494,7 @@ def train(config, device): os.remove(video_paths[env_name]) # Save model checkpoints based on conditions (success rate, validation loss, etc) - if should_save_ckpt: + if should_save_ckpt: TrainUtils.save_model( model=model, config=config, @@ -320,6 +502,7 @@ def train(config, device): shape_meta=shape_meta, ckpt_path=os.path.join(ckpt_dir, epoch_ckpt_name + ".pth"), obs_normalization_stats=obs_normalization_stats, + action_normalization_stats=action_normalization_stats, ) # Finally, log memory usage in MB @@ -344,7 +527,7 @@ def main(args): else: config = config_factory(args.algo) - if args.dataset is not None: + if config.train.data_format != "r2d2_rlds" and args.dataset is not None: config.train.data = args.dataset if args.name is not None: @@ -362,7 +545,9 @@ def main(args): # train and validate (if enabled) for 3 gradient steps, for 2 epochs config.experiment.epoch_every_n_steps = 3 config.experiment.validation_epoch_every_n_steps = 3 - config.train.num_epochs = 2 + config.train.num_epochs = 200 + config.experiment.mse.every_n_epochs = 2 + config.experiment.save.every_n_epochs = 1 # if rollouts are enabled, try 2 rollouts at end of each epoch, with 10 environment steps config.experiment.rollout.rate = 1 @@ -428,4 +613,3 @@ def main(args): args = parser.parse_args() main(args) - diff --git a/robomimic/utils/action_utils.py b/robomimic/utils/action_utils.py new file mode 100644 index 00000000..a191194b --- /dev/null +++ b/robomimic/utils/action_utils.py @@ -0,0 +1,51 @@ + +from typing import Union, Sequence, Dict, Optional, Tuple + +from copy import deepcopy +from collections import OrderedDict +import functools + +import numpy as np + + +def action_dict_to_vector( + action_dict: Dict[str, np.ndarray], + action_keys: Optional[Sequence[str]]=None) -> np.ndarray: + if action_keys is None: + action_keys = list(action_dict.keys()) + actions = [action_dict[k] for k in action_keys] + + action_vec = np.concatenate(actions, axis=-1) + return action_vec + + +def vector_to_action_dict( + action: np.ndarray, + action_shapes: Dict[str, Tuple[int]], + action_keys: Sequence[str]) -> Dict[str, np.ndarray]: + action_dict = dict() + start_idx = 0 + for key in action_keys: + this_act_shape = action_shapes[key] + this_act_dim = np.prod(this_act_shape) + end_idx = start_idx + this_act_dim + action_dict[key] = action[...,start_idx:end_idx].reshape( + action.shape[:-1]+this_act_shape) + start_idx = end_idx + return action_dict + +def get_action_stats_dict(rlds_dataset_stats, action_keys, action_shapes): + action_stats = dict() + start_idx = 0 + for key, shape in zip(action_keys, action_shapes): + this_act_shape = shape + this_act_dim = np.prod(this_act_shape) + end_idx = start_idx + this_act_dim + action_stats[key] = dict() + for sub_key in rlds_dataset_stats.keys(): + action = rlds_dataset_stats[sub_key] + action_stats[key][sub_key] = action[...,start_idx:end_idx].reshape( + action.shape[:-1]+tuple(this_act_shape)) + start_idx = end_idx + + return action_stats diff --git a/robomimic/utils/camera_utils.py b/robomimic/utils/camera_utils.py new file mode 100644 index 00000000..e4f1ba1f --- /dev/null +++ b/robomimic/utils/camera_utils.py @@ -0,0 +1,2471 @@ +""" +Almost all the code in this file has been copied from TRI's VIDAR +https://github.com/TRI-ML/vidar +""" + + +from abc import ABC +import abc +import torch +import torch.nn as nn +import torch.nn.functional as tfn +import os +import numpy as np +from collections import OrderedDict +import torchvision +from einops import rearrange + +def is_int(data): + """Checks if data is an integer.""" + return isinstance(data, int) + +def is_tensor(data): + """Checks if data is a torch tensor.""" + return type(data) == torch.Tensor + +def to_global_pose_broken(pose, zero_origin=False): + """Get global pose coordinates from current and context poses""" + tgt = 0 if 0 in pose else (0, 0) + base = None if zero_origin else pose[tgt].T.clone() + pose[tgt].T = torch.eye( + 4, device=pose[tgt].device, dtype=pose[tgt].dtype).repeat(pose[tgt].T.shape[0], 1, 1) + + keys = pose.keys() + # steps = sorted(set([key[0] for key in keys])) + steps = {key[1]: [key2[0] for key2 in keys if key2[1] == key[1]] for key in keys} + cams = sorted(set([key[1] for key in keys])) + + for cam in cams: + if cam != tgt[1]: + pose[(tgt[0], cam)].T = (pose[(tgt[0], cam)] * pose[tgt]).T.float() + for cam in cams: + for step in steps[cam]: + if step != tgt[0]: + pose[(step, cam)] = (pose[(step, cam)] * pose[(tgt[0], cam)]) + # for step in steps: + # if step != tgt[0]: + # for cam in cams: + # pose[(step, cam)] = (pose[(step, cam)] * pose[(tgt[0], cam)]) + if not zero_origin: + for key in keys: + pose[key].T = pose[key].T @ base + return pose + +def to_global_pose(pose, zero_origin=False): + """Get global pose coordinates from current and context poses""" + tgt = 0 if 0 in pose else (0, 0) + base = None if zero_origin else pose[tgt].T[[0]].clone() + pose[tgt].T[[0]] = torch.eye(4, device=pose[tgt].device, dtype=pose[tgt].dtype) + for b in range(1, len(pose[tgt])): + pose[tgt].T[[b]] = (pose[tgt][b] * pose[tgt][0]).T.float() + for key in pose.keys(): + if key != tgt: + pose[key] = pose[key] * pose[tgt] + if not zero_origin: + for key in pose.keys(): + for b in range(len(pose[key])): + pose[key].T[[b]] = pose[key].T[[b]] @ base + return pose + +def from_dict_sample(T, to_global=False, zero_origin=False, to_matrix=False, broken=False): + """Helper function to convert sample poses to Pose objects""" + pose = {key: Pose(val) for key, val in T.items()} + if to_global: + to_global_pose_fn = to_global_pose_broken if broken else to_global_pose + pose = to_global_pose_fn(pose, zero_origin=zero_origin) + if to_matrix: + pose = {key: val.T for key, val in pose.items()} + return pose + + +def pose_vec2mat(vec, mode='euler', dtype=None): + """Convert Euler parameters to transformation matrix.""" + if mode is None: + return vec + trans, rot = vec[:, :3].unsqueeze(-1), vec[:, 3:] + if mode == 'euler': + rot_mat = euler2mat(rot, dtype) + else: + raise ValueError('Rotation mode not supported {}'.format(mode)) + mat = torch.cat([rot_mat, trans], dim=2) # [B,3,4] + return mat + + +def euler2mat(angle, dtype=None): + """Convert euler angles to rotation matrix""" + B = angle.size(0) + if dtype is not None: + angle = angle.to(dtype) + x, y, z = angle[:, 0], angle[:, 1], angle[:, 2] + + cosz = torch.cos(z) + sinz = torch.sin(z) + + zeros = z.detach() * 0 + ones = zeros.detach() + 1 + zmat = torch.stack([ cosz, -sinz, zeros, + sinz, cosz, zeros, + zeros, zeros, ones], dim=1).view(B, 3, 3) + + cosy = torch.cos(y) + siny = torch.sin(y) + + ymat = torch.stack([ cosy, zeros, siny, + zeros, ones, zeros, + -siny, zeros, cosy], dim=1).view(B, 3, 3) + + cosx = torch.cos(x) + sinx = torch.sin(x) + + xmat = torch.stack([ ones, zeros, zeros, + zeros, cosx, -sinx, + zeros, sinx, cosx], dim=1).view(B, 3, 3) + + rot_mat = xmat.bmm(ymat).bmm(zmat) + return rot_mat + +def is_tuple(data): + """Checks if data is a tuple.""" + return isinstance(data, tuple) + +def is_list(data): + """Checks if data is a list.""" + return isinstance(data, list) or isinstance(data, torch.nn.ModuleList) + +def is_seq(data): + """Checks if data is a list or tuple.""" + return is_tuple(data) or is_list(data) + +def is_dict(data): + """Checks if data is a dictionary.""" + return isinstance(data, dict) or isinstance(data, torch.nn.ModuleDict) + +def iterate1(func): + """Decorator to iterate over a list (first argument)""" + def inner(var, *args, **kwargs): + if is_seq(var): + return [func(v, *args, **kwargs) for v in var] + elif is_dict(var): + return {key: func(val, *args, **kwargs) for key, val in var.items()} + else: + return func(var, *args, **kwargs) + return inner + +@iterate1 +def invert_pose(T): + """Inverts a [B,4,4] torch.tensor pose""" + Tinv = torch.eye(4, device=T.device, dtype=T.dtype).repeat([len(T), 1, 1]) + Tinv[:, :3, :3] = torch.transpose(T[:, :3, :3], -2, -1) + Tinv[:, :3, -1] = torch.bmm(-1. * Tinv[:, :3, :3], T[:, :3, -1].unsqueeze(-1)).squeeze(-1) + return Tinv + # return torch.linalg.inv(T) + +def from_dict_batch(T, **kwargs): + """Helper function to convert a dicionary of tensor poses to Pose objects""" + pose_batch = [from_dict_sample({key: val[b] for key, val in T.items()}, **kwargs) + for b in range(T[0].shape[0])] + return {key: torch.stack([v[key] for v in pose_batch], 0) for key in pose_batch[0]} + + +def align_corners(value=None): + """Get the align_corners value from the environment variable""" + if value is not None: + return value + if os.getenv('ALIGN_CORNERS') is not None: + return True if os.getenv('ALIGN_CORNERS') == 'True' else \ + False if os.getenv('ALIGN_CORNERS') == 'False' else None + return True + + +@iterate1 +def interpolate(tensor, size, scale_factor, mode): + """Helper function to interpolate tensors""" + if size is None and scale_factor is None: + return tensor + if is_tensor(size): + size = size.shape[-2:] + return tfn.interpolate( + tensor, size=size, scale_factor=scale_factor, + recompute_scale_factor=False, mode=mode, + align_corners=None if mode == 'nearest' else align_corners(), + ) + + +def interpolate_nearest(tensor, size=None, scale_factor=None): + """Helper function to interpolate tensors using nearest neighbor interpolation""" + if size is not None and is_tensor(size): + size = size.shape[-2:] + return interpolate(tensor.float(), size, scale_factor, mode='nearest') + + +def masked_average(loss, mask, eps=1e-7): + """Mask average for loss given mask""" + return (loss * mask).sum() / (mask.sum() + eps) + + +def multiply_mask(data, mask): + """Multiply data with masks""" + return data if (data is None or mask is None) else data * mask + + +def multiply_args(*args): + """Multiply all arguments""" + valids = [v for v in args if v is not None] + return None if not valids else reduce((lambda x, y: x * y), valids) + + +def grid_sample(tensor, grid, padding_mode, mode): + """Helper function for grid sampling""" + return tfn.grid_sample( + tensor, grid, + padding_mode=padding_mode, mode=mode, + align_corners=align_corners(), + ) + + +def grid_sample_volume(tensor, grid, padding_mode, mode): + """Helper function for multi-layer grid sampling""" + b, d, h, w, _ = grid.shape + return grid_sample( + tensor, grid.reshape(b, d, h * w, 2), + padding_mode=padding_mode, mode=mode + ).reshape(b, 3, d, h, w) + + +def pixel_grid(hw, b=None, with_ones=False, device=None, normalize=False, shake=False): + """Helper function to generate a pixel grid given [H,W] or [B,H,W]""" + if is_tensor(hw): + b, hw, device = hw.shape[0], hw.shape[-2:], hw.device + if is_tensor(device): + device = device.device + if align_corners(): + hi, hf = 0, hw[0] - 1 + wi, wf = 0, hw[1] - 1 + else: + hi, hf = 0.5, hw[0] - 0.5 + wi, wf = 0.5, hw[1] - 0.5 + yy, xx = torch.meshgrid([torch.linspace(hi, hf, hw[0], device=device), + torch.linspace(wi, wf, hw[1], device=device)], indexing='ij') + if with_ones: + grid = torch.stack([xx, yy, torch.ones(hw, device=device)], 0) + else: + grid = torch.stack([xx, yy], 0) + if b is not None: + grid = grid.unsqueeze(0).repeat(b, 1, 1, 1) + if shake: + if align_corners(): + rand = torch.rand((b, 2, *hw), device=device) + else: + rand = torch.rand((b, 2, *hw), device=device) - 0.5 + grid[:, :2, :, :] += rand + if normalize: + grid = norm_pixel_grid(grid) + return grid + + +def norm_pixel_grid(grid, hw=None, in_place=False): + """Normalize a pixel grid from [W,H] to [-1,+1].""" + if hw is None: + hw = grid.shape[-2:] + if not in_place: + grid = grid.clone() + if align_corners(): + grid[:, 0] = 2.0 * grid[:, 0] / (hw[1] - 1) - 1.0 + grid[:, 1] = 2.0 * grid[:, 1] / (hw[0] - 1) - 1.0 + else: + grid[:, 0] = 2.0 * grid[:, 0] / hw[1] - 1.0 + grid[:, 1] = 2.0 * grid[:, 1] / hw[0] - 1.0 + return grid + + +def unnorm_pixel_grid(grid, hw=None, in_place=False): + """Unnormalize a pixel grid from [-1,+1] to [W,H].""" + if hw is None: + hw = grid.shape[-2:] + if not in_place: + grid = grid.clone() + if align_corners(): + grid[:, 0] = 0.5 * (hw[1] - 1) * (grid[:, 0] + 1) + grid[:, 1] = 0.5 * (hw[0] - 1) * (grid[:, 1] + 1) + else: + grid[:, 0] = 0.5 * hw[1] * (grid[:, 0] + 1) + grid[:, 1] = 0.5 * hw[0] * (grid[:, 1] + 1) + return grid + + +def match_scales(image, targets, num_scales, mode='bilinear'): + """Match scales of image given target scales and resolutions + + Parameters + ---------- + image : torch.Tensor + Input image to be scaled + targets : list of torch.Tensor + Targes to match scale + num_scales : int + Number of scales to match + mode : str, optional + Interpolation mode, by default 'bilinear' + + Returns + ------- + _type_ + _description_ + """ + # For all scales + images = [] + image_shape = image.shape[-2:] + for i in range(num_scales): + target_shape = targets[i].shape + # If image shape is equal to target shape + if same_shape(image_shape, target_shape): + images.append(image) + else: + # Otherwise, interpolate + images.append(interpolate_image(image, target_shape, mode=mode)) + # Return scaled images + return images + + +def cat_channel_ones(tensor, n=1): + """ + Concatenate tensor with an extra channel of ones + + Parameters + ---------- + tensor : torch.Tensor + Tensor to be concatenated + n : int + Which channel will be concatenated + + Returns + ------- + cat_tensor : torch.Tensor + Concatenated tensor + """ + # Get tensor shape with 1 channel + shape = list(tensor.shape) + shape[n] = 1 + # Return concatenation of tensor with ones + return torch.cat([tensor, torch.ones(shape, + device=tensor.device, dtype=tensor.dtype)], n) + + +def same_shape(shape1, shape2): + """Checks if two shapes are the same""" + if len(shape1) != len(shape2): + return False + for i in range(len(shape1)): + if shape1[i] != shape2[i]: + return False + return True + + +def interpolate_image(image, shape=None, scale_factor=None, + mode='bilinear', recompute_scale_factor=False): + """ + Interpolate an image to a different resolution + + Parameters + ---------- + image : torch.Tensor + Image to be interpolated [B,?,h,w] + shape : torch.Tensor or tuple + Output shape [H,W] + scale_factor : float + Scale factor for output shape + mode : str + Interpolation mode + recompute_scale_factor : bool + True if scale factor is recomputed + + Returns + ------- + image : torch.Tensor + Interpolated image [B,?,H,W] + """ + assert shape is not None or scale_factor is not None, 'Invalid option for interpolate_image' + # Take last two dimensions as shape + if shape is not None: + if is_tensor(shape): + shape = shape.shape + if len(shape) > 2: + shape = shape[-2:] + # If the shapes are the same, do nothing + if same_shape(image.shape[-2:], shape): + return image + # Interpolate image to match the shape + return interpolate(image, size=shape, scale_factor=scale_factor, mode=mode) + + +def check_assert(pred, gt, atol=1e-5, rtol=1e-5): + """Check if two dictionaries are equal""" + for key in gt.keys(): + if key in pred.keys(): + # assert key in pred and key in gt + if is_dict(pred[key]): + check_assert(pred[key], gt[key]) + elif is_seq(pred[key]): + for val1, val2 in zip(pred[key], gt[key]): + if is_tensor(val1): + assert torch.allclose(val1, val2, atol=atol, rtol=rtol), \ + f'Assert error in {key} : {val1.mean().item()} x {val2.mean().item()}' + else: + assert val1 == val2, \ + f'Assert error in {key} : {val1} x {val2}' + else: + if is_tensor(pred[key]): + assert torch.allclose(pred[key], gt[key], atol=atol, rtol=rtol), \ + f'Assert error in {key} : {pred[key].mean().item()} x {gt[key].mean().item()}' + else: + assert pred[key] == gt[key], \ + f'Assert error in {key} : {pred[key]} x {gt[key]}' + + +def interleave(data, b): + """Interleave data across multiple batches""" + data_interleave = data.unsqueeze(1).expand(-1, b, *data.shape[1:]) + return data_interleave.reshape(-1, *data.shape[1:]) + +def invert_intrinsics(K): + """Invert camera intrinsics""" + + Kinv = K.clone() + Kinv[:, 0, 0] = 1. / K[:, 0, 0] + Kinv[:, 1, 1] = 1. / K[:, 1, 1] + Kinv[:, 0, 2] = -1. * K[:, 0, 2] / K[:, 0, 0] + Kinv[:, 1, 2] = -1. * K[:, 1, 2] / K[:, 1, 1] + return Kinv + + +def scale_intrinsics(K, ratio): + """Scale intrinsics given x_scale and y_scale factors""" + + if is_seq(ratio): + ratio_h, ratio_w = ratio + else: + ratio_h = ratio_w = ratio + + K = K.clone() + + K[..., 0, 0] *= ratio_w + K[..., 1, 1] *= ratio_h + + K[..., 0, 2] = K[..., 0, 2] * ratio_w + K[..., 1, 2] = K[..., 1, 2] * ratio_h + + return K + +class Pose: + """ + Pose class, that encapsulates a [4,4] transformation matrix + for a specific reference frame + """ + def __init__(self, T=1): + """ + Initializes a Pose object. + + Parameters + ---------- + T : int or torch.Tensor + Transformation matrix [B,4,4] + """ + if is_int(T): + T = torch.eye(4).repeat(T, 1, 1) + self.T = T if T.dim() == 3 else T.unsqueeze(0) + + def __len__(self): + """Batch size of the transformation matrix""" + return len(self.T) + + def __getitem__(self, idx): + """Return batch-wise pose""" + if not is_tensor(idx): + idx = [idx] + return Pose(self.T[idx]) + + def __mul__(self, data): + """Transforms the input (Pose or 3D points) using this object""" + if isinstance(data, Pose): + return Pose(self.T.bmm(data.T)) + elif isinstance(data, torch.Tensor): + return self.T[:, :3, :3].bmm(data) + self.T[:, :3, -1].unsqueeze(-1) + else: + raise NotImplementedError() + + def detach(self): + """Detach pose from graph""" + return Pose(self.T.detach()) + + def clone(self): + """Clone pose""" + return type(self)( + T=self.T.clone() + ) + + @property + def shape(self): + """Return pose shape""" + return self.T.shape + + @property + def device(self): + """Return pose device""" + return self.T.device + + @property + def dtype(self): + """Return pose type""" + return self.T.dtype + + @classmethod + def identity(cls, N=1, device=None, dtype=torch.float): + """Initializes as a [4,4] identity matrix""" + return cls(torch.eye(4, device=device, dtype=dtype).repeat([N,1,1])) + + @staticmethod + def from_dict(T, to_global=False, zero_origin=False, to_matrix=False, broken=False): + if T is None: + return None + tgt = (0, 0) if broken else 0 + if T[tgt].dim() == 3: + return from_dict_sample( + T, to_global=to_global, zero_origin=zero_origin, to_matrix=to_matrix, broken=broken) + elif T[tgt].dim() == 4: + return from_dict_batch( + T, to_global=to_global, zero_origin=zero_origin, to_matrix=True, broken=broken) + + @classmethod + def from_vec(cls, vec, mode): + """Initializes from a [B,6] batch vector""" + mat = pose_vec2mat(vec, mode) + pose = torch.eye(4, device=vec.device, dtype=vec.dtype).repeat([len(vec), 1, 1]) + pose[:, :3, :3] = mat[:, :3, :3] + pose[:, :3, -1] = mat[:, :3, -1] + return cls(pose) + + def repeat(self, *args, **kwargs): + """Repeats the transformation matrix multiple times""" + self.T = self.T.repeat(*args, **kwargs) + return self + + def inverse(self): + """Returns a new Pose that is the inverse of this one""" + return Pose(invert_pose(self.T)) + + def to(self, *args, **kwargs): + """Moves object to a specific device""" + self.T = self.T.to(*args, **kwargs) + return self + + def cuda(self, *args, **kwargs): + """Moves pose to GPU""" + self.to('cuda') + return self + + def translate(self, xyz): + """Translate pose by a vector""" + self.T[:, :3, -1] = self.T[:, :3, -1] + xyz.to(self.device) + return self + + def rotate(self, rpw): + """Rotate pose by a vector""" + rot = euler2mat(rpw) + T = invert_pose(self.T).clone() + T[:, :3, :3] = T[:, :3, :3] @ rot.to(self.device) + self.T = invert_pose(T) + return self + + def rotateRoll(self, r): + """Rotate pose by a roll angle""" + return self.rotate(torch.tensor([[0, 0, r]])) + + def rotatePitch(self, p): + """Rotate pose by a pitch angle""" + return self.rotate(torch.tensor([[p, 0, 0]])) + + def rotateYaw(self, w): + """Rotate pose by a yaw angle""" + return self.rotate(torch.tensor([[0, w, 0]])) + + def translateForward(self, t): + """Translate pose on its z axis (forward)""" + return self.translate(torch.tensor([[0, 0, -t]])) + + def translateBackward(self, t): + """Translate pose on its z axis (backward)""" + return self.translate(torch.tensor([[0, 0, +t]])) + + def translateLeft(self, t): + """Translate pose on its x axis (left)""" + return self.translate(torch.tensor([[+t, 0, 0]])) + + def translateRight(self, t): + """Translate pose on its x axis (right)""" + return self.translate(torch.tensor([[-t, 0, 0]])) + + def translateUp(self, t): + """Translate pose on its y axis (up)""" + return self.translate(torch.tensor([[0, +t, 0]])) + + def translateDown(self, t): + """Translate pose on its y axis (down)""" + return self.translate(torch.tensor([[0, -t, 0]])) + + +class CameraBase(nn.Module, ABC): + """Base camera class + + Parameters + ---------- + hw : tuple + Camera resolution + Twc : torch.Tensor, optional + Camera pose (world to camera), by default None + Tcw : torch.Tensor, optional + Camera pose (camera to world), by default None + """ + def __init__(self, hw, Twc=None, Tcw=None): + super().__init__() + assert Twc is None or Tcw is None + + # Pose + + if Twc is None and Tcw is None: + self._Twc = torch.eye( + 4, dtype=self._K.dtype, device=self._K.device).unsqueeze(0).repeat(self._K.shape[0], 1, 1) + else: + self._Twc = invert_pose(Tcw) if Tcw is not None else Twc + if is_tensor(self._Twc): + self._Twc = Pose(self._Twc) + + # Resolution + + self._hw = hw + if is_tensor(self._hw): + self._hw = self._hw.shape[-2:] + + def __getitem__(self, idx): + """Return batch-wise pose""" + if is_seq(idx): + return type(self).from_list([self.__getitem__(i) for i in idx]) + else: + if not is_tensor(idx): + idx = [idx] + return type(self)( + K=self._K[idx], + Twc=self._Twc[idx] if self._Twc is not None else None, + hw=self._hw, + ) + + def __len__(self): + """Return length as intrinsics batch""" + return self._K.shape[0] + + def __eq__(self, cam): + """Check camera equality""" + if not isinstance(cam, type(self)): + return False + if self._hw[0] != cam.hw[0] or self._hw[1] != cam.hw[1]: + return False + if not torch.allclose(self._K, cam.K): + return False + if not torch.allclose(self._Twc.T, cam.Twc.T): + return False + return True + + def clone(self): + """Clone camera""" + return type(self)( + K=self.K.clone(), + Twc=self.Twc.clone(), + hw=[v for v in self._hw], + ) + + @property + def pose(self): + """Return camera pose (world to camera)""" + return self._Twc.T + + @property + def K(self): + """Return camera intrinsics""" + return self._K + + @K.setter + def K(self, K): + """Set camera intrinsics""" + self._K = K + + @property + def batch_size(self): + """Return batch size""" + return self._Twc.T.shape[0] + + @property + def b(self): + """Return batch size""" + return self._Twc.T.shape[0] + + @property + def bhw(self): + """Return batch size and resolution""" + return self.b, self.hw + + @property + def bdhw(self): + """Return batch size, device, and resolution""" + return self.b, self.device, self.hw + + @property + def hw(self): + """Return camera resolution""" + return self._hw + + @hw.setter + def hw(self, hw): + """Set camera resolution""" + self._hw = hw + + @property + def wh(self): + """Return camera resolution""" + return self._hw[::-1] + + @property + def n_pixels(self): + """Return number of pixels""" + return self._hw[0] * self._hw[1] + + @property + def Tcw(self): + """Return camera pose (camera to world)""" + return None if self._Twc is None else self._Twc.inverse() + + @Tcw.setter + def Tcw(self, Tcw): + """Set camera pose (camera to world)""" + self._Twc = Tcw.inverse() + + @property + def Twc(self): + """Return camera pose (world to camera)""" + return self._Twc + + @Twc.setter + def Twc(self, Twc): + """Set camera pose (world to camera)""" + self._Twc = Twc + + @property + def dtype(self): + """Get camera data type""" + return self._K.dtype + + @property + def device(self): + """Get camera device""" + return self._K.device + + def detach_pose(self): + """Detach camera pose""" + return type(self)(K=self._K, hw=self._hw, + Twc=self._Twc.detach() if self._Twc is not None else None) + + def detach_K(self): + """Detach camera intrinsics""" + return type(self)(K=self._K.detach(), hw=self._hw, Twc=self._Twc) + + def detach(self): + """Detach camera""" + return type(self)(K=self._K.detach(), hw=self._hw, + Twc=self._Twc.detach() if self._Twc is not None else None) + + def inverted_pose(self): + """Return camera with inverted pose""" + return type(self)(K=self._K, hw=self._hw, + Twc=self._Twc.inverse() if self._Twc is not None else None) + + def no_translation(self): + """Return camera with no translation""" + Twc = self.pose.clone() + Twc[:, :-1, -1] = 0 + return type(self)(K=self._K, hw=self._hw, Twc=Twc) + + def no_pose(self): + """Return camera with no pose""" + return type(self)(K=self._K, hw=self._hw) + + def interpolate(self, rgb): + """Interpolate RGB image""" + if rgb.dim() == 5: + rgb = rearrange(rgb, 'b n c h w -> (b n) c h w') + return interpolate(rgb, scale_factor=None, size=self.hw, mode='bilinear') + + def interleave_K(self, b): + """Interleave intrinsics for multiple batches""" + return type(self)( + K=interleave(self._K, b), + Twc=self._Twc, + hw=self._hw, + ) + + def interleave_Twc(self, b): + """Interleave pose for multiple batches""" + return type(self)( + K=self._K, + Twc=interleave(self._Twc, b), + hw=self._hw, + ) + + def interleave(self, b): + """Interleave camera for multiple batches""" + return type(self)( + K=interleave(self._K, b), + Twc=interleave(self._Twc, b), + hw=self._hw, + ) + + def repeat_bidir(self): + """Repeat camera for bidirectional training""" + return type(self)( + K=self._K.repeat(2, 1, 1), + Twc=torch.cat([self._Twc.T, self.Tcw.T], 0), + hw=self.hw, + ) + + def Pwc(self, from_world=True): + """Return camera projection matrix (world to camera)""" + return self._K[:, :3] if not from_world or self._Twc is None else \ + torch.matmul(self._K, self._Twc.T)[:, :3] + + def to_world(self, points): + """Moves pointcloud to world coordinates""" + if points.dim() > 3: + points = points.reshape(points.shape[0], 3, -1) + return points if self.Tcw is None else self.Tcw * points + + def from_world(self, points): + """Moves pointcloud to camera coordinates""" + if points.dim() > 3: + shape = points.shape + points = points.reshape(points.shape[0], 3, -1) + else: + shape = None + local_points = points if self._Twc is None else \ + torch.matmul(self._Twc.T, cat_channel_ones(points, 1))[:, :3] + return local_points if shape is None else local_points.view(shape) + + def from_world2(self, points): + """Moves pointcloud to camera coordinates""" + if points.dim() > 3: + points = points.reshape(points.shape[0], 3, -1) + return points if self._Twc is None else \ + torch.matmul(self._Twc.T[:, :3, :3], points[:, :3]) + self._Twc.T[:, :3, 3:] + + def to(self, *args, **kwargs): + """Moves camera to device""" + self._K = self._K.to(*args, **kwargs) + if self._Twc is not None: + self._Twc = self._Twc.to(*args, **kwargs) + return self + + def cuda(self, *args, **kwargs): + """Moves camera to GPU""" + return self.to('cuda') + + def relative_to(self, cam): + """Returns camera relative to another camera""" + return type(self)(K=self._K, hw=self._hw, Twc=self._Twc * cam.Twc.inverse()) + + def global_from(self, cam): + """Returns global camera from one relative to another camera""" + return type(self)(K=self._K, hw=self._hw, Twc=self._Twc * cam.Twc) + + def pixel_grid(self, shake=False): + """Returns pixel grid""" + return pixel_grid( + b=self.batch_size, hw=self.hw, with_ones=True, + shake=shake, device=self.device).view(self.batch_size, 3, -1) + + def reconstruct_depth_map(self, depth, to_world=False, grid=None, scene_flow=None, world_scene_flow=None): + """Reconstruct 3D pointcloud from z-buffer depth map""" + if depth is None: + return None + b, _, h, w = depth.shape + if grid is None: + grid = pixel_grid(depth, with_ones=True, device=depth.device).view(b, 3, -1) + points = self.lift(grid) * depth.view(depth.shape[0], 1, -1) + if scene_flow is not None: + points = points + scene_flow.view(b, 3, -1) + if to_world and self.Tcw is not None: + points = self.Tcw * points + if world_scene_flow is not None: + points = points + world_scene_flow.view(b, 3, -1) + return points.view(b, 3, h, w) + + def reconstruct_euclidean(self, depth, to_world=False, grid=None, scene_flow=None, world_scene_flow=None): + """Reconstruct 3D pointcloud from euclidean depth map""" + if depth is None: + return None + b, _, h, w = depth.shape + rays = self.get_viewdirs(normalize=True, to_world=False, grid=grid).view(b, 3, -1) + points = rays * depth.view(depth.shape[0], 1, -1) + if scene_flow is not None: + points = points + scene_flow.view(b, 3, -1) + if to_world and self.Tcw is not None: + points = self.Tcw * points + if world_scene_flow is not None: + points = points + world_scene_flow.view(b, 3, -1) + return points.view(b, 3, h, w) + + def reconstruct_volume(self, depth, euclidean=False, **kwargs): + """Reconstruct 3D pointcloud from depth volume""" + if euclidean: + return torch.stack([self.reconstruct_euclidean(depth[:, :, i], **kwargs) + for i in range(depth.shape[2])], 2) + else: + return torch.stack([self.reconstruct_depth_map(depth[:, :, i], **kwargs) + for i in range(depth.shape[2])], 2) + + def reconstruct_cost_volume(self, volume, to_world=True, flatten=True): + """Reconstruct 3D pointcloud from cost volume""" + c, d, h, w = volume.shape + grid = pixel_grid((h, w), with_ones=True, device=volume.device).view(3, -1).repeat(1, d) + points = torch.stack([ + (volume.view(c, -1) * torch.matmul(invK[:3, :3].unsqueeze(0), grid)).view(3, d * h * w) + for invK in self.invK], 0) + if to_world and self.Tcw is not None: + points = self.Tcw * points + if flatten: + return points.view(-1, 3, d, h * w).permute(0, 2, 1, 3) + else: + return points.view(-1, 3, d, h, w) + + def project_points(self, points, from_world=True, normalize=True, + return_z=False, return_e=False, flag_invalid=True): + """Projects 3D points to 2D image plane""" + + is_depth_map = points.dim() == 4 + hw = self._hw if not is_depth_map else points.shape[-2:] + return_depth = return_z or return_e + + if is_depth_map: + points = points.reshape(points.shape[0], 3, -1) + b, _, n = points.shape + + coords, depth = self.unlift(points, from_world=from_world, euclidean=return_e) + + if not is_depth_map: + if normalize: + coords = norm_pixel_grid(coords, hw=self._hw, in_place=True) + if flag_invalid: + invalid = (coords[:, 0] < -1) | (coords[:, 0] > 1) | \ + (coords[:, 1] < -1) | (coords[:, 1] > 1) | (depth < 0) + coords[invalid.unsqueeze(1).repeat(1, 2, 1)] = -2 + if return_depth: + return coords.permute(0, 2, 1), depth + else: + return coords.permute(0, 2, 1) + + coords = coords.view(b, 2, *hw) + depth = depth.view(b, 1, *hw) + + if normalize: + coords = norm_pixel_grid(coords, hw=self._hw, in_place=True) + if flag_invalid: + invalid = (coords[:, 0] < -1) | (coords[:, 0] > 1) | \ + (coords[:, 1] < -1) | (coords[:, 1] > 1) | (depth[:, 0] < 0) + coords[invalid.unsqueeze(1).repeat(1, 2, 1, 1)] = -2 + + if return_depth: + return coords.permute(0, 2, 3, 1), depth + else: + return coords.permute(0, 2, 3, 1) + + def project_cost_volume(self, points, from_world=True, normalize=True): + """Projects 3D points from a cost volume to 2D image plane""" + + if points.dim() == 4: + points = points.permute(0, 2, 1, 3).reshape(points.shape[0], 3, -1) + b, _, n = points.shape + + points = torch.matmul(self.Pwc(from_world), cat_channel_ones(points, 1)) + + coords = points[:, :2] / (points[:, 2].unsqueeze(1) + 1e-7) + coords = coords.view(b, 2, -1, *self._hw).permute(0, 2, 3, 4, 1) + + if normalize: + coords[..., 0] /= self._hw[1] - 1 + coords[..., 1] /= self._hw[0] - 1 + return 2 * coords - 1 + else: + return coords + + def create_radial_volume(self, bins, to_world=True): + """Create a volume of radial depth bins""" + ones = torch.ones((1, *self.hw), device=self.device) + volume = torch.stack([depth * ones for depth in bins], 1).unsqueeze(0) + return self.reconstruct_volume(volume, to_world=to_world) + + def project_volume(self, volume, from_world=True): + """Project a volume to 2D image plane""" + b, c, d, h, w = volume.shape + return self.project_points(volume.view(b, c, -1), from_world=from_world).view(b, d, h, w, 2) + + def coords_from_cost_volume(self, volume, ref_cam=None): + """Project a cost volume to 2D image plane""" + if ref_cam is None: + return self.project_cost_volume(self.reconstruct_cost_volume(volume, to_world=False), from_world=True) + else: + return ref_cam.project_cost_volume(self.reconstruct_cost_volume(volume, to_world=True), from_world=True) + + def z2e(self, z_depth): + """Convert z-buffer depth to euclidean depth""" + points = self.reconstruct_depth_map(z_depth, to_world=False) + return self.project_points(points, from_world=False, return_e=True)[1] + + def e2z(self, e_depth): + """Convert euclidean depth to z-buffer depth""" + points = self.reconstruct_euclidean(e_depth, to_world=False) + return self.project_points(points, from_world=False, return_z=True)[1] + + def control(self, draw, tvel=0.2, rvel=0.1): + """Control camera with keyboard (requires camviz)""" + change = False + if draw.UP: + self.Twc.translateForward(tvel) + change = True + if draw.DOWN: + self.Twc.translateBackward(tvel) + change = True + if draw.LEFT: + self.Twc.translateLeft(tvel) + change = True + if draw.RIGHT: + self.Twc.translateRight(tvel) + change = True + if draw.KEY_Z: + self.Twc.translateUp(tvel) + change = True + if draw.KEY_X: + self.Twc.translateDown(tvel) + change = True + if draw.KEY_A: + self.Twc.rotateYaw(-rvel) + change = True + if draw.KEY_D: + self.Twc.rotateYaw(+rvel) + change = True + if draw.KEY_W: + self.Twc.rotatePitch(+rvel) + change = True + if draw.KEY_S: + self.Twc.rotatePitch(-rvel) + change = True + if draw.KEY_Q: + self.Twc.rotateRoll(-rvel) + change = True + if draw.KEY_E: + self.Twc.rotateRoll(+rvel) + change = True + return change + + +class CameraNerf(CameraBase): + """Camera class with NeRF functionalities""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + # Matrix to convert from vidar to NeRF coordinate system + self.convert_matrix = torch.tensor( + [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], + dtype=torch.float32, + ).unsqueeze(0) + + @staticmethod + def from_list(cams): + """Get a CameraNerf object from a list of cameras""" + K = torch.cat([cam.K for cam in cams], 0) + Twc = torch.cat([cam.Twc.T for cam in cams], 0) + return CameraNerf(K=K, Twc=Twc, hw=cams[0].hw) + + @staticmethod + def from_dict(K, hw, Twc=None): + """Get a CameraNerf object from a dictionary of cameras""" + return {key: CameraNerf(K=K[0], hw=hw[0], Twc=val) for key, val in Twc.items()} + + def switch(self): + """Convert from vidar to NeRF coordinate system""" + T = self.convert_matrix.to(self.device) + Twc = T @ self.Twc.T @ T + return type(self)(K=self.K, Twc=Twc, hw=self.hw) + + def bwd(self): + """Go from NeRF to vidar coordinate system""" + T = self.convert_matrix.to(self.device) + Tcw = T @ self.Twc.T @ T + return type(self)(K=self.K, Tcw=Tcw, hw=self.hw) + + def fwd(self): + """Go to vidar from NeRF coordinate system""" + T = self.convert_matrix.to(self.device) + Twc = T @ self.Tcw.T @ T + return type(self)(K=self.K, Twc=Twc, hw=self.hw) + + def look_at(self, at, up=torch.Tensor([0, 1, 0])): + """Point a camera to a particular 3D point""" + + eps = 1e-5 + eye = self.Tcw.T[:, :3, -1] + + at = at.unsqueeze(0) + up = up.unsqueeze(0).to(at.device) + up /= up.norm(dim=-1, keepdim=True) + eps + + z_axis = at - eye + z_axis /= z_axis.norm(dim=-1, keepdim=True) + eps + + up = up.expand(z_axis.shape) + x_axis = torch.cross(up, z_axis) + x_axis /= x_axis.norm(dim=-1, keepdim=True) + eps + + y_axis = torch.cross(z_axis, x_axis) + y_axis /= y_axis.norm(dim=-1, keepdim=True) + eps + + R = torch.stack((x_axis, y_axis, z_axis), dim=-1) + + Tcw = self.Tcw + Tcw.T[:, :3, :3] = R + self.Twc = Tcw.inverse() + + def get_origin(self, flatten=False): + """Get camera origin""" + orig = self.Tcw.T[:, :3, -1].view(len(self), 3, 1, 1).repeat(1, 1, *self.hw) + if flatten: + orig = orig.reshape(len(self), 3, -1).permute(0, 2, 1) + return orig + + def get_viewdirs(self, normalize=False, flatten=False, to_world=False): + """Get camera viewing rays""" + ones = torch.ones((len(self), 1, *self.hw), dtype=self.dtype, device=self.device) + rays = self.reconstruct_depth_map(ones, to_world=False) + if normalize: + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + if to_world: + rays = self.to_world(rays).reshape(len(self), 3, *self.hw) + if flatten: + rays = rays.reshape(len(self), 3, -1).permute(0, 2, 1) + return rays + + def get_render_rays(self, near=None, far=None, n_rays=None, gt=None): + """Get camera render rays""" + b = len(self) + + ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) + + rays = self.reconstruct_depth_map(ones, to_world=False) + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + + rays[:, 1] = - rays[:, 1] + rays[:, 2] = - rays[:, 2] + + orig = self.pose[:, :3, -1].view(b, 3, 1, 1).repeat(1, 1, *self.hw) + rays = self.no_translation().inverted_pose().to_world(rays).reshape(b, 3, *self.hw) + + info = [orig, rays] + if near is not None: + info = info + [near * ones] + if far is not None: + info = info + [far * ones] + if gt is not None: + info = info + [gt] + + rays = torch.cat(info, 1) + rays = rays.permute(0, 2, 3, 1).reshape(b, -1, rays.shape[1]) + + if n_rays is not None: + idx = torch.randint(0, self.n_pixels, (n_rays,)) + rays = rays[:, idx, :] + + return rays + + def get_plucker(self): + """Get camera plucker rays""" + b = len(self) + ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) + rays = self.reconstruct_depth_map(ones, to_world=False) + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + orig = self.Tcw.T[:, :3, -1].view(b, 3, 1, 1).repeat(1, 1, *self.hw) + + orig = orig.view(1, 3, -1).permute(0, 2, 1) + rays = rays.view(1, 3, -1).permute(0, 2, 1) + + cross = torch.cross(orig, rays, dim=-1) + plucker = torch.cat((rays, cross), dim=-1) + + return plucker + + def project_pointcloud(self, pcl_src, rgb_src, thr=1): + """Project pointcloud to the image plane""" + + if rgb_src.dim() == 4: + rgb_src = rgb_src.view(*rgb_src.shape[:2], -1) + + # Get projected coordinates and depth values + uv_all, z_all = self.project_points(pcl_src, return_z=True, from_world=True) + + rgbs_tgt, depths_tgt = [], [] + + b = pcl_src.shape[0] + for i in range(b): + uv, z = uv_all[i].reshape(-1, 2), z_all[i].reshape(-1, 1) + + # Remove out-of-bounds coordinates and points behind the camera + idx = (uv[:, 0] >= -1) & (uv[:, 0] <= 1) & \ + (uv[:, 1] >= -1) & (uv[:, 1] <= 1) & (z[:, 0] > 0.0) + + # Unormalize and stack coordinates for scatter operation + uv = (unnorm_pixel_grid(uv[idx], self.hw)).round().long() + uv = uv[:, 0] + uv[:, 1] * self.hw[1] + + # Min scatter operation (only keep the closest depth) + depth_tgt = 1e10 * torch.ones((self.hw[0] * self.hw[1], 1), device=pcl_src.device) + depth_tgt, argmin = scatter_min(src=z[idx], index=uv.unsqueeze(1), dim=0, out=depth_tgt) + depth_tgt[depth_tgt == 1e10] = 0. + + num_valid = (depth_tgt > 0).sum() + if num_valid > thr: + + # Substitute invalid values with zero + invalid = argmin == argmin.max() + argmin[invalid] = 0 + rgb_tgt = rgb_src[i].permute(1, 0)[idx][argmin] + rgb_tgt[invalid] = -1 + + else: + + rgb_tgt = -1 * torch.ones(1, self.n_pixels, 3, device=self.device, dtype=self.dtype) + + # Reshape outputs + rgb_tgt = rgb_tgt.reshape(1, self.hw[0], self.hw[1], 3).permute(0, 3, 1, 2) + depth_tgt = depth_tgt.reshape(1, 1, self.hw[0], self.hw[1]) + + rgbs_tgt.append(rgb_tgt) + depths_tgt.append(depth_tgt) + + rgb_tgt = torch.cat(rgbs_tgt, 0) + depth_tgt = torch.cat(depths_tgt, 0) + + return rgb_tgt, depth_tgt + + def reconstruct_depth_map_rays(self, depth, to_world=False): + """Reconstruct 3D points from depth map""" + if depth is None: + return None + b, _, h, w = depth.shape + rays = self.get_viewdirs(normalize=True, to_world=False) + points = (rays * depth).view(b, 3, -1) + if to_world and self.Tcw is not None: + points = self.Tcw * points + return points.view(b, 3, h, w) + + +class CameraPinhole(CameraBase): + """Pinhole camera model""" + def __init__(self, K, *args, **kwargs): + # Intrinsics + if same_shape(K.shape[-2:], (3, 3)): + self._K = torch.eye(4, dtype=K.dtype, device=K.device).repeat(K.shape[0], 1, 1) + self._K[:, :3, :3] = K + else: + self._K = K + super().__init__(*args, **kwargs) + + self.convert_matrix = torch.tensor( + [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], + dtype=torch.float32, + ).unsqueeze(0) + + @staticmethod + def from_list(cams): + """Create a single camera from a list of cameras""" + K = torch.cat([cam.K for cam in cams], 0) + Twc = torch.cat([cam.Twc.T for cam in cams], 0) + return CameraPinhole(K=K, Twc=Twc, hw=cams[0].hw) + + @staticmethod + def from_dict(K, hw, Twc=None, broken=False): + """Create a single camera from a dictionary of cameras""" + if broken: + return {key: CameraPinhole( + K=K[key] if key in K else K[(0, key[1])], + hw=hw[key], Twc=val + ) for key, val in Twc.items()} + else: + return {key: CameraPinhole( + K=K[key] if key in K else K[0], + hw=hw[key], Twc=val + ) for key, val in Twc.items()} + + @property + def fx(self): + """Focal length in x direction""" + return self._K[:, 0, 0] + + @property + def fy(self): + """Focal length in y direction""" + return self._K[:, 1, 1] + + @property + def cx(self): + """Principal point in x direction""" + return self._K[:, 0, 2] + + @property + def cy(self): + """Principal point in y direction""" + return self._K[:, 1, 2] + + @property + def fxy(self): + """Focal length in x and y direction""" + return torch.tensor([self.fx, self.fy], dtype=self.dtype, device=self.device) + + @property + def cxy(self): + """Principal point in x and y direction""" + return torch.tensor([self.cx, self.cy], dtype=self.dtype, device=self.device) + + @property + def invK(self): + """Inverse of camera intrinsics""" + return invert_intrinsics(self._K) + + def offset_start(self, start): + """Offset the principal point""" + new_cam = self.clone() + if is_seq(start): + new_cam.K[:, 0, 2] -= start[1] + new_cam.K[:, 1, 2] -= start[0] + else: + start = start.to(self.device) + new_cam.K[:, 0, 2] -= start[:, 1] + new_cam.K[:, 1, 2] -= start[:, 0] + return new_cam + + def scaled(self, scale_factor): + """Scale the camera intrinsics""" + if scale_factor is None or scale_factor == 1: + return self + if is_seq(scale_factor): + if len(scale_factor) == 4: + scale_factor = scale_factor[-2:] + scale_factor = [float(scale_factor[i]) / float(self._hw[i]) for i in range(2)] + else: + scale_factor = [scale_factor] * 2 + return type(self)( + K=scale_intrinsics(self._K, scale_factor), + hw=[int(self._hw[i] * scale_factor[i]) for i in range(len(self._hw))], + Twc=self._Twc + ) + + def lift(self, grid): + """Lift a grid of points to 3D""" + return torch.matmul(self.invK[:, :3, :3], grid) + + def unlift(self, points, from_world=True, euclidean=False): + """Unlift a grid of points to 2D""" + projected = torch.matmul(self.Pwc(from_world), cat_channel_ones(points, 1)) + coords = projected[:, :2] / (projected[:, 2].unsqueeze(1) + 1e-7) + if not euclidean: + depth = projected[:, 2] + else: + points = self.from_world(points) if from_world else points + depth = torch.linalg.vector_norm(points, dim=1, keepdim=True) + return coords, depth + + def switch(self): + """Switch the camera from world to camera coordinates""" + T = self.convert_matrix.to(self.device) + Twc = T @ self.Twc.T @ T + return type(self)(K=self.K, Twc=Twc, hw=self.hw) + + def bwd(self): + """Switch the camera from world to camera coordinates""" + T = self.convert_matrix.to(self.device) + Tcw = T @ self.Twc.T @ T + return type(self)(K=self.K, Tcw=Tcw, hw=self.hw) + + def fwd(self): + """Switch the camera from world to camera coordinates""" + T = self.convert_matrix.to(self.device) + Twc = T @ self.Tcw.T @ T + return type(self)(K=self.K, Twc=Twc, hw=self.hw) + + def up(self): + """Get the up vector of the camera""" + up = self.clone() + up.Twc.translateUp(1) + return up.get_center() - self.get_center() + + def forward(self): + """Get the forward vector of the camera""" + forward = self.clone() + forward.Twc.translateForward(1) + return forward.get_center() - self.get_center() + + def look_at(self, at, up=None): + """Look at a point""" + + if up is None: + up = self.up() + + eps = 1e-5 + eye = self.get_center() + + at = at.unsqueeze(0) + up = up.unsqueeze(0).to(at.device) + up /= up.norm(dim=-1, keepdim=True) + eps + + z_axis = at - eye + z_axis /= z_axis.norm(dim=-1, keepdim=True) + eps + + up = up.expand(z_axis.shape) + x_axis = torch.cross(up, z_axis) + x_axis /= x_axis.norm(dim=-1, keepdim=True) + eps + + y_axis = torch.cross(z_axis, x_axis) + y_axis /= y_axis.norm(dim=-1, keepdim=True) + eps + + R = torch.stack((x_axis, y_axis, z_axis), dim=-1) + + Tcw = self.Tcw + Tcw.T[:, :3, :3] = R + self.Twc = Tcw.inverse() + + def get_center(self): + """Get the center of the camera""" + return self.Tcw.T[:, :3, -1] + + def get_origin(self, flatten=False): + """Get the origin of the camera""" + orig = self.get_center().view(len(self), 3, 1, 1).repeat(1, 1, *self.hw) + if flatten: + orig = orig.reshape(len(self), 3, -1).permute(0, 2, 1) + return orig + + def get_viewdirs(self, normalize=None, to_world=None, flatten=False, reflect=False, grid=None): + """Get the view directions of the camera""" + + ones = torch.ones((len(self), 1, *self.hw), dtype=self.dtype, device=self.device) + rays = self.reconstruct_depth_map(ones, to_world=False, grid=grid) + + if reflect: + rays[:, 1] = - rays[:, 1] + rays[:, 2] = - rays[:, 2] + + if normalize is True or normalize == 'unit': + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + if normalize == 'plane': + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + rays = rays / rays[:, [2]] + + if to_world: + # rays = self.to_world(rays).reshape(len(self), 3, *self.hw) + rays = self.no_translation().to_world(rays).reshape(len(self), 3, *self.hw) + + if flatten: + rays = rays.reshape(len(self), 3, -1).permute(0, 2, 1) + + return rays + + def get_render_rays(self, near=None, far=None, n_rays=None, gt=None): + """Get the rays for rendering""" + + b = len(self) + + ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) + + rays = self.reconstruct_depth_map(ones, to_world=False) + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + + rays[:, 1] = - rays[:, 1] + rays[:, 2] = - rays[:, 2] + + orig = self.pose[:, :3, -1].view(b, 3, 1, 1).repeat(1, 1, *self.hw) + rays = self.no_translation().inverted_pose().to_world(rays).reshape(b, 3, *self.hw) + + info = [orig, rays] + if near is not None: + info = info + [near * ones] + if far is not None: + info = info + [far * ones] + if gt is not None: + info = info + [gt] + + rays = torch.cat(info, 1) + rays = rays.permute(0, 2, 3, 1).reshape(b, -1, rays.shape[1]) + + if n_rays is not None: + idx = torch.randint(0, self.n_pixels, (n_rays,)) + rays = rays[:, idx, :] + + return rays + + def get_plucker(self): + """Get the plucker coordinates of the camera""" + + b = len(self) + ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) + rays = self.reconstruct_depth_map(ones, to_world=False) + rays = rays / torch.norm(rays, dim=1).unsqueeze(1) + orig = self.get_center().view(b, 3, 1, 1).repeat(1, 1, *self.hw) + + orig = orig.view(1, 3, -1).permute(0, 2, 1) + rays = rays.view(1, 3, -1).permute(0, 2, 1) + + cross = torch.cross(orig, rays, dim=-1) + plucker = torch.cat((rays, cross), dim=-1) + + return plucker + + def project_pointcloud(self, pcl_src, rgb_src=None, thr=1): + """Project a pointcloud to the image plane""" + + if rgb_src is not None and rgb_src.dim() == 4: + rgb_src = rgb_src.view(*rgb_src.shape[:2], -1) + + # Get projected coordinates and depth values + uv_all, z_all = self.project_points(pcl_src, return_z=True, from_world=True) + + rgbs_tgt, depths_tgt = [], [] + + b = pcl_src.shape[0] + for i in range(b): + uv, z = uv_all[i].reshape(-1, 2), z_all[i].reshape(-1, 1) + + # Remove out-of-bounds coordinates and points behind the camera + idx = (uv[:, 0] >= -1) & (uv[:, 0] <= 1) & \ + (uv[:, 1] >= -1) & (uv[:, 1] <= 1) & (z[:, 0] > 0.0) + + # Unormalize and stack coordinates for scatter operation + uv = (unnorm_pixel_grid(uv[idx], self.hw)).round().long() + uv = uv[:, 0] + uv[:, 1] * self.hw[1] + + # Min scatter operation (only keep the closest depth) + depth_tgt = 1e10 * torch.ones((self.hw[0] * self.hw[1], 1), device=pcl_src.device) + depth_tgt, argmin = scatter_min(src=z[idx], index=uv.unsqueeze(1), dim=0, out=depth_tgt) + depth_tgt[depth_tgt == 1e10] = 0. + + num_valid = (depth_tgt > 0).sum() + if num_valid > thr: + + # Substitute invalid values with zero + invalid = argmin == argmin.max() + argmin[invalid] = 0 + if rgb_src is not None: + rgb_tgt = rgb_src[i].permute(1, 0)[idx][argmin] + rgb_tgt[invalid] = 0 + + else: + + if rgb_src is not None: + rgb_tgt = -1 * torch.ones(1, self.n_pixels, 3, device=self.device, dtype=self.dtype) + + # Reshape outputs + depth_tgt = depth_tgt.reshape(1, 1, self.hw[0], self.hw[1]) + depths_tgt.append(depth_tgt) + + if rgb_src is not None: + rgb_tgt = rgb_tgt.reshape(1, self.hw[0], self.hw[1], 3).permute(0, 3, 1, 2) + rgbs_tgt.append(rgb_tgt) + + if rgb_src is not None: + rgb_tgt = torch.cat(rgbs_tgt, 0) + else: + rgb_tgt = None + + depth_tgt = torch.cat(depths_tgt, 0) + + return rgb_tgt, depth_tgt + + def reconstruct_depth_map_rays(self, depth, to_world=False): + """Reconstruct a depth map from a depth image""" + if depth is None: + return None + b, _, h, w = depth.shape + rays = self.get_viewdirs(normalize=True, to_world=False) + points = (rays * depth).view(b, 3, -1) + if to_world and self.Tcw is not None: + points = self.Tcw * points + return points.view(b, 3, h, w) + + def to_ndc_rays(self, rays_o, rays_d, near=1.0): + """Transform rays from camera coordinates to NDC coordinates""" + H, W = self.hw + focal = self.fy[0].item() + + t = - (near + rays_o[..., 2]) / rays_d[..., 2] + rays_o = rays_o + t[..., None] * rays_d + + o0 = -1. / (W / (2. * focal)) * rays_o[..., 0] / rays_o[..., 2] + o1 = -1. / (H / (2. * focal)) * rays_o[..., 1] / rays_o[..., 2] + o2 = 1. + 2. * near / rays_o[..., 2] + + d0 = -1. / (W / (2. * focal)) * (rays_d[..., 0] / rays_d[..., 2] - rays_o[..., 0] / rays_o[..., 2]) + d1 = -1. / (H / (2. * focal)) * (rays_d[..., 1] / rays_d[..., 2] - rays_o[..., 1] / rays_o[..., 2]) + d2 = -2. * near / rays_o[..., 2] + + rays_o = torch.stack([o0, o1, o2], -1) + rays_d = torch.stack([d0, d1, d2], -1) + + return rays_o, rays_d + + def from_ndc(self, xyz_ndc): + """Transform points from NDC coordinates to camera coordinates""" + wh = self.wh + fx = fy = self.fy[0].item() + + z_e = 2. / (xyz_ndc[..., 2:3] - 1. + 1e-6) + x_e = - xyz_ndc[..., 0:1] * z_e * wh[0] / (2. * fx) + y_e = - xyz_ndc[..., 1:2] * z_e * wh[1] / (2. * fy) + + return torch.cat([x_e, y_e, z_e], -1) + + +class PerceiverAbstractPositionEncoding(nn.Module, metaclass=abc.ABCMeta): + """Perceiver abstract position encoding.""" + + @property + @abc.abstractmethod + def num_dimensions(self) -> int: + raise NotImplementedError + + @abc.abstractmethod + def output_size(self, *args, **kwargs) -> int: + raise NotImplementedError + + @abc.abstractmethod + def forward(self, batch_size, pos): + raise NotImplementedError + + +class PerceiverTrainablePositionEncoding(PerceiverAbstractPositionEncoding): + """Trainable position encoding.""" + + def __init__(self, index_dims, num_channels=128): + super().__init__() + self._num_channels = num_channels + self._index_dims = index_dims + index_dim = np.prod(index_dims) + self.position_embeddings = nn.Parameter( + torch.randn(index_dim, num_channels)) + + @property + def num_dimensions(self) -> int: + if isinstance(self._index_dims, int): + return 1 + return len(self._index_dims) + + def output_size(self, *args, **kwargs) -> int: + return self._num_channels + + def forward(self, batch_size): + position_embeddings = self.position_embeddings + + if batch_size is not None: + position_embeddings = position_embeddings.expand( + batch_size, -1, -1) + return position_embeddings + +def build_linear_positions(index_dims, output_range=(-1.0, 1.0)): + """ + Generate an array of position indices for an N-D input array. + + Args: + index_dims (`List[int]`): + The shape of the index dimensions of the input array. + output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`): + The min and max values taken by each input index dimension. + + Returns: + `torch.FloatTensor` of shape `(index_dims[0], index_dims[1], .., index_dims[-1], N)`. + """ + + def _linspace(n_xels_per_dim): + return torch.linspace(start=output_range[0], end=output_range[1], steps=n_xels_per_dim, dtype=torch.float32) + + dim_ranges = [_linspace(n_xels_per_dim) for n_xels_per_dim in index_dims] + array_index_grid = torch.meshgrid(*dim_ranges) + + return torch.stack(array_index_grid, dim=-1) + +def _check_or_build_spatial_positions(pos, index_dims, batch_size): + """ + Checks or builds spatial position features (x, y, ...). + + Args: + pos (`torch.FloatTensor`): + None, or an array of position features. If None, position features are built. Otherwise, their size is checked. + index_dims (`List[int]`): + An iterable giving the spatial/index size of the data to be featurized. + batch_size (`int`): + The batch size of the data to be featurized. + + Returns: + `torch.FloatTensor` of shape `(batch_size, prod(index_dims))` an array of position features. + """ + if pos is None: + pos = build_linear_positions(index_dims) + pos = torch.broadcast_to(pos[None], (batch_size,) + pos.shape) + pos = torch.reshape(pos, [batch_size, np.prod(index_dims), -1]) + # else: + # # Just a warning label: you probably don't want your spatial features to + # # have a different spatial layout than your pos coordinate system. + # # But feel free to override if you think it'll work! + # if pos.shape[-1] != len(index_dims): + # raise ValueError("Spatial features have the wrong number of dimensions.") + return pos + +def generate_fourier_features(pos, num_bands, max_resolution=(224, 224), concat_pos=True, sine_only=False): + """ + Generate a Fourier frequency position encoding with linear spacing. + + Args: + pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`): + The Tensor containing the position of n points in d dimensional space. + num_bands (`int`): + The number of frequency bands (K) to use. + max_resolution (`Tuple[int]`, *optional*, defaults to (224, 224)): + The maximum resolution (i.e. the number of pixels per dim). A tuple representing resolution for each dimension. + concat_pos (`bool`, *optional*, defaults to `True`): + Whether to concatenate the input position encoding to the Fourier features. + sine_only (`bool`, *optional*, defaults to `False`): + Whether to use a single phase (sin) or two (sin/cos) for each frequency band. + + Returns: + `torch.FloatTensor` of shape `(batch_size, sequence_length, n_channels)`: The Fourier position embeddings. If + `concat_pos` is `True` and `sine_only` is `False`, output dimensions are ordered as: [dim_1, dim_2, ..., dim_d, + sin(pi*f_1*dim_1), ..., sin(pi*f_K*dim_1), ..., sin(pi*f_1*dim_d), ..., sin(pi*f_K*dim_d), cos(pi*f_1*dim_1), + ..., cos(pi*f_K*dim_1), ..., cos(pi*f_1*dim_d), ..., cos(pi*f_K*dim_d)], where dim_i is pos[:, i] and f_k is the + kth frequency band. + """ + + batch_size = pos.shape[0] + device = pos.device + + min_freq = 1.0 + # Nyquist frequency at the target resolution: + freq_bands = torch.stack( + [torch.linspace(start=min_freq, end=res / 2, steps=num_bands, device=device) for res in max_resolution], dim=0 + ) + + # Get frequency bands for each spatial dimension. + # Output is size [n, d * num_bands] + per_pos_features = pos[0, :, :][:, :, None] * freq_bands[None, :, :] + per_pos_features = torch.reshape( + per_pos_features, [-1, np.prod(per_pos_features.shape[1:])]) + + if sine_only: + # Output is size [n, d * num_bands] + per_pos_features = torch.sin(np.pi * (per_pos_features)) + else: + # Output is size [n, 2 * d * num_bands] + per_pos_features = torch.cat( + [torch.sin(np.pi * per_pos_features), torch.cos(np.pi * per_pos_features)], dim=-1 + ) + # Concatenate the raw input positions. + if concat_pos: + # Adds d bands to the encoding. + per_pos_features = torch.cat( + [pos, per_pos_features.expand(batch_size, -1, -1)], dim=-1) + return per_pos_features + +class PerceiverFourierPositionEncoding(PerceiverAbstractPositionEncoding): + """Fourier (Sinusoidal) position encoding.""" + + def __init__(self, num_bands, max_resolution, concat_pos=True, sine_only=False): + super().__init__() + self.num_bands = num_bands + self.max_resolution = max_resolution + self.concat_pos = concat_pos + self.sine_only = sine_only + + @property + def num_dimensions(self) -> int: + return len(self.max_resolution) + + def output_size(self): + """Returns size of positional encodings last dimension.""" + num_dims = len(self.max_resolution) + encoding_size = self.num_bands * num_dims + if not self.sine_only: + encoding_size *= 2 + if self.concat_pos: + encoding_size += self.num_dimensions + + return encoding_size + + def forward(self, index_dims, batch_size, device, pos=None): + pos = _check_or_build_spatial_positions(pos, index_dims, batch_size) + fourier_pos_enc = generate_fourier_features( + pos, + num_bands=self.num_bands, + max_resolution=self.max_resolution, + concat_pos=self.concat_pos, + sine_only=self.sine_only, + ).to(device) + return fourier_pos_enc + + +def build_position_encoding( + position_encoding_type, + out_channels=None, + project_pos_dim=-1, + trainable_position_encoding_kwargs=None, + fourier_position_encoding_kwargs=None, +): + """ + Builds the position encoding. + + Args: + + - out_channels: refers to the number of channels of the position encodings. + - project_pos_dim: if specified, will project the position encodings to this dimension. + + """ + + if position_encoding_type == "trainable": + if not trainable_position_encoding_kwargs: + raise ValueError( + "Make sure to pass trainable_position_encoding_kwargs") + output_pos_enc = PerceiverTrainablePositionEncoding( + **trainable_position_encoding_kwargs) + elif position_encoding_type == "fourier": + # We don't use the index_dims argument, as this is only known during the forward pass + if not fourier_position_encoding_kwargs: + raise ValueError( + "Make sure to pass fourier_position_encoding_kwargs") + output_pos_enc = PerceiverFourierPositionEncoding( + **fourier_position_encoding_kwargs) + else: + raise ValueError( + f"Unknown position encoding type: {position_encoding_type}.") + + # Optionally, project the position encoding to a target dimension: + positions_projection = nn.Linear( + out_channels, project_pos_dim) if project_pos_dim > 0 else nn.Identity() + + return output_pos_enc, positions_projection + +import torchvision.models as models +RESNET_VERSIONS = { # general resblock + 18: models.resnet18, + 34: models.resnet34, + 50: models.resnet50, + 101: models.resnet101, + 152: models.resnet152 +} + +RESNET_WEIGHTS = 'IMAGENET1K_V1' + +class ResNetMultiInput(models.ResNet, ABC): + def __init__(self, block_type, block_channels, num_input_rgb): + """Multi-input ResNet model + + Parameters + ---------- + block_type : nn.Module + Residual block type + block_channels : int + Number of channels in each block + num_input_rgb : int + Number of input images + """ + super().__init__(block_type, block_channels) + + self.inplanes = 64 + self.conv1 = nn.Conv2d( + num_input_rgb * 3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block_type, 64, block_channels[0]) + self.layer2 = self._make_layer(block_type, 128, block_channels[1], stride=2) + self.layer3 = self._make_layer(block_type, 256, block_channels[2], stride=2) + self.layer4 = self._make_layer(block_type, 512, block_channels[3], stride=2) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + +def resnet_multi_input(num_layers, num_input_rgb, pretrained=True): + """Generates a multi-input ResNet model + + Parameters + ---------- + num_layers : int + Number of layers in the ResNet + num_input_rgb : int + Number of input images + pretrained : bool, optional + True is pre-trained ImageNet weights are used, by default True + + Returns + ------- + nn.Module + Multi-input ResNet model + """ + assert num_layers in [18, 50], 'Can only run with 18 or 50 layer resnet' + + block_channels = { + 18: [2, 2, 2, 2], + 50: [3, 4, 6, 3] + }[num_layers] + + block_type = { + 18: models.resnet.BasicBlock, + 50: models.resnet.Bottleneck + }[num_layers] + + model = ResNetMultiInput(block_type, block_channels, num_input_rgb) + + if pretrained: + loaded = RESNET_VERSIONS[num_layers](weights=RESNET_WEIGHTS).state_dict() + loaded['conv1.weight'] = torch.cat( + [loaded['conv1.weight']] * num_input_rgb, 1) / num_input_rgb + model.load_state_dict(loaded) + + return model + +''' +appends spatial co-ordinates to the feature map +the range of the spatial co-ordinates can be defined as [min_val, max_val] + +Arguments: + feature_maps : input feature maps (N,C,H,W) + min_val : minimum value of spatial co-ordinate + max_val : maximum value of spatial co-ordinate + +Returns: + feature_maps : input feature maps concatenated with spatial co-ordinates (N,C+2,H,W) +''' +def append_spatial_location(feature_maps, min_val=-1, max_val=1): + batch_size, channels, height, width = feature_maps.data.shape + + # arange spatial co-ordiantes for height + h_array = Variable(torch.stack([torch.linspace(min_val, max_val, height)]*width, dim=1).cuda()) + # arange spatial co-ordinates for width + w_array = Variable(torch.stack([torch.linspace(min_val, max_val, width)]*height, dim=0).cuda()) + # stack the h_array and w_array to get the spatial co-ordinates at each location + spatial_array = torch.stack([h_array, w_array], dim=0) + # expand the spatial co-ordinates across the batch size + spatial_array = torch.stack([spatial_array]*batch_size, dim=0) + # concatenate feature maps with spatial co-ordinates + feature_maps = torch.cat([feature_maps, spatial_array], dim=1) + + return feature_maps + + +class DynamicFC(nn.Module): + + def __init__(self): + super(DynamicFC, self).__init__() + + self.in_planes = None + self.out_planes = None + self.activation = None + self.use_bias = None + + self.activation = None + self.linear = None + + ''' + Arguments: + embedding : input to the MLP (N,*,C) + out_planes : total ch dddannels in the output + activation : 'relu' or 'tanh' + use_bias : True / False + + Returns: + out : output of the MLP (N,*,out_planes) + ''' + def forward(self, embedding, out_planes=1, activation=None, use_bias=True): + + self.in_planes = embedding.data.shape[-1] + self.out_planes = out_planes + self.use_bias = use_bias + + self.linear = nn.Linear(self.in_planes, self.out_planes, bias=use_bias).cuda() + if activation == 'relu': + self.activation = nn.ReLU(inplace=True).cuda() + elif activation == 'tanh': + self.activation = nn.Tanh().cuda() + + for m in self.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform(m.weight) + if self.use_bias: + nn.init.constant(m.bias, 0.1) + + out = self.linear(embedding) + if self.activation is not None: + out = self.activation(out) + + return out + + +''' +A very basic FiLM layer with a linear transformation from context to FiLM parameters +''' +class FilmLayer(nn.Module): + + def __init__(self, feature_size): + super(FilmLayer, self).__init__() + + self.batch_size = None + self.channels = None + self.height = None + self.width = None + self.feature_size = feature_size + + # self.fc = DynamicFC().cuda() + self.fc = nn.Linear(768, self.feature_size * 2).cuda() # Hardcode language embedding shape for now + + ''' + Arguments: + feature_maps : input feature maps (N,C,H,W) + context : context embedding (N,L) + + Return: + output : feature maps modulated with betas and gammas (FiLM parameters) + ''' + def forward(self, feature_maps, context): + self.batch_size, self.channels, self.height, self.width = feature_maps.data.shape + # FiLM parameters needed for each channel in the feature map + # hence, feature_size defined to be same as no. of channels + # self.feature_size = feature_maps.data.shape[1] + + # import pdb; pdb.set_trace() + + # linear transformation of context to FiLM parameters + # film_params = self.fc(context, out_planes=2*self.feature_size, activation=None) + film_params = self.fc(context) + + # stack the FiLM parameters across the spatial dimension + film_params = torch.stack([film_params]*self.height, dim=2) + film_params = torch.stack([film_params]*self.width, dim=3) + + # slice the film_params to get betas and gammas + gammas = film_params[:, :self.feature_size, :, :] + betas = film_params[:, self.feature_size:, :, :] + + # modulate the feature map with FiLM parameters + output = (1 + gammas) * feature_maps + betas + + return output + +''' +Modualted ResNet block with FiLM layer +''' +class FilmResBlock(nn.Module): + + ''' + Arguments: + in_channels : no. of channels in the input + feature_size : feature size required + spatial_location : whether to append spatial co-ordinates + ''' + def __init__(self, in_channels, feature_size, spatial_location=True): + super(FilmResBlock, self).__init__() + + self.spatial_location = spatial_location + self.feature_size = feature_size + self.in_channels = in_channels + # add 2 channels for spatial location + if spatial_location: + self.in_channels += 2 + + # modulated resnet block with FiLM layer + self.conv1 = nn.Conv2d(self.in_channels, self.feature_size, kernel_size=1) + self.relu1 = nn.ReLU() + self.conv2 = nn.Conv2d(self.feature_size, self.feature_size, kernel_size=3, padding=1) + self.bn2 = nn.BatchNorm2d(self.feature_size) + self.film_layer = FilmLayer(self.feature_size).cuda() + self.relu2 = nn.ReLU() + + ''' + Arguments: + feature_maps : input feature maps (N,C,H,W) + context : context embedding (N,L) + + Returns: + out : input feature maps modulated with FiLM parameters computed using context embedding + ''' + def forward(self, feature_maps, context): + + if self.spatial_location: + feature_maps = append_spatial_location(feature_maps) + + conv1 = self.conv1(feature_maps) + out1 = self.relu1(conv1) + + conv2 = self.conv2(out1) + bn = self.bn2(conv2) + film_out = self.film_layer(bn, context) + out2 = self.relu2(film_out) + + # residual connection + out = out1 + out2 + + return out + + +class ResNetFiLMFeaturizer(nn.Module, ABC): + """ + Single-frame depth encoder + + Parameters + ---------- + cfg : Config + Configuration with parameters + """ + def __init__(self, version=18, num_rgb_in=1, pretrained=False): + super().__init__() + + self.num_ch_enc = np.array([64, 64, 128, 256, 512]) + # features = [] + + assert version in RESNET_VERSIONS, f'Invalid ResNet version: {version}' + + if num_rgb_in > 1: + self.encoder = resnet_multi_input( + version, num_rgb_in, pretrained) + else: + self.encoder = RESNET_VERSIONS[version]( + weights=None if not pretrained else RESNET_WEIGHTS) + + if version > 34: + self.num_ch_enc[1:] *= 4 + + self.film1 = FilmLayer(self.num_ch_enc[1]).cuda() + self.film2 = FilmLayer(self.num_ch_enc[2]).cuda() + self.film3 = FilmLayer(self.num_ch_enc[3]).cuda() + self.film4 = FilmLayer(self.num_ch_enc[4]).cuda() + + def forward(self, input_image, context): + """Network forward pass""" + + features = [] + x = (input_image - 0.45) / 0.225 + x = self.encoder.conv1(x) + x = self.encoder.bn1(x) + + features.clear() + features.append(self.encoder.relu(x)) + if context is None: + features.append(self.encoder.layer1(self.encoder.maxpool(features[-1]))) + features.append(self.encoder.layer2(features[-1])) + features.append(self.encoder.layer3(features[-1])) + features.append(self.encoder.layer4(features[-1])) + else: + features.append(self.film1(self.encoder.layer1(self.encoder.maxpool(features[-1])), context)) + features.append(self.film2(self.encoder.layer2(features[-1]), context)) + features.append(self.film3(self.encoder.layer3(features[-1]), context)) + features.append(self.film4(self.encoder.layer4(features[-1]), context)) + + return features + + +class ResNetFiLMEncoder(nn.Module, ABC): + """ + Single-frame depth encoder + + Parameters + ---------- + cfg : Config + Configuration with parameters + """ + def __init__(self, pretrained=False, input_channel=3): + super().__init__() + self.resnet_film_featurizer = ResNetFiLMFeaturizer(version=50, pretrained=pretrained, num_rgb_in=int(input_channel / 3)) + + def forward(self, image, lang_emb): + """Network forward pass""" + film_features = self.resnet_film_featurizer(image, lang_emb)[-1] + film_features_flat = film_features.view(*film_features.shape[:-2], -1).permute(0, 2, 1) + return film_features_flat + + def output_shape(self, input_shape): + return ( (input_shape[1] // 29) * (input_shape[2] // 29), 2048) + + + +class DeFiNeImageCamEncoder(nn.Module, ABC): + def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): + super().__init__() + + """ + pulled these from here: + https://github.com/TRI-ML/vidar/blob/6fdbee65bff2cc94a42aaedd121a2179ac48d441/configs/papers/define/hub_define_temporal.yaml#L21 + """ + num_bands_orig = 20 + num_bands_dirs = 10 + max_resolution_orig = 60 + max_resolution_dirs = 60 + + # whether to use camera information (intrinsics + extrinsics) in the encoder + self.use_cam = use_cam + self.downsample = downsample + + self.image_feature_encoder = ResNetFiLMFeaturizer(version=50, pretrained=pretrained, num_rgb_in=int(input_channel / 3)) + + # Fourier encoding for camera origin + self._fourier_encoding_orig, _ = build_position_encoding( + position_encoding_type='fourier', + fourier_position_encoding_kwargs={ + 'num_bands': num_bands_orig, + 'max_resolution': [max_resolution_orig] * 3, + 'concat_pos': True, + 'sine_only': False, + } + ) + + # Fourier encoding for camera viewing rays + self._fourier_encoding_dirs, _ = build_position_encoding( + position_encoding_type='fourier', + fourier_position_encoding_kwargs={ + 'num_bands': num_bands_dirs, + 'max_resolution': [num_bands_dirs] * 3, + 'concat_pos': True, + 'sine_only': False, + } + ) + + if self.downsample: + # Adding in another Conv2 layer that is applied [H/4, W/4, features] to + # reduce size + self.num_cam_channels = 186 + self.num_image_channels = 3840 # ResNet50 + if self.use_cam: + num_channels = self.num_image_channels + self.num_cam_channels + else: + num_channels = self.num_image_channels + self.finalconv = torch.nn.Conv2d(num_channels, 1024, 2, stride=2) + + + def get_rgb_feat(self, rgb, lang=None, rgb_feat_type="resnet_all"): + """Exract image features""" + if rgb_feat_type == 'convnet': + return { + 'feat': self.image_feature_encoder(rgb, lang) + } + elif rgb_feat_type == 'resnet': + return { + 'feat': self.image_feature_encoder(rgb, lang)[1] + } + elif rgb_feat_type.startswith('resnet_all'): + all_feats = self.image_feature_encoder(rgb, lang) + feats = all_feats[1:] + for i in range(1, len(feats)): + feats[i] = interpolate( + feats[i], size=feats[0], scale_factor=None, mode='bilinear') + if rgb_feat_type.endswith('rgb'): + feats = feats + [interpolate( + rgb, size=feats[0], scale_factor=None, mode='bilinear')] + feat = torch.cat(feats, 1) + return { + 'all_feats': all_feats, + 'feat': feat + } + + + def embeddings(self, data, sources, downsample): + """Compute embeddings for encoder and decoder data""" + if 'rgb' in sources: + assert 'rgb' in data[0].keys() + b = [datum['rgb'].shape[0] for datum in data] + + rgb = torch.cat([datum['rgb'] for datum in data], 0) + + if 'lang' in sources: + lang = torch.cat([datum['lang'] for datum in data], 0) + output_feats = self.get_rgb_feat(rgb, lang) + else: + output_feats = self.get_rgb_feat(rgb) + + feats = torch.split(output_feats['feat'], b) + for i in range(len(data)): + data[i]['feat'] = feats[i] + + encodings = [] + for datum in data: + + encoding = OrderedDict() + + # Camera embeddings + if 'cam' in sources: + assert 'cam' in data[0].keys() + + cam = datum['cam'].scaled(1. / downsample) + orig = cam.get_origin(flatten=True) + + to_world = True # should it be True or False? idk + + if to_world: + dirs = cam.get_viewdirs(normalize=True, flatten=True, to_world=True) + else: + dirs = cam.no_translation().get_viewdirs(normalize=True, flatten=True, to_world=True) + + orig_encodings = self._fourier_encoding_orig( + index_dims=None, pos=orig, batch_size=orig.shape[0], device=orig.device) + dirs_encodings = self._fourier_encoding_dirs( + index_dims=None, pos=dirs, batch_size=dirs.shape[0], device=dirs.device) + + encoding['cam'] = torch.cat([orig_encodings, dirs_encodings], -1) + + # Image embeddings + if 'rgb' in sources: + rgb = datum['feat'] + rgb_flat = rgb.view(*rgb.shape[:-2], -1).permute(0, 2, 1) + encoding['rgb'] = rgb_flat + + # Adding in another Conv2 layer that is applied [H/4, W/4, features] to + # reduce size + if self.downsample: + stacked = torch.cat([val for val in encoding.values()], -1) + st_sh = stacked.shape + stacked = stacked.reshape(st_sh[0], int(np.sqrt(st_sh[1])), int(np.sqrt(st_sh[1])), st_sh[-1]) + stacked = self.finalconv(stacked.permute(0, 3, 1, 2)) + encoding['all'] = stacked.reshape(stacked.shape[0], stacked.shape[1], -1).permute(0, 2, 1) + else: + encoding['all'] = torch.cat([val for val in encoding.values()], -1) + encodings.append(encoding) + + return encodings + + def forward(self, image, extrinsics, intrinsics, lang_emb): + if extrinsics is None or intrinsics is None: + sources = ["rgb"] # camera info not present + data = [{ + "rgb": image, + }] + else: + sources = ["rgb", "cam"] + cam = CameraPinhole( + K=intrinsics, + Twc=Pose(extrinsics).to(image.device), + hw=image.shape[-2:], + ) + data = [{ + "cam": cam, + "rgb": image, + }] + if lang_emb is not None: + sources += ["lang"] + data[0]["lang"] = lang_emb + + emb = self.embeddings( + data, + sources=sources, + downsample=4, + ) + result = torch.cat([emb[i]["all"] for i in range(len(emb))]) + return result + + def output_shape(self, input_shape): + if self.downsample: + # num_cam_channels = 186 + # num_image_channels = 3840 # ResNet18: 512 + 256 + 128 + 64 + num_sptial_feats = int(np.floor((input_shape[1] / 8))) * int(np.floor(input_shape[1] / 8)) + # Updated to have the shapes outputted by the final conv2. + return (num_sptial_feats, 1024) + else: + num_cam_channels = 186 + num_image_channels = 3840 # ResNet18: 512 + 256 + 128 + 64 + num_sptial_feats = int(input_shape[1] / 4 * input_shape[2] / 4) + if self.use_cam: + num_channels = num_image_channels + num_cam_channels + else: + num_channels = num_image_channels + return (num_sptial_feats, num_channels) + + +class VoltronImageCamEncoder(nn.Module, ABC): + def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): + super().__init__() + + from vtx import load + from torchvision import transforms as T + + from torchvision.transforms.functional import InterpolationMode + + device = "cuda" if torch.cuda.is_available() else "cpu" + self.device = device + self.img_transform, self.ctcn = load("ctcn-base+x7", "/mnt/fsx/x-voltron/ctcn-base+x7/ctcn-base+x7.pt", device=device, freeze_backbone=False) + + def forward(self, image, lang_emb): + all_langs = [] + for b in range(len(lang_emb[0])): + for h in range(len(lang_emb)): + all_langs.append(lang_emb[h][b]) + processed_images = torch.stack([self.img_transform(i) for i in image]).to(self.device) + embedding: torch.Tensor = self.ctcn.embed(processed_images, all_langs) + return embedding + + def output_shape(self, input_shape): + return (768) + + +class R3MImageCamEncoder(nn.Module, ABC): + def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): + super().__init__() + + from r3m import load_r3m + from torchvision import transforms as T + + device = "cuda" if torch.cuda.is_available() else "cpu" + self.device = device + + self.r3m = load_r3m("resnet50") # resnet18, resnet34 + self.r3m.train() + self.r3m.to(self.device) + + self.transforms = T.Compose([ + T.ToPILImage(), + T.Resize(256), + T.CenterCrop(224), + T.ToTensor() + ]) + + def forward(self, image, lang_emb): + processed_images = torch.stack([self.transforms(i) for i in image]).to(self.device) + embedding: torch.Tensor = self.r3m(processed_images * 255.0) + return embedding + + def output_shape(self, input_shape): + return (2048,) + +class VC1ImageCamEncoder(nn.Module, ABC): + def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): + super().__init__() + + from vc_models.models.vit import model_utils + self.model, self.embd_size, self.model_transforms, self.model_info = model_utils.load_model(model_utils.VC1_BASE_NAME) + self.model.train() + + device = "cuda" if torch.cuda.is_available() else "cpu" + self.device = device + + def forward(self, image, lang_emb): + processed_images = self.model_transforms(image) + embedding: torch.Tensor = self.model(processed_images) + return embedding + + def output_shape(self, input_shape): + return (768,) \ No newline at end of file diff --git a/robomimic/utils/dataset.py b/robomimic/utils/dataset.py index 40430ca8..b90dcd13 100644 --- a/robomimic/utils/dataset.py +++ b/robomimic/utils/dataset.py @@ -5,13 +5,18 @@ import os import h5py import numpy as np +import random from copy import deepcopy from contextlib import contextmanager +from collections import OrderedDict +import time +import psutil import torch.utils.data import robomimic.utils.tensor_utils as TensorUtils import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.action_utils as AcUtils import robomimic.utils.log_utils as LogUtils @@ -22,6 +27,7 @@ def __init__( obs_keys, action_keys, dataset_keys, + action_config, frame_stack=1, seq_length=1, pad_frame_stack=True, @@ -33,6 +39,8 @@ def __init__( hdf5_normalize_obs=False, filter_by_attribute=None, load_next_obs=True, + shuffled_obs_key_groups=None, + truncated_geom_factor=None, ): """ Dataset class for fetching sequences of experience. @@ -43,7 +51,7 @@ def __init__( obs_keys (tuple, list): keys to observation items (image, object, etc) to be fetched from the dataset - action_keys (tuple, list): keys to action items (end effector velocity, gripper velocity, etc) to be fetched from the dataset + action_config (dict): TODO dataset_keys (tuple, list): keys to dataset items (actions, rewards, etc) to be fetched from the dataset @@ -82,6 +90,8 @@ def __init__( demonstrations to load load_next_obs (bool): whether to load next_obs from the dataset + + shuffled_obs_key_groups (list): TODO """ super(SequenceDataset, self).__init__() @@ -90,6 +100,8 @@ def __init__( self.hdf5_normalize_obs = hdf5_normalize_obs self._hdf5_file = None + self.truncated_geom_factor = truncated_geom_factor + assert hdf5_cache_mode in ["all", "low_dim", None] self.hdf5_cache_mode = hdf5_cache_mode @@ -104,6 +116,8 @@ def __init__( if self.action_keys is not None: self.dataset_keys = tuple(set(self.dataset_keys).union(set(self.action_keys))) + self.action_config = action_config + self.n_frame_stack = frame_stack assert self.n_frame_stack >= 1 @@ -112,9 +126,7 @@ def __init__( self.goal_mode = goal_mode if self.goal_mode is not None: - assert self.goal_mode in ["last"] - if not self.load_next_obs: - assert self.goal_mode != "last" # we use last next_obs as goal + assert self.goal_mode in ["last", "geom"] self.pad_seq_length = pad_seq_length self.pad_frame_stack = pad_frame_stack @@ -127,6 +139,11 @@ def __init__( if self.hdf5_normalize_obs: self.obs_normalization_stats = self.normalize_obs() + # prepare for action normalization + self.action_normalization_stats = None + + print(self.hdf5_cache_mode) # Should be None + # maybe store dataset in memory for fast access if self.hdf5_cache_mode in ["all", "low_dim"]: obs_keys_in_memory = self.obs_keys @@ -156,8 +173,35 @@ def __init__( del self.hdf5_cache self.hdf5_cache = None else: + + ### EVEN WHEN CACHING IS NONE, TRY AND LOAD THEN DELETE + ### WHY? TO FILTER OUT BAD DATASETS AHEAD OF TIME + obs_keys_in_memory = self.obs_keys + # only store low-dim observations + obs_keys_in_memory = [] + for k in self.obs_keys: + if ObsUtils.key_is_obs_modality(k, "low_dim"): + obs_keys_in_memory.append(k) + self.obs_keys_in_memory = obs_keys_in_memory + + self.hdf5_cache = self.load_dataset_in_memory( + demo_list=self.demos, + hdf5_file=self.hdf5_file, + obs_keys=self.obs_keys_in_memory, + dataset_keys=self.dataset_keys, + load_next_obs=self.load_next_obs + ) + del self.hdf5_cache + del self.obs_keys_in_memory + self.obs_keys_in_memory = None + self.hdf5_cache = None + if shuffled_obs_key_groups is None: + self.shuffled_obs_key_groups = list() + else: + self.shuffled_obs_key_groups = shuffled_obs_key_groups + self.close_and_delete_hdf5_handle() def load_demo_info(self, filter_by_attribute=None, demos=None): @@ -311,33 +355,6 @@ def normalize_obs(self): Computes a dataset-wide mean and standard deviation for the observations (per dimension and per obs key) and returns it. """ - def _compute_traj_stats(traj_obs_dict): - """ - Helper function to compute statistics over a single trajectory of observations. - """ - traj_stats = { k : {} for k in traj_obs_dict } - for k in traj_obs_dict: - traj_stats[k]["n"] = traj_obs_dict[k].shape[0] - traj_stats[k]["mean"] = traj_obs_dict[k].mean(axis=0, keepdims=True) # [1, ...] - traj_stats[k]["sqdiff"] = ((traj_obs_dict[k] - traj_stats[k]["mean"]) ** 2).sum(axis=0, keepdims=True) # [1, ...] - return traj_stats - - def _aggregate_traj_stats(traj_stats_a, traj_stats_b): - """ - Helper function to aggregate trajectory statistics. - See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm - for more information. - """ - merged_stats = {} - for k in traj_stats_a: - n_a, avg_a, M2_a = traj_stats_a[k]["n"], traj_stats_a[k]["mean"], traj_stats_a[k]["sqdiff"] - n_b, avg_b, M2_b = traj_stats_b[k]["n"], traj_stats_b[k]["mean"], traj_stats_b[k]["sqdiff"] - n = n_a + n_b - mean = (n_a * avg_a + n_b * avg_b) / n - delta = (avg_b - avg_a) - M2 = M2_a + M2_b + (delta ** 2) * (n_a * n_b) / n - merged_stats[k] = dict(n=n, mean=mean, sqdiff=M2) - return merged_stats # Run through all trajectories. For each one, compute minimal observation statistics, and then aggregate # with the previous statistics. @@ -373,6 +390,41 @@ def get_obs_normalization_stats(self): assert self.hdf5_normalize_obs, "not using observation normalization!" return deepcopy(self.obs_normalization_stats) + def get_action_traj(self, ep): + action_traj = dict() + for key in self.action_keys: + action_traj[key] = self.hdf5_file["data/{}/{}".format(ep, key)][()].astype('float32') + self.close_and_delete_hdf5_handle() + return action_traj + + def get_action_stats(self): + ep = self.demos[0] + action_traj = self.get_action_traj(ep) + action_stats = _compute_traj_stats(action_traj) + print("SequenceDataset: normalizing actions...") + for ep in LogUtils.custom_tqdm(self.demos[1:]): + action_traj = self.get_action_traj(ep) + traj_stats = _compute_traj_stats(action_traj) + action_stats = _aggregate_traj_stats(action_stats, traj_stats) + return action_stats + + def set_action_normalization_stats(self, action_normalization_stats): + self.action_normalization_stats = action_normalization_stats + + def get_action_normalization_stats(self): + """ + Computes a dataset-wide min, max, mean and standard deviation for the actions + (per dimension) and returns it. + """ + + # Run through all trajectories. For each one, compute minimal observation statistics, and then aggregate + # with the previous statistics. + if self.action_normalization_stats is None: + action_stats = self.get_action_stats() + self.action_normalization_stats = action_stats_to_normalization_stats( + action_stats, self.action_config) + return self.action_normalization_stats + def get_dataset_for_ep(self, ep, key): """ Helper utility to get a dataset for a specific demonstration. @@ -385,7 +437,7 @@ def get_dataset_for_ep(self, ep, key): # if key is an observation, it may not be in memory if '/' in key: key1, key2 = key.split('/') - assert(key1 in ['obs', 'next_obs']) + assert(key1 in ['obs', 'next_obs', 'action_dict']) if key2 not in self.obs_keys_in_memory: key_should_be_in_memory = False @@ -393,7 +445,7 @@ def get_dataset_for_ep(self, ep, key): # read cache if '/' in key: key1, key2 = key.split('/') - assert(key1 in ['obs', 'next_obs']) + assert(key1 in ['obs', 'next_obs', 'action_dict']) ret = self.hdf5_cache[ep][key1][key2] else: ret = self.hdf5_cache[ep][key] @@ -408,8 +460,24 @@ def __getitem__(self, index): Fetch dataset sequence @index (inferred through internal index map), using the getitem_cache if available. """ if self.hdf5_cache_mode == "all": - return self.getitem_cache[index] - return self.get_item(index) + output = self.getitem_cache[index] + else: + output = self.get_item(index) + + for (g1, g2) in self.shuffled_obs_key_groups: + assert len(g1) == len(g2) + if random.random() > 0.5: + # shuffle the keys accordingly + for (o1, o2) in zip(g1, g2): + for otype in ["obs", "next_obs", "goal_obs"]: + if output.get(otype, None) is None: + continue + if o1 not in output[otype] or o2 not in output[otype]: + continue + # swap values + output[otype][o1], output[otype][o2] = output[otype][o2], output[otype][o1] + + return output def get_item(self, index): """ @@ -438,8 +506,24 @@ def get_item(self, index): # determine goal index goal_index = None + action_padding_len = None if self.goal_mode == "last": goal_index = end_index_in_demo - 1 + elif self.goal_mode == "geom": + assert self.truncated_geom_factor is not None + num_options = end_index_in_demo - 1 - index_in_demo + if num_options <= self.seq_length: + goal_index = end_index_in_demo - 1 + else: + geom_sample_index = 1 + int(truncated_geometric(p=self.truncated_geom_factor / num_options, truncate_threshold=num_options-1, size=1)) + goal_index = index_in_demo + geom_sample_index + + # Clamp to end of demo if goal_index is too high + if goal_index >= end_index_in_demo: + goal_index = end_index_in_demo - 1 + elif geom_sample_index <= self.seq_length: + # If it happens that you sample a goal_index within seq_length away from the current index, compute appropriate action padding + action_padding_len = self.seq_length - geom_sample_index - 1 meta["obs"] = self.get_obs_sequence_from_demo( demo_id, @@ -447,10 +531,9 @@ def get_item(self, index): keys=self.obs_keys, num_frames_to_stack=self.n_frame_stack - 1, seq_length=self.seq_length, - prefix="obs" + prefix="obs", + action_padding_len=action_padding_len ) - if self.hdf5_normalize_obs: - meta["obs"] = ObsUtils.normalize_obs(meta["obs"], obs_normalization_stats=self.obs_normalization_stats) if self.load_next_obs: meta["next_obs"] = self.get_obs_sequence_from_demo( @@ -461,8 +544,6 @@ def get_item(self, index): seq_length=self.seq_length, prefix="next_obs" ) - if self.hdf5_normalize_obs: - meta["next_obs"] = ObsUtils.normalize_obs(meta["next_obs"], obs_normalization_stats=self.obs_normalization_stats) if goal_index is not None: goal = self.get_obs_sequence_from_demo( @@ -471,28 +552,32 @@ def get_item(self, index): keys=self.obs_keys, num_frames_to_stack=0, seq_length=1, - prefix="next_obs", + prefix="next_obs" if self.load_next_obs else "obs", ) - if self.hdf5_normalize_obs: - goal = ObsUtils.normalize_obs(goal, obs_normalization_stats=self.obs_normalization_stats) meta["goal_obs"] = {k: goal[k][0] for k in goal} # remove sequence dimension for goal - # get actions - if self.action_keys is not None: - actions_to_concat = [] - for k in self.action_keys: - ac = meta[k] - if len(ac.shape) == 1: - ac = ac.reshape(-1, 1) - actions_to_concat.append(ac) - meta["actions"] = np.concatenate(actions_to_concat, axis=1) + # get action components + ac_dict = OrderedDict() + for k in self.action_keys: + ac = meta[k] + # expand action shape if needed + if len(ac.shape) == 1: + ac = ac.reshape(-1, 1) + ac_dict[k] = ac + + # normalize actions + action_normalization_stats = self.get_action_normalization_stats() + ac_dict = ObsUtils.normalize_dict(ac_dict, normalization_stats=action_normalization_stats) + + # concatenate all action components + meta["actions"] = AcUtils.action_dict_to_vector(ac_dict) # also return the sampled index meta["index"] = index return meta - def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_stack=0, seq_length=1): + def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_stack=0, seq_length=1, action_padding_len=None): """ Extract a (sub)sequence of data items from a demo given the @keys of the items. @@ -520,6 +605,10 @@ def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_sta seq_begin_pad = max(0, num_frames_to_stack - index_in_demo) # pad for frame stacking seq_end_pad = max(0, index_in_demo + seq_length - demo_length) # pad for sequence length + if action_padding_len is not None: + seq_end_pad = action_padding_len + 1 + seq_end_index = seq_end_index - action_padding_len - 1 + # make sure we are not padding if specified. if not self.pad_frame_stack: assert seq_begin_pad == 0 @@ -534,11 +623,11 @@ def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_sta seq = TensorUtils.pad_sequence(seq, padding=(seq_begin_pad, seq_end_pad), pad_same=True) pad_mask = np.array([0] * seq_begin_pad + [1] * (seq_end_index - seq_begin_index) + [0] * seq_end_pad) - pad_mask = pad_mask[:, None].astype(np.bool) + pad_mask = pad_mask[:, None].astype(bool) return seq, pad_mask - def get_obs_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_stack=0, seq_length=1, prefix="obs"): + def get_obs_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_stack=0, seq_length=1, prefix="obs", action_padding_len=None): """ Extract a (sub)sequence of observation items from a demo given the @keys of the items. @@ -559,6 +648,7 @@ def get_obs_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to keys=tuple('{}/{}'.format(prefix, k) for k in keys), num_frames_to_stack=num_frames_to_stack, seq_length=seq_length, + action_padding_len=action_padding_len ) obs = {'/'.join(k.split('/')[1:]): obs[k] for k in obs} # strip the prefix if self.get_pad_mask: @@ -636,6 +726,16 @@ def get_dataset_sampler(self): class R2D2Dataset(SequenceDataset): + def get_action_traj(self, ep): + action_traj = dict() + for key in self.action_keys: + action_traj[key] = self.hdf5_file[key][()].astype('float32') + if len(action_traj[key].shape) == 1: + action_traj[key] = np.reshape(action_traj[key], (-1, 1)) + self.close_and_delete_hdf5_handle() + + return action_traj + def load_demo_info(self, filter_by_attribute=None, demos=None, n_demos=None): """ Args: @@ -663,12 +763,12 @@ def load_demo_info(self, filter_by_attribute=None, demos=None, n_demos=None): # determine index mapping self.total_num_sequences = 0 - demo_length = self.hdf5_file["action/cartesian_velocity"].shape[0] + demo_length = self.hdf5_file["action/abs_pos"].shape[0] self._demo_id_to_start_indices[ep] = self.total_num_sequences self._demo_id_to_demo_length[ep] = demo_length # seperate demo into segments for better alignment - gripper_actions = list(self.hdf5_file["action/target_gripper_position"]) + gripper_actions = list(self.hdf5_file["action/gripper_position"]) gripper_closed = [1 if x > 0 else 0 for x in gripper_actions] try: @@ -720,9 +820,10 @@ def load_dataset_in_memory(self, demo_list, hdf5_file, obs_keys, dataset_keys, l for ep in LogUtils.custom_tqdm(demo_list): all_data[ep] = {} all_data[ep]["attrs"] = {} - all_data[ep]["attrs"]["num_samples"] = hdf5_file["action/cartesian_velocity"].shape[0] # hack to get traj len + all_data[ep]["attrs"]["num_samples"] = hdf5_file["action/abs_pos"].shape[0] # hack to get traj len # get obs - all_data[ep]["obs"] = {k: hdf5_file["observation/{}".format(k)][()].astype('float32') for k in obs_keys} + ## Dont make strings floats + all_data[ep]["obs"] = {k: hdf5_file["observation/{}".format(k)][()].astype('float32') if ("raw" not in k) else hdf5_file["observation/{}".format(k)][()] for k in obs_keys} if load_next_obs: raise NotImplementedError # get other dataset keys @@ -766,11 +867,12 @@ def get_dataset_for_ep(self, ep, key, try_to_use_cache=True): else: # read from file hd5key = "{}".format(key) #"data/{}/{}".format(ep, key) - ret = self.hdf5_file[hd5key] + ret = self.hdf5_file[hd5key][:] + self.close_and_delete_hdf5_handle() return ret - def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_stack=0, seq_length=1): + def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_stack=0, seq_length=1, action_padding_len=None): """ Extract a (sub)sequence of data items from a demo given the @keys of the items. @@ -798,6 +900,10 @@ def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_sta seq_begin_pad = max(0, num_frames_to_stack - index_in_demo) # pad for frame stacking seq_end_pad = max(0, index_in_demo + seq_length - demo_length) # pad for sequence length + if action_padding_len is not None: + seq_end_pad = action_padding_len + 1 + seq_end_index = seq_end_index - action_padding_len - 1 + # make sure we are not padding if specified. if not self.pad_frame_stack: assert seq_begin_pad == 0 @@ -808,7 +914,11 @@ def get_sequence_from_demo(self, demo_id, index_in_demo, keys, num_frames_to_sta seq = dict() for k in keys: data = self.get_dataset_for_ep(demo_id, k) - seq[k] = data[seq_begin_index: seq_end_index].astype("float32") + # Dont make strings floats + if "raw" not in k: + seq[k] = data[seq_begin_index: seq_end_index].astype("float32") + else: + seq[k] = data[seq_begin_index: seq_end_index].astype("string_") seq = TensorUtils.pad_sequence(seq, padding=(seq_begin_pad, seq_end_pad), pad_same=True) pad_mask = np.array([0] * seq_begin_pad + [1] * (seq_end_index - seq_begin_index) + [0] * seq_end_pad) @@ -833,7 +943,7 @@ def get_item(self, index): # end at offset index if not padding for seq length demo_length_offset = 0 if self.pad_seq_length else (self.seq_length - 1) end_index_in_demo = demo_length - demo_length_offset - + meta = self.get_dataset_sequence_from_demo( demo_id, index_in_demo=index_in_demo, @@ -844,8 +954,24 @@ def get_item(self, index): # determine goal index goal_index = None + action_padding_len = None if self.goal_mode == "last": goal_index = end_index_in_demo - 1 + elif self.goal_mode == "geom": + assert self.truncated_geom_factor is not None + num_options = end_index_in_demo - 1 - index_in_demo + if num_options <= self.seq_length: + goal_index = end_index_in_demo - 1 + else: + geom_sample_index = 1 + int(truncated_geometric(p=self.truncated_geom_factor / num_options, truncate_threshold=num_options-1, size=1)) + goal_index = index_in_demo + geom_sample_index + + # Clamp to end of demo if goal_index is too high + if goal_index >= end_index_in_demo: + goal_index = end_index_in_demo - 1 + elif geom_sample_index <= self.seq_length: + # If it happens that you sample a goal_index within seq_length away from the current index, compute appropriate action padding + action_padding_len = self.seq_length - geom_sample_index - 1 meta["obs"] = self.get_obs_sequence_from_demo( demo_id, @@ -853,10 +979,9 @@ def get_item(self, index): keys=self.obs_keys, num_frames_to_stack=self.n_frame_stack - 1, seq_length=self.seq_length, - prefix="observation" + prefix="observation", + action_padding_len=action_padding_len ) - if self.hdf5_normalize_obs: - meta["obs"] = ObsUtils.normalize_obs(meta["obs"], obs_normalization_stats=self.obs_normalization_stats) if self.load_next_obs: meta["next_obs"] = self.get_obs_sequence_from_demo( @@ -867,8 +992,6 @@ def get_item(self, index): seq_length=self.seq_length, prefix="next_obs" ) - if self.hdf5_normalize_obs: - meta["next_obs"] = ObsUtils.normalize_obs(meta["next_obs"], obs_normalization_stats=self.obs_normalization_stats) if goal_index is not None: goal = self.get_obs_sequence_from_demo( @@ -877,21 +1000,32 @@ def get_item(self, index): keys=self.obs_keys, num_frames_to_stack=0, seq_length=1, - prefix="next_obs", + prefix="next_obs" if self.load_next_obs else "observation", ) - if self.hdf5_normalize_obs: - goal = ObsUtils.normalize_obs(goal, obs_normalization_stats=self.obs_normalization_stats) - meta["goal_obs"] = {k: goal[k][0] for k in goal} # remove sequence dimension for goal - - # get actions - if self.action_keys is not None: - actions_to_concat = [] - for k in self.action_keys: - ac = meta[k] - if len(ac.shape) == 1: - ac = ac.reshape(-1, 1) - actions_to_concat.append(ac) - meta["actions"] = np.concatenate(actions_to_concat, axis=1) + + image_keys = [k for k in goal.keys() if "camera/image" in k] + for k in image_keys: + obs_image = meta['obs'][k] + N, H, W, C = obs_image.shape + goal_image = goal[k] + meta['obs'][k] = np.concatenate([obs_image, goal_image.repeat(N, 0)], axis = -1) + + # get action components + ac_dict = OrderedDict() + for k in self.action_keys: + ac = meta[k] + # expand action shape if needed + if len(ac.shape) == 1: + ac = ac.reshape(-1, 1) + ac_dict[k] = ac + + # normalize actions + # if "oxe_hdf5" not in self.hdf5_path: # IF ONLY NORMALIZING IN DOMAIN + action_normalization_stats = self.get_action_normalization_stats() + ac_dict = ObsUtils.normalize_dict(ac_dict, normalization_stats=action_normalization_stats) + + # concatenate all action components + meta["actions"] = AcUtils.action_dict_to_vector(ac_dict) # keys to reshape for k in meta["obs"]: @@ -901,6 +1035,10 @@ def get_item(self, index): # also return the sampled index meta["index"] = index + ## Moves instructions from numpy strings to a normal list of strings and does some cleanup + for k in meta['obs'].keys(): + if "raw" in k: + meta['obs'][k] = [str(s[0])[2:-1] for s in meta['obs'][k]] return meta @@ -915,11 +1053,17 @@ def __init__( super(MetaDataset, self).__init__() self.datasets = datasets ds_lens = np.array([len(ds) for ds in self.datasets]) + self.ds_lens = ds_lens if normalize_weights_by_ds_size: self.ds_weights = np.array(ds_weights) / ds_lens else: self.ds_weights = ds_weights self._ds_ind_bins = np.cumsum([0] + list(ds_lens)) + + # cache mode "all" not supported! The action normalization stats of each + # dataset will change after the datasets are already initialized + for ds in self.datasets: + assert ds.hdf5_cache_mode != "all" # compute ds_labels to one hot ids if ds_labels is None: @@ -934,6 +1078,15 @@ def __init__( one_hot_id = np.zeros(len(unique_labels)) one_hot_id[i] = 1.0 self.ds_labels_to_ids[label] = one_hot_id + + # TODO: comment + action_stats = self.get_action_stats() + ## IF ONLY NORMALZING IN DOMAIN + # non_oxe_datasets = [p for p in self.datasets if "oxe_hdf5" not in p.hdf5_path] + # print(non_oxe_datasets[0].action_config) + self.action_normalization_stats = action_stats_to_normalization_stats( + action_stats, self.datasets[0].action_config) + self.set_action_normalization_stats(self.action_normalization_stats) def __len__(self): return np.sum([len(ds) for ds in self.datasets]) @@ -964,11 +1117,195 @@ def __repr__(self): def get_dataset_sampler(self): weights = np.ones(len(self)) for i, (start, end) in enumerate(zip(self._ds_ind_bins[:-1], self._ds_ind_bins[1:])): - weights[start:end] = self.ds_weights[i] + ### IMPORTANT TO DIVIDE BY LEN + ### MAKES SURE THE WEIGHTING ASSIGNED TO A DEMO IS EVENLY DISTRIBUTED ACROSS + ### ITS TRANSITIONS. OTHERWISE LONGER TRAJECTORIES GET ARTIFICIALLY UPWEIGHTED + weights[start:end] = self.ds_weights[i] / self.ds_lens[i] - sampler = torch.utils.data.WeightedRandomSampler( + sampler = CustomWeightedRandomSampler( weights=weights, num_samples=len(self), replacement=True, ) - return sampler \ No newline at end of file + ## CANT HANDLE OXE + # sampler = torch.utils.data.WeightedRandomSampler( + # weights=weights, + # num_samples=len(self), + # replacement=True, + # ) + return sampler + + def get_action_stats(self): + ## IF ONLY NORMALIZING IN DOMAIN + # non_oxe_datasets = [p for p in self.datasets if "oxe_hdf5" not in p.hdf5_path] + meta_action_stats = self.datasets[0].get_action_stats() + numd = len(self.datasets) + # for i, dataset in enumerate(self.datasets[1:]): + for i in range(1, numd): + t0 = time.time() + dataset = self.datasets[i] + ds_action_stats = dataset.get_action_stats() + t1 = time.time() + meta_action_stats = _aggregate_traj_stats(meta_action_stats, ds_action_stats) + t2 = time.time() + del dataset + del ds_action_stats + print(f"{i} / {numd} Normalizations completed in {t2 - t0}, {t1-t0}, {t2-t1}") + print(f"TOTAL RAM: {psutil.Process().memory_info().rss / (1024 ** 2)}") + + return meta_action_stats + + def set_action_normalization_stats(self, action_normalization_stats): + self.action_normalization_stats = action_normalization_stats + for ds in self.datasets: + ds.set_action_normalization_stats(self.action_normalization_stats) + + def get_action_normalization_stats(self): + """ + Computes a dataset-wide min, max, mean and standard deviation for the actions + (per dimension) and returns it. + """ + + # Run through all trajectories. For each one, compute minimal observation statistics, and then aggregate + # with the previous statistics. + if self.action_normalization_stats is None: + action_stats = self.get_action_stats() + self.action_normalization_stats = action_stats_to_normalization_stats( + action_stats, self.datasets[0].action_config) + return self.action_normalization_stats + +class CustomWeightedRandomSampler(torch.utils.data.WeightedRandomSampler): + """WeightedRandomSampler except allows for more than 2^24 samples to be sampled""" + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def __iter__(self): + rand_tensor = np.random.choice(range(0, len(self.weights)), + size=self.num_samples, + p=self.weights.numpy() / torch.sum(self.weights).numpy(), + replace=self.replacement) + rand_tensor = torch.from_numpy(rand_tensor) + return iter(rand_tensor.tolist()) + +def _compute_traj_stats(traj_obs_dict): + """ + Helper function to compute statistics over a single trajectory of observations. + """ + traj_stats = { k : {} for k in traj_obs_dict } + for k in traj_obs_dict: + traj_stats[k]["n"] = traj_obs_dict[k].shape[0] + traj_stats[k]["mean"] = traj_obs_dict[k].mean(axis=0, keepdims=True) # [1, ...] + traj_stats[k]["sqdiff"] = ((traj_obs_dict[k] - traj_stats[k]["mean"]) ** 2).sum(axis=0, keepdims=True) # [1, ...] + traj_stats[k]["min"] = traj_obs_dict[k].min(axis=0, keepdims=True) + traj_stats[k]["max"] = traj_obs_dict[k].max(axis=0, keepdims=True) + return traj_stats + +def _aggregate_traj_stats(traj_stats_a, traj_stats_b): + """ + Helper function to aggregate trajectory statistics. + See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm + for more information. + """ + merged_stats = {} + for k in traj_stats_a: + n_a, avg_a, M2_a, min_a, max_a = traj_stats_a[k]["n"], traj_stats_a[k]["mean"], traj_stats_a[k]["sqdiff"], traj_stats_a[k]["min"], traj_stats_a[k]["max"] + n_b, avg_b, M2_b, min_b, max_b = traj_stats_b[k]["n"], traj_stats_b[k]["mean"], traj_stats_b[k]["sqdiff"], traj_stats_b[k]["min"], traj_stats_b[k]["max"] + n = n_a + n_b + mean = (n_a * avg_a + n_b * avg_b) / n + delta = (avg_b - avg_a) + M2 = M2_a + M2_b + (delta ** 2) * (n_a * n_b) / n + min_ = np.minimum(min_a, min_b) + max_ = np.maximum(max_a, max_b) + merged_stats[k] = dict(n=n, mean=mean, sqdiff=M2, min=min_, max=max_) + return merged_stats + +def action_stats_to_normalization_stats(action_stats, action_config): + action_normalization_stats = OrderedDict() + for action_key in action_stats.keys(): + # get how this action should be normalized from config, default to None + norm_method = action_config[action_key].get("normalization", None) + if norm_method is None: + # no normalization, unit scale, zero offset + action_normalization_stats[action_key] = { + "scale": np.ones_like(action_stats[action_key]["mean"], dtype=np.float32), + "offset": np.zeros_like(action_stats[action_key]["mean"], dtype=np.float32) + } + elif norm_method == "min_max": + # normalize min to -1 and max to 1 + range_eps = 1e-4 + input_min = action_stats[action_key]["min"].astype(np.float32) + input_max = action_stats[action_key]["max"].astype(np.float32) + # instead of -1 and 1 use numbers just below threshold to prevent numerical instability issues + output_min = -0.999999 + output_max = 0.999999 + + # ignore input dimentions that is too small to prevent division by zero + input_range = input_max - input_min + ignore_dim = input_range < range_eps + input_range[ignore_dim] = output_max - output_min + + # expected usage of scale and offset + # normalized_action = (raw_action - offset) / scale + # raw_action = scale * normalized_action + offset + + # eq1: input_max = scale * output_max + offset + # eq2: input_min = scale * output_min + offset + + # solution for scale and offset + # eq1 - eq2: + # input_max - input_min = scale * (output_max - output_min) + # (input_max - input_min) / (output_max - output_min) = scale <- eq3 + # offset = input_min - scale * output_min <- eq4 + scale = input_range / (output_max - output_min) + offset = input_min - scale * output_min + + offset[ignore_dim] = input_min[ignore_dim] - (output_max + output_min) / 2 + + action_normalization_stats[action_key] = { + "scale": scale, + "offset": offset + } + elif norm_method == "gaussian": + # normalize to zero mean unit variance + input_mean = action_stats[action_key]["mean"].astype(np.float32) + input_std = np.sqrt(action_stats[action_key]["sqdiff"] / action_stats[action_key]["n"]).astype(np.float32) + + # ignore input dimentions that is too small to prevent division by zero + std_eps = 1e-6 + ignore_dim = input_std < std_eps + input_std[ignore_dim] = 1.0 + + action_normalization_stats[action_key] = { + "scale": input_mean, + "offset": input_std + } + else: + raise NotImplementedError( + 'action_config.actions.normalization: "{}" is not supported'.format(norm_method)) + + return action_normalization_stats + +def truncated_geometric(p, truncate_threshold, size, new_value=None): + """ + Sample from geometric, but truncated values to `truncated_threshold`. + This geometric has support from {0, 1, 2, ...}, meaning it includes 0. + All values greater than `truncated_threshold` will be set to `new_value`. + If `new_value` is None, then they will be assigned random integers from 0 to + `truncate_threshold`. + :param p: probability parameter for geometric distribution + :param truncate_threshold: Cut-off + :param size: size of sample + :param new_value: + :return: + """ + # numpy default does not include zero + samples = np.random.geometric(p, size) - 1 + samples_too_large = samples > truncate_threshold + num_bad = sum(samples_too_large) + if new_value is None: + samples[samples > truncate_threshold] = ( + np.random.randint(0, truncate_threshold, num_bad) + ) + else: + samples[samples > truncate_threshold] = new_value + return samples diff --git a/robomimic/utils/env_utils.py b/robomimic/utils/env_utils.py index d3d55ebe..6514de32 100644 --- a/robomimic/utils/env_utils.py +++ b/robomimic/utils/env_utils.py @@ -214,10 +214,10 @@ def create_env_from_metadata( env_name = env_meta["env_name"] env_type = get_env_type(env_meta=env_meta) env_kwargs = env_meta["env_kwargs"] + env_kwargs["env_name"] = env_name env = create_env( env_type=env_type, - env_name=env_name, render=render, render_offscreen=render_offscreen, use_image_obs=use_image_obs, diff --git a/robomimic/utils/file_utils.py b/robomimic/utils/file_utils.py index 2cb12a68..011f4cf5 100644 --- a/robomimic/utils/file_utils.py +++ b/robomimic/utils/file_utils.py @@ -96,24 +96,31 @@ def get_env_metadata_from_dataset(dataset_path, ds_format="robomimic"): :`'type'`: type of environment, should be a value in EB.EnvType :`'env_kwargs'`: dictionary of keyword arguments to pass to environment constructor """ - dataset_path = os.path.expanduser(dataset_path) - f = h5py.File(dataset_path, "r") + if ds_format != "r2d2_rlds": + dataset_path = os.path.expanduser(dataset_path) + f = h5py.File(dataset_path, "r") + if ds_format == "robomimic": env_meta = json.loads(f["data"].attrs["env_args"]) + f.close() elif ds_format == "r2d2": env_meta = dict(f.attrs) + f.close() + elif ds_format == "r2d2_rlds": + # TODO(Ashwin): find a proper way to extract this; this info isn't actually used though + env_meta = {} else: raise ValueError - f.close() return env_meta -def get_shape_metadata_from_dataset(dataset_path, all_obs_keys=None, ds_format="robomimic", verbose=False): +def get_shape_metadata_from_dataset(dataset_path, batch, action_keys, all_obs_keys=None, ds_format="robomimic", verbose=False, config=None): """ Retrieves shape metadata from dataset. Args: dataset_path (str): path to dataset + action_keys (list): list of all action key strings all_obs_keys (list): list of all modalities used by the model. If not provided, all modalities present in the file are used. verbose (bool): if True, include print statements @@ -128,17 +135,19 @@ def get_shape_metadata_from_dataset(dataset_path, all_obs_keys=None, ds_format=" """ shape_meta = {} - - # read demo file for some metadata - dataset_path = os.path.expanduser(dataset_path) - f = h5py.File(dataset_path, "r") if ds_format == "robomimic": + # read demo file for some metadata + dataset_path = os.path.expanduser(dataset_path) + f = h5py.File(dataset_path, "r") + demo_id = list(f["data"].keys())[0] demo = f["data/{}".format(demo_id)] - - # action dimension - shape_meta['ac_dim'] = f["data/{}/actions".format(demo_id)].shape[1] + + for key in action_keys: + assert len(demo[key].shape) == 2 # shape should be (B, D) + action_dim = sum([demo[key].shape[1] for key in action_keys]) + shape_meta["ac_dim"] = action_dim # observation dimensions all_shapes = OrderedDict() @@ -156,8 +165,18 @@ def get_shape_metadata_from_dataset(dataset_path, all_obs_keys=None, ds_format=" obs_modality=ObsUtils.OBS_KEYS_TO_MODALITIES[k], input_shape=initial_shape, ) + f.close() elif ds_format == "r2d2": - shape_meta['ac_dim'] = f["action/cartesian_velocity"].shape[1] + 1 # 1 for "action/gripper_velocity" + # read demo file for some metadata + dataset_path = os.path.expanduser(dataset_path) + f = h5py.File(dataset_path, "r") + + print(dataset_path) + for key in action_keys: + print(key, f[key].shape) + assert len(f[key].shape) == 2 # shape should be (B, D) + action_dim = sum([f[key].shape[1] for key in action_keys]) + shape_meta["ac_dim"] = action_dim # observation dimensions all_shapes = OrderedDict() @@ -166,10 +185,29 @@ def get_shape_metadata_from_dataset(dataset_path, all_obs_keys=None, ds_format=" for k in [ "robot_state/cartesian_position", "robot_state/gripper_position", - "robot_state/joint_positions", - "camera/image/hand_camera_image", - "camera/image/varied_camera_left_image", - "camera/image/varied_camera_right_image", + # "robot_state/joint_positions", + "camera/image/hand_camera_left_image", + # "camera/image/hand_camera_right_image", + "camera/image/varied_camera_1_left_image", + # "camera/image/varied_camera_1_right_image", + "camera/image/varied_camera_2_left_image", + # "camera/image/varied_camera_2_right_image", + # "camera/extrinsics/hand_camera_left", + # "camera/extrinsics/hand_camera_left_gripper_offset", + # "camera/extrinsics/hand_camera_right", + # "camera/extrinsics/hand_camera_right_gripper_offset", + # "camera/extrinsics/varied_camera_1_left", + # "camera/extrinsics/varied_camera_1_right", + # "camera/extrinsics/varied_camera_2_left", + # "camera/extrinsics/varied_camera_2_right", + # "camera/intrinsics/hand_camera_left", + # "camera/intrinsics/hand_camera_right", + # "camera/intrinsics/varied_camera_1_left", + # "camera/intrinsics/varied_camera_1_right", + # "camera/intrinsics/varied_camera_2_left", + # "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert", + "lang_fixed/language_raw" ]: initial_shape = f["observation/{}".format(k)].shape[1:] if len(initial_shape) == 0: @@ -179,15 +217,38 @@ def get_shape_metadata_from_dataset(dataset_path, all_obs_keys=None, ds_format=" obs_modality=ObsUtils.OBS_KEYS_TO_MODALITIES[k], input_shape=initial_shape, ) + + ## Special case for goal image conditioning, images become 6 channel + if (config.train.goal_mode is not None) and (ObsUtils.OBS_KEYS_TO_MODALITIES[k] == 'rgb'): + all_shapes[k][0] *= 2 + f.close() + elif ds_format == "r2d2_rlds": + all_shapes = OrderedDict() + for k in [ + "robot_state/cartesian_position", + "robot_state/gripper_position", + "camera/image/varied_camera_1_left_image", + # "camera/image/varied_camera_1_right_image", + "camera/image/varied_camera_2_left_image", + ]: + if k in batch["obs"]: + initial_shape = batch["obs"][k].shape[2:] + + if len(initial_shape) == 0: + initial_shape = (1,) + + all_shapes[k] = ObsUtils.get_processed_shape( + obs_modality=ObsUtils.OBS_KEYS_TO_MODALITIES[k], + input_shape=initial_shape, + ) + + shape_meta = {'ac_dim': batch["actions"].shape[-1]} else: raise ValueError - f.close() - shape_meta['all_shapes'] = all_shapes shape_meta['all_obs_keys'] = all_obs_keys shape_meta['use_images'] = ObsUtils.has_modality("rgb", all_obs_keys) - return shape_meta @@ -418,6 +479,13 @@ def policy_from_checkpoint(device=None, ckpt_path=None, ckpt_dict=None, verbose= for k in obs_normalization_stats[m]: obs_normalization_stats[m][k] = np.array(obs_normalization_stats[m][k]) + # maybe restore action normalization stats + action_normalization_stats = ckpt_dict.get("action_normalization_stats", None) + if action_normalization_stats is not None: + for m in action_normalization_stats: + for k in action_normalization_stats[m]: + action_normalization_stats[m][k] = np.array(action_normalization_stats[m][k]) + if device is None: # get torch device device = TorchUtils.get_torch_device(try_to_use_cuda=config.train.cuda) @@ -432,7 +500,11 @@ def policy_from_checkpoint(device=None, ckpt_path=None, ckpt_dict=None, verbose= ) model.deserialize(ckpt_dict["model"]) model.set_eval() - model = RolloutPolicy(model, obs_normalization_stats=obs_normalization_stats) + model = RolloutPolicy( + model, + obs_normalization_stats=obs_normalization_stats, + action_normalization_stats=action_normalization_stats + ) if verbose: print("============= Loaded Policy =============") print(model) @@ -474,6 +546,8 @@ def env_from_checkpoint(ckpt_path=None, ckpt_dict=None, env_name=None, render=Fa render_offscreen=render_offscreen, use_image_obs=shape_meta["use_images"], ) + config, _ = config_from_checkpoint(algo_name=ckpt_dict["algo_name"], ckpt_dict=ckpt_dict, verbose=False) + env = EnvUtils.wrap_env_from_config(env, config=config) # apply environment warpper, if applicable if verbose: print("============= Loaded Environment =============") print(env) diff --git a/robomimic/utils/hyperparam_utils.py b/robomimic/utils/hyperparam_utils.py index d74321bc..0ff6397e 100644 --- a/robomimic/utils/hyperparam_utils.py +++ b/robomimic/utils/hyperparam_utils.py @@ -41,7 +41,7 @@ def __init__(self, base_config_file, wandb_proj_name="debug", script_file=None, assert isinstance(wandb_proj_name, str) self.wandb_proj_name = wandb_proj_name - def add_param(self, key, name, group, values, value_names=None, hidename=False): + def add_param(self, key, name, group, values, value_names=None, hidename=False, prepend=False): """ Add parameter to the hyperparameter sweep. @@ -70,8 +70,10 @@ def add_param(self, key, name, group, values, value_names=None, hidename=False): value_names=value_names, hidename=hidename, ) + if prepend: + self.parameters.move_to_end(key, last=False) - def generate(self): + def generate(self, override_base_name=False): """ Generates json configs for the hyperparameter sweep using attributes @self.parameters, @self.base_config_file, and @self.script_file, @@ -79,7 +81,7 @@ def generate(self): @add_param, @set_base_config_file, and @set_script_file. """ assert len(self.parameters) > 0, "must add parameters using add_param first!" - generated_json_paths = self._generate_jsons() + generated_json_paths = self._generate_jsons(override_base_name=override_base_name) self._script_from_jsons(generated_json_paths) def _name_for_experiment(self, base_name, parameter_values, parameter_value_names): @@ -103,9 +105,6 @@ def _name_for_experiment(self, base_name, parameter_values, parameter_value_name if len(self.parameters[k].name) == 0 or self.parameters[k].hidename: # empty string indicates that naming should be skipped continue - if len(self.parameters[k].name) == 0: - # empty string indicates that naming should be skipped - continue if parameter_value_names[k] is not None: # take name from passed dictionary val_str = parameter_value_names[k] @@ -115,7 +114,9 @@ def _name_for_experiment(self, base_name, parameter_values, parameter_value_name # convert list to string to avoid weird spaces and naming problems val_str = "_".join([str(x) for x in parameter_values[k]]) val_str = str(val_str) - name += '_{}'.format(self.parameters[k].name) + if len(name) > 0: + name += "_" + name += '{}'.format(self.parameters[k].name) if len(val_str) > 0: name += '_{}'.format(val_str) return name @@ -194,7 +195,7 @@ def _get_parameter_ranges(self): return parameter_ranges, parameter_names - def _generate_jsons(self): + def _generate_jsons(self, override_base_name=False): """ Generates json configs for the hyperparameter sweep, using @self.parameters and @self.base_config_file. @@ -215,7 +216,10 @@ def _generate_jsons(self): base_config = load_json(self.base_config_file, verbose=False) # base exp name from this base config - base_exp_name = base_config['experiment']['name'] + if override_base_name: + base_exp_name = "" + else: + base_exp_name = base_config['experiment']['name'] # use base json to determine the parameter ranges parameter_ranges, parameter_names = self._get_parameter_ranges() @@ -289,7 +293,8 @@ def _script_from_jsons(self, json_paths): f.write("#!/bin/bash\n\n") for path in json_paths: # write python command to file - cmd = "python train.py --config {}\n".format(path) + import robomimic + cmd = "python {}/scripts/train.py --config {}\n".format(robomimic.__path__[0], path) print() print(cmd) diff --git a/robomimic/utils/log_utils.py b/robomimic/utils/log_utils.py index bc9ae55d..9edd6e5c 100644 --- a/robomimic/utils/log_utils.py +++ b/robomimic/utils/log_utils.py @@ -67,7 +67,7 @@ def __init__(self, log_dir, config, log_tb=True, log_wandb=False): assert Macros.WANDB_ENTITY is not None, "WANDB_ENTITY macro is set to None." \ "\nSet this macro in {base_path}/macros_private.py" \ - "\nIf this file does not exist, first run {base_path}/scripts/setup_macros.py".format(base_path=robomimic.__path__[0]) + "\nIf this file does not exist, first run python {base_path}/scripts/setup_macros.py".format(base_path=robomimic.__path__[0]) # attempt to set up wandb 10 times. If unsuccessful after these trials, don't use wandb num_attempts = 10 @@ -128,6 +128,8 @@ def record(self, k, v, epoch, data_type='scalar', log_stats=False): stat_k_name = '{}-{}'.format(k, stat_k) self._tb_logger.add_scalar(stat_k_name, stat_v, epoch) elif data_type == 'image': + if len(v.shape) == 3: + v = v[None, ...] self._tb_logger.add_images(k, img_tensor=v, global_step=epoch, dataformats="NHWC") if self._wandb_logger is not None: @@ -139,7 +141,8 @@ def record(self, k, v, epoch, data_type='scalar', log_stats=False): for (stat_k, stat_v) in stats.items(): self._wandb_logger.log({stat_k: stat_v}, step=epoch) elif data_type == 'image': - raise NotImplementedError + import wandb + self._wandb_logger.log({k: wandb.Image(v)}, step=epoch) except Exception as e: log_warning("wandb logging: {}".format(e)) diff --git a/robomimic/utils/obs_utils.py b/robomimic/utils/obs_utils.py index 5768b959..85b0218a 100644 --- a/robomimic/utils/obs_utils.py +++ b/robomimic/utils/obs_utils.py @@ -93,14 +93,15 @@ def obs_encoder_kwargs_from_config(obs_encoder_config): # Unlock encoder config obs_encoder_config.unlock() for obs_modality, encoder_kwargs in obs_encoder_config.items(): - # First run some sanity checks and store the classes - for cls_name, cores in zip(("core", "obs_randomizer"), (OBS_ENCODER_CORES, OBS_RANDOMIZERS)): - # Make sure the requested encoder for each obs_modality exists - cfg_cls = encoder_kwargs[f"{cls_name}_class"] - if cfg_cls is not None: - assert cfg_cls in cores, f"No {cls_name} class with name {cfg_cls} found, must register this class before" \ - f"creating model!" - # encoder_kwargs[f"{cls_name}_class"] = cores[cfg_cls] + ### TODO: fix sanity checks. Disabling this code snippet to allow chaining multiple randomizers. + # # First run some sanity checks and store the classes + # for cls_name, cores in zip(("core", "obs_randomizer"), (OBS_ENCODER_CORES, OBS_RANDOMIZERS)): + # # Make sure the requested encoder for each obs_modality exists + # cfg_cls = encoder_kwargs[f"{cls_name}_class"] + # if cfg_cls is not None: + # assert cfg_cls in cores, f"No {cls_name} class with name {cfg_cls} found, must register this class before" \ + # f"creating model!" + # # encoder_kwargs[f"{cls_name}_class"] = cores[cfg_cls] # Process core and randomizer kwargs encoder_kwargs.core_kwargs = dict() if encoder_kwargs.core_kwargs is None else \ @@ -278,7 +279,7 @@ def center_crop(im, t_h, t_w): im (np.array or torch.Tensor): center cropped image """ assert(im.shape[-3] >= t_h and im.shape[-2] >= t_w) - assert(im.shape[-1] in [1, 3]) + assert(im.shape[-1] in [1, 3, 6]) crop_h = int((im.shape[-3] - t_h) / 2) crop_w = int((im.shape[-2] - t_w) / 2) return im[..., crop_h:crop_h + t_h, crop_w:crop_w + t_w, :] @@ -376,8 +377,8 @@ def process_frame(frame, channel_dim, scale): Returns: processed_frame (np.array or torch.Tensor): processed frame """ - # Channel size should either be 3 (RGB) or 1 (depth) - assert (frame.shape[-1] == channel_dim) + # Channel size should either be 3 (RGB) or 1 (depth) or 6 (goal image RGB) + assert (frame.shape[-1] == channel_dim) or (frame.shape[-1] == channel_dim*2) frame = TU.to_float(frame) frame /= scale frame = frame.clip(0.0, 1.0) @@ -460,44 +461,86 @@ def get_processed_shape(obs_modality, input_shape): return list(process_obs(obs=np.zeros(input_shape), obs_modality=obs_modality).shape) -def normalize_obs(obs_dict, obs_normalization_stats): +def normalize_dict(dict, normalization_stats): """ - Normalize observations using the provided "mean" and "std" entries - for each observation key. The observation dictionary will be + Normalize dict using the provided "offset" and "scale" entries + for each observation key. The dictionary will be modified in-place. Args: - obs_dict (dict): dictionary mapping observation key to np.array or + dict (dict): dictionary mapping key to np.array or + torch.Tensor. Leading batch dimensions are optional. + + normalization_stats (dict): this should map keys to dicts + with a "offset" and "scale" of shape (1, ...) where ... is the default + shape for the dict value. + + Returns: + dict (dict): obs dict with normalized arrays + """ + + # ensure we have statistics for each modality key in the dict + assert set(dict.keys()).issubset(normalization_stats) + + for m in dict: + offset = normalization_stats[m]["offset"] + scale = normalization_stats[m]["scale"] + + # check shape consistency + shape_len_diff = len(offset.shape) - len(dict[m].shape) + assert shape_len_diff in [0, 1], "shape length mismatch in @normalize_dict" + # if dict has no leading batch dim, check shapes match exactly, else allow first dim to broadcast + assert offset.shape[1:] == dict[m].shape[(1 - shape_len_diff):], "shape mismatch in @normalize_dict" + + # handle case where obs dict is not batched by removing stats batch dimension + if shape_len_diff == 1: + offset = offset[0] + scale = scale[0] + + dict[m] = (dict[m] - offset) / scale + + return dict + + +def unnormalize_dict(dict, normalization_stats): + """ + Unnormalize dict using the provided "offset" and "scale" entries + for each observation key. The dictionary will be + modified in-place. + + Args: + dict (dict): dictionary mapping key to np.array or torch.Tensor. Leading batch dimensions are optional. - obs_normalization_stats (dict): this should map observation keys to dicts - with a "mean" and "std" of shape (1, ...) where ... is the default - shape for the observation. + normalization_stats (dict): this should map keys to dicts + with a "offset" and "scale" of shape (1, ...) where ... is the default + shape for the dict value. Returns: - obs_dict (dict): obs dict with normalized observation arrays + dict (dict): obs dict with normalized arrays """ - # ensure we have statistics for each modality key in the observation - assert set(obs_dict.keys()).issubset(obs_normalization_stats) + # ensure we have statistics for each modality key in the dict + assert set(dict.keys()).issubset(normalization_stats) - for m in obs_dict: - mean = obs_normalization_stats[m]["mean"] - std = obs_normalization_stats[m]["std"] + for m in dict: + offset = normalization_stats[m]["offset"] + scale = normalization_stats[m]["scale"] # check shape consistency - shape_len_diff = len(mean.shape) - len(obs_dict[m].shape) - assert shape_len_diff in [0, 1], "shape length mismatch in @normalize_obs" - assert mean.shape[shape_len_diff:] == obs_dict[m].shape, "shape mismatch in @normalize obs" + shape_len_diff = len(offset.shape) - len(dict[m].shape) + assert shape_len_diff in [0, 1], "shape length mismatch in @unnormalize_dict" + # if dict has no leading batch dim, check shapes match exactly, else allow first dim to broadcast + assert offset.shape[1:] == dict[m].shape[(1 - shape_len_diff):], "shape mismatch in @unnormalize_dict" # handle case where obs dict is not batched by removing stats batch dimension if shape_len_diff == 1: - mean = mean[0] - std = std[0] + offset = offset[0] + scale = scale[0] - obs_dict[m] = (obs_dict[m] - mean) / std + dict[m] = (dict[m] * scale) + offset - return obs_dict + return dict def has_modality(modality, obs_keys): diff --git a/robomimic/utils/rlds_utils.py b/robomimic/utils/rlds_utils.py new file mode 100644 index 00000000..5c2ce9e4 --- /dev/null +++ b/robomimic/utils/rlds_utils.py @@ -0,0 +1,89 @@ +"""Episode transforms for different RLDS datasets to canonical dataset definition.""" +from typing import Any, Dict + +import tensorflow as tf +import torch +import tensorflow_graphics.geometry.transformation as tfg + + +def euler_to_rmat(euler): + return tfg.rotation_matrix_3d.from_euler(euler) + + +def mat_to_rot6d(mat): + r6 = mat[..., :2, :] + r6_0, r6_1 = r6[..., 0, :], r6[..., 1, :] + r6_flat = tf.concat([r6_0, r6_1], axis=-1) + return r6_flat + + +def droid_dataset_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + # every input feature is batched, ie has leading batch dimension + T = trajectory["action_dict"]["cartesian_position"][:, :3] + R = mat_to_rot6d(euler_to_rmat(trajectory["action_dict"]["cartesian_position"][:, 3:6])) + trajectory["action"] = tf.concat( + ( + T, + R, + trajectory["action_dict"]["gripper_position"], + ), + axis=-1, + ) + return trajectory + + +def robomimic_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: + return { + "obs": { + "camera/image/varied_camera_1_left_image": + tf.cast(trajectory["observation"]["image_primary"], tf.float32) / 255., + "camera/image/varied_camera_2_left_image": + tf.cast(trajectory["observation"]["image_secondary"], tf.float32) / 255., + "raw_language": trajectory["task"]["language_instruction"], + "robot_state/cartesian_position": trajectory["observation"]["proprio"][..., :6], + "robot_state/gripper_position": trajectory["observation"]["proprio"][..., -1:], + "pad_mask": trajectory["observation"]["pad_mask"][..., None], + }, + "actions": trajectory["action"][1:], + } + +R2D2_TO_RLDS_OBS_KEY_MAP = { + "camera/image/varied_camera_1_left_image": "exterior_image_1_left", + "camera/image/varied_camera_2_left_image": "exterior_image_2_left" +} + +R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP = { + "robot_state/cartesian_position": "cartesian_position", + "robot_state/gripper_position": "gripper_position", +} + +class TorchRLDSDataset(torch.utils.data.IterableDataset): + """Thin wrapper around RLDS dataset for use with PyTorch dataloaders.""" + + def __init__( + self, + rlds_dataset, + train=True, + ): + self._rlds_dataset = rlds_dataset + self._is_train = train + + def __iter__(self): + for sample in self._rlds_dataset.as_numpy_iterator(): + yield sample + + def __len__(self): + lengths = np.array( + [ + stats["num_transitions"] + for stats in self._rlds_dataset.dataset_statistics + ] + ) + if hasattr(self._rlds_dataset, "sample_weights"): + lengths *= np.array(self._rlds_dataset.sample_weights) + total_len = lengths.sum() + if self._is_train: + return int(0.95 * total_len) + else: + return int(0.05 * total_len) + diff --git a/robomimic/utils/script_utils.py b/robomimic/utils/script_utils.py new file mode 100644 index 00000000..e42eaf62 --- /dev/null +++ b/robomimic/utils/script_utils.py @@ -0,0 +1,15 @@ +""" +Collection of miscellaneous utility tools +""" + +def deep_update(d, u): + """ + Copied from https://stackoverflow.com/a/3233356 + """ + import collections + for k, v in u.items(): + if isinstance(v, collections.abc.Mapping): + d[k] = deep_update(d.get(k, {}), v) + else: + d[k] = v + return d \ No newline at end of file diff --git a/robomimic/utils/tensor_utils.py b/robomimic/utils/tensor_utils.py index ec2063b2..b5ccfb1e 100644 --- a/robomimic/utils/tensor_utils.py +++ b/robomimic/utils/tensor_utils.py @@ -39,8 +39,11 @@ def recursive_dict_list_tuple_apply(x, type_func_dict): if isinstance(x, t): return f(x) else: - raise NotImplementedError( - 'Cannot handle data type %s' % str(type(x))) + ## Pretty hacky fix to avoid error when strings get converted to tensors + ## TODO (surajnair) try and clean this up at some point + return x + # raise NotImplementedError( + # 'Cannot handle data type %s' % str(type(x))) def map_tensor(x, func): diff --git a/robomimic/utils/torch_utils.py b/robomimic/utils/torch_utils.py index 433c8797..b604621b 100644 --- a/robomimic/utils/torch_utils.py +++ b/robomimic/utils/torch_utils.py @@ -4,6 +4,7 @@ import numpy as np import torch import torch.optim as optim +import torch.nn.functional as F def soft_update(source, target, tau): @@ -204,6 +205,42 @@ def backprop_for_loss(net, optim, loss, max_grad_norm=None, retain_graph=False): return grad_norms +def rot_6d_to_axis_angle(rot_6d): + """ + Converts tensor with rot_6d representation to axis-angle representation. + """ + rot_mat = rotation_6d_to_matrix(rot_6d) + rot = matrix_to_axis_angle(rot_mat) + return rot + + +def rot_6d_to_euler_angles(rot_6d, convention="XYZ"): + """ + Converts tensor with rot_6d representation to euler representation. + """ + rot_mat = rotation_6d_to_matrix(rot_6d) + rot = matrix_to_euler_angles(rot_mat, convention=convention) + return rot + + +def axis_angle_to_rot_6d(axis_angle): + """ + Converts tensor with rot_6d representation to axis-angle representation. + """ + rot_mat = axis_angle_to_matrix(axis_angle) + rot_6d = matrix_to_rotation_6d(rot_mat) + return rot_6d + + +def euler_angles_to_rot_6d(euler_angles, convention="XYZ"): + """ + Converts tensor with rot_6d representation to euler representation. + """ + rot_mat = euler_angles_to_matrix(euler_angles, convention="XYZ") + rot_6d = matrix_to_rotation_6d(rot_mat) + return rot_6d + + class dummy_context_mgr(): """ A dummy context manager - useful for having conditional scopes (such @@ -222,3 +259,378 @@ def maybe_no_grad(no_grad): it will be a dummy context """ return torch.no_grad() if no_grad else dummy_context_mgr() + + +""" +The following utility functions were taken from PyTorch3D: +https://github.com/facebookresearch/pytorch3d/blob/d84f274a0822da969668d00e831870fd88327845/pytorch3d/transforms/rotation_conversions.py +""" +def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: + """ + Returns torch.sqrt(torch.max(0, x)) + but with a zero subgradient where x is 0. + """ + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + + +def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as quaternions to rotation matrices. + Args: + quaternions: quaternions with real part first, + as tensor of shape (..., 4). + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + r, i, j, k = torch.unbind(quaternions, -1) + # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`. + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + +def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to quaternions. + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + Returns: + quaternions with real part first, as tensor of shape (..., 4). + """ + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + + batch_dim = matrix.shape[:-2] + m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind( + matrix.reshape(batch_dim + (9,)), dim=-1 + ) + + q_abs = _sqrt_positive_part( + torch.stack( + [ + 1.0 + m00 + m11 + m22, + 1.0 + m00 - m11 - m22, + 1.0 - m00 + m11 - m22, + 1.0 - m00 - m11 + m22, + ], + dim=-1, + ) + ) + + # we produce the desired quaternion multiplied by each of r, i, j, k + quat_by_rijk = torch.stack( + [ + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), + ], + dim=-2, + ) + + # We floor here at 0.1 but the exact level is not important; if q_abs is small, + # the candidate won't be picked. + flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device) + quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr)) + + # if not for numerical problems, quat_candidates[i] should be same (up to a sign), + # forall i; we pick the best-conditioned one (with the largest denominator) + + return quat_candidates[ + F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : + ].reshape(batch_dim + (4,)) + + +def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as axis/angle to rotation matrices. + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) + + +def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to axis/angle. + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + Returns: + Rotations given as a vector in axis angle form, as a tensor + of shape (..., 3), where the magnitude is the angle + turned anticlockwise in radians around the vector's + direction. + """ + return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) + + +def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as axis/angle to quaternions. + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + Returns: + quaternions with real part first, as tensor of shape (..., 4). + """ + angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) + half_angles = angles * 0.5 + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + quaternions = torch.cat( + [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 + ) + return quaternions + + +def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as quaternions to axis/angle. + Args: + quaternions: quaternions with real part first, + as tensor of shape (..., 4). + Returns: + Rotations given as a vector in axis angle form, as a tensor + of shape (..., 3), where the magnitude is the angle + turned anticlockwise in radians around the vector's + direction. + """ + norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) + half_angles = torch.atan2(norms, quaternions[..., :1]) + angles = 2 * half_angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + return quaternions[..., 1:] / sin_half_angles_over_angles + + +def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: + """ + Converts 6D rotation representation by Zhou et al. [1] to rotation matrix + using Gram--Schmidt orthogonalization per Section B of [1]. + Args: + d6: 6D rotation representation, of size (*, 6) + Returns: + batch of rotation matrices of size (*, 3, 3) + [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. + On the Continuity of Rotation Representations in Neural Networks. + IEEE Conference on Computer Vision and Pattern Recognition, 2019. + Retrieved from http://arxiv.org/abs/1812.07035 + """ + + a1, a2 = d6[..., :3], d6[..., 3:] + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + return torch.stack((b1, b2, b3), dim=-2) + + +def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: + """ + Converts rotation matrices to 6D rotation representation by Zhou et al. [1] + by dropping the last row. Note that 6D representation is not unique. + Args: + matrix: batch of rotation matrices of size (*, 3, 3) + Returns: + 6D rotation representation, of size (*, 6) + [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. + On the Continuity of Rotation Representations in Neural Networks. + IEEE Conference on Computer Vision and Pattern Recognition, 2019. + Retrieved from http://arxiv.org/abs/1812.07035 + """ + batch_dim = matrix.size()[:-2] + return matrix[..., :2, :].clone().reshape(batch_dim + (6,)) + + +def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to Euler angles in radians. + + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + convention: Convention string of three uppercase letters. + + Returns: + Euler angles in radians as tensor of shape (..., 3). + """ + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + i0 = _index_from_letter(convention[0]) + i2 = _index_from_letter(convention[2]) + tait_bryan = i0 != i2 + if tait_bryan: + central_angle = torch.asin( + matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) + ) + else: + central_angle = torch.acos(matrix[..., i0, i0]) + + o = ( + _angle_from_tan( + convention[0], convention[1], matrix[..., i2], False, tait_bryan + ), + central_angle, + _angle_from_tan( + convention[2], convention[1], matrix[..., i0, :], True, tait_bryan + ), + ) + return torch.stack(o, -1) + + +def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor: + """ + Convert rotations given as Euler angles in radians to rotation matrices. + + Args: + euler_angles: Euler angles in radians as tensor of shape (..., 3). + convention: Convention string of three uppercase letters from + {"X", "Y", and "Z"}. + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: + raise ValueError("Invalid input euler angles.") + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + matrices = [ + _axis_angle_rotation(c, e) + for c, e in zip(convention, torch.unbind(euler_angles, -1)) + ] + # return functools.reduce(torch.matmul, matrices) + return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2]) + + +def _index_from_letter(letter: str) -> int: + if letter == "X": + return 0 + if letter == "Y": + return 1 + if letter == "Z": + return 2 + raise ValueError("letter must be either X, Y or Z.") + + +def _angle_from_tan( + axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool +) -> torch.Tensor: + """ + Extract the first or third Euler angle from the two members of + the matrix which are positive constant times its sine and cosine. + + Args: + axis: Axis label "X" or "Y or "Z" for the angle we are finding. + other_axis: Axis label "X" or "Y or "Z" for the middle axis in the + convention. + data: Rotation matrices as tensor of shape (..., 3, 3). + horizontal: Whether we are looking for the angle for the third axis, + which means the relevant entries are in the same row of the + rotation matrix. If not, they are in the same column. + tait_bryan: Whether the first and third axes in the convention differ. + + Returns: + Euler Angles in radians for each matrix in data as a tensor + of shape (...). + """ + + i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] + if horizontal: + i2, i1 = i1, i2 + even = (axis + other_axis) in ["XY", "YZ", "ZX"] + if horizontal == even: + return torch.atan2(data[..., i1], data[..., i2]) + if tait_bryan: + return torch.atan2(-data[..., i2], data[..., i1]) + return torch.atan2(data[..., i2], -data[..., i1]) + + +def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor: + """ + Return the rotation matrices for one of the rotations about an axis + of which Euler angles describe, for each value of the angle given. + + Args: + axis: Axis label "X" or "Y or "Z". + angle: any shape tensor of Euler angles in radians + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + + cos = torch.cos(angle) + sin = torch.sin(angle) + one = torch.ones_like(angle) + zero = torch.zeros_like(angle) + + if axis == "X": + R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) + elif axis == "Y": + R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) + elif axis == "Z": + R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) + else: + raise ValueError("letter must be either X, Y or Z.") + + return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) diff --git a/robomimic/utils/train_utils.py b/robomimic/utils/train_utils.py index 7d99c89e..54dd09bf 100644 --- a/robomimic/utils/train_utils.py +++ b/robomimic/utils/train_utils.py @@ -77,7 +77,12 @@ def get_exp_dir(config, auto_remove_exp_dir=False): # video directory video_dir = os.path.join(base_output_dir, time_str, "videos") os.makedirs(video_dir) - return log_dir, output_dir, video_dir + + # vis directory + vis_dir = os.path.join(base_output_dir, time_str, "vis") + os.makedirs(vis_dir) + + return log_dir, output_dir, video_dir, vis_dir def load_data_for_training(config, obs_keys): @@ -100,8 +105,6 @@ def load_data_for_training(config, obs_keys): if valid_filter_by_attribute is not None: assert config.experiment.validate, "specified validation filter key {}, but config.experiment.validate is not set".format(valid_filter_by_attribute) - action_keys = config.train.action_keys - # load the dataset into memory if config.experiment.validate: assert not config.train.hdf5_normalize_obs, "no support for observation normalization with validation data yet" @@ -118,16 +121,16 @@ def load_data_for_training(config, obs_keys): ) assert set(train_demo_keys).isdisjoint(set(valid_demo_keys)), "training demonstrations overlap with " \ "validation demonstrations!" - train_dataset = dataset_factory(config, obs_keys, action_keys, filter_by_attribute=train_filter_by_attribute) - valid_dataset = dataset_factory(config, obs_keys, action_keys, filter_by_attribute=valid_filter_by_attribute) + train_dataset = dataset_factory(config, obs_keys, filter_by_attribute=train_filter_by_attribute) + valid_dataset = dataset_factory(config, obs_keys, filter_by_attribute=valid_filter_by_attribute) else: - train_dataset = dataset_factory(config, obs_keys, action_keys, filter_by_attribute=train_filter_by_attribute) + train_dataset = dataset_factory(config, obs_keys, filter_by_attribute=train_filter_by_attribute) valid_dataset = None return train_dataset, valid_dataset -def dataset_factory(config, obs_keys, action_keys, filter_by_attribute=None, dataset_path=None): +def dataset_factory(config, obs_keys, filter_by_attribute=None, dataset_path=None): """ Create a SequenceDataset instance to pass to a torch DataLoader. @@ -152,19 +155,22 @@ def dataset_factory(config, obs_keys, action_keys, filter_by_attribute=None, dat ds_kwargs = dict( hdf5_path=dataset_path, obs_keys=obs_keys, - action_keys=action_keys, + action_keys=config.train.action_keys, dataset_keys=config.train.dataset_keys, + action_config=config.train.action_config, load_next_obs=config.train.hdf5_load_next_obs, # whether to load next observations (s') from dataset frame_stack=config.train.frame_stack, seq_length=config.train.seq_length, pad_frame_stack=config.train.pad_frame_stack, pad_seq_length=config.train.pad_seq_length, - get_pad_mask=False, + get_pad_mask=True, goal_mode=config.train.goal_mode, + truncated_geom_factor=config.train.truncated_geom_factor, hdf5_cache_mode=config.train.hdf5_cache_mode, hdf5_use_swmr=config.train.hdf5_use_swmr, hdf5_normalize_obs=config.train.hdf5_normalize_obs, - filter_by_attribute=filter_by_attribute + filter_by_attribute=filter_by_attribute, + shuffled_obs_key_groups=config.train.shuffled_obs_key_groups, ) ds_kwargs["hdf5_path"] = [ds_cfg["path"] for ds_cfg in config.train.data] @@ -197,25 +203,40 @@ def get_dataset( meta_ds_kwargs=None, ): ds_list = [] - for i in range(len(ds_weights)): + ds_weights_filtered = [] + + alllen = len(ds_weights) + for i in range(alllen): + t0 = time.time() + # The old way + # ds_kwargs_copy = deepcopy(ds_kwargs) + # keys = ["hdf5_path", "filter_by_attribute"] + # for k in keys: + # ds_kwargs_copy[k] = ds_kwargs[k][i] + ds_kwargs_copy = {} + for k in ds_kwargs.keys(): + if k in ["hdf5_path", "filter_by_attribute"]: + ds_kwargs_copy[k] = ds_kwargs[k][i] + else: + ds_kwargs_copy[k] = ds_kwargs[k] - ds_kwargs_copy = deepcopy(ds_kwargs) - - keys = ["hdf5_path", "filter_by_attribute"] - - for k in keys: - ds_kwargs_copy[k] = ds_kwargs[k][i] - - ds_list.append(ds_class(**ds_kwargs_copy)) + try: + ds_list.append(ds_class(**ds_kwargs_copy)) + ds_weights_filtered.append(ds_weights[i]) + except: + pass + t3 = time.time() + print(f"{i} / {alllen} in {t3-t0}") - if len(ds_weights) == 1: + assert len(ds_weights_filtered) == len(ds_list) + if len(ds_weights_filtered) == 1: ds = ds_list[0] else: if meta_ds_kwargs is None: meta_ds_kwargs = dict() ds = meta_ds_class( datasets=ds_list, - ds_weights=ds_weights, + ds_weights=ds_weights_filtered, ds_labels=ds_labels, normalize_weights_by_ds_size=normalize_weights_by_ds_size, **meta_ds_kwargs @@ -516,7 +537,7 @@ def should_save_from_rollout_logs( ) -def save_model(model, config, env_meta, shape_meta, ckpt_path, obs_normalization_stats=None): +def save_model(model, config, env_meta, shape_meta, ckpt_path, obs_normalization_stats=None, action_normalization_stats=None): """ Save model to a torch pth file. @@ -535,6 +556,8 @@ def save_model(model, config, env_meta, shape_meta, ckpt_path, obs_normalization normalization. This should map observation keys to dicts with a "mean" and "std" of shape (1, ...) where ... is the default shape for the observation. + + action_normalization_stats (dict): TODO """ env_meta = deepcopy(env_meta) shape_meta = deepcopy(shape_meta) @@ -549,11 +572,14 @@ def save_model(model, config, env_meta, shape_meta, ckpt_path, obs_normalization assert config.train.hdf5_normalize_obs obs_normalization_stats = deepcopy(obs_normalization_stats) params["obs_normalization_stats"] = TensorUtils.to_list(obs_normalization_stats) + if action_normalization_stats is not None: + action_normalization_stats = deepcopy(action_normalization_stats) + params["action_normalization_stats"] = TensorUtils.to_list(action_normalization_stats) torch.save(params, ckpt_path) print("save checkpoint to {}".format(ckpt_path)) -def run_epoch(model, data_loader, epoch, validate=False, num_steps=None, obs_normalization_stats=None): +def run_epoch(model, data_loader, epoch, validate=False, num_steps=None, obs_normalization_stats=None, data_loader_iter=None): """ Run an epoch of training or validation. @@ -583,14 +609,17 @@ def run_epoch(model, data_loader, epoch, validate=False, num_steps=None, obs_nor model.set_eval() else: model.set_train() - if num_steps is None: - num_steps = len(data_loader) + # if num_steps is None: + # num_steps = len(data_loader) step_log_all = [] timing_stats = dict(Data_Loading=[], Process_Batch=[], Train_Batch=[], Log_Info=[]) start_time = time.time() - data_loader_iter = iter(data_loader) + if data_loader_iter is None: + data_loader_iter = iter(data_loader) + else: + data_loader_iter = data_loader_iter for _ in LogUtils.custom_tqdm(range(num_steps)): # load next batch from data loader @@ -636,7 +665,7 @@ def run_epoch(model, data_loader, epoch, validate=False, num_steps=None, obs_nor step_log_all["Time_{}".format(k)] = np.sum(timing_stats[k]) / 60. step_log_all["Time_Epoch"] = (time.time() - epoch_timestamp) / 60. - return step_log_all + return step_log_all, data_loader_iter def is_every_n_steps(interval, current_step, skip_zero=False): diff --git a/robomimic/utils/vis_utils.py b/robomimic/utils/vis_utils.py index 48abd7c1..fe80e66c 100644 --- a/robomimic/utils/vis_utils.py +++ b/robomimic/utils/vis_utils.py @@ -4,6 +4,7 @@ """ import numpy as np import matplotlib.pyplot as plt +import os import robomimic.utils.tensor_utils as TensorUtils import robomimic.utils.obs_utils as ObsUtils @@ -90,3 +91,94 @@ def visualize_image_randomizer(original_image, randomized_image, randomizer_name # Show the entire grid of subplots plt.show() + + +def make_model_prediction_plot( + hdf5_path, + save_path, + images, + action_names, + actual_actions, + predicted_actions, +): + """ + TODO: documentation + actual_actions: (T, D) + predicted_actions: (T, D) + """ + image_keys = sorted(list(images.keys())) + action_dim = actual_actions.shape[1] + traj_length = len(actual_actions) + if traj_length < 15: + return + + # Plot + fig, axs = plt.subplots(len(images) + action_dim, 1, figsize=(30, (len(images) + action_dim) * 3)) + for i, image_key in enumerate(image_keys): + interval = int(traj_length/15) # plot `5` images + images[image_key] = images[image_key][::interval] + combined_images = np.concatenate(images[image_key], axis=1) + + ## If doing goal image conditioniing visualize goal ims below + if combined_images.shape[-1] == 6: + c1 = combined_images[:, :, :3] + c2 = combined_images[:, :, 3:] + combined_images = np.concatenate([c1, c2], 0) + + axs[i].imshow(combined_images) + if i == 0: + axs[i].set_title(hdf5_path + '\n' + image_key, fontsize=30) + else: + axs[i].set_title(image_key, fontsize=30) + axs[i].axis("off") + for dim in range(action_dim): + ax = axs[len(images)+dim] + ax.plot(range(traj_length), actual_actions[:, dim], label='Actual Action', color='blue') + ax.plot(range(traj_length), predicted_actions[:, dim], label='Predicted Action', color='red') + # ax.set_xlabel('Timestep') + # ax.set_ylabel('Action Dimension {}'.format(dim + 1)) + ax.set_title(action_names[dim], fontsize=30) + ax.xaxis.set_tick_params(labelsize=24) + ax.yaxis.set_tick_params(labelsize=24) + ax.legend(fontsize=20) + plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3, hspace=0.6) + + # Save the figure with the specified path and filename + save_dir = os.path.dirname(save_path) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + plt.savefig(save_path) + + fig.clear() + plt.close() + plt.cla() + plt.clf() + + +def make_batch_vis_plot( + save_path, + images, +): + image_keys = sorted(list(images.keys())) + + # Plot + fig, axs = plt.subplots(len(images), 1, figsize=(30, (len(images)) * 3)) + for i, image_key in enumerate(image_keys): + combined_images = images[image_key].cpu().detach().numpy() + combined_images = [combined_images[i] for i in range(combined_images.shape[0])] + combined_images = np.concatenate(combined_images, axis=1) + axs[i].imshow(combined_images) + axs[i].set_title(image_key, fontsize=30) + axs[i].axis("off") + plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3, hspace=0.6) + + # Save the figure with the specified path and filename + save_dir = os.path.dirname(save_path) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + plt.savefig(save_path) + + fig.clear() + plt.close() + plt.cla() + plt.clf() \ No newline at end of file diff --git a/runallruns.sh b/runallruns.sh new file mode 100644 index 00000000..8cb238f4 --- /dev/null +++ b/runallruns.sh @@ -0,0 +1,89 @@ +#!/bin/bash + +# bash /mnt/fsx/surajnair/code/robomimic/runallruns.sh +# List of commands to run + +# # P4D1 +# commands=( +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_multi_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_foodinbowl_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_openmicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_closemicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_multi_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_foodinbowl_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_openmicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_closemicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# ) + +# # P4D2 +# commands=( +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_multi.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_foodinbowl.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_openmicro.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_closemicro.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_multi.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_foodinbowl.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_openmicro.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_closemicro.json" +# ) + +# # P4D3 (Ashwin P4D1) +commands=( +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_multi.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_foodinbowl.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_openmicro.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_closemicro.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_multi.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_foodinbowl.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_openmicro.json" +"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_closemicro.json" +) + +# # P4DE 1 +# commands=( +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_multi_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_foodinbowl_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_openmicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_closemicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_multi_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_foodinbowl_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_openmicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_closemicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" +# ) + +# # P4DE 2 +# commands=( +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_multi.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_foodinbowl.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_openmicro.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_closemicro.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_multi.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_foodinbowl.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_openmicro.json" +# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_closemicro.json" +# ) + + + +# Number of GPUs available (adjust as needed) +num_gpus=8 + +# Loop through the commands and launch them in separate screen sessions +for ((i=0; i<${#commands[@]}; i++)); do + # Get the current command + cmd="${commands[$i]}" + + # Calculate the GPU index for this command + gpu_index=$((i % num_gpus)) + + # Set the GPU index for this command + export CUDA_VISIBLE_DEVICES=$gpu_index + + # Create a unique screen session for each command + screen -dmS r2d2_$i $cmd + + # Sleep for a few seconds to allow screen to start + sleep 5 +done + +echo "Launched ${#commands[@]} commands in separate screen sessions." diff --git a/setup.py b/setup.py index afd46643..54405a5a 100644 --- a/setup.py +++ b/setup.py @@ -29,6 +29,9 @@ "egl_probe>=1.0.1", "torch", "torchvision", + "diffusers==0.11.1", + "opencv-python", + "transformers==4.34.0" ], eager_resources=['*'], include_package_data=True, From 851ca6e242994a83eb4e33871d214e704ad36eb5 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 28 Feb 2024 18:52:17 +0000 Subject: [PATCH 02/43] remove act stuff --- act/.gitignore | 140 ------ act/LICENSE | 21 - act/README.md | 89 ---- act/assets/bimanual_viperx_ee_insertion.xml | 59 --- .../bimanual_viperx_ee_transfer_cube.xml | 48 -- act/assets/bimanual_viperx_insertion.xml | 53 -- act/assets/bimanual_viperx_transfer_cube.xml | 42 -- act/assets/scene.xml | 38 -- act/assets/tabletop.stl | Bin 684 -> 0 bytes act/assets/vx300s_10_custom_finger_left.stl | Bin 83384 -> 0 bytes act/assets/vx300s_10_custom_finger_right.stl | Bin 83384 -> 0 bytes act/assets/vx300s_10_gripper_finger.stl | Bin 42884 -> 0 bytes act/assets/vx300s_11_ar_tag.stl | Bin 3884 -> 0 bytes act/assets/vx300s_1_base.stl | Bin 99984 -> 0 bytes act/assets/vx300s_2_shoulder.stl | Bin 63884 -> 0 bytes act/assets/vx300s_3_upper_arm.stl | Bin 102984 -> 0 bytes act/assets/vx300s_4_upper_forearm.stl | Bin 49584 -> 0 bytes act/assets/vx300s_5_lower_forearm.stl | Bin 99884 -> 0 bytes act/assets/vx300s_6_wrist.stl | Bin 70784 -> 0 bytes act/assets/vx300s_7_gripper.stl | Bin 450084 -> 0 bytes act/assets/vx300s_8_gripper_prop.stl | Bin 31684 -> 0 bytes act/assets/vx300s_9_gripper_bar.stl | Bin 379484 -> 0 bytes act/assets/vx300s_dependencies.xml | 17 - act/assets/vx300s_left.xml | 59 --- act/assets/vx300s_right.xml | 59 --- act/conda_env.yaml | 23 - act/constants.py | 76 --- act/detr/LICENSE | 201 -------- act/detr/README.md | 9 - act/detr/main.py | 109 ---- act/detr/models/__init__.py | 9 - act/detr/models/backbone.py | 122 ----- act/detr/models/detr_vae.py | 284 ----------- act/detr/models/position_encoding.py | 93 ---- act/detr/models/transformer.py | 314 ------------ act/detr/setup.py | 10 - act/detr/util/__init__.py | 1 - act/detr/util/box_ops.py | 88 ---- act/detr/util/misc.py | 468 ------------------ act/detr/util/plot_utils.py | 107 ---- act/ee_sim_env.py | 267 ---------- act/imitate_episodes.py | 435 ---------------- act/policy.py | 84 ---- act/record_sim_episodes.py | 189 ------- act/scripted_policy.py | 194 -------- act/sim_env.py | 278 ----------- act/utils.py | 189 ------- act/visualize_episodes.py | 147 ------ 48 files changed, 4322 deletions(-) delete mode 100644 act/.gitignore delete mode 100644 act/LICENSE delete mode 100644 act/README.md delete mode 100644 act/assets/bimanual_viperx_ee_insertion.xml delete mode 100644 act/assets/bimanual_viperx_ee_transfer_cube.xml delete mode 100644 act/assets/bimanual_viperx_insertion.xml delete mode 100644 act/assets/bimanual_viperx_transfer_cube.xml delete mode 100644 act/assets/scene.xml delete mode 100644 act/assets/tabletop.stl delete mode 100644 act/assets/vx300s_10_custom_finger_left.stl delete mode 100644 act/assets/vx300s_10_custom_finger_right.stl delete mode 100644 act/assets/vx300s_10_gripper_finger.stl delete mode 100644 act/assets/vx300s_11_ar_tag.stl delete mode 100644 act/assets/vx300s_1_base.stl delete mode 100644 act/assets/vx300s_2_shoulder.stl delete mode 100644 act/assets/vx300s_3_upper_arm.stl delete mode 100644 act/assets/vx300s_4_upper_forearm.stl delete mode 100644 act/assets/vx300s_5_lower_forearm.stl delete mode 100644 act/assets/vx300s_6_wrist.stl delete mode 100644 act/assets/vx300s_7_gripper.stl delete mode 100644 act/assets/vx300s_8_gripper_prop.stl delete mode 100644 act/assets/vx300s_9_gripper_bar.stl delete mode 100644 act/assets/vx300s_dependencies.xml delete mode 100644 act/assets/vx300s_left.xml delete mode 100644 act/assets/vx300s_right.xml delete mode 100644 act/conda_env.yaml delete mode 100644 act/constants.py delete mode 100644 act/detr/LICENSE delete mode 100644 act/detr/README.md delete mode 100644 act/detr/main.py delete mode 100644 act/detr/models/__init__.py delete mode 100644 act/detr/models/backbone.py delete mode 100644 act/detr/models/detr_vae.py delete mode 100644 act/detr/models/position_encoding.py delete mode 100644 act/detr/models/transformer.py delete mode 100644 act/detr/setup.py delete mode 100644 act/detr/util/__init__.py delete mode 100644 act/detr/util/box_ops.py delete mode 100644 act/detr/util/misc.py delete mode 100644 act/detr/util/plot_utils.py delete mode 100644 act/ee_sim_env.py delete mode 100644 act/imitate_episodes.py delete mode 100644 act/policy.py delete mode 100644 act/record_sim_episodes.py delete mode 100644 act/scripted_policy.py delete mode 100644 act/sim_env.py delete mode 100644 act/utils.py delete mode 100644 act/visualize_episodes.py diff --git a/act/.gitignore b/act/.gitignore deleted file mode 100644 index d1e4c693..00000000 --- a/act/.gitignore +++ /dev/null @@ -1,140 +0,0 @@ -bin -logs -wandb -outputs -data -data_local -.vscode -_wandb - -**/.DS_Store - -fuse.cfg - -*.ai - -# Generation results -results/ - -ray/auth.json - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ \ No newline at end of file diff --git a/act/LICENSE b/act/LICENSE deleted file mode 100644 index 35e5f5e2..00000000 --- a/act/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 Tony Z. Zhao - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/act/README.md b/act/README.md deleted file mode 100644 index 2a345157..00000000 --- a/act/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# ACT: Action Chunking with Transformers - -### *New*: [ACT tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) -TL;DR: if your ACT policy is jerky or pauses in the middle of an episode, just train for longer! Success rate and smoothness can improve way after loss plateaus. - -#### Project Website: https://tonyzhaozh.github.io/aloha/ - -This repo contains the implementation of ACT, together with 2 simulated environments: -Transfer Cube and Bimanual Insertion. You can train and evaluate ACT in sim or real. -For real, you would also need to install [ALOHA](https://github.com/tonyzhaozh/aloha). - -### Updates: -You can find all scripted/human demo for simulated environments [here](https://drive.google.com/drive/folders/1gPR03v05S1xiInoVJn7G7VJ9pDCnxq9O?usp=share_link). - - -### Repo Structure -- ``imitate_episodes.py`` Train and Evaluate ACT -- ``policy.py`` An adaptor for ACT policy -- ``detr`` Model definitions of ACT, modified from DETR -- ``sim_env.py`` Mujoco + DM_Control environments with joint space control -- ``ee_sim_env.py`` Mujoco + DM_Control environments with EE space control -- ``scripted_policy.py`` Scripted policies for sim environments -- ``constants.py`` Constants shared across files -- ``utils.py`` Utils such as data loading and helper functions -- ``visualize_episodes.py`` Save videos from a .hdf5 dataset - - -### Installation - - conda create -n aloha python=3.8 - conda activate aloha - pip install torchvision - pip install torch - pip install pyquaternion - pip install pyyaml - pip install rospkg - pip install pexpect - pip install mujoco - pip install dm_control - pip install opencv-python - pip install matplotlib - pip install einops - pip install packaging - pip install h5py - pip install ipython - cd act/detr && pip install -e . - -### Example Usages - -To set up a new terminal, run: - - conda activate aloha - cd - -### Simulated experiments - -We use ``sim_transfer_cube_scripted`` task in the examples below. Another option is ``sim_insertion_scripted``. -To generated 50 episodes of scripted data, run: - - python3 record_sim_episodes.py \ - --task_name sim_transfer_cube_scripted \ - --dataset_dir \ - --num_episodes 50 - -To can add the flag ``--onscreen_render`` to see real-time rendering. -To visualize the episode after it is collected, run - - python3 visualize_episodes.py --dataset_dir --episode_idx 0 - -To train ACT: - - # Transfer Cube task - python3 imitate_episodes.py \ - --task_name sim_transfer_cube_scripted \ - --ckpt_dir \ - --policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \ - --num_epochs 2000 --lr 1e-5 \ - --seed 0 - - -To evaluate the policy, run the same command but add ``--eval``. This loads the best validation checkpoint. -The success rate should be around 90% for transfer cube, and around 50% for insertion. -To enable temporal ensembling, add flag ``--temporal_agg``. -Videos will be saved to ```` for each rollout. -You can also add ``--onscreen_render`` to see real-time rendering during evaluation. - -For real-world data where things can be harder to model, train for at least 5000 epochs or 3-4 times the length after the loss has plateaued. -Please refer to [tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) for more info. - diff --git a/act/assets/bimanual_viperx_ee_insertion.xml b/act/assets/bimanual_viperx_ee_insertion.xml deleted file mode 100644 index 700aaac5..00000000 --- a/act/assets/bimanual_viperx_ee_insertion.xml +++ /dev/null @@ -1,59 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file 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- - \ No newline at end of file diff --git a/act/assets/vx300s_right.xml b/act/assets/vx300s_right.xml deleted file mode 100644 index 2c6f007c..00000000 --- a/act/assets/vx300s_right.xml +++ /dev/null @@ -1,59 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/act/conda_env.yaml b/act/conda_env.yaml deleted file mode 100644 index 0f44d6b0..00000000 --- a/act/conda_env.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: aloha -channels: - - pytorch - - nvidia - - conda-forge -dependencies: - - python=3.9 - - pip=23.0.1 - - pytorch=2.0.0 - - torchvision=0.15.0 - - pytorch-cuda=11.8 - - pyquaternion=0.9.9 - - pyyaml=6.0 - - rospkg=1.5.0 - - pexpect=4.8.0 - - mujoco=2.3.3 - - dm_control=1.0.9 - - py-opencv=4.7.0 - - matplotlib=3.7.1 - - einops=0.6.0 - - packaging=23.0 - - h5py=3.8.0 - - ipython=8.12.0 diff --git a/act/constants.py b/act/constants.py deleted file mode 100644 index f445350a..00000000 --- a/act/constants.py +++ /dev/null @@ -1,76 +0,0 @@ -import pathlib - -### Task parameters -DATA_DIR = '' -SIM_TASK_CONFIGS = { - 'sim_transfer_cube_scripted':{ - 'dataset_dir': DATA_DIR + '/sim_transfer_cube_scripted', - 'num_episodes': 50, - 'episode_len': 400, - 'camera_names': ['top'] - }, - - 'sim_transfer_cube_human':{ - 'dataset_dir': DATA_DIR + '/sim_transfer_cube_human', - 'num_episodes': 50, - 'episode_len': 400, - 'camera_names': ['top'] - }, - - 'sim_insertion_scripted': { - 'dataset_dir': DATA_DIR + '/sim_insertion_scripted', - 'num_episodes': 50, - 'episode_len': 400, - 'camera_names': ['top'] - }, - - 'sim_insertion_human': { - 'dataset_dir': DATA_DIR + '/sim_insertion_human', - 'num_episodes': 50, - 'episode_len': 500, - 'camera_names': ['top'] - }, -} - -### Simulation envs fixed constants -DT = 0.02 -JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] -START_ARM_POSE = [0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239, 0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239] - -XML_DIR = str(pathlib.Path(__file__).parent.resolve()) + '/assets/' # note: absolute path - -# Left finger position limits (qpos[7]), right_finger = -1 * left_finger -MASTER_GRIPPER_POSITION_OPEN = 0.02417 -MASTER_GRIPPER_POSITION_CLOSE = 0.01244 -PUPPET_GRIPPER_POSITION_OPEN = 0.05800 -PUPPET_GRIPPER_POSITION_CLOSE = 0.01844 - -# Gripper joint limits (qpos[6]) -MASTER_GRIPPER_JOINT_OPEN = 0.3083 -MASTER_GRIPPER_JOINT_CLOSE = -0.6842 -PUPPET_GRIPPER_JOINT_OPEN = 1.4910 -PUPPET_GRIPPER_JOINT_CLOSE = -0.6213 - -############################ Helper functions ############################ - -MASTER_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_POSITION_CLOSE) / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) -PUPPET_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_POSITION_CLOSE) / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) -MASTER_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) + MASTER_GRIPPER_POSITION_CLOSE -PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + PUPPET_GRIPPER_POSITION_CLOSE -MASTER2PUPPET_POSITION_FN = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(MASTER_GRIPPER_POSITION_NORMALIZE_FN(x)) - -MASTER_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) -PUPPET_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) -MASTER_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE -PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE -MASTER2PUPPET_JOINT_FN = lambda x: PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN(MASTER_GRIPPER_JOINT_NORMALIZE_FN(x)) - -MASTER_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) -PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) - -MASTER_POS2JOINT = lambda x: MASTER_GRIPPER_POSITION_NORMALIZE_FN(x) * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE -MASTER_JOINT2POS = lambda x: MASTER_GRIPPER_POSITION_UNNORMALIZE_FN((x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)) -PUPPET_POS2JOINT = lambda x: PUPPET_GRIPPER_POSITION_NORMALIZE_FN(x) * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE -PUPPET_JOINT2POS = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN((x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)) - -MASTER_GRIPPER_JOINT_MID = (MASTER_GRIPPER_JOINT_OPEN + MASTER_GRIPPER_JOINT_CLOSE)/2 diff --git a/act/detr/LICENSE b/act/detr/LICENSE deleted file mode 100644 index b1395e94..00000000 --- a/act/detr/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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All Rights Reserved -import argparse -from pathlib import Path - -import numpy as np -import torch -from .models import build_ACT_model, build_CNNMLP_model - -import IPython -e = IPython.embed - -def get_args_parser(): - parser = argparse.ArgumentParser('Set transformer detector', add_help=False) - parser.add_argument('--lr', default=1e-4, type=float) # will be overridden - parser.add_argument('--lr_backbone', default=1e-5, type=float) # will be overridden - parser.add_argument('--batch_size', default=2, type=int) # not used - parser.add_argument('--weight_decay', default=1e-4, type=float) - parser.add_argument('--epochs', default=300, type=int) # not used - parser.add_argument('--lr_drop', default=200, type=int) # not used - parser.add_argument('--clip_max_norm', default=0.1, type=float, # not used - help='gradient clipping max norm') - - # Model parameters - # * Backbone - parser.add_argument('--backbone', default='resnet18', type=str, # will be overridden - help="Name of the convolutional backbone to use") - parser.add_argument('--dilation', action='store_true', - help="If true, we replace stride with dilation in the last convolutional block (DC5)") - parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'), - help="Type of positional embedding to use on top of the image features") - parser.add_argument('--camera_names', default=[], type=list, # will be overridden - help="A list of camera names") - - # * Transformer - parser.add_argument('--enc_layers', default=4, type=int, # will be overridden - help="Number of encoding layers in the transformer") - parser.add_argument('--dec_layers', default=6, type=int, # will be overridden - help="Number of decoding layers in the transformer") - parser.add_argument('--dim_feedforward', default=2048, type=int, # will be overridden - help="Intermediate size of the feedforward layers in the transformer blocks") - parser.add_argument('--hidden_dim', default=256, type=int, # will be overridden - help="Size of the embeddings (dimension of the transformer)") - parser.add_argument('--dropout', default=0.1, type=float, - help="Dropout applied in the transformer") - parser.add_argument('--nheads', default=8, type=int, # will be overridden - help="Number of attention heads inside the transformer's attentions") - parser.add_argument('--num_queries', default=400, type=int, # will be overridden - help="Number of query slots") - parser.add_argument('--pre_norm', action='store_true') - - # * Segmentation - parser.add_argument('--masks', action='store_true', - help="Train segmentation head if the flag is provided") - - parser.add_argument('--a_dim', default=-1, type=float, - help="Action dim") - parser.add_argument('--latent_dim', default=-1, type=float, - help="Latent dim") - parser.add_argument('--state_dim', default=-1, type=float, - help="State dim") - - return parser - - -def build_ACT_model_and_optimizer(args_override): - parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) - args, _ = parser.parse_known_args() - - for k, v in args_override.items(): - setattr(args, k, v) - - model = build_ACT_model(args) - model.cuda() - - param_dicts = [ - {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, - { - "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], - "lr": args.lr_backbone, - }, - ] - optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, - weight_decay=args.weight_decay) - - return model, optimizer - - -def build_CNNMLP_model_and_optimizer(args_override): - parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) - args = parser.parse_args() - - for k, v in args_override.items(): - setattr(args, k, v) - - model = build_CNNMLP_model(args) - model.cuda() - - param_dicts = [ - {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, - { - "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], - "lr": args.lr_backbone, - }, - ] - optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, - weight_decay=args.weight_decay) - - return model, optimizer - diff --git a/act/detr/models/__init__.py b/act/detr/models/__init__.py deleted file mode 100644 index cc78db10..00000000 --- a/act/detr/models/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -from .detr_vae import build as build_vae -from .detr_vae import build_cnnmlp as build_cnnmlp - -def build_ACT_model(args): - return build_vae(args) - -def build_CNNMLP_model(args): - return build_cnnmlp(args) \ No newline at end of file diff --git a/act/detr/models/backbone.py b/act/detr/models/backbone.py deleted file mode 100644 index f28637ea..00000000 --- a/act/detr/models/backbone.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Backbone modules. -""" -from collections import OrderedDict - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter -from typing import Dict, List - -from util.misc import NestedTensor, is_main_process - -from .position_encoding import build_position_encoding - -import IPython -e = IPython.embed - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs): - num_batches_tracked_key = prefix + 'num_batches_tracked' - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - - def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): - super().__init__() - # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this? - # if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: - # parameter.requires_grad_(False) - if return_interm_layers: - return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - else: - return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor): - xs = self.body(tensor) - return xs - # out: Dict[str, NestedTensor] = {} - # for name, x in xs.items(): - # m = tensor_list.mask - # assert m is not None - # mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - # out[name] = NestedTensor(x, mask) - # return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - def __init__(self, name: str, - train_backbone: bool, - return_interm_layers: bool, - dilation: bool): - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) # pretrained # TODO do we want frozen batch_norm?? - num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - super().__init__(backbone, train_backbone, num_channels, return_interm_layers) - - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.dtype)) - - return out, pos - - -def build_backbone(args): - position_embedding = build_position_encoding(args) - train_backbone = args.lr_backbone > 0 - return_interm_layers = args.masks - backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation) - model = Joiner(backbone, position_embedding) - model.num_channels = backbone.num_channels - return model diff --git a/act/detr/models/detr_vae.py b/act/detr/models/detr_vae.py deleted file mode 100644 index dbde581e..00000000 --- a/act/detr/models/detr_vae.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR model and criterion classes. -""" -import torch -from torch import nn -from torch.autograd import Variable -from .backbone import build_backbone -from .transformer import build_transformer, TransformerEncoder, TransformerEncoderLayer - -import numpy as np - -import IPython -e = IPython.embed - - -def reparametrize(mu, logvar): - std = logvar.div(2).exp() - eps = Variable(std.data.new(std.size()).normal_()) - return mu + std * eps - - -def get_sinusoid_encoding_table(n_position, d_hid): - def get_position_angle_vec(position): - return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] - - sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) - sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i - sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 - - return torch.FloatTensor(sinusoid_table).unsqueeze(0) - - -class DETRVAE(nn.Module): - """ This is the DETR module that performs object detection """ - def __init__(self, backbones, transformer, encoder, latent_dim, a_dim, state_dim, num_queries, camera_names): - """ Initializes the model. - Parameters: - backbones: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - state_dim: robot state dimension of the environment - num_queries: number of object queries, ie detection slot. This is the maximal number of objects - DETR can detect in a single image. For COCO, we recommend 100 queries. - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - """ - super().__init__() - self.action_dim = a_dim - self.latent_dim = latent_dim - self.state_dim = state_dim - - self.num_queries = num_queries - self.camera_names = camera_names - self.transformer = transformer - self.encoder = encoder - hidden_dim = transformer.d_model - self.action_head = nn.Linear(hidden_dim, self.action_dim) - self.is_pad_head = nn.Linear(hidden_dim, 1) - self.query_embed = nn.Embedding(num_queries, hidden_dim) - if backbones is not None: - self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1) - self.backbones = nn.ModuleList(backbones) - self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) - else: - # input_dim = 14 + 7 # robot_state + env_state - self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) - self.input_proj_env_state = nn.Linear(10, hidden_dim) # TODO not used in robomimic - self.pos = torch.nn.Embedding(2, hidden_dim) - self.backbones = None - - # encoder extra parameters - self.latent_dim = self.latent_dim # final size of latent z # TODO tune - self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding - self.encoder_action_proj = nn.Linear(self.action_dim, hidden_dim) # project action to embedding - self.encoder_joint_proj = nn.Linear(state_dim, hidden_dim) # project qpos to embedding - self.latent_proj = nn.Linear(hidden_dim, self.latent_dim*2) # project hidden state to latent std, var - self.register_buffer('pos_table', get_sinusoid_encoding_table(1+1+num_queries, hidden_dim)) # [CLS], qpos, a_seq - - # decoder extra parameters - self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim) # project latent sample to embedding - self.additional_pos_embed = nn.Embedding(2, hidden_dim) # learned position embedding for proprio and latent - - def forward(self, qpos, image, env_state, actions=None, is_pad=None): - """ - qpos: batch, qpos_dim - image: batch, num_cam, channel, height, width - env_state: None - actions: batch, seq, action_dim - """ - is_training = actions is not None # train or val - bs, _ = qpos.shape - ### Obtain latent z from action sequence - if is_training: - # project action sequence to embedding dim, and concat with a CLS token - action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim) - qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim) - qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim) - cls_embed = self.cls_embed.weight # (1, hidden_dim) - cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim) - encoder_input = torch.cat([cls_embed, qpos_embed, action_embed], axis=1) # (bs, seq+2, hidden_dim) - encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim) - # do not mask cls token - cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding - is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1) - # obtain position embedding - pos_embed = self.pos_table.clone().detach() - pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim) - # query model - encoder_output = self.encoder(encoder_input, pos=pos_embed, src_key_padding_mask=is_pad) - encoder_output = encoder_output[0] # take cls output only - latent_info = self.latent_proj(encoder_output) - mu = latent_info[:, :self.latent_dim] - logvar = latent_info[:, self.latent_dim:] - latent_sample = reparametrize(mu, logvar) - latent_input = self.latent_out_proj(latent_sample) - else: - mu = logvar = None - latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device) - latent_input = self.latent_out_proj(latent_sample) - - if self.backbones is not None: - # Image observation features and position embeddings - all_cam_features = [] - all_cam_pos = [] - for cam_id, cam_name in enumerate(self.camera_names): - features, pos = self.backbones[cam_id](image[:, cam_id]) - features = features[0] # take the last layer feature - pos = pos[0] - all_cam_features.append(self.input_proj(features)) - all_cam_pos.append(pos) - # proprioception features - proprio_input = self.input_proj_robot_state(qpos) - # fold camera dimension into width dimension - src = torch.cat(all_cam_features, axis=3) - pos = torch.cat(all_cam_pos, axis=3) - hs = self.transformer(src, None, self.query_embed.weight, pos, latent_input, proprio_input, self.additional_pos_embed.weight)[0] - else: - qpos = self.input_proj_robot_state(qpos).unsqueeze(dim=1) - env_state = self.input_proj_env_state(env_state).unsqueeze(dim=1) - transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2 - hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0] - a_hat = self.action_head(hs) - is_pad_hat = self.is_pad_head(hs) - return a_hat, is_pad_hat, [mu, logvar] - - - -class CNNMLP(nn.Module): - def __init__(self, backbones, state_dim, camera_names): - """ Initializes the model. - Parameters: - backbones: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - state_dim: robot state dimension of the environment - num_queries: number of object queries, ie detection slot. This is the maximal number of objects - DETR can detect in a single image. For COCO, we recommend 100 queries. - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - """ - super().__init__() - self.camera_names = camera_names - self.action_head = nn.Linear(1000, state_dim) # TODO add more - if backbones is not None: - self.backbones = nn.ModuleList(backbones) - backbone_down_projs = [] - for backbone in backbones: - down_proj = nn.Sequential( - nn.Conv2d(backbone.num_channels, 128, kernel_size=5), - nn.Conv2d(128, 64, kernel_size=5), - nn.Conv2d(64, 32, kernel_size=5) - ) - backbone_down_projs.append(down_proj) - self.backbone_down_projs = nn.ModuleList(backbone_down_projs) - - mlp_in_dim = 768 * len(backbones) + 14 - self.mlp = mlp(input_dim=mlp_in_dim, hidden_dim=1024, output_dim=14, hidden_depth=2) - else: - raise NotImplementedError - - def forward(self, qpos, image, env_state, actions=None): - """ - qpos: batch, qpos_dim - image: batch, num_cam, channel, height, width - env_state: None - actions: batch, seq, action_dim - """ - is_training = actions is not None # train or val - bs, _ = qpos.shape - # Image observation features and position embeddings - all_cam_features = [] - for cam_id, cam_name in enumerate(self.camera_names): - features, pos = self.backbones[cam_id](image[:, cam_id]) - features = features[0] # take the last layer feature - pos = pos[0] # not used - all_cam_features.append(self.backbone_down_projs[cam_id](features)) - # flatten everything - flattened_features = [] - for cam_feature in all_cam_features: - flattened_features.append(cam_feature.reshape([bs, -1])) - flattened_features = torch.cat(flattened_features, axis=1) # 768 each - features = torch.cat([flattened_features, qpos], axis=1) # qpos: 14 - a_hat = self.mlp(features) - return a_hat - - -def mlp(input_dim, hidden_dim, output_dim, hidden_depth): - if hidden_depth == 0: - mods = [nn.Linear(input_dim, output_dim)] - else: - mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)] - for i in range(hidden_depth - 1): - mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)] - mods.append(nn.Linear(hidden_dim, output_dim)) - trunk = nn.Sequential(*mods) - return trunk - - -def build_encoder(args): - d_model = args.hidden_dim # 256 - dropout = args.dropout # 0.1 - nhead = args.nheads # 8 - dim_feedforward = args.dim_feedforward # 2048 - num_encoder_layers = args.enc_layers # 4 # TODO shared with VAE decoder - normalize_before = args.pre_norm # False - activation = "relu" - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - return encoder - - -def build(args): - - # From state - # backbone = None # from state for now, no need for conv nets - # From image - backbones = [] - for _ in args.camera_names: - backbone = build_backbone(args) - backbones.append(backbone) - - transformer = build_transformer(args) - - encoder = build_encoder(args) - - model = DETRVAE( - backbones, - transformer, - encoder, - latent_dim=args.latent_dim, - a_dim=args.a_dim, - state_dim=args.state_dim, - num_queries=args.num_queries, - camera_names=args.camera_names, - ) - - n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("number of parameters: %.2fM" % (n_parameters/1e6,)) - - return model - -def build_cnnmlp(args): - state_dim = 14 # TODO hardcode - - # From state - # backbone = None # from state for now, no need for conv nets - # From image - backbones = [] - for _ in args.camera_names: - backbone = build_backbone(args) - backbones.append(backbone) - - model = CNNMLP( - backbones, - state_dim=state_dim, - camera_names=args.camera_names, - ) - - n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("number of parameters: %.2fM" % (n_parameters/1e6,)) - - return model - diff --git a/act/detr/models/position_encoding.py b/act/detr/models/position_encoding.py deleted file mode 100644 index 209d9171..00000000 --- a/act/detr/models/position_encoding.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Various positional encodings for the transformer. -""" -import math -import torch -from torch import nn - -from util.misc import NestedTensor - -import IPython -e = IPython.embed - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor): - x = tensor - # mask = tensor_list.mask - # assert mask is not None - # not_mask = ~mask - - not_mask = torch.ones_like(x[0, [0]]) - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PositionEmbeddingLearned(nn.Module): - """ - Absolute pos embedding, learned. - """ - def __init__(self, num_pos_feats=256): - super().__init__() - self.row_embed = nn.Embedding(50, num_pos_feats) - self.col_embed = nn.Embedding(50, num_pos_feats) - self.reset_parameters() - - def reset_parameters(self): - nn.init.uniform_(self.row_embed.weight) - nn.init.uniform_(self.col_embed.weight) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - h, w = x.shape[-2:] - i = torch.arange(w, device=x.device) - j = torch.arange(h, device=x.device) - x_emb = self.col_embed(i) - y_emb = self.row_embed(j) - pos = torch.cat([ - x_emb.unsqueeze(0).repeat(h, 1, 1), - y_emb.unsqueeze(1).repeat(1, w, 1), - ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) - return pos - - -def build_position_encoding(args): - N_steps = args.hidden_dim // 2 - if args.position_embedding in ('v2', 'sine'): - # TODO find a better way of exposing other arguments - position_embedding = PositionEmbeddingSine(N_steps, normalize=True) - elif args.position_embedding in ('v3', 'learned'): - position_embedding = PositionEmbeddingLearned(N_steps) - else: - raise ValueError(f"not supported {args.position_embedding}") - - return position_embedding diff --git a/act/detr/models/transformer.py b/act/detr/models/transformer.py deleted file mode 100644 index f38afd0e..00000000 --- a/act/detr/models/transformer.py +++ /dev/null @@ -1,314 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR Transformer class. - -Copy-paste from torch.nn.Transformer with modifications: - * positional encodings are passed in MHattention - * extra LN at the end of encoder is removed - * decoder returns a stack of activations from all decoding layers -""" -import copy -from typing import Optional, List - -import torch -import torch.nn.functional as F -from torch import nn, Tensor - -import IPython -e = IPython.embed - -class Transformer(nn.Module): - - def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, - num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False, - return_intermediate_dec=False): - super().__init__() - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - decoder_norm = nn.LayerNorm(d_model) - self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, - return_intermediate=return_intermediate_dec) - - self._reset_parameters() - - self.d_model = d_model - self.nhead = nhead - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward(self, src, mask, query_embed, pos_embed, latent_input=None, proprio_input=None, additional_pos_embed=None): - # TODO flatten only when input has H and W - if len(src.shape) == 4: # has H and W - # flatten NxCxHxW to HWxNxC - bs, c, h, w = src.shape - src = src.flatten(2).permute(2, 0, 1) - pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - # mask = mask.flatten(1) - - additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1) # seq, bs, dim - pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0) - - addition_input = torch.stack([latent_input, proprio_input], axis=0) - src = torch.cat([addition_input, src], axis=0) - else: - assert len(src.shape) == 3 - # flatten NxHWxC to HWxNxC - bs, hw, c = src.shape - src = src.permute(1, 0, 2) - pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - - tgt = torch.zeros_like(query_embed) - memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) - hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, - pos=pos_embed, query_pos=query_embed) - hs = hs.transpose(1, 2) - return hs - -class TransformerEncoder(nn.Module): - - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward(self, src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - output = src - - for layer in self.layers: - output = layer(output, src_mask=mask, - src_key_padding_mask=src_key_padding_mask, pos=pos) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerDecoder(nn.Module): - - def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - self.return_intermediate = return_intermediate - - def forward(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - output = tgt - - intermediate = [] - - for layer in self.layers: - output = layer(output, memory, tgt_mask=tgt_mask, - memory_mask=memory_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - pos=pos, query_pos=query_pos) - if self.return_intermediate: - intermediate.append(self.norm(output)) - - if self.norm is not None: - output = self.norm(output) - if self.return_intermediate: - intermediate.pop() - intermediate.append(output) - - if self.return_intermediate: - return torch.stack(intermediate) - - return output.unsqueeze(0) - - -class TransformerEncoderLayer(nn.Module): - - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(src, pos) - src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) - - -class TransformerDecoderLayer(nn.Module): - - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.norm3 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - self.dropout3 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout3(tgt2) - tgt = self.norm3(tgt) - return tgt - - def forward_pre(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt2 = self.norm2(tgt) - tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] - tgt = tgt + self.dropout2(tgt2) - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout3(tgt2) - return tgt - - def forward(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(tgt, memory, tgt_mask, memory_mask, - tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) - return self.forward_post(tgt, memory, tgt_mask, memory_mask, - tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def build_transformer(args): - return Transformer( - d_model=args.hidden_dim, - dropout=args.dropout, - nhead=args.nheads, - dim_feedforward=args.dim_feedforward, - num_encoder_layers=args.enc_layers, - num_decoder_layers=args.dec_layers, - normalize_before=args.pre_norm, - return_intermediate_dec=True, - ) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/act/detr/setup.py b/act/detr/setup.py deleted file mode 100644 index 55d18c0d..00000000 --- a/act/detr/setup.py +++ /dev/null @@ -1,10 +0,0 @@ -from distutils.core import setup -from setuptools import find_packages - -setup( - name='detr', - version='0.0.0', - packages=find_packages(), - license='MIT License', - long_description=open('README.md').read(), -) \ No newline at end of file diff --git a/act/detr/util/__init__.py b/act/detr/util/__init__.py deleted file mode 100644 index 168f9979..00000000 --- a/act/detr/util/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/act/detr/util/box_ops.py b/act/detr/util/box_ops.py deleted file mode 100644 index 9c088e5b..00000000 --- a/act/detr/util/box_ops.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Utilities for bounding box manipulation and GIoU. -""" -import torch -from torchvision.ops.boxes import box_area - - -def box_cxcywh_to_xyxy(x): - x_c, y_c, w, h = x.unbind(-1) - b = [(x_c - 0.5 * w), (y_c - 0.5 * h), - (x_c + 0.5 * w), (y_c + 0.5 * h)] - return torch.stack(b, dim=-1) - - -def box_xyxy_to_cxcywh(x): - x0, y0, x1, y1 = x.unbind(-1) - b = [(x0 + x1) / 2, (y0 + y1) / 2, - (x1 - x0), (y1 - y0)] - return torch.stack(b, dim=-1) - - -# modified from torchvision to also return the union -def box_iou(boxes1, boxes2): - area1 = box_area(boxes1) - area2 = box_area(boxes2) - - lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] - rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] - - wh = (rb - lt).clamp(min=0) # [N,M,2] - inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] - - union = area1[:, None] + area2 - inter - - iou = inter / union - return iou, union - - -def generalized_box_iou(boxes1, boxes2): - """ - Generalized IoU from https://giou.stanford.edu/ - - The boxes should be in [x0, y0, x1, y1] format - - Returns a [N, M] pairwise matrix, where N = len(boxes1) - and M = len(boxes2) - """ - # degenerate boxes gives inf / nan results - # so do an early check - assert (boxes1[:, 2:] >= boxes1[:, :2]).all() - assert (boxes2[:, 2:] >= boxes2[:, :2]).all() - iou, union = box_iou(boxes1, boxes2) - - lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) - rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) - - wh = (rb - lt).clamp(min=0) # [N,M,2] - area = wh[:, :, 0] * wh[:, :, 1] - - return iou - (area - union) / area - - -def masks_to_boxes(masks): - """Compute the bounding boxes around the provided masks - - The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. - - Returns a [N, 4] tensors, with the boxes in xyxy format - """ - if masks.numel() == 0: - return torch.zeros((0, 4), device=masks.device) - - h, w = masks.shape[-2:] - - y = torch.arange(0, h, dtype=torch.float) - x = torch.arange(0, w, dtype=torch.float) - y, x = torch.meshgrid(y, x) - - x_mask = (masks * x.unsqueeze(0)) - x_max = x_mask.flatten(1).max(-1)[0] - x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] - - y_mask = (masks * y.unsqueeze(0)) - y_max = y_mask.flatten(1).max(-1)[0] - y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] - - return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/act/detr/util/misc.py b/act/detr/util/misc.py deleted file mode 100644 index dfa9fb5b..00000000 --- a/act/detr/util/misc.py +++ /dev/null @@ -1,468 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Misc functions, including distributed helpers. - -Mostly copy-paste from torchvision references. -""" -import os -import subprocess -import time -from collections import defaultdict, deque -import datetime -import pickle -from packaging import version -from typing import Optional, List - -import torch -import torch.distributed as dist -from torch import Tensor - -# needed due to empty tensor bug in pytorch and torchvision 0.5 -import torchvision -if version.parse(torchvision.__version__) < version.parse('0.7'): - from torchvision.ops import _new_empty_tensor - from torchvision.ops.misc import _output_size - - -class SmoothedValue(object): - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20, fmt=None): - if fmt is None: - fmt = "{median:.4f} ({global_avg:.4f})" - self.deque = deque(maxlen=window_size) - self.total = 0.0 - self.count = 0 - self.fmt = fmt - - def update(self, value, n=1): - self.deque.append(value) - self.count += n - self.total += value * n - - def synchronize_between_processes(self): - """ - Warning: does not synchronize the deque! - """ - if not is_dist_avail_and_initialized(): - return - t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') - dist.barrier() - dist.all_reduce(t) - t = t.tolist() - self.count = int(t[0]) - self.total = t[1] - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() - - @property - def avg(self): - d = torch.tensor(list(self.deque), dtype=torch.float32) - return d.mean().item() - - @property - def global_avg(self): - return self.total / self.count - - @property - def max(self): - return max(self.deque) - - @property - def value(self): - return self.deque[-1] - - def __str__(self): - return self.fmt.format( - median=self.median, - avg=self.avg, - global_avg=self.global_avg, - max=self.max, - value=self.value) - - -def all_gather(data): - """ - Run all_gather on arbitrary picklable data (not necessarily tensors) - Args: - data: any picklable object - Returns: - list[data]: list of data gathered from each rank - """ - world_size = get_world_size() - if world_size == 1: - return [data] - - # serialized to a Tensor - buffer = pickle.dumps(data) - storage = torch.ByteStorage.from_buffer(buffer) - tensor = torch.ByteTensor(storage).to("cuda") - - # obtain Tensor size of each rank - local_size = torch.tensor([tensor.numel()], device="cuda") - size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] - dist.all_gather(size_list, local_size) - size_list = [int(size.item()) for size in size_list] - max_size = max(size_list) - - # receiving Tensor from all ranks - # we pad the tensor because torch all_gather does not support - # gathering tensors of different shapes - tensor_list = [] - for _ in size_list: - tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) - if local_size != max_size: - padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") - tensor = torch.cat((tensor, padding), dim=0) - dist.all_gather(tensor_list, tensor) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - buffer = tensor.cpu().numpy().tobytes()[:size] - data_list.append(pickle.loads(buffer)) - - return data_list - - -def reduce_dict(input_dict, average=True): - """ - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dict with the same fields as - input_dict, after reduction. - """ - world_size = get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.all_reduce(values) - if average: - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict - - -class MetricLogger(object): - def __init__(self, delimiter="\t"): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - if attr in self.__dict__: - return self.__dict__[attr] - raise AttributeError("'{}' object has no attribute '{}'".format( - type(self).__name__, attr)) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append( - "{}: {}".format(name, str(meter)) - ) - return self.delimiter.join(loss_str) - - def synchronize_between_processes(self): - for meter in self.meters.values(): - meter.synchronize_between_processes() - - def add_meter(self, name, meter): - self.meters[name] = meter - - def log_every(self, iterable, print_freq, header=None): - i = 0 - if not header: - header = '' - start_time = time.time() - end = time.time() - iter_time = SmoothedValue(fmt='{avg:.4f}') - data_time = SmoothedValue(fmt='{avg:.4f}') - space_fmt = ':' + str(len(str(len(iterable)))) + 'd' - if torch.cuda.is_available(): - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}', - 'max mem: {memory:.0f}' - ]) - else: - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}' - ]) - MB = 1024.0 * 1024.0 - for obj in iterable: - data_time.update(time.time() - end) - yield obj - iter_time.update(time.time() - end) - if i % print_freq == 0 or i == len(iterable) - 1: - eta_seconds = iter_time.global_avg * (len(iterable) - i) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - if torch.cuda.is_available(): - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time), - memory=torch.cuda.max_memory_allocated() / MB)) - else: - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time))) - i += 1 - end = time.time() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print('{} Total time: {} ({:.4f} s / it)'.format( - header, total_time_str, total_time / len(iterable))) - - -def get_sha(): - cwd = os.path.dirname(os.path.abspath(__file__)) - - def _run(command): - return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() - sha = 'N/A' - diff = "clean" - branch = 'N/A' - try: - sha = _run(['git', 'rev-parse', 'HEAD']) - subprocess.check_output(['git', 'diff'], cwd=cwd) - diff = _run(['git', 'diff-index', 'HEAD']) - diff = "has uncommited changes" if diff else "clean" - branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) - except Exception: - pass - message = f"sha: {sha}, status: {diff}, branch: {branch}" - return message - - -def collate_fn(batch): - batch = list(zip(*batch)) - batch[0] = nested_tensor_from_tensor_list(batch[0]) - return tuple(batch) - - -def _max_by_axis(the_list): - # type: (List[List[int]]) -> List[int] - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - return maxes - - -class NestedTensor(object): - def __init__(self, tensors, mask: Optional[Tensor]): - self.tensors = tensors - self.mask = mask - - def to(self, device): - # type: (Device) -> NestedTensor # noqa - cast_tensor = self.tensors.to(device) - mask = self.mask - if mask is not None: - assert mask is not None - cast_mask = mask.to(device) - else: - cast_mask = None - return NestedTensor(cast_tensor, cast_mask) - - def decompose(self): - return self.tensors, self.mask - - def __repr__(self): - return str(self.tensors) - - -def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): - # TODO make this more general - if tensor_list[0].ndim == 3: - if torchvision._is_tracing(): - # nested_tensor_from_tensor_list() does not export well to ONNX - # call _onnx_nested_tensor_from_tensor_list() instead - return _onnx_nested_tensor_from_tensor_list(tensor_list) - - # TODO make it support different-sized images - max_size = _max_by_axis([list(img.shape) for img in tensor_list]) - # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) - batch_shape = [len(tensor_list)] + max_size - b, c, h, w = batch_shape - dtype = tensor_list[0].dtype - device = tensor_list[0].device - tensor = torch.zeros(batch_shape, dtype=dtype, device=device) - mask = torch.ones((b, h, w), dtype=torch.bool, device=device) - for img, pad_img, m in zip(tensor_list, tensor, mask): - pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - m[: img.shape[1], :img.shape[2]] = False - else: - raise ValueError('not supported') - return NestedTensor(tensor, mask) - - -# _onnx_nested_tensor_from_tensor_list() is an implementation of -# nested_tensor_from_tensor_list() that is supported by ONNX tracing. -@torch.jit.unused -def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: - max_size = [] - for i in range(tensor_list[0].dim()): - max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) - max_size.append(max_size_i) - max_size = tuple(max_size) - - # work around for - # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - # m[: img.shape[1], :img.shape[2]] = False - # which is not yet supported in onnx - padded_imgs = [] - padded_masks = [] - for img in tensor_list: - padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] - padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) - padded_imgs.append(padded_img) - - m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) - padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) - padded_masks.append(padded_mask.to(torch.bool)) - - tensor = torch.stack(padded_imgs) - mask = torch.stack(padded_masks) - - return NestedTensor(tensor, mask=mask) - - -def setup_for_distributed(is_master): - """ - This function disables printing when not in master process - """ - import builtins as __builtin__ - builtin_print = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop('force', False) - if is_master or force: - builtin_print(*args, **kwargs) - - __builtin__.print = print - - -def is_dist_avail_and_initialized(): - if not dist.is_available(): - return False - if not dist.is_initialized(): - return False - return True - - -def get_world_size(): - if not is_dist_avail_and_initialized(): - return 1 - return dist.get_world_size() - - -def get_rank(): - if not is_dist_avail_and_initialized(): - return 0 - return dist.get_rank() - - -def is_main_process(): - return get_rank() == 0 - - -def save_on_master(*args, **kwargs): - if is_main_process(): - torch.save(*args, **kwargs) - - -def init_distributed_mode(args): - if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ['WORLD_SIZE']) - args.gpu = int(os.environ['LOCAL_RANK']) - elif 'SLURM_PROCID' in os.environ: - args.rank = int(os.environ['SLURM_PROCID']) - args.gpu = args.rank % torch.cuda.device_count() - else: - print('Not using distributed mode') - args.distributed = False - return - - args.distributed = True - - torch.cuda.set_device(args.gpu) - args.dist_backend = 'nccl' - print('| distributed init (rank {}): {}'.format( - args.rank, args.dist_url), flush=True) - torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - torch.distributed.barrier() - setup_for_distributed(args.rank == 0) - - -@torch.no_grad() -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - if target.numel() == 0: - return [torch.zeros([], device=output.device)] - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): - # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor - """ - Equivalent to nn.functional.interpolate, but with support for empty batch sizes. - This will eventually be supported natively by PyTorch, and this - class can go away. - """ - if version.parse(torchvision.__version__) < version.parse('0.7'): - if input.numel() > 0: - return torch.nn.functional.interpolate( - input, size, scale_factor, mode, align_corners - ) - - output_shape = _output_size(2, input, size, scale_factor) - output_shape = list(input.shape[:-2]) + list(output_shape) - return _new_empty_tensor(input, output_shape) - else: - return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/act/detr/util/plot_utils.py b/act/detr/util/plot_utils.py deleted file mode 100644 index 0f24bed0..00000000 --- a/act/detr/util/plot_utils.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Plotting utilities to visualize training logs. -""" -import torch -import pandas as pd -import numpy as np -import seaborn as sns -import matplotlib.pyplot as plt - -from pathlib import Path, PurePath - - -def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): - ''' - Function to plot specific fields from training log(s). Plots both training and test results. - - :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file - - fields = which results to plot from each log file - plots both training and test for each field. - - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots - - log_name = optional, name of log file if different than default 'log.txt'. - - :: Outputs - matplotlib plots of results in fields, color coded for each log file. - - solid lines are training results, dashed lines are test results. - - ''' - func_name = "plot_utils.py::plot_logs" - - # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, - # convert single Path to list to avoid 'not iterable' error - - if not isinstance(logs, list): - if isinstance(logs, PurePath): - logs = [logs] - print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") - else: - raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ - Expect list[Path] or single Path obj, received {type(logs)}") - - # Quality checks - verify valid dir(s), that every item in list is Path object, and that log_name exists in each dir - for i, dir in enumerate(logs): - if not isinstance(dir, PurePath): - raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") - if not dir.exists(): - raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") - # verify log_name exists - fn = Path(dir / log_name) - if not fn.exists(): - print(f"-> missing {log_name}. Have you gotten to Epoch 1 in training?") - print(f"--> full path of missing log file: {fn}") - return - - # load log file(s) and plot - dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] - - fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) - - for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): - for j, field in enumerate(fields): - if field == 'mAP': - coco_eval = pd.DataFrame( - np.stack(df.test_coco_eval_bbox.dropna().values)[:, 1] - ).ewm(com=ewm_col).mean() - axs[j].plot(coco_eval, c=color) - else: - df.interpolate().ewm(com=ewm_col).mean().plot( - y=[f'train_{field}', f'test_{field}'], - ax=axs[j], - color=[color] * 2, - style=['-', '--'] - ) - for ax, field in zip(axs, fields): - ax.legend([Path(p).name for p in logs]) - ax.set_title(field) - - -def plot_precision_recall(files, naming_scheme='iter'): - if naming_scheme == 'exp_id': - # name becomes exp_id - names = [f.parts[-3] for f in files] - elif naming_scheme == 'iter': - names = [f.stem for f in files] - else: - raise ValueError(f'not supported {naming_scheme}') - fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) - for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): - data = torch.load(f) - # precision is n_iou, n_points, n_cat, n_area, max_det - precision = data['precision'] - recall = data['params'].recThrs - scores = data['scores'] - # take precision for all classes, all areas and 100 detections - precision = precision[0, :, :, 0, -1].mean(1) - scores = scores[0, :, :, 0, -1].mean(1) - prec = precision.mean() - rec = data['recall'][0, :, 0, -1].mean() - print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + - f'score={scores.mean():0.3f}, ' + - f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' - ) - axs[0].plot(recall, precision, c=color) - axs[1].plot(recall, scores, c=color) - - axs[0].set_title('Precision / Recall') - axs[0].legend(names) - axs[1].set_title('Scores / Recall') - axs[1].legend(names) - return fig, axs diff --git a/act/ee_sim_env.py b/act/ee_sim_env.py deleted file mode 100644 index 01df2337..00000000 --- a/act/ee_sim_env.py +++ /dev/null @@ -1,267 +0,0 @@ -import numpy as np -import collections -import os - -from constants import DT, XML_DIR, START_ARM_POSE -from constants import PUPPET_GRIPPER_POSITION_CLOSE -from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN -from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN -from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN - -from utils import sample_box_pose, sample_insertion_pose -from dm_control import mujoco -from dm_control.rl import control -from dm_control.suite import base - -import IPython -e = IPython.embed - - -def make_ee_sim_env(task_name): - """ - Environment for simulated robot bi-manual manipulation, with end-effector control. - Action space: [left_arm_pose (7), # position and quaternion for end effector - left_gripper_positions (1), # normalized gripper position (0: close, 1: open) - right_arm_pose (7), # position and quaternion for end effector - right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) - - Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position - left_gripper_position (1), # normalized gripper position (0: close, 1: open) - right_arm_qpos (6), # absolute joint position - right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) - "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) - left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) - right_arm_qvel (6), # absolute joint velocity (rad) - right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) - "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' - """ - if 'sim_transfer_cube' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_transfer_cube.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = TransferCubeEETask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - elif 'sim_insertion' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_insertion.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = InsertionEETask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - else: - raise NotImplementedError - return env - -class BimanualViperXEETask(base.Task): - def __init__(self, random=None): - super().__init__(random=random) - - def before_step(self, action, physics): - a_len = len(action) // 2 - action_left = action[:a_len] - action_right = action[a_len:] - - # set mocap position and quat - # left - np.copyto(physics.data.mocap_pos[0], action_left[:3]) - np.copyto(physics.data.mocap_quat[0], action_left[3:7]) - # right - np.copyto(physics.data.mocap_pos[1], action_right[:3]) - np.copyto(physics.data.mocap_quat[1], action_right[3:7]) - - # set gripper - g_left_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_left[7]) - g_right_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_right[7]) - np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl])) - - def initialize_robots(self, physics): - # reset joint position - physics.named.data.qpos[:16] = START_ARM_POSE - - # reset mocap to align with end effector - # to obtain these numbers: - # (1) make an ee_sim env and reset to the same start_pose - # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link'] - # get env._physics.named.data.xquat['vx300s_left/gripper_link'] - # repeat the same for right side - np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084]) - np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0]) - # right - np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084])) - np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0]) - - # reset gripper control - close_gripper_control = np.array([ - PUPPET_GRIPPER_POSITION_CLOSE, - -PUPPET_GRIPPER_POSITION_CLOSE, - PUPPET_GRIPPER_POSITION_CLOSE, - -PUPPET_GRIPPER_POSITION_CLOSE, - ]) - np.copyto(physics.data.ctrl, close_gripper_control) - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - super().initialize_episode(physics) - - @staticmethod - def get_qpos(physics): - qpos_raw = physics.data.qpos.copy() - left_qpos_raw = qpos_raw[:8] - right_qpos_raw = qpos_raw[8:16] - left_arm_qpos = left_qpos_raw[:6] - right_arm_qpos = right_qpos_raw[:6] - left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] - right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] - return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) - - @staticmethod - def get_qvel(physics): - qvel_raw = physics.data.qvel.copy() - left_qvel_raw = qvel_raw[:8] - right_qvel_raw = qvel_raw[8:16] - left_arm_qvel = left_qvel_raw[:6] - right_arm_qvel = right_qvel_raw[:6] - left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] - right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] - return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) - - @staticmethod - def get_env_state(physics): - raise NotImplementedError - - def get_observation(self, physics): - # note: it is important to do .copy() - obs = collections.OrderedDict() - obs['qpos'] = self.get_qpos(physics) - obs['qvel'] = self.get_qvel(physics) - obs['env_state'] = self.get_env_state(physics) - obs['images'] = dict() - obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') - obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') - obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') - # used in scripted policy to obtain starting pose - obs['mocap_pose_left'] = np.concatenate([physics.data.mocap_pos[0], physics.data.mocap_quat[0]]).copy() - obs['mocap_pose_right'] = np.concatenate([physics.data.mocap_pos[1], physics.data.mocap_quat[1]]).copy() - - # used when replaying joint trajectory - obs['gripper_ctrl'] = physics.data.ctrl.copy() - return obs - - def get_reward(self, physics): - raise NotImplementedError - - -class TransferCubeEETask(BimanualViperXEETask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - self.initialize_robots(physics) - # randomize box position - cube_pose = sample_box_pose() - box_start_idx = physics.model.name2id('red_box_joint', 'joint') - np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose) - # print(f"randomized cube position to {cube_position}") - - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether left gripper is holding the box - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_table = ("red_box", "table") in all_contact_pairs - - reward = 0 - if touch_right_gripper: - reward = 1 - if touch_right_gripper and not touch_table: # lifted - reward = 2 - if touch_left_gripper: # attempted transfer - reward = 3 - if touch_left_gripper and not touch_table: # successful transfer - reward = 4 - return reward - - -class InsertionEETask(BimanualViperXEETask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - self.initialize_robots(physics) - # randomize peg and socket position - peg_pose, socket_pose = sample_insertion_pose() - id2index = lambda j_id: 16 + (j_id - 16) * 7 # first 16 is robot qpos, 7 is pose dim # hacky - - peg_start_id = physics.model.name2id('red_peg_joint', 'joint') - peg_start_idx = id2index(peg_start_id) - np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose) - # print(f"randomized cube position to {cube_position}") - - socket_start_id = physics.model.name2id('blue_socket_joint', 'joint') - socket_start_idx = id2index(socket_start_id) - np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose) - # print(f"randomized cube position to {cube_position}") - - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether peg touches the pin - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - - peg_touch_table = ("red_peg", "table") in all_contact_pairs - socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ - ("socket-2", "table") in all_contact_pairs or \ - ("socket-3", "table") in all_contact_pairs or \ - ("socket-4", "table") in all_contact_pairs - peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ - ("red_peg", "socket-2") in all_contact_pairs or \ - ("red_peg", "socket-3") in all_contact_pairs or \ - ("red_peg", "socket-4") in all_contact_pairs - pin_touched = ("red_peg", "pin") in all_contact_pairs - - reward = 0 - if touch_left_gripper and touch_right_gripper: # touch both - reward = 1 - if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both - reward = 2 - if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching - reward = 3 - if pin_touched: # successful insertion - reward = 4 - return reward diff --git a/act/imitate_episodes.py b/act/imitate_episodes.py deleted file mode 100644 index 34f9a372..00000000 --- a/act/imitate_episodes.py +++ /dev/null @@ -1,435 +0,0 @@ -import torch -import numpy as np -import os -import pickle -import argparse -import matplotlib.pyplot as plt -from copy import deepcopy -from tqdm import tqdm -from einops import rearrange - -from constants import DT -from constants import PUPPET_GRIPPER_JOINT_OPEN -from utils import load_data # data functions -from utils import sample_box_pose, sample_insertion_pose # robot functions -from utils import compute_dict_mean, set_seed, detach_dict # helper functions -from policy import ACTPolicy, CNNMLPPolicy -from visualize_episodes import save_videos - -from sim_env import BOX_POSE - -import IPython -e = IPython.embed - -def main(args): - set_seed(1) - # command line parameters - is_eval = args['eval'] - ckpt_dir = args['ckpt_dir'] - policy_class = args['policy_class'] - onscreen_render = args['onscreen_render'] - task_name = args['task_name'] - batch_size_train = args['batch_size'] - batch_size_val = args['batch_size'] - num_epochs = args['num_epochs'] - - # get task parameters - is_sim = task_name[:4] == 'sim_' - if is_sim: - from constants import SIM_TASK_CONFIGS - task_config = SIM_TASK_CONFIGS[task_name] - else: - from aloha_scripts.constants import TASK_CONFIGS - task_config = TASK_CONFIGS[task_name] - dataset_dir = task_config['dataset_dir'] - num_episodes = task_config['num_episodes'] - episode_len = task_config['episode_len'] - camera_names = task_config['camera_names'] - - # fixed parameters - state_dim = 14 - lr_backbone = 1e-5 - backbone = 'resnet18' - if policy_class == 'ACT': - enc_layers = 4 - dec_layers = 7 - nheads = 8 - policy_config = {'lr': args['lr'], - 'num_queries': args['chunk_size'], - 'kl_weight': args['kl_weight'], - 'hidden_dim': args['hidden_dim'], - 'dim_feedforward': args['dim_feedforward'], - 'lr_backbone': lr_backbone, - 'backbone': backbone, - 'enc_layers': enc_layers, - 'dec_layers': dec_layers, - 'nheads': nheads, - 'camera_names': camera_names, - } - elif policy_class == 'CNNMLP': - policy_config = {'lr': args['lr'], 'lr_backbone': lr_backbone, 'backbone' : backbone, 'num_queries': 1, - 'camera_names': camera_names,} - else: - raise NotImplementedError - - config = { - 'num_epochs': num_epochs, - 'ckpt_dir': ckpt_dir, - 'episode_len': episode_len, - 'state_dim': state_dim, - 'lr': args['lr'], - 'policy_class': policy_class, - 'onscreen_render': onscreen_render, - 'policy_config': policy_config, - 'task_name': task_name, - 'seed': args['seed'], - 'temporal_agg': args['temporal_agg'], - 'camera_names': camera_names, - 'real_robot': not is_sim - } - - if is_eval: - ckpt_names = [f'policy_best.ckpt'] - results = [] - for ckpt_name in ckpt_names: - success_rate, avg_return = eval_bc(config, ckpt_name, save_episode=True) - results.append([ckpt_name, success_rate, avg_return]) - - for ckpt_name, success_rate, avg_return in results: - print(f'{ckpt_name}: {success_rate=} {avg_return=}') - print() - exit() - - train_dataloader, val_dataloader, stats, _ = load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val) - - # save dataset stats - if not os.path.isdir(ckpt_dir): - os.makedirs(ckpt_dir) - stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') - with open(stats_path, 'wb') as f: - pickle.dump(stats, f) - - best_ckpt_info = train_bc(train_dataloader, val_dataloader, config) - best_epoch, min_val_loss, best_state_dict = best_ckpt_info - - # save best checkpoint - ckpt_path = os.path.join(ckpt_dir, f'policy_best.ckpt') - torch.save(best_state_dict, ckpt_path) - print(f'Best ckpt, val loss {min_val_loss:.6f} @ epoch{best_epoch}') - - -def make_policy(policy_class, policy_config): - if policy_class == 'ACT': - policy = ACTPolicy(policy_config) - elif policy_class == 'CNNMLP': - policy = CNNMLPPolicy(policy_config) - else: - raise NotImplementedError - return policy - - -def make_optimizer(policy_class, policy): - if policy_class == 'ACT': - optimizer = policy.configure_optimizers() - elif policy_class == 'CNNMLP': - optimizer = policy.configure_optimizers() - else: - raise NotImplementedError - return optimizer - - -def get_image(ts, camera_names): - curr_images = [] - for cam_name in camera_names: - curr_image = rearrange(ts.observation['images'][cam_name], 'h w c -> c h w') - curr_images.append(curr_image) - curr_image = np.stack(curr_images, axis=0) - curr_image = torch.from_numpy(curr_image / 255.0).float().cuda().unsqueeze(0) - return curr_image - - -def eval_bc(config, ckpt_name, save_episode=True): - set_seed(1000) - ckpt_dir = config['ckpt_dir'] - state_dim = config['state_dim'] - real_robot = config['real_robot'] - policy_class = config['policy_class'] - onscreen_render = config['onscreen_render'] - policy_config = config['policy_config'] - camera_names = config['camera_names'] - max_timesteps = config['episode_len'] - task_name = config['task_name'] - temporal_agg = config['temporal_agg'] - onscreen_cam = 'angle' - - # load policy and stats - ckpt_path = os.path.join(ckpt_dir, ckpt_name) - policy = make_policy(policy_class, policy_config) - loading_status = policy.load_state_dict(torch.load(ckpt_path)) - print(loading_status) - policy.cuda() - policy.eval() - print(f'Loaded: {ckpt_path}') - stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') - with open(stats_path, 'rb') as f: - stats = pickle.load(f) - - pre_process = lambda s_qpos: (s_qpos - stats['qpos_mean']) / stats['qpos_std'] - post_process = lambda a: a * stats['action_std'] + stats['action_mean'] - - # load environment - if real_robot: - from aloha_scripts.robot_utils import move_grippers # requires aloha - from aloha_scripts.real_env import make_real_env # requires aloha - env = make_real_env(init_node=True) - env_max_reward = 0 - else: - from sim_env import make_sim_env - env = make_sim_env(task_name) - env_max_reward = env.task.max_reward - - query_frequency = policy_config['num_queries'] - if temporal_agg: - query_frequency = 1 - num_queries = policy_config['num_queries'] - - max_timesteps = int(max_timesteps * 1) # may increase for real-world tasks - - num_rollouts = 50 - episode_returns = [] - highest_rewards = [] - for rollout_id in range(num_rollouts): - rollout_id += 0 - ### set task - if 'sim_transfer_cube' in task_name: - BOX_POSE[0] = sample_box_pose() # used in sim reset - elif 'sim_insertion' in task_name: - BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset - - ts = env.reset() - - ### onscreen render - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(env._physics.render(height=480, width=640, camera_id=onscreen_cam)) - plt.ion() - - ### evaluation loop - if temporal_agg: - all_time_actions = torch.zeros([max_timesteps, max_timesteps+num_queries, state_dim]).cuda() - - qpos_history = torch.zeros((1, max_timesteps, state_dim)).cuda() - image_list = [] # for visualization - qpos_list = [] - target_qpos_list = [] - rewards = [] - with torch.inference_mode(): - for t in range(max_timesteps): - ### update onscreen render and wait for DT - if onscreen_render: - image = env._physics.render(height=480, width=640, camera_id=onscreen_cam) - plt_img.set_data(image) - plt.pause(DT) - - ### process previous timestep to get qpos and image_list - obs = ts.observation - if 'images' in obs: - image_list.append(obs['images']) - else: - image_list.append({'main': obs['image']}) - qpos_numpy = np.array(obs['qpos']) - qpos = pre_process(qpos_numpy) - qpos = torch.from_numpy(qpos).float().cuda().unsqueeze(0) - qpos_history[:, t] = qpos - curr_image = get_image(ts, camera_names) - - ### query policy - if config['policy_class'] == "ACT": - if t % query_frequency == 0: - all_actions = policy(qpos, curr_image) - if temporal_agg: - all_time_actions[[t], t:t+num_queries] = all_actions - actions_for_curr_step = all_time_actions[:, t] - actions_populated = torch.all(actions_for_curr_step != 0, axis=1) - actions_for_curr_step = actions_for_curr_step[actions_populated] - k = 0.01 - exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step))) - exp_weights = exp_weights / exp_weights.sum() - exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1) - raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True) - else: - raw_action = all_actions[:, t % query_frequency] - elif config['policy_class'] == "CNNMLP": - raw_action = policy(qpos, curr_image) - else: - raise NotImplementedError - - ### post-process actions - raw_action = raw_action.squeeze(0).cpu().numpy() - action = post_process(raw_action) - target_qpos = action - - ### step the environment - ts = env.step(target_qpos) - - ### for visualization - qpos_list.append(qpos_numpy) - target_qpos_list.append(target_qpos) - rewards.append(ts.reward) - - plt.close() - if real_robot: - move_grippers([env.puppet_bot_left, env.puppet_bot_right], [PUPPET_GRIPPER_JOINT_OPEN] * 2, move_time=0.5) # open - pass - - rewards = np.array(rewards) - episode_return = np.sum(rewards[rewards!=None]) - episode_returns.append(episode_return) - episode_highest_reward = np.max(rewards) - highest_rewards.append(episode_highest_reward) - print(f'Rollout {rollout_id}\n{episode_return=}, {episode_highest_reward=}, {env_max_reward=}, Success: {episode_highest_reward==env_max_reward}') - - if save_episode: - save_videos(image_list, DT, video_path=os.path.join(ckpt_dir, f'video{rollout_id}.mp4')) - - success_rate = np.mean(np.array(highest_rewards) == env_max_reward) - avg_return = np.mean(episode_returns) - summary_str = f'\nSuccess rate: {success_rate}\nAverage return: {avg_return}\n\n' - for r in range(env_max_reward+1): - more_or_equal_r = (np.array(highest_rewards) >= r).sum() - more_or_equal_r_rate = more_or_equal_r / num_rollouts - summary_str += f'Reward >= {r}: {more_or_equal_r}/{num_rollouts} = {more_or_equal_r_rate*100}%\n' - - print(summary_str) - - # save success rate to txt - result_file_name = 'result_' + ckpt_name.split('.')[0] + '.txt' - with open(os.path.join(ckpt_dir, result_file_name), 'w') as f: - f.write(summary_str) - f.write(repr(episode_returns)) - f.write('\n\n') - f.write(repr(highest_rewards)) - - return success_rate, avg_return - - -def forward_pass(data, policy): - image_data, qpos_data, action_data, is_pad = data - image_data, qpos_data, action_data, is_pad = image_data.cuda(), qpos_data.cuda(), action_data.cuda(), is_pad.cuda() - return policy(qpos_data, image_data, action_data, is_pad) # TODO remove None - - -def train_bc(train_dataloader, val_dataloader, config): - num_epochs = config['num_epochs'] - ckpt_dir = config['ckpt_dir'] - seed = config['seed'] - policy_class = config['policy_class'] - policy_config = config['policy_config'] - - set_seed(seed) - - policy = make_policy(policy_class, policy_config) - policy.cuda() - optimizer = make_optimizer(policy_class, policy) - - train_history = [] - validation_history = [] - min_val_loss = np.inf - best_ckpt_info = None - for epoch in tqdm(range(num_epochs)): - print(f'\nEpoch {epoch}') - # validation - with torch.inference_mode(): - policy.eval() - epoch_dicts = [] - for batch_idx, data in enumerate(val_dataloader): - forward_dict = forward_pass(data, policy) - epoch_dicts.append(forward_dict) - epoch_summary = compute_dict_mean(epoch_dicts) - validation_history.append(epoch_summary) - - epoch_val_loss = epoch_summary['loss'] - if epoch_val_loss < min_val_loss: - min_val_loss = epoch_val_loss - best_ckpt_info = (epoch, min_val_loss, deepcopy(policy.state_dict())) - print(f'Val loss: {epoch_val_loss:.5f}') - summary_string = '' - for k, v in epoch_summary.items(): - summary_string += f'{k}: {v.item():.3f} ' - print(summary_string) - - # training - policy.train() - optimizer.zero_grad() - for batch_idx, data in enumerate(train_dataloader): - forward_dict = forward_pass(data, policy) - # backward - loss = forward_dict['loss'] - loss.backward() - optimizer.step() - optimizer.zero_grad() - train_history.append(detach_dict(forward_dict)) - epoch_summary = compute_dict_mean(train_history[(batch_idx+1)*epoch:(batch_idx+1)*(epoch+1)]) - epoch_train_loss = epoch_summary['loss'] - print(f'Train loss: {epoch_train_loss:.5f}') - summary_string = '' - for k, v in epoch_summary.items(): - summary_string += f'{k}: {v.item():.3f} ' - print(summary_string) - - if epoch % 100 == 0: - ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{epoch}_seed_{seed}.ckpt') - torch.save(policy.state_dict(), ckpt_path) - plot_history(train_history, validation_history, epoch, ckpt_dir, seed) - - ckpt_path = os.path.join(ckpt_dir, f'policy_last.ckpt') - torch.save(policy.state_dict(), ckpt_path) - - best_epoch, min_val_loss, best_state_dict = best_ckpt_info - ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{best_epoch}_seed_{seed}.ckpt') - torch.save(best_state_dict, ckpt_path) - print(f'Training finished:\nSeed {seed}, val loss {min_val_loss:.6f} at epoch {best_epoch}') - - # save training curves - plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed) - - return best_ckpt_info - - -def plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed): - # save training curves - for key in train_history[0]: - plot_path = os.path.join(ckpt_dir, f'train_val_{key}_seed_{seed}.png') - plt.figure() - train_values = [summary[key].item() for summary in train_history] - val_values = [summary[key].item() for summary in validation_history] - plt.plot(np.linspace(0, num_epochs-1, len(train_history)), train_values, label='train') - plt.plot(np.linspace(0, num_epochs-1, len(validation_history)), val_values, label='validation') - # plt.ylim([-0.1, 1]) - plt.tight_layout() - plt.legend() - plt.title(key) - plt.savefig(plot_path) - print(f'Saved plots to {ckpt_dir}') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--eval', action='store_true') - parser.add_argument('--onscreen_render', action='store_true') - parser.add_argument('--ckpt_dir', action='store', type=str, help='ckpt_dir', required=True) - parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True) - parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) - parser.add_argument('--batch_size', action='store', type=int, help='batch_size', required=True) - parser.add_argument('--seed', action='store', type=int, help='seed', required=True) - parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True) - parser.add_argument('--lr', action='store', type=float, help='lr', required=True) - - # for ACT - parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False) - parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False) - parser.add_argument('--hidden_dim', action='store', type=int, help='hidden_dim', required=False) - parser.add_argument('--dim_feedforward', action='store', type=int, help='dim_feedforward', required=False) - parser.add_argument('--temporal_agg', action='store_true') - - main(vars(parser.parse_args())) diff --git a/act/policy.py b/act/policy.py deleted file mode 100644 index 7b091e5e..00000000 --- a/act/policy.py +++ /dev/null @@ -1,84 +0,0 @@ -import torch.nn as nn -from torch.nn import functional as F -import torchvision.transforms as transforms - -from detr.main import build_ACT_model_and_optimizer, build_CNNMLP_model_and_optimizer -import IPython -e = IPython.embed - -class ACTPolicy(nn.Module): - def __init__(self, args_override): - super().__init__() - model, optimizer = build_ACT_model_and_optimizer(args_override) - self.model = model # CVAE decoder - self.optimizer = optimizer - self.kl_weight = args_override['kl_weight'] - print(f'KL Weight {self.kl_weight}') - - def __call__(self, qpos, image, actions=None, is_pad=None): - env_state = None - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - image = normalize(image) - if actions is not None: # training time - actions = actions[:, :self.model.num_queries] - is_pad = is_pad[:, :self.model.num_queries] - - a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad) - total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar) - loss_dict = dict() - all_l1 = F.l1_loss(actions, a_hat, reduction='none') - l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() - loss_dict['l1'] = l1 - loss_dict['kl'] = total_kld[0] - loss_dict['loss'] = loss_dict['l1'] + loss_dict['kl'] * self.kl_weight - return loss_dict - else: # inference time - a_hat, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior - return a_hat - - def configure_optimizers(self): - return self.optimizer - - -class CNNMLPPolicy(nn.Module): - def __init__(self, args_override): - super().__init__() - model, optimizer = build_CNNMLP_model_and_optimizer(args_override) - self.model = model # decoder - self.optimizer = optimizer - - def __call__(self, qpos, image, actions=None, is_pad=None): - env_state = None # TODO - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - image = normalize(image) - if actions is not None: # training time - actions = actions[:, 0] - a_hat = self.model(qpos, image, env_state, actions) - mse = F.mse_loss(actions, a_hat) - loss_dict = dict() - loss_dict['mse'] = mse - loss_dict['loss'] = loss_dict['mse'] - return loss_dict - else: # inference time - a_hat = self.model(qpos, image, env_state) # no action, sample from prior - return a_hat - - def configure_optimizers(self): - return self.optimizer - -def kl_divergence(mu, logvar): - batch_size = mu.size(0) - assert batch_size != 0 - if mu.data.ndimension() == 4: - mu = mu.view(mu.size(0), mu.size(1)) - if logvar.data.ndimension() == 4: - logvar = logvar.view(logvar.size(0), logvar.size(1)) - - klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) - total_kld = klds.sum(1).mean(0, True) - dimension_wise_kld = klds.mean(0) - mean_kld = klds.mean(1).mean(0, True) - - return total_kld, dimension_wise_kld, mean_kld diff --git a/act/record_sim_episodes.py b/act/record_sim_episodes.py deleted file mode 100644 index 253fdea1..00000000 --- a/act/record_sim_episodes.py +++ /dev/null @@ -1,189 +0,0 @@ -import time -import os -import numpy as np -import argparse -import matplotlib.pyplot as plt -import h5py - -from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN, SIM_TASK_CONFIGS -from ee_sim_env import make_ee_sim_env -from sim_env import make_sim_env, BOX_POSE -from scripted_policy import PickAndTransferPolicy, InsertionPolicy - -import IPython -e = IPython.embed - - -def main(args): - """ - Generate demonstration data in simulation. - First rollout the policy (defined in ee space) in ee_sim_env. Obtain the joint trajectory. - Replace the gripper joint positions with the commanded joint position. - Replay this joint trajectory (as action sequence) in sim_env, and record all observations. - Save this episode of data, and continue to next episode of data collection. - """ - - task_name = args['task_name'] - dataset_dir = args['dataset_dir'] - num_episodes = args['num_episodes'] - onscreen_render = args['onscreen_render'] - inject_noise = False - render_cam_name = 'angle' - - if not os.path.isdir(dataset_dir): - os.makedirs(dataset_dir, exist_ok=True) - - episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] - camera_names = SIM_TASK_CONFIGS[task_name]['camera_names'] - if task_name == 'sim_transfer_cube_scripted': - policy_cls = PickAndTransferPolicy - elif task_name == 'sim_insertion_scripted': - policy_cls = InsertionPolicy - else: - raise NotImplementedError - - success = [] - for episode_idx in range(num_episodes): - print(f'{episode_idx=}') - print('Rollout out EE space scripted policy') - # setup the environment - env = make_ee_sim_env(task_name) - ts = env.reset() - episode = [ts] - policy = policy_cls(inject_noise) - # setup plotting - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images'][render_cam_name]) - plt.ion() - for step in range(episode_len): - action = policy(ts) - ts = env.step(action) - episode.append(ts) - if onscreen_render: - plt_img.set_data(ts.observation['images'][render_cam_name]) - plt.pause(0.002) - plt.close() - - episode_return = np.sum([ts.reward for ts in episode[1:]]) - episode_max_reward = np.max([ts.reward for ts in episode[1:]]) - if episode_max_reward == env.task.max_reward: - print(f"{episode_idx=} Successful, {episode_return=}") - else: - print(f"{episode_idx=} Failed") - - joint_traj = [ts.observation['qpos'] for ts in episode] - # replace gripper pose with gripper control - gripper_ctrl_traj = [ts.observation['gripper_ctrl'] for ts in episode] - for joint, ctrl in zip(joint_traj, gripper_ctrl_traj): - left_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[0]) - right_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[2]) - joint[6] = left_ctrl - joint[6+7] = right_ctrl - - subtask_info = episode[0].observation['env_state'].copy() # box pose at step 0 - - # clear unused variables - del env - del episode - del policy - - # setup the environment - print('Replaying joint commands') - env = make_sim_env(task_name) - BOX_POSE[0] = subtask_info # make sure the sim_env has the same object configurations as ee_sim_env - ts = env.reset() - - episode_replay = [ts] - # setup plotting - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images'][render_cam_name]) - plt.ion() - for t in range(len(joint_traj)): # note: this will increase episode length by 1 - action = joint_traj[t] - ts = env.step(action) - episode_replay.append(ts) - if onscreen_render: - plt_img.set_data(ts.observation['images'][render_cam_name]) - plt.pause(0.02) - - episode_return = np.sum([ts.reward for ts in episode_replay[1:]]) - episode_max_reward = np.max([ts.reward for ts in episode_replay[1:]]) - if episode_max_reward == env.task.max_reward: - success.append(1) - print(f"{episode_idx=} Successful, {episode_return=}") - else: - success.append(0) - print(f"{episode_idx=} Failed") - - plt.close() - - """ - For each timestep: - observations - - images - - each_cam_name (480, 640, 3) 'uint8' - - qpos (14,) 'float64' - - qvel (14,) 'float64' - - action (14,) 'float64' - """ - - data_dict = { - '/observations/qpos': [], - '/observations/qvel': [], - '/action': [], - } - for cam_name in camera_names: - data_dict[f'/observations/images/{cam_name}'] = [] - - # because the replaying, there will be eps_len + 1 actions and eps_len + 2 timesteps - # truncate here to be consistent - joint_traj = joint_traj[:-1] - episode_replay = episode_replay[:-1] - - # len(joint_traj) i.e. actions: max_timesteps - # len(episode_replay) i.e. time steps: max_timesteps + 1 - max_timesteps = len(joint_traj) - while joint_traj: - action = joint_traj.pop(0) - ts = episode_replay.pop(0) - data_dict['/observations/qpos'].append(ts.observation['qpos']) - data_dict['/observations/qvel'].append(ts.observation['qvel']) - data_dict['/action'].append(action) - for cam_name in camera_names: - data_dict[f'/observations/images/{cam_name}'].append(ts.observation['images'][cam_name]) - - # HDF5 - t0 = time.time() - dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}') - with h5py.File(dataset_path + '.hdf5', 'w', rdcc_nbytes=1024 ** 2 * 2) as root: - root.attrs['sim'] = True - obs = root.create_group('observations') - image = obs.create_group('images') - for cam_name in camera_names: - _ = image.create_dataset(cam_name, (max_timesteps, 480, 640, 3), dtype='uint8', - chunks=(1, 480, 640, 3), ) - # compression='gzip',compression_opts=2,) - # compression=32001, compression_opts=(0, 0, 0, 0, 9, 1, 1), shuffle=False) - qpos = obs.create_dataset('qpos', (max_timesteps, 14)) - qvel = obs.create_dataset('qvel', (max_timesteps, 14)) - action = root.create_dataset('action', (max_timesteps, 14)) - - for name, array in data_dict.items(): - root[name][...] = array - print(f'Saving: {time.time() - t0:.1f} secs\n') - - print(f'Saved to {dataset_dir}') - print(f'Success: {np.sum(success)} / {len(success)}') - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) - parser.add_argument('--dataset_dir', action='store', type=str, help='dataset saving dir', required=True) - parser.add_argument('--num_episodes', action='store', type=int, help='num_episodes', required=False) - parser.add_argument('--onscreen_render', action='store_true') - - main(vars(parser.parse_args())) - diff --git a/act/scripted_policy.py b/act/scripted_policy.py deleted file mode 100644 index 4fd8f000..00000000 --- a/act/scripted_policy.py +++ /dev/null @@ -1,194 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from pyquaternion import Quaternion - -from constants import SIM_TASK_CONFIGS -from ee_sim_env import make_ee_sim_env - -import IPython -e = IPython.embed - - -class BasePolicy: - def __init__(self, inject_noise=False): - self.inject_noise = inject_noise - self.step_count = 0 - self.left_trajectory = None - self.right_trajectory = None - - def generate_trajectory(self, ts_first): - raise NotImplementedError - - @staticmethod - def interpolate(curr_waypoint, next_waypoint, t): - t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"]) - curr_xyz = curr_waypoint['xyz'] - curr_quat = curr_waypoint['quat'] - curr_grip = curr_waypoint['gripper'] - next_xyz = next_waypoint['xyz'] - next_quat = next_waypoint['quat'] - next_grip = next_waypoint['gripper'] - xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac - quat = curr_quat + (next_quat - curr_quat) * t_frac - gripper = curr_grip + (next_grip - curr_grip) * t_frac - return xyz, quat, gripper - - def __call__(self, ts): - # generate trajectory at first timestep, then open-loop execution - if self.step_count == 0: - self.generate_trajectory(ts) - - # obtain left and right waypoints - if self.left_trajectory[0]['t'] == self.step_count: - self.curr_left_waypoint = self.left_trajectory.pop(0) - next_left_waypoint = self.left_trajectory[0] - - if self.right_trajectory[0]['t'] == self.step_count: - self.curr_right_waypoint = self.right_trajectory.pop(0) - next_right_waypoint = self.right_trajectory[0] - - # interpolate between waypoints to obtain current pose and gripper command - left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count) - right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count) - - # Inject noise - if self.inject_noise: - scale = 0.01 - left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape) - right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape) - - action_left = np.concatenate([left_xyz, left_quat, [left_gripper]]) - action_right = np.concatenate([right_xyz, right_quat, [right_gripper]]) - - self.step_count += 1 - return np.concatenate([action_left, action_right]) - - -class PickAndTransferPolicy(BasePolicy): - - def generate_trajectory(self, ts_first): - init_mocap_pose_right = ts_first.observation['mocap_pose_right'] - init_mocap_pose_left = ts_first.observation['mocap_pose_left'] - - box_info = np.array(ts_first.observation['env_state']) - box_xyz = box_info[:3] - box_quat = box_info[3:] - # print(f"Generate trajectory for {box_xyz=}") - - gripper_pick_quat = Quaternion(init_mocap_pose_right[3:]) - gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) - - meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90) - - meet_xyz = np.array([0, 0.5, 0.25]) - - self.left_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep - {"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position - {"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position - {"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper - {"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left - {"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay - ] - - self.right_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep - {"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube - {"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down - {"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper - {"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position - {"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position - {"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper - {"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right - {"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay - ] - - -class InsertionPolicy(BasePolicy): - - def generate_trajectory(self, ts_first): - init_mocap_pose_right = ts_first.observation['mocap_pose_right'] - init_mocap_pose_left = ts_first.observation['mocap_pose_left'] - - peg_info = np.array(ts_first.observation['env_state'])[:7] - peg_xyz = peg_info[:3] - peg_quat = peg_info[3:] - - socket_info = np.array(ts_first.observation['env_state'])[7:] - socket_xyz = socket_info[:3] - socket_quat = socket_info[3:] - - gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:]) - gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) - - gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:]) - gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60) - - meet_xyz = np.array([0, 0.5, 0.15]) - lift_right = 0.00715 - - self.left_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep - {"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube - {"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down - {"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper - {"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position - {"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0}, # insertion - {"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # insertion - ] - - self.right_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep - {"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube - {"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down - {"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper - {"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position - {"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion - {"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion - - ] - - -def test_policy(task_name): - # example rolling out pick_and_transfer policy - onscreen_render = True - inject_noise = False - - # setup the environment - episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] - if 'sim_transfer_cube' in task_name: - env = make_ee_sim_env('sim_transfer_cube') - elif 'sim_insertion' in task_name: - env = make_ee_sim_env('sim_insertion') - else: - raise NotImplementedError - - for episode_idx in range(2): - ts = env.reset() - episode = [ts] - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images']['angle']) - plt.ion() - - policy = PickAndTransferPolicy(inject_noise) - for step in range(episode_len): - action = policy(ts) - ts = env.step(action) - episode.append(ts) - if onscreen_render: - plt_img.set_data(ts.observation['images']['angle']) - plt.pause(0.02) - plt.close() - - episode_return = np.sum([ts.reward for ts in episode[1:]]) - if episode_return > 0: - print(f"{episode_idx=} Successful, {episode_return=}") - else: - print(f"{episode_idx=} Failed") - - -if __name__ == '__main__': - test_task_name = 'sim_transfer_cube_scripted' - test_policy(test_task_name) - diff --git a/act/sim_env.py b/act/sim_env.py deleted file mode 100644 index b79b935b..00000000 --- a/act/sim_env.py +++ /dev/null @@ -1,278 +0,0 @@ -import numpy as np -import os -import collections -import matplotlib.pyplot as plt -from dm_control import mujoco -from dm_control.rl import control -from dm_control.suite import base - -from constants import DT, XML_DIR, START_ARM_POSE -from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN -from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN -from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN -from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN - -import IPython -e = IPython.embed - -BOX_POSE = [None] # to be changed from outside - -def make_sim_env(task_name): - """ - Environment for simulated robot bi-manual manipulation, with joint position control - Action space: [left_arm_qpos (6), # absolute joint position - left_gripper_positions (1), # normalized gripper position (0: close, 1: open) - right_arm_qpos (6), # absolute joint position - right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) - - Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position - left_gripper_position (1), # normalized gripper position (0: close, 1: open) - right_arm_qpos (6), # absolute joint position - right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) - "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) - left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) - right_arm_qvel (6), # absolute joint velocity (rad) - right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) - "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' - """ - if 'sim_transfer_cube' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_transfer_cube.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = TransferCubeTask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - elif 'sim_insertion' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_insertion.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = InsertionTask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - else: - raise NotImplementedError - return env - -class BimanualViperXTask(base.Task): - def __init__(self, random=None): - super().__init__(random=random) - - def before_step(self, action, physics): - left_arm_action = action[:6] - right_arm_action = action[7:7+6] - normalized_left_gripper_action = action[6] - normalized_right_gripper_action = action[7+6] - - left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_left_gripper_action) - right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_right_gripper_action) - - full_left_gripper_action = [left_gripper_action, -left_gripper_action] - full_right_gripper_action = [right_gripper_action, -right_gripper_action] - - env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action]) - super().before_step(env_action, physics) - return - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - super().initialize_episode(physics) - - @staticmethod - def get_qpos(physics): - qpos_raw = physics.data.qpos.copy() - left_qpos_raw = qpos_raw[:8] - right_qpos_raw = qpos_raw[8:16] - left_arm_qpos = left_qpos_raw[:6] - right_arm_qpos = right_qpos_raw[:6] - left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] - right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] - return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) - - @staticmethod - def get_qvel(physics): - qvel_raw = physics.data.qvel.copy() - left_qvel_raw = qvel_raw[:8] - right_qvel_raw = qvel_raw[8:16] - left_arm_qvel = left_qvel_raw[:6] - right_arm_qvel = right_qvel_raw[:6] - left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] - right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] - return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) - - @staticmethod - def get_env_state(physics): - raise NotImplementedError - - def get_observation(self, physics): - obs = collections.OrderedDict() - obs['qpos'] = self.get_qpos(physics) - obs['qvel'] = self.get_qvel(physics) - obs['env_state'] = self.get_env_state(physics) - obs['images'] = dict() - obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') - obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') - obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') - - return obs - - def get_reward(self, physics): - # return whether left gripper is holding the box - raise NotImplementedError - - -class TransferCubeTask(BimanualViperXTask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside - # reset qpos, control and box position - with physics.reset_context(): - physics.named.data.qpos[:16] = START_ARM_POSE - np.copyto(physics.data.ctrl, START_ARM_POSE) - assert BOX_POSE[0] is not None - physics.named.data.qpos[-7:] = BOX_POSE[0] - # print(f"{BOX_POSE=}") - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether left gripper is holding the box - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_table = ("red_box", "table") in all_contact_pairs - - reward = 0 - if touch_right_gripper: - reward = 1 - if touch_right_gripper and not touch_table: # lifted - reward = 2 - if touch_left_gripper: # attempted transfer - reward = 3 - if touch_left_gripper and not touch_table: # successful transfer - reward = 4 - return reward - - -class InsertionTask(BimanualViperXTask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside - # reset qpos, control and box position - with physics.reset_context(): - physics.named.data.qpos[:16] = START_ARM_POSE - np.copyto(physics.data.ctrl, START_ARM_POSE) - assert BOX_POSE[0] is not None - physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects - # print(f"{BOX_POSE=}") - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether peg touches the pin - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - - peg_touch_table = ("red_peg", "table") in all_contact_pairs - socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ - ("socket-2", "table") in all_contact_pairs or \ - ("socket-3", "table") in all_contact_pairs or \ - ("socket-4", "table") in all_contact_pairs - peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ - ("red_peg", "socket-2") in all_contact_pairs or \ - ("red_peg", "socket-3") in all_contact_pairs or \ - ("red_peg", "socket-4") in all_contact_pairs - pin_touched = ("red_peg", "pin") in all_contact_pairs - - reward = 0 - if touch_left_gripper and touch_right_gripper: # touch both - reward = 1 - if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both - reward = 2 - if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching - reward = 3 - if pin_touched: # successful insertion - reward = 4 - return reward - - -def get_action(master_bot_left, master_bot_right): - action = np.zeros(14) - # arm action - action[:6] = master_bot_left.dxl.joint_states.position[:6] - action[7:7+6] = master_bot_right.dxl.joint_states.position[:6] - # gripper action - left_gripper_pos = master_bot_left.dxl.joint_states.position[7] - right_gripper_pos = master_bot_right.dxl.joint_states.position[7] - normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos) - normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos) - action[6] = normalized_left_pos - action[7+6] = normalized_right_pos - return action - -def test_sim_teleop(): - """ Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """ - from interbotix_xs_modules.arm import InterbotixManipulatorXS - - BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0] - - # source of data - master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", - robot_name=f'master_left', init_node=True) - master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", - robot_name=f'master_right', init_node=False) - - # setup the environment - env = make_sim_env('sim_transfer_cube') - ts = env.reset() - episode = [ts] - # setup plotting - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images']['angle']) - plt.ion() - - for t in range(1000): - action = get_action(master_bot_left, master_bot_right) - ts = env.step(action) - episode.append(ts) - - plt_img.set_data(ts.observation['images']['angle']) - plt.pause(0.02) - - -if __name__ == '__main__': - test_sim_teleop() - diff --git a/act/utils.py b/act/utils.py deleted file mode 100644 index 673cbb10..00000000 --- a/act/utils.py +++ /dev/null @@ -1,189 +0,0 @@ -import numpy as np -import torch -import os -import h5py -from torch.utils.data import TensorDataset, DataLoader - -import IPython -e = IPython.embed - -class EpisodicDataset(torch.utils.data.Dataset): - def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats): - super(EpisodicDataset).__init__() - self.episode_ids = episode_ids - self.dataset_dir = dataset_dir - self.camera_names = camera_names - self.norm_stats = norm_stats - self.is_sim = None - self.__getitem__(0) # initialize self.is_sim - - def __len__(self): - return len(self.episode_ids) - - def __getitem__(self, index): - sample_full_episode = False # hardcode - - episode_id = self.episode_ids[index] - dataset_path = os.path.join(self.dataset_dir, f'episode_{episode_id}.hdf5') - with h5py.File(dataset_path, 'r') as root: - is_sim = root.attrs['sim'] - original_action_shape = root['/action'].shape - episode_len = original_action_shape[0] - if sample_full_episode: - start_ts = 0 - else: - start_ts = np.random.choice(episode_len) - # get observation at start_ts only - qpos = root['/observations/qpos'][start_ts] - qvel = root['/observations/qvel'][start_ts] - image_dict = dict() - for cam_name in self.camera_names: - image_dict[cam_name] = root[f'/observations/images/{cam_name}'][start_ts] - # get all actions after and including start_ts - if is_sim: - action = root['/action'][start_ts:] - action_len = episode_len - start_ts - else: - action = root['/action'][max(0, start_ts - 1):] # hack, to make timesteps more aligned - action_len = episode_len - max(0, start_ts - 1) # hack, to make timesteps more aligned - - self.is_sim = is_sim - padded_action = np.zeros(original_action_shape, dtype=np.float32) - padded_action[:action_len] = action - is_pad = np.zeros(episode_len) - is_pad[action_len:] = 1 - - # new axis for different cameras - all_cam_images = [] - for cam_name in self.camera_names: - all_cam_images.append(image_dict[cam_name]) - all_cam_images = np.stack(all_cam_images, axis=0) - - # construct observations - image_data = torch.from_numpy(all_cam_images) - qpos_data = torch.from_numpy(qpos).float() - action_data = torch.from_numpy(padded_action).float() - is_pad = torch.from_numpy(is_pad).bool() - - # channel last - image_data = torch.einsum('k h w c -> k c h w', image_data) - - # normalize image and change dtype to float - image_data = image_data / 255.0 - action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"] - qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"] - - return image_data, qpos_data, action_data, is_pad - - -def get_norm_stats(dataset_dir, num_episodes): - all_qpos_data = [] - all_action_data = [] - for episode_idx in range(num_episodes): - dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5') - with h5py.File(dataset_path, 'r') as root: - qpos = root['/observations/qpos'][()] - qvel = root['/observations/qvel'][()] - action = root['/action'][()] - all_qpos_data.append(torch.from_numpy(qpos)) - all_action_data.append(torch.from_numpy(action)) - all_qpos_data = torch.stack(all_qpos_data) - all_action_data = torch.stack(all_action_data) - all_action_data = all_action_data - - # normalize action data - action_mean = all_action_data.mean(dim=[0, 1], keepdim=True) - action_std = all_action_data.std(dim=[0, 1], keepdim=True) - action_std = torch.clip(action_std, 1e-2, 10) # clipping - - # normalize qpos data - qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True) - qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True) - qpos_std = torch.clip(qpos_std, 1e-2, 10) # clipping - - stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(), - "qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(), - "example_qpos": qpos} - - return stats - - -def load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val): - print(f'\nData from: {dataset_dir}\n') - # obtain train test split - train_ratio = 0.8 - shuffled_indices = np.random.permutation(num_episodes) - train_indices = shuffled_indices[:int(train_ratio * num_episodes)] - val_indices = shuffled_indices[int(train_ratio * num_episodes):] - - # obtain normalization stats for qpos and action - norm_stats = get_norm_stats(dataset_dir, num_episodes) - - # construct dataset and dataloader - train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats) - val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats) - train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) - val_dataloader = DataLoader(val_dataset, batch_size=batch_size_val, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) - - return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim - - -### env utils - -def sample_box_pose(): - x_range = [0.0, 0.2] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - cube_quat = np.array([1, 0, 0, 0]) - return np.concatenate([cube_position, cube_quat]) - -def sample_insertion_pose(): - # Peg - x_range = [0.1, 0.2] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - peg_quat = np.array([1, 0, 0, 0]) - peg_pose = np.concatenate([peg_position, peg_quat]) - - # Socket - x_range = [-0.2, -0.1] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - socket_quat = np.array([1, 0, 0, 0]) - socket_pose = np.concatenate([socket_position, socket_quat]) - - return peg_pose, socket_pose - -### helper functions - -def compute_dict_mean(epoch_dicts): - result = {k: None for k in epoch_dicts[0]} - num_items = len(epoch_dicts) - for k in result: - value_sum = 0 - for epoch_dict in epoch_dicts: - value_sum += epoch_dict[k] - result[k] = value_sum / num_items - return result - -def detach_dict(d): - new_d = dict() - for k, v in d.items(): - new_d[k] = v.detach() - return new_d - -def set_seed(seed): - torch.manual_seed(seed) - np.random.seed(seed) diff --git a/act/visualize_episodes.py b/act/visualize_episodes.py deleted file mode 100644 index 4e55e471..00000000 --- a/act/visualize_episodes.py +++ /dev/null @@ -1,147 +0,0 @@ -import os -import numpy as np -import cv2 -import h5py -import argparse - -import matplotlib.pyplot as plt -from constants import DT - -import IPython -e = IPython.embed - -JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] -STATE_NAMES = JOINT_NAMES + ["gripper"] - -def load_hdf5(dataset_dir, dataset_name): - dataset_path = os.path.join(dataset_dir, dataset_name + '.hdf5') - if not os.path.isfile(dataset_path): - print(f'Dataset does not exist at \n{dataset_path}\n') - exit() - - with h5py.File(dataset_path, 'r') as root: - is_sim = root.attrs['sim'] - qpos = root['/observations/qpos'][()] - qvel = root['/observations/qvel'][()] - action = root['/action'][()] - image_dict = dict() - for cam_name in root[f'/observations/images/'].keys(): - image_dict[cam_name] = root[f'/observations/images/{cam_name}'][()] - - return qpos, qvel, action, image_dict - -def main(args): - dataset_dir = args['dataset_dir'] - episode_idx = args['episode_idx'] - dataset_name = f'episode_{episode_idx}' - - qpos, qvel, action, image_dict = load_hdf5(dataset_dir, dataset_name) - save_videos(image_dict, DT, video_path=os.path.join(dataset_dir, dataset_name + '_video.mp4')) - visualize_joints(qpos, action, plot_path=os.path.join(dataset_dir, dataset_name + '_qpos.png')) - # visualize_timestamp(t_list, dataset_path) # TODO addn timestamp back - - -def save_videos(video, dt, video_path=None): - if isinstance(video, list): - cam_names = list(video[0].keys()) - h, w, _ = video[0][cam_names[0]].shape - w = w * len(cam_names) - fps = int(1/dt) - out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - for ts, image_dict in enumerate(video): - images = [] - for cam_name in cam_names: - image = image_dict[cam_name] - image = image[:, :, [2, 1, 0]] # swap B and R channel - images.append(image) - images = np.concatenate(images, axis=1) - out.write(images) - out.release() - print(f'Saved video to: {video_path}') - elif isinstance(video, dict): - cam_names = list(video.keys()) - all_cam_videos = [] - for cam_name in cam_names: - all_cam_videos.append(video[cam_name]) - all_cam_videos = np.concatenate(all_cam_videos, axis=2) # width dimension - - n_frames, h, w, _ = all_cam_videos.shape - fps = int(1 / dt) - out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - for t in range(n_frames): - image = all_cam_videos[t] - image = image[:, :, [2, 1, 0]] # swap B and R channel - out.write(image) - out.release() - print(f'Saved video to: {video_path}') - - -def visualize_joints(qpos_list, command_list, plot_path=None, ylim=None, label_overwrite=None): - if label_overwrite: - label1, label2 = label_overwrite - else: - label1, label2 = 'State', 'Command' - - qpos = np.array(qpos_list) # ts, dim - command = np.array(command_list) - num_ts, num_dim = qpos.shape - h, w = 2, num_dim - num_figs = num_dim - fig, axs = plt.subplots(num_figs, 1, figsize=(w, h * num_figs)) - - # plot joint state - all_names = [name + '_left' for name in STATE_NAMES] + [name + '_right' for name in STATE_NAMES] - for dim_idx in range(num_dim): - ax = axs[dim_idx] - ax.plot(qpos[:, dim_idx], label=label1) - ax.set_title(f'Joint {dim_idx}: {all_names[dim_idx]}') - ax.legend() - - # plot arm command - for dim_idx in range(num_dim): - ax = axs[dim_idx] - ax.plot(command[:, dim_idx], label=label2) - ax.legend() - - if ylim: - for dim_idx in range(num_dim): - ax = axs[dim_idx] - ax.set_ylim(ylim) - - plt.tight_layout() - plt.savefig(plot_path) - print(f'Saved qpos plot to: {plot_path}') - plt.close() - -def visualize_timestamp(t_list, dataset_path): - plot_path = dataset_path.replace('.pkl', '_timestamp.png') - h, w = 4, 10 - fig, axs = plt.subplots(2, 1, figsize=(w, h*2)) - # process t_list - t_float = [] - for secs, nsecs in t_list: - t_float.append(secs + nsecs * 10E-10) - t_float = np.array(t_float) - - ax = axs[0] - ax.plot(np.arange(len(t_float)), t_float) - ax.set_title(f'Camera frame timestamps') - ax.set_xlabel('timestep') - ax.set_ylabel('time (sec)') - - ax = axs[1] - ax.plot(np.arange(len(t_float)-1), t_float[:-1] - t_float[1:]) - ax.set_title(f'dt') - ax.set_xlabel('timestep') - ax.set_ylabel('time (sec)') - - plt.tight_layout() - plt.savefig(plot_path) - print(f'Saved timestamp plot to: {plot_path}') - plt.close() - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--dataset_dir', action='store', type=str, help='Dataset dir.', required=True) - parser.add_argument('--episode_idx', action='store', type=int, help='Episode index.', required=False) - main(vars(parser.parse_args())) From 94fe84b0a1efe06663a25069fcd4de305ea5b9a3 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 28 Feb 2024 23:46:23 +0000 Subject: [PATCH 03/43] start clean --- requirements.txt | 1 - robomimic/algo/__init__.py | 1 - robomimic/algo/act.py | 265 -------- robomimic/config/__init__.py | 2 - robomimic/config/act_config.py | 47 -- robomimic/exps/templates/act.json | 160 ----- robomimic/exps/templates/bc_transformer.json | 172 ----- robomimic/macros.py | 4 +- robomimic/models/obs_core.py | 278 -------- robomimic/scripts/config_gen/act_gen.py | 131 ---- .../scripts/config_gen/act_gen_no_define.py | 266 -------- robomimic/scripts/config_gen/diffusion_gen.py | 480 -------------- .../config_gen/diffusion_gen_no_define.py | 399 ------------ .../scripts/config_gen/r2d2_runs_bcxfmr.py | 456 -------------- robomimic/scripts/config_gen/voltron.py | 593 ------------------ .../scripts/conversion/extract_action_dict.py | 81 --- .../robosuite_add_absolute_actions.py | 298 --------- .../scripts/conversion/set_dataset_attr.py | 98 --- runallruns.sh | 89 --- 19 files changed, 2 insertions(+), 3819 deletions(-) delete mode 100644 robomimic/algo/act.py delete mode 100644 robomimic/config/act_config.py delete mode 100644 robomimic/exps/templates/act.json delete mode 100644 robomimic/exps/templates/bc_transformer.json delete mode 100644 robomimic/scripts/config_gen/act_gen.py delete mode 100644 robomimic/scripts/config_gen/act_gen_no_define.py delete mode 100644 robomimic/scripts/config_gen/diffusion_gen.py delete mode 100644 robomimic/scripts/config_gen/diffusion_gen_no_define.py delete mode 100644 robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py delete mode 100644 robomimic/scripts/config_gen/voltron.py delete mode 100644 robomimic/scripts/conversion/extract_action_dict.py delete mode 100644 robomimic/scripts/conversion/robosuite_add_absolute_actions.py delete mode 100644 robomimic/scripts/conversion/set_dataset_attr.py delete mode 100644 runallruns.sh diff --git a/requirements.txt b/requirements.txt index c451769d..ac8ea89a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,3 @@ egl_probe>=1.0.1 torch torchvision diffusers==0.11.1 -pytorch3d==0.7.3 diff --git a/robomimic/algo/__init__.py b/robomimic/algo/__init__.py index 5fd1d53f..dedba35c 100644 --- a/robomimic/algo/__init__.py +++ b/robomimic/algo/__init__.py @@ -10,4 +10,3 @@ from robomimic.algo.iris import IRIS from robomimic.algo.td3_bc import TD3_BC from robomimic.algo.diffusion_policy import DiffusionPolicyUNet -from robomimic.algo.act import ACT diff --git a/robomimic/algo/act.py b/robomimic/algo/act.py deleted file mode 100644 index d0c0024d..00000000 --- a/robomimic/algo/act.py +++ /dev/null @@ -1,265 +0,0 @@ -""" -Implementation of Action Chunking with Transformers (ACT). -""" -from collections import OrderedDict -from functools import reduce - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision.transforms as transforms - -import robomimic.utils.tensor_utils as TensorUtils - -from robomimic.algo import register_algo_factory_func, PolicyAlgo -from robomimic.algo.bc import BC_VAE - - -@register_algo_factory_func("act") -def algo_config_to_class(algo_config): - """ - Maps algo config to the BC algo class to instantiate, along with additional algo kwargs. - - Args: - algo_config (Config instance): algo config - - Returns: - algo_class: subclass of Algo - algo_kwargs (dict): dictionary of additional kwargs to pass to algorithm - """ - algo_class, algo_kwargs = ACT, {} - - return algo_class, algo_kwargs - - -class ACT(BC_VAE): - """ - BC training with a VAE policy. - """ - def _create_networks(self): - """ - Creates networks and places them into @self.nets. - """ - - self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - self.nets = nn.ModuleDict() - self.chunk_size = self.global_config["train"]["seq_length"] - self.camera_keys = self.obs_config['modalities']['obs']['rgb'].copy() - self.proprio_keys = self.obs_config['modalities']['obs']['low_dim'].copy() - self.obs_keys = self.proprio_keys + self.camera_keys - - self.proprio_dim = 0 - for k in self.proprio_keys: - self.proprio_dim += reduce(lambda x, y: x*y, self.obs_key_shapes[k]) - - from act.detr.main import build_ACT_model_and_optimizer - policy_config = {'num_queries': self.chunk_size, - 'hidden_dim': self.algo_config.act.hidden_dim, - 'dim_feedforward': self.algo_config.act.dim_feedforward, - 'backbone': self.algo_config.act.backbone, - 'enc_layers': self.algo_config.act.enc_layers, - 'dec_layers': self.algo_config.act.dec_layers, - 'nheads': self.algo_config.act.nheads, - 'latent_dim': self.algo_config.act.latent_dim, - 'a_dim': self.ac_dim, - 'state_dim': self.proprio_dim, - 'camera_names': self.camera_keys - } - self.kl_weight = self.algo_config.act.kl_weight - model, optimizer = build_ACT_model_and_optimizer(policy_config) - self.nets["policy"] = torch.nn.parallel.DataParallel(model, device_ids=list(range(0,torch.cuda.device_count()))) - self.nets = self.nets.float().to(self.device) - - self.temporal_agg = False - self.query_frequency = self.chunk_size # TODO maybe tune - - self._step_counter = 0 - self.a_hat_store = None - - - def process_batch_for_training(self, batch): - """ - Processes input batch from a data loader to filter out - relevant information and prepare the batch for training. - Args: - batch (dict): dictionary with torch.Tensors sampled - from a data loader - Returns: - input_batch (dict): processed and filtered batch that - will be used for training - """ - - input_batch = dict() - input_batch["obs"] = {k: batch["obs"][k][:, 0, :] for k in batch["obs"] if k != 'pad_mask'} - input_batch["obs"]['pad_mask'] = batch["obs"]['pad_mask'] - input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present - # For the goal, only keep the camera observations - if input_batch["goal_obs"] is not None: - input_batch["goal_obs"] = {key: value for key, value in batch["goal_obs"].items() if key.startswith('camera')} - input_batch["actions"] = batch["actions"][:, :, :] - # we move to device first before float conversion because image observation modalities will be uint8 - - # this minimizes the amount of data transferred to GPU - return TensorUtils.to_float(TensorUtils.to_device(input_batch, self.device)) - - def train_on_batch(self, batch, epoch, validate=False): - """ - Update from superclass to set categorical temperature, for categorcal VAEs. - """ - - return super(BC_VAE, self).train_on_batch(batch, epoch, validate=validate) - - def _forward_training(self, batch): - """ - Internal helper function for BC algo class. Compute forward pass - and return network outputs in @predictions dict. - Args: - batch (dict): dictionary with torch.Tensors sampled - from a data loader and filtered by @process_batch_for_training - Returns: - predictions (dict): dictionary containing network outputs - """ - # TODO (Ashwin): remove this once we add in the robomimic observation encoders - for k in self.proprio_keys: - batch["obs"][k] = batch["obs"][k].reshape((batch["obs"][k].shape[0], -1)) - proprio = [batch["obs"][k] for k in self.proprio_keys] - proprio = torch.cat(proprio, axis=1) - qpos = proprio - - images = [] - goal_images = [] - for cam_name in self.camera_keys: - image = batch['obs'][cam_name] - image = self.normalize(image) - image = image.unsqueeze(axis=1) - images.append(image) - goal_image = batch['goal_obs'][cam_name] - goal_image = self.normalize(goal_image) - goal_images.append(goal_image) - - images = torch.cat(images, axis=1) - goal_images = torch.cat(goal_images, axis=1) - - # Concatenate images and goal images channel-wise - images = torch.cat((images, goal_images), axis=1) - - env_state = torch.zeros([qpos.shape[0], 10]).cuda() # this is not used - - actions = batch['actions'] - is_pad = batch['obs']['pad_mask'] == 0 # from 1.0 or 0 to False and True - is_pad = is_pad.squeeze(dim=-1) - - a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state, actions, is_pad) - total_kld, dim_wise_kld, mean_kld = self.kl_divergence(mu, logvar) - loss_dict = dict() - all_l1 = F.l1_loss(actions, a_hat, reduction='none') - l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() - loss_dict['l1'] = l1 - loss_dict['kl'] = total_kld[0] - - - predictions = OrderedDict( - actions=actions, - kl_loss=loss_dict['kl'], - reconstruction_loss=loss_dict['l1'], - ) - - return predictions - - def get_action(self, obs_dict, goal_dict=None): - """ - Get policy action outputs. - Args: - obs_dict (dict): current observation - goal_dict (dict): (optional) goal - Returns: - action (torch.Tensor): action tensor - """ - assert not self.nets.training - - # TODO (Ashwin): remove this once we add in the robomimic observation encoders - for k in self.proprio_keys: - obs_dict[k] = obs_dict[k].reshape((obs_dict[k].shape[0], -1)) - proprio = [obs_dict[k] for k in self.proprio_keys] - proprio = torch.cat(proprio, axis=1) - qpos = proprio - - images = [] - for cam_name in self.camera_keys: - image = obs_dict[cam_name] - image = self.normalize(image) - image = image.unsqueeze(axis=1) - images.append(image) - images = torch.cat(images, axis=1) - - env_state = torch.zeros([qpos.shape[0], 10]).cuda() # not used - - if self._step_counter % self.query_frequency == 0: - a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state) - self.a_hat_store = a_hat - - action = self.a_hat_store[:, self._step_counter % self.query_frequency, :] - self._step_counter += 1 - return action - - - def reset(self): - """ - Reset algo state to prepare for environment rollouts. - """ - self._step_counter = 0 - - def _compute_losses(self, predictions, batch): - """ - Internal helper function for BC algo class. Compute losses based on - network outputs in @predictions dict, using reference labels in @batch. - Args: - predictions (dict): dictionary containing network outputs, from @_forward_training - batch (dict): dictionary with torch.Tensors sampled - from a data loader and filtered by @process_batch_for_training - Returns: - losses (dict): dictionary of losses computed over the batch - """ - - # total loss is sum of reconstruction and KL, weighted by beta - kl_loss = predictions["kl_loss"] - recons_loss = predictions["reconstruction_loss"] - action_loss = recons_loss + self.kl_weight * kl_loss - return OrderedDict( - recons_loss=recons_loss, - kl_loss=kl_loss, - action_loss=action_loss, - ) - - def log_info(self, info): - """ - Process info dictionary from @train_on_batch to summarize - information to pass to tensorboard for logging. - Args: - info (dict): dictionary of info - Returns: - loss_log (dict): name -> summary statistic - """ - log = PolicyAlgo.log_info(self, info) - log["Loss"] = info["losses"]["action_loss"].item() - log["KL_Loss"] = info["losses"]["kl_loss"].item() - log["Reconstruction_Loss"] = info["losses"]["recons_loss"].item() - if "policy_grad_norms" in info: - log["Policy_Grad_Norms"] = info["policy_grad_norms"] - return log - - def kl_divergence(self, mu, logvar): - batch_size = mu.size(0) - assert batch_size != 0 - if mu.data.ndimension() == 4: - mu = mu.view(mu.size(0), mu.size(1)) - if logvar.data.ndimension() == 4: - logvar = logvar.view(logvar.size(0), logvar.size(1)) - - klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) - total_kld = klds.sum(1).mean(0, True) - dimension_wise_kld = klds.mean(0) - mean_kld = klds.mean(1).mean(0, True) - - return total_kld, dimension_wise_kld, mean_kld - diff --git a/robomimic/config/__init__.py b/robomimic/config/__init__.py index b8b9da73..8b0ea02e 100644 --- a/robomimic/config/__init__.py +++ b/robomimic/config/__init__.py @@ -9,6 +9,4 @@ from robomimic.config.gl_config import GLConfig from robomimic.config.hbc_config import HBCConfig from robomimic.config.iris_config import IRISConfig -from robomimic.config.td3_bc_config import TD3_BCConfig from robomimic.config.diffusion_policy_config import DiffusionPolicyConfig -from robomimic.config.act_config import ACTConfig diff --git a/robomimic/config/act_config.py b/robomimic/config/act_config.py deleted file mode 100644 index 9be3926b..00000000 --- a/robomimic/config/act_config.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Config for BC algorithm. -""" - -from robomimic.config.base_config import BaseConfig - - -class ACTConfig(BaseConfig): - ALGO_NAME = "act" - - def train_config(self): - """ - BC algorithms don't need "next_obs" from hdf5 - so save on storage and compute by disabling it. - """ - super(ACTConfig, self).train_config() - self.train.hdf5_load_next_obs = False - - def algo_config(self): - """ - This function populates the `config.algo` attribute of the config, and is given to the - `Algo` subclass (see `algo/algo.py`) for each algorithm through the `algo_config` - argument to the constructor. Any parameter that an algorithm needs to determine its - training and test-time behavior should be populated here. - """ - - # optimization parameters - self.algo.optim_params.policy.optimizer_type = "adamw" - self.algo.optim_params.policy.learning_rate.initial = 5e-5 # policy learning rate - self.algo.optim_params.policy.learning_rate.decay_factor = 1 # factor to decay LR by (if epoch schedule non-empty) - self.algo.optim_params.policy.learning_rate.epoch_schedule = [] # epochs where LR decay occurs - self.algo.optim_params.policy.learning_rate.scheduler_type = "linear" # learning rate scheduler ("multistep", "linear", etc) - self.algo.optim_params.policy.regularization.L2 = 0.0001 # L2 regularization strength - - # loss weights - self.algo.loss.l2_weight = 0.0 # L2 loss weight - self.algo.loss.l1_weight = 1.0 # L1 loss weight - self.algo.loss.cos_weight = 0.0 # cosine loss weight - - # ACT policy settings - self.algo.act.hidden_dim = 512 # length of (s, a) seqeunces to feed to transformer - should usually match train.frame_stack - self.algo.act.dim_feedforward = 3200 # dimension for embeddings used by transformer - self.algo.act.backbone = "resnet18" # number of transformer blocks to stack - self.algo.act.enc_layers = 4 # number of attention heads for each transformer block (should divide embed_dim evenly) - self.algo.act.dec_layers = 7 # dropout probability for embedding inputs in transformer - self.algo.act.nheads = 8 # dropout probability for attention outputs for each transformer block - self.algo.act.latent_dim = 32 # latent dim of VAE - self.algo.act.kl_weight = 20 # KL weight of VAE diff --git a/robomimic/exps/templates/act.json b/robomimic/exps/templates/act.json deleted file mode 100644 index 4512ecdf..00000000 --- a/robomimic/exps/templates/act.json +++ /dev/null @@ -1,160 +0,0 @@ -{ - "algo_name": "act", - "experiment": { - "name": "test", - "validate": false, - "logging": { - "terminal_output_to_txt": true, - "log_tb": true, - "log_wandb": false, - "wandb_proj_name": "debug" - }, - "mse":{}, - "save": { - "enabled": true, - "every_n_seconds": null, - "every_n_epochs": 40, - "epochs": [], - "on_best_validation": false, - "on_best_rollout_return": false, - "on_best_rollout_success_rate": true - }, - "epoch_every_n_steps": 500, - "validation_epoch_every_n_steps": 10, - "env": null, - "additional_envs": null, - "render": false, - "render_video": true, - "keep_all_videos": false, - "video_skip": 5, - "rollout": { - "enabled": true, - "n": 50, - "horizon": 400, - "rate": 40, - "warmstart": 0, - "terminate_on_success": true - } - }, - "train": { - "data": null, - "output_dir":"../act_trained_models", - "num_data_workers": 4, - "hdf5_cache_mode": "low_dim", - "hdf5_use_swmr": true, - "hdf5_load_next_obs": false, - "hdf5_normalize_obs": false, - "hdf5_filter_key": null, - "seq_length": 10, - "pad_seq_length": true, - "frame_stack": 1, - "pad_frame_stack": true, - "dataset_keys": [ - "actions" - ], - "goal_mode": null, - "cuda": true, - "batch_size": 128, - "num_epochs": 10000, - "seed": 1 - }, - "algo": { - "optim_params": { - "policy": { - "optimizer_type": "adamw", - "learning_rate": { - "initial": 0.00005, - "decay_factor": 1, - "epoch_schedule": [ - 100 - ], - "scheduler_type": "linear" - }, - "regularization": { - "L2": 0.0001 - } - } - }, - "loss": { - "l2_weight": 0.0, - "l1_weight": 1.0, - "cos_weight": 0.0 - }, - "act": { - "hidden_dim": 512, - "dim_feedforward": 3200, - "backbone": "resnet18", - "enc_layers": 4, - "dec_layers": 7, - "nheads": 8, - "latent_dim": 32, - "kl_weight": 20 - } - }, - "observation": { - "modalities": { - "obs": { - "low_dim": [ - "robot0_eef_pos", - "robot0_eef_quat", - "robot0_gripper_qpos", - "object" - ], - "rgb": [], - "depth": [], - "scan": [] - }, - "goal": { - "low_dim": [], - "rgb": [], - "depth": [], - "scan": [] - } - }, - "encoder": { - "low_dim": { - "core_class": null, - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - }, - "rgb": { - "core_class": "VisualCore", - "core_kwargs": { - "feature_dimension": 64, - "backbone_class": "ResNet18Conv", - "backbone_kwargs": { - "pretrained": false, - "input_coord_conv": false - }, - "pool_class": "SpatialSoftmax", - "pool_kwargs": { - "num_kp": 32, - "learnable_temperature": false, - "temperature": 1.0, - "noise_std": 0.0 - } - }, - "obs_randomizer_class": "CropRandomizer", - "obs_randomizer_kwargs": { - "crop_height": 76, - "crop_width": 76, - "num_crops": 1, - "pos_enc": false - } - }, - "depth": { - "core_class": "VisualCore", - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - }, - "scan": { - "core_class": "ScanCore", - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - } - } - } -} \ No newline at end of file diff --git a/robomimic/exps/templates/bc_transformer.json b/robomimic/exps/templates/bc_transformer.json deleted file mode 100644 index 0bb5c3d9..00000000 --- a/robomimic/exps/templates/bc_transformer.json +++ /dev/null @@ -1,172 +0,0 @@ -{ - "algo_name": "bc", - "experiment": { - "name": "test", - "validate": true, - "logging": { - "terminal_output_to_txt": true, - "log_tb": true, - "log_wandb": false, - "wandb_proj_name": "debug" - }, - "mse":{}, - "save": { - "enabled": true, - "every_n_seconds": null, - "every_n_epochs": 50, - "epochs": [], - "on_best_validation": false, - "on_best_rollout_return": false, - "on_best_rollout_success_rate": true - }, - "epoch_every_n_steps": 100, - "validation_epoch_every_n_steps": 10, - "env": null, - "additional_envs": null, - "render": false, - "render_video": true, - "keep_all_videos": false, - "video_skip": 5, - "rollout": { - "enabled": true, - "n": 50, - "horizon": 400, - "rate": 50, - "warmstart": 0, - "terminate_on_success": true - } - }, - "train": { - "data": null, - "output_dir": "../bc_transformer_trained_models", - "num_data_workers": 0, - "hdf5_cache_mode": "low_dim", - "hdf5_use_swmr": true, - "hdf5_load_next_obs": false, - "hdf5_normalize_obs": false, - "hdf5_filter_key": null, - "seq_length": 1, - "pad_seq_length": true, - "frame_stack": 10, - "pad_frame_stack": true, - "dataset_keys": [ - "actions" - ], - "goal_mode": null, - "cuda": true, - "batch_size": 100, - "num_epochs": 2000, - "seed": 1 - }, - "algo": { - "optim_params": { - "policy": { - "optimizer_type": "adamw", - "learning_rate": { - "initial": 0.0001, - "decay_factor": 0.1, - "epoch_schedule": [], - "scheduler_type": "linear" - }, - "regularization": { - "L2": 0.01 - } - } - }, - "loss": { - "l2_weight": 1.0, - "l1_weight": 0.0, - "cos_weight": 0.0 - }, - "actor_layer_dims": [], - "gaussian": { - "enabled": false - }, - "gmm": { - "enabled": true, - "num_modes": 5, - "min_std": 0.0001, - "std_activation": "softplus", - "low_noise_eval": true - }, - "vae": { - "enabled": false - }, - "rnn": { - "enabled": false - }, - "transformer": { - "enabled": true, - "supervise_all_steps": false, - "num_layers": 6, - "embed_dim": 512, - "num_heads": 8 - } - }, - "observation": { - "modalities": { - "obs": { - "low_dim": [ - "robot0_eef_pos", - "robot0_eef_quat", - "robot0_gripper_qpos", - "object" - ], - "rgb": [], - "depth": [], - "scan": [] - }, - "goal": { - "low_dim": [], - "rgb": [], - "depth": [], - "scan": [] - } - }, - "encoder": { - "low_dim": { - "core_class": null, - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - }, - "rgb": { - "core_class": "VisualCore", - "core_kwargs": { - "feature_dimension": 64, - "backbone_class": "ResNet18Conv", - "backbone_kwargs": { - "pretrained": false, - "input_coord_conv": false - }, - "pool_class": "SpatialSoftmax", - "pool_kwargs": { - "num_kp": 32, - "learnable_temperature": false, - "temperature": 1.0, - "noise_std": 0.0 - } - }, - "obs_randomizer_class": "CropRandomizer", - "obs_randomizer_kwargs": { - "crop_height": 76, - "crop_width": 76, - "num_crops": 1, - "pos_enc": false - } - }, - "depth": { - "core_class": "VisualCore", - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - }, - "scan": { - "core_class": "ScanCore", - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - } - } - } -} \ No newline at end of file diff --git a/robomimic/macros.py b/robomimic/macros.py index 9d54a4cc..3b6c0503 100644 --- a/robomimic/macros.py +++ b/robomimic/macros.py @@ -9,11 +9,11 @@ VISUALIZE_RANDOMIZER = False # wandb entity (eg. username or team name) -WANDB_ENTITY = "tri" +WANDB_ENTITY = None # wandb api key (obtain from https://wandb.ai/authorize) # alternatively, set up wandb from terminal with `wandb login` -WANDB_API_KEY = "10eb447cdaa881ddb7748e1ebb4814706c2be903" +WANDB_API_KEY = None try: from robomimic.macros_private import * diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index 4c9d35c9..54976419 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -19,7 +19,6 @@ # NOTE: this is required for the backbone classes to be found by the `eval` call in the core networks from robomimic.models.base_nets import * -from robomimic.utils.camera_utils import DeFiNeImageCamEncoder, VoltronImageCamEncoder, R3MImageCamEncoder, VC1ImageCamEncoder from robomimic.utils.camera_utils import ResNetFiLMEncoder from robomimic.utils.vis_utils import visualize_image_randomizer from robomimic.macros import VISUALIZE_RANDOMIZER @@ -184,283 +183,6 @@ def __repr__(self): msg += textwrap.indent("\npool_net={}".format(self.pool), indent) msg = header + '(' + msg + '\n)' return msg - - -class PretrainedVisualCore(EncoderCore, BaseNets.ConvBase): - """ - Extension of VisualCore that uses a pretrained model - """ - def __init__( - self, - input_shape, - backbone_class="ResNet18Conv", - pool_class="SpatialSoftmax", - backbone_kwargs=None, - pool_kwargs=None, - flatten=True, - feature_dimension=64, - ): - """ - Args: - input_shape (tuple): shape of input (not including batch dimension) - backbone_class (str): class name for the visual backbone network. Defaults - to "ResNet18Conv". - pool_class (str): class name for the visual feature pooler (optional) - Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to - "SpatialSoftmax". - backbone_kwargs (dict): kwargs for the visual backbone network (optional) - pool_kwargs (dict): kwargs for the visual feature pooler (optional) - flatten (bool): whether to flatten the visual features - feature_dimension (int): if not None, add a Linear layer to - project output into a desired feature dimension - """ - super(PretrainedVisualCore, self).__init__(input_shape=input_shape) - self.flatten = flatten - - self.nets = nn.ModuleDict() - - if backbone_kwargs is None: - backbone_kwargs = dict() - - # add input channel dimension to visual core inputs - backbone_kwargs["input_channel"] = input_shape[0] - - # extract only relevant kwargs for this specific backbone - backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) - - # visual backbone - assert isinstance(backbone_class, str) - self.backbone = eval(backbone_class)(**backbone_kwargs) - - feat_shape = self.backbone.output_shape(input_shape) - self.feat_shape = feat_shape - # net_list = [self.backbone] - self.nets["backbone"] = self.backbone - - post_proc_list = [] - - # maybe make pool net - if pool_class is not None: - assert isinstance(pool_class, str) - # feed output shape of backbone to pool net - if pool_kwargs is None: - pool_kwargs = dict() - # extract only relevant kwargs for this specific backbone - pool_kwargs["input_shape"] = feat_shape - pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) - self.pool = eval(pool_class)(**pool_kwargs) - assert isinstance(self.pool, BaseNets.Module) - - feat_shape = self.pool.output_shape(feat_shape) - post_proc_list.append(self.pool) - else: - self.pool = None - - # flatten layer - if self.flatten: - post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) - - # maybe linear layer - self.feature_dimension = feature_dimension - if feature_dimension is not None: - assert self.flatten - post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) - - self.nets["post_proc"] = nn.Sequential(*post_proc_list) - - def output_shape(self, input_shape): - """ - Function to compute output shape from inputs to this module. - - Args: - input_shape (iterable of int): shape of input. Does not include batch dimension. - Some modules may not need this argument, if their output does not depend - on the size of the input, or if they assume fixed size input. - - Returns: - out_shape ([int]): list of integers corresponding to output shape - """ - if self.feature_dimension is not None: - # linear output - return [self.feature_dimension] - feat_shape = self.backbone.output_shape(input_shape) - if self.pool is not None: - # pool output - feat_shape = self.pool.output_shape(feat_shape) - # backbone + flat output - if self.flatten: - return [np.prod(feat_shape)] - else: - return feat_shape - - def forward(self, inputs): - """ - Forward pass through visual core. - """ - ndim = len(self.input_shape) - assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) - image = inputs["image"] - # Sometimes will be language conditioned image representation - lang_emb = inputs.get("lang_emb", None) - - x = self.nets["backbone"].forward(image=image, lang_emb=lang_emb) - # x = self.nets["backbone"].forward(inputs["image"]) - x = self.nets["post_proc"](x) - - return x - - def __repr__(self): - """Pretty print network.""" - header = '{}'.format(str(self.__class__.__name__)) - msg = '' - indent = ' ' * 2 - msg += textwrap.indent( - "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) - msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) - msg += textwrap.indent("\npool_net={}".format(self.pool), indent) - msg = header + '(' + msg + '\n)' - return msg - -class DeFiNeVisualCore(EncoderCore, BaseNets.ConvBase): - """ - Extension of VisualCore that takes in camera information - """ - def __init__( - self, - input_shape, - backbone_class="ResNet18Conv", - pool_class="SpatialSoftmax", - backbone_kwargs=None, - pool_kwargs=None, - flatten=True, - feature_dimension=64, - ): - """ - Args: - input_shape (tuple): shape of input (not including batch dimension) - backbone_class (str): class name for the visual backbone network. Defaults - to "ResNet18Conv". - pool_class (str): class name for the visual feature pooler (optional) - Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to - "SpatialSoftmax". - backbone_kwargs (dict): kwargs for the visual backbone network (optional) - pool_kwargs (dict): kwargs for the visual feature pooler (optional) - flatten (bool): whether to flatten the visual features - feature_dimension (int): if not None, add a Linear layer to - project output into a desired feature dimension - """ - super(DeFiNeVisualCore, self).__init__(input_shape=input_shape) - self.flatten = flatten - - self.nets = nn.ModuleDict() - - if backbone_kwargs is None: - backbone_kwargs = dict() - - # add input channel dimension to visual core inputs - backbone_kwargs["input_channel"] = input_shape[0] - - # extract only relevant kwargs for this specific backbone - backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) - - # visual backbone - assert isinstance(backbone_class, str) - self.backbone = eval(backbone_class)(**backbone_kwargs) - - feat_shape = self.backbone.output_shape(input_shape) - self.feat_shape = feat_shape - # net_list = [self.backbone] - self.nets["backbone"] = self.backbone - - post_proc_list = [] - - # maybe make pool net - if pool_class is not None: - assert isinstance(pool_class, str) - # feed output shape of backbone to pool net - if pool_kwargs is None: - pool_kwargs = dict() - # extract only relevant kwargs for this specific backbone - pool_kwargs["input_shape"] = feat_shape - pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) - self.pool = eval(pool_class)(**pool_kwargs) - assert isinstance(self.pool, BaseNets.Module) - - feat_shape = self.pool.output_shape(feat_shape) - post_proc_list.append(self.pool) - else: - self.pool = None - - # flatten layer - if self.flatten: - post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) - - # maybe linear layer - self.feature_dimension = feature_dimension - if feature_dimension is not None: - assert self.flatten - post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) - - self.nets["post_proc"] = nn.Sequential(*post_proc_list) - - def output_shape(self, input_shape): - """ - Function to compute output shape from inputs to this module. - - Args: - input_shape (iterable of int): shape of input. Does not include batch dimension. - Some modules may not need this argument, if their output does not depend - on the size of the input, or if they assume fixed size input. - - Returns: - out_shape ([int]): list of integers corresponding to output shape - """ - if self.feature_dimension is not None: - # linear output - return [self.feature_dimension] - feat_shape = self.backbone.output_shape(input_shape) - if self.pool is not None: - # pool output - feat_shape = self.pool.output_shape(feat_shape) - # backbone + flat output - if self.flatten: - return [np.prod(feat_shape)] - else: - return feat_shape - - def forward(self, inputs): - """ - Forward pass through visual core. - """ - ndim = len(self.input_shape) - assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) - image = inputs["image"] - intrinsics = inputs.get("intrinsics", None) - extrinsics = inputs.get("extrinsics", None) - lang_emb = inputs.get("lang_emb", None) - - # # TODO: replace with actual extrinsics - # extrinsics = TensorUtils.to_torch(np.array([[ - # [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], - # ]] * image.shape[0]), image.device) - - x = self.nets["backbone"].forward(image=image, intrinsics=intrinsics, extrinsics=extrinsics, lang_emb=lang_emb) - # x = self.nets["backbone"].forward(inputs["image"]) - x = self.nets["post_proc"](x) - - return x - - def __repr__(self): - """Pretty print network.""" - header = '{}'.format(str(self.__class__.__name__)) - msg = '' - indent = ' ' * 2 - msg += textwrap.indent( - "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) - msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) - msg += textwrap.indent("\npool_net={}".format(self.pool), indent) - msg = header + '(' + msg + '\n)' - return msg class FiLMVisualCore(EncoderCore, BaseNets.ConvBase): diff --git a/robomimic/scripts/config_gen/act_gen.py b/robomimic/scripts/config_gen/act_gen.py deleted file mode 100644 index 8962941d..00000000 --- a/robomimic/scripts/config_gen/act_gen.py +++ /dev/null @@ -1,131 +0,0 @@ -from robomimic.scripts.config_gen.helper import * - -def make_generator_helper(args): - algo_name_short = "act" - generator = get_generator( - algo_name="act", - config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[64], - ) - - generator.add_param( - key="train.max_grad_norm", - name="", - group=-1, - values=[100.0], - ) - - if args.env == "r2d2": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [{"path": p} for p in scan_datasets("~/Downloads/example_pen_in_cup", postfix="trajectory_im128.h5")], - ], - value_names=[ - "pen-in-cup", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - ) - elif args.env == "kitchen": - raise NotImplementedError - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "TODO.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "~/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/act_gen_no_define.py b/robomimic/scripts/config_gen/act_gen_no_define.py deleted file mode 100644 index 51658b16..00000000 --- a/robomimic/scripts/config_gen/act_gen_no_define.py +++ /dev/null @@ -1,266 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# evaldataset = evaldataset[:200] -# broaddataset = broaddataset[:2000] -random.shuffle(evaldataset) -# random.shuffle(broaddataset) -N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -def make_generator_helper(args): - algo_name_short = "act" - generator = get_generator( - algo_name="act", - config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[2048], # 4096 works for wrist - ) - - generator.add_param( - key="train.max_grad_norm", - name="", - group=-1, - values=[100.0], - ) - - if args.env == "r2d2": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # broaddataset + evaldataset, - # [{"path": p["path"], "weight": (0.5 / N_EVAL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset], - # broaddataset, - evaldataset, - ], - value_names=[ - # "mixed_unbalanced", - # "mixed_balanced", - # "broad", - "eval", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - # None, # Change this to "geom" to do goal conditioning - "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - # 0.5, - 0.3 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - # "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=2498, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - ] - ], - value_names=[ - # "proprio", - "proprio-cam", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="backbone", - group=1234, - values=[ - # "ResNet18Conv", - "ResNet50Conv", - ], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - # 64, - 512, - ], - ) - elif args.env == "kitchen": - raise NotImplementedError - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "TODO.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "~/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/diffusion_gen.py b/robomimic/scripts/config_gen/diffusion_gen.py deleted file mode 100644 index 7c1bba00..00000000 --- a/robomimic/scripts/config_gen/diffusion_gen.py +++ /dev/null @@ -1,480 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] -broaddataset = broaddataset[:2000] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] -N_EVAL_MULTI = len(evaldataset_multitask) -print(N_BROAD, N_EVAL_MULTI) - - -def make_generator_helper(args): - algo_name_short = "diffusion_policy" - - generator = get_generator( - algo_name="diffusion_policy", - config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[8], - ) - - # use ddim by default - generator.add_param( - key="algo.ddim.enabled", - name="ddim", - group=1001, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.ddpm.enabled", - name="ddpm", - group=1001, - values=[ - False, - # True, - ], - hidename=True, - ) - - if args.env == "r2d2": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # broaddataset + evaldataset, - [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], - # broaddataset, - evaldataset_multitask, - ], - value_names=[ - # "mixed_unbalanced", - "mixed_balanced", - # "broad", - "eval_multi", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - # ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - # "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - None, # Change this to "geom" to do goal conditioning - # "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - 0.3, - # 0.5 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - # "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24986, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ] - ], - value_names=[ - # "proprio", - "proprio-cam", - "proprio-cam-lang", - ], - hidename=False, - ) - ## All of the following are needed for DeFiNe style encoding - generator.add_param( - key="observation.encoder.rgb.input_maps", - name="", - group=24986, - values=[ - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - }, - ], - value_names=[ - "define", - "define-lang", - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=-1, - values=["DeFiNeVisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="", - group=-1, - values=["DeFiNeImageCamEncoder"], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - 512, - # None, - # None - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.flatten", - name="flatten", - group=1234, - values=[ - True, - # False, - # False - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.fuser", - name="fuser", - group=1234, - values=[ - None, - # "transformer", - # "perceiver" - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", - name="", - group=1234, - values=[ - True, - # True, - # False, - ], - hidename=False, - ) - - - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.backbone_class", - # name="backbone", - # group=1234, - # values=[ - # # "ResNet18Conv", - # "ResNet50Conv", - # ], - # ) - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.feature_dimension", - # name="visdim", - # group=1234, - # values=[ - # # 64, - # 512, - # ], - # ) - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/diffusion_gen_no_define.py b/robomimic/scripts/config_gen/diffusion_gen_no_define.py deleted file mode 100644 index c022b77f..00000000 --- a/robomimic/scripts/config_gen/diffusion_gen_no_define.py +++ /dev/null @@ -1,399 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] -broaddataset = broaddataset[:2000] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] -N_EVAL_MULTI = len(evaldataset_multitask) -print(N_BROAD, N_EVAL_MULTI) - -def make_generator_helper(args): - algo_name_short = "diffusion_policy" - - generator = get_generator( - algo_name="diffusion_policy", - config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[16], - ) - - # use ddim by default - generator.add_param( - key="algo.ddim.enabled", - name="ddim", - group=1001, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.ddpm.enabled", - name="ddpm", - group=1001, - values=[ - False, - # True, - ], - hidename=True, - ) - - if args.env == "r2d2": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # broaddataset + evaldataset, - [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], - # broaddataset, - evaldataset_multitask, - ], - value_names=[ - # "mixed_unbalanced", - "mixed_balanced", - # "broad", - "eval_multi", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - # ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - # "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - None, # Change this to "geom" to do goal conditioning - # "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - # 0.5, - 0.3 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - # "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24987, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ] - ], - value_names=[ - # "proprio", - "proprio-cam", - "proprio-cam-lang", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.input_maps", - name="", - group=24987, - values=[ - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "lang_emb" : "lang_fixed/language_distilbert" - }, - }, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=-1, - values=["FiLMVisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="backbone", - group=1234, - values=[ - # "ResNet18Conv", - # "ResNet50Conv", - "ResNetFiLMEncoder" - ], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - # 64, - 512, - ], - ) - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py deleted file mode 100644 index e09f172d..00000000 --- a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py +++ /dev/null @@ -1,456 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] -N_BROAD_FULL = len(broaddataset_full) - -chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] -N_CHIPS = len(chips_singletask) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang_filtered.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -# evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl' in l['path']] -# N_EVAL_MULTI = len(evaldataset_multitask) - -# apple_singletask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl/apple' in l['path']] -# N_APPLE = len(apple_singletask) - -## Getting single task eval data (food in bowl) -# evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] -# evaldataset_foodinbowl = evaldataset_foodinbowl[:20] -# N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) - -## Getting single task eval data (open microwave) -# evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] -# N_EVAL_OPENMICRO = len(evaldataset_openmicro) - -## Getting single task eval data (close microwave) -# evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] -# N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) - -print(f"Broad Datapoints: {N_BROAD, N_BROAD_FULL} \ - \nSingle Task CHIPS Datapoints: {N_CHIPS} ") - # \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ - # \nSingle Task APPLE Datapoints: {N_APPLE}") - # # \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") - - -def make_generator_helper(args): - algo_name_short = "bc_xfmr" - - generator = get_generator( - algo_name="bc", - config_file=os.path.join(base_path, 'robomimic/exps/templates/bc_transformer.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[100000], - ) - - generator.add_param( - key="train.batch_size", - name="bz", - group=1212111, - values=[128], - hidename=False, - ) - - if args.env == "r2d2": - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - "wrist", - # "wrist-stereo", - "2cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=130, - values=[ - {}, # jitter only - [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - ### CONDITIONING - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=24986, - values = [ - "geom", - None, # Change this to "geom" to do goal conditioning - - ] - ) - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - 0.3, - ] - ) - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24986, - values=[ - ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "lang_fixed/language_distilbert" - ] - ], - value_names=[ - # "proprio", - "proprio", - "proprio-lang", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", - name="", - group=2498, - values=[ - False, - # True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=-1, - values=["VisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="", - group=-1, - values=["ResNet50Conv"], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - 512, - # None, - # None - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.flatten", - name="flatten", - group=1234, - values=[ - True, - # False, - # False - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.fuser", - name="fuser", - group=1234, - values=[ - None, - # "transformer", - # "perceiver" - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", - name="", - group=1234, - values=[ - False, - ], - hidename=False, - ) - - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - chips_singletask, - [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], - [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], - ], - value_names=[ - "eval_chips", - "balanced_chips", - "full_balanced_chips", - ], - ) - - generator.add_param( - key="algo.transformer.supervise_all_steps", - name="supallsteps", - group=1, - values=[ - True, - ], - hidename=True, - ) - generator.add_param( - key="algo.transformer.pred_future_acs", - name="futureacs", - group=1, - values=[ - False, - ], - hidename=True, - ) - generator.add_param( - key="algo.transformer.causal", - name="causal", - group=1, - values=[ - False, - ], - hidename=True, - ) - generator.add_param( - key="train.frame_stack", - name="", - group=-1, - values=[2], - hidename=True, - ) - generator.add_param( - key="train.seq_length", - name="H", - group=-1, - values=[2], - hidename=False, - ) - generator.add_param( - key="algo.transformer.context_length", - name="", - group=-1, - values=[2], - hidename=True, - ) - generator.add_param( - key="algo.gmm.enabled", - name="", - group=-1, - values=[False], - hidename=True, - ) - - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/voltron.py b/robomimic/scripts/config_gen/voltron.py deleted file mode 100644 index 3d1f29b0..00000000 --- a/robomimic/scripts/config_gen/voltron.py +++ /dev/null @@ -1,593 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] -N_EVAL_MULTI = len(evaldataset_multitask) - -## Getting single task eval data (food in bowl) -evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] -N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) - -## Getting single task eval data (open microwave) -evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] -N_EVAL_OPENMICRO = len(evaldataset_openmicro) - -## Getting single task eval data (close microwave) -evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] -N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) - -print(f"Broad Datapoints: {N_BROAD} \ - \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ - \nSingle Task Food in Bowl Datapoints: {N_EVAL_FOODINBOWL} \ - \nSingle Task Microwave Open Datapoints: {N_EVAL_OPENMICRO} \ - \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") - - -def make_generator_helper(args): - algo_name_short = "diffusion_policy" - - generator = get_generator( - algo_name="diffusion_policy", - config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[100000], - ) - - generator.add_param( - key="train.batch_size", - name="bs", - group=-1, - values=[32], - ) - - # use ddim by default - generator.add_param( - key="algo.ddim.enabled", - name="ddim", - group=1001, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.ddpm.enabled", - name="ddpm", - group=1001, - values=[ - False, - # True, - ], - hidename=True, - ) - - if args.env == "r2d2": - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - # ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - # "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - None, # Change this to "geom" to do goal conditioning - # "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - 0.3, - # 0.5 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=138, - values=[ - "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - # ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - # [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24986, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - # [ - # "robot_state/cartesian_position", "robot_state/gripper_position", - # "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - # "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - # "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - # "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - # "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - # "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - # ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_raw" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - ], - value_names=[ - # "proprio", - "proprio-cam-lang-bert2", - "proprio-cam-lang-bert", - "proprio-cam-lang-raw", - "proprio-cam-lang-bert3", - "proprio-cam-lang-bert4", - ], - hidename=False, - ) - ## All of the following are needed for DeFiNe style encoding - generator.add_param( - key="observation.encoder.rgb.input_maps", - name="", - group=24986, - values=[ - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "lang_emb" : "lang_fixed/language_raw" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "lang_emb" : "lang_fixed/language_raw" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "lang_emb" : "lang_fixed/language_raw" - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - }, - }, - ], - value_names=[ - "r3m-no-lang", - "vc1-no-lang", - "voltron-lang", - "define-film-lang", - "define-nofilm-lang", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=24986, - values=["PretrainedVisualCore", "PretrainedVisualCore", "PretrainedVisualCore", "DeFiNeVisualCore", "DeFiNeVisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="enc", - group=24986, - values=["R3MImageCamEncoder", "VC1ImageCamEncoder", "VoltronImageCamEncoder", "DeFiNeImageCamEncoder", "DeFiNeImageCamEncoder"], - hidename=False, - ) - - - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - - - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - 512, - # None, - # None - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.flatten", - name="flatten", - group=1234, - values=[ - True, - # False, - # False - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.fuser", - name="fuser", - group=1234, - values=[ - None, - # "transformer", - # "perceiver" - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", - name="", - group=1234, - values=[ - True, - # True, - # False, - ], - hidename=False, - ) - - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - broaddataset, - # evaldataset_multitask, - # evaldataset_foodinbowl, - # evaldataset_openmicro, - # evaldataset_closemicro, - # [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], - # [{"path": p["path"], "weight": (0.5 / N_EVAL_FOODINBOWL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_foodinbowl], - # [{"path": p["path"], "weight": (0.5 / N_EVAL_OPENMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_openmicro], - # [{"path": p["path"], "weight": (0.5 / N_EVAL_CLOSEMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_closemicro], - ], - value_names=[ - "broad", - # "eval_multi", - # "eval_foodinbowl", - # "eval_openmicro", - # "eval_closemicro", - # "balanced_broad_eval_multi", - # "balanced_broad_eval_foodinbowl", - # "balanced_broad_eval_openmicro", - # "balanced_broad_eval_closemicro", - ], - ) - - - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.backbone_class", - # name="backbone", - # group=1234, - # values=[ - # # "ResNet18Conv", - # "ResNet50Conv", - # ], - # ) - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.feature_dimension", - # name="visdim", - # group=1234, - # values=[ - # # 64, - # 512, - # ], - # ) - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/conversion/extract_action_dict.py b/robomimic/scripts/conversion/extract_action_dict.py deleted file mode 100644 index 2d3a50e3..00000000 --- a/robomimic/scripts/conversion/extract_action_dict.py +++ /dev/null @@ -1,81 +0,0 @@ -import argparse -import pathlib -import sys -import tqdm -import h5py -import numpy as np -import torch -import os - -import robomimic.utils.torch_utils as TorchUtils - -def extract_action_dict(dataset): - # find files - f = h5py.File(os.path.expanduser(dataset), mode="r+") - - SPECS = [ - dict( - key="actions", - is_absolute=False, - ), - dict( - key="actions_abs", - is_absolute=True, - ) - ] - - # execute - for spec in SPECS: - input_action_key = spec["key"] - is_absolute = spec["is_absolute"] - - if is_absolute: - prefix = "abs_" - else: - prefix = "rel_" - - for demo in f["data"].values(): - in_action = demo[str(input_action_key)][:] - in_pos = in_action[:,:3].astype(np.float32) - in_rot = in_action[:,3:6].astype(np.float32) - in_grip = in_action[:,6:7].astype(np.float32) - - rot_6d = TorchUtils.axis_angle_to_rot_6d( - axis_angle=torch.from_numpy(in_rot) - ) - rot_6d = rot_6d.numpy().astype(np.float32) # convert to numpy - - this_action_dict = { - prefix + "pos": in_pos, - prefix + "rot_axis_angle": in_rot, - prefix + "rot_6d": rot_6d, - "gripper": in_grip - } - - # special case: 8 dim actions mean there is a mobile base mode in the action space - if in_action.shape[1] == 8: - this_action_dict["base_mode"] = in_action[:,7:8].astype(np.float32) - - action_dict_group = demo.require_group("action_dict") - for key, data in this_action_dict.items(): - if key in action_dict_group: - del action_dict_group[key] - action_dict_group.create_dataset(key, data=data) - - f.close() - - -def main(): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--dataset", - type=str, - required=True - ) - - args = parser.parse_args() - extract_action_dict(args.dataset) - -if __name__ == "__main__": - main() diff --git a/robomimic/scripts/conversion/robosuite_add_absolute_actions.py b/robomimic/scripts/conversion/robosuite_add_absolute_actions.py deleted file mode 100644 index 8d634f20..00000000 --- a/robomimic/scripts/conversion/robosuite_add_absolute_actions.py +++ /dev/null @@ -1,298 +0,0 @@ -import multiprocessing -import os -import pathlib -import h5py -from tqdm import tqdm -import collections -import pickle -import argparse -import numpy as np -import copy - -import h5py -import robomimic.utils.obs_utils as ObsUtils -import robomimic.utils.file_utils as FileUtils -import robomimic.utils.env_utils as EnvUtils -from scipy.spatial.transform import Rotation - -from robomimic.config import config_factory - -""" -copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/common/robomimic_util.py -""" -class RobomimicAbsoluteActionConverter: - def __init__(self, dataset_path, algo_name='bc'): - # default BC config - config = config_factory(algo_name=algo_name) - - # read config to set up metadata for observation modalities (e.g. detecting rgb observations) - # must ran before create dataset - ObsUtils.initialize_obs_utils_with_config(config) - - env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path) - abs_env_meta = copy.deepcopy(env_meta) - abs_env_meta['env_kwargs']['controller_configs']['control_delta'] = False - - env = EnvUtils.create_env_from_metadata( - env_meta=env_meta, - render=False, - render_offscreen=False, - use_image_obs=False, - ) - assert len(env.env.robots) in (1, 2) - - abs_env = EnvUtils.create_env_from_metadata( - env_meta=abs_env_meta, - render=False, - render_offscreen=False, - use_image_obs=False, - ) - assert not abs_env.env.robots[0].controller.use_delta - - self.env = env - self.abs_env = abs_env - self.file = h5py.File(dataset_path, 'r') - - def get_demo_keys(self): - return list(self.file['data'].keys()) - - def convert_actions(self, - states: np.ndarray, - actions: np.ndarray) -> np.ndarray: - """ - Given state and delta action sequence - generate equivalent goal position and orientation for each step - keep the original gripper action intact. - """ - env = self.env - d_a = len(env.env.robots[0].action_limits[0]) - - # in case of multi robot - # reshape (N,14) to (N,2,7) - # or (N,7) to (N,1,7) - stacked_actions = actions.reshape(*actions.shape[:-1], -1, d_a) - - # generate abs actions - action_goal_pos = np.zeros( - stacked_actions.shape[:-1]+(3,), - dtype=stacked_actions.dtype) - action_goal_ori = np.zeros( - stacked_actions.shape[:-1]+(3,), - dtype=stacked_actions.dtype) - action_remainder = stacked_actions[...,6:] - for i in range(len(states)): - _ = env.reset_to({'states': states[i]}) - - # taken from robot_env.py L#454 - for idx, robot in enumerate(env.env.robots): - # run controller goal generator - robot.control(stacked_actions[i,idx], policy_step=True) - - # read pos and ori from robots - controller = robot.controller - action_goal_pos[i,idx] = controller.goal_pos - action_goal_ori[i,idx] = Rotation.from_matrix( - controller.goal_ori).as_rotvec() - - stacked_abs_actions = np.concatenate([ - action_goal_pos, - action_goal_ori, - action_remainder - ], axis=-1) - abs_actions = stacked_abs_actions.reshape(actions.shape) - return abs_actions - - def convert_demo(self, demo_key): - file = self.file - demo = file["data/{}".format(demo_key)] - # input - states = demo['states'][:] - actions = demo['actions'][:] - - # generate abs actions - abs_actions = self.convert_actions(states, actions) - return abs_actions - - def convert_and_eval_demo(self, demo_key): - env = self.env - abs_env = self.abs_env - file = self.file - # first step have high error for some reason, not representative - eval_skip_steps = 1 - - demo = file["data/{}".format(demo_key)] - # input - states = demo['states'][:] - actions = demo['actions'][:] - - # generate abs actions - abs_actions = self.convert_actions(states, actions) - - # verify - robot0_eef_pos = demo['obs']['robot0_eef_pos'][:] - robot0_eef_quat = demo['obs']['robot0_eef_quat'][:] - - delta_error_info = self.evaluate_rollout_error( - env, states, actions, robot0_eef_pos, robot0_eef_quat, - metric_skip_steps=eval_skip_steps) - abs_error_info = self.evaluate_rollout_error( - abs_env, states, abs_actions, robot0_eef_pos, robot0_eef_quat, - metric_skip_steps=eval_skip_steps) - - info = { - 'delta_max_error': delta_error_info, - 'abs_max_error': abs_error_info - } - return abs_actions, info - - @staticmethod - def evaluate_rollout_error(env, - states, actions, - robot0_eef_pos, - robot0_eef_quat, - metric_skip_steps=1): - # first step have high error for some reason, not representative - - # evaluate abs actions - rollout_next_states = list() - rollout_next_eef_pos = list() - rollout_next_eef_quat = list() - obs = env.reset_to({'states': states[0]}) - for i in range(len(states)): - obs = env.reset_to({'states': states[i]}) - obs, reward, done, info = env.step(actions[i]) - obs = env.get_observation() - rollout_next_states.append(env.get_state()['states']) - rollout_next_eef_pos.append(obs['robot0_eef_pos']) - rollout_next_eef_quat.append(obs['robot0_eef_quat']) - rollout_next_states = np.array(rollout_next_states) - rollout_next_eef_pos = np.array(rollout_next_eef_pos) - rollout_next_eef_quat = np.array(rollout_next_eef_quat) - - next_state_diff = states[1:] - rollout_next_states[:-1] - max_next_state_diff = np.max(np.abs(next_state_diff[metric_skip_steps:])) - - next_eef_pos_diff = robot0_eef_pos[1:] - rollout_next_eef_pos[:-1] - next_eef_pos_dist = np.linalg.norm(next_eef_pos_diff, axis=-1) - max_next_eef_pos_dist = next_eef_pos_dist[metric_skip_steps:].max() - - next_eef_rot_diff = Rotation.from_quat(robot0_eef_quat[1:]) \ - * Rotation.from_quat(rollout_next_eef_quat[:-1]).inv() - next_eef_rot_dist = next_eef_rot_diff.magnitude() - max_next_eef_rot_dist = next_eef_rot_dist[metric_skip_steps:].max() - - info = { - 'state': max_next_state_diff, - 'pos': max_next_eef_pos_dist, - 'rot': max_next_eef_rot_dist - } - return info - -""" -copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/scripts/robomimic_dataset_conversion.py -""" -def worker(x): - path, demo_key, do_eval = x - converter = RobomimicAbsoluteActionConverter(path) - if do_eval: - abs_actions, info = converter.convert_and_eval_demo(demo_key) - else: - abs_actions = converter.convert_demo(demo_key) - info = dict() - return abs_actions, info - - -def add_absolute_actions_to_dataset(dataset, eval_dir, num_workers): - # process inputs - dataset = pathlib.Path(dataset).expanduser() - assert dataset.is_file() - - do_eval = False - if eval_dir is not None: - eval_dir = pathlib.Path(eval_dir).expanduser() - assert eval_dir.parent.exists() - do_eval = True - - converter = RobomimicAbsoluteActionConverter(dataset) - demo_keys = converter.get_demo_keys() - del converter - - # run - with multiprocessing.Pool(num_workers) as pool: - results = pool.map(worker, [(dataset, demo_key, do_eval) for demo_key in demo_keys]) - - # modify action - with h5py.File(dataset, 'r+') as out_file: - for i in tqdm(range(len(results)), desc="Writing to output"): - abs_actions, info = results[i] - demo = out_file["data/{}".format(demo_keys[i])] - if "actions_abs" not in demo: - demo.create_dataset("actions_abs", data=np.array(abs_actions)) - else: - demo['actions_abs'][:] = abs_actions - - # save eval - if do_eval: - eval_dir.mkdir(parents=False, exist_ok=True) - - print("Writing error_stats.pkl") - infos = [info for _, info in results] - pickle.dump(infos, eval_dir.joinpath('error_stats.pkl').open('wb')) - - print("Generating visualization") - metrics = ['pos', 'rot'] - metrics_dicts = dict() - for m in metrics: - metrics_dicts[m] = collections.defaultdict(list) - - for i in range(len(infos)): - info = infos[i] - for k, v in info.items(): - for m in metrics: - metrics_dicts[m][k].append(v[m]) - - from matplotlib import pyplot as plt - plt.switch_backend('PDF') - - fig, ax = plt.subplots(1, len(metrics)) - for i in range(len(metrics)): - axis = ax[i] - data = metrics_dicts[metrics[i]] - for key, value in data.items(): - axis.plot(value, label=key) - axis.legend() - axis.set_title(metrics[i]) - fig.set_size_inches(10,4) - fig.savefig(str(eval_dir.joinpath('error_stats.pdf'))) - fig.savefig(str(eval_dir.joinpath('error_stats.png'))) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--dataset", - type=str, - required=True - ) - - parser.add_argument( - "--eval_dir", - type=str, - help="directory to output evaluation metrics", - ) - - parser.add_argument( - "--num_workers", - type=int, - default=10, - ) - - args = parser.parse_args() - - - add_absolute_actions_to_dataset( - dataset=args.dataset, - eval_dir=args.eval_dir, - num_workers=args.num_workers, - ) \ No newline at end of file diff --git a/robomimic/scripts/conversion/set_dataset_attr.py b/robomimic/scripts/conversion/set_dataset_attr.py deleted file mode 100644 index 4f148d08..00000000 --- a/robomimic/scripts/conversion/set_dataset_attr.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -Example: -python robomimic/scripts/set_dataset_attr.py --glob 'datasets/**/*_abs.hdf5' --env_args env_kwargs.controller_configs.control_delta=false absolute_actions=true -""" -import argparse -import pathlib -import json -import sys -import tqdm -import h5py - -def update_env_args_dict(env_args_dict: dict, key: tuple, value): - if key is None: - return env_args_dict - elif len(key) == 0: - return env_args_dict - elif len(key) == 1: - env_args_dict[key[0]] = value - return env_args_dict - else: - this_key = key[0] - if this_key not in env_args_dict: - env_args_dict[this_key] = dict() - update_env_args_dict(env_args_dict[this_key], key[1:], value) - return env_args_dict - -def main(): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--glob", - type=str, - required=True - ) - - parser.add_argument( - "--env_args", - type=str, - default=None - ) - - parser.add_argument( - 'attrs', - nargs='*' - ) - - args = parser.parse_args() - - # parse attrs to set - # format: key=value - # values are parsed with json - attrs_dict = dict() - for attr_arg in args.attrs: - key, svalue = attr_arg.split("=") - value = json.loads(svalue) - attrs_dict[key] = value - - # parse env_args update - env_args_key = None - env_args_value = None - if args.env_args is not None: - key, svalue = args.env_args.split('=') - env_args_key = key.split('.') - env_args_value = json.loads(svalue) - - # find files - file_paths = list(pathlib.Path.cwd().glob(args.glob)) - - # confirm with the user - print("Found matching files:") - for f in file_paths: - print(f) - print("Are you sure to modify these files with the following attributes:") - print(json.dumps(attrs_dict, indent=2)) - if env_args_key is not None: - print("env_args."+'.'.join(env_args_key)+'='+str(env_args_value)) - result = input("[y/n]?") - if 'y' not in result: - sys.exit(0) - - # execute - for file_path in tqdm.tqdm(file_paths): - with h5py.File(str(file_path), mode='r+') as file: - # update env_args - if env_args_key is not None: - env_args = file['data'].attrs['env_args'] - env_args_dict = json.loads(env_args) - env_args_dict = update_env_args_dict( - env_args_dict=env_args_dict, - key=env_args_key, value=env_args_value) - env_args = json.dumps(env_args_dict) - file['data'].attrs['env_args'] = env_args - - # update other attrs - file['data'].attrs.update(attrs_dict) - -if __name__ == "__main__": - main() diff --git a/runallruns.sh b/runallruns.sh deleted file mode 100644 index 8cb238f4..00000000 --- a/runallruns.sh +++ /dev/null @@ -1,89 +0,0 @@ -#!/bin/bash - -# bash /mnt/fsx/surajnair/code/robomimic/runallruns.sh -# List of commands to run - -# # P4D1 -# commands=( -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_multi_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_foodinbowl_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_openmicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_closemicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_multi_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_foodinbowl_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_openmicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_closemicro_cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# ) - -# # P4D2 -# commands=( -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_multi.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_foodinbowl.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_openmicro.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_eval_closemicro.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_multi.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_foodinbowl.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_openmicro.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-40-14/json/cams_wrist_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_closemicro.json" -# ) - -# # P4D3 (Ashwin P4D1) -commands=( -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_multi.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_foodinbowl.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_openmicro.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_closemicro.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_multi.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_foodinbowl.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_openmicro.json" -"/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_wrist_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_closemicro.json" -) - -# # P4DE 1 -# commands=( -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_multi_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_foodinbowl_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_openmicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_eval_closemicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_multi_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_foodinbowl_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_openmicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-16-01/json/ds_balanced_broad_eval_closemicro_cams_3cams_goal_mode_None_truncated_geom_factor_0.3_ldkeys_proprio-cam-lang_visenc_DeFiNeVisualCore_fuser_None.json" -# ) - -# # P4DE 2 -# commands=( -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_multi.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_foodinbowl.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_openmicro.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_eval_closemicro.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_multi.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_foodinbowl.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_openmicro.json" -# "/mnt/fsx/surajnair/mambaforge/envs/r2d2/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/train.py --config /mnt/fsx/surajnair/tmp/autogen_configs/ril/diffusion_policy/r2d2/im/12-29-None/12-29-23-01-43-10/json/cams_3cams_goal_mode_geom_truncated_geom_factor_0.3_ldkeys_proprio-cam_visenc_DeFiNeVisualCore_fuser_None_ds_balanced_broad_eval_closemicro.json" -# ) - - - -# Number of GPUs available (adjust as needed) -num_gpus=8 - -# Loop through the commands and launch them in separate screen sessions -for ((i=0; i<${#commands[@]}; i++)); do - # Get the current command - cmd="${commands[$i]}" - - # Calculate the GPU index for this command - gpu_index=$((i % num_gpus)) - - # Set the GPU index for this command - export CUDA_VISIBLE_DEVICES=$gpu_index - - # Create a unique screen session for each command - screen -dmS r2d2_$i $cmd - - # Sleep for a few seconds to allow screen to start - sleep 5 -done - -echo "Launched ${#commands[@]} commands in separate screen sessions." From c0ddf13a2c93344971135db0563dc863a7ae8d71 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Thu, 7 Mar 2024 01:19:27 +0000 Subject: [PATCH 04/43] release code --- act/.gitignore | 140 +++++ act/LICENSE | 21 + act/README.md | 89 +++ act/assets/bimanual_viperx_ee_insertion.xml | 59 ++ .../bimanual_viperx_ee_transfer_cube.xml | 48 ++ act/assets/bimanual_viperx_insertion.xml | 53 ++ act/assets/bimanual_viperx_transfer_cube.xml | 42 ++ act/assets/scene.xml | 38 ++ act/assets/tabletop.stl | Bin 0 -> 684 bytes act/assets/vx300s_10_custom_finger_left.stl | Bin 0 -> 83384 bytes act/assets/vx300s_10_custom_finger_right.stl | Bin 0 -> 83384 bytes act/assets/vx300s_10_gripper_finger.stl | Bin 0 -> 42884 bytes act/assets/vx300s_11_ar_tag.stl | Bin 0 -> 3884 bytes act/assets/vx300s_1_base.stl | Bin 0 -> 99984 bytes act/assets/vx300s_2_shoulder.stl | Bin 0 -> 63884 bytes act/assets/vx300s_3_upper_arm.stl | Bin 0 -> 102984 bytes act/assets/vx300s_4_upper_forearm.stl | Bin 0 -> 49584 bytes act/assets/vx300s_5_lower_forearm.stl | Bin 0 -> 99884 bytes act/assets/vx300s_6_wrist.stl | Bin 0 -> 70784 bytes act/assets/vx300s_7_gripper.stl | Bin 0 -> 450084 bytes act/assets/vx300s_8_gripper_prop.stl | Bin 0 -> 31684 bytes act/assets/vx300s_9_gripper_bar.stl | Bin 0 -> 379484 bytes act/assets/vx300s_dependencies.xml | 17 + act/assets/vx300s_left.xml | 59 ++ act/assets/vx300s_right.xml | 59 ++ act/conda_env.yaml | 23 + act/constants.py | 76 +++ act/detr/LICENSE | 201 ++++++ act/detr/README.md | 9 + act/detr/main.py | 109 ++++ act/detr/models/__init__.py | 9 + act/detr/models/backbone.py | 122 ++++ act/detr/models/detr_vae.py | 284 +++++++++ act/detr/models/position_encoding.py | 93 +++ act/detr/models/transformer.py | 314 ++++++++++ act/detr/setup.py | 10 + act/detr/util/__init__.py | 1 + act/detr/util/box_ops.py | 88 +++ act/detr/util/misc.py | 468 ++++++++++++++ act/detr/util/plot_utils.py | 107 ++++ act/ee_sim_env.py | 267 ++++++++ act/imitate_episodes.py | 435 +++++++++++++ act/policy.py | 84 +++ act/record_sim_episodes.py | 189 ++++++ act/scripted_policy.py | 194 ++++++ act/sim_env.py | 278 ++++++++ act/utils.py | 189 ++++++ act/visualize_episodes.py | 147 +++++ requirements.txt | 1 + robomimic/algo/__init__.py | 1 + robomimic/algo/act.py | 265 ++++++++ robomimic/config/__init__.py | 2 + robomimic/config/act_config.py | 47 ++ robomimic/exps/templates/act.json | 160 +++++ robomimic/exps/templates/bc_transformer.json | 172 +++++ robomimic/models/obs_core.py | 278 ++++++++ robomimic/scripts/config_gen/act_gen.py | 131 ++++ .../scripts/config_gen/act_gen_no_define.py | 266 ++++++++ robomimic/scripts/config_gen/diffusion_gen.py | 480 ++++++++++++++ .../config_gen/diffusion_gen_no_define.py | 399 ++++++++++++ .../scripts/config_gen/r2d2_runs_bcxfmr.py | 456 ++++++++++++++ .../r2d2_runs_language_conditioned.py | 8 +- .../r2d2_runs_language_conditioned_rlds.py | 6 +- robomimic/scripts/config_gen/voltron.py | 593 ++++++++++++++++++ .../scripts/conversion/extract_action_dict.py | 81 +++ .../robosuite_add_absolute_actions.py | 298 +++++++++ .../scripts/conversion/set_dataset_attr.py | 98 +++ robomimic/scripts/train.py | 13 +- 68 files changed, 8062 insertions(+), 15 deletions(-) create mode 100644 act/.gitignore create mode 100644 act/LICENSE create mode 100644 act/README.md create mode 100644 act/assets/bimanual_viperx_ee_insertion.xml create mode 100644 act/assets/bimanual_viperx_ee_transfer_cube.xml create mode 100644 act/assets/bimanual_viperx_insertion.xml create mode 100644 act/assets/bimanual_viperx_transfer_cube.xml create mode 100644 act/assets/scene.xml create mode 100644 act/assets/tabletop.stl create mode 100644 act/assets/vx300s_10_custom_finger_left.stl create mode 100644 act/assets/vx300s_10_custom_finger_right.stl create mode 100644 act/assets/vx300s_10_gripper_finger.stl create mode 100644 act/assets/vx300s_11_ar_tag.stl create mode 100644 act/assets/vx300s_1_base.stl create mode 100644 act/assets/vx300s_2_shoulder.stl create mode 100644 act/assets/vx300s_3_upper_arm.stl create mode 100644 act/assets/vx300s_4_upper_forearm.stl create mode 100644 act/assets/vx300s_5_lower_forearm.stl create mode 100644 act/assets/vx300s_6_wrist.stl create mode 100644 act/assets/vx300s_7_gripper.stl create mode 100644 act/assets/vx300s_8_gripper_prop.stl create mode 100644 act/assets/vx300s_9_gripper_bar.stl create mode 100644 act/assets/vx300s_dependencies.xml create mode 100644 act/assets/vx300s_left.xml create mode 100644 act/assets/vx300s_right.xml create mode 100644 act/conda_env.yaml create mode 100644 act/constants.py create mode 100644 act/detr/LICENSE create mode 100644 act/detr/README.md create mode 100644 act/detr/main.py create mode 100644 act/detr/models/__init__.py create mode 100644 act/detr/models/backbone.py create mode 100644 act/detr/models/detr_vae.py create mode 100644 act/detr/models/position_encoding.py create mode 100644 act/detr/models/transformer.py create mode 100644 act/detr/setup.py create mode 100644 act/detr/util/__init__.py create mode 100644 act/detr/util/box_ops.py create mode 100644 act/detr/util/misc.py create mode 100644 act/detr/util/plot_utils.py create mode 100644 act/ee_sim_env.py create mode 100644 act/imitate_episodes.py create mode 100644 act/policy.py create mode 100644 act/record_sim_episodes.py create mode 100644 act/scripted_policy.py create mode 100644 act/sim_env.py create mode 100644 act/utils.py create mode 100644 act/visualize_episodes.py create mode 100644 robomimic/algo/act.py create mode 100644 robomimic/config/act_config.py create mode 100644 robomimic/exps/templates/act.json create mode 100644 robomimic/exps/templates/bc_transformer.json create mode 100644 robomimic/scripts/config_gen/act_gen.py create mode 100644 robomimic/scripts/config_gen/act_gen_no_define.py create mode 100644 robomimic/scripts/config_gen/diffusion_gen.py create mode 100644 robomimic/scripts/config_gen/diffusion_gen_no_define.py create mode 100644 robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py create mode 100644 robomimic/scripts/config_gen/voltron.py create mode 100644 robomimic/scripts/conversion/extract_action_dict.py create mode 100644 robomimic/scripts/conversion/robosuite_add_absolute_actions.py create mode 100644 robomimic/scripts/conversion/set_dataset_attr.py diff --git a/act/.gitignore b/act/.gitignore new file mode 100644 index 00000000..d1e4c693 --- /dev/null +++ b/act/.gitignore @@ -0,0 +1,140 @@ +bin +logs +wandb +outputs +data +data_local +.vscode +_wandb + +**/.DS_Store + +fuse.cfg + +*.ai + +# Generation results +results/ + +ray/auth.json + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ \ No newline at end of file diff --git a/act/LICENSE b/act/LICENSE new file mode 100644 index 00000000..35e5f5e2 --- /dev/null +++ b/act/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Tony Z. Zhao + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/act/README.md b/act/README.md new file mode 100644 index 00000000..2a345157 --- /dev/null +++ b/act/README.md @@ -0,0 +1,89 @@ +# ACT: Action Chunking with Transformers + +### *New*: [ACT tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) +TL;DR: if your ACT policy is jerky or pauses in the middle of an episode, just train for longer! Success rate and smoothness can improve way after loss plateaus. + +#### Project Website: https://tonyzhaozh.github.io/aloha/ + +This repo contains the implementation of ACT, together with 2 simulated environments: +Transfer Cube and Bimanual Insertion. You can train and evaluate ACT in sim or real. +For real, you would also need to install [ALOHA](https://github.com/tonyzhaozh/aloha). + +### Updates: +You can find all scripted/human demo for simulated environments [here](https://drive.google.com/drive/folders/1gPR03v05S1xiInoVJn7G7VJ9pDCnxq9O?usp=share_link). + + +### Repo Structure +- ``imitate_episodes.py`` Train and Evaluate ACT +- ``policy.py`` An adaptor for ACT policy +- ``detr`` Model definitions of ACT, modified from DETR +- ``sim_env.py`` Mujoco + DM_Control environments with joint space control +- ``ee_sim_env.py`` Mujoco + DM_Control environments with EE space control +- ``scripted_policy.py`` Scripted policies for sim environments +- ``constants.py`` Constants shared across files +- ``utils.py`` Utils such as data loading and helper functions +- ``visualize_episodes.py`` Save videos from a .hdf5 dataset + + +### Installation + + conda create -n aloha python=3.8 + conda activate aloha + pip install torchvision + pip install torch + pip install pyquaternion + pip install pyyaml + pip install rospkg + pip install pexpect + pip install mujoco + pip install dm_control + pip install opencv-python + pip install matplotlib + pip install einops + pip install packaging + pip install h5py + pip install ipython + cd act/detr && pip install -e . + +### Example Usages + +To set up a new terminal, run: + + conda activate aloha + cd + +### Simulated experiments + +We use ``sim_transfer_cube_scripted`` task in the examples below. Another option is ``sim_insertion_scripted``. +To generated 50 episodes of scripted data, run: + + python3 record_sim_episodes.py \ + --task_name sim_transfer_cube_scripted \ + --dataset_dir \ + --num_episodes 50 + +To can add the flag ``--onscreen_render`` to see real-time rendering. +To visualize the episode after it is collected, run + + python3 visualize_episodes.py --dataset_dir --episode_idx 0 + +To train ACT: + + # Transfer Cube task + python3 imitate_episodes.py \ + --task_name sim_transfer_cube_scripted \ + --ckpt_dir \ + --policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \ + --num_epochs 2000 --lr 1e-5 \ + --seed 0 + + +To evaluate the policy, run the same command but add ``--eval``. This loads the best validation checkpoint. +The success rate should be around 90% for transfer cube, and around 50% for insertion. +To enable temporal ensembling, add flag ``--temporal_agg``. +Videos will be saved to ```` for each rollout. +You can also add ``--onscreen_render`` to see real-time rendering during evaluation. + +For real-world data where things can be harder to model, train for at least 5000 epochs or 3-4 times the length after the loss has plateaued. +Please refer to [tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) for more info. + diff --git a/act/assets/bimanual_viperx_ee_insertion.xml b/act/assets/bimanual_viperx_ee_insertion.xml new file mode 100644 index 00000000..700aaac5 --- /dev/null +++ b/act/assets/bimanual_viperx_ee_insertion.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/bimanual_viperx_ee_transfer_cube.xml b/act/assets/bimanual_viperx_ee_transfer_cube.xml new file mode 100644 index 00000000..25893842 --- /dev/null +++ b/act/assets/bimanual_viperx_ee_transfer_cube.xml @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/bimanual_viperx_insertion.xml b/act/assets/bimanual_viperx_insertion.xml new file mode 100644 index 00000000..f701d70a --- /dev/null +++ b/act/assets/bimanual_viperx_insertion.xml @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/bimanual_viperx_transfer_cube.xml b/act/assets/bimanual_viperx_transfer_cube.xml new file mode 100644 index 00000000..bdc9e644 --- /dev/null +++ b/act/assets/bimanual_viperx_transfer_cube.xml @@ -0,0 +1,42 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + 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zi)T!CA^Hl@PzaXVIO*#0jxBy;cWtrvo;;X@I9-S>LU0Zf6PsOIKB3RR$X(~Z8Z<8a z#}(u3`W<|lae_7cjR(K?T4vSjH%71iX1wf0cE3B%2kbfWo%|YFNdBmDb%b~-f=HK2_roZP9hI5$E^yE{n6CJ%o#}}@31IZyple#$2@TG>GUSN;Vd zOY!P!l5l=%V0lMHa;HV@{#q#;tt&cAkd#t8@OxfDpuw5zuU{V3)ZpFHr4QAdY|}%o ze&P#aOGwf7549VdsV!^pYD$yt_Xgh6t-k41;B5sW_4P9;D8`x}7 zeY3Wk5c1^l>TZb|&kod3{sQ;U5-gRi72A;|0AyQO?XXgLh{t0cF~L%v<+ZKg>Mv^S y-(as+WI2_TB?1BDpChvE!#X^|jlwLEp+2|zxOH9sJ4xL)>g#Z{Yb&H3@&5pgFc6pk literal 0 HcmV?d00001 diff --git a/act/assets/vx300s_dependencies.xml b/act/assets/vx300s_dependencies.xml new file mode 100644 index 00000000..c75d3ad5 --- /dev/null +++ b/act/assets/vx300s_dependencies.xml @@ -0,0 +1,17 @@ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/vx300s_left.xml b/act/assets/vx300s_left.xml new file mode 100644 index 00000000..61e6219e --- /dev/null +++ b/act/assets/vx300s_left.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/assets/vx300s_right.xml b/act/assets/vx300s_right.xml new file mode 100644 index 00000000..2c6f007c --- /dev/null +++ b/act/assets/vx300s_right.xml @@ -0,0 +1,59 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/act/conda_env.yaml b/act/conda_env.yaml new file mode 100644 index 00000000..0f44d6b0 --- /dev/null +++ b/act/conda_env.yaml @@ -0,0 +1,23 @@ +name: aloha +channels: + - pytorch + - nvidia + - conda-forge +dependencies: + - python=3.9 + - pip=23.0.1 + - pytorch=2.0.0 + - torchvision=0.15.0 + - pytorch-cuda=11.8 + - pyquaternion=0.9.9 + - pyyaml=6.0 + - rospkg=1.5.0 + - pexpect=4.8.0 + - mujoco=2.3.3 + - dm_control=1.0.9 + - py-opencv=4.7.0 + - matplotlib=3.7.1 + - einops=0.6.0 + - packaging=23.0 + - h5py=3.8.0 + - ipython=8.12.0 diff --git a/act/constants.py b/act/constants.py new file mode 100644 index 00000000..f445350a --- /dev/null +++ b/act/constants.py @@ -0,0 +1,76 @@ +import pathlib + +### Task parameters +DATA_DIR = '' +SIM_TASK_CONFIGS = { + 'sim_transfer_cube_scripted':{ + 'dataset_dir': DATA_DIR + '/sim_transfer_cube_scripted', + 'num_episodes': 50, + 'episode_len': 400, + 'camera_names': ['top'] + }, + + 'sim_transfer_cube_human':{ + 'dataset_dir': DATA_DIR + '/sim_transfer_cube_human', + 'num_episodes': 50, + 'episode_len': 400, + 'camera_names': ['top'] + }, + + 'sim_insertion_scripted': { + 'dataset_dir': DATA_DIR + '/sim_insertion_scripted', + 'num_episodes': 50, + 'episode_len': 400, + 'camera_names': ['top'] + }, + + 'sim_insertion_human': { + 'dataset_dir': DATA_DIR + '/sim_insertion_human', + 'num_episodes': 50, + 'episode_len': 500, + 'camera_names': ['top'] + }, +} + +### Simulation envs fixed constants +DT = 0.02 +JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] +START_ARM_POSE = [0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239, 0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239] + +XML_DIR = str(pathlib.Path(__file__).parent.resolve()) + '/assets/' # note: absolute path + +# Left finger position limits (qpos[7]), right_finger = -1 * left_finger +MASTER_GRIPPER_POSITION_OPEN = 0.02417 +MASTER_GRIPPER_POSITION_CLOSE = 0.01244 +PUPPET_GRIPPER_POSITION_OPEN = 0.05800 +PUPPET_GRIPPER_POSITION_CLOSE = 0.01844 + +# Gripper joint limits (qpos[6]) +MASTER_GRIPPER_JOINT_OPEN = 0.3083 +MASTER_GRIPPER_JOINT_CLOSE = -0.6842 +PUPPET_GRIPPER_JOINT_OPEN = 1.4910 +PUPPET_GRIPPER_JOINT_CLOSE = -0.6213 + +############################ Helper functions ############################ + +MASTER_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_POSITION_CLOSE) / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) +PUPPET_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_POSITION_CLOSE) / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) +MASTER_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) + MASTER_GRIPPER_POSITION_CLOSE +PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + PUPPET_GRIPPER_POSITION_CLOSE +MASTER2PUPPET_POSITION_FN = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(MASTER_GRIPPER_POSITION_NORMALIZE_FN(x)) + +MASTER_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) +PUPPET_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) +MASTER_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE +PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE +MASTER2PUPPET_JOINT_FN = lambda x: PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN(MASTER_GRIPPER_JOINT_NORMALIZE_FN(x)) + +MASTER_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) +PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + +MASTER_POS2JOINT = lambda x: MASTER_GRIPPER_POSITION_NORMALIZE_FN(x) * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE +MASTER_JOINT2POS = lambda x: MASTER_GRIPPER_POSITION_UNNORMALIZE_FN((x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)) +PUPPET_POS2JOINT = lambda x: PUPPET_GRIPPER_POSITION_NORMALIZE_FN(x) * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE +PUPPET_JOINT2POS = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN((x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)) + +MASTER_GRIPPER_JOINT_MID = (MASTER_GRIPPER_JOINT_OPEN + MASTER_GRIPPER_JOINT_CLOSE)/2 diff --git a/act/detr/LICENSE b/act/detr/LICENSE new file mode 100644 index 00000000..b1395e94 --- /dev/null +++ b/act/detr/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2020 - present, Facebook, Inc + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/act/detr/README.md b/act/detr/README.md new file mode 100644 index 00000000..500b1b8d --- /dev/null +++ b/act/detr/README.md @@ -0,0 +1,9 @@ +This part of the codebase is modified from DETR https://github.com/facebookresearch/detr under APACHE 2.0. + + @article{Carion2020EndtoEndOD, + title={End-to-End Object Detection with Transformers}, + author={Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, + journal={ArXiv}, + year={2020}, + volume={abs/2005.12872} + } \ No newline at end of file diff --git a/act/detr/main.py b/act/detr/main.py new file mode 100644 index 00000000..1e44949e --- /dev/null +++ b/act/detr/main.py @@ -0,0 +1,109 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import argparse +from pathlib import Path + +import numpy as np +import torch +from .models import build_ACT_model, build_CNNMLP_model + +import IPython +e = IPython.embed + +def get_args_parser(): + parser = argparse.ArgumentParser('Set transformer detector', add_help=False) + parser.add_argument('--lr', default=1e-4, type=float) # will be overridden + parser.add_argument('--lr_backbone', default=1e-5, type=float) # will be overridden + parser.add_argument('--batch_size', default=2, type=int) # not used + parser.add_argument('--weight_decay', default=1e-4, type=float) + parser.add_argument('--epochs', default=300, type=int) # not used + parser.add_argument('--lr_drop', default=200, type=int) # not used + parser.add_argument('--clip_max_norm', default=0.1, type=float, # not used + help='gradient clipping max norm') + + # Model parameters + # * Backbone + parser.add_argument('--backbone', default='resnet18', type=str, # will be overridden + help="Name of the convolutional backbone to use") + parser.add_argument('--dilation', action='store_true', + help="If true, we replace stride with dilation in the last convolutional block (DC5)") + parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'), + help="Type of positional embedding to use on top of the image features") + parser.add_argument('--camera_names', default=[], type=list, # will be overridden + help="A list of camera names") + + # * Transformer + parser.add_argument('--enc_layers', default=4, type=int, # will be overridden + help="Number of encoding layers in the transformer") + parser.add_argument('--dec_layers', default=6, type=int, # will be overridden + help="Number of decoding layers in the transformer") + parser.add_argument('--dim_feedforward', default=2048, type=int, # will be overridden + help="Intermediate size of the feedforward layers in the transformer blocks") + parser.add_argument('--hidden_dim', default=256, type=int, # will be overridden + help="Size of the embeddings (dimension of the transformer)") + parser.add_argument('--dropout', default=0.1, type=float, + help="Dropout applied in the transformer") + parser.add_argument('--nheads', default=8, type=int, # will be overridden + help="Number of attention heads inside the transformer's attentions") + parser.add_argument('--num_queries', default=400, type=int, # will be overridden + help="Number of query slots") + parser.add_argument('--pre_norm', action='store_true') + + # * Segmentation + parser.add_argument('--masks', action='store_true', + help="Train segmentation head if the flag is provided") + + parser.add_argument('--a_dim', default=-1, type=float, + help="Action dim") + parser.add_argument('--latent_dim', default=-1, type=float, + help="Latent dim") + parser.add_argument('--state_dim', default=-1, type=float, + help="State dim") + + return parser + + +def build_ACT_model_and_optimizer(args_override): + parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) + args, _ = parser.parse_known_args() + + for k, v in args_override.items(): + setattr(args, k, v) + + model = build_ACT_model(args) + model.cuda() + + param_dicts = [ + {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, + { + "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], + "lr": args.lr_backbone, + }, + ] + optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, + weight_decay=args.weight_decay) + + return model, optimizer + + +def build_CNNMLP_model_and_optimizer(args_override): + parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) + args = parser.parse_args() + + for k, v in args_override.items(): + setattr(args, k, v) + + model = build_CNNMLP_model(args) + model.cuda() + + param_dicts = [ + {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, + { + "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], + "lr": args.lr_backbone, + }, + ] + optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, + weight_decay=args.weight_decay) + + return model, optimizer + diff --git a/act/detr/models/__init__.py b/act/detr/models/__init__.py new file mode 100644 index 00000000..cc78db10 --- /dev/null +++ b/act/detr/models/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +from .detr_vae import build as build_vae +from .detr_vae import build_cnnmlp as build_cnnmlp + +def build_ACT_model(args): + return build_vae(args) + +def build_CNNMLP_model(args): + return build_cnnmlp(args) \ No newline at end of file diff --git a/act/detr/models/backbone.py b/act/detr/models/backbone.py new file mode 100644 index 00000000..f28637ea --- /dev/null +++ b/act/detr/models/backbone.py @@ -0,0 +1,122 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Backbone modules. +""" +from collections import OrderedDict + +import torch +import torch.nn.functional as F +import torchvision +from torch import nn +from torchvision.models._utils import IntermediateLayerGetter +from typing import Dict, List + +from util.misc import NestedTensor, is_main_process + +from .position_encoding import build_position_encoding + +import IPython +e = IPython.embed + +class FrozenBatchNorm2d(torch.nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, + without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101] + produce nans. + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + num_batches_tracked_key = prefix + 'num_batches_tracked' + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super(FrozenBatchNorm2d, self)._load_from_state_dict( + state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs) + + def forward(self, x): + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + eps = 1e-5 + scale = w * (rv + eps).rsqrt() + bias = b - rm * scale + return x * scale + bias + + +class BackboneBase(nn.Module): + + def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): + super().__init__() + # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this? + # if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: + # parameter.requires_grad_(False) + if return_interm_layers: + return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} + else: + return_layers = {'layer4': "0"} + self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) + self.num_channels = num_channels + + def forward(self, tensor): + xs = self.body(tensor) + return xs + # out: Dict[str, NestedTensor] = {} + # for name, x in xs.items(): + # m = tensor_list.mask + # assert m is not None + # mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] + # out[name] = NestedTensor(x, mask) + # return out + + +class Backbone(BackboneBase): + """ResNet backbone with frozen BatchNorm.""" + def __init__(self, name: str, + train_backbone: bool, + return_interm_layers: bool, + dilation: bool): + backbone = getattr(torchvision.models, name)( + replace_stride_with_dilation=[False, False, dilation], + pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) # pretrained # TODO do we want frozen batch_norm?? + num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 + super().__init__(backbone, train_backbone, num_channels, return_interm_layers) + + +class Joiner(nn.Sequential): + def __init__(self, backbone, position_embedding): + super().__init__(backbone, position_embedding) + + def forward(self, tensor_list: NestedTensor): + xs = self[0](tensor_list) + out: List[NestedTensor] = [] + pos = [] + for name, x in xs.items(): + out.append(x) + # position encoding + pos.append(self[1](x).to(x.dtype)) + + return out, pos + + +def build_backbone(args): + position_embedding = build_position_encoding(args) + train_backbone = args.lr_backbone > 0 + return_interm_layers = args.masks + backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation) + model = Joiner(backbone, position_embedding) + model.num_channels = backbone.num_channels + return model diff --git a/act/detr/models/detr_vae.py b/act/detr/models/detr_vae.py new file mode 100644 index 00000000..dbde581e --- /dev/null +++ b/act/detr/models/detr_vae.py @@ -0,0 +1,284 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DETR model and criterion classes. +""" +import torch +from torch import nn +from torch.autograd import Variable +from .backbone import build_backbone +from .transformer import build_transformer, TransformerEncoder, TransformerEncoderLayer + +import numpy as np + +import IPython +e = IPython.embed + + +def reparametrize(mu, logvar): + std = logvar.div(2).exp() + eps = Variable(std.data.new(std.size()).normal_()) + return mu + std * eps + + +def get_sinusoid_encoding_table(n_position, d_hid): + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.FloatTensor(sinusoid_table).unsqueeze(0) + + +class DETRVAE(nn.Module): + """ This is the DETR module that performs object detection """ + def __init__(self, backbones, transformer, encoder, latent_dim, a_dim, state_dim, num_queries, camera_names): + """ Initializes the model. + Parameters: + backbones: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + state_dim: robot state dimension of the environment + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + DETR can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + """ + super().__init__() + self.action_dim = a_dim + self.latent_dim = latent_dim + self.state_dim = state_dim + + self.num_queries = num_queries + self.camera_names = camera_names + self.transformer = transformer + self.encoder = encoder + hidden_dim = transformer.d_model + self.action_head = nn.Linear(hidden_dim, self.action_dim) + self.is_pad_head = nn.Linear(hidden_dim, 1) + self.query_embed = nn.Embedding(num_queries, hidden_dim) + if backbones is not None: + self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1) + self.backbones = nn.ModuleList(backbones) + self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) + else: + # input_dim = 14 + 7 # robot_state + env_state + self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) + self.input_proj_env_state = nn.Linear(10, hidden_dim) # TODO not used in robomimic + self.pos = torch.nn.Embedding(2, hidden_dim) + self.backbones = None + + # encoder extra parameters + self.latent_dim = self.latent_dim # final size of latent z # TODO tune + self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding + self.encoder_action_proj = nn.Linear(self.action_dim, hidden_dim) # project action to embedding + self.encoder_joint_proj = nn.Linear(state_dim, hidden_dim) # project qpos to embedding + self.latent_proj = nn.Linear(hidden_dim, self.latent_dim*2) # project hidden state to latent std, var + self.register_buffer('pos_table', get_sinusoid_encoding_table(1+1+num_queries, hidden_dim)) # [CLS], qpos, a_seq + + # decoder extra parameters + self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim) # project latent sample to embedding + self.additional_pos_embed = nn.Embedding(2, hidden_dim) # learned position embedding for proprio and latent + + def forward(self, qpos, image, env_state, actions=None, is_pad=None): + """ + qpos: batch, qpos_dim + image: batch, num_cam, channel, height, width + env_state: None + actions: batch, seq, action_dim + """ + is_training = actions is not None # train or val + bs, _ = qpos.shape + ### Obtain latent z from action sequence + if is_training: + # project action sequence to embedding dim, and concat with a CLS token + action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim) + qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim) + qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim) + cls_embed = self.cls_embed.weight # (1, hidden_dim) + cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim) + encoder_input = torch.cat([cls_embed, qpos_embed, action_embed], axis=1) # (bs, seq+2, hidden_dim) + encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim) + # do not mask cls token + cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding + is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1) + # obtain position embedding + pos_embed = self.pos_table.clone().detach() + pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim) + # query model + encoder_output = self.encoder(encoder_input, pos=pos_embed, src_key_padding_mask=is_pad) + encoder_output = encoder_output[0] # take cls output only + latent_info = self.latent_proj(encoder_output) + mu = latent_info[:, :self.latent_dim] + logvar = latent_info[:, self.latent_dim:] + latent_sample = reparametrize(mu, logvar) + latent_input = self.latent_out_proj(latent_sample) + else: + mu = logvar = None + latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device) + latent_input = self.latent_out_proj(latent_sample) + + if self.backbones is not None: + # Image observation features and position embeddings + all_cam_features = [] + all_cam_pos = [] + for cam_id, cam_name in enumerate(self.camera_names): + features, pos = self.backbones[cam_id](image[:, cam_id]) + features = features[0] # take the last layer feature + pos = pos[0] + all_cam_features.append(self.input_proj(features)) + all_cam_pos.append(pos) + # proprioception features + proprio_input = self.input_proj_robot_state(qpos) + # fold camera dimension into width dimension + src = torch.cat(all_cam_features, axis=3) + pos = torch.cat(all_cam_pos, axis=3) + hs = self.transformer(src, None, self.query_embed.weight, pos, latent_input, proprio_input, self.additional_pos_embed.weight)[0] + else: + qpos = self.input_proj_robot_state(qpos).unsqueeze(dim=1) + env_state = self.input_proj_env_state(env_state).unsqueeze(dim=1) + transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2 + hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0] + a_hat = self.action_head(hs) + is_pad_hat = self.is_pad_head(hs) + return a_hat, is_pad_hat, [mu, logvar] + + + +class CNNMLP(nn.Module): + def __init__(self, backbones, state_dim, camera_names): + """ Initializes the model. + Parameters: + backbones: torch module of the backbone to be used. See backbone.py + transformer: torch module of the transformer architecture. See transformer.py + state_dim: robot state dimension of the environment + num_queries: number of object queries, ie detection slot. This is the maximal number of objects + DETR can detect in a single image. For COCO, we recommend 100 queries. + aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. + """ + super().__init__() + self.camera_names = camera_names + self.action_head = nn.Linear(1000, state_dim) # TODO add more + if backbones is not None: + self.backbones = nn.ModuleList(backbones) + backbone_down_projs = [] + for backbone in backbones: + down_proj = nn.Sequential( + nn.Conv2d(backbone.num_channels, 128, kernel_size=5), + nn.Conv2d(128, 64, kernel_size=5), + nn.Conv2d(64, 32, kernel_size=5) + ) + backbone_down_projs.append(down_proj) + self.backbone_down_projs = nn.ModuleList(backbone_down_projs) + + mlp_in_dim = 768 * len(backbones) + 14 + self.mlp = mlp(input_dim=mlp_in_dim, hidden_dim=1024, output_dim=14, hidden_depth=2) + else: + raise NotImplementedError + + def forward(self, qpos, image, env_state, actions=None): + """ + qpos: batch, qpos_dim + image: batch, num_cam, channel, height, width + env_state: None + actions: batch, seq, action_dim + """ + is_training = actions is not None # train or val + bs, _ = qpos.shape + # Image observation features and position embeddings + all_cam_features = [] + for cam_id, cam_name in enumerate(self.camera_names): + features, pos = self.backbones[cam_id](image[:, cam_id]) + features = features[0] # take the last layer feature + pos = pos[0] # not used + all_cam_features.append(self.backbone_down_projs[cam_id](features)) + # flatten everything + flattened_features = [] + for cam_feature in all_cam_features: + flattened_features.append(cam_feature.reshape([bs, -1])) + flattened_features = torch.cat(flattened_features, axis=1) # 768 each + features = torch.cat([flattened_features, qpos], axis=1) # qpos: 14 + a_hat = self.mlp(features) + return a_hat + + +def mlp(input_dim, hidden_dim, output_dim, hidden_depth): + if hidden_depth == 0: + mods = [nn.Linear(input_dim, output_dim)] + else: + mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)] + for i in range(hidden_depth - 1): + mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)] + mods.append(nn.Linear(hidden_dim, output_dim)) + trunk = nn.Sequential(*mods) + return trunk + + +def build_encoder(args): + d_model = args.hidden_dim # 256 + dropout = args.dropout # 0.1 + nhead = args.nheads # 8 + dim_feedforward = args.dim_feedforward # 2048 + num_encoder_layers = args.enc_layers # 4 # TODO shared with VAE decoder + normalize_before = args.pre_norm # False + activation = "relu" + + encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + return encoder + + +def build(args): + + # From state + # backbone = None # from state for now, no need for conv nets + # From image + backbones = [] + for _ in args.camera_names: + backbone = build_backbone(args) + backbones.append(backbone) + + transformer = build_transformer(args) + + encoder = build_encoder(args) + + model = DETRVAE( + backbones, + transformer, + encoder, + latent_dim=args.latent_dim, + a_dim=args.a_dim, + state_dim=args.state_dim, + num_queries=args.num_queries, + camera_names=args.camera_names, + ) + + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print("number of parameters: %.2fM" % (n_parameters/1e6,)) + + return model + +def build_cnnmlp(args): + state_dim = 14 # TODO hardcode + + # From state + # backbone = None # from state for now, no need for conv nets + # From image + backbones = [] + for _ in args.camera_names: + backbone = build_backbone(args) + backbones.append(backbone) + + model = CNNMLP( + backbones, + state_dim=state_dim, + camera_names=args.camera_names, + ) + + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + print("number of parameters: %.2fM" % (n_parameters/1e6,)) + + return model + diff --git a/act/detr/models/position_encoding.py b/act/detr/models/position_encoding.py new file mode 100644 index 00000000..209d9171 --- /dev/null +++ b/act/detr/models/position_encoding.py @@ -0,0 +1,93 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Various positional encodings for the transformer. +""" +import math +import torch +from torch import nn + +from util.misc import NestedTensor + +import IPython +e = IPython.embed + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, tensor): + x = tensor + # mask = tensor_list.mask + # assert mask is not None + # not_mask = ~mask + + not_mask = torch.ones_like(x[0, [0]]) + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + def __init__(self, num_pos_feats=256): + super().__init__() + self.row_embed = nn.Embedding(50, num_pos_feats) + self.col_embed = nn.Embedding(50, num_pos_feats) + self.reset_parameters() + + def reset_parameters(self): + nn.init.uniform_(self.row_embed.weight) + nn.init.uniform_(self.col_embed.weight) + + def forward(self, tensor_list: NestedTensor): + x = tensor_list.tensors + h, w = x.shape[-2:] + i = torch.arange(w, device=x.device) + j = torch.arange(h, device=x.device) + x_emb = self.col_embed(i) + y_emb = self.row_embed(j) + pos = torch.cat([ + x_emb.unsqueeze(0).repeat(h, 1, 1), + y_emb.unsqueeze(1).repeat(1, w, 1), + ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) + return pos + + +def build_position_encoding(args): + N_steps = args.hidden_dim // 2 + if args.position_embedding in ('v2', 'sine'): + # TODO find a better way of exposing other arguments + position_embedding = PositionEmbeddingSine(N_steps, normalize=True) + elif args.position_embedding in ('v3', 'learned'): + position_embedding = PositionEmbeddingLearned(N_steps) + else: + raise ValueError(f"not supported {args.position_embedding}") + + return position_embedding diff --git a/act/detr/models/transformer.py b/act/detr/models/transformer.py new file mode 100644 index 00000000..f38afd0e --- /dev/null +++ b/act/detr/models/transformer.py @@ -0,0 +1,314 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +DETR Transformer class. + +Copy-paste from torch.nn.Transformer with modifications: + * positional encodings are passed in MHattention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers +""" +import copy +from typing import Optional, List + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +import IPython +e = IPython.embed + +class Transformer(nn.Module): + + def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, + num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False, + return_intermediate_dec=False): + super().__init__() + + encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + encoder_norm = nn.LayerNorm(d_model) if normalize_before else None + self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) + + decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, + dropout, activation, normalize_before) + decoder_norm = nn.LayerNorm(d_model) + self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, + return_intermediate=return_intermediate_dec) + + self._reset_parameters() + + self.d_model = d_model + self.nhead = nhead + + def _reset_parameters(self): + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + + def forward(self, src, mask, query_embed, pos_embed, latent_input=None, proprio_input=None, additional_pos_embed=None): + # TODO flatten only when input has H and W + if len(src.shape) == 4: # has H and W + # flatten NxCxHxW to HWxNxC + bs, c, h, w = src.shape + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + # mask = mask.flatten(1) + + additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1) # seq, bs, dim + pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0) + + addition_input = torch.stack([latent_input, proprio_input], axis=0) + src = torch.cat([addition_input, src], axis=0) + else: + assert len(src.shape) == 3 + # flatten NxHWxC to HWxNxC + bs, hw, c = src.shape + src = src.permute(1, 0, 2) + pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) + + tgt = torch.zeros_like(query_embed) + memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) + hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, + pos=pos_embed, query_pos=query_embed) + hs = hs.transpose(1, 2) + return hs + +class TransformerEncoder(nn.Module): + + def __init__(self, encoder_layer, num_layers, norm=None): + super().__init__() + self.layers = _get_clones(encoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + + def forward(self, src, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + output = src + + for layer in self.layers: + output = layer(output, src_mask=mask, + src_key_padding_mask=src_key_padding_mask, pos=pos) + + if self.norm is not None: + output = self.norm(output) + + return output + + +class TransformerDecoder(nn.Module): + + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + output = tgt + + intermediate = [] + + for layer in self.layers: + output = layer(output, memory, tgt_mask=tgt_mask, + memory_mask=memory_mask, + tgt_key_padding_mask=tgt_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, query_pos=query_pos) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class TransformerEncoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, + src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(src, pos) + src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + return src + + def forward_pre(self, src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + src2 = self.norm1(src) + q = k = self.with_pos_embed(src2, pos) + src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, + key_padding_mask=src_key_padding_mask)[0] + src = src + self.dropout1(src2) + src2 = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) + src = src + self.dropout2(src2) + return src + + def forward(self, src, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(src, src_mask, src_key_padding_mask, pos) + return self.forward_post(src, src_mask, src_key_padding_mask, pos) + + +class TransformerDecoderLayer(nn.Module): + + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, + activation="relu", normalize_before=False): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward_post(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + q = k = self.with_pos_embed(tgt, query_pos) + tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt = self.norm1(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) + tgt = tgt + self.dropout3(tgt2) + tgt = self.norm3(tgt) + return tgt + + def forward_pre(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + tgt2 = self.norm2(tgt) + tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask)[0] + tgt = tgt + self.dropout2(tgt2) + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + def forward(self, tgt, memory, + tgt_mask: Optional[Tensor] = None, + memory_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + memory_key_padding_mask: Optional[Tensor] = None, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + if self.normalize_before: + return self.forward_pre(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + return self.forward_post(tgt, memory, tgt_mask, memory_mask, + tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def build_transformer(args): + return Transformer( + d_model=args.hidden_dim, + dropout=args.dropout, + nhead=args.nheads, + dim_feedforward=args.dim_feedforward, + num_encoder_layers=args.enc_layers, + num_decoder_layers=args.dec_layers, + normalize_before=args.pre_norm, + return_intermediate_dec=True, + ) + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/act/detr/setup.py b/act/detr/setup.py new file mode 100644 index 00000000..55d18c0d --- /dev/null +++ b/act/detr/setup.py @@ -0,0 +1,10 @@ +from distutils.core import setup +from setuptools import find_packages + +setup( + name='detr', + version='0.0.0', + packages=find_packages(), + license='MIT License', + long_description=open('README.md').read(), +) \ No newline at end of file diff --git a/act/detr/util/__init__.py b/act/detr/util/__init__.py new file mode 100644 index 00000000..168f9979 --- /dev/null +++ b/act/detr/util/__init__.py @@ -0,0 +1 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/act/detr/util/box_ops.py b/act/detr/util/box_ops.py new file mode 100644 index 00000000..9c088e5b --- /dev/null +++ b/act/detr/util/box_ops.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Utilities for bounding box manipulation and GIoU. +""" +import torch +from torchvision.ops.boxes import box_area + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), + (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, + (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +# modified from torchvision to also return the union +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + wh = (rb - lt).clamp(min=0) # [N,M,2] + inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/ + + The boxes should be in [x0, y0, x1, y1] format + + Returns a [N, M] pairwise matrix, where N = len(boxes1) + and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + assert (boxes1[:, 2:] >= boxes1[:, :2]).all() + assert (boxes2[:, 2:] >= boxes2[:, :2]).all() + iou, union = box_iou(boxes1, boxes2) + + lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + wh = (rb - lt).clamp(min=0) # [N,M,2] + area = wh[:, :, 0] * wh[:, :, 1] + + return iou - (area - union) / area + + +def masks_to_boxes(masks): + """Compute the bounding boxes around the provided masks + + The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. + + Returns a [N, 4] tensors, with the boxes in xyxy format + """ + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device) + + h, w = masks.shape[-2:] + + y = torch.arange(0, h, dtype=torch.float) + x = torch.arange(0, w, dtype=torch.float) + y, x = torch.meshgrid(y, x) + + x_mask = (masks * x.unsqueeze(0)) + x_max = x_mask.flatten(1).max(-1)[0] + x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + y_mask = (masks * y.unsqueeze(0)) + y_max = y_mask.flatten(1).max(-1)[0] + y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] + + return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/act/detr/util/misc.py b/act/detr/util/misc.py new file mode 100644 index 00000000..dfa9fb5b --- /dev/null +++ b/act/detr/util/misc.py @@ -0,0 +1,468 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +""" +Misc functions, including distributed helpers. + +Mostly copy-paste from torchvision references. +""" +import os +import subprocess +import time +from collections import defaultdict, deque +import datetime +import pickle +from packaging import version +from typing import Optional, List + +import torch +import torch.distributed as dist +from torch import Tensor + +# needed due to empty tensor bug in pytorch and torchvision 0.5 +import torchvision +if version.parse(torchvision.__version__) < version.parse('0.7'): + from torchvision.ops import _new_empty_tensor + from torchvision.ops.misc import _output_size + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +def all_gather(data): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors) + Args: + data: any picklable object + Returns: + list[data]: list of data gathered from each rank + """ + world_size = get_world_size() + if world_size == 1: + return [data] + + # serialized to a Tensor + buffer = pickle.dumps(data) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to("cuda") + + # obtain Tensor size of each rank + local_size = torch.tensor([tensor.numel()], device="cuda") + size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] + dist.all_gather(size_list, local_size) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # receiving Tensor from all ranks + # we pad the tensor because torch all_gather does not support + # gathering tensors of different shapes + tensor_list = [] + for _ in size_list: + tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) + if local_size != max_size: + padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") + tensor = torch.cat((tensor, padding), dim=0) + dist.all_gather(tensor_list, tensor) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + + return data_list + + +def reduce_dict(input_dict, average=True): + """ + Args: + input_dict (dict): all the values will be reduced + average (bool): whether to do average or sum + Reduce the values in the dictionary from all processes so that all processes + have the averaged results. Returns a dict with the same fields as + input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.all_reduce(values) + if average: + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.4f}') + data_time = SmoothedValue(fmt='{avg:.4f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + if torch.cuda.is_available(): + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}', + 'max mem: {memory:.0f}' + ]) + else: + log_msg = self.delimiter.join([ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ]) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.4f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def get_sha(): + cwd = os.path.dirname(os.path.abspath(__file__)) + + def _run(command): + return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() + sha = 'N/A' + diff = "clean" + branch = 'N/A' + try: + sha = _run(['git', 'rev-parse', 'HEAD']) + subprocess.check_output(['git', 'diff'], cwd=cwd) + diff = _run(['git', 'diff-index', 'HEAD']) + diff = "has uncommited changes" if diff else "clean" + branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) + except Exception: + pass + message = f"sha: {sha}, status: {diff}, branch: {branch}" + return message + + +def collate_fn(batch): + batch = list(zip(*batch)) + batch[0] = nested_tensor_from_tensor_list(batch[0]) + return tuple(batch) + + +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + # type: (Device) -> NestedTensor # noqa + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + assert mask is not None + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + # TODO make this more general + if tensor_list[0].ndim == 3: + if torchvision._is_tracing(): + # nested_tensor_from_tensor_list() does not export well to ONNX + # call _onnx_nested_tensor_from_tensor_list() instead + return _onnx_nested_tensor_from_tensor_list(tensor_list) + + # TODO make it support different-sized images + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) + batch_shape = [len(tensor_list)] + max_size + b, c, h, w = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((b, h, w), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], :img.shape[2]] = False + else: + raise ValueError('not supported') + return NestedTensor(tensor, mask) + + +# _onnx_nested_tensor_from_tensor_list() is an implementation of +# nested_tensor_from_tensor_list() that is supported by ONNX tracing. +@torch.jit.unused +def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: + max_size = [] + for i in range(tensor_list[0].dim()): + max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) + max_size.append(max_size_i) + max_size = tuple(max_size) + + # work around for + # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + # m[: img.shape[1], :img.shape[2]] = False + # which is not yet supported in onnx + padded_imgs = [] + padded_masks = [] + for img in tensor_list: + padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] + padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) + padded_imgs.append(padded_img) + + m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) + padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) + padded_masks.append(padded_mask.to(torch.bool)) + + tensor = torch.stack(padded_imgs) + mask = torch.stack(padded_masks) + + return NestedTensor(tensor, mask=mask) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + import builtins as __builtin__ + builtin_print = __builtin__.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + if is_master or force: + builtin_print(*args, **kwargs) + + __builtin__.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + else: + print('Not using distributed mode') + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = 'nccl' + print('| distributed init (rank {}): {}'.format( + args.rank, args.dist_url), flush=True) + torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +@torch.no_grad() +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k""" + if target.numel() == 0: + return [torch.zeros([], device=output.device)] + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + +def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): + # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor + """ + Equivalent to nn.functional.interpolate, but with support for empty batch sizes. + This will eventually be supported natively by PyTorch, and this + class can go away. + """ + if version.parse(torchvision.__version__) < version.parse('0.7'): + if input.numel() > 0: + return torch.nn.functional.interpolate( + input, size, scale_factor, mode, align_corners + ) + + output_shape = _output_size(2, input, size, scale_factor) + output_shape = list(input.shape[:-2]) + list(output_shape) + return _new_empty_tensor(input, output_shape) + else: + return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/act/detr/util/plot_utils.py b/act/detr/util/plot_utils.py new file mode 100644 index 00000000..0f24bed0 --- /dev/null +++ b/act/detr/util/plot_utils.py @@ -0,0 +1,107 @@ +""" +Plotting utilities to visualize training logs. +""" +import torch +import pandas as pd +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt + +from pathlib import Path, PurePath + + +def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): + ''' + Function to plot specific fields from training log(s). Plots both training and test results. + + :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file + - fields = which results to plot from each log file - plots both training and test for each field. + - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots + - log_name = optional, name of log file if different than default 'log.txt'. + + :: Outputs - matplotlib plots of results in fields, color coded for each log file. + - solid lines are training results, dashed lines are test results. + + ''' + func_name = "plot_utils.py::plot_logs" + + # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, + # convert single Path to list to avoid 'not iterable' error + + if not isinstance(logs, list): + if isinstance(logs, PurePath): + logs = [logs] + print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") + else: + raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ + Expect list[Path] or single Path obj, received {type(logs)}") + + # Quality checks - verify valid dir(s), that every item in list is Path object, and that log_name exists in each dir + for i, dir in enumerate(logs): + if not isinstance(dir, PurePath): + raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") + if not dir.exists(): + raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") + # verify log_name exists + fn = Path(dir / log_name) + if not fn.exists(): + print(f"-> missing {log_name}. Have you gotten to Epoch 1 in training?") + print(f"--> full path of missing log file: {fn}") + return + + # load log file(s) and plot + dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] + + fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) + + for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): + for j, field in enumerate(fields): + if field == 'mAP': + coco_eval = pd.DataFrame( + np.stack(df.test_coco_eval_bbox.dropna().values)[:, 1] + ).ewm(com=ewm_col).mean() + axs[j].plot(coco_eval, c=color) + else: + df.interpolate().ewm(com=ewm_col).mean().plot( + y=[f'train_{field}', f'test_{field}'], + ax=axs[j], + color=[color] * 2, + style=['-', '--'] + ) + for ax, field in zip(axs, fields): + ax.legend([Path(p).name for p in logs]) + ax.set_title(field) + + +def plot_precision_recall(files, naming_scheme='iter'): + if naming_scheme == 'exp_id': + # name becomes exp_id + names = [f.parts[-3] for f in files] + elif naming_scheme == 'iter': + names = [f.stem for f in files] + else: + raise ValueError(f'not supported {naming_scheme}') + fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) + for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): + data = torch.load(f) + # precision is n_iou, n_points, n_cat, n_area, max_det + precision = data['precision'] + recall = data['params'].recThrs + scores = data['scores'] + # take precision for all classes, all areas and 100 detections + precision = precision[0, :, :, 0, -1].mean(1) + scores = scores[0, :, :, 0, -1].mean(1) + prec = precision.mean() + rec = data['recall'][0, :, 0, -1].mean() + print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + + f'score={scores.mean():0.3f}, ' + + f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' + ) + axs[0].plot(recall, precision, c=color) + axs[1].plot(recall, scores, c=color) + + axs[0].set_title('Precision / Recall') + axs[0].legend(names) + axs[1].set_title('Scores / Recall') + axs[1].legend(names) + return fig, axs diff --git a/act/ee_sim_env.py b/act/ee_sim_env.py new file mode 100644 index 00000000..01df2337 --- /dev/null +++ b/act/ee_sim_env.py @@ -0,0 +1,267 @@ +import numpy as np +import collections +import os + +from constants import DT, XML_DIR, START_ARM_POSE +from constants import PUPPET_GRIPPER_POSITION_CLOSE +from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN +from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN +from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN + +from utils import sample_box_pose, sample_insertion_pose +from dm_control import mujoco +from dm_control.rl import control +from dm_control.suite import base + +import IPython +e = IPython.embed + + +def make_ee_sim_env(task_name): + """ + Environment for simulated robot bi-manual manipulation, with end-effector control. + Action space: [left_arm_pose (7), # position and quaternion for end effector + left_gripper_positions (1), # normalized gripper position (0: close, 1: open) + right_arm_pose (7), # position and quaternion for end effector + right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) + + Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position + left_gripper_position (1), # normalized gripper position (0: close, 1: open) + right_arm_qpos (6), # absolute joint position + right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) + "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) + left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) + right_arm_qvel (6), # absolute joint velocity (rad) + right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) + "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' + """ + if 'sim_transfer_cube' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_transfer_cube.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = TransferCubeEETask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + elif 'sim_insertion' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_insertion.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = InsertionEETask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + else: + raise NotImplementedError + return env + +class BimanualViperXEETask(base.Task): + def __init__(self, random=None): + super().__init__(random=random) + + def before_step(self, action, physics): + a_len = len(action) // 2 + action_left = action[:a_len] + action_right = action[a_len:] + + # set mocap position and quat + # left + np.copyto(physics.data.mocap_pos[0], action_left[:3]) + np.copyto(physics.data.mocap_quat[0], action_left[3:7]) + # right + np.copyto(physics.data.mocap_pos[1], action_right[:3]) + np.copyto(physics.data.mocap_quat[1], action_right[3:7]) + + # set gripper + g_left_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_left[7]) + g_right_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_right[7]) + np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl])) + + def initialize_robots(self, physics): + # reset joint position + physics.named.data.qpos[:16] = START_ARM_POSE + + # reset mocap to align with end effector + # to obtain these numbers: + # (1) make an ee_sim env and reset to the same start_pose + # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link'] + # get env._physics.named.data.xquat['vx300s_left/gripper_link'] + # repeat the same for right side + np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084]) + np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0]) + # right + np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084])) + np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0]) + + # reset gripper control + close_gripper_control = np.array([ + PUPPET_GRIPPER_POSITION_CLOSE, + -PUPPET_GRIPPER_POSITION_CLOSE, + PUPPET_GRIPPER_POSITION_CLOSE, + -PUPPET_GRIPPER_POSITION_CLOSE, + ]) + np.copyto(physics.data.ctrl, close_gripper_control) + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + super().initialize_episode(physics) + + @staticmethod + def get_qpos(physics): + qpos_raw = physics.data.qpos.copy() + left_qpos_raw = qpos_raw[:8] + right_qpos_raw = qpos_raw[8:16] + left_arm_qpos = left_qpos_raw[:6] + right_arm_qpos = right_qpos_raw[:6] + left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] + right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] + return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) + + @staticmethod + def get_qvel(physics): + qvel_raw = physics.data.qvel.copy() + left_qvel_raw = qvel_raw[:8] + right_qvel_raw = qvel_raw[8:16] + left_arm_qvel = left_qvel_raw[:6] + right_arm_qvel = right_qvel_raw[:6] + left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] + right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] + return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) + + @staticmethod + def get_env_state(physics): + raise NotImplementedError + + def get_observation(self, physics): + # note: it is important to do .copy() + obs = collections.OrderedDict() + obs['qpos'] = self.get_qpos(physics) + obs['qvel'] = self.get_qvel(physics) + obs['env_state'] = self.get_env_state(physics) + obs['images'] = dict() + obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') + obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') + obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') + # used in scripted policy to obtain starting pose + obs['mocap_pose_left'] = np.concatenate([physics.data.mocap_pos[0], physics.data.mocap_quat[0]]).copy() + obs['mocap_pose_right'] = np.concatenate([physics.data.mocap_pos[1], physics.data.mocap_quat[1]]).copy() + + # used when replaying joint trajectory + obs['gripper_ctrl'] = physics.data.ctrl.copy() + return obs + + def get_reward(self, physics): + raise NotImplementedError + + +class TransferCubeEETask(BimanualViperXEETask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + self.initialize_robots(physics) + # randomize box position + cube_pose = sample_box_pose() + box_start_idx = physics.model.name2id('red_box_joint', 'joint') + np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose) + # print(f"randomized cube position to {cube_position}") + + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether left gripper is holding the box + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_table = ("red_box", "table") in all_contact_pairs + + reward = 0 + if touch_right_gripper: + reward = 1 + if touch_right_gripper and not touch_table: # lifted + reward = 2 + if touch_left_gripper: # attempted transfer + reward = 3 + if touch_left_gripper and not touch_table: # successful transfer + reward = 4 + return reward + + +class InsertionEETask(BimanualViperXEETask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + self.initialize_robots(physics) + # randomize peg and socket position + peg_pose, socket_pose = sample_insertion_pose() + id2index = lambda j_id: 16 + (j_id - 16) * 7 # first 16 is robot qpos, 7 is pose dim # hacky + + peg_start_id = physics.model.name2id('red_peg_joint', 'joint') + peg_start_idx = id2index(peg_start_id) + np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose) + # print(f"randomized cube position to {cube_position}") + + socket_start_id = physics.model.name2id('blue_socket_joint', 'joint') + socket_start_idx = id2index(socket_start_id) + np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose) + # print(f"randomized cube position to {cube_position}") + + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether peg touches the pin + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + + peg_touch_table = ("red_peg", "table") in all_contact_pairs + socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ + ("socket-2", "table") in all_contact_pairs or \ + ("socket-3", "table") in all_contact_pairs or \ + ("socket-4", "table") in all_contact_pairs + peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ + ("red_peg", "socket-2") in all_contact_pairs or \ + ("red_peg", "socket-3") in all_contact_pairs or \ + ("red_peg", "socket-4") in all_contact_pairs + pin_touched = ("red_peg", "pin") in all_contact_pairs + + reward = 0 + if touch_left_gripper and touch_right_gripper: # touch both + reward = 1 + if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both + reward = 2 + if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching + reward = 3 + if pin_touched: # successful insertion + reward = 4 + return reward diff --git a/act/imitate_episodes.py b/act/imitate_episodes.py new file mode 100644 index 00000000..34f9a372 --- /dev/null +++ b/act/imitate_episodes.py @@ -0,0 +1,435 @@ +import torch +import numpy as np +import os +import pickle +import argparse +import matplotlib.pyplot as plt +from copy import deepcopy +from tqdm import tqdm +from einops import rearrange + +from constants import DT +from constants import PUPPET_GRIPPER_JOINT_OPEN +from utils import load_data # data functions +from utils import sample_box_pose, sample_insertion_pose # robot functions +from utils import compute_dict_mean, set_seed, detach_dict # helper functions +from policy import ACTPolicy, CNNMLPPolicy +from visualize_episodes import save_videos + +from sim_env import BOX_POSE + +import IPython +e = IPython.embed + +def main(args): + set_seed(1) + # command line parameters + is_eval = args['eval'] + ckpt_dir = args['ckpt_dir'] + policy_class = args['policy_class'] + onscreen_render = args['onscreen_render'] + task_name = args['task_name'] + batch_size_train = args['batch_size'] + batch_size_val = args['batch_size'] + num_epochs = args['num_epochs'] + + # get task parameters + is_sim = task_name[:4] == 'sim_' + if is_sim: + from constants import SIM_TASK_CONFIGS + task_config = SIM_TASK_CONFIGS[task_name] + else: + from aloha_scripts.constants import TASK_CONFIGS + task_config = TASK_CONFIGS[task_name] + dataset_dir = task_config['dataset_dir'] + num_episodes = task_config['num_episodes'] + episode_len = task_config['episode_len'] + camera_names = task_config['camera_names'] + + # fixed parameters + state_dim = 14 + lr_backbone = 1e-5 + backbone = 'resnet18' + if policy_class == 'ACT': + enc_layers = 4 + dec_layers = 7 + nheads = 8 + policy_config = {'lr': args['lr'], + 'num_queries': args['chunk_size'], + 'kl_weight': args['kl_weight'], + 'hidden_dim': args['hidden_dim'], + 'dim_feedforward': args['dim_feedforward'], + 'lr_backbone': lr_backbone, + 'backbone': backbone, + 'enc_layers': enc_layers, + 'dec_layers': dec_layers, + 'nheads': nheads, + 'camera_names': camera_names, + } + elif policy_class == 'CNNMLP': + policy_config = {'lr': args['lr'], 'lr_backbone': lr_backbone, 'backbone' : backbone, 'num_queries': 1, + 'camera_names': camera_names,} + else: + raise NotImplementedError + + config = { + 'num_epochs': num_epochs, + 'ckpt_dir': ckpt_dir, + 'episode_len': episode_len, + 'state_dim': state_dim, + 'lr': args['lr'], + 'policy_class': policy_class, + 'onscreen_render': onscreen_render, + 'policy_config': policy_config, + 'task_name': task_name, + 'seed': args['seed'], + 'temporal_agg': args['temporal_agg'], + 'camera_names': camera_names, + 'real_robot': not is_sim + } + + if is_eval: + ckpt_names = [f'policy_best.ckpt'] + results = [] + for ckpt_name in ckpt_names: + success_rate, avg_return = eval_bc(config, ckpt_name, save_episode=True) + results.append([ckpt_name, success_rate, avg_return]) + + for ckpt_name, success_rate, avg_return in results: + print(f'{ckpt_name}: {success_rate=} {avg_return=}') + print() + exit() + + train_dataloader, val_dataloader, stats, _ = load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val) + + # save dataset stats + if not os.path.isdir(ckpt_dir): + os.makedirs(ckpt_dir) + stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') + with open(stats_path, 'wb') as f: + pickle.dump(stats, f) + + best_ckpt_info = train_bc(train_dataloader, val_dataloader, config) + best_epoch, min_val_loss, best_state_dict = best_ckpt_info + + # save best checkpoint + ckpt_path = os.path.join(ckpt_dir, f'policy_best.ckpt') + torch.save(best_state_dict, ckpt_path) + print(f'Best ckpt, val loss {min_val_loss:.6f} @ epoch{best_epoch}') + + +def make_policy(policy_class, policy_config): + if policy_class == 'ACT': + policy = ACTPolicy(policy_config) + elif policy_class == 'CNNMLP': + policy = CNNMLPPolicy(policy_config) + else: + raise NotImplementedError + return policy + + +def make_optimizer(policy_class, policy): + if policy_class == 'ACT': + optimizer = policy.configure_optimizers() + elif policy_class == 'CNNMLP': + optimizer = policy.configure_optimizers() + else: + raise NotImplementedError + return optimizer + + +def get_image(ts, camera_names): + curr_images = [] + for cam_name in camera_names: + curr_image = rearrange(ts.observation['images'][cam_name], 'h w c -> c h w') + curr_images.append(curr_image) + curr_image = np.stack(curr_images, axis=0) + curr_image = torch.from_numpy(curr_image / 255.0).float().cuda().unsqueeze(0) + return curr_image + + +def eval_bc(config, ckpt_name, save_episode=True): + set_seed(1000) + ckpt_dir = config['ckpt_dir'] + state_dim = config['state_dim'] + real_robot = config['real_robot'] + policy_class = config['policy_class'] + onscreen_render = config['onscreen_render'] + policy_config = config['policy_config'] + camera_names = config['camera_names'] + max_timesteps = config['episode_len'] + task_name = config['task_name'] + temporal_agg = config['temporal_agg'] + onscreen_cam = 'angle' + + # load policy and stats + ckpt_path = os.path.join(ckpt_dir, ckpt_name) + policy = make_policy(policy_class, policy_config) + loading_status = policy.load_state_dict(torch.load(ckpt_path)) + print(loading_status) + policy.cuda() + policy.eval() + print(f'Loaded: {ckpt_path}') + stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') + with open(stats_path, 'rb') as f: + stats = pickle.load(f) + + pre_process = lambda s_qpos: (s_qpos - stats['qpos_mean']) / stats['qpos_std'] + post_process = lambda a: a * stats['action_std'] + stats['action_mean'] + + # load environment + if real_robot: + from aloha_scripts.robot_utils import move_grippers # requires aloha + from aloha_scripts.real_env import make_real_env # requires aloha + env = make_real_env(init_node=True) + env_max_reward = 0 + else: + from sim_env import make_sim_env + env = make_sim_env(task_name) + env_max_reward = env.task.max_reward + + query_frequency = policy_config['num_queries'] + if temporal_agg: + query_frequency = 1 + num_queries = policy_config['num_queries'] + + max_timesteps = int(max_timesteps * 1) # may increase for real-world tasks + + num_rollouts = 50 + episode_returns = [] + highest_rewards = [] + for rollout_id in range(num_rollouts): + rollout_id += 0 + ### set task + if 'sim_transfer_cube' in task_name: + BOX_POSE[0] = sample_box_pose() # used in sim reset + elif 'sim_insertion' in task_name: + BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset + + ts = env.reset() + + ### onscreen render + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(env._physics.render(height=480, width=640, camera_id=onscreen_cam)) + plt.ion() + + ### evaluation loop + if temporal_agg: + all_time_actions = torch.zeros([max_timesteps, max_timesteps+num_queries, state_dim]).cuda() + + qpos_history = torch.zeros((1, max_timesteps, state_dim)).cuda() + image_list = [] # for visualization + qpos_list = [] + target_qpos_list = [] + rewards = [] + with torch.inference_mode(): + for t in range(max_timesteps): + ### update onscreen render and wait for DT + if onscreen_render: + image = env._physics.render(height=480, width=640, camera_id=onscreen_cam) + plt_img.set_data(image) + plt.pause(DT) + + ### process previous timestep to get qpos and image_list + obs = ts.observation + if 'images' in obs: + image_list.append(obs['images']) + else: + image_list.append({'main': obs['image']}) + qpos_numpy = np.array(obs['qpos']) + qpos = pre_process(qpos_numpy) + qpos = torch.from_numpy(qpos).float().cuda().unsqueeze(0) + qpos_history[:, t] = qpos + curr_image = get_image(ts, camera_names) + + ### query policy + if config['policy_class'] == "ACT": + if t % query_frequency == 0: + all_actions = policy(qpos, curr_image) + if temporal_agg: + all_time_actions[[t], t:t+num_queries] = all_actions + actions_for_curr_step = all_time_actions[:, t] + actions_populated = torch.all(actions_for_curr_step != 0, axis=1) + actions_for_curr_step = actions_for_curr_step[actions_populated] + k = 0.01 + exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step))) + exp_weights = exp_weights / exp_weights.sum() + exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1) + raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True) + else: + raw_action = all_actions[:, t % query_frequency] + elif config['policy_class'] == "CNNMLP": + raw_action = policy(qpos, curr_image) + else: + raise NotImplementedError + + ### post-process actions + raw_action = raw_action.squeeze(0).cpu().numpy() + action = post_process(raw_action) + target_qpos = action + + ### step the environment + ts = env.step(target_qpos) + + ### for visualization + qpos_list.append(qpos_numpy) + target_qpos_list.append(target_qpos) + rewards.append(ts.reward) + + plt.close() + if real_robot: + move_grippers([env.puppet_bot_left, env.puppet_bot_right], [PUPPET_GRIPPER_JOINT_OPEN] * 2, move_time=0.5) # open + pass + + rewards = np.array(rewards) + episode_return = np.sum(rewards[rewards!=None]) + episode_returns.append(episode_return) + episode_highest_reward = np.max(rewards) + highest_rewards.append(episode_highest_reward) + print(f'Rollout {rollout_id}\n{episode_return=}, {episode_highest_reward=}, {env_max_reward=}, Success: {episode_highest_reward==env_max_reward}') + + if save_episode: + save_videos(image_list, DT, video_path=os.path.join(ckpt_dir, f'video{rollout_id}.mp4')) + + success_rate = np.mean(np.array(highest_rewards) == env_max_reward) + avg_return = np.mean(episode_returns) + summary_str = f'\nSuccess rate: {success_rate}\nAverage return: {avg_return}\n\n' + for r in range(env_max_reward+1): + more_or_equal_r = (np.array(highest_rewards) >= r).sum() + more_or_equal_r_rate = more_or_equal_r / num_rollouts + summary_str += f'Reward >= {r}: {more_or_equal_r}/{num_rollouts} = {more_or_equal_r_rate*100}%\n' + + print(summary_str) + + # save success rate to txt + result_file_name = 'result_' + ckpt_name.split('.')[0] + '.txt' + with open(os.path.join(ckpt_dir, result_file_name), 'w') as f: + f.write(summary_str) + f.write(repr(episode_returns)) + f.write('\n\n') + f.write(repr(highest_rewards)) + + return success_rate, avg_return + + +def forward_pass(data, policy): + image_data, qpos_data, action_data, is_pad = data + image_data, qpos_data, action_data, is_pad = image_data.cuda(), qpos_data.cuda(), action_data.cuda(), is_pad.cuda() + return policy(qpos_data, image_data, action_data, is_pad) # TODO remove None + + +def train_bc(train_dataloader, val_dataloader, config): + num_epochs = config['num_epochs'] + ckpt_dir = config['ckpt_dir'] + seed = config['seed'] + policy_class = config['policy_class'] + policy_config = config['policy_config'] + + set_seed(seed) + + policy = make_policy(policy_class, policy_config) + policy.cuda() + optimizer = make_optimizer(policy_class, policy) + + train_history = [] + validation_history = [] + min_val_loss = np.inf + best_ckpt_info = None + for epoch in tqdm(range(num_epochs)): + print(f'\nEpoch {epoch}') + # validation + with torch.inference_mode(): + policy.eval() + epoch_dicts = [] + for batch_idx, data in enumerate(val_dataloader): + forward_dict = forward_pass(data, policy) + epoch_dicts.append(forward_dict) + epoch_summary = compute_dict_mean(epoch_dicts) + validation_history.append(epoch_summary) + + epoch_val_loss = epoch_summary['loss'] + if epoch_val_loss < min_val_loss: + min_val_loss = epoch_val_loss + best_ckpt_info = (epoch, min_val_loss, deepcopy(policy.state_dict())) + print(f'Val loss: {epoch_val_loss:.5f}') + summary_string = '' + for k, v in epoch_summary.items(): + summary_string += f'{k}: {v.item():.3f} ' + print(summary_string) + + # training + policy.train() + optimizer.zero_grad() + for batch_idx, data in enumerate(train_dataloader): + forward_dict = forward_pass(data, policy) + # backward + loss = forward_dict['loss'] + loss.backward() + optimizer.step() + optimizer.zero_grad() + train_history.append(detach_dict(forward_dict)) + epoch_summary = compute_dict_mean(train_history[(batch_idx+1)*epoch:(batch_idx+1)*(epoch+1)]) + epoch_train_loss = epoch_summary['loss'] + print(f'Train loss: {epoch_train_loss:.5f}') + summary_string = '' + for k, v in epoch_summary.items(): + summary_string += f'{k}: {v.item():.3f} ' + print(summary_string) + + if epoch % 100 == 0: + ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{epoch}_seed_{seed}.ckpt') + torch.save(policy.state_dict(), ckpt_path) + plot_history(train_history, validation_history, epoch, ckpt_dir, seed) + + ckpt_path = os.path.join(ckpt_dir, f'policy_last.ckpt') + torch.save(policy.state_dict(), ckpt_path) + + best_epoch, min_val_loss, best_state_dict = best_ckpt_info + ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{best_epoch}_seed_{seed}.ckpt') + torch.save(best_state_dict, ckpt_path) + print(f'Training finished:\nSeed {seed}, val loss {min_val_loss:.6f} at epoch {best_epoch}') + + # save training curves + plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed) + + return best_ckpt_info + + +def plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed): + # save training curves + for key in train_history[0]: + plot_path = os.path.join(ckpt_dir, f'train_val_{key}_seed_{seed}.png') + plt.figure() + train_values = [summary[key].item() for summary in train_history] + val_values = [summary[key].item() for summary in validation_history] + plt.plot(np.linspace(0, num_epochs-1, len(train_history)), train_values, label='train') + plt.plot(np.linspace(0, num_epochs-1, len(validation_history)), val_values, label='validation') + # plt.ylim([-0.1, 1]) + plt.tight_layout() + plt.legend() + plt.title(key) + plt.savefig(plot_path) + print(f'Saved plots to {ckpt_dir}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--eval', action='store_true') + parser.add_argument('--onscreen_render', action='store_true') + parser.add_argument('--ckpt_dir', action='store', type=str, help='ckpt_dir', required=True) + parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True) + parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) + parser.add_argument('--batch_size', action='store', type=int, help='batch_size', required=True) + parser.add_argument('--seed', action='store', type=int, help='seed', required=True) + parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True) + parser.add_argument('--lr', action='store', type=float, help='lr', required=True) + + # for ACT + parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False) + parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False) + parser.add_argument('--hidden_dim', action='store', type=int, help='hidden_dim', required=False) + parser.add_argument('--dim_feedforward', action='store', type=int, help='dim_feedforward', required=False) + parser.add_argument('--temporal_agg', action='store_true') + + main(vars(parser.parse_args())) diff --git a/act/policy.py b/act/policy.py new file mode 100644 index 00000000..7b091e5e --- /dev/null +++ b/act/policy.py @@ -0,0 +1,84 @@ +import torch.nn as nn +from torch.nn import functional as F +import torchvision.transforms as transforms + +from detr.main import build_ACT_model_and_optimizer, build_CNNMLP_model_and_optimizer +import IPython +e = IPython.embed + +class ACTPolicy(nn.Module): + def __init__(self, args_override): + super().__init__() + model, optimizer = build_ACT_model_and_optimizer(args_override) + self.model = model # CVAE decoder + self.optimizer = optimizer + self.kl_weight = args_override['kl_weight'] + print(f'KL Weight {self.kl_weight}') + + def __call__(self, qpos, image, actions=None, is_pad=None): + env_state = None + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + image = normalize(image) + if actions is not None: # training time + actions = actions[:, :self.model.num_queries] + is_pad = is_pad[:, :self.model.num_queries] + + a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad) + total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar) + loss_dict = dict() + all_l1 = F.l1_loss(actions, a_hat, reduction='none') + l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() + loss_dict['l1'] = l1 + loss_dict['kl'] = total_kld[0] + loss_dict['loss'] = loss_dict['l1'] + loss_dict['kl'] * self.kl_weight + return loss_dict + else: # inference time + a_hat, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior + return a_hat + + def configure_optimizers(self): + return self.optimizer + + +class CNNMLPPolicy(nn.Module): + def __init__(self, args_override): + super().__init__() + model, optimizer = build_CNNMLP_model_and_optimizer(args_override) + self.model = model # decoder + self.optimizer = optimizer + + def __call__(self, qpos, image, actions=None, is_pad=None): + env_state = None # TODO + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + image = normalize(image) + if actions is not None: # training time + actions = actions[:, 0] + a_hat = self.model(qpos, image, env_state, actions) + mse = F.mse_loss(actions, a_hat) + loss_dict = dict() + loss_dict['mse'] = mse + loss_dict['loss'] = loss_dict['mse'] + return loss_dict + else: # inference time + a_hat = self.model(qpos, image, env_state) # no action, sample from prior + return a_hat + + def configure_optimizers(self): + return self.optimizer + +def kl_divergence(mu, logvar): + batch_size = mu.size(0) + assert batch_size != 0 + if mu.data.ndimension() == 4: + mu = mu.view(mu.size(0), mu.size(1)) + if logvar.data.ndimension() == 4: + logvar = logvar.view(logvar.size(0), logvar.size(1)) + + klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) + total_kld = klds.sum(1).mean(0, True) + dimension_wise_kld = klds.mean(0) + mean_kld = klds.mean(1).mean(0, True) + + return total_kld, dimension_wise_kld, mean_kld diff --git a/act/record_sim_episodes.py b/act/record_sim_episodes.py new file mode 100644 index 00000000..253fdea1 --- /dev/null +++ b/act/record_sim_episodes.py @@ -0,0 +1,189 @@ +import time +import os +import numpy as np +import argparse +import matplotlib.pyplot as plt +import h5py + +from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN, SIM_TASK_CONFIGS +from ee_sim_env import make_ee_sim_env +from sim_env import make_sim_env, BOX_POSE +from scripted_policy import PickAndTransferPolicy, InsertionPolicy + +import IPython +e = IPython.embed + + +def main(args): + """ + Generate demonstration data in simulation. + First rollout the policy (defined in ee space) in ee_sim_env. Obtain the joint trajectory. + Replace the gripper joint positions with the commanded joint position. + Replay this joint trajectory (as action sequence) in sim_env, and record all observations. + Save this episode of data, and continue to next episode of data collection. + """ + + task_name = args['task_name'] + dataset_dir = args['dataset_dir'] + num_episodes = args['num_episodes'] + onscreen_render = args['onscreen_render'] + inject_noise = False + render_cam_name = 'angle' + + if not os.path.isdir(dataset_dir): + os.makedirs(dataset_dir, exist_ok=True) + + episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] + camera_names = SIM_TASK_CONFIGS[task_name]['camera_names'] + if task_name == 'sim_transfer_cube_scripted': + policy_cls = PickAndTransferPolicy + elif task_name == 'sim_insertion_scripted': + policy_cls = InsertionPolicy + else: + raise NotImplementedError + + success = [] + for episode_idx in range(num_episodes): + print(f'{episode_idx=}') + print('Rollout out EE space scripted policy') + # setup the environment + env = make_ee_sim_env(task_name) + ts = env.reset() + episode = [ts] + policy = policy_cls(inject_noise) + # setup plotting + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images'][render_cam_name]) + plt.ion() + for step in range(episode_len): + action = policy(ts) + ts = env.step(action) + episode.append(ts) + if onscreen_render: + plt_img.set_data(ts.observation['images'][render_cam_name]) + plt.pause(0.002) + plt.close() + + episode_return = np.sum([ts.reward for ts in episode[1:]]) + episode_max_reward = np.max([ts.reward for ts in episode[1:]]) + if episode_max_reward == env.task.max_reward: + print(f"{episode_idx=} Successful, {episode_return=}") + else: + print(f"{episode_idx=} Failed") + + joint_traj = [ts.observation['qpos'] for ts in episode] + # replace gripper pose with gripper control + gripper_ctrl_traj = [ts.observation['gripper_ctrl'] for ts in episode] + for joint, ctrl in zip(joint_traj, gripper_ctrl_traj): + left_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[0]) + right_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[2]) + joint[6] = left_ctrl + joint[6+7] = right_ctrl + + subtask_info = episode[0].observation['env_state'].copy() # box pose at step 0 + + # clear unused variables + del env + del episode + del policy + + # setup the environment + print('Replaying joint commands') + env = make_sim_env(task_name) + BOX_POSE[0] = subtask_info # make sure the sim_env has the same object configurations as ee_sim_env + ts = env.reset() + + episode_replay = [ts] + # setup plotting + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images'][render_cam_name]) + plt.ion() + for t in range(len(joint_traj)): # note: this will increase episode length by 1 + action = joint_traj[t] + ts = env.step(action) + episode_replay.append(ts) + if onscreen_render: + plt_img.set_data(ts.observation['images'][render_cam_name]) + plt.pause(0.02) + + episode_return = np.sum([ts.reward for ts in episode_replay[1:]]) + episode_max_reward = np.max([ts.reward for ts in episode_replay[1:]]) + if episode_max_reward == env.task.max_reward: + success.append(1) + print(f"{episode_idx=} Successful, {episode_return=}") + else: + success.append(0) + print(f"{episode_idx=} Failed") + + plt.close() + + """ + For each timestep: + observations + - images + - each_cam_name (480, 640, 3) 'uint8' + - qpos (14,) 'float64' + - qvel (14,) 'float64' + + action (14,) 'float64' + """ + + data_dict = { + '/observations/qpos': [], + '/observations/qvel': [], + '/action': [], + } + for cam_name in camera_names: + data_dict[f'/observations/images/{cam_name}'] = [] + + # because the replaying, there will be eps_len + 1 actions and eps_len + 2 timesteps + # truncate here to be consistent + joint_traj = joint_traj[:-1] + episode_replay = episode_replay[:-1] + + # len(joint_traj) i.e. actions: max_timesteps + # len(episode_replay) i.e. time steps: max_timesteps + 1 + max_timesteps = len(joint_traj) + while joint_traj: + action = joint_traj.pop(0) + ts = episode_replay.pop(0) + data_dict['/observations/qpos'].append(ts.observation['qpos']) + data_dict['/observations/qvel'].append(ts.observation['qvel']) + data_dict['/action'].append(action) + for cam_name in camera_names: + data_dict[f'/observations/images/{cam_name}'].append(ts.observation['images'][cam_name]) + + # HDF5 + t0 = time.time() + dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}') + with h5py.File(dataset_path + '.hdf5', 'w', rdcc_nbytes=1024 ** 2 * 2) as root: + root.attrs['sim'] = True + obs = root.create_group('observations') + image = obs.create_group('images') + for cam_name in camera_names: + _ = image.create_dataset(cam_name, (max_timesteps, 480, 640, 3), dtype='uint8', + chunks=(1, 480, 640, 3), ) + # compression='gzip',compression_opts=2,) + # compression=32001, compression_opts=(0, 0, 0, 0, 9, 1, 1), shuffle=False) + qpos = obs.create_dataset('qpos', (max_timesteps, 14)) + qvel = obs.create_dataset('qvel', (max_timesteps, 14)) + action = root.create_dataset('action', (max_timesteps, 14)) + + for name, array in data_dict.items(): + root[name][...] = array + print(f'Saving: {time.time() - t0:.1f} secs\n') + + print(f'Saved to {dataset_dir}') + print(f'Success: {np.sum(success)} / {len(success)}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) + parser.add_argument('--dataset_dir', action='store', type=str, help='dataset saving dir', required=True) + parser.add_argument('--num_episodes', action='store', type=int, help='num_episodes', required=False) + parser.add_argument('--onscreen_render', action='store_true') + + main(vars(parser.parse_args())) + diff --git a/act/scripted_policy.py b/act/scripted_policy.py new file mode 100644 index 00000000..4fd8f000 --- /dev/null +++ b/act/scripted_policy.py @@ -0,0 +1,194 @@ +import numpy as np +import matplotlib.pyplot as plt +from pyquaternion import Quaternion + +from constants import SIM_TASK_CONFIGS +from ee_sim_env import make_ee_sim_env + +import IPython +e = IPython.embed + + +class BasePolicy: + def __init__(self, inject_noise=False): + self.inject_noise = inject_noise + self.step_count = 0 + self.left_trajectory = None + self.right_trajectory = None + + def generate_trajectory(self, ts_first): + raise NotImplementedError + + @staticmethod + def interpolate(curr_waypoint, next_waypoint, t): + t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"]) + curr_xyz = curr_waypoint['xyz'] + curr_quat = curr_waypoint['quat'] + curr_grip = curr_waypoint['gripper'] + next_xyz = next_waypoint['xyz'] + next_quat = next_waypoint['quat'] + next_grip = next_waypoint['gripper'] + xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac + quat = curr_quat + (next_quat - curr_quat) * t_frac + gripper = curr_grip + (next_grip - curr_grip) * t_frac + return xyz, quat, gripper + + def __call__(self, ts): + # generate trajectory at first timestep, then open-loop execution + if self.step_count == 0: + self.generate_trajectory(ts) + + # obtain left and right waypoints + if self.left_trajectory[0]['t'] == self.step_count: + self.curr_left_waypoint = self.left_trajectory.pop(0) + next_left_waypoint = self.left_trajectory[0] + + if self.right_trajectory[0]['t'] == self.step_count: + self.curr_right_waypoint = self.right_trajectory.pop(0) + next_right_waypoint = self.right_trajectory[0] + + # interpolate between waypoints to obtain current pose and gripper command + left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count) + right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count) + + # Inject noise + if self.inject_noise: + scale = 0.01 + left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape) + right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape) + + action_left = np.concatenate([left_xyz, left_quat, [left_gripper]]) + action_right = np.concatenate([right_xyz, right_quat, [right_gripper]]) + + self.step_count += 1 + return np.concatenate([action_left, action_right]) + + +class PickAndTransferPolicy(BasePolicy): + + def generate_trajectory(self, ts_first): + init_mocap_pose_right = ts_first.observation['mocap_pose_right'] + init_mocap_pose_left = ts_first.observation['mocap_pose_left'] + + box_info = np.array(ts_first.observation['env_state']) + box_xyz = box_info[:3] + box_quat = box_info[3:] + # print(f"Generate trajectory for {box_xyz=}") + + gripper_pick_quat = Quaternion(init_mocap_pose_right[3:]) + gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) + + meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90) + + meet_xyz = np.array([0, 0.5, 0.25]) + + self.left_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep + {"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position + {"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position + {"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper + {"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left + {"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay + ] + + self.right_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep + {"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube + {"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down + {"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper + {"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position + {"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position + {"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper + {"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right + {"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay + ] + + +class InsertionPolicy(BasePolicy): + + def generate_trajectory(self, ts_first): + init_mocap_pose_right = ts_first.observation['mocap_pose_right'] + init_mocap_pose_left = ts_first.observation['mocap_pose_left'] + + peg_info = np.array(ts_first.observation['env_state'])[:7] + peg_xyz = peg_info[:3] + peg_quat = peg_info[3:] + + socket_info = np.array(ts_first.observation['env_state'])[7:] + socket_xyz = socket_info[:3] + socket_quat = socket_info[3:] + + gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:]) + gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) + + gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:]) + gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60) + + meet_xyz = np.array([0, 0.5, 0.15]) + lift_right = 0.00715 + + self.left_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep + {"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube + {"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down + {"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper + {"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position + {"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0}, # insertion + {"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # insertion + ] + + self.right_trajectory = [ + {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep + {"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube + {"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down + {"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper + {"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position + {"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion + {"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion + + ] + + +def test_policy(task_name): + # example rolling out pick_and_transfer policy + onscreen_render = True + inject_noise = False + + # setup the environment + episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] + if 'sim_transfer_cube' in task_name: + env = make_ee_sim_env('sim_transfer_cube') + elif 'sim_insertion' in task_name: + env = make_ee_sim_env('sim_insertion') + else: + raise NotImplementedError + + for episode_idx in range(2): + ts = env.reset() + episode = [ts] + if onscreen_render: + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images']['angle']) + plt.ion() + + policy = PickAndTransferPolicy(inject_noise) + for step in range(episode_len): + action = policy(ts) + ts = env.step(action) + episode.append(ts) + if onscreen_render: + plt_img.set_data(ts.observation['images']['angle']) + plt.pause(0.02) + plt.close() + + episode_return = np.sum([ts.reward for ts in episode[1:]]) + if episode_return > 0: + print(f"{episode_idx=} Successful, {episode_return=}") + else: + print(f"{episode_idx=} Failed") + + +if __name__ == '__main__': + test_task_name = 'sim_transfer_cube_scripted' + test_policy(test_task_name) + diff --git a/act/sim_env.py b/act/sim_env.py new file mode 100644 index 00000000..b79b935b --- /dev/null +++ b/act/sim_env.py @@ -0,0 +1,278 @@ +import numpy as np +import os +import collections +import matplotlib.pyplot as plt +from dm_control import mujoco +from dm_control.rl import control +from dm_control.suite import base + +from constants import DT, XML_DIR, START_ARM_POSE +from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN +from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN +from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN +from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN + +import IPython +e = IPython.embed + +BOX_POSE = [None] # to be changed from outside + +def make_sim_env(task_name): + """ + Environment for simulated robot bi-manual manipulation, with joint position control + Action space: [left_arm_qpos (6), # absolute joint position + left_gripper_positions (1), # normalized gripper position (0: close, 1: open) + right_arm_qpos (6), # absolute joint position + right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) + + Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position + left_gripper_position (1), # normalized gripper position (0: close, 1: open) + right_arm_qpos (6), # absolute joint position + right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) + "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) + left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) + right_arm_qvel (6), # absolute joint velocity (rad) + right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) + "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' + """ + if 'sim_transfer_cube' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_transfer_cube.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = TransferCubeTask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + elif 'sim_insertion' in task_name: + xml_path = os.path.join(XML_DIR, f'bimanual_viperx_insertion.xml') + physics = mujoco.Physics.from_xml_path(xml_path) + task = InsertionTask(random=False) + env = control.Environment(physics, task, time_limit=20, control_timestep=DT, + n_sub_steps=None, flat_observation=False) + else: + raise NotImplementedError + return env + +class BimanualViperXTask(base.Task): + def __init__(self, random=None): + super().__init__(random=random) + + def before_step(self, action, physics): + left_arm_action = action[:6] + right_arm_action = action[7:7+6] + normalized_left_gripper_action = action[6] + normalized_right_gripper_action = action[7+6] + + left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_left_gripper_action) + right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_right_gripper_action) + + full_left_gripper_action = [left_gripper_action, -left_gripper_action] + full_right_gripper_action = [right_gripper_action, -right_gripper_action] + + env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action]) + super().before_step(env_action, physics) + return + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + super().initialize_episode(physics) + + @staticmethod + def get_qpos(physics): + qpos_raw = physics.data.qpos.copy() + left_qpos_raw = qpos_raw[:8] + right_qpos_raw = qpos_raw[8:16] + left_arm_qpos = left_qpos_raw[:6] + right_arm_qpos = right_qpos_raw[:6] + left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] + right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] + return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) + + @staticmethod + def get_qvel(physics): + qvel_raw = physics.data.qvel.copy() + left_qvel_raw = qvel_raw[:8] + right_qvel_raw = qvel_raw[8:16] + left_arm_qvel = left_qvel_raw[:6] + right_arm_qvel = right_qvel_raw[:6] + left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] + right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] + return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) + + @staticmethod + def get_env_state(physics): + raise NotImplementedError + + def get_observation(self, physics): + obs = collections.OrderedDict() + obs['qpos'] = self.get_qpos(physics) + obs['qvel'] = self.get_qvel(physics) + obs['env_state'] = self.get_env_state(physics) + obs['images'] = dict() + obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') + obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') + obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') + + return obs + + def get_reward(self, physics): + # return whether left gripper is holding the box + raise NotImplementedError + + +class TransferCubeTask(BimanualViperXTask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside + # reset qpos, control and box position + with physics.reset_context(): + physics.named.data.qpos[:16] = START_ARM_POSE + np.copyto(physics.data.ctrl, START_ARM_POSE) + assert BOX_POSE[0] is not None + physics.named.data.qpos[-7:] = BOX_POSE[0] + # print(f"{BOX_POSE=}") + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether left gripper is holding the box + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_table = ("red_box", "table") in all_contact_pairs + + reward = 0 + if touch_right_gripper: + reward = 1 + if touch_right_gripper and not touch_table: # lifted + reward = 2 + if touch_left_gripper: # attempted transfer + reward = 3 + if touch_left_gripper and not touch_table: # successful transfer + reward = 4 + return reward + + +class InsertionTask(BimanualViperXTask): + def __init__(self, random=None): + super().__init__(random=random) + self.max_reward = 4 + + def initialize_episode(self, physics): + """Sets the state of the environment at the start of each episode.""" + # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside + # reset qpos, control and box position + with physics.reset_context(): + physics.named.data.qpos[:16] = START_ARM_POSE + np.copyto(physics.data.ctrl, START_ARM_POSE) + assert BOX_POSE[0] is not None + physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects + # print(f"{BOX_POSE=}") + super().initialize_episode(physics) + + @staticmethod + def get_env_state(physics): + env_state = physics.data.qpos.copy()[16:] + return env_state + + def get_reward(self, physics): + # return whether peg touches the pin + all_contact_pairs = [] + for i_contact in range(physics.data.ncon): + id_geom_1 = physics.data.contact[i_contact].geom1 + id_geom_2 = physics.data.contact[i_contact].geom2 + name_geom_1 = physics.model.id2name(id_geom_1, 'geom') + name_geom_2 = physics.model.id2name(id_geom_2, 'geom') + contact_pair = (name_geom_1, name_geom_2) + all_contact_pairs.append(contact_pair) + + touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs + touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ + ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs + + peg_touch_table = ("red_peg", "table") in all_contact_pairs + socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ + ("socket-2", "table") in all_contact_pairs or \ + ("socket-3", "table") in all_contact_pairs or \ + ("socket-4", "table") in all_contact_pairs + peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ + ("red_peg", "socket-2") in all_contact_pairs or \ + ("red_peg", "socket-3") in all_contact_pairs or \ + ("red_peg", "socket-4") in all_contact_pairs + pin_touched = ("red_peg", "pin") in all_contact_pairs + + reward = 0 + if touch_left_gripper and touch_right_gripper: # touch both + reward = 1 + if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both + reward = 2 + if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching + reward = 3 + if pin_touched: # successful insertion + reward = 4 + return reward + + +def get_action(master_bot_left, master_bot_right): + action = np.zeros(14) + # arm action + action[:6] = master_bot_left.dxl.joint_states.position[:6] + action[7:7+6] = master_bot_right.dxl.joint_states.position[:6] + # gripper action + left_gripper_pos = master_bot_left.dxl.joint_states.position[7] + right_gripper_pos = master_bot_right.dxl.joint_states.position[7] + normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos) + normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos) + action[6] = normalized_left_pos + action[7+6] = normalized_right_pos + return action + +def test_sim_teleop(): + """ Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """ + from interbotix_xs_modules.arm import InterbotixManipulatorXS + + BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0] + + # source of data + master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", + robot_name=f'master_left', init_node=True) + master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", + robot_name=f'master_right', init_node=False) + + # setup the environment + env = make_sim_env('sim_transfer_cube') + ts = env.reset() + episode = [ts] + # setup plotting + ax = plt.subplot() + plt_img = ax.imshow(ts.observation['images']['angle']) + plt.ion() + + for t in range(1000): + action = get_action(master_bot_left, master_bot_right) + ts = env.step(action) + episode.append(ts) + + plt_img.set_data(ts.observation['images']['angle']) + plt.pause(0.02) + + +if __name__ == '__main__': + test_sim_teleop() + diff --git a/act/utils.py b/act/utils.py new file mode 100644 index 00000000..673cbb10 --- /dev/null +++ b/act/utils.py @@ -0,0 +1,189 @@ +import numpy as np +import torch +import os +import h5py +from torch.utils.data import TensorDataset, DataLoader + +import IPython +e = IPython.embed + +class EpisodicDataset(torch.utils.data.Dataset): + def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats): + super(EpisodicDataset).__init__() + self.episode_ids = episode_ids + self.dataset_dir = dataset_dir + self.camera_names = camera_names + self.norm_stats = norm_stats + self.is_sim = None + self.__getitem__(0) # initialize self.is_sim + + def __len__(self): + return len(self.episode_ids) + + def __getitem__(self, index): + sample_full_episode = False # hardcode + + episode_id = self.episode_ids[index] + dataset_path = os.path.join(self.dataset_dir, f'episode_{episode_id}.hdf5') + with h5py.File(dataset_path, 'r') as root: + is_sim = root.attrs['sim'] + original_action_shape = root['/action'].shape + episode_len = original_action_shape[0] + if sample_full_episode: + start_ts = 0 + else: + start_ts = np.random.choice(episode_len) + # get observation at start_ts only + qpos = root['/observations/qpos'][start_ts] + qvel = root['/observations/qvel'][start_ts] + image_dict = dict() + for cam_name in self.camera_names: + image_dict[cam_name] = root[f'/observations/images/{cam_name}'][start_ts] + # get all actions after and including start_ts + if is_sim: + action = root['/action'][start_ts:] + action_len = episode_len - start_ts + else: + action = root['/action'][max(0, start_ts - 1):] # hack, to make timesteps more aligned + action_len = episode_len - max(0, start_ts - 1) # hack, to make timesteps more aligned + + self.is_sim = is_sim + padded_action = np.zeros(original_action_shape, dtype=np.float32) + padded_action[:action_len] = action + is_pad = np.zeros(episode_len) + is_pad[action_len:] = 1 + + # new axis for different cameras + all_cam_images = [] + for cam_name in self.camera_names: + all_cam_images.append(image_dict[cam_name]) + all_cam_images = np.stack(all_cam_images, axis=0) + + # construct observations + image_data = torch.from_numpy(all_cam_images) + qpos_data = torch.from_numpy(qpos).float() + action_data = torch.from_numpy(padded_action).float() + is_pad = torch.from_numpy(is_pad).bool() + + # channel last + image_data = torch.einsum('k h w c -> k c h w', image_data) + + # normalize image and change dtype to float + image_data = image_data / 255.0 + action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"] + qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"] + + return image_data, qpos_data, action_data, is_pad + + +def get_norm_stats(dataset_dir, num_episodes): + all_qpos_data = [] + all_action_data = [] + for episode_idx in range(num_episodes): + dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5') + with h5py.File(dataset_path, 'r') as root: + qpos = root['/observations/qpos'][()] + qvel = root['/observations/qvel'][()] + action = root['/action'][()] + all_qpos_data.append(torch.from_numpy(qpos)) + all_action_data.append(torch.from_numpy(action)) + all_qpos_data = torch.stack(all_qpos_data) + all_action_data = torch.stack(all_action_data) + all_action_data = all_action_data + + # normalize action data + action_mean = all_action_data.mean(dim=[0, 1], keepdim=True) + action_std = all_action_data.std(dim=[0, 1], keepdim=True) + action_std = torch.clip(action_std, 1e-2, 10) # clipping + + # normalize qpos data + qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True) + qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True) + qpos_std = torch.clip(qpos_std, 1e-2, 10) # clipping + + stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(), + "qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(), + "example_qpos": qpos} + + return stats + + +def load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val): + print(f'\nData from: {dataset_dir}\n') + # obtain train test split + train_ratio = 0.8 + shuffled_indices = np.random.permutation(num_episodes) + train_indices = shuffled_indices[:int(train_ratio * num_episodes)] + val_indices = shuffled_indices[int(train_ratio * num_episodes):] + + # obtain normalization stats for qpos and action + norm_stats = get_norm_stats(dataset_dir, num_episodes) + + # construct dataset and dataloader + train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats) + val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats) + train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) + val_dataloader = DataLoader(val_dataset, batch_size=batch_size_val, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) + + return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim + + +### env utils + +def sample_box_pose(): + x_range = [0.0, 0.2] + y_range = [0.4, 0.6] + z_range = [0.05, 0.05] + + ranges = np.vstack([x_range, y_range, z_range]) + cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) + + cube_quat = np.array([1, 0, 0, 0]) + return np.concatenate([cube_position, cube_quat]) + +def sample_insertion_pose(): + # Peg + x_range = [0.1, 0.2] + y_range = [0.4, 0.6] + z_range = [0.05, 0.05] + + ranges = np.vstack([x_range, y_range, z_range]) + peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) + + peg_quat = np.array([1, 0, 0, 0]) + peg_pose = np.concatenate([peg_position, peg_quat]) + + # Socket + x_range = [-0.2, -0.1] + y_range = [0.4, 0.6] + z_range = [0.05, 0.05] + + ranges = np.vstack([x_range, y_range, z_range]) + socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) + + socket_quat = np.array([1, 0, 0, 0]) + socket_pose = np.concatenate([socket_position, socket_quat]) + + return peg_pose, socket_pose + +### helper functions + +def compute_dict_mean(epoch_dicts): + result = {k: None for k in epoch_dicts[0]} + num_items = len(epoch_dicts) + for k in result: + value_sum = 0 + for epoch_dict in epoch_dicts: + value_sum += epoch_dict[k] + result[k] = value_sum / num_items + return result + +def detach_dict(d): + new_d = dict() + for k, v in d.items(): + new_d[k] = v.detach() + return new_d + +def set_seed(seed): + torch.manual_seed(seed) + np.random.seed(seed) diff --git a/act/visualize_episodes.py b/act/visualize_episodes.py new file mode 100644 index 00000000..4e55e471 --- /dev/null +++ b/act/visualize_episodes.py @@ -0,0 +1,147 @@ +import os +import numpy as np +import cv2 +import h5py +import argparse + +import matplotlib.pyplot as plt +from constants import DT + +import IPython +e = IPython.embed + +JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] +STATE_NAMES = JOINT_NAMES + ["gripper"] + +def load_hdf5(dataset_dir, dataset_name): + dataset_path = os.path.join(dataset_dir, dataset_name + '.hdf5') + if not os.path.isfile(dataset_path): + print(f'Dataset does not exist at \n{dataset_path}\n') + exit() + + with h5py.File(dataset_path, 'r') as root: + is_sim = root.attrs['sim'] + qpos = root['/observations/qpos'][()] + qvel = root['/observations/qvel'][()] + action = root['/action'][()] + image_dict = dict() + for cam_name in root[f'/observations/images/'].keys(): + image_dict[cam_name] = root[f'/observations/images/{cam_name}'][()] + + return qpos, qvel, action, image_dict + +def main(args): + dataset_dir = args['dataset_dir'] + episode_idx = args['episode_idx'] + dataset_name = f'episode_{episode_idx}' + + qpos, qvel, action, image_dict = load_hdf5(dataset_dir, dataset_name) + save_videos(image_dict, DT, video_path=os.path.join(dataset_dir, dataset_name + '_video.mp4')) + visualize_joints(qpos, action, plot_path=os.path.join(dataset_dir, dataset_name + '_qpos.png')) + # visualize_timestamp(t_list, dataset_path) # TODO addn timestamp back + + +def save_videos(video, dt, video_path=None): + if isinstance(video, list): + cam_names = list(video[0].keys()) + h, w, _ = video[0][cam_names[0]].shape + w = w * len(cam_names) + fps = int(1/dt) + out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + for ts, image_dict in enumerate(video): + images = [] + for cam_name in cam_names: + image = image_dict[cam_name] + image = image[:, :, [2, 1, 0]] # swap B and R channel + images.append(image) + images = np.concatenate(images, axis=1) + out.write(images) + out.release() + print(f'Saved video to: {video_path}') + elif isinstance(video, dict): + cam_names = list(video.keys()) + all_cam_videos = [] + for cam_name in cam_names: + all_cam_videos.append(video[cam_name]) + all_cam_videos = np.concatenate(all_cam_videos, axis=2) # width dimension + + n_frames, h, w, _ = all_cam_videos.shape + fps = int(1 / dt) + out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + for t in range(n_frames): + image = all_cam_videos[t] + image = image[:, :, [2, 1, 0]] # swap B and R channel + out.write(image) + out.release() + print(f'Saved video to: {video_path}') + + +def visualize_joints(qpos_list, command_list, plot_path=None, ylim=None, label_overwrite=None): + if label_overwrite: + label1, label2 = label_overwrite + else: + label1, label2 = 'State', 'Command' + + qpos = np.array(qpos_list) # ts, dim + command = np.array(command_list) + num_ts, num_dim = qpos.shape + h, w = 2, num_dim + num_figs = num_dim + fig, axs = plt.subplots(num_figs, 1, figsize=(w, h * num_figs)) + + # plot joint state + all_names = [name + '_left' for name in STATE_NAMES] + [name + '_right' for name in STATE_NAMES] + for dim_idx in range(num_dim): + ax = axs[dim_idx] + ax.plot(qpos[:, dim_idx], label=label1) + ax.set_title(f'Joint {dim_idx}: {all_names[dim_idx]}') + ax.legend() + + # plot arm command + for dim_idx in range(num_dim): + ax = axs[dim_idx] + ax.plot(command[:, dim_idx], label=label2) + ax.legend() + + if ylim: + for dim_idx in range(num_dim): + ax = axs[dim_idx] + ax.set_ylim(ylim) + + plt.tight_layout() + plt.savefig(plot_path) + print(f'Saved qpos plot to: {plot_path}') + plt.close() + +def visualize_timestamp(t_list, dataset_path): + plot_path = dataset_path.replace('.pkl', '_timestamp.png') + h, w = 4, 10 + fig, axs = plt.subplots(2, 1, figsize=(w, h*2)) + # process t_list + t_float = [] + for secs, nsecs in t_list: + t_float.append(secs + nsecs * 10E-10) + t_float = np.array(t_float) + + ax = axs[0] + ax.plot(np.arange(len(t_float)), t_float) + ax.set_title(f'Camera frame timestamps') + ax.set_xlabel('timestep') + ax.set_ylabel('time (sec)') + + ax = axs[1] + ax.plot(np.arange(len(t_float)-1), t_float[:-1] - t_float[1:]) + ax.set_title(f'dt') + ax.set_xlabel('timestep') + ax.set_ylabel('time (sec)') + + plt.tight_layout() + plt.savefig(plot_path) + print(f'Saved timestamp plot to: {plot_path}') + plt.close() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_dir', action='store', type=str, help='Dataset dir.', required=True) + parser.add_argument('--episode_idx', action='store', type=int, help='Episode index.', required=False) + main(vars(parser.parse_args())) diff --git a/requirements.txt b/requirements.txt index ac8ea89a..c451769d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,3 +12,4 @@ egl_probe>=1.0.1 torch torchvision diffusers==0.11.1 +pytorch3d==0.7.3 diff --git a/robomimic/algo/__init__.py b/robomimic/algo/__init__.py index dedba35c..5fd1d53f 100644 --- a/robomimic/algo/__init__.py +++ b/robomimic/algo/__init__.py @@ -10,3 +10,4 @@ from robomimic.algo.iris import IRIS from robomimic.algo.td3_bc import TD3_BC from robomimic.algo.diffusion_policy import DiffusionPolicyUNet +from robomimic.algo.act import ACT diff --git a/robomimic/algo/act.py b/robomimic/algo/act.py new file mode 100644 index 00000000..d0c0024d --- /dev/null +++ b/robomimic/algo/act.py @@ -0,0 +1,265 @@ +""" +Implementation of Action Chunking with Transformers (ACT). +""" +from collections import OrderedDict +from functools import reduce + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms as transforms + +import robomimic.utils.tensor_utils as TensorUtils + +from robomimic.algo import register_algo_factory_func, PolicyAlgo +from robomimic.algo.bc import BC_VAE + + +@register_algo_factory_func("act") +def algo_config_to_class(algo_config): + """ + Maps algo config to the BC algo class to instantiate, along with additional algo kwargs. + + Args: + algo_config (Config instance): algo config + + Returns: + algo_class: subclass of Algo + algo_kwargs (dict): dictionary of additional kwargs to pass to algorithm + """ + algo_class, algo_kwargs = ACT, {} + + return algo_class, algo_kwargs + + +class ACT(BC_VAE): + """ + BC training with a VAE policy. + """ + def _create_networks(self): + """ + Creates networks and places them into @self.nets. + """ + + self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + + self.nets = nn.ModuleDict() + self.chunk_size = self.global_config["train"]["seq_length"] + self.camera_keys = self.obs_config['modalities']['obs']['rgb'].copy() + self.proprio_keys = self.obs_config['modalities']['obs']['low_dim'].copy() + self.obs_keys = self.proprio_keys + self.camera_keys + + self.proprio_dim = 0 + for k in self.proprio_keys: + self.proprio_dim += reduce(lambda x, y: x*y, self.obs_key_shapes[k]) + + from act.detr.main import build_ACT_model_and_optimizer + policy_config = {'num_queries': self.chunk_size, + 'hidden_dim': self.algo_config.act.hidden_dim, + 'dim_feedforward': self.algo_config.act.dim_feedforward, + 'backbone': self.algo_config.act.backbone, + 'enc_layers': self.algo_config.act.enc_layers, + 'dec_layers': self.algo_config.act.dec_layers, + 'nheads': self.algo_config.act.nheads, + 'latent_dim': self.algo_config.act.latent_dim, + 'a_dim': self.ac_dim, + 'state_dim': self.proprio_dim, + 'camera_names': self.camera_keys + } + self.kl_weight = self.algo_config.act.kl_weight + model, optimizer = build_ACT_model_and_optimizer(policy_config) + self.nets["policy"] = torch.nn.parallel.DataParallel(model, device_ids=list(range(0,torch.cuda.device_count()))) + self.nets = self.nets.float().to(self.device) + + self.temporal_agg = False + self.query_frequency = self.chunk_size # TODO maybe tune + + self._step_counter = 0 + self.a_hat_store = None + + + def process_batch_for_training(self, batch): + """ + Processes input batch from a data loader to filter out + relevant information and prepare the batch for training. + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader + Returns: + input_batch (dict): processed and filtered batch that + will be used for training + """ + + input_batch = dict() + input_batch["obs"] = {k: batch["obs"][k][:, 0, :] for k in batch["obs"] if k != 'pad_mask'} + input_batch["obs"]['pad_mask'] = batch["obs"]['pad_mask'] + input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present + # For the goal, only keep the camera observations + if input_batch["goal_obs"] is not None: + input_batch["goal_obs"] = {key: value for key, value in batch["goal_obs"].items() if key.startswith('camera')} + input_batch["actions"] = batch["actions"][:, :, :] + # we move to device first before float conversion because image observation modalities will be uint8 - + # this minimizes the amount of data transferred to GPU + return TensorUtils.to_float(TensorUtils.to_device(input_batch, self.device)) + + def train_on_batch(self, batch, epoch, validate=False): + """ + Update from superclass to set categorical temperature, for categorcal VAEs. + """ + + return super(BC_VAE, self).train_on_batch(batch, epoch, validate=validate) + + def _forward_training(self, batch): + """ + Internal helper function for BC algo class. Compute forward pass + and return network outputs in @predictions dict. + Args: + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + Returns: + predictions (dict): dictionary containing network outputs + """ + # TODO (Ashwin): remove this once we add in the robomimic observation encoders + for k in self.proprio_keys: + batch["obs"][k] = batch["obs"][k].reshape((batch["obs"][k].shape[0], -1)) + proprio = [batch["obs"][k] for k in self.proprio_keys] + proprio = torch.cat(proprio, axis=1) + qpos = proprio + + images = [] + goal_images = [] + for cam_name in self.camera_keys: + image = batch['obs'][cam_name] + image = self.normalize(image) + image = image.unsqueeze(axis=1) + images.append(image) + goal_image = batch['goal_obs'][cam_name] + goal_image = self.normalize(goal_image) + goal_images.append(goal_image) + + images = torch.cat(images, axis=1) + goal_images = torch.cat(goal_images, axis=1) + + # Concatenate images and goal images channel-wise + images = torch.cat((images, goal_images), axis=1) + + env_state = torch.zeros([qpos.shape[0], 10]).cuda() # this is not used + + actions = batch['actions'] + is_pad = batch['obs']['pad_mask'] == 0 # from 1.0 or 0 to False and True + is_pad = is_pad.squeeze(dim=-1) + + a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state, actions, is_pad) + total_kld, dim_wise_kld, mean_kld = self.kl_divergence(mu, logvar) + loss_dict = dict() + all_l1 = F.l1_loss(actions, a_hat, reduction='none') + l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() + loss_dict['l1'] = l1 + loss_dict['kl'] = total_kld[0] + + + predictions = OrderedDict( + actions=actions, + kl_loss=loss_dict['kl'], + reconstruction_loss=loss_dict['l1'], + ) + + return predictions + + def get_action(self, obs_dict, goal_dict=None): + """ + Get policy action outputs. + Args: + obs_dict (dict): current observation + goal_dict (dict): (optional) goal + Returns: + action (torch.Tensor): action tensor + """ + assert not self.nets.training + + # TODO (Ashwin): remove this once we add in the robomimic observation encoders + for k in self.proprio_keys: + obs_dict[k] = obs_dict[k].reshape((obs_dict[k].shape[0], -1)) + proprio = [obs_dict[k] for k in self.proprio_keys] + proprio = torch.cat(proprio, axis=1) + qpos = proprio + + images = [] + for cam_name in self.camera_keys: + image = obs_dict[cam_name] + image = self.normalize(image) + image = image.unsqueeze(axis=1) + images.append(image) + images = torch.cat(images, axis=1) + + env_state = torch.zeros([qpos.shape[0], 10]).cuda() # not used + + if self._step_counter % self.query_frequency == 0: + a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state) + self.a_hat_store = a_hat + + action = self.a_hat_store[:, self._step_counter % self.query_frequency, :] + self._step_counter += 1 + return action + + + def reset(self): + """ + Reset algo state to prepare for environment rollouts. + """ + self._step_counter = 0 + + def _compute_losses(self, predictions, batch): + """ + Internal helper function for BC algo class. Compute losses based on + network outputs in @predictions dict, using reference labels in @batch. + Args: + predictions (dict): dictionary containing network outputs, from @_forward_training + batch (dict): dictionary with torch.Tensors sampled + from a data loader and filtered by @process_batch_for_training + Returns: + losses (dict): dictionary of losses computed over the batch + """ + + # total loss is sum of reconstruction and KL, weighted by beta + kl_loss = predictions["kl_loss"] + recons_loss = predictions["reconstruction_loss"] + action_loss = recons_loss + self.kl_weight * kl_loss + return OrderedDict( + recons_loss=recons_loss, + kl_loss=kl_loss, + action_loss=action_loss, + ) + + def log_info(self, info): + """ + Process info dictionary from @train_on_batch to summarize + information to pass to tensorboard for logging. + Args: + info (dict): dictionary of info + Returns: + loss_log (dict): name -> summary statistic + """ + log = PolicyAlgo.log_info(self, info) + log["Loss"] = info["losses"]["action_loss"].item() + log["KL_Loss"] = info["losses"]["kl_loss"].item() + log["Reconstruction_Loss"] = info["losses"]["recons_loss"].item() + if "policy_grad_norms" in info: + log["Policy_Grad_Norms"] = info["policy_grad_norms"] + return log + + def kl_divergence(self, mu, logvar): + batch_size = mu.size(0) + assert batch_size != 0 + if mu.data.ndimension() == 4: + mu = mu.view(mu.size(0), mu.size(1)) + if logvar.data.ndimension() == 4: + logvar = logvar.view(logvar.size(0), logvar.size(1)) + + klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) + total_kld = klds.sum(1).mean(0, True) + dimension_wise_kld = klds.mean(0) + mean_kld = klds.mean(1).mean(0, True) + + return total_kld, dimension_wise_kld, mean_kld + diff --git a/robomimic/config/__init__.py b/robomimic/config/__init__.py index 8b0ea02e..b8b9da73 100644 --- a/robomimic/config/__init__.py +++ b/robomimic/config/__init__.py @@ -9,4 +9,6 @@ from robomimic.config.gl_config import GLConfig from robomimic.config.hbc_config import HBCConfig from robomimic.config.iris_config import IRISConfig +from robomimic.config.td3_bc_config import TD3_BCConfig from robomimic.config.diffusion_policy_config import DiffusionPolicyConfig +from robomimic.config.act_config import ACTConfig diff --git a/robomimic/config/act_config.py b/robomimic/config/act_config.py new file mode 100644 index 00000000..9be3926b --- /dev/null +++ b/robomimic/config/act_config.py @@ -0,0 +1,47 @@ +""" +Config for BC algorithm. +""" + +from robomimic.config.base_config import BaseConfig + + +class ACTConfig(BaseConfig): + ALGO_NAME = "act" + + def train_config(self): + """ + BC algorithms don't need "next_obs" from hdf5 - so save on storage and compute by disabling it. + """ + super(ACTConfig, self).train_config() + self.train.hdf5_load_next_obs = False + + def algo_config(self): + """ + This function populates the `config.algo` attribute of the config, and is given to the + `Algo` subclass (see `algo/algo.py`) for each algorithm through the `algo_config` + argument to the constructor. Any parameter that an algorithm needs to determine its + training and test-time behavior should be populated here. + """ + + # optimization parameters + self.algo.optim_params.policy.optimizer_type = "adamw" + self.algo.optim_params.policy.learning_rate.initial = 5e-5 # policy learning rate + self.algo.optim_params.policy.learning_rate.decay_factor = 1 # factor to decay LR by (if epoch schedule non-empty) + self.algo.optim_params.policy.learning_rate.epoch_schedule = [] # epochs where LR decay occurs + self.algo.optim_params.policy.learning_rate.scheduler_type = "linear" # learning rate scheduler ("multistep", "linear", etc) + self.algo.optim_params.policy.regularization.L2 = 0.0001 # L2 regularization strength + + # loss weights + self.algo.loss.l2_weight = 0.0 # L2 loss weight + self.algo.loss.l1_weight = 1.0 # L1 loss weight + self.algo.loss.cos_weight = 0.0 # cosine loss weight + + # ACT policy settings + self.algo.act.hidden_dim = 512 # length of (s, a) seqeunces to feed to transformer - should usually match train.frame_stack + self.algo.act.dim_feedforward = 3200 # dimension for embeddings used by transformer + self.algo.act.backbone = "resnet18" # number of transformer blocks to stack + self.algo.act.enc_layers = 4 # number of attention heads for each transformer block (should divide embed_dim evenly) + self.algo.act.dec_layers = 7 # dropout probability for embedding inputs in transformer + self.algo.act.nheads = 8 # dropout probability for attention outputs for each transformer block + self.algo.act.latent_dim = 32 # latent dim of VAE + self.algo.act.kl_weight = 20 # KL weight of VAE diff --git a/robomimic/exps/templates/act.json b/robomimic/exps/templates/act.json new file mode 100644 index 00000000..4512ecdf --- /dev/null +++ b/robomimic/exps/templates/act.json @@ -0,0 +1,160 @@ +{ + "algo_name": "act", + "experiment": { + "name": "test", + "validate": false, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": false, + "wandb_proj_name": "debug" + }, + "mse":{}, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 40, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 500, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": true, + "n": 50, + "horizon": 400, + "rate": 40, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir":"../act_trained_models", + "num_data_workers": 4, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "seq_length": 10, + "pad_seq_length": true, + "frame_stack": 1, + "pad_frame_stack": true, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 128, + "num_epochs": 10000, + "seed": 1 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adamw", + "learning_rate": { + "initial": 0.00005, + "decay_factor": 1, + "epoch_schedule": [ + 100 + ], + "scheduler_type": "linear" + }, + "regularization": { + "L2": 0.0001 + } + } + }, + "loss": { + "l2_weight": 0.0, + "l1_weight": 1.0, + "cos_weight": 0.0 + }, + "act": { + "hidden_dim": 512, + "dim_feedforward": 3200, + "backbone": "resnet18", + "enc_layers": 4, + "dec_layers": 7, + "nheads": 8, + "latent_dim": 32, + "kl_weight": 20 + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "robot0_eef_pos", + "robot0_eef_quat", + "robot0_gripper_qpos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0 + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} \ No newline at end of file diff --git a/robomimic/exps/templates/bc_transformer.json b/robomimic/exps/templates/bc_transformer.json new file mode 100644 index 00000000..0bb5c3d9 --- /dev/null +++ b/robomimic/exps/templates/bc_transformer.json @@ -0,0 +1,172 @@ +{ + "algo_name": "bc", + "experiment": { + "name": "test", + "validate": true, + "logging": { + "terminal_output_to_txt": true, + "log_tb": true, + "log_wandb": false, + "wandb_proj_name": "debug" + }, + "mse":{}, + "save": { + "enabled": true, + "every_n_seconds": null, + "every_n_epochs": 50, + "epochs": [], + "on_best_validation": false, + "on_best_rollout_return": false, + "on_best_rollout_success_rate": true + }, + "epoch_every_n_steps": 100, + "validation_epoch_every_n_steps": 10, + "env": null, + "additional_envs": null, + "render": false, + "render_video": true, + "keep_all_videos": false, + "video_skip": 5, + "rollout": { + "enabled": true, + "n": 50, + "horizon": 400, + "rate": 50, + "warmstart": 0, + "terminate_on_success": true + } + }, + "train": { + "data": null, + "output_dir": "../bc_transformer_trained_models", + "num_data_workers": 0, + "hdf5_cache_mode": "low_dim", + "hdf5_use_swmr": true, + "hdf5_load_next_obs": false, + "hdf5_normalize_obs": false, + "hdf5_filter_key": null, + "seq_length": 1, + "pad_seq_length": true, + "frame_stack": 10, + "pad_frame_stack": true, + "dataset_keys": [ + "actions" + ], + "goal_mode": null, + "cuda": true, + "batch_size": 100, + "num_epochs": 2000, + "seed": 1 + }, + "algo": { + "optim_params": { + "policy": { + "optimizer_type": "adamw", + "learning_rate": { + "initial": 0.0001, + "decay_factor": 0.1, + "epoch_schedule": [], + "scheduler_type": "linear" + }, + "regularization": { + "L2": 0.01 + } + } + }, + "loss": { + "l2_weight": 1.0, + "l1_weight": 0.0, + "cos_weight": 0.0 + }, + "actor_layer_dims": [], + "gaussian": { + "enabled": false + }, + "gmm": { + "enabled": true, + "num_modes": 5, + "min_std": 0.0001, + "std_activation": "softplus", + "low_noise_eval": true + }, + "vae": { + "enabled": false + }, + "rnn": { + "enabled": false + }, + "transformer": { + "enabled": true, + "supervise_all_steps": false, + "num_layers": 6, + "embed_dim": 512, + "num_heads": 8 + } + }, + "observation": { + "modalities": { + "obs": { + "low_dim": [ + "robot0_eef_pos", + "robot0_eef_quat", + "robot0_gripper_qpos", + "object" + ], + "rgb": [], + "depth": [], + "scan": [] + }, + "goal": { + "low_dim": [], + "rgb": [], + "depth": [], + "scan": [] + } + }, + "encoder": { + "low_dim": { + "core_class": null, + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "rgb": { + "core_class": "VisualCore", + "core_kwargs": { + "feature_dimension": 64, + "backbone_class": "ResNet18Conv", + "backbone_kwargs": { + "pretrained": false, + "input_coord_conv": false + }, + "pool_class": "SpatialSoftmax", + "pool_kwargs": { + "num_kp": 32, + "learnable_temperature": false, + "temperature": 1.0, + "noise_std": 0.0 + } + }, + "obs_randomizer_class": "CropRandomizer", + "obs_randomizer_kwargs": { + "crop_height": 76, + "crop_width": 76, + "num_crops": 1, + "pos_enc": false + } + }, + "depth": { + "core_class": "VisualCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + }, + "scan": { + "core_class": "ScanCore", + "core_kwargs": {}, + "obs_randomizer_class": null, + "obs_randomizer_kwargs": {} + } + } + } +} \ No newline at end of file diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index 54976419..4c9d35c9 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -19,6 +19,7 @@ # NOTE: this is required for the backbone classes to be found by the `eval` call in the core networks from robomimic.models.base_nets import * +from robomimic.utils.camera_utils import DeFiNeImageCamEncoder, VoltronImageCamEncoder, R3MImageCamEncoder, VC1ImageCamEncoder from robomimic.utils.camera_utils import ResNetFiLMEncoder from robomimic.utils.vis_utils import visualize_image_randomizer from robomimic.macros import VISUALIZE_RANDOMIZER @@ -183,6 +184,283 @@ def __repr__(self): msg += textwrap.indent("\npool_net={}".format(self.pool), indent) msg = header + '(' + msg + '\n)' return msg + + +class PretrainedVisualCore(EncoderCore, BaseNets.ConvBase): + """ + Extension of VisualCore that uses a pretrained model + """ + def __init__( + self, + input_shape, + backbone_class="ResNet18Conv", + pool_class="SpatialSoftmax", + backbone_kwargs=None, + pool_kwargs=None, + flatten=True, + feature_dimension=64, + ): + """ + Args: + input_shape (tuple): shape of input (not including batch dimension) + backbone_class (str): class name for the visual backbone network. Defaults + to "ResNet18Conv". + pool_class (str): class name for the visual feature pooler (optional) + Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to + "SpatialSoftmax". + backbone_kwargs (dict): kwargs for the visual backbone network (optional) + pool_kwargs (dict): kwargs for the visual feature pooler (optional) + flatten (bool): whether to flatten the visual features + feature_dimension (int): if not None, add a Linear layer to + project output into a desired feature dimension + """ + super(PretrainedVisualCore, self).__init__(input_shape=input_shape) + self.flatten = flatten + + self.nets = nn.ModuleDict() + + if backbone_kwargs is None: + backbone_kwargs = dict() + + # add input channel dimension to visual core inputs + backbone_kwargs["input_channel"] = input_shape[0] + + # extract only relevant kwargs for this specific backbone + backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) + + # visual backbone + assert isinstance(backbone_class, str) + self.backbone = eval(backbone_class)(**backbone_kwargs) + + feat_shape = self.backbone.output_shape(input_shape) + self.feat_shape = feat_shape + # net_list = [self.backbone] + self.nets["backbone"] = self.backbone + + post_proc_list = [] + + # maybe make pool net + if pool_class is not None: + assert isinstance(pool_class, str) + # feed output shape of backbone to pool net + if pool_kwargs is None: + pool_kwargs = dict() + # extract only relevant kwargs for this specific backbone + pool_kwargs["input_shape"] = feat_shape + pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) + self.pool = eval(pool_class)(**pool_kwargs) + assert isinstance(self.pool, BaseNets.Module) + + feat_shape = self.pool.output_shape(feat_shape) + post_proc_list.append(self.pool) + else: + self.pool = None + + # flatten layer + if self.flatten: + post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) + + # maybe linear layer + self.feature_dimension = feature_dimension + if feature_dimension is not None: + assert self.flatten + post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) + + self.nets["post_proc"] = nn.Sequential(*post_proc_list) + + def output_shape(self, input_shape): + """ + Function to compute output shape from inputs to this module. + + Args: + input_shape (iterable of int): shape of input. Does not include batch dimension. + Some modules may not need this argument, if their output does not depend + on the size of the input, or if they assume fixed size input. + + Returns: + out_shape ([int]): list of integers corresponding to output shape + """ + if self.feature_dimension is not None: + # linear output + return [self.feature_dimension] + feat_shape = self.backbone.output_shape(input_shape) + if self.pool is not None: + # pool output + feat_shape = self.pool.output_shape(feat_shape) + # backbone + flat output + if self.flatten: + return [np.prod(feat_shape)] + else: + return feat_shape + + def forward(self, inputs): + """ + Forward pass through visual core. + """ + ndim = len(self.input_shape) + assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) + image = inputs["image"] + # Sometimes will be language conditioned image representation + lang_emb = inputs.get("lang_emb", None) + + x = self.nets["backbone"].forward(image=image, lang_emb=lang_emb) + # x = self.nets["backbone"].forward(inputs["image"]) + x = self.nets["post_proc"](x) + + return x + + def __repr__(self): + """Pretty print network.""" + header = '{}'.format(str(self.__class__.__name__)) + msg = '' + indent = ' ' * 2 + msg += textwrap.indent( + "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) + msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) + msg += textwrap.indent("\npool_net={}".format(self.pool), indent) + msg = header + '(' + msg + '\n)' + return msg + +class DeFiNeVisualCore(EncoderCore, BaseNets.ConvBase): + """ + Extension of VisualCore that takes in camera information + """ + def __init__( + self, + input_shape, + backbone_class="ResNet18Conv", + pool_class="SpatialSoftmax", + backbone_kwargs=None, + pool_kwargs=None, + flatten=True, + feature_dimension=64, + ): + """ + Args: + input_shape (tuple): shape of input (not including batch dimension) + backbone_class (str): class name for the visual backbone network. Defaults + to "ResNet18Conv". + pool_class (str): class name for the visual feature pooler (optional) + Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to + "SpatialSoftmax". + backbone_kwargs (dict): kwargs for the visual backbone network (optional) + pool_kwargs (dict): kwargs for the visual feature pooler (optional) + flatten (bool): whether to flatten the visual features + feature_dimension (int): if not None, add a Linear layer to + project output into a desired feature dimension + """ + super(DeFiNeVisualCore, self).__init__(input_shape=input_shape) + self.flatten = flatten + + self.nets = nn.ModuleDict() + + if backbone_kwargs is None: + backbone_kwargs = dict() + + # add input channel dimension to visual core inputs + backbone_kwargs["input_channel"] = input_shape[0] + + # extract only relevant kwargs for this specific backbone + backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) + + # visual backbone + assert isinstance(backbone_class, str) + self.backbone = eval(backbone_class)(**backbone_kwargs) + + feat_shape = self.backbone.output_shape(input_shape) + self.feat_shape = feat_shape + # net_list = [self.backbone] + self.nets["backbone"] = self.backbone + + post_proc_list = [] + + # maybe make pool net + if pool_class is not None: + assert isinstance(pool_class, str) + # feed output shape of backbone to pool net + if pool_kwargs is None: + pool_kwargs = dict() + # extract only relevant kwargs for this specific backbone + pool_kwargs["input_shape"] = feat_shape + pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) + self.pool = eval(pool_class)(**pool_kwargs) + assert isinstance(self.pool, BaseNets.Module) + + feat_shape = self.pool.output_shape(feat_shape) + post_proc_list.append(self.pool) + else: + self.pool = None + + # flatten layer + if self.flatten: + post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) + + # maybe linear layer + self.feature_dimension = feature_dimension + if feature_dimension is not None: + assert self.flatten + post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) + + self.nets["post_proc"] = nn.Sequential(*post_proc_list) + + def output_shape(self, input_shape): + """ + Function to compute output shape from inputs to this module. + + Args: + input_shape (iterable of int): shape of input. Does not include batch dimension. + Some modules may not need this argument, if their output does not depend + on the size of the input, or if they assume fixed size input. + + Returns: + out_shape ([int]): list of integers corresponding to output shape + """ + if self.feature_dimension is not None: + # linear output + return [self.feature_dimension] + feat_shape = self.backbone.output_shape(input_shape) + if self.pool is not None: + # pool output + feat_shape = self.pool.output_shape(feat_shape) + # backbone + flat output + if self.flatten: + return [np.prod(feat_shape)] + else: + return feat_shape + + def forward(self, inputs): + """ + Forward pass through visual core. + """ + ndim = len(self.input_shape) + assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) + image = inputs["image"] + intrinsics = inputs.get("intrinsics", None) + extrinsics = inputs.get("extrinsics", None) + lang_emb = inputs.get("lang_emb", None) + + # # TODO: replace with actual extrinsics + # extrinsics = TensorUtils.to_torch(np.array([[ + # [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], + # ]] * image.shape[0]), image.device) + + x = self.nets["backbone"].forward(image=image, intrinsics=intrinsics, extrinsics=extrinsics, lang_emb=lang_emb) + # x = self.nets["backbone"].forward(inputs["image"]) + x = self.nets["post_proc"](x) + + return x + + def __repr__(self): + """Pretty print network.""" + header = '{}'.format(str(self.__class__.__name__)) + msg = '' + indent = ' ' * 2 + msg += textwrap.indent( + "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) + msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) + msg += textwrap.indent("\npool_net={}".format(self.pool), indent) + msg = header + '(' + msg + '\n)' + return msg class FiLMVisualCore(EncoderCore, BaseNets.ConvBase): diff --git a/robomimic/scripts/config_gen/act_gen.py b/robomimic/scripts/config_gen/act_gen.py new file mode 100644 index 00000000..8962941d --- /dev/null +++ b/robomimic/scripts/config_gen/act_gen.py @@ -0,0 +1,131 @@ +from robomimic.scripts.config_gen.helper import * + +def make_generator_helper(args): + algo_name_short = "act" + generator = get_generator( + algo_name="act", + config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[64], + ) + + generator.add_param( + key="train.max_grad_norm", + name="", + group=-1, + values=[100.0], + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [{"path": p} for p in scan_datasets("~/Downloads/example_pen_in_cup", postfix="trajectory_im128.h5")], + ], + value_names=[ + "pen-in-cup", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + ) + elif args.env == "kitchen": + raise NotImplementedError + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "TODO.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "~/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/act_gen_no_define.py b/robomimic/scripts/config_gen/act_gen_no_define.py new file mode 100644 index 00000000..51658b16 --- /dev/null +++ b/robomimic/scripts/config_gen/act_gen_no_define.py @@ -0,0 +1,266 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# evaldataset = evaldataset[:200] +# broaddataset = broaddataset[:2000] +random.shuffle(evaldataset) +# random.shuffle(broaddataset) +N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +def make_generator_helper(args): + algo_name_short = "act" + generator = get_generator( + algo_name="act", + config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[2048], # 4096 works for wrist + ) + + generator.add_param( + key="train.max_grad_norm", + name="", + group=-1, + values=[100.0], + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # broaddataset + evaldataset, + # [{"path": p["path"], "weight": (0.5 / N_EVAL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset], + # broaddataset, + evaldataset, + ], + value_names=[ + # "mixed_unbalanced", + # "mixed_balanced", + # "broad", + "eval", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + # None, # Change this to "geom" to do goal conditioning + "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + # 0.5, + 0.3 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=2498, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + ] + ], + value_names=[ + # "proprio", + "proprio-cam", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="backbone", + group=1234, + values=[ + # "ResNet18Conv", + "ResNet50Conv", + ], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + # 64, + 512, + ], + ) + elif args.env == "kitchen": + raise NotImplementedError + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "TODO.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "~/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/diffusion_gen.py b/robomimic/scripts/config_gen/diffusion_gen.py new file mode 100644 index 00000000..7c1bba00 --- /dev/null +++ b/robomimic/scripts/config_gen/diffusion_gen.py @@ -0,0 +1,480 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = broaddataset[:2000] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] +N_EVAL_MULTI = len(evaldataset_multitask) +print(N_BROAD, N_EVAL_MULTI) + + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[8], + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # broaddataset + evaldataset, + [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], + # broaddataset, + evaldataset_multitask, + ], + value_names=[ + # "mixed_unbalanced", + "mixed_balanced", + # "broad", + "eval_multi", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + None, # Change this to "geom" to do goal conditioning + # "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + # 0.5 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + "proprio-cam", + "proprio-cam-lang", + ], + hidename=False, + ) + ## All of the following are needed for DeFiNe style encoding + generator.add_param( + key="observation.encoder.rgb.input_maps", + name="", + group=24986, + values=[ + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + }, + ], + value_names=[ + "define", + "define-lang", + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["DeFiNeVisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="", + group=-1, + values=["DeFiNeImageCamEncoder"], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + True, + # True, + # False, + ], + hidename=False, + ) + + + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.backbone_class", + # name="backbone", + # group=1234, + # values=[ + # # "ResNet18Conv", + # "ResNet50Conv", + # ], + # ) + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.feature_dimension", + # name="visdim", + # group=1234, + # values=[ + # # 64, + # 512, + # ], + # ) + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/diffusion_gen_no_define.py b/robomimic/scripts/config_gen/diffusion_gen_no_define.py new file mode 100644 index 00000000..c022b77f --- /dev/null +++ b/robomimic/scripts/config_gen/diffusion_gen_no_define.py @@ -0,0 +1,399 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = broaddataset[:2000] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] +N_EVAL_MULTI = len(evaldataset_multitask) +print(N_BROAD, N_EVAL_MULTI) + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[1000], + ) + + generator.add_param( + key="train.batch_size", + name="", + group=-1, + values=[16], + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # broaddataset + evaldataset, + [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], + # broaddataset, + evaldataset_multitask, + ], + value_names=[ + # "mixed_unbalanced", + "mixed_balanced", + # "broad", + "eval_multi", + ], + ) + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + None, # Change this to "geom" to do goal conditioning + # "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + # 0.5, + 0.3 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + # "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24987, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + "proprio-cam", + "proprio-cam-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.input_maps", + name="", + group=24987, + values=[ + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "lang_emb" : "lang_fixed/language_distilbert" + }, + }, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["FiLMVisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="backbone", + group=1234, + values=[ + # "ResNet18Conv", + # "ResNet50Conv", + "ResNetFiLMEncoder" + ], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + # 64, + 512, + ], + ) + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py new file mode 100644 index 00000000..e09f172d --- /dev/null +++ b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py @@ -0,0 +1,456 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD_FULL = len(broaddataset_full) + +chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] +N_CHIPS = len(chips_singletask) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang_filtered.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +# evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl' in l['path']] +# N_EVAL_MULTI = len(evaldataset_multitask) + +# apple_singletask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl/apple' in l['path']] +# N_APPLE = len(apple_singletask) + +## Getting single task eval data (food in bowl) +# evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] +# evaldataset_foodinbowl = evaldataset_foodinbowl[:20] +# N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) + +## Getting single task eval data (open microwave) +# evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] +# N_EVAL_OPENMICRO = len(evaldataset_openmicro) + +## Getting single task eval data (close microwave) +# evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] +# N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) + +print(f"Broad Datapoints: {N_BROAD, N_BROAD_FULL} \ + \nSingle Task CHIPS Datapoints: {N_CHIPS} ") + # \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ + # \nSingle Task APPLE Datapoints: {N_APPLE}") + # # \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") + + +def make_generator_helper(args): + algo_name_short = "bc_xfmr" + + generator = get_generator( + algo_name="bc", + config_file=os.path.join(base_path, 'robomimic/exps/templates/bc_transformer.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[100000], + ) + + generator.add_param( + key="train.batch_size", + name="bz", + group=1212111, + values=[128], + hidename=False, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + "wrist", + # "wrist-stereo", + "2cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=130, + values=[ + "ColorRandomizer", # jitter only + ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=130, + values=[ + {}, # jitter only + [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + ### CONDITIONING + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=24986, + values = [ + "geom", + None, # Change this to "geom" to do goal conditioning + + ] + ) + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + ] + ) + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + ["robot_state/cartesian_position", "robot_state/gripper_position"], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "lang_fixed/language_distilbert" + ] + ], + value_names=[ + # "proprio", + "proprio", + "proprio-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + False, + # True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=-1, + values=["VisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="", + group=-1, + values=["ResNet50Conv"], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + False, + ], + hidename=False, + ) + + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + chips_singletask, + [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], + [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], + ], + value_names=[ + "eval_chips", + "balanced_chips", + "full_balanced_chips", + ], + ) + + generator.add_param( + key="algo.transformer.supervise_all_steps", + name="supallsteps", + group=1, + values=[ + True, + ], + hidename=True, + ) + generator.add_param( + key="algo.transformer.pred_future_acs", + name="futureacs", + group=1, + values=[ + False, + ], + hidename=True, + ) + generator.add_param( + key="algo.transformer.causal", + name="causal", + group=1, + values=[ + False, + ], + hidename=True, + ) + generator.add_param( + key="train.frame_stack", + name="", + group=-1, + values=[2], + hidename=True, + ) + generator.add_param( + key="train.seq_length", + name="H", + group=-1, + values=[2], + hidename=False, + ) + generator.add_param( + key="algo.transformer.context_length", + name="", + group=-1, + values=[2], + hidename=True, + ) + generator.add_param( + key="algo.gmm.enabled", + name="", + group=-1, + values=[False], + hidename=True, + ) + + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py index 7d5708ba..5b0af96b 100644 --- a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py +++ b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py @@ -357,10 +357,10 @@ def make_generator_helper(args): # stanford_singletask_eraser, # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_eraser], # cmu_multi, - # cmutoast_singletask, + cmutoast_singletask, # [{"path": p["path"], "weight": (0.5 / N_CMU_MULTI if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + cmu_multi], # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + cmutoast_singletask], - broaddataset_full, + # broaddataset_full, # chips_singletask, # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], @@ -397,10 +397,10 @@ def make_generator_helper(args): # "stanford_eraser", # "fullr2d2_balanced_stanford_eraser" # "cmu_multi", - # "cmu_toast", + "cmu_toast_hdf5", # "fullr2d2_balanced_cmu_multi", # "fullr2d2_balanced_cmu_toast", - "broad", + # "broad", # "eval_chips", # "balanced_chips", # "full_balanced_chips", diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py index 41cb37e8..e84ed06c 100644 --- a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py @@ -46,7 +46,7 @@ def make_generator_helper(args): key="train.shuffle_buffer_size", name="", group=-1, - values=[50000], + values=[500000], ) generator.add_param( @@ -101,12 +101,10 @@ def make_generator_helper(args): name="dataset_names", group=24988, values=[ - ["r2_d2", "r2_d2_cmu_waffle"], ["r2_d2", "r2_d2_cmu_toaster"], ], value_names=[ - "r2_d2__r2_d2_cmu_waffle_fix_norm_fix_obs", - "r2_d2__r2_d2_cmu_toaster_fix_norm_fix_obs", + "r2_d2__r2_d2_cmu_toaster" ] ) generator.add_param( diff --git a/robomimic/scripts/config_gen/voltron.py b/robomimic/scripts/config_gen/voltron.py new file mode 100644 index 00000000..3d1f29b0 --- /dev/null +++ b/robomimic/scripts/config_gen/voltron.py @@ -0,0 +1,593 @@ +from robomimic.scripts.config_gen.helper import * +import random +import json + +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# import pdb; pdb.set_trace() +# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# # evaldataset = evaldataset[:200] +# # broaddataset = broaddataset[:2000] +# random.shuffle(evaldataset) +# random.shuffle(broaddataset) +# N_EVAL = len(evaldataset) +# N_BROAD = len(broaddataset) + +## Getting all language labeled data +with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + langs = json.load(file) + +## Getting broad data +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +N_BROAD = len(broaddataset) + +## Getting multitask eval data +evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] +N_EVAL_MULTI = len(evaldataset_multitask) + +## Getting single task eval data (food in bowl) +evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] +N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) + +## Getting single task eval data (open microwave) +evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] +N_EVAL_OPENMICRO = len(evaldataset_openmicro) + +## Getting single task eval data (close microwave) +evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] +N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) + +print(f"Broad Datapoints: {N_BROAD} \ + \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ + \nSingle Task Food in Bowl Datapoints: {N_EVAL_FOODINBOWL} \ + \nSingle Task Microwave Open Datapoints: {N_EVAL_OPENMICRO} \ + \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") + + +def make_generator_helper(args): + algo_name_short = "diffusion_policy" + + generator = get_generator( + algo_name="diffusion_policy", + config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), + args=args, + algo_name_short=algo_name_short, + pt=True, + ) + if args.ckpt_mode is None: + args.ckpt_mode = "off" + + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[8], + ) + + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[100000], + ) + + generator.add_param( + key="train.batch_size", + name="bs", + group=-1, + values=[32], + ) + + # use ddim by default + generator.add_param( + key="algo.ddim.enabled", + name="ddim", + group=1001, + values=[ + True, + # False, + ], + hidename=True, + ) + generator.add_param( + key="algo.ddpm.enabled", + name="ddpm", + group=1001, + values=[ + False, + # True, + ], + hidename=True, + ) + + if args.env == "r2d2": + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + generator.add_param( + key="observation.modalities.obs.rgb", + name="cams", + group=130, + values=[ + # ["camera/image/hand_camera_left_image"], + # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], + ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + # [ + # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", + # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", + # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", + # ], + ], + value_names=[ + # "wrist", + # "wrist-stereo", + "3cams", + # "3cams-stereo", + ] + ) + generator.add_param( + key="train.goal_mode", + name="goal_mode", + group=5678, + values = [ + None, # Change this to "geom" to do goal conditioning + # "geom" + ] + ) + + generator.add_param( + key="train.truncated_geom_factor", + name="truncated_geom_factor", + group=5555, + values = [ + 0.3, + # 0.5 + ] + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_class", + name="obsrand", + group=138, + values=[ + "CropRandomizer", # crop only + # "ColorRandomizer", # jitter only + # ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.obs_randomizer_kwargs", + name="obsrandargs", + group=-1, + values=[ + {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}, # crop only + # {}, # jitter only + # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + # [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop + ], + hidename=True, + ) + + generator.add_param( + key="observation.modalities.obs.low_dim", + name="ldkeys", + group=24986, + values=[ + # ["robot_state/cartesian_position", "robot_state/gripper_position"], + # [ + # "robot_state/cartesian_position", "robot_state/gripper_position", + # "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + # "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + # "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + # "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + # "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + # "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + # ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_raw" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + [ + "robot_state/cartesian_position", "robot_state/gripper_position", + "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", + "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", + "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", + "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", + "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", + "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", + "lang_fixed/language_distilbert" + ], + ], + value_names=[ + # "proprio", + "proprio-cam-lang-bert2", + "proprio-cam-lang-bert", + "proprio-cam-lang-raw", + "proprio-cam-lang-bert3", + "proprio-cam-lang-bert4", + ], + hidename=False, + ) + ## All of the following are needed for DeFiNe style encoding + generator.add_param( + key="observation.encoder.rgb.input_maps", + name="", + group=24986, + values=[ + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "lang_emb" : "lang_fixed/language_raw" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "lang_emb" : "lang_fixed/language_raw" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "lang_emb" : "lang_fixed/language_raw" + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + "lang_emb" : "lang_fixed/language_distilbert" + }, + }, + { + "camera/image/hand_camera_left_image": { + "image": "camera/image/hand_camera_left_image", + "intrinsics": "camera/intrinsics/hand_camera_left", + "extrinsics": "camera/extrinsics/hand_camera_left", + }, + "camera/image/varied_camera_1_left_image": { + "image": "camera/image/varied_camera_1_left_image", + "intrinsics": "camera/intrinsics/varied_camera_1_left", + "extrinsics": "camera/extrinsics/varied_camera_1_left", + }, + "camera/image/varied_camera_2_left_image": { + "image": "camera/image/varied_camera_2_left_image", + "intrinsics": "camera/intrinsics/varied_camera_2_left", + "extrinsics": "camera/extrinsics/varied_camera_2_left", + }, + }, + ], + value_names=[ + "r3m-no-lang", + "vc1-no-lang", + "voltron-lang", + "define-film-lang", + "define-nofilm-lang", + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_class", + name="visenc", + group=24986, + values=["PretrainedVisualCore", "PretrainedVisualCore", "PretrainedVisualCore", "DeFiNeVisualCore", "DeFiNeVisualCore"], + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_class", + name="enc", + group=24986, + values=["R3MImageCamEncoder", "VC1ImageCamEncoder", "VoltronImageCamEncoder", "DeFiNeImageCamEncoder", "DeFiNeImageCamEncoder"], + hidename=False, + ) + + + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", + name="", + group=2498, + values=[ + # False, + True, + ], + hidename=True, + ) + + + generator.add_param( + key="observation.encoder.rgb.core_kwargs.feature_dimension", + name="visdim", + group=1234, + values=[ + 512, + # None, + # None + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.flatten", + name="flatten", + group=1234, + values=[ + True, + # False, + # False + ], + hidename=True, + ) + generator.add_param( + key="observation.encoder.rgb.fuser", + name="fuser", + group=1234, + values=[ + None, + # "transformer", + # "perceiver" + ], + hidename=False, + ) + generator.add_param( + key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", + name="", + group=1234, + values=[ + True, + # True, + # False, + ], + hidename=False, + ) + + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + broaddataset, + # evaldataset_multitask, + # evaldataset_foodinbowl, + # evaldataset_openmicro, + # evaldataset_closemicro, + # [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], + # [{"path": p["path"], "weight": (0.5 / N_EVAL_FOODINBOWL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_foodinbowl], + # [{"path": p["path"], "weight": (0.5 / N_EVAL_OPENMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_openmicro], + # [{"path": p["path"], "weight": (0.5 / N_EVAL_CLOSEMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_closemicro], + ], + value_names=[ + "broad", + # "eval_multi", + # "eval_foodinbowl", + # "eval_openmicro", + # "eval_closemicro", + # "balanced_broad_eval_multi", + # "balanced_broad_eval_foodinbowl", + # "balanced_broad_eval_openmicro", + # "balanced_broad_eval_closemicro", + ], + ) + + + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.backbone_class", + # name="backbone", + # group=1234, + # values=[ + # # "ResNet18Conv", + # "ResNet50Conv", + # ], + # ) + # generator.add_param( + # key="observation.encoder.rgb.core_kwargs.feature_dimension", + # name="visdim", + # group=1234, + # values=[ + # # 64, + # 512, + # ], + # ) + + elif args.env == "kitchen": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], + [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], + # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + ], + value_names=[ + # "bowls-human-100", + "human-100", + # "mg-100", + # "mg-1000", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + "action_dict/base_mode", + # "actions", + ], + ], + value_names=[ + "abs", + ], + hidename=True, + ) + elif args.env == "square": + generator.add_param( + key="train.data", + name="ds", + group=2, + values=[ + [ + {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path + ], + ], + value_names=[ + "square", + ], + ) + + # update env config to use absolute action control + generator.add_param( + key="experiment.env_meta_update_dict", + name="", + group=-1, + values=[ + {"env_kwargs": {"controller_configs": {"control_delta": False}}} + ], + ) + + generator.add_param( + key="train.action_keys", + name="ac_keys", + group=-1, + values=[ + [ + "action_dict/abs_pos", + "action_dict/abs_rot_6d", + "action_dict/gripper", + # "actions", + ], + ], + value_names=[ + "abs", + ], + ) + + + else: + raise ValueError + + generator.add_param( + key="train.output_dir", + name="", + group=-1, + values=[ + "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + env=args.env, + mod=args.mod, + algo_name_short=algo_name_short, + ) + ], + ) + + return generator + +if __name__ == "__main__": + parser = get_argparser() + + args = parser.parse_args() + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/conversion/extract_action_dict.py b/robomimic/scripts/conversion/extract_action_dict.py new file mode 100644 index 00000000..2d3a50e3 --- /dev/null +++ b/robomimic/scripts/conversion/extract_action_dict.py @@ -0,0 +1,81 @@ +import argparse +import pathlib +import sys +import tqdm +import h5py +import numpy as np +import torch +import os + +import robomimic.utils.torch_utils as TorchUtils + +def extract_action_dict(dataset): + # find files + f = h5py.File(os.path.expanduser(dataset), mode="r+") + + SPECS = [ + dict( + key="actions", + is_absolute=False, + ), + dict( + key="actions_abs", + is_absolute=True, + ) + ] + + # execute + for spec in SPECS: + input_action_key = spec["key"] + is_absolute = spec["is_absolute"] + + if is_absolute: + prefix = "abs_" + else: + prefix = "rel_" + + for demo in f["data"].values(): + in_action = demo[str(input_action_key)][:] + in_pos = in_action[:,:3].astype(np.float32) + in_rot = in_action[:,3:6].astype(np.float32) + in_grip = in_action[:,6:7].astype(np.float32) + + rot_6d = TorchUtils.axis_angle_to_rot_6d( + axis_angle=torch.from_numpy(in_rot) + ) + rot_6d = rot_6d.numpy().astype(np.float32) # convert to numpy + + this_action_dict = { + prefix + "pos": in_pos, + prefix + "rot_axis_angle": in_rot, + prefix + "rot_6d": rot_6d, + "gripper": in_grip + } + + # special case: 8 dim actions mean there is a mobile base mode in the action space + if in_action.shape[1] == 8: + this_action_dict["base_mode"] = in_action[:,7:8].astype(np.float32) + + action_dict_group = demo.require_group("action_dict") + for key, data in this_action_dict.items(): + if key in action_dict_group: + del action_dict_group[key] + action_dict_group.create_dataset(key, data=data) + + f.close() + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--dataset", + type=str, + required=True + ) + + args = parser.parse_args() + extract_action_dict(args.dataset) + +if __name__ == "__main__": + main() diff --git a/robomimic/scripts/conversion/robosuite_add_absolute_actions.py b/robomimic/scripts/conversion/robosuite_add_absolute_actions.py new file mode 100644 index 00000000..8d634f20 --- /dev/null +++ b/robomimic/scripts/conversion/robosuite_add_absolute_actions.py @@ -0,0 +1,298 @@ +import multiprocessing +import os +import pathlib +import h5py +from tqdm import tqdm +import collections +import pickle +import argparse +import numpy as np +import copy + +import h5py +import robomimic.utils.obs_utils as ObsUtils +import robomimic.utils.file_utils as FileUtils +import robomimic.utils.env_utils as EnvUtils +from scipy.spatial.transform import Rotation + +from robomimic.config import config_factory + +""" +copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/common/robomimic_util.py +""" +class RobomimicAbsoluteActionConverter: + def __init__(self, dataset_path, algo_name='bc'): + # default BC config + config = config_factory(algo_name=algo_name) + + # read config to set up metadata for observation modalities (e.g. detecting rgb observations) + # must ran before create dataset + ObsUtils.initialize_obs_utils_with_config(config) + + env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path) + abs_env_meta = copy.deepcopy(env_meta) + abs_env_meta['env_kwargs']['controller_configs']['control_delta'] = False + + env = EnvUtils.create_env_from_metadata( + env_meta=env_meta, + render=False, + render_offscreen=False, + use_image_obs=False, + ) + assert len(env.env.robots) in (1, 2) + + abs_env = EnvUtils.create_env_from_metadata( + env_meta=abs_env_meta, + render=False, + render_offscreen=False, + use_image_obs=False, + ) + assert not abs_env.env.robots[0].controller.use_delta + + self.env = env + self.abs_env = abs_env + self.file = h5py.File(dataset_path, 'r') + + def get_demo_keys(self): + return list(self.file['data'].keys()) + + def convert_actions(self, + states: np.ndarray, + actions: np.ndarray) -> np.ndarray: + """ + Given state and delta action sequence + generate equivalent goal position and orientation for each step + keep the original gripper action intact. + """ + env = self.env + d_a = len(env.env.robots[0].action_limits[0]) + + # in case of multi robot + # reshape (N,14) to (N,2,7) + # or (N,7) to (N,1,7) + stacked_actions = actions.reshape(*actions.shape[:-1], -1, d_a) + + # generate abs actions + action_goal_pos = np.zeros( + stacked_actions.shape[:-1]+(3,), + dtype=stacked_actions.dtype) + action_goal_ori = np.zeros( + stacked_actions.shape[:-1]+(3,), + dtype=stacked_actions.dtype) + action_remainder = stacked_actions[...,6:] + for i in range(len(states)): + _ = env.reset_to({'states': states[i]}) + + # taken from robot_env.py L#454 + for idx, robot in enumerate(env.env.robots): + # run controller goal generator + robot.control(stacked_actions[i,idx], policy_step=True) + + # read pos and ori from robots + controller = robot.controller + action_goal_pos[i,idx] = controller.goal_pos + action_goal_ori[i,idx] = Rotation.from_matrix( + controller.goal_ori).as_rotvec() + + stacked_abs_actions = np.concatenate([ + action_goal_pos, + action_goal_ori, + action_remainder + ], axis=-1) + abs_actions = stacked_abs_actions.reshape(actions.shape) + return abs_actions + + def convert_demo(self, demo_key): + file = self.file + demo = file["data/{}".format(demo_key)] + # input + states = demo['states'][:] + actions = demo['actions'][:] + + # generate abs actions + abs_actions = self.convert_actions(states, actions) + return abs_actions + + def convert_and_eval_demo(self, demo_key): + env = self.env + abs_env = self.abs_env + file = self.file + # first step have high error for some reason, not representative + eval_skip_steps = 1 + + demo = file["data/{}".format(demo_key)] + # input + states = demo['states'][:] + actions = demo['actions'][:] + + # generate abs actions + abs_actions = self.convert_actions(states, actions) + + # verify + robot0_eef_pos = demo['obs']['robot0_eef_pos'][:] + robot0_eef_quat = demo['obs']['robot0_eef_quat'][:] + + delta_error_info = self.evaluate_rollout_error( + env, states, actions, robot0_eef_pos, robot0_eef_quat, + metric_skip_steps=eval_skip_steps) + abs_error_info = self.evaluate_rollout_error( + abs_env, states, abs_actions, robot0_eef_pos, robot0_eef_quat, + metric_skip_steps=eval_skip_steps) + + info = { + 'delta_max_error': delta_error_info, + 'abs_max_error': abs_error_info + } + return abs_actions, info + + @staticmethod + def evaluate_rollout_error(env, + states, actions, + robot0_eef_pos, + robot0_eef_quat, + metric_skip_steps=1): + # first step have high error for some reason, not representative + + # evaluate abs actions + rollout_next_states = list() + rollout_next_eef_pos = list() + rollout_next_eef_quat = list() + obs = env.reset_to({'states': states[0]}) + for i in range(len(states)): + obs = env.reset_to({'states': states[i]}) + obs, reward, done, info = env.step(actions[i]) + obs = env.get_observation() + rollout_next_states.append(env.get_state()['states']) + rollout_next_eef_pos.append(obs['robot0_eef_pos']) + rollout_next_eef_quat.append(obs['robot0_eef_quat']) + rollout_next_states = np.array(rollout_next_states) + rollout_next_eef_pos = np.array(rollout_next_eef_pos) + rollout_next_eef_quat = np.array(rollout_next_eef_quat) + + next_state_diff = states[1:] - rollout_next_states[:-1] + max_next_state_diff = np.max(np.abs(next_state_diff[metric_skip_steps:])) + + next_eef_pos_diff = robot0_eef_pos[1:] - rollout_next_eef_pos[:-1] + next_eef_pos_dist = np.linalg.norm(next_eef_pos_diff, axis=-1) + max_next_eef_pos_dist = next_eef_pos_dist[metric_skip_steps:].max() + + next_eef_rot_diff = Rotation.from_quat(robot0_eef_quat[1:]) \ + * Rotation.from_quat(rollout_next_eef_quat[:-1]).inv() + next_eef_rot_dist = next_eef_rot_diff.magnitude() + max_next_eef_rot_dist = next_eef_rot_dist[metric_skip_steps:].max() + + info = { + 'state': max_next_state_diff, + 'pos': max_next_eef_pos_dist, + 'rot': max_next_eef_rot_dist + } + return info + +""" +copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/scripts/robomimic_dataset_conversion.py +""" +def worker(x): + path, demo_key, do_eval = x + converter = RobomimicAbsoluteActionConverter(path) + if do_eval: + abs_actions, info = converter.convert_and_eval_demo(demo_key) + else: + abs_actions = converter.convert_demo(demo_key) + info = dict() + return abs_actions, info + + +def add_absolute_actions_to_dataset(dataset, eval_dir, num_workers): + # process inputs + dataset = pathlib.Path(dataset).expanduser() + assert dataset.is_file() + + do_eval = False + if eval_dir is not None: + eval_dir = pathlib.Path(eval_dir).expanduser() + assert eval_dir.parent.exists() + do_eval = True + + converter = RobomimicAbsoluteActionConverter(dataset) + demo_keys = converter.get_demo_keys() + del converter + + # run + with multiprocessing.Pool(num_workers) as pool: + results = pool.map(worker, [(dataset, demo_key, do_eval) for demo_key in demo_keys]) + + # modify action + with h5py.File(dataset, 'r+') as out_file: + for i in tqdm(range(len(results)), desc="Writing to output"): + abs_actions, info = results[i] + demo = out_file["data/{}".format(demo_keys[i])] + if "actions_abs" not in demo: + demo.create_dataset("actions_abs", data=np.array(abs_actions)) + else: + demo['actions_abs'][:] = abs_actions + + # save eval + if do_eval: + eval_dir.mkdir(parents=False, exist_ok=True) + + print("Writing error_stats.pkl") + infos = [info for _, info in results] + pickle.dump(infos, eval_dir.joinpath('error_stats.pkl').open('wb')) + + print("Generating visualization") + metrics = ['pos', 'rot'] + metrics_dicts = dict() + for m in metrics: + metrics_dicts[m] = collections.defaultdict(list) + + for i in range(len(infos)): + info = infos[i] + for k, v in info.items(): + for m in metrics: + metrics_dicts[m][k].append(v[m]) + + from matplotlib import pyplot as plt + plt.switch_backend('PDF') + + fig, ax = plt.subplots(1, len(metrics)) + for i in range(len(metrics)): + axis = ax[i] + data = metrics_dicts[metrics[i]] + for key, value in data.items(): + axis.plot(value, label=key) + axis.legend() + axis.set_title(metrics[i]) + fig.set_size_inches(10,4) + fig.savefig(str(eval_dir.joinpath('error_stats.pdf'))) + fig.savefig(str(eval_dir.joinpath('error_stats.png'))) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--dataset", + type=str, + required=True + ) + + parser.add_argument( + "--eval_dir", + type=str, + help="directory to output evaluation metrics", + ) + + parser.add_argument( + "--num_workers", + type=int, + default=10, + ) + + args = parser.parse_args() + + + add_absolute_actions_to_dataset( + dataset=args.dataset, + eval_dir=args.eval_dir, + num_workers=args.num_workers, + ) \ No newline at end of file diff --git a/robomimic/scripts/conversion/set_dataset_attr.py b/robomimic/scripts/conversion/set_dataset_attr.py new file mode 100644 index 00000000..4f148d08 --- /dev/null +++ b/robomimic/scripts/conversion/set_dataset_attr.py @@ -0,0 +1,98 @@ +""" +Example: +python robomimic/scripts/set_dataset_attr.py --glob 'datasets/**/*_abs.hdf5' --env_args env_kwargs.controller_configs.control_delta=false absolute_actions=true +""" +import argparse +import pathlib +import json +import sys +import tqdm +import h5py + +def update_env_args_dict(env_args_dict: dict, key: tuple, value): + if key is None: + return env_args_dict + elif len(key) == 0: + return env_args_dict + elif len(key) == 1: + env_args_dict[key[0]] = value + return env_args_dict + else: + this_key = key[0] + if this_key not in env_args_dict: + env_args_dict[this_key] = dict() + update_env_args_dict(env_args_dict[this_key], key[1:], value) + return env_args_dict + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "--glob", + type=str, + required=True + ) + + parser.add_argument( + "--env_args", + type=str, + default=None + ) + + parser.add_argument( + 'attrs', + nargs='*' + ) + + args = parser.parse_args() + + # parse attrs to set + # format: key=value + # values are parsed with json + attrs_dict = dict() + for attr_arg in args.attrs: + key, svalue = attr_arg.split("=") + value = json.loads(svalue) + attrs_dict[key] = value + + # parse env_args update + env_args_key = None + env_args_value = None + if args.env_args is not None: + key, svalue = args.env_args.split('=') + env_args_key = key.split('.') + env_args_value = json.loads(svalue) + + # find files + file_paths = list(pathlib.Path.cwd().glob(args.glob)) + + # confirm with the user + print("Found matching files:") + for f in file_paths: + print(f) + print("Are you sure to modify these files with the following attributes:") + print(json.dumps(attrs_dict, indent=2)) + if env_args_key is not None: + print("env_args."+'.'.join(env_args_key)+'='+str(env_args_value)) + result = input("[y/n]?") + if 'y' not in result: + sys.exit(0) + + # execute + for file_path in tqdm.tqdm(file_paths): + with h5py.File(str(file_path), mode='r+') as file: + # update env_args + if env_args_key is not None: + env_args = file['data'].attrs['env_args'] + env_args_dict = json.loads(env_args) + env_args_dict = update_env_args_dict( + env_args_dict=env_args_dict, + key=env_args_key, value=env_args_value) + env_args = json.dumps(env_args_dict) + file['data'].attrs['env_args'] = env_args + + # update other attrs + file['data'].attrs.update(attrs_dict) + +if __name__ == "__main__": + main() diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index 2787bc06..c745195f 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -90,15 +90,17 @@ def train(config, device): obs_modalities = config.observation.modalities.obs.rgb # NOTE(Ashwin): must be 2 cam for now, can clean this up later assert(len(obs_modalities) == 2) + ac_dim = sum([ac_comp[1] for ac_comp in config.train.action_shapes]) BASE_DATASET_KWARGS = { "data_dir": config.train.data_path, "image_obs_keys": {"primary": R2D2_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": R2D2_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, "state_obs_keys": [R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], "language_key": "language_instruction", + "keys_to_normalize": {"action": "action"}, "action_proprio_normalization_type": "bounds", - "absolute_action_mask": [True] * 10, - "action_normalization_mask": [True] * 10, + "absolute_action_mask": [True] * ac_dim, + "action_normalization_mask": [True] * ac_dim, "standardize_fn": droid_dataset_transform, } @@ -115,7 +117,7 @@ def train(config, device): train=True, shuffle_buffer_size=config.train.shuffle_buffer_size, batch_size=None, # batching will be handles in PyTorch Dataloader object - balance_weights=True, + balance_weights=False, traj_transform_kwargs=dict( # NOTE(Ashwin): window_size and future_action_window_size may break if # not using diffusion policy @@ -136,9 +138,7 @@ def train(config, device): traj_transform_threads=48, traj_read_threads=48, ) - # TODO(Ashwin): this assumes that you are doing co-training and that the co-training dataset - # that corresponds to the eval setting is last - rlds_dataset_stats = dataset.dataset_statistics[-1]["action"] + rlds_dataset_stats = dataset.dataset_statistics["action"] action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats, config.train.action_keys, config.train.action_shapes) action_config = config.train.action_config action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) @@ -151,7 +151,6 @@ def train(config, device): num_workers=0, # important to keep this to 0 so PyTorch does not mess with the parallelism ) - # For RLDS, get batch from train loader to compute shapes data_loader_iter = iter(train_loader) rlds_batch = next(data_loader_iter) From 721ab4854a3f846f0621c69902d25c2aab93bd93 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Thu, 7 Mar 2024 02:04:41 +0000 Subject: [PATCH 05/43] fix args --- robomimic/scripts/train.py | 1 + 1 file changed, 1 insertion(+) diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index c745195f..b9159394 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -118,6 +118,7 @@ def train(config, device): shuffle_buffer_size=config.train.shuffle_buffer_size, batch_size=None, # batching will be handles in PyTorch Dataloader object balance_weights=False, + do_combined_normalization=True, traj_transform_kwargs=dict( # NOTE(Ashwin): window_size and future_action_window_size may break if # not using diffusion policy From 1943abf37b37aaa97f5fe44baa2aba61b254d958 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 00:00:09 +0000 Subject: [PATCH 06/43] fix, using this as default from now on --- robomimic/scripts/train.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index b9159394..ed79f918 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -117,7 +117,7 @@ def train(config, device): train=True, shuffle_buffer_size=config.train.shuffle_buffer_size, batch_size=None, # batching will be handles in PyTorch Dataloader object - balance_weights=False, + balance_weights=True, do_combined_normalization=True, traj_transform_kwargs=dict( # NOTE(Ashwin): window_size and future_action_window_size may break if @@ -139,8 +139,10 @@ def train(config, device): traj_transform_threads=48, traj_read_threads=48, ) - rlds_dataset_stats = dataset.dataset_statistics["action"] - action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats, config.train.action_keys, config.train.action_shapes) + # Note: If we have statistics for multiple datasets separately, use the last one (assumes last one is the dataset from the target domain) + # rlds_dataset_stats will only be a list if do_combined_normalization is set to False + rlds_dataset_stats = dataset.dataset_statistics[-1] if isinstance(dataset.dataset_statistics, list) else dataset.dataset_statistics + action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats["action"], config.train.action_keys, config.train.action_shapes) action_config = config.train.action_config action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) dataset = dataset.map(robomimic_transform, num_parallel_calls=48) From c49ec0365f77923cb1ba43be2110c7cd5e68fd8f Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 05:04:50 +0000 Subject: [PATCH 07/43] cleanup, r2d2 --> droid --- README.md | 14 ++--- robomimic/algo/act.py | 4 +- robomimic/algo/algo.py | 2 +- robomimic/algo/diffusion_policy.py | 2 +- robomimic/config/base_config.py | 7 ++- .../exps/templates/diffusion_policy.json | 4 ++ robomimic/models/obs_nets.py | 2 +- robomimic/scripts/config_gen/act_gen.py | 2 +- .../scripts/config_gen/act_gen_no_define.py | 12 ++-- robomimic/scripts/config_gen/bc_xfmr_gen.py | 2 +- robomimic/scripts/config_gen/diffusion_gen.py | 16 ++--- .../config_gen/diffusion_gen_no_define.py | 16 ++--- robomimic/scripts/config_gen/helper.py | 8 +-- .../scripts/config_gen/r2d2_runs_bcxfmr.py | 20 +++--- .../r2d2_runs_language_conditioned.py | 62 +++++++++---------- .../r2d2_runs_language_conditioned_rlds.py | 48 ++++++++++++-- robomimic/scripts/config_gen/voltron.py | 16 ++--- .../conversion/add_lang_to_converted_data.py | 14 ++--- robomimic/scripts/conversion/convert_r2d2.py | 26 ++++---- .../conversion/get_eval_lang_annotations.py | 4 +- .../get_lang_annotations_in_manifest.py | 8 +-- robomimic/scripts/plot_model_predictions.py | 10 +-- robomimic/scripts/train.py | 30 +++++---- robomimic/utils/dataset.py | 2 +- robomimic/utils/file_utils.py | 10 +-- robomimic/utils/rlds_utils.py | 4 +- robomimic/utils/train_utils.py | 4 +- 27 files changed, 197 insertions(+), 152 deletions(-) diff --git a/README.md b/README.md index 88a0a951..03d9bdbb 100644 --- a/README.md +++ b/README.md @@ -5,17 +5,17 @@ ------- ## Installation 1. Clone the repo with the `--recurse-submodules` flag. -2. (if applicable) switch to `r2d2` branch +2. (if applicable) switch to `droid` branch 3. Run `pip install -e .` in `robomimic` 4. Run `pip install -e .` in `robomimic/act/detr` ------- ## Pre-processing datasets Before training, you need to pre-process your datasets to ensure they're the correct format. -### r2d2 datasets -Convert the raw r2d2 data using this simple script: +### droid datasets +Convert the raw droid data using this simple script: ``` -python robomimic/scripts/convertion/convert_r2d2.py --folder +python robomimic/scripts/convertion/convert_droid.py --folder ``` ### robosuite datasets 1. Convert the raw robosuite dataset to robomimic format @@ -53,7 +53,7 @@ Want to intialize your model with weights from a previous model checkpoint? Set Read this short [tutorial page](https://robomimic.github.io/docs/tutorials/viewing_results.html). ------- -## Real robot evaluation (r2d2 only) -Use this forked r2d2: https://github.com/snasiriany/r2d2/tree/robomimic-eval. Note that the branch is `robomimic-eval`. +## Real robot evaluation (droid only) +Use this forked droid: https://github.com/snasiriany/droid/tree/robomimic-eval. Note that the branch is `robomimic-eval`. -Run this script: https://github.com/snasiriany/r2d2/blob/robomimic-eval/scripts/evaluation/evaluate_policy.py. Before doing so, make sure to fill out `CKPT_PATH` with the path to the saved robomimic checkpoint you wish to evaluate. +Run this script: https://github.com/snasiriany/droid/blob/robomimic-eval/scripts/evaluation/evaluate_policy.py. Before doing so, make sure to fill out `CKPT_PATH` with the path to the saved robomimic checkpoint you wish to evaluate. diff --git a/robomimic/algo/act.py b/robomimic/algo/act.py index d0c0024d..ad5b6493 100644 --- a/robomimic/algo/act.py +++ b/robomimic/algo/act.py @@ -119,7 +119,7 @@ def _forward_training(self, batch): Returns: predictions (dict): dictionary containing network outputs """ - # TODO (Ashwin): remove this once we add in the robomimic observation encoders + # TODO(Ashwin): remove this once we add in the robomimic observation encoders for k in self.proprio_keys: batch["obs"][k] = batch["obs"][k].reshape((batch["obs"][k].shape[0], -1)) proprio = [batch["obs"][k] for k in self.proprio_keys] @@ -177,7 +177,7 @@ def get_action(self, obs_dict, goal_dict=None): """ assert not self.nets.training - # TODO (Ashwin): remove this once we add in the robomimic observation encoders + # TODO(Ashwin): remove this once we add in the robomimic observation encoders for k in self.proprio_keys: obs_dict[k] = obs_dict[k].reshape((obs_dict[k].shape[0], -1)) proprio = [obs_dict[k] for k in self.proprio_keys] diff --git a/robomimic/algo/algo.py b/robomimic/algo/algo.py index 1e1e93e6..32995930 100644 --- a/robomimic/algo/algo.py +++ b/robomimic/algo/algo.py @@ -347,7 +347,7 @@ def get_action(self, obs_dict, goal_dict=None): def compute_traj_pred_actual_actions(self, traj, return_images=False): """ - traj is an R2D2Dataset object representing one trajectory + traj is an DROIDDataset object representing one trajectory This function is slow (>1s per trajectory) because there is no batching and instead loops through all timesteps one by one TODO: documentation diff --git a/robomimic/algo/diffusion_policy.py b/robomimic/algo/diffusion_policy.py index a38e8c8b..70311f92 100644 --- a/robomimic/algo/diffusion_policy.py +++ b/robomimic/algo/diffusion_policy.py @@ -336,7 +336,7 @@ def get_action(self, obs_dict, goal_mode=None, eval_mode=False): Ta = self.algo_config.horizon.action_horizon if eval_mode: - from r2d2.misc.parameters import hand_camera_id, varied_camera_1_id, varied_camera_2_id + from droid.misc.parameters import hand_camera_id, varied_camera_1_id, varied_camera_2_id root_path = os.path.join(os. getcwd(), "eval_params") if goal_mode is not None: diff --git a/robomimic/config/base_config.py b/robomimic/config/base_config.py index 5137b5e9..256acd9b 100644 --- a/robomimic/config/base_config.py +++ b/robomimic/config/base_config.py @@ -225,7 +225,6 @@ class has a default implementation that usually doesn't need to be overriden. # Used only if using geometric goal sampling self.train.truncated_geom_factor = None - ## learning config ## self.train.cuda = True # use GPU or not self.train.batch_size = 100 # batch size @@ -234,7 +233,7 @@ class has a default implementation that usually doesn't need to be overriden. self.train.max_grad_norm = None # clip gradient norms (see `backprop_for_loss` function in torch_utils.py) - self.train.data_format = "robomimic" # either "robomimic" or "r2d2" + self.train.data_format = "robomimic" # either "robomimic" or "droid" # list of observation keys to shuffle randomly in the dataset. # must be list of tuples pairs, with each pair representing @@ -246,6 +245,10 @@ class has a default implementation that usually doesn't need to be overriden. self.train.shuffle_buffer_size = 100000 self.train.sample_weights = [1, 1] self.train.dataset_names = ["", ""] + self.train.subsample_length = 100 + self.train.num_parallel_calls = 200 + self.train.traj_transform_threads = 48 + self.train.traj_read_threads = 48 def algo_config(self): """ diff --git a/robomimic/exps/templates/diffusion_policy.json b/robomimic/exps/templates/diffusion_policy.json index 315a3907..cb3611d8 100644 --- a/robomimic/exps/templates/diffusion_policy.json +++ b/robomimic/exps/templates/diffusion_policy.json @@ -54,6 +54,10 @@ ], "goal_mode": null, "truncated_geom_factor": null, + "subsample_length": 100, + "num_parallel_calls": 200, + "traj_transform_threads": 48, + "traj_read_threads": 48, "cuda": true, "batch_size": 256, "num_epochs": 2000, diff --git a/robomimic/models/obs_nets.py b/robomimic/models/obs_nets.py index 270c4d26..47ec6dd3 100644 --- a/robomimic/models/obs_nets.py +++ b/robomimic/models/obs_nets.py @@ -304,7 +304,7 @@ def final_collation(self, feats): all_image_feats = torch.cat(all_image_feats, dim = 1) all_image_feats_posttrans = self.fusernetwork(all_image_feats).last_hidden_state output = self.l1(TensorUtils.flatten(all_image_feats_posttrans, begin_axis=1)) - # TODO (Ashwin): Ideally should include low-dim features in perceiver input too + # TODO(Ashwin): Ideally should include low-dim features in perceiver input too return torch.cat(non_image_feats + [output], -1) else: raise NotImplementedError("Unsupported fuser") diff --git a/robomimic/scripts/config_gen/act_gen.py b/robomimic/scripts/config_gen/act_gen.py index 8962941d..57c83263 100644 --- a/robomimic/scripts/config_gen/act_gen.py +++ b/robomimic/scripts/config_gen/act_gen.py @@ -34,7 +34,7 @@ def make_generator_helper(args): values=[100.0], ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.data", name="ds", diff --git a/robomimic/scripts/config_gen/act_gen_no_define.py b/robomimic/scripts/config_gen/act_gen_no_define.py index 51658b16..cf9db391 100644 --- a/robomimic/scripts/config_gen/act_gen_no_define.py +++ b/robomimic/scripts/config_gen/act_gen_no_define.py @@ -2,12 +2,12 @@ import random import json -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] # import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) +evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) # evaldataset = evaldataset[:200] # broaddataset = broaddataset[:2000] random.shuffle(evaldataset) @@ -49,7 +49,7 @@ def make_generator_helper(args): values=[100.0], ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.data", name="ds", diff --git a/robomimic/scripts/config_gen/bc_xfmr_gen.py b/robomimic/scripts/config_gen/bc_xfmr_gen.py index 713085de..f3d43332 100644 --- a/robomimic/scripts/config_gen/bc_xfmr_gen.py +++ b/robomimic/scripts/config_gen/bc_xfmr_gen.py @@ -13,7 +13,7 @@ def make_generator_helper(args): if args.ckpt_mode is None: args.ckpt_mode = "off" - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.data", name="ds", diff --git a/robomimic/scripts/config_gen/diffusion_gen.py b/robomimic/scripts/config_gen/diffusion_gen.py index 7c1bba00..072bd266 100644 --- a/robomimic/scripts/config_gen/diffusion_gen.py +++ b/robomimic/scripts/config_gen/diffusion_gen.py @@ -2,12 +2,12 @@ import random import json -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] # import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) # # evaldataset = evaldataset[:200] # # broaddataset = broaddataset[:2000] # random.shuffle(evaldataset) @@ -16,11 +16,11 @@ # N_BROAD = len(broaddataset) ## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: langs = json.load(file) ## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] broaddataset = broaddataset[:2000] N_BROAD = len(broaddataset) @@ -86,7 +86,7 @@ def make_generator_helper(args): hidename=True, ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.data", name="ds", diff --git a/robomimic/scripts/config_gen/diffusion_gen_no_define.py b/robomimic/scripts/config_gen/diffusion_gen_no_define.py index c022b77f..a5c81b56 100644 --- a/robomimic/scripts/config_gen/diffusion_gen_no_define.py +++ b/robomimic/scripts/config_gen/diffusion_gen_no_define.py @@ -2,12 +2,12 @@ import random import json -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] # import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) # # evaldataset = evaldataset[:200] # # broaddataset = broaddataset[:2000] # random.shuffle(evaldataset) @@ -16,11 +16,11 @@ # N_BROAD = len(broaddataset) ## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: langs = json.load(file) ## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] broaddataset = broaddataset[:2000] N_BROAD = len(broaddataset) @@ -85,7 +85,7 @@ def make_generator_helper(args): hidename=True, ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.data", name="ds", diff --git a/robomimic/scripts/config_gen/helper.py b/robomimic/scripts/config_gen/helper.py index 6cc009e0..c84e1df8 100644 --- a/robomimic/scripts/config_gen/helper.py +++ b/robomimic/scripts/config_gen/helper.py @@ -49,7 +49,7 @@ def get_generator(algo_name, config_file, args, algo_name_short=None, pt=False): def set_env_settings(generator, args): - if args.env in ["r2d2"]: + if args.env in ["droid"]: assert args.mod == "im" generator.add_param( key="experiment.rollout.enabled", @@ -134,7 +134,7 @@ def set_env_settings(generator, args): 116 ], ) - # remove spatial softmax by default for r2d2 dataset + # remove spatial softmax by default for droid dataset generator.add_param( key="observation.encoder.rgb.core_kwargs.pool_class", name="", @@ -667,7 +667,7 @@ def set_mod_settings(generator, args): name="", group=-1, values=[ - "r2d2" + "droid" ], ) if "train.num_epochs" not in generator.parameters: @@ -823,7 +823,7 @@ def get_argparser(): parser.add_argument( "--env", type=str, - default='r2d2', + default='droid', ) parser.add_argument( diff --git a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py index e09f172d..16feace6 100644 --- a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py +++ b/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py @@ -2,12 +2,12 @@ import random import json -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] # import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) # # evaldataset = evaldataset[:200] # # broaddataset = broaddataset[:2000] # random.shuffle(evaldataset) @@ -16,22 +16,22 @@ # N_BROAD = len(broaddataset) ## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: langs = json.load(file) ## Getting broad data -broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] N_BROAD_FULL = len(broaddataset_full) chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] N_CHIPS = len(chips_singletask) ## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang_filtered.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang_filtered.json", 'r') as file: langs = json.load(file) ## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] N_BROAD = len(broaddataset) ## Getting multitask eval data @@ -96,7 +96,7 @@ def make_generator_helper(args): hidename=False, ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.action_keys", name="ac_keys", diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py index 5b0af96b..a85f2d44 100644 --- a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py +++ b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py @@ -3,12 +3,12 @@ import json import numpy as np -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] # import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) # # evaldataset = evaldataset[:200] # # broaddataset = broaddataset[:2000] # random.shuffle(evaldataset) @@ -22,43 +22,43 @@ N_OXE = len(oxe) ## Getting all stanford data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang_stanford_eval.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang_stanford_eval.json", 'r') as file: langs = json.load(file) stanford_singletask_eraser = [{'path': l['path']} for l in langs if 'stanford_eval_0125' in l['path']] N_STANFORD_ERASER = len(stanford_singletask_eraser) -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_stanford_evgr_laundry.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_stanford_evgr_laundry.json", 'r') as file: stanford_singletask_laundry = json.load(file) N_STANFORD_LAUNDRY = len(stanford_singletask_laundry) -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_stanford_evgr_cooking.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_stanford_evgr_cooking.json", 'r') as file: stanford_singletask_cooking = json.load(file) N_STANFORD_COOKING = len(stanford_singletask_cooking) ## Getting all TRI data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_tri_chips.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_tri_chips.json", 'r') as file: langs = json.load(file) chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] N_CHIPS = len(chips_singletask) -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_tri_frenchpress.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_tri_frenchpress.json", 'r') as file: tri_frenchpress = json.load(file) N_FRENCHPRESS = len(tri_frenchpress) -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_tri_pot.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_tri_pot.json", 'r') as file: tri_pot = json.load(file) N_POT = len(tri_pot) ## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: langs = json.load(file) ## Getting broad data -broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] N_BROAD_FULL = len(broaddataset_full) -with open("/mnt/fsx/surajnair/datasets/r2d2-data/filter_traj_20scene_10k_episodes_success.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/filter_traj_20scene_10k_episodes_success.json", 'r') as file: subset = json.load(file) broaddataset_filtered = [{'path': l['path']} for l in broaddataset_full if "/".join(l['path'].split("/")[7:-1])+"/" in subset] @@ -74,7 +74,7 @@ N_CMU_MULTI = len(cmu_multi) -print(f"Broad Datapoints: R2D2 {N_BROAD_FULL, N_BROAD_FILTERED, N_BROAD_FILTERED_RANDOM} OXE {N_OXE} \ +print(f"Broad Datapoints: DROID {N_BROAD_FULL, N_BROAD_FILTERED, N_BROAD_FILTERED_RANDOM} OXE {N_OXE} \ \nSingle Task TRI Datapoints: CHIPS {N_CHIPS} POT {N_POT} FRENCHPRESS {N_FRENCHPRESS} \ \nStanford Datapoints: Eraser {N_STANFORD_ERASER} Laundry {N_STANFORD_LAUNDRY} Cooking {N_STANFORD_COOKING} \ \nCMU Datapoints: {N_CMU_MULTI, N_CMU_TOASTER} ") @@ -148,7 +148,7 @@ def make_generator_helper(args): hidename=True, ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.action_keys", name="ac_keys", @@ -368,38 +368,38 @@ def make_generator_helper(args): value_names=[ # "stanford_cooking", # "stanford_laundry", - # "filteredr2d2_stanford_cooking", - # "filteredr2d2_stanford_laundry", - # "filteredrandomr2d2_stanford_cooking", - # "filteredrandomr2d2_stanford_laundry", + # "filtereddroid_stanford_cooking", + # "filtereddroid_stanford_laundry", + # "filteredrandomdroid_stanford_cooking", + # "filteredrandomdroid_stanford_laundry", # "stanford_eraser", # "cmu_toast", - # "filteredr2d2_stanford_eraser", - # "filteredr2d2_cmu_toast", - # "filteredrandomr2d2_stanford_eraser", - # "filteredrandomr2d2_cmu_toast", + # "filtereddroid_stanford_eraser", + # "filtereddroid_cmu_toast", + # "filteredrandomdroid_stanford_eraser", + # "filteredrandomdroid_cmu_toast", # "stanford_cooking", # "stanford_laundry", # "oxe_balanced_stanford_cooking", # "oxe_balanced_stanford_laundry", - # "fullr2d2_balanced_stanford_cooking", - # "fullr2d2_balanced_stanford_laundry", + # "fulldroid_balanced_stanford_cooking", + # "fulldroid_balanced_stanford_laundry", # "tri_pot", # "tri_frenchpress", # "oxe_balanced_tri_pot", # "oxe_balanced_tri_frenchpress", - # "fullr2d2_balanced_tri_pot", - # "fullr2d2_balanced_tri_frenchpress", + # "fulldroid_balanced_tri_pot", + # "fulldroid_balanced_tri_frenchpress", # "oxe_balanced_stanford_eraser", # "oxe_balanced_chips", # "oxe_balanced_cmu_multi", # "oxe_balanced_cmu_toast", # "stanford_eraser", - # "fullr2d2_balanced_stanford_eraser" + # "fulldroid_balanced_stanford_eraser" # "cmu_multi", "cmu_toast_hdf5", - # "fullr2d2_balanced_cmu_multi", - # "fullr2d2_balanced_cmu_toast", + # "fulldroid_balanced_cmu_multi", + # "fulldroid_balanced_cmu_toast", # "broad", # "eval_chips", # "balanced_chips", diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py index e84ed06c..39e7a3df 100644 --- a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py @@ -3,7 +3,7 @@ import json import numpy as np -DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" +DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid" def make_generator_helper(args): @@ -24,7 +24,7 @@ def make_generator_helper(args): name="", group=-1, values=[ - "r2d2_rlds" + "droid_rlds" ], ) @@ -39,7 +39,7 @@ def make_generator_helper(args): key="train.data_path", name="", group=-1, - values=["/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2"], + values=["/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid"], ) generator.add_param( @@ -57,6 +57,46 @@ def make_generator_helper(args): hidename=False, ) + generator.add_param( + key="train.subsample_length", + name="subsample_length", + group=7070707, + values=[ + 100 + ], + hidename=True, + ) + + generator.add_param( + key="train.num_parallel_calls", + name="num_parallel_calls", + group=404040404, + values=[ + 200 + ], + hidename=True, + ) + + generator.add_param( + key="train.traj_transform_threads", + name="traj_transform_threads", + group=303030303, + values=[ + 48 + ], + hidename=True, + ) + + generator.add_param( + key="train.traj_read_threads", + name="traj_read_threads", + group=908090809, + values=[ + 48 + ], + hidename=True, + ) + generator.add_param( key="algo.noise_samples", name="noise_samples", @@ -87,7 +127,7 @@ def make_generator_helper(args): hidename=True, ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.sample_weights", name="sample_weights", diff --git a/robomimic/scripts/config_gen/voltron.py b/robomimic/scripts/config_gen/voltron.py index 3d1f29b0..a649072f 100644 --- a/robomimic/scripts/config_gen/voltron.py +++ b/robomimic/scripts/config_gen/voltron.py @@ -2,12 +2,12 @@ import random import json -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/", postfix="trajectory_im128.h5")] +# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] # import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/r2d2_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/r2d2_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/r2d2_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) +# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) +# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) +# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] +# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) # # evaldataset = evaldataset[:200] # # broaddataset = broaddataset[:2000] # random.shuffle(evaldataset) @@ -16,11 +16,11 @@ # N_BROAD = len(broaddataset) ## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: +with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: langs = json.load(file) ## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/' in l['path']] +broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] N_BROAD = len(broaddataset) ## Getting multitask eval data @@ -102,7 +102,7 @@ def make_generator_helper(args): hidename=True, ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.action_keys", name="ac_keys", diff --git a/robomimic/scripts/conversion/add_lang_to_converted_data.py b/robomimic/scripts/conversion/add_lang_to_converted_data.py index f98a0b6d..b900285f 100644 --- a/robomimic/scripts/conversion/add_lang_to_converted_data.py +++ b/robomimic/scripts/conversion/add_lang_to_converted_data.py @@ -1,5 +1,5 @@ """ -Add image information to existing r2d2 hdf5 file +Add image information to existing droid hdf5 file """ import h5py import os @@ -54,7 +54,7 @@ def add_lang(path, raw_lang, args): "--folder", type=str, help="folder containing hdf5's to add camera images to", - default="~/datasets/r2d2/success" + default="~/datasets/droid/success" ) parser.add_argument( @@ -66,7 +66,7 @@ def add_lang(path, raw_lang, args): args = parser.parse_args() - with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json", 'r') as file: + with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: langs = json.load(file) langs = [l for l in langs if "TRI_chips_only_1_21" in l["path"]] datasets = langs @@ -90,8 +90,8 @@ def add_lang(path, raw_lang, args): -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/AUTOLab/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/CLVR/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/GuptaLab/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/AUTOLab/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/CLVR/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/GuptaLab/success --imsize 128 -# python robomimic/scripts/conversion/add_lang_to_converted_data.py --folder /mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval/TRI_1_10_multitask_food_in_bowl/ --imsize 256 \ No newline at end of file +# python robomimic/scripts/conversion/add_lang_to_converted_data.py --folder /mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval/TRI_1_10_multitask_food_in_bowl/ --imsize 256 \ No newline at end of file diff --git a/robomimic/scripts/conversion/convert_r2d2.py b/robomimic/scripts/conversion/convert_r2d2.py index e3436223..471036f5 100644 --- a/robomimic/scripts/conversion/convert_r2d2.py +++ b/robomimic/scripts/conversion/convert_r2d2.py @@ -1,5 +1,5 @@ """ -Add image information to existing r2d2 hdf5 file +Add image information to existing droid hdf5 file """ import h5py import os @@ -23,11 +23,11 @@ import robomimic.utils.torch_utils as TorchUtils import robomimic.utils.tensor_utils as TensorUtils -from r2d2.camera_utils.wrappers.recorded_multi_camera_wrapper import RecordedMultiCameraWrapper -from r2d2.trajectory_utils.trajectory_reader import TrajectoryReader -from r2d2.camera_utils.info import camera_type_to_string_dict +from droid.camera_utils.wrappers.recorded_multi_camera_wrapper import RecordedMultiCameraWrapper +from droid.trajectory_utils.trajectory_reader import TrajectoryReader +from droid.camera_utils.info import camera_type_to_string_dict -from r2d2.camera_utils.camera_readers.zed_camera import ZedCamera, standard_params +from droid.camera_utils.camera_readers.zed_camera import ZedCamera, standard_params DISNEY_IMAGE_NAME_TO_CAM_KEY_MAPPING = {'hand_camera_left_image': '16522755_left', 'hand_camera_right_image': '16522755_right', 'varied_camera_1_left_image': '29431508_left', 'varied_camera_1_right_image': '29431508_right', 'varied_camera_2_left_image': '29513368_left', 'varied_camera_2_right_image': '29513368_right'} @@ -291,7 +291,7 @@ def remove_timesteps_for_group(g): "--folder", type=str, help="folder containing hdf5's to add camera images to", - default="~/datasets/r2d2/success" + default="~/datasets/droid/success" ) parser.add_argument( @@ -309,8 +309,8 @@ def remove_timesteps_for_group(g): args = parser.parse_args() - # langs = json.load("/mnt/fsx/surajnair/datasets/r2d2-data/manifest_lang.json") - # with open("/mnt/fsx/surajnair/datasets/r2d2-data/manifest.json", 'r') as file: + # langs = json.load("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json") + # with open("/mnt/fsx/surajnair/datasets/droid-data/manifest.json", 'r') as file: # files = json.load(file) # # files = [os.path.join(os.path.dirname(l["path"]), "trajectory.h5") for l in langs if ("eval" in l["path"])] # datasets = files @@ -345,9 +345,9 @@ def remove_timesteps_for_group(g): # pass -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/AUTOLab/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/CLVR/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --folder /mnt/fsx/surajnair/datasets/r2d2_full_raw/GuptaLab/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/AUTOLab/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/CLVR/success --imsize 128 +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/GuptaLab/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --imsize 256 -# /mnt/fsx/surajnair/mambaforge/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_r2d2.py --imsize 128 \ No newline at end of file +# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --imsize 256 +# /mnt/fsx/surajnair/mambaforge/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --imsize 128 \ No newline at end of file diff --git a/robomimic/scripts/conversion/get_eval_lang_annotations.py b/robomimic/scripts/conversion/get_eval_lang_annotations.py index 562c79b2..797f33da 100644 --- a/robomimic/scripts/conversion/get_eval_lang_annotations.py +++ b/robomimic/scripts/conversion/get_eval_lang_annotations.py @@ -4,9 +4,9 @@ import os import csv -EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval/" +EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval/" RAW_ANNOTATIONS = os.path.join(EVAL_DATA_ROOT_DIR, "tri-multi-task-eval-lang-annotations.csv") -METADATA_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval-json/" +METADATA_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval-json/" def read_csv(file_path): data_list = [] diff --git a/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py b/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py index e6f2d073..7b6fc722 100644 --- a/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py +++ b/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py @@ -3,12 +3,12 @@ import numpy as np import os -DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads/" -ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-json/" +DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/" +ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-json/" AGGREGATED_ANNOTATIONS_PATH = os.path.join(ANNOTATIONS_DIR, "aggregated-annotations.json") -EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval/" -EVAL_ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/r2d2-data/lab-uploads-eval-json/" +EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval/" +EVAL_ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval-json/" AGGREGATED_ANNOTATIONS_EVAL_PATH = os.path.join(EVAL_ANNOTATIONS_DIR, "aggregated_annotations_eval.json") with open(AGGREGATED_ANNOTATIONS_PATH, "r") as a_file: diff --git a/robomimic/scripts/plot_model_predictions.py b/robomimic/scripts/plot_model_predictions.py index 403f2756..6a9fa772 100644 --- a/robomimic/scripts/plot_model_predictions.py +++ b/robomimic/scripts/plot_model_predictions.py @@ -23,15 +23,15 @@ # the configs of the models to be plotted model_config_mapping = { # "bottle_less_obs": { - # "model":"/home/zehan/expdata/r2d2/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/models/model_epoch_60.pth", - # 'folder':"/home/zehan/expdata/r2d2/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/test_inference_figures/", + # "model":"/home/zehan/expdata/droid/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/models/model_epoch_60.pth", + # 'folder':"/home/zehan/expdata/droid/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/test_inference_figures/", # # "action_names": ['x', 'y', 'z', 'roll', 'pitch', 'yaw', "gripper_action" , 'terminate'], # "action_names": None, # "trajectory_name_regex": r'(\d+_trajectory_im\d+)' # }, - "r2d2_wire": { - # "model": "/home/soroushn/expdata/r2d2/im/diffusion_policy/debug/ds_pen-in-cup_cams_3cams/20230830160945/models/model_epoch_2.pth", - "model": "/home/soroushn/expdata/r2d2/im/bc_xfmr/debug/ds_pen-in-cup_cams_3cams_predfuture_True_ac_keys_rel/20230830161631/models/model_epoch_2.pth", + "droid_wire": { + # "model": "/home/soroushn/expdata/droid/im/diffusion_policy/debug/ds_pen-in-cup_cams_3cams/20230830160945/models/model_epoch_2.pth", + "model": "/home/soroushn/expdata/droid/im/bc_xfmr/debug/ds_pen-in-cup_cams_3cams_predfuture_True_ac_keys_rel/20230830161631/models/model_epoch_2.pth", "folder": "/home/soroushn/tmp/model_predictions", # "action_names": ['x', 'y', 'z', 'r', 'p', 'y', "gripper_pos"], # use custom names "action_names": None, # use default names, see line 71 diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index ed79f918..b803a162 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -44,7 +44,7 @@ from robomimic.config import config_factory from robomimic.algo import algo_factory, RolloutPolicy from robomimic.utils.log_utils import PrintLogger, DataLogger, flush_warnings -from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, R2D2_TO_RLDS_OBS_KEY_MAP, R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset +from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, droid_TO_RLDS_OBS_KEY_MAP, droid_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset from octo.data.dataset import make_interleaved_dataset @@ -77,25 +77,24 @@ def train(config, device): ds_format = config.train.data_format - if ds_format == "r2d2_rlds": + if ds_format == "droid_rlds": # # load basic metadata from training file # print("\n============= Loaded Environment Metadata =============") env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path=None, ds_format=ds_format) - # TODO (Ashwin): make sure setting to None here is OK, are observations pre-normalized the way we want? obs_normalization_stats = None # FOR RLDS tf.config.set_visible_devices([], "GPU") obs_modalities = config.observation.modalities.obs.rgb - # NOTE(Ashwin): must be 2 cam for now, can clean this up later + # NOTE: Must be 2 cam for now, can clean this up later assert(len(obs_modalities) == 2) ac_dim = sum([ac_comp[1] for ac_comp in config.train.action_shapes]) BASE_DATASET_KWARGS = { "data_dir": config.train.data_path, - "image_obs_keys": {"primary": R2D2_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": R2D2_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, - "state_obs_keys": [R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], + "image_obs_keys": {"primary": droid_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": droid_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, + "state_obs_keys": [droid_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], "language_key": "language_instruction", "keys_to_normalize": {"action": "action"}, "action_proprio_normalization_type": "bounds", @@ -110,21 +109,20 @@ def train(config, device): {"name": d_name, **BASE_DATASET_KWARGS} for d_name in dataset_names ] - # TODO(Ashwin): Wrap more of the parameters below in the robomimic configs dataset = make_interleaved_dataset( dataset_kwargs_list, config.train.sample_weights, train=True, shuffle_buffer_size=config.train.shuffle_buffer_size, batch_size=None, # batching will be handles in PyTorch Dataloader object - balance_weights=True, + balance_weights=False, do_combined_normalization=True, traj_transform_kwargs=dict( # NOTE(Ashwin): window_size and future_action_window_size may break if # not using diffusion policy window_size=config.algo.horizon.observation_horizon, future_action_window_size=config.algo.horizon.prediction_horizon-1, - subsample_length=100, + subsample_length=config.train.subsample_length, skip_unlabeled=True, # skip all trajectories without language ), frame_transform_kwargs=dict( @@ -134,10 +132,10 @@ def train(config, device): primary=config.observation.image_dim, secondary=config.observation.image_dim, ), - num_parallel_calls=200, + num_parallel_calls=config.train.num_parallel_calls, ), - traj_transform_threads=48, - traj_read_threads=48, + traj_transform_threads=config.train.traj_transform_threads, + traj_read_threads=config.train.traj_read_threads, ) # Note: If we have statistics for multiple datasets separately, use the last one (assumes last one is the dataset from the target domain) # rlds_dataset_stats will only be a list if do_combined_normalization is set to False @@ -145,7 +143,7 @@ def train(config, device): action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats["action"], config.train.action_keys, config.train.action_shapes) action_config = config.train.action_config action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) - dataset = dataset.map(robomimic_transform, num_parallel_calls=48) + dataset = dataset.map(robomimic_transform, num_parallel_calls=config.train.traj_transform_threads) pytorch_dataset = TorchRLDSDataset(dataset) train_loader = DataLoader( @@ -285,7 +283,7 @@ def train(config, device): ##### ------------------------------------------------------------------------------------ ###### # TODO(Ashwin): Support loading validation splits for RLDS - if ds_format != "r2d2_rlds" and config.experiment.validate: + if ds_format != "droid_rlds" and config.experiment.validate: # cap num workers for validation dataset at 1 num_workers = min(config.train.num_data_workers, 1) valid_sampler = validset.get_dataset_sampler() @@ -435,7 +433,7 @@ def train(config, device): # check if we need to save model MSE #TODO(Ashwin): support MSE Logging with RLDS dataloading - if ds_format != "r2d2_rlds": + if ds_format != "droid_rlds": should_save_mse = False if config.experiment.mse.enabled: if config.experiment.mse.every_n_epochs is not None and epoch % config.experiment.mse.every_n_epochs == 0: @@ -529,7 +527,7 @@ def main(args): else: config = config_factory(args.algo) - if config.train.data_format != "r2d2_rlds" and args.dataset is not None: + if config.train.data_format != "droid_rlds" and args.dataset is not None: config.train.data = args.dataset if args.name is not None: diff --git a/robomimic/utils/dataset.py b/robomimic/utils/dataset.py index b90dcd13..a65b1e24 100644 --- a/robomimic/utils/dataset.py +++ b/robomimic/utils/dataset.py @@ -725,7 +725,7 @@ def get_dataset_sampler(self): return None -class R2D2Dataset(SequenceDataset): +class DROIDDataset(SequenceDataset): def get_action_traj(self, ep): action_traj = dict() for key in self.action_keys: diff --git a/robomimic/utils/file_utils.py b/robomimic/utils/file_utils.py index 011f4cf5..50532f6d 100644 --- a/robomimic/utils/file_utils.py +++ b/robomimic/utils/file_utils.py @@ -96,17 +96,17 @@ def get_env_metadata_from_dataset(dataset_path, ds_format="robomimic"): :`'type'`: type of environment, should be a value in EB.EnvType :`'env_kwargs'`: dictionary of keyword arguments to pass to environment constructor """ - if ds_format != "r2d2_rlds": + if ds_format != "droid_rlds": dataset_path = os.path.expanduser(dataset_path) f = h5py.File(dataset_path, "r") if ds_format == "robomimic": env_meta = json.loads(f["data"].attrs["env_args"]) f.close() - elif ds_format == "r2d2": + elif ds_format == "droid": env_meta = dict(f.attrs) f.close() - elif ds_format == "r2d2_rlds": + elif ds_format == "droid_rlds": # TODO(Ashwin): find a proper way to extract this; this info isn't actually used though env_meta = {} else: @@ -166,7 +166,7 @@ def get_shape_metadata_from_dataset(dataset_path, batch, action_keys, all_obs_ke input_shape=initial_shape, ) f.close() - elif ds_format == "r2d2": + elif ds_format == "droid": # read demo file for some metadata dataset_path = os.path.expanduser(dataset_path) f = h5py.File(dataset_path, "r") @@ -222,7 +222,7 @@ def get_shape_metadata_from_dataset(dataset_path, batch, action_keys, all_obs_ke if (config.train.goal_mode is not None) and (ObsUtils.OBS_KEYS_TO_MODALITIES[k] == 'rgb'): all_shapes[k][0] *= 2 f.close() - elif ds_format == "r2d2_rlds": + elif ds_format == "droid_rlds": all_shapes = OrderedDict() for k in [ "robot_state/cartesian_position", diff --git a/robomimic/utils/rlds_utils.py b/robomimic/utils/rlds_utils.py index 5c2ce9e4..ff16f53a 100644 --- a/robomimic/utils/rlds_utils.py +++ b/robomimic/utils/rlds_utils.py @@ -47,12 +47,12 @@ def robomimic_transform(trajectory: Dict[str, Any]) -> Dict[str, Any]: "actions": trajectory["action"][1:], } -R2D2_TO_RLDS_OBS_KEY_MAP = { +DROID_TO_RLDS_OBS_KEY_MAP = { "camera/image/varied_camera_1_left_image": "exterior_image_1_left", "camera/image/varied_camera_2_left_image": "exterior_image_2_left" } -R2D2_TO_RLDS_LOW_DIM_OBS_KEY_MAP = { +DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP = { "robot_state/cartesian_position": "cartesian_position", "robot_state/gripper_position": "gripper_position", } diff --git a/robomimic/utils/train_utils.py b/robomimic/utils/train_utils.py index 54dd09bf..7075391b 100644 --- a/robomimic/utils/train_utils.py +++ b/robomimic/utils/train_utils.py @@ -21,7 +21,7 @@ import robomimic.utils.log_utils as LogUtils import robomimic.utils.file_utils as FileUtils -from robomimic.utils.dataset import SequenceDataset, R2D2Dataset, MetaDataset +from robomimic.utils.dataset import SequenceDataset, DROIDDataset, MetaDataset from robomimic.envs.env_base import EnvBase from robomimic.envs.wrappers import EnvWrapper from robomimic.algo import RolloutPolicy @@ -181,7 +181,7 @@ def dataset_factory(config, obs_keys, filter_by_attribute=None, dataset_path=Non meta_ds_kwargs = dict() dataset = get_dataset( - ds_class=R2D2Dataset if config.train.data_format == "r2d2" else SequenceDataset, + ds_class=DROIDDataset if config.train.data_format == "droid" else SequenceDataset, ds_kwargs=ds_kwargs, ds_weights=ds_weights, ds_labels=ds_labels, From fe2e60c5791d6b697479fdfa64ab29348daa801d Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 06:47:31 +0000 Subject: [PATCH 08/43] update README, install deps, tested fresh install --- README.md | 72 ++++++++----------- requirements.txt | 3 +- ...d2_runs_bcxfmr.py => droid_runs_bcxfmr.py} | 0 ....py => droid_runs_language_conditioned.py} | 0 ...> droid_runs_language_conditioned_rlds.py} | 27 ++++--- robomimic/scripts/train.py | 6 +- 6 files changed, 51 insertions(+), 57 deletions(-) rename robomimic/scripts/config_gen/{r2d2_runs_bcxfmr.py => droid_runs_bcxfmr.py} (100%) rename robomimic/scripts/config_gen/{r2d2_runs_language_conditioned.py => droid_runs_language_conditioned.py} (100%) rename robomimic/scripts/config_gen/{r2d2_runs_language_conditioned_rlds.py => droid_runs_language_conditioned_rlds.py} (93%) diff --git a/README.md b/README.md index 03d9bdbb..cb49607f 100644 --- a/README.md +++ b/README.md @@ -1,59 +1,45 @@ -# robomimic +# DROID Policy Learning and Evaluation Codebase -[**[Homepage]**](https://robomimic.github.io/)   [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html)   [**[Study Paper]**](https://arxiv.org/abs/2108.03298)   [**[Study Website]**](https://robomimic.github.io/study/)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative) +This repository contains all code for the policy learning and evaluation experiments in DROID(TODO(Karl/Sasha): add link to DROID website), a project aimed at collecting a large-scale dataset in-the-wild robot manipulation dataset for the robot learning resaerch community. This codebase is built as a fork of the popular repository for imitation learning algorithm development, `robomimic`, about which further information can be found here: [**[Homepage]**](https://robomimic.github.io/)   [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html)   [**[Study Paper]**](https://arxiv.org/abs/2108.03298)   [**[Study Website]**](https://robomimic.github.io/study/)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative). ------- ## Installation -1. Clone the repo with the `--recurse-submodules` flag. -2. (if applicable) switch to `droid` branch -3. Run `pip install -e .` in `robomimic` -4. Run `pip install -e .` in `robomimic/act/detr` +Create a python3 conda environment (tested with Python 3.10) and run the following: + +1. Create python 3.10 conda environment: `conda create --name droid_policy_learning python=3.10` +2. Install [octo](https://github.com/octo-models/octo) +3. Clone the repo +4. Switch to the `r2d2` branch +5. Run `pip install -e .` in `robomimic` + +If you are running policy evaluation on a DROID robot station, then you also need to have DROID installed in the same conda environment. To do that, please follow the instructions [here](https://github.com/AlexanderKhazatsky/DROID). ------- -## Pre-processing datasets -Before training, you need to pre-process your datasets to ensure they're the correct format. -### droid datasets -Convert the raw droid data using this simple script: -``` -python robomimic/scripts/convertion/convert_droid.py --folder -``` -### robosuite datasets -1. Convert the raw robosuite dataset to robomimic format -``` -python robomimic/scripts/conversion/convert_robosuite.py --dataset --filter_num_demos -``` -`--filter_num_demos` corresponds to the number of demos to filter by. It's a list, eg. `10 30 50 100 200 500 1000` - -This script will extract absolute actions, extract the action dict, and add filter keys. - -2. Extract image observations from robomimic dataset -``` -python robomimic/scripts/dataset_states_to_obs.py --camera_names robot0_agentview_left robot0_agentview_right robot0_eye_in_hand --compress --exclude-next-obs --dataset -``` -This script will generate a new dataset with the suffix `_im84.hdf5` in the same directory as `--dataset` +## Preparing Datasets +We provide all DROID datasets in RLDS, which makes it easy to co-train with various other robot-learning datasets (such as those in the [Open X-Embodiment](https://robotics-transformer-x.github.io/)). + +To prepare datasets, first download the desired training split of DROID here(TODO(Karl/Sasha): insert link and details based on what the splits are). +If you want to additionally train DROID policies on task-specific data collected in your own DROID hardware platform, follow the instructions [here](https://github.com/kpertsch/droid_dataset_builder?tab=readme-ov-file) to convert the data to an RLDS format that can be used for DROID policy learning. Make sure +that all datasets you want to train on are under the same root directory `DATA_PATH`. ------- ## Training -There are a number of algorithms to choose from: Diffusion Policy, ACT, BC-Transformer, etc. +To train policies, update `DATA_PATH`, `EXP_LOG_PATH`, and `EXP_NAMES` in `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` and then run: -Each algorithm has its own config generator script. For example for diffusion policy run: -``` -python robomimic/scripts/config_gen/diffusion_gen.py --name -``` -You can add `--debug` to generate small runs for testing. Running this script will generate training run commands. You can use this script for generating a single run or multiple (for comparing settings and hyperparameter tuning). -After running this script you just need to run the command(s) outputted. +`python robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` --wandb_proj_name ` -Want to learn how to set your own config values and sweep them? Read this short [tutorial section](https://robomimic.github.io/docs/tutorials/hyperparam_scan.html#step-3-set-hyperparameter-values). +This will generate a python command that can be run to launch training. You can also update other training parameters within `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py`. Please see the `robomimic` documentation for more information on how `robomimic` configs are defined. The three +most important parameters in this file are: -### Loading model checkpoint weights -Want to intialize your model with weights from a previous model checkpoint? Set the checkpoint path under `experiment.ckpt_path` in the config. +- `DATA_PATH`: This is the directory in which all RLDS datasets were prepared. +- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. +- `EXP_NAMES`: This defines the name of each experiment (as will be logged in `wandb`), the RLDS datasets corresponding to that experiment, and the desired sample weights between those datasets. See `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` for a template on how this should be formatted. -------- -## Logging and viewing results -Read this short [tutorial page](https://robomimic.github.io/docs/tutorials/viewing_results.html). +The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce this based on your RAM availability. For best results, we recommend using `shuffle_buffer_size >= 100000` if possible. ------- -## Real robot evaluation (droid only) -Use this forked droid: https://github.com/snasiriany/droid/tree/robomimic-eval. Note that the branch is `robomimic-eval`. +## Evaluation on a DROID robot station +Make sure DROID is installed and follow the policy evaluation instructions at the bottom of the README. -Run this script: https://github.com/snasiriany/droid/blob/robomimic-eval/scripts/evaluation/evaluate_policy.py. Before doing so, make sure to fill out `CKPT_PATH` with the path to the saved robomimic checkpoint you wish to evaluate. +------- +## TODO(Suraj): Debugging with HDF5 based policies \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index c451769d..1978b695 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,5 @@ egl_probe>=1.0.1 torch torchvision diffusers==0.11.1 -pytorch3d==0.7.3 +opencv-python +transformers==4.34.0 diff --git a/robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py b/robomimic/scripts/config_gen/droid_runs_bcxfmr.py similarity index 100% rename from robomimic/scripts/config_gen/r2d2_runs_bcxfmr.py rename to robomimic/scripts/config_gen/droid_runs_bcxfmr.py diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py similarity index 100% rename from robomimic/scripts/config_gen/r2d2_runs_language_conditioned.py rename to robomimic/scripts/config_gen/droid_runs_language_conditioned.py diff --git a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py similarity index 93% rename from robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py rename to robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py index 39e7a3df..2fde524e 100644 --- a/robomimic/scripts/config_gen/r2d2_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py @@ -2,9 +2,17 @@ import random import json import numpy as np +from collections import OrderedDict -DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid" - +DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" +EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" +EXP_NAMES = OrderedDict( + [ + ("r2_d2__r2_d2_cmu_toaster", {"datasets": ["r2_d2", "r2_d2_cmu_toaster"], + "sample_weights": [1, 1]}), + ("r2_d2_cmu_toaster", {"datasets": ["r2_d2_cmu_toaster"], + "sample_weights": [1]}) + ]) def make_generator_helper(args): algo_name_short = "diffusion_policy" @@ -39,7 +47,7 @@ def make_generator_helper(args): key="train.data_path", name="", group=-1, - values=["/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid"], + values=[DATA_PATH], ) generator.add_param( @@ -131,9 +139,9 @@ def make_generator_helper(args): generator.add_param( key="train.sample_weights", name="sample_weights", - group=-1, + group=24988, values=[ - [1, 1], + EXP_NAMES[k]["sample_weights"] for k in EXP_NAMES.keys() ], ) generator.add_param( @@ -141,11 +149,9 @@ def make_generator_helper(args): name="dataset_names", group=24988, values=[ - ["r2_d2", "r2_d2_cmu_toaster"], + EXP_NAMES[k]["datasets"] for k in EXP_NAMES.keys() ], - value_names=[ - "r2_d2__r2_d2_cmu_toaster" - ] + value_names=list(EXP_NAMES.keys()) ) generator.add_param( key="train.action_keys", @@ -437,7 +443,8 @@ def make_generator_helper(args): name="", group=-1, values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + "{exp_log_path}/{env}/{mod}/{algo_name_short}".format( + exp_log_path=EXP_LOG_PATH, env=args.env, mod=args.mod, algo_name_short=algo_name_short, diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index b803a162..b534aac4 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -44,7 +44,7 @@ from robomimic.config import config_factory from robomimic.algo import algo_factory, RolloutPolicy from robomimic.utils.log_utils import PrintLogger, DataLogger, flush_warnings -from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, droid_TO_RLDS_OBS_KEY_MAP, droid_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset +from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, DROID_TO_RLDS_OBS_KEY_MAP, DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset from octo.data.dataset import make_interleaved_dataset @@ -93,8 +93,8 @@ def train(config, device): BASE_DATASET_KWARGS = { "data_dir": config.train.data_path, - "image_obs_keys": {"primary": droid_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": droid_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, - "state_obs_keys": [droid_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], + "image_obs_keys": {"primary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, + "state_obs_keys": [DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], "language_key": "language_instruction", "keys_to_normalize": {"action": "action"}, "action_proprio_normalization_type": "bounds", From 21cab26a9f0f6d8dc7c7a53a8d5e5decb101c7b0 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 06:58:52 +0000 Subject: [PATCH 09/43] clean up requirements --- README.md | 8 +++++--- requirements.txt | 2 +- setup.py | 8 +++----- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index cb49607f..5cb1d5c5 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# DROID Policy Learning and Evaluation Codebase +# DROID Policy Learning and Evaluation This repository contains all code for the policy learning and evaluation experiments in DROID(TODO(Karl/Sasha): add link to DROID website), a project aimed at collecting a large-scale dataset in-the-wild robot manipulation dataset for the robot learning resaerch community. This codebase is built as a fork of the popular repository for imitation learning algorithm development, `robomimic`, about which further information can be found here: [**[Homepage]**](https://robomimic.github.io/)   [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html)   [**[Study Paper]**](https://arxiv.org/abs/2108.03298)   [**[Study Website]**](https://robomimic.github.io/study/)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative). @@ -35,11 +35,13 @@ most important parameters in this file are: - `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. - `EXP_NAMES`: This defines the name of each experiment (as will be logged in `wandb`), the RLDS datasets corresponding to that experiment, and the desired sample weights between those datasets. See `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` for a template on how this should be formatted. -The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce this based on your RAM availability. For best results, we recommend using `shuffle_buffer_size >= 100000` if possible. +The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce this based on your RAM availability. For best results, we recommend using `shuffle_buffer_size >= 100000` if possible. All polices were trained on a single NVIDIA A100 GPU. + +To specify your information for Weights and Biases logging, make sure to update the `WANDB_ENTITY` and `WANDB_API_KEY` values in `robomimic/macros.py`. ------- ## Evaluation on a DROID robot station Make sure DROID is installed and follow the policy evaluation instructions at the bottom of the README. ------- -## TODO(Suraj): Debugging with HDF5 based policies \ No newline at end of file +## TODO(Suraj): Debugging with HDF5 based policies + help cleanup unnecessary files \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 1978b695..c2ab691c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,7 +9,7 @@ imageio imageio-ffmpeg matplotlib egl_probe>=1.0.1 -torch +torch==2.0.1 torchvision diffusers==0.11.1 opencv-python diff --git a/setup.py b/setup.py index 54405a5a..0ecb291d 100644 --- a/setup.py +++ b/setup.py @@ -27,7 +27,7 @@ "imageio-ffmpeg", "matplotlib", "egl_probe>=1.0.1", - "torch", + "torch==2.0.1", "torchvision", "diffusers==0.11.1", "opencv-python", @@ -36,10 +36,8 @@ eager_resources=['*'], include_package_data=True, python_requires='>=3', - description="robomimic: A Modular Framework for Robot Learning from Demonstration", - author="Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang", - url="https://github.com/ARISE-Initiative/robomimic", - author_email="amandlek@cs.stanford.edu", + author="Suraj Nair, Ashwin Balakrishna, Soroush Nasiriany", + author_email="ashwin.balakrishna@tri.global", version="0.3.0", long_description=long_description, long_description_content_type='text/markdown' From 03b19a8727206d09d4a03a345b10ab30eb53579d Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 07:07:01 +0000 Subject: [PATCH 10/43] update todos --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 5cb1d5c5..68b256b6 100644 --- a/README.md +++ b/README.md @@ -44,4 +44,5 @@ To specify your information for Weights and Biases logging, make sure to update Make sure DROID is installed and follow the policy evaluation instructions at the bottom of the README. ------- -## TODO(Suraj): Debugging with HDF5 based policies + help cleanup unnecessary files \ No newline at end of file +## TODO(Suraj): Describe debugging with HDF5 based policies and how to train lang/goal conditioned policies with them +## TODO(Suraj): Help cleanup unnecessary files \ No newline at end of file From fa1215febc663034cf8579d6d6206b6eb608988e Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 07:10:10 +0000 Subject: [PATCH 11/43] clean --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 68b256b6..e1f1266f 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ that all datasets you want to train on are under the same root directory `DATA_P ## Training To train policies, update `DATA_PATH`, `EXP_LOG_PATH`, and `EXP_NAMES` in `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` and then run: -`python robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` --wandb_proj_name ` +`python robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py --wandb_proj_name ` This will generate a python command that can be run to launch training. You can also update other training parameters within `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py`. Please see the `robomimic` documentation for more information on how `robomimic` configs are defined. The three most important parameters in this file are: From dfe3ed27d8475c1b44bb69c5fd99a99146792891 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Fri, 8 Mar 2024 07:11:19 +0000 Subject: [PATCH 12/43] update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e1f1266f..6260ba3e 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ To specify your information for Weights and Biases logging, make sure to update ------- ## Evaluation on a DROID robot station -Make sure DROID is installed and follow the policy evaluation instructions at the bottom of the README. +Make sure [DROID](https://github.com/AlexanderKhazatsky/DROID) is installed and follow the policy evaluation instructions at the bottom of the README. ------- ## TODO(Suraj): Describe debugging with HDF5 based policies and how to train lang/goal conditioned policies with them From bbe0c5bb936f5fc3c98f883f57675fff15733355 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Sat, 9 Mar 2024 06:39:21 +0000 Subject: [PATCH 13/43] clean --- .gitignore | 3 - act/.gitignore | 140 --- act/LICENSE | 21 - act/README.md | 89 -- act/assets/bimanual_viperx_ee_insertion.xml | 59 - .../bimanual_viperx_ee_transfer_cube.xml | 48 - act/assets/bimanual_viperx_insertion.xml | 53 - act/assets/bimanual_viperx_transfer_cube.xml | 42 - act/assets/scene.xml | 38 - act/assets/tabletop.stl | Bin 684 -> 0 bytes act/assets/vx300s_10_custom_finger_left.stl | Bin 83384 -> 0 bytes act/assets/vx300s_10_custom_finger_right.stl | Bin 83384 -> 0 bytes act/assets/vx300s_10_gripper_finger.stl | Bin 42884 -> 0 bytes act/assets/vx300s_11_ar_tag.stl | Bin 3884 -> 0 bytes act/assets/vx300s_1_base.stl | Bin 99984 -> 0 bytes act/assets/vx300s_2_shoulder.stl | Bin 63884 -> 0 bytes act/assets/vx300s_3_upper_arm.stl | Bin 102984 -> 0 bytes act/assets/vx300s_4_upper_forearm.stl | Bin 49584 -> 0 bytes act/assets/vx300s_5_lower_forearm.stl | Bin 99884 -> 0 bytes act/assets/vx300s_6_wrist.stl | Bin 70784 -> 0 bytes act/assets/vx300s_7_gripper.stl | Bin 450084 -> 0 bytes act/assets/vx300s_8_gripper_prop.stl | Bin 31684 -> 0 bytes act/assets/vx300s_9_gripper_bar.stl | Bin 379484 -> 0 bytes act/assets/vx300s_dependencies.xml | 17 - act/assets/vx300s_left.xml | 59 - act/assets/vx300s_right.xml | 59 - act/conda_env.yaml | 23 - act/constants.py | 76 -- act/detr/LICENSE | 201 --- act/detr/README.md | 9 - act/detr/main.py | 109 -- act/detr/models/__init__.py | 9 - act/detr/models/backbone.py | 122 -- act/detr/models/detr_vae.py | 284 ----- act/detr/models/position_encoding.py | 93 -- act/detr/models/transformer.py | 314 ----- act/detr/setup.py | 10 - act/detr/util/__init__.py | 1 - act/detr/util/box_ops.py | 88 -- act/detr/util/misc.py | 468 ------- act/detr/util/plot_utils.py | 107 -- act/ee_sim_env.py | 267 ---- act/imitate_episodes.py | 435 ------- act/policy.py | 84 -- act/record_sim_episodes.py | 189 --- act/scripted_policy.py | 194 --- act/sim_env.py | 278 ----- act/utils.py | 189 --- act/visualize_episodes.py | 147 --- docs/api/robomimic.rst | 8 + docs/datasets/mimicgen.md | 37 + docs/datasets/overview.md | 3 +- docs/datasets/robomimic_v0.1.md | 10 +- docs/datasets/robosuite.md | 3 + docs/images/mimicgen_mosaic.gif | Bin 0 -> 8967711 bytes docs/index.rst | 2 + docs/introduction/getting_started.md | 2 +- docs/introduction/installation.md | 1 + docs/miscellaneous/contributing.md | 2 +- docs/miscellaneous/references.md | 58 +- docs/miscellaneous/team.md | 5 +- docs/miscellaneous/troubleshooting.md | 4 +- docs/model_zoo/robomimic_v0.1.md | 11 +- docs/modules/dataset.md | 16 +- docs/modules/environments.md | 2 +- docs/modules/models.md | 6 +- docs/tutorials/configs.md | 4 +- docs/tutorials/dataset_contents.md | 17 +- docs/tutorials/hyperparam_scan.md | 26 +- docs/tutorials/pretrained_representations.md | 12 +- docs/tutorials/training_transformers.md | 43 + docs/tutorials/viewing_results.md | 10 +- examples/notebooks/datasets.ipynb | 1091 +---------------- examples/notebooks/run_policy.ipynb | 2 +- examples/train_bc_rnn.py | 2 +- robomimic/macros.py | 4 +- 76 files changed, 256 insertions(+), 5450 deletions(-) delete mode 100644 act/.gitignore delete mode 100644 act/LICENSE delete mode 100644 act/README.md delete mode 100644 act/assets/bimanual_viperx_ee_insertion.xml delete mode 100644 act/assets/bimanual_viperx_ee_transfer_cube.xml delete mode 100644 act/assets/bimanual_viperx_insertion.xml delete mode 100644 act/assets/bimanual_viperx_transfer_cube.xml delete mode 100644 act/assets/scene.xml delete mode 100644 act/assets/tabletop.stl delete mode 100644 act/assets/vx300s_10_custom_finger_left.stl delete mode 100644 act/assets/vx300s_10_custom_finger_right.stl delete mode 100644 act/assets/vx300s_10_gripper_finger.stl delete mode 100644 act/assets/vx300s_11_ar_tag.stl delete mode 100644 act/assets/vx300s_1_base.stl delete mode 100644 act/assets/vx300s_2_shoulder.stl delete mode 100644 act/assets/vx300s_3_upper_arm.stl delete mode 100644 act/assets/vx300s_4_upper_forearm.stl delete mode 100644 act/assets/vx300s_5_lower_forearm.stl delete mode 100644 act/assets/vx300s_6_wrist.stl delete mode 100644 act/assets/vx300s_7_gripper.stl delete mode 100644 act/assets/vx300s_8_gripper_prop.stl delete mode 100644 act/assets/vx300s_9_gripper_bar.stl delete mode 100644 act/assets/vx300s_dependencies.xml delete mode 100644 act/assets/vx300s_left.xml delete mode 100644 act/assets/vx300s_right.xml delete mode 100644 act/conda_env.yaml delete mode 100644 act/constants.py delete mode 100644 act/detr/LICENSE delete mode 100644 act/detr/README.md delete mode 100644 act/detr/main.py delete mode 100644 act/detr/models/__init__.py delete mode 100644 act/detr/models/backbone.py delete mode 100644 act/detr/models/detr_vae.py delete mode 100644 act/detr/models/position_encoding.py delete mode 100644 act/detr/models/transformer.py delete mode 100644 act/detr/setup.py delete mode 100644 act/detr/util/__init__.py delete mode 100644 act/detr/util/box_ops.py delete mode 100644 act/detr/util/misc.py delete mode 100644 act/detr/util/plot_utils.py delete mode 100644 act/ee_sim_env.py delete mode 100644 act/imitate_episodes.py delete mode 100644 act/policy.py delete mode 100644 act/record_sim_episodes.py delete mode 100644 act/scripted_policy.py delete mode 100644 act/sim_env.py delete mode 100644 act/utils.py delete mode 100644 act/visualize_episodes.py create mode 100644 docs/datasets/mimicgen.md create mode 100644 docs/images/mimicgen_mosaic.gif create mode 100644 docs/tutorials/training_transformers.md diff --git a/.gitignore b/.gitignore index 0bbf99ff..65249c39 100644 --- a/.gitignore +++ b/.gitignore @@ -119,6 +119,3 @@ venv.bak/ .mypy_cache/ *.mp4 *.pth - -# private macros -macros_private.py diff --git a/act/.gitignore b/act/.gitignore deleted file mode 100644 index d1e4c693..00000000 --- a/act/.gitignore +++ /dev/null @@ -1,140 +0,0 @@ -bin -logs -wandb -outputs -data -data_local -.vscode -_wandb - -**/.DS_Store - -fuse.cfg - -*.ai - -# Generation results -results/ - -ray/auth.json - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -pip-wheel-metadata/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ \ No newline at end of file diff --git a/act/LICENSE b/act/LICENSE deleted file mode 100644 index 35e5f5e2..00000000 --- a/act/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2023 Tony Z. Zhao - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/act/README.md b/act/README.md deleted file mode 100644 index 2a345157..00000000 --- a/act/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# ACT: Action Chunking with Transformers - -### *New*: [ACT tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) -TL;DR: if your ACT policy is jerky or pauses in the middle of an episode, just train for longer! Success rate and smoothness can improve way after loss plateaus. - -#### Project Website: https://tonyzhaozh.github.io/aloha/ - -This repo contains the implementation of ACT, together with 2 simulated environments: -Transfer Cube and Bimanual Insertion. You can train and evaluate ACT in sim or real. -For real, you would also need to install [ALOHA](https://github.com/tonyzhaozh/aloha). - -### Updates: -You can find all scripted/human demo for simulated environments [here](https://drive.google.com/drive/folders/1gPR03v05S1xiInoVJn7G7VJ9pDCnxq9O?usp=share_link). - - -### Repo Structure -- ``imitate_episodes.py`` Train and Evaluate ACT -- ``policy.py`` An adaptor for ACT policy -- ``detr`` Model definitions of ACT, modified from DETR -- ``sim_env.py`` Mujoco + DM_Control environments with joint space control -- ``ee_sim_env.py`` Mujoco + DM_Control environments with EE space control -- ``scripted_policy.py`` Scripted policies for sim environments -- ``constants.py`` Constants shared across files -- ``utils.py`` Utils such as data loading and helper functions -- ``visualize_episodes.py`` Save videos from a .hdf5 dataset - - -### Installation - - conda create -n aloha python=3.8 - conda activate aloha - pip install torchvision - pip install torch - pip install pyquaternion - pip install pyyaml - pip install rospkg - pip install pexpect - pip install mujoco - pip install dm_control - pip install opencv-python - pip install matplotlib - pip install einops - pip install packaging - pip install h5py - pip install ipython - cd act/detr && pip install -e . - -### Example Usages - -To set up a new terminal, run: - - conda activate aloha - cd - -### Simulated experiments - -We use ``sim_transfer_cube_scripted`` task in the examples below. Another option is ``sim_insertion_scripted``. -To generated 50 episodes of scripted data, run: - - python3 record_sim_episodes.py \ - --task_name sim_transfer_cube_scripted \ - --dataset_dir \ - --num_episodes 50 - -To can add the flag ``--onscreen_render`` to see real-time rendering. -To visualize the episode after it is collected, run - - python3 visualize_episodes.py --dataset_dir --episode_idx 0 - -To train ACT: - - # Transfer Cube task - python3 imitate_episodes.py \ - --task_name sim_transfer_cube_scripted \ - --ckpt_dir \ - --policy_class ACT --kl_weight 10 --chunk_size 100 --hidden_dim 512 --batch_size 8 --dim_feedforward 3200 \ - --num_epochs 2000 --lr 1e-5 \ - --seed 0 - - -To evaluate the policy, run the same command but add ``--eval``. This loads the best validation checkpoint. -The success rate should be around 90% for transfer cube, and around 50% for insertion. -To enable temporal ensembling, add flag ``--temporal_agg``. -Videos will be saved to ```` for each rollout. -You can also add ``--onscreen_render`` to see real-time rendering during evaluation. - -For real-world data where things can be harder to model, train for at least 5000 epochs or 3-4 times the length after the loss has plateaued. -Please refer to [tuning tips](https://docs.google.com/document/d/1FVIZfoALXg_ZkYKaYVh-qOlaXveq5CtvJHXkY25eYhs/edit?usp=sharing) for more info. - diff --git a/act/assets/bimanual_viperx_ee_insertion.xml b/act/assets/bimanual_viperx_ee_insertion.xml deleted file mode 100644 index 700aaac5..00000000 --- a/act/assets/bimanual_viperx_ee_insertion.xml +++ /dev/null @@ -1,59 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file 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- - \ No newline at end of file diff --git a/act/assets/vx300s_right.xml b/act/assets/vx300s_right.xml deleted file mode 100644 index 2c6f007c..00000000 --- a/act/assets/vx300s_right.xml +++ /dev/null @@ -1,59 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/act/conda_env.yaml b/act/conda_env.yaml deleted file mode 100644 index 0f44d6b0..00000000 --- a/act/conda_env.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: aloha -channels: - - pytorch - - nvidia - - conda-forge -dependencies: - - python=3.9 - - pip=23.0.1 - - pytorch=2.0.0 - - torchvision=0.15.0 - - pytorch-cuda=11.8 - - pyquaternion=0.9.9 - - pyyaml=6.0 - - rospkg=1.5.0 - - pexpect=4.8.0 - - mujoco=2.3.3 - - dm_control=1.0.9 - - py-opencv=4.7.0 - - matplotlib=3.7.1 - - einops=0.6.0 - - packaging=23.0 - - h5py=3.8.0 - - ipython=8.12.0 diff --git a/act/constants.py b/act/constants.py deleted file mode 100644 index f445350a..00000000 --- a/act/constants.py +++ /dev/null @@ -1,76 +0,0 @@ -import pathlib - -### Task parameters -DATA_DIR = '' -SIM_TASK_CONFIGS = { - 'sim_transfer_cube_scripted':{ - 'dataset_dir': DATA_DIR + '/sim_transfer_cube_scripted', - 'num_episodes': 50, - 'episode_len': 400, - 'camera_names': ['top'] - }, - - 'sim_transfer_cube_human':{ - 'dataset_dir': DATA_DIR + '/sim_transfer_cube_human', - 'num_episodes': 50, - 'episode_len': 400, - 'camera_names': ['top'] - }, - - 'sim_insertion_scripted': { - 'dataset_dir': DATA_DIR + '/sim_insertion_scripted', - 'num_episodes': 50, - 'episode_len': 400, - 'camera_names': ['top'] - }, - - 'sim_insertion_human': { - 'dataset_dir': DATA_DIR + '/sim_insertion_human', - 'num_episodes': 50, - 'episode_len': 500, - 'camera_names': ['top'] - }, -} - -### Simulation envs fixed constants -DT = 0.02 -JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] -START_ARM_POSE = [0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239, 0, -0.96, 1.16, 0, -0.3, 0, 0.02239, -0.02239] - -XML_DIR = str(pathlib.Path(__file__).parent.resolve()) + '/assets/' # note: absolute path - -# Left finger position limits (qpos[7]), right_finger = -1 * left_finger -MASTER_GRIPPER_POSITION_OPEN = 0.02417 -MASTER_GRIPPER_POSITION_CLOSE = 0.01244 -PUPPET_GRIPPER_POSITION_OPEN = 0.05800 -PUPPET_GRIPPER_POSITION_CLOSE = 0.01844 - -# Gripper joint limits (qpos[6]) -MASTER_GRIPPER_JOINT_OPEN = 0.3083 -MASTER_GRIPPER_JOINT_CLOSE = -0.6842 -PUPPET_GRIPPER_JOINT_OPEN = 1.4910 -PUPPET_GRIPPER_JOINT_CLOSE = -0.6213 - -############################ Helper functions ############################ - -MASTER_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_POSITION_CLOSE) / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) -PUPPET_GRIPPER_POSITION_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_POSITION_CLOSE) / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) -MASTER_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) + MASTER_GRIPPER_POSITION_CLOSE -PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) + PUPPET_GRIPPER_POSITION_CLOSE -MASTER2PUPPET_POSITION_FN = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(MASTER_GRIPPER_POSITION_NORMALIZE_FN(x)) - -MASTER_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) -PUPPET_GRIPPER_JOINT_NORMALIZE_FN = lambda x: (x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) -MASTER_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE -PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN = lambda x: x * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE -MASTER2PUPPET_JOINT_FN = lambda x: PUPPET_GRIPPER_JOINT_UNNORMALIZE_FN(MASTER_GRIPPER_JOINT_NORMALIZE_FN(x)) - -MASTER_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (MASTER_GRIPPER_POSITION_OPEN - MASTER_GRIPPER_POSITION_CLOSE) -PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN = lambda x: x / (PUPPET_GRIPPER_POSITION_OPEN - PUPPET_GRIPPER_POSITION_CLOSE) - -MASTER_POS2JOINT = lambda x: MASTER_GRIPPER_POSITION_NORMALIZE_FN(x) * (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE) + MASTER_GRIPPER_JOINT_CLOSE -MASTER_JOINT2POS = lambda x: MASTER_GRIPPER_POSITION_UNNORMALIZE_FN((x - MASTER_GRIPPER_JOINT_CLOSE) / (MASTER_GRIPPER_JOINT_OPEN - MASTER_GRIPPER_JOINT_CLOSE)) -PUPPET_POS2JOINT = lambda x: PUPPET_GRIPPER_POSITION_NORMALIZE_FN(x) * (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE) + PUPPET_GRIPPER_JOINT_CLOSE -PUPPET_JOINT2POS = lambda x: PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN((x - PUPPET_GRIPPER_JOINT_CLOSE) / (PUPPET_GRIPPER_JOINT_OPEN - PUPPET_GRIPPER_JOINT_CLOSE)) - -MASTER_GRIPPER_JOINT_MID = (MASTER_GRIPPER_JOINT_OPEN + MASTER_GRIPPER_JOINT_CLOSE)/2 diff --git a/act/detr/LICENSE b/act/detr/LICENSE deleted file mode 100644 index b1395e94..00000000 --- a/act/detr/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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All Rights Reserved -import argparse -from pathlib import Path - -import numpy as np -import torch -from .models import build_ACT_model, build_CNNMLP_model - -import IPython -e = IPython.embed - -def get_args_parser(): - parser = argparse.ArgumentParser('Set transformer detector', add_help=False) - parser.add_argument('--lr', default=1e-4, type=float) # will be overridden - parser.add_argument('--lr_backbone', default=1e-5, type=float) # will be overridden - parser.add_argument('--batch_size', default=2, type=int) # not used - parser.add_argument('--weight_decay', default=1e-4, type=float) - parser.add_argument('--epochs', default=300, type=int) # not used - parser.add_argument('--lr_drop', default=200, type=int) # not used - parser.add_argument('--clip_max_norm', default=0.1, type=float, # not used - help='gradient clipping max norm') - - # Model parameters - # * Backbone - parser.add_argument('--backbone', default='resnet18', type=str, # will be overridden - help="Name of the convolutional backbone to use") - parser.add_argument('--dilation', action='store_true', - help="If true, we replace stride with dilation in the last convolutional block (DC5)") - parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'), - help="Type of positional embedding to use on top of the image features") - parser.add_argument('--camera_names', default=[], type=list, # will be overridden - help="A list of camera names") - - # * Transformer - parser.add_argument('--enc_layers', default=4, type=int, # will be overridden - help="Number of encoding layers in the transformer") - parser.add_argument('--dec_layers', default=6, type=int, # will be overridden - help="Number of decoding layers in the transformer") - parser.add_argument('--dim_feedforward', default=2048, type=int, # will be overridden - help="Intermediate size of the feedforward layers in the transformer blocks") - parser.add_argument('--hidden_dim', default=256, type=int, # will be overridden - help="Size of the embeddings (dimension of the transformer)") - parser.add_argument('--dropout', default=0.1, type=float, - help="Dropout applied in the transformer") - parser.add_argument('--nheads', default=8, type=int, # will be overridden - help="Number of attention heads inside the transformer's attentions") - parser.add_argument('--num_queries', default=400, type=int, # will be overridden - help="Number of query slots") - parser.add_argument('--pre_norm', action='store_true') - - # * Segmentation - parser.add_argument('--masks', action='store_true', - help="Train segmentation head if the flag is provided") - - parser.add_argument('--a_dim', default=-1, type=float, - help="Action dim") - parser.add_argument('--latent_dim', default=-1, type=float, - help="Latent dim") - parser.add_argument('--state_dim', default=-1, type=float, - help="State dim") - - return parser - - -def build_ACT_model_and_optimizer(args_override): - parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) - args, _ = parser.parse_known_args() - - for k, v in args_override.items(): - setattr(args, k, v) - - model = build_ACT_model(args) - model.cuda() - - param_dicts = [ - {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, - { - "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], - "lr": args.lr_backbone, - }, - ] - optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, - weight_decay=args.weight_decay) - - return model, optimizer - - -def build_CNNMLP_model_and_optimizer(args_override): - parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()]) - args = parser.parse_args() - - for k, v in args_override.items(): - setattr(args, k, v) - - model = build_CNNMLP_model(args) - model.cuda() - - param_dicts = [ - {"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]}, - { - "params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad], - "lr": args.lr_backbone, - }, - ] - optimizer = torch.optim.AdamW(param_dicts, lr=args.lr, - weight_decay=args.weight_decay) - - return model, optimizer - diff --git a/act/detr/models/__init__.py b/act/detr/models/__init__.py deleted file mode 100644 index cc78db10..00000000 --- a/act/detr/models/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -from .detr_vae import build as build_vae -from .detr_vae import build_cnnmlp as build_cnnmlp - -def build_ACT_model(args): - return build_vae(args) - -def build_CNNMLP_model(args): - return build_cnnmlp(args) \ No newline at end of file diff --git a/act/detr/models/backbone.py b/act/detr/models/backbone.py deleted file mode 100644 index f28637ea..00000000 --- a/act/detr/models/backbone.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Backbone modules. -""" -from collections import OrderedDict - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter -from typing import Dict, List - -from util.misc import NestedTensor, is_main_process - -from .position_encoding import build_position_encoding - -import IPython -e = IPython.embed - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other policy_models than torchvision.policy_models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs): - num_batches_tracked_key = prefix + 'num_batches_tracked' - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - - def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): - super().__init__() - # for name, parameter in backbone.named_parameters(): # only train later layers # TODO do we want this? - # if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: - # parameter.requires_grad_(False) - if return_interm_layers: - return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - else: - return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor): - xs = self.body(tensor) - return xs - # out: Dict[str, NestedTensor] = {} - # for name, x in xs.items(): - # m = tensor_list.mask - # assert m is not None - # mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - # out[name] = NestedTensor(x, mask) - # return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - def __init__(self, name: str, - train_backbone: bool, - return_interm_layers: bool, - dilation: bool): - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) # pretrained # TODO do we want frozen batch_norm?? - num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - super().__init__(backbone, train_backbone, num_channels, return_interm_layers) - - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.dtype)) - - return out, pos - - -def build_backbone(args): - position_embedding = build_position_encoding(args) - train_backbone = args.lr_backbone > 0 - return_interm_layers = args.masks - backbone = Backbone(args.backbone, train_backbone, return_interm_layers, args.dilation) - model = Joiner(backbone, position_embedding) - model.num_channels = backbone.num_channels - return model diff --git a/act/detr/models/detr_vae.py b/act/detr/models/detr_vae.py deleted file mode 100644 index dbde581e..00000000 --- a/act/detr/models/detr_vae.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR model and criterion classes. -""" -import torch -from torch import nn -from torch.autograd import Variable -from .backbone import build_backbone -from .transformer import build_transformer, TransformerEncoder, TransformerEncoderLayer - -import numpy as np - -import IPython -e = IPython.embed - - -def reparametrize(mu, logvar): - std = logvar.div(2).exp() - eps = Variable(std.data.new(std.size()).normal_()) - return mu + std * eps - - -def get_sinusoid_encoding_table(n_position, d_hid): - def get_position_angle_vec(position): - return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] - - sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) - sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i - sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 - - return torch.FloatTensor(sinusoid_table).unsqueeze(0) - - -class DETRVAE(nn.Module): - """ This is the DETR module that performs object detection """ - def __init__(self, backbones, transformer, encoder, latent_dim, a_dim, state_dim, num_queries, camera_names): - """ Initializes the model. - Parameters: - backbones: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - state_dim: robot state dimension of the environment - num_queries: number of object queries, ie detection slot. This is the maximal number of objects - DETR can detect in a single image. For COCO, we recommend 100 queries. - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - """ - super().__init__() - self.action_dim = a_dim - self.latent_dim = latent_dim - self.state_dim = state_dim - - self.num_queries = num_queries - self.camera_names = camera_names - self.transformer = transformer - self.encoder = encoder - hidden_dim = transformer.d_model - self.action_head = nn.Linear(hidden_dim, self.action_dim) - self.is_pad_head = nn.Linear(hidden_dim, 1) - self.query_embed = nn.Embedding(num_queries, hidden_dim) - if backbones is not None: - self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1) - self.backbones = nn.ModuleList(backbones) - self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) - else: - # input_dim = 14 + 7 # robot_state + env_state - self.input_proj_robot_state = nn.Linear(state_dim, hidden_dim) - self.input_proj_env_state = nn.Linear(10, hidden_dim) # TODO not used in robomimic - self.pos = torch.nn.Embedding(2, hidden_dim) - self.backbones = None - - # encoder extra parameters - self.latent_dim = self.latent_dim # final size of latent z # TODO tune - self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding - self.encoder_action_proj = nn.Linear(self.action_dim, hidden_dim) # project action to embedding - self.encoder_joint_proj = nn.Linear(state_dim, hidden_dim) # project qpos to embedding - self.latent_proj = nn.Linear(hidden_dim, self.latent_dim*2) # project hidden state to latent std, var - self.register_buffer('pos_table', get_sinusoid_encoding_table(1+1+num_queries, hidden_dim)) # [CLS], qpos, a_seq - - # decoder extra parameters - self.latent_out_proj = nn.Linear(self.latent_dim, hidden_dim) # project latent sample to embedding - self.additional_pos_embed = nn.Embedding(2, hidden_dim) # learned position embedding for proprio and latent - - def forward(self, qpos, image, env_state, actions=None, is_pad=None): - """ - qpos: batch, qpos_dim - image: batch, num_cam, channel, height, width - env_state: None - actions: batch, seq, action_dim - """ - is_training = actions is not None # train or val - bs, _ = qpos.shape - ### Obtain latent z from action sequence - if is_training: - # project action sequence to embedding dim, and concat with a CLS token - action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim) - qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim) - qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim) - cls_embed = self.cls_embed.weight # (1, hidden_dim) - cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim) - encoder_input = torch.cat([cls_embed, qpos_embed, action_embed], axis=1) # (bs, seq+2, hidden_dim) - encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim) - # do not mask cls token - cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding - is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1) - # obtain position embedding - pos_embed = self.pos_table.clone().detach() - pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim) - # query model - encoder_output = self.encoder(encoder_input, pos=pos_embed, src_key_padding_mask=is_pad) - encoder_output = encoder_output[0] # take cls output only - latent_info = self.latent_proj(encoder_output) - mu = latent_info[:, :self.latent_dim] - logvar = latent_info[:, self.latent_dim:] - latent_sample = reparametrize(mu, logvar) - latent_input = self.latent_out_proj(latent_sample) - else: - mu = logvar = None - latent_sample = torch.zeros([bs, self.latent_dim], dtype=torch.float32).to(qpos.device) - latent_input = self.latent_out_proj(latent_sample) - - if self.backbones is not None: - # Image observation features and position embeddings - all_cam_features = [] - all_cam_pos = [] - for cam_id, cam_name in enumerate(self.camera_names): - features, pos = self.backbones[cam_id](image[:, cam_id]) - features = features[0] # take the last layer feature - pos = pos[0] - all_cam_features.append(self.input_proj(features)) - all_cam_pos.append(pos) - # proprioception features - proprio_input = self.input_proj_robot_state(qpos) - # fold camera dimension into width dimension - src = torch.cat(all_cam_features, axis=3) - pos = torch.cat(all_cam_pos, axis=3) - hs = self.transformer(src, None, self.query_embed.weight, pos, latent_input, proprio_input, self.additional_pos_embed.weight)[0] - else: - qpos = self.input_proj_robot_state(qpos).unsqueeze(dim=1) - env_state = self.input_proj_env_state(env_state).unsqueeze(dim=1) - transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2 - hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0] - a_hat = self.action_head(hs) - is_pad_hat = self.is_pad_head(hs) - return a_hat, is_pad_hat, [mu, logvar] - - - -class CNNMLP(nn.Module): - def __init__(self, backbones, state_dim, camera_names): - """ Initializes the model. - Parameters: - backbones: torch module of the backbone to be used. See backbone.py - transformer: torch module of the transformer architecture. See transformer.py - state_dim: robot state dimension of the environment - num_queries: number of object queries, ie detection slot. This is the maximal number of objects - DETR can detect in a single image. For COCO, we recommend 100 queries. - aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used. - """ - super().__init__() - self.camera_names = camera_names - self.action_head = nn.Linear(1000, state_dim) # TODO add more - if backbones is not None: - self.backbones = nn.ModuleList(backbones) - backbone_down_projs = [] - for backbone in backbones: - down_proj = nn.Sequential( - nn.Conv2d(backbone.num_channels, 128, kernel_size=5), - nn.Conv2d(128, 64, kernel_size=5), - nn.Conv2d(64, 32, kernel_size=5) - ) - backbone_down_projs.append(down_proj) - self.backbone_down_projs = nn.ModuleList(backbone_down_projs) - - mlp_in_dim = 768 * len(backbones) + 14 - self.mlp = mlp(input_dim=mlp_in_dim, hidden_dim=1024, output_dim=14, hidden_depth=2) - else: - raise NotImplementedError - - def forward(self, qpos, image, env_state, actions=None): - """ - qpos: batch, qpos_dim - image: batch, num_cam, channel, height, width - env_state: None - actions: batch, seq, action_dim - """ - is_training = actions is not None # train or val - bs, _ = qpos.shape - # Image observation features and position embeddings - all_cam_features = [] - for cam_id, cam_name in enumerate(self.camera_names): - features, pos = self.backbones[cam_id](image[:, cam_id]) - features = features[0] # take the last layer feature - pos = pos[0] # not used - all_cam_features.append(self.backbone_down_projs[cam_id](features)) - # flatten everything - flattened_features = [] - for cam_feature in all_cam_features: - flattened_features.append(cam_feature.reshape([bs, -1])) - flattened_features = torch.cat(flattened_features, axis=1) # 768 each - features = torch.cat([flattened_features, qpos], axis=1) # qpos: 14 - a_hat = self.mlp(features) - return a_hat - - -def mlp(input_dim, hidden_dim, output_dim, hidden_depth): - if hidden_depth == 0: - mods = [nn.Linear(input_dim, output_dim)] - else: - mods = [nn.Linear(input_dim, hidden_dim), nn.ReLU(inplace=True)] - for i in range(hidden_depth - 1): - mods += [nn.Linear(hidden_dim, hidden_dim), nn.ReLU(inplace=True)] - mods.append(nn.Linear(hidden_dim, output_dim)) - trunk = nn.Sequential(*mods) - return trunk - - -def build_encoder(args): - d_model = args.hidden_dim # 256 - dropout = args.dropout # 0.1 - nhead = args.nheads # 8 - dim_feedforward = args.dim_feedforward # 2048 - num_encoder_layers = args.enc_layers # 4 # TODO shared with VAE decoder - normalize_before = args.pre_norm # False - activation = "relu" - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - return encoder - - -def build(args): - - # From state - # backbone = None # from state for now, no need for conv nets - # From image - backbones = [] - for _ in args.camera_names: - backbone = build_backbone(args) - backbones.append(backbone) - - transformer = build_transformer(args) - - encoder = build_encoder(args) - - model = DETRVAE( - backbones, - transformer, - encoder, - latent_dim=args.latent_dim, - a_dim=args.a_dim, - state_dim=args.state_dim, - num_queries=args.num_queries, - camera_names=args.camera_names, - ) - - n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("number of parameters: %.2fM" % (n_parameters/1e6,)) - - return model - -def build_cnnmlp(args): - state_dim = 14 # TODO hardcode - - # From state - # backbone = None # from state for now, no need for conv nets - # From image - backbones = [] - for _ in args.camera_names: - backbone = build_backbone(args) - backbones.append(backbone) - - model = CNNMLP( - backbones, - state_dim=state_dim, - camera_names=args.camera_names, - ) - - n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) - print("number of parameters: %.2fM" % (n_parameters/1e6,)) - - return model - diff --git a/act/detr/models/position_encoding.py b/act/detr/models/position_encoding.py deleted file mode 100644 index 209d9171..00000000 --- a/act/detr/models/position_encoding.py +++ /dev/null @@ -1,93 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Various positional encodings for the transformer. -""" -import math -import torch -from torch import nn - -from util.misc import NestedTensor - -import IPython -e = IPython.embed - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, tensor): - x = tensor - # mask = tensor_list.mask - # assert mask is not None - # not_mask = ~mask - - not_mask = torch.ones_like(x[0, [0]]) - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - - -class PositionEmbeddingLearned(nn.Module): - """ - Absolute pos embedding, learned. - """ - def __init__(self, num_pos_feats=256): - super().__init__() - self.row_embed = nn.Embedding(50, num_pos_feats) - self.col_embed = nn.Embedding(50, num_pos_feats) - self.reset_parameters() - - def reset_parameters(self): - nn.init.uniform_(self.row_embed.weight) - nn.init.uniform_(self.col_embed.weight) - - def forward(self, tensor_list: NestedTensor): - x = tensor_list.tensors - h, w = x.shape[-2:] - i = torch.arange(w, device=x.device) - j = torch.arange(h, device=x.device) - x_emb = self.col_embed(i) - y_emb = self.row_embed(j) - pos = torch.cat([ - x_emb.unsqueeze(0).repeat(h, 1, 1), - y_emb.unsqueeze(1).repeat(1, w, 1), - ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) - return pos - - -def build_position_encoding(args): - N_steps = args.hidden_dim // 2 - if args.position_embedding in ('v2', 'sine'): - # TODO find a better way of exposing other arguments - position_embedding = PositionEmbeddingSine(N_steps, normalize=True) - elif args.position_embedding in ('v3', 'learned'): - position_embedding = PositionEmbeddingLearned(N_steps) - else: - raise ValueError(f"not supported {args.position_embedding}") - - return position_embedding diff --git a/act/detr/models/transformer.py b/act/detr/models/transformer.py deleted file mode 100644 index f38afd0e..00000000 --- a/act/detr/models/transformer.py +++ /dev/null @@ -1,314 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -DETR Transformer class. - -Copy-paste from torch.nn.Transformer with modifications: - * positional encodings are passed in MHattention - * extra LN at the end of encoder is removed - * decoder returns a stack of activations from all decoding layers -""" -import copy -from typing import Optional, List - -import torch -import torch.nn.functional as F -from torch import nn, Tensor - -import IPython -e = IPython.embed - -class Transformer(nn.Module): - - def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, - num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False, - return_intermediate_dec=False): - super().__init__() - - encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - encoder_norm = nn.LayerNorm(d_model) if normalize_before else None - self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) - - decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, - dropout, activation, normalize_before) - decoder_norm = nn.LayerNorm(d_model) - self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, - return_intermediate=return_intermediate_dec) - - self._reset_parameters() - - self.d_model = d_model - self.nhead = nhead - - def _reset_parameters(self): - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward(self, src, mask, query_embed, pos_embed, latent_input=None, proprio_input=None, additional_pos_embed=None): - # TODO flatten only when input has H and W - if len(src.shape) == 4: # has H and W - # flatten NxCxHxW to HWxNxC - bs, c, h, w = src.shape - src = src.flatten(2).permute(2, 0, 1) - pos_embed = pos_embed.flatten(2).permute(2, 0, 1).repeat(1, bs, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - # mask = mask.flatten(1) - - additional_pos_embed = additional_pos_embed.unsqueeze(1).repeat(1, bs, 1) # seq, bs, dim - pos_embed = torch.cat([additional_pos_embed, pos_embed], axis=0) - - addition_input = torch.stack([latent_input, proprio_input], axis=0) - src = torch.cat([addition_input, src], axis=0) - else: - assert len(src.shape) == 3 - # flatten NxHWxC to HWxNxC - bs, hw, c = src.shape - src = src.permute(1, 0, 2) - pos_embed = pos_embed.unsqueeze(1).repeat(1, bs, 1) - query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1) - - tgt = torch.zeros_like(query_embed) - memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) - hs = self.decoder(tgt, memory, memory_key_padding_mask=mask, - pos=pos_embed, query_pos=query_embed) - hs = hs.transpose(1, 2) - return hs - -class TransformerEncoder(nn.Module): - - def __init__(self, encoder_layer, num_layers, norm=None): - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward(self, src, - mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - output = src - - for layer in self.layers: - output = layer(output, src_mask=mask, - src_key_padding_mask=src_key_padding_mask, pos=pos) - - if self.norm is not None: - output = self.norm(output) - - return output - - -class TransformerDecoder(nn.Module): - - def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - self.return_intermediate = return_intermediate - - def forward(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - output = tgt - - intermediate = [] - - for layer in self.layers: - output = layer(output, memory, tgt_mask=tgt_mask, - memory_mask=memory_mask, - tgt_key_padding_mask=tgt_key_padding_mask, - memory_key_padding_mask=memory_key_padding_mask, - pos=pos, query_pos=query_pos) - if self.return_intermediate: - intermediate.append(self.norm(output)) - - if self.norm is not None: - output = self.norm(output) - if self.return_intermediate: - intermediate.pop() - intermediate.append(output) - - if self.return_intermediate: - return torch.stack(intermediate) - - return output.unsqueeze(0) - - -class TransformerEncoderLayer(nn.Module): - - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, - src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(src, pos) - src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src - - def forward_pre(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - src2 = self.norm1(src) - q = k = self.with_pos_embed(src2, pos) - src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src2 = self.norm2(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) - src = src + self.dropout2(src2) - return src - - def forward(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(src, src_mask, src_key_padding_mask, pos) - return self.forward_post(src, src_mask, src_key_padding_mask, pos) - - -class TransformerDecoderLayer(nn.Module): - - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super().__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.norm3 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - self.dropout3 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward_post(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - q = k = self.with_pos_embed(tgt, query_pos) - tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt = self.norm1(tgt) - tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] - tgt = tgt + self.dropout2(tgt2) - tgt = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) - tgt = tgt + self.dropout3(tgt2) - tgt = self.norm3(tgt) - return tgt - - def forward_pre(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - tgt2 = self.norm2(tgt) - tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), - key=self.with_pos_embed(memory, pos), - value=memory, attn_mask=memory_mask, - key_padding_mask=memory_key_padding_mask)[0] - tgt = tgt + self.dropout2(tgt2) - tgt2 = self.norm3(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout3(tgt2) - return tgt - - def forward(self, tgt, memory, - tgt_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - memory_key_padding_mask: Optional[Tensor] = None, - pos: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - if self.normalize_before: - return self.forward_pre(tgt, memory, tgt_mask, memory_mask, - tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) - return self.forward_post(tgt, memory, tgt_mask, memory_mask, - tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def build_transformer(args): - return Transformer( - d_model=args.hidden_dim, - dropout=args.dropout, - nhead=args.nheads, - dim_feedforward=args.dim_feedforward, - num_encoder_layers=args.enc_layers, - num_decoder_layers=args.dec_layers, - normalize_before=args.pre_norm, - return_intermediate_dec=True, - ) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/act/detr/setup.py b/act/detr/setup.py deleted file mode 100644 index 55d18c0d..00000000 --- a/act/detr/setup.py +++ /dev/null @@ -1,10 +0,0 @@ -from distutils.core import setup -from setuptools import find_packages - -setup( - name='detr', - version='0.0.0', - packages=find_packages(), - license='MIT License', - long_description=open('README.md').read(), -) \ No newline at end of file diff --git a/act/detr/util/__init__.py b/act/detr/util/__init__.py deleted file mode 100644 index 168f9979..00000000 --- a/act/detr/util/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved diff --git a/act/detr/util/box_ops.py b/act/detr/util/box_ops.py deleted file mode 100644 index 9c088e5b..00000000 --- a/act/detr/util/box_ops.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Utilities for bounding box manipulation and GIoU. -""" -import torch -from torchvision.ops.boxes import box_area - - -def box_cxcywh_to_xyxy(x): - x_c, y_c, w, h = x.unbind(-1) - b = [(x_c - 0.5 * w), (y_c - 0.5 * h), - (x_c + 0.5 * w), (y_c + 0.5 * h)] - return torch.stack(b, dim=-1) - - -def box_xyxy_to_cxcywh(x): - x0, y0, x1, y1 = x.unbind(-1) - b = [(x0 + x1) / 2, (y0 + y1) / 2, - (x1 - x0), (y1 - y0)] - return torch.stack(b, dim=-1) - - -# modified from torchvision to also return the union -def box_iou(boxes1, boxes2): - area1 = box_area(boxes1) - area2 = box_area(boxes2) - - lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] - rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] - - wh = (rb - lt).clamp(min=0) # [N,M,2] - inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] - - union = area1[:, None] + area2 - inter - - iou = inter / union - return iou, union - - -def generalized_box_iou(boxes1, boxes2): - """ - Generalized IoU from https://giou.stanford.edu/ - - The boxes should be in [x0, y0, x1, y1] format - - Returns a [N, M] pairwise matrix, where N = len(boxes1) - and M = len(boxes2) - """ - # degenerate boxes gives inf / nan results - # so do an early check - assert (boxes1[:, 2:] >= boxes1[:, :2]).all() - assert (boxes2[:, 2:] >= boxes2[:, :2]).all() - iou, union = box_iou(boxes1, boxes2) - - lt = torch.min(boxes1[:, None, :2], boxes2[:, :2]) - rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) - - wh = (rb - lt).clamp(min=0) # [N,M,2] - area = wh[:, :, 0] * wh[:, :, 1] - - return iou - (area - union) / area - - -def masks_to_boxes(masks): - """Compute the bounding boxes around the provided masks - - The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. - - Returns a [N, 4] tensors, with the boxes in xyxy format - """ - if masks.numel() == 0: - return torch.zeros((0, 4), device=masks.device) - - h, w = masks.shape[-2:] - - y = torch.arange(0, h, dtype=torch.float) - x = torch.arange(0, w, dtype=torch.float) - y, x = torch.meshgrid(y, x) - - x_mask = (masks * x.unsqueeze(0)) - x_max = x_mask.flatten(1).max(-1)[0] - x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] - - y_mask = (masks * y.unsqueeze(0)) - y_max = y_mask.flatten(1).max(-1)[0] - y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0] - - return torch.stack([x_min, y_min, x_max, y_max], 1) diff --git a/act/detr/util/misc.py b/act/detr/util/misc.py deleted file mode 100644 index dfa9fb5b..00000000 --- a/act/detr/util/misc.py +++ /dev/null @@ -1,468 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Misc functions, including distributed helpers. - -Mostly copy-paste from torchvision references. -""" -import os -import subprocess -import time -from collections import defaultdict, deque -import datetime -import pickle -from packaging import version -from typing import Optional, List - -import torch -import torch.distributed as dist -from torch import Tensor - -# needed due to empty tensor bug in pytorch and torchvision 0.5 -import torchvision -if version.parse(torchvision.__version__) < version.parse('0.7'): - from torchvision.ops import _new_empty_tensor - from torchvision.ops.misc import _output_size - - -class SmoothedValue(object): - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20, fmt=None): - if fmt is None: - fmt = "{median:.4f} ({global_avg:.4f})" - self.deque = deque(maxlen=window_size) - self.total = 0.0 - self.count = 0 - self.fmt = fmt - - def update(self, value, n=1): - self.deque.append(value) - self.count += n - self.total += value * n - - def synchronize_between_processes(self): - """ - Warning: does not synchronize the deque! - """ - if not is_dist_avail_and_initialized(): - return - t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') - dist.barrier() - dist.all_reduce(t) - t = t.tolist() - self.count = int(t[0]) - self.total = t[1] - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() - - @property - def avg(self): - d = torch.tensor(list(self.deque), dtype=torch.float32) - return d.mean().item() - - @property - def global_avg(self): - return self.total / self.count - - @property - def max(self): - return max(self.deque) - - @property - def value(self): - return self.deque[-1] - - def __str__(self): - return self.fmt.format( - median=self.median, - avg=self.avg, - global_avg=self.global_avg, - max=self.max, - value=self.value) - - -def all_gather(data): - """ - Run all_gather on arbitrary picklable data (not necessarily tensors) - Args: - data: any picklable object - Returns: - list[data]: list of data gathered from each rank - """ - world_size = get_world_size() - if world_size == 1: - return [data] - - # serialized to a Tensor - buffer = pickle.dumps(data) - storage = torch.ByteStorage.from_buffer(buffer) - tensor = torch.ByteTensor(storage).to("cuda") - - # obtain Tensor size of each rank - local_size = torch.tensor([tensor.numel()], device="cuda") - size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)] - dist.all_gather(size_list, local_size) - size_list = [int(size.item()) for size in size_list] - max_size = max(size_list) - - # receiving Tensor from all ranks - # we pad the tensor because torch all_gather does not support - # gathering tensors of different shapes - tensor_list = [] - for _ in size_list: - tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda")) - if local_size != max_size: - padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda") - tensor = torch.cat((tensor, padding), dim=0) - dist.all_gather(tensor_list, tensor) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - buffer = tensor.cpu().numpy().tobytes()[:size] - data_list.append(pickle.loads(buffer)) - - return data_list - - -def reduce_dict(input_dict, average=True): - """ - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dict with the same fields as - input_dict, after reduction. - """ - world_size = get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.all_reduce(values) - if average: - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict - - -class MetricLogger(object): - def __init__(self, delimiter="\t"): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - if attr in self.__dict__: - return self.__dict__[attr] - raise AttributeError("'{}' object has no attribute '{}'".format( - type(self).__name__, attr)) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append( - "{}: {}".format(name, str(meter)) - ) - return self.delimiter.join(loss_str) - - def synchronize_between_processes(self): - for meter in self.meters.values(): - meter.synchronize_between_processes() - - def add_meter(self, name, meter): - self.meters[name] = meter - - def log_every(self, iterable, print_freq, header=None): - i = 0 - if not header: - header = '' - start_time = time.time() - end = time.time() - iter_time = SmoothedValue(fmt='{avg:.4f}') - data_time = SmoothedValue(fmt='{avg:.4f}') - space_fmt = ':' + str(len(str(len(iterable)))) + 'd' - if torch.cuda.is_available(): - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}', - 'max mem: {memory:.0f}' - ]) - else: - log_msg = self.delimiter.join([ - header, - '[{0' + space_fmt + '}/{1}]', - 'eta: {eta}', - '{meters}', - 'time: {time}', - 'data: {data}' - ]) - MB = 1024.0 * 1024.0 - for obj in iterable: - data_time.update(time.time() - end) - yield obj - iter_time.update(time.time() - end) - if i % print_freq == 0 or i == len(iterable) - 1: - eta_seconds = iter_time.global_avg * (len(iterable) - i) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - if torch.cuda.is_available(): - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time), - memory=torch.cuda.max_memory_allocated() / MB)) - else: - print(log_msg.format( - i, len(iterable), eta=eta_string, - meters=str(self), - time=str(iter_time), data=str(data_time))) - i += 1 - end = time.time() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print('{} Total time: {} ({:.4f} s / it)'.format( - header, total_time_str, total_time / len(iterable))) - - -def get_sha(): - cwd = os.path.dirname(os.path.abspath(__file__)) - - def _run(command): - return subprocess.check_output(command, cwd=cwd).decode('ascii').strip() - sha = 'N/A' - diff = "clean" - branch = 'N/A' - try: - sha = _run(['git', 'rev-parse', 'HEAD']) - subprocess.check_output(['git', 'diff'], cwd=cwd) - diff = _run(['git', 'diff-index', 'HEAD']) - diff = "has uncommited changes" if diff else "clean" - branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) - except Exception: - pass - message = f"sha: {sha}, status: {diff}, branch: {branch}" - return message - - -def collate_fn(batch): - batch = list(zip(*batch)) - batch[0] = nested_tensor_from_tensor_list(batch[0]) - return tuple(batch) - - -def _max_by_axis(the_list): - # type: (List[List[int]]) -> List[int] - maxes = the_list[0] - for sublist in the_list[1:]: - for index, item in enumerate(sublist): - maxes[index] = max(maxes[index], item) - return maxes - - -class NestedTensor(object): - def __init__(self, tensors, mask: Optional[Tensor]): - self.tensors = tensors - self.mask = mask - - def to(self, device): - # type: (Device) -> NestedTensor # noqa - cast_tensor = self.tensors.to(device) - mask = self.mask - if mask is not None: - assert mask is not None - cast_mask = mask.to(device) - else: - cast_mask = None - return NestedTensor(cast_tensor, cast_mask) - - def decompose(self): - return self.tensors, self.mask - - def __repr__(self): - return str(self.tensors) - - -def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): - # TODO make this more general - if tensor_list[0].ndim == 3: - if torchvision._is_tracing(): - # nested_tensor_from_tensor_list() does not export well to ONNX - # call _onnx_nested_tensor_from_tensor_list() instead - return _onnx_nested_tensor_from_tensor_list(tensor_list) - - # TODO make it support different-sized images - max_size = _max_by_axis([list(img.shape) for img in tensor_list]) - # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list])) - batch_shape = [len(tensor_list)] + max_size - b, c, h, w = batch_shape - dtype = tensor_list[0].dtype - device = tensor_list[0].device - tensor = torch.zeros(batch_shape, dtype=dtype, device=device) - mask = torch.ones((b, h, w), dtype=torch.bool, device=device) - for img, pad_img, m in zip(tensor_list, tensor, mask): - pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - m[: img.shape[1], :img.shape[2]] = False - else: - raise ValueError('not supported') - return NestedTensor(tensor, mask) - - -# _onnx_nested_tensor_from_tensor_list() is an implementation of -# nested_tensor_from_tensor_list() that is supported by ONNX tracing. -@torch.jit.unused -def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor: - max_size = [] - for i in range(tensor_list[0].dim()): - max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64) - max_size.append(max_size_i) - max_size = tuple(max_size) - - # work around for - # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) - # m[: img.shape[1], :img.shape[2]] = False - # which is not yet supported in onnx - padded_imgs = [] - padded_masks = [] - for img in tensor_list: - padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))] - padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0])) - padded_imgs.append(padded_img) - - m = torch.zeros_like(img[0], dtype=torch.int, device=img.device) - padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1) - padded_masks.append(padded_mask.to(torch.bool)) - - tensor = torch.stack(padded_imgs) - mask = torch.stack(padded_masks) - - return NestedTensor(tensor, mask=mask) - - -def setup_for_distributed(is_master): - """ - This function disables printing when not in master process - """ - import builtins as __builtin__ - builtin_print = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop('force', False) - if is_master or force: - builtin_print(*args, **kwargs) - - __builtin__.print = print - - -def is_dist_avail_and_initialized(): - if not dist.is_available(): - return False - if not dist.is_initialized(): - return False - return True - - -def get_world_size(): - if not is_dist_avail_and_initialized(): - return 1 - return dist.get_world_size() - - -def get_rank(): - if not is_dist_avail_and_initialized(): - return 0 - return dist.get_rank() - - -def is_main_process(): - return get_rank() == 0 - - -def save_on_master(*args, **kwargs): - if is_main_process(): - torch.save(*args, **kwargs) - - -def init_distributed_mode(args): - if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ['WORLD_SIZE']) - args.gpu = int(os.environ['LOCAL_RANK']) - elif 'SLURM_PROCID' in os.environ: - args.rank = int(os.environ['SLURM_PROCID']) - args.gpu = args.rank % torch.cuda.device_count() - else: - print('Not using distributed mode') - args.distributed = False - return - - args.distributed = True - - torch.cuda.set_device(args.gpu) - args.dist_backend = 'nccl' - print('| distributed init (rank {}): {}'.format( - args.rank, args.dist_url), flush=True) - torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - torch.distributed.barrier() - setup_for_distributed(args.rank == 0) - - -@torch.no_grad() -def accuracy(output, target, topk=(1,)): - """Computes the precision@k for the specified values of k""" - if target.numel() == 0: - return [torch.zeros([], device=output.device)] - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].view(-1).float().sum(0) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None): - # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor - """ - Equivalent to nn.functional.interpolate, but with support for empty batch sizes. - This will eventually be supported natively by PyTorch, and this - class can go away. - """ - if version.parse(torchvision.__version__) < version.parse('0.7'): - if input.numel() > 0: - return torch.nn.functional.interpolate( - input, size, scale_factor, mode, align_corners - ) - - output_shape = _output_size(2, input, size, scale_factor) - output_shape = list(input.shape[:-2]) + list(output_shape) - return _new_empty_tensor(input, output_shape) - else: - return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/act/detr/util/plot_utils.py b/act/detr/util/plot_utils.py deleted file mode 100644 index 0f24bed0..00000000 --- a/act/detr/util/plot_utils.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Plotting utilities to visualize training logs. -""" -import torch -import pandas as pd -import numpy as np -import seaborn as sns -import matplotlib.pyplot as plt - -from pathlib import Path, PurePath - - -def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): - ''' - Function to plot specific fields from training log(s). Plots both training and test results. - - :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file - - fields = which results to plot from each log file - plots both training and test for each field. - - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots - - log_name = optional, name of log file if different than default 'log.txt'. - - :: Outputs - matplotlib plots of results in fields, color coded for each log file. - - solid lines are training results, dashed lines are test results. - - ''' - func_name = "plot_utils.py::plot_logs" - - # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, - # convert single Path to list to avoid 'not iterable' error - - if not isinstance(logs, list): - if isinstance(logs, PurePath): - logs = [logs] - print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") - else: - raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ - Expect list[Path] or single Path obj, received {type(logs)}") - - # Quality checks - verify valid dir(s), that every item in list is Path object, and that log_name exists in each dir - for i, dir in enumerate(logs): - if not isinstance(dir, PurePath): - raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") - if not dir.exists(): - raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") - # verify log_name exists - fn = Path(dir / log_name) - if not fn.exists(): - print(f"-> missing {log_name}. Have you gotten to Epoch 1 in training?") - print(f"--> full path of missing log file: {fn}") - return - - # load log file(s) and plot - dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] - - fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) - - for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): - for j, field in enumerate(fields): - if field == 'mAP': - coco_eval = pd.DataFrame( - np.stack(df.test_coco_eval_bbox.dropna().values)[:, 1] - ).ewm(com=ewm_col).mean() - axs[j].plot(coco_eval, c=color) - else: - df.interpolate().ewm(com=ewm_col).mean().plot( - y=[f'train_{field}', f'test_{field}'], - ax=axs[j], - color=[color] * 2, - style=['-', '--'] - ) - for ax, field in zip(axs, fields): - ax.legend([Path(p).name for p in logs]) - ax.set_title(field) - - -def plot_precision_recall(files, naming_scheme='iter'): - if naming_scheme == 'exp_id': - # name becomes exp_id - names = [f.parts[-3] for f in files] - elif naming_scheme == 'iter': - names = [f.stem for f in files] - else: - raise ValueError(f'not supported {naming_scheme}') - fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) - for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): - data = torch.load(f) - # precision is n_iou, n_points, n_cat, n_area, max_det - precision = data['precision'] - recall = data['params'].recThrs - scores = data['scores'] - # take precision for all classes, all areas and 100 detections - precision = precision[0, :, :, 0, -1].mean(1) - scores = scores[0, :, :, 0, -1].mean(1) - prec = precision.mean() - rec = data['recall'][0, :, 0, -1].mean() - print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + - f'score={scores.mean():0.3f}, ' + - f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' - ) - axs[0].plot(recall, precision, c=color) - axs[1].plot(recall, scores, c=color) - - axs[0].set_title('Precision / Recall') - axs[0].legend(names) - axs[1].set_title('Scores / Recall') - axs[1].legend(names) - return fig, axs diff --git a/act/ee_sim_env.py b/act/ee_sim_env.py deleted file mode 100644 index 01df2337..00000000 --- a/act/ee_sim_env.py +++ /dev/null @@ -1,267 +0,0 @@ -import numpy as np -import collections -import os - -from constants import DT, XML_DIR, START_ARM_POSE -from constants import PUPPET_GRIPPER_POSITION_CLOSE -from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN -from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN -from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN - -from utils import sample_box_pose, sample_insertion_pose -from dm_control import mujoco -from dm_control.rl import control -from dm_control.suite import base - -import IPython -e = IPython.embed - - -def make_ee_sim_env(task_name): - """ - Environment for simulated robot bi-manual manipulation, with end-effector control. - Action space: [left_arm_pose (7), # position and quaternion for end effector - left_gripper_positions (1), # normalized gripper position (0: close, 1: open) - right_arm_pose (7), # position and quaternion for end effector - right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) - - Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position - left_gripper_position (1), # normalized gripper position (0: close, 1: open) - right_arm_qpos (6), # absolute joint position - right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) - "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) - left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) - right_arm_qvel (6), # absolute joint velocity (rad) - right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) - "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' - """ - if 'sim_transfer_cube' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_transfer_cube.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = TransferCubeEETask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - elif 'sim_insertion' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_ee_insertion.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = InsertionEETask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - else: - raise NotImplementedError - return env - -class BimanualViperXEETask(base.Task): - def __init__(self, random=None): - super().__init__(random=random) - - def before_step(self, action, physics): - a_len = len(action) // 2 - action_left = action[:a_len] - action_right = action[a_len:] - - # set mocap position and quat - # left - np.copyto(physics.data.mocap_pos[0], action_left[:3]) - np.copyto(physics.data.mocap_quat[0], action_left[3:7]) - # right - np.copyto(physics.data.mocap_pos[1], action_right[:3]) - np.copyto(physics.data.mocap_quat[1], action_right[3:7]) - - # set gripper - g_left_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_left[7]) - g_right_ctrl = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(action_right[7]) - np.copyto(physics.data.ctrl, np.array([g_left_ctrl, -g_left_ctrl, g_right_ctrl, -g_right_ctrl])) - - def initialize_robots(self, physics): - # reset joint position - physics.named.data.qpos[:16] = START_ARM_POSE - - # reset mocap to align with end effector - # to obtain these numbers: - # (1) make an ee_sim env and reset to the same start_pose - # (2) get env._physics.named.data.xpos['vx300s_left/gripper_link'] - # get env._physics.named.data.xquat['vx300s_left/gripper_link'] - # repeat the same for right side - np.copyto(physics.data.mocap_pos[0], [-0.31718881, 0.5, 0.29525084]) - np.copyto(physics.data.mocap_quat[0], [1, 0, 0, 0]) - # right - np.copyto(physics.data.mocap_pos[1], np.array([0.31718881, 0.49999888, 0.29525084])) - np.copyto(physics.data.mocap_quat[1], [1, 0, 0, 0]) - - # reset gripper control - close_gripper_control = np.array([ - PUPPET_GRIPPER_POSITION_CLOSE, - -PUPPET_GRIPPER_POSITION_CLOSE, - PUPPET_GRIPPER_POSITION_CLOSE, - -PUPPET_GRIPPER_POSITION_CLOSE, - ]) - np.copyto(physics.data.ctrl, close_gripper_control) - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - super().initialize_episode(physics) - - @staticmethod - def get_qpos(physics): - qpos_raw = physics.data.qpos.copy() - left_qpos_raw = qpos_raw[:8] - right_qpos_raw = qpos_raw[8:16] - left_arm_qpos = left_qpos_raw[:6] - right_arm_qpos = right_qpos_raw[:6] - left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] - right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] - return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) - - @staticmethod - def get_qvel(physics): - qvel_raw = physics.data.qvel.copy() - left_qvel_raw = qvel_raw[:8] - right_qvel_raw = qvel_raw[8:16] - left_arm_qvel = left_qvel_raw[:6] - right_arm_qvel = right_qvel_raw[:6] - left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] - right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] - return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) - - @staticmethod - def get_env_state(physics): - raise NotImplementedError - - def get_observation(self, physics): - # note: it is important to do .copy() - obs = collections.OrderedDict() - obs['qpos'] = self.get_qpos(physics) - obs['qvel'] = self.get_qvel(physics) - obs['env_state'] = self.get_env_state(physics) - obs['images'] = dict() - obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') - obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') - obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') - # used in scripted policy to obtain starting pose - obs['mocap_pose_left'] = np.concatenate([physics.data.mocap_pos[0], physics.data.mocap_quat[0]]).copy() - obs['mocap_pose_right'] = np.concatenate([physics.data.mocap_pos[1], physics.data.mocap_quat[1]]).copy() - - # used when replaying joint trajectory - obs['gripper_ctrl'] = physics.data.ctrl.copy() - return obs - - def get_reward(self, physics): - raise NotImplementedError - - -class TransferCubeEETask(BimanualViperXEETask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - self.initialize_robots(physics) - # randomize box position - cube_pose = sample_box_pose() - box_start_idx = physics.model.name2id('red_box_joint', 'joint') - np.copyto(physics.data.qpos[box_start_idx : box_start_idx + 7], cube_pose) - # print(f"randomized cube position to {cube_position}") - - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether left gripper is holding the box - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_table = ("red_box", "table") in all_contact_pairs - - reward = 0 - if touch_right_gripper: - reward = 1 - if touch_right_gripper and not touch_table: # lifted - reward = 2 - if touch_left_gripper: # attempted transfer - reward = 3 - if touch_left_gripper and not touch_table: # successful transfer - reward = 4 - return reward - - -class InsertionEETask(BimanualViperXEETask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - self.initialize_robots(physics) - # randomize peg and socket position - peg_pose, socket_pose = sample_insertion_pose() - id2index = lambda j_id: 16 + (j_id - 16) * 7 # first 16 is robot qpos, 7 is pose dim # hacky - - peg_start_id = physics.model.name2id('red_peg_joint', 'joint') - peg_start_idx = id2index(peg_start_id) - np.copyto(physics.data.qpos[peg_start_idx : peg_start_idx + 7], peg_pose) - # print(f"randomized cube position to {cube_position}") - - socket_start_id = physics.model.name2id('blue_socket_joint', 'joint') - socket_start_idx = id2index(socket_start_id) - np.copyto(physics.data.qpos[socket_start_idx : socket_start_idx + 7], socket_pose) - # print(f"randomized cube position to {cube_position}") - - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether peg touches the pin - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - - peg_touch_table = ("red_peg", "table") in all_contact_pairs - socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ - ("socket-2", "table") in all_contact_pairs or \ - ("socket-3", "table") in all_contact_pairs or \ - ("socket-4", "table") in all_contact_pairs - peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ - ("red_peg", "socket-2") in all_contact_pairs or \ - ("red_peg", "socket-3") in all_contact_pairs or \ - ("red_peg", "socket-4") in all_contact_pairs - pin_touched = ("red_peg", "pin") in all_contact_pairs - - reward = 0 - if touch_left_gripper and touch_right_gripper: # touch both - reward = 1 - if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both - reward = 2 - if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching - reward = 3 - if pin_touched: # successful insertion - reward = 4 - return reward diff --git a/act/imitate_episodes.py b/act/imitate_episodes.py deleted file mode 100644 index 34f9a372..00000000 --- a/act/imitate_episodes.py +++ /dev/null @@ -1,435 +0,0 @@ -import torch -import numpy as np -import os -import pickle -import argparse -import matplotlib.pyplot as plt -from copy import deepcopy -from tqdm import tqdm -from einops import rearrange - -from constants import DT -from constants import PUPPET_GRIPPER_JOINT_OPEN -from utils import load_data # data functions -from utils import sample_box_pose, sample_insertion_pose # robot functions -from utils import compute_dict_mean, set_seed, detach_dict # helper functions -from policy import ACTPolicy, CNNMLPPolicy -from visualize_episodes import save_videos - -from sim_env import BOX_POSE - -import IPython -e = IPython.embed - -def main(args): - set_seed(1) - # command line parameters - is_eval = args['eval'] - ckpt_dir = args['ckpt_dir'] - policy_class = args['policy_class'] - onscreen_render = args['onscreen_render'] - task_name = args['task_name'] - batch_size_train = args['batch_size'] - batch_size_val = args['batch_size'] - num_epochs = args['num_epochs'] - - # get task parameters - is_sim = task_name[:4] == 'sim_' - if is_sim: - from constants import SIM_TASK_CONFIGS - task_config = SIM_TASK_CONFIGS[task_name] - else: - from aloha_scripts.constants import TASK_CONFIGS - task_config = TASK_CONFIGS[task_name] - dataset_dir = task_config['dataset_dir'] - num_episodes = task_config['num_episodes'] - episode_len = task_config['episode_len'] - camera_names = task_config['camera_names'] - - # fixed parameters - state_dim = 14 - lr_backbone = 1e-5 - backbone = 'resnet18' - if policy_class == 'ACT': - enc_layers = 4 - dec_layers = 7 - nheads = 8 - policy_config = {'lr': args['lr'], - 'num_queries': args['chunk_size'], - 'kl_weight': args['kl_weight'], - 'hidden_dim': args['hidden_dim'], - 'dim_feedforward': args['dim_feedforward'], - 'lr_backbone': lr_backbone, - 'backbone': backbone, - 'enc_layers': enc_layers, - 'dec_layers': dec_layers, - 'nheads': nheads, - 'camera_names': camera_names, - } - elif policy_class == 'CNNMLP': - policy_config = {'lr': args['lr'], 'lr_backbone': lr_backbone, 'backbone' : backbone, 'num_queries': 1, - 'camera_names': camera_names,} - else: - raise NotImplementedError - - config = { - 'num_epochs': num_epochs, - 'ckpt_dir': ckpt_dir, - 'episode_len': episode_len, - 'state_dim': state_dim, - 'lr': args['lr'], - 'policy_class': policy_class, - 'onscreen_render': onscreen_render, - 'policy_config': policy_config, - 'task_name': task_name, - 'seed': args['seed'], - 'temporal_agg': args['temporal_agg'], - 'camera_names': camera_names, - 'real_robot': not is_sim - } - - if is_eval: - ckpt_names = [f'policy_best.ckpt'] - results = [] - for ckpt_name in ckpt_names: - success_rate, avg_return = eval_bc(config, ckpt_name, save_episode=True) - results.append([ckpt_name, success_rate, avg_return]) - - for ckpt_name, success_rate, avg_return in results: - print(f'{ckpt_name}: {success_rate=} {avg_return=}') - print() - exit() - - train_dataloader, val_dataloader, stats, _ = load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val) - - # save dataset stats - if not os.path.isdir(ckpt_dir): - os.makedirs(ckpt_dir) - stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') - with open(stats_path, 'wb') as f: - pickle.dump(stats, f) - - best_ckpt_info = train_bc(train_dataloader, val_dataloader, config) - best_epoch, min_val_loss, best_state_dict = best_ckpt_info - - # save best checkpoint - ckpt_path = os.path.join(ckpt_dir, f'policy_best.ckpt') - torch.save(best_state_dict, ckpt_path) - print(f'Best ckpt, val loss {min_val_loss:.6f} @ epoch{best_epoch}') - - -def make_policy(policy_class, policy_config): - if policy_class == 'ACT': - policy = ACTPolicy(policy_config) - elif policy_class == 'CNNMLP': - policy = CNNMLPPolicy(policy_config) - else: - raise NotImplementedError - return policy - - -def make_optimizer(policy_class, policy): - if policy_class == 'ACT': - optimizer = policy.configure_optimizers() - elif policy_class == 'CNNMLP': - optimizer = policy.configure_optimizers() - else: - raise NotImplementedError - return optimizer - - -def get_image(ts, camera_names): - curr_images = [] - for cam_name in camera_names: - curr_image = rearrange(ts.observation['images'][cam_name], 'h w c -> c h w') - curr_images.append(curr_image) - curr_image = np.stack(curr_images, axis=0) - curr_image = torch.from_numpy(curr_image / 255.0).float().cuda().unsqueeze(0) - return curr_image - - -def eval_bc(config, ckpt_name, save_episode=True): - set_seed(1000) - ckpt_dir = config['ckpt_dir'] - state_dim = config['state_dim'] - real_robot = config['real_robot'] - policy_class = config['policy_class'] - onscreen_render = config['onscreen_render'] - policy_config = config['policy_config'] - camera_names = config['camera_names'] - max_timesteps = config['episode_len'] - task_name = config['task_name'] - temporal_agg = config['temporal_agg'] - onscreen_cam = 'angle' - - # load policy and stats - ckpt_path = os.path.join(ckpt_dir, ckpt_name) - policy = make_policy(policy_class, policy_config) - loading_status = policy.load_state_dict(torch.load(ckpt_path)) - print(loading_status) - policy.cuda() - policy.eval() - print(f'Loaded: {ckpt_path}') - stats_path = os.path.join(ckpt_dir, f'dataset_stats.pkl') - with open(stats_path, 'rb') as f: - stats = pickle.load(f) - - pre_process = lambda s_qpos: (s_qpos - stats['qpos_mean']) / stats['qpos_std'] - post_process = lambda a: a * stats['action_std'] + stats['action_mean'] - - # load environment - if real_robot: - from aloha_scripts.robot_utils import move_grippers # requires aloha - from aloha_scripts.real_env import make_real_env # requires aloha - env = make_real_env(init_node=True) - env_max_reward = 0 - else: - from sim_env import make_sim_env - env = make_sim_env(task_name) - env_max_reward = env.task.max_reward - - query_frequency = policy_config['num_queries'] - if temporal_agg: - query_frequency = 1 - num_queries = policy_config['num_queries'] - - max_timesteps = int(max_timesteps * 1) # may increase for real-world tasks - - num_rollouts = 50 - episode_returns = [] - highest_rewards = [] - for rollout_id in range(num_rollouts): - rollout_id += 0 - ### set task - if 'sim_transfer_cube' in task_name: - BOX_POSE[0] = sample_box_pose() # used in sim reset - elif 'sim_insertion' in task_name: - BOX_POSE[0] = np.concatenate(sample_insertion_pose()) # used in sim reset - - ts = env.reset() - - ### onscreen render - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(env._physics.render(height=480, width=640, camera_id=onscreen_cam)) - plt.ion() - - ### evaluation loop - if temporal_agg: - all_time_actions = torch.zeros([max_timesteps, max_timesteps+num_queries, state_dim]).cuda() - - qpos_history = torch.zeros((1, max_timesteps, state_dim)).cuda() - image_list = [] # for visualization - qpos_list = [] - target_qpos_list = [] - rewards = [] - with torch.inference_mode(): - for t in range(max_timesteps): - ### update onscreen render and wait for DT - if onscreen_render: - image = env._physics.render(height=480, width=640, camera_id=onscreen_cam) - plt_img.set_data(image) - plt.pause(DT) - - ### process previous timestep to get qpos and image_list - obs = ts.observation - if 'images' in obs: - image_list.append(obs['images']) - else: - image_list.append({'main': obs['image']}) - qpos_numpy = np.array(obs['qpos']) - qpos = pre_process(qpos_numpy) - qpos = torch.from_numpy(qpos).float().cuda().unsqueeze(0) - qpos_history[:, t] = qpos - curr_image = get_image(ts, camera_names) - - ### query policy - if config['policy_class'] == "ACT": - if t % query_frequency == 0: - all_actions = policy(qpos, curr_image) - if temporal_agg: - all_time_actions[[t], t:t+num_queries] = all_actions - actions_for_curr_step = all_time_actions[:, t] - actions_populated = torch.all(actions_for_curr_step != 0, axis=1) - actions_for_curr_step = actions_for_curr_step[actions_populated] - k = 0.01 - exp_weights = np.exp(-k * np.arange(len(actions_for_curr_step))) - exp_weights = exp_weights / exp_weights.sum() - exp_weights = torch.from_numpy(exp_weights).cuda().unsqueeze(dim=1) - raw_action = (actions_for_curr_step * exp_weights).sum(dim=0, keepdim=True) - else: - raw_action = all_actions[:, t % query_frequency] - elif config['policy_class'] == "CNNMLP": - raw_action = policy(qpos, curr_image) - else: - raise NotImplementedError - - ### post-process actions - raw_action = raw_action.squeeze(0).cpu().numpy() - action = post_process(raw_action) - target_qpos = action - - ### step the environment - ts = env.step(target_qpos) - - ### for visualization - qpos_list.append(qpos_numpy) - target_qpos_list.append(target_qpos) - rewards.append(ts.reward) - - plt.close() - if real_robot: - move_grippers([env.puppet_bot_left, env.puppet_bot_right], [PUPPET_GRIPPER_JOINT_OPEN] * 2, move_time=0.5) # open - pass - - rewards = np.array(rewards) - episode_return = np.sum(rewards[rewards!=None]) - episode_returns.append(episode_return) - episode_highest_reward = np.max(rewards) - highest_rewards.append(episode_highest_reward) - print(f'Rollout {rollout_id}\n{episode_return=}, {episode_highest_reward=}, {env_max_reward=}, Success: {episode_highest_reward==env_max_reward}') - - if save_episode: - save_videos(image_list, DT, video_path=os.path.join(ckpt_dir, f'video{rollout_id}.mp4')) - - success_rate = np.mean(np.array(highest_rewards) == env_max_reward) - avg_return = np.mean(episode_returns) - summary_str = f'\nSuccess rate: {success_rate}\nAverage return: {avg_return}\n\n' - for r in range(env_max_reward+1): - more_or_equal_r = (np.array(highest_rewards) >= r).sum() - more_or_equal_r_rate = more_or_equal_r / num_rollouts - summary_str += f'Reward >= {r}: {more_or_equal_r}/{num_rollouts} = {more_or_equal_r_rate*100}%\n' - - print(summary_str) - - # save success rate to txt - result_file_name = 'result_' + ckpt_name.split('.')[0] + '.txt' - with open(os.path.join(ckpt_dir, result_file_name), 'w') as f: - f.write(summary_str) - f.write(repr(episode_returns)) - f.write('\n\n') - f.write(repr(highest_rewards)) - - return success_rate, avg_return - - -def forward_pass(data, policy): - image_data, qpos_data, action_data, is_pad = data - image_data, qpos_data, action_data, is_pad = image_data.cuda(), qpos_data.cuda(), action_data.cuda(), is_pad.cuda() - return policy(qpos_data, image_data, action_data, is_pad) # TODO remove None - - -def train_bc(train_dataloader, val_dataloader, config): - num_epochs = config['num_epochs'] - ckpt_dir = config['ckpt_dir'] - seed = config['seed'] - policy_class = config['policy_class'] - policy_config = config['policy_config'] - - set_seed(seed) - - policy = make_policy(policy_class, policy_config) - policy.cuda() - optimizer = make_optimizer(policy_class, policy) - - train_history = [] - validation_history = [] - min_val_loss = np.inf - best_ckpt_info = None - for epoch in tqdm(range(num_epochs)): - print(f'\nEpoch {epoch}') - # validation - with torch.inference_mode(): - policy.eval() - epoch_dicts = [] - for batch_idx, data in enumerate(val_dataloader): - forward_dict = forward_pass(data, policy) - epoch_dicts.append(forward_dict) - epoch_summary = compute_dict_mean(epoch_dicts) - validation_history.append(epoch_summary) - - epoch_val_loss = epoch_summary['loss'] - if epoch_val_loss < min_val_loss: - min_val_loss = epoch_val_loss - best_ckpt_info = (epoch, min_val_loss, deepcopy(policy.state_dict())) - print(f'Val loss: {epoch_val_loss:.5f}') - summary_string = '' - for k, v in epoch_summary.items(): - summary_string += f'{k}: {v.item():.3f} ' - print(summary_string) - - # training - policy.train() - optimizer.zero_grad() - for batch_idx, data in enumerate(train_dataloader): - forward_dict = forward_pass(data, policy) - # backward - loss = forward_dict['loss'] - loss.backward() - optimizer.step() - optimizer.zero_grad() - train_history.append(detach_dict(forward_dict)) - epoch_summary = compute_dict_mean(train_history[(batch_idx+1)*epoch:(batch_idx+1)*(epoch+1)]) - epoch_train_loss = epoch_summary['loss'] - print(f'Train loss: {epoch_train_loss:.5f}') - summary_string = '' - for k, v in epoch_summary.items(): - summary_string += f'{k}: {v.item():.3f} ' - print(summary_string) - - if epoch % 100 == 0: - ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{epoch}_seed_{seed}.ckpt') - torch.save(policy.state_dict(), ckpt_path) - plot_history(train_history, validation_history, epoch, ckpt_dir, seed) - - ckpt_path = os.path.join(ckpt_dir, f'policy_last.ckpt') - torch.save(policy.state_dict(), ckpt_path) - - best_epoch, min_val_loss, best_state_dict = best_ckpt_info - ckpt_path = os.path.join(ckpt_dir, f'policy_epoch_{best_epoch}_seed_{seed}.ckpt') - torch.save(best_state_dict, ckpt_path) - print(f'Training finished:\nSeed {seed}, val loss {min_val_loss:.6f} at epoch {best_epoch}') - - # save training curves - plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed) - - return best_ckpt_info - - -def plot_history(train_history, validation_history, num_epochs, ckpt_dir, seed): - # save training curves - for key in train_history[0]: - plot_path = os.path.join(ckpt_dir, f'train_val_{key}_seed_{seed}.png') - plt.figure() - train_values = [summary[key].item() for summary in train_history] - val_values = [summary[key].item() for summary in validation_history] - plt.plot(np.linspace(0, num_epochs-1, len(train_history)), train_values, label='train') - plt.plot(np.linspace(0, num_epochs-1, len(validation_history)), val_values, label='validation') - # plt.ylim([-0.1, 1]) - plt.tight_layout() - plt.legend() - plt.title(key) - plt.savefig(plot_path) - print(f'Saved plots to {ckpt_dir}') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--eval', action='store_true') - parser.add_argument('--onscreen_render', action='store_true') - parser.add_argument('--ckpt_dir', action='store', type=str, help='ckpt_dir', required=True) - parser.add_argument('--policy_class', action='store', type=str, help='policy_class, capitalize', required=True) - parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) - parser.add_argument('--batch_size', action='store', type=int, help='batch_size', required=True) - parser.add_argument('--seed', action='store', type=int, help='seed', required=True) - parser.add_argument('--num_epochs', action='store', type=int, help='num_epochs', required=True) - parser.add_argument('--lr', action='store', type=float, help='lr', required=True) - - # for ACT - parser.add_argument('--kl_weight', action='store', type=int, help='KL Weight', required=False) - parser.add_argument('--chunk_size', action='store', type=int, help='chunk_size', required=False) - parser.add_argument('--hidden_dim', action='store', type=int, help='hidden_dim', required=False) - parser.add_argument('--dim_feedforward', action='store', type=int, help='dim_feedforward', required=False) - parser.add_argument('--temporal_agg', action='store_true') - - main(vars(parser.parse_args())) diff --git a/act/policy.py b/act/policy.py deleted file mode 100644 index 7b091e5e..00000000 --- a/act/policy.py +++ /dev/null @@ -1,84 +0,0 @@ -import torch.nn as nn -from torch.nn import functional as F -import torchvision.transforms as transforms - -from detr.main import build_ACT_model_and_optimizer, build_CNNMLP_model_and_optimizer -import IPython -e = IPython.embed - -class ACTPolicy(nn.Module): - def __init__(self, args_override): - super().__init__() - model, optimizer = build_ACT_model_and_optimizer(args_override) - self.model = model # CVAE decoder - self.optimizer = optimizer - self.kl_weight = args_override['kl_weight'] - print(f'KL Weight {self.kl_weight}') - - def __call__(self, qpos, image, actions=None, is_pad=None): - env_state = None - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - image = normalize(image) - if actions is not None: # training time - actions = actions[:, :self.model.num_queries] - is_pad = is_pad[:, :self.model.num_queries] - - a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad) - total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar) - loss_dict = dict() - all_l1 = F.l1_loss(actions, a_hat, reduction='none') - l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() - loss_dict['l1'] = l1 - loss_dict['kl'] = total_kld[0] - loss_dict['loss'] = loss_dict['l1'] + loss_dict['kl'] * self.kl_weight - return loss_dict - else: # inference time - a_hat, _, (_, _) = self.model(qpos, image, env_state) # no action, sample from prior - return a_hat - - def configure_optimizers(self): - return self.optimizer - - -class CNNMLPPolicy(nn.Module): - def __init__(self, args_override): - super().__init__() - model, optimizer = build_CNNMLP_model_and_optimizer(args_override) - self.model = model # decoder - self.optimizer = optimizer - - def __call__(self, qpos, image, actions=None, is_pad=None): - env_state = None # TODO - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - image = normalize(image) - if actions is not None: # training time - actions = actions[:, 0] - a_hat = self.model(qpos, image, env_state, actions) - mse = F.mse_loss(actions, a_hat) - loss_dict = dict() - loss_dict['mse'] = mse - loss_dict['loss'] = loss_dict['mse'] - return loss_dict - else: # inference time - a_hat = self.model(qpos, image, env_state) # no action, sample from prior - return a_hat - - def configure_optimizers(self): - return self.optimizer - -def kl_divergence(mu, logvar): - batch_size = mu.size(0) - assert batch_size != 0 - if mu.data.ndimension() == 4: - mu = mu.view(mu.size(0), mu.size(1)) - if logvar.data.ndimension() == 4: - logvar = logvar.view(logvar.size(0), logvar.size(1)) - - klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) - total_kld = klds.sum(1).mean(0, True) - dimension_wise_kld = klds.mean(0) - mean_kld = klds.mean(1).mean(0, True) - - return total_kld, dimension_wise_kld, mean_kld diff --git a/act/record_sim_episodes.py b/act/record_sim_episodes.py deleted file mode 100644 index 253fdea1..00000000 --- a/act/record_sim_episodes.py +++ /dev/null @@ -1,189 +0,0 @@ -import time -import os -import numpy as np -import argparse -import matplotlib.pyplot as plt -import h5py - -from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN, SIM_TASK_CONFIGS -from ee_sim_env import make_ee_sim_env -from sim_env import make_sim_env, BOX_POSE -from scripted_policy import PickAndTransferPolicy, InsertionPolicy - -import IPython -e = IPython.embed - - -def main(args): - """ - Generate demonstration data in simulation. - First rollout the policy (defined in ee space) in ee_sim_env. Obtain the joint trajectory. - Replace the gripper joint positions with the commanded joint position. - Replay this joint trajectory (as action sequence) in sim_env, and record all observations. - Save this episode of data, and continue to next episode of data collection. - """ - - task_name = args['task_name'] - dataset_dir = args['dataset_dir'] - num_episodes = args['num_episodes'] - onscreen_render = args['onscreen_render'] - inject_noise = False - render_cam_name = 'angle' - - if not os.path.isdir(dataset_dir): - os.makedirs(dataset_dir, exist_ok=True) - - episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] - camera_names = SIM_TASK_CONFIGS[task_name]['camera_names'] - if task_name == 'sim_transfer_cube_scripted': - policy_cls = PickAndTransferPolicy - elif task_name == 'sim_insertion_scripted': - policy_cls = InsertionPolicy - else: - raise NotImplementedError - - success = [] - for episode_idx in range(num_episodes): - print(f'{episode_idx=}') - print('Rollout out EE space scripted policy') - # setup the environment - env = make_ee_sim_env(task_name) - ts = env.reset() - episode = [ts] - policy = policy_cls(inject_noise) - # setup plotting - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images'][render_cam_name]) - plt.ion() - for step in range(episode_len): - action = policy(ts) - ts = env.step(action) - episode.append(ts) - if onscreen_render: - plt_img.set_data(ts.observation['images'][render_cam_name]) - plt.pause(0.002) - plt.close() - - episode_return = np.sum([ts.reward for ts in episode[1:]]) - episode_max_reward = np.max([ts.reward for ts in episode[1:]]) - if episode_max_reward == env.task.max_reward: - print(f"{episode_idx=} Successful, {episode_return=}") - else: - print(f"{episode_idx=} Failed") - - joint_traj = [ts.observation['qpos'] for ts in episode] - # replace gripper pose with gripper control - gripper_ctrl_traj = [ts.observation['gripper_ctrl'] for ts in episode] - for joint, ctrl in zip(joint_traj, gripper_ctrl_traj): - left_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[0]) - right_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[2]) - joint[6] = left_ctrl - joint[6+7] = right_ctrl - - subtask_info = episode[0].observation['env_state'].copy() # box pose at step 0 - - # clear unused variables - del env - del episode - del policy - - # setup the environment - print('Replaying joint commands') - env = make_sim_env(task_name) - BOX_POSE[0] = subtask_info # make sure the sim_env has the same object configurations as ee_sim_env - ts = env.reset() - - episode_replay = [ts] - # setup plotting - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images'][render_cam_name]) - plt.ion() - for t in range(len(joint_traj)): # note: this will increase episode length by 1 - action = joint_traj[t] - ts = env.step(action) - episode_replay.append(ts) - if onscreen_render: - plt_img.set_data(ts.observation['images'][render_cam_name]) - plt.pause(0.02) - - episode_return = np.sum([ts.reward for ts in episode_replay[1:]]) - episode_max_reward = np.max([ts.reward for ts in episode_replay[1:]]) - if episode_max_reward == env.task.max_reward: - success.append(1) - print(f"{episode_idx=} Successful, {episode_return=}") - else: - success.append(0) - print(f"{episode_idx=} Failed") - - plt.close() - - """ - For each timestep: - observations - - images - - each_cam_name (480, 640, 3) 'uint8' - - qpos (14,) 'float64' - - qvel (14,) 'float64' - - action (14,) 'float64' - """ - - data_dict = { - '/observations/qpos': [], - '/observations/qvel': [], - '/action': [], - } - for cam_name in camera_names: - data_dict[f'/observations/images/{cam_name}'] = [] - - # because the replaying, there will be eps_len + 1 actions and eps_len + 2 timesteps - # truncate here to be consistent - joint_traj = joint_traj[:-1] - episode_replay = episode_replay[:-1] - - # len(joint_traj) i.e. actions: max_timesteps - # len(episode_replay) i.e. time steps: max_timesteps + 1 - max_timesteps = len(joint_traj) - while joint_traj: - action = joint_traj.pop(0) - ts = episode_replay.pop(0) - data_dict['/observations/qpos'].append(ts.observation['qpos']) - data_dict['/observations/qvel'].append(ts.observation['qvel']) - data_dict['/action'].append(action) - for cam_name in camera_names: - data_dict[f'/observations/images/{cam_name}'].append(ts.observation['images'][cam_name]) - - # HDF5 - t0 = time.time() - dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}') - with h5py.File(dataset_path + '.hdf5', 'w', rdcc_nbytes=1024 ** 2 * 2) as root: - root.attrs['sim'] = True - obs = root.create_group('observations') - image = obs.create_group('images') - for cam_name in camera_names: - _ = image.create_dataset(cam_name, (max_timesteps, 480, 640, 3), dtype='uint8', - chunks=(1, 480, 640, 3), ) - # compression='gzip',compression_opts=2,) - # compression=32001, compression_opts=(0, 0, 0, 0, 9, 1, 1), shuffle=False) - qpos = obs.create_dataset('qpos', (max_timesteps, 14)) - qvel = obs.create_dataset('qvel', (max_timesteps, 14)) - action = root.create_dataset('action', (max_timesteps, 14)) - - for name, array in data_dict.items(): - root[name][...] = array - print(f'Saving: {time.time() - t0:.1f} secs\n') - - print(f'Saved to {dataset_dir}') - print(f'Success: {np.sum(success)} / {len(success)}') - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True) - parser.add_argument('--dataset_dir', action='store', type=str, help='dataset saving dir', required=True) - parser.add_argument('--num_episodes', action='store', type=int, help='num_episodes', required=False) - parser.add_argument('--onscreen_render', action='store_true') - - main(vars(parser.parse_args())) - diff --git a/act/scripted_policy.py b/act/scripted_policy.py deleted file mode 100644 index 4fd8f000..00000000 --- a/act/scripted_policy.py +++ /dev/null @@ -1,194 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from pyquaternion import Quaternion - -from constants import SIM_TASK_CONFIGS -from ee_sim_env import make_ee_sim_env - -import IPython -e = IPython.embed - - -class BasePolicy: - def __init__(self, inject_noise=False): - self.inject_noise = inject_noise - self.step_count = 0 - self.left_trajectory = None - self.right_trajectory = None - - def generate_trajectory(self, ts_first): - raise NotImplementedError - - @staticmethod - def interpolate(curr_waypoint, next_waypoint, t): - t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"]) - curr_xyz = curr_waypoint['xyz'] - curr_quat = curr_waypoint['quat'] - curr_grip = curr_waypoint['gripper'] - next_xyz = next_waypoint['xyz'] - next_quat = next_waypoint['quat'] - next_grip = next_waypoint['gripper'] - xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac - quat = curr_quat + (next_quat - curr_quat) * t_frac - gripper = curr_grip + (next_grip - curr_grip) * t_frac - return xyz, quat, gripper - - def __call__(self, ts): - # generate trajectory at first timestep, then open-loop execution - if self.step_count == 0: - self.generate_trajectory(ts) - - # obtain left and right waypoints - if self.left_trajectory[0]['t'] == self.step_count: - self.curr_left_waypoint = self.left_trajectory.pop(0) - next_left_waypoint = self.left_trajectory[0] - - if self.right_trajectory[0]['t'] == self.step_count: - self.curr_right_waypoint = self.right_trajectory.pop(0) - next_right_waypoint = self.right_trajectory[0] - - # interpolate between waypoints to obtain current pose and gripper command - left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count) - right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count) - - # Inject noise - if self.inject_noise: - scale = 0.01 - left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape) - right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape) - - action_left = np.concatenate([left_xyz, left_quat, [left_gripper]]) - action_right = np.concatenate([right_xyz, right_quat, [right_gripper]]) - - self.step_count += 1 - return np.concatenate([action_left, action_right]) - - -class PickAndTransferPolicy(BasePolicy): - - def generate_trajectory(self, ts_first): - init_mocap_pose_right = ts_first.observation['mocap_pose_right'] - init_mocap_pose_left = ts_first.observation['mocap_pose_left'] - - box_info = np.array(ts_first.observation['env_state']) - box_xyz = box_info[:3] - box_quat = box_info[3:] - # print(f"Generate trajectory for {box_xyz=}") - - gripper_pick_quat = Quaternion(init_mocap_pose_right[3:]) - gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) - - meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90) - - meet_xyz = np.array([0, 0.5, 0.25]) - - self.left_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep - {"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position - {"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position - {"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper - {"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left - {"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay - ] - - self.right_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep - {"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube - {"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down - {"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper - {"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position - {"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position - {"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper - {"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right - {"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay - ] - - -class InsertionPolicy(BasePolicy): - - def generate_trajectory(self, ts_first): - init_mocap_pose_right = ts_first.observation['mocap_pose_right'] - init_mocap_pose_left = ts_first.observation['mocap_pose_left'] - - peg_info = np.array(ts_first.observation['env_state'])[:7] - peg_xyz = peg_info[:3] - peg_quat = peg_info[3:] - - socket_info = np.array(ts_first.observation['env_state'])[7:] - socket_xyz = socket_info[:3] - socket_quat = socket_info[3:] - - gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:]) - gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60) - - gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:]) - gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60) - - meet_xyz = np.array([0, 0.5, 0.15]) - lift_right = 0.00715 - - self.left_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep - {"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube - {"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down - {"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper - {"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position - {"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0}, # insertion - {"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # insertion - ] - - self.right_trajectory = [ - {"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep - {"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube - {"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down - {"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper - {"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position - {"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion - {"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion - - ] - - -def test_policy(task_name): - # example rolling out pick_and_transfer policy - onscreen_render = True - inject_noise = False - - # setup the environment - episode_len = SIM_TASK_CONFIGS[task_name]['episode_len'] - if 'sim_transfer_cube' in task_name: - env = make_ee_sim_env('sim_transfer_cube') - elif 'sim_insertion' in task_name: - env = make_ee_sim_env('sim_insertion') - else: - raise NotImplementedError - - for episode_idx in range(2): - ts = env.reset() - episode = [ts] - if onscreen_render: - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images']['angle']) - plt.ion() - - policy = PickAndTransferPolicy(inject_noise) - for step in range(episode_len): - action = policy(ts) - ts = env.step(action) - episode.append(ts) - if onscreen_render: - plt_img.set_data(ts.observation['images']['angle']) - plt.pause(0.02) - plt.close() - - episode_return = np.sum([ts.reward for ts in episode[1:]]) - if episode_return > 0: - print(f"{episode_idx=} Successful, {episode_return=}") - else: - print(f"{episode_idx=} Failed") - - -if __name__ == '__main__': - test_task_name = 'sim_transfer_cube_scripted' - test_policy(test_task_name) - diff --git a/act/sim_env.py b/act/sim_env.py deleted file mode 100644 index b79b935b..00000000 --- a/act/sim_env.py +++ /dev/null @@ -1,278 +0,0 @@ -import numpy as np -import os -import collections -import matplotlib.pyplot as plt -from dm_control import mujoco -from dm_control.rl import control -from dm_control.suite import base - -from constants import DT, XML_DIR, START_ARM_POSE -from constants import PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN -from constants import MASTER_GRIPPER_POSITION_NORMALIZE_FN -from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN -from constants import PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN - -import IPython -e = IPython.embed - -BOX_POSE = [None] # to be changed from outside - -def make_sim_env(task_name): - """ - Environment for simulated robot bi-manual manipulation, with joint position control - Action space: [left_arm_qpos (6), # absolute joint position - left_gripper_positions (1), # normalized gripper position (0: close, 1: open) - right_arm_qpos (6), # absolute joint position - right_gripper_positions (1),] # normalized gripper position (0: close, 1: open) - - Observation space: {"qpos": Concat[ left_arm_qpos (6), # absolute joint position - left_gripper_position (1), # normalized gripper position (0: close, 1: open) - right_arm_qpos (6), # absolute joint position - right_gripper_qpos (1)] # normalized gripper position (0: close, 1: open) - "qvel": Concat[ left_arm_qvel (6), # absolute joint velocity (rad) - left_gripper_velocity (1), # normalized gripper velocity (pos: opening, neg: closing) - right_arm_qvel (6), # absolute joint velocity (rad) - right_gripper_qvel (1)] # normalized gripper velocity (pos: opening, neg: closing) - "images": {"main": (480x640x3)} # h, w, c, dtype='uint8' - """ - if 'sim_transfer_cube' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_transfer_cube.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = TransferCubeTask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - elif 'sim_insertion' in task_name: - xml_path = os.path.join(XML_DIR, f'bimanual_viperx_insertion.xml') - physics = mujoco.Physics.from_xml_path(xml_path) - task = InsertionTask(random=False) - env = control.Environment(physics, task, time_limit=20, control_timestep=DT, - n_sub_steps=None, flat_observation=False) - else: - raise NotImplementedError - return env - -class BimanualViperXTask(base.Task): - def __init__(self, random=None): - super().__init__(random=random) - - def before_step(self, action, physics): - left_arm_action = action[:6] - right_arm_action = action[7:7+6] - normalized_left_gripper_action = action[6] - normalized_right_gripper_action = action[7+6] - - left_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_left_gripper_action) - right_gripper_action = PUPPET_GRIPPER_POSITION_UNNORMALIZE_FN(normalized_right_gripper_action) - - full_left_gripper_action = [left_gripper_action, -left_gripper_action] - full_right_gripper_action = [right_gripper_action, -right_gripper_action] - - env_action = np.concatenate([left_arm_action, full_left_gripper_action, right_arm_action, full_right_gripper_action]) - super().before_step(env_action, physics) - return - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - super().initialize_episode(physics) - - @staticmethod - def get_qpos(physics): - qpos_raw = physics.data.qpos.copy() - left_qpos_raw = qpos_raw[:8] - right_qpos_raw = qpos_raw[8:16] - left_arm_qpos = left_qpos_raw[:6] - right_arm_qpos = right_qpos_raw[:6] - left_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(left_qpos_raw[6])] - right_gripper_qpos = [PUPPET_GRIPPER_POSITION_NORMALIZE_FN(right_qpos_raw[6])] - return np.concatenate([left_arm_qpos, left_gripper_qpos, right_arm_qpos, right_gripper_qpos]) - - @staticmethod - def get_qvel(physics): - qvel_raw = physics.data.qvel.copy() - left_qvel_raw = qvel_raw[:8] - right_qvel_raw = qvel_raw[8:16] - left_arm_qvel = left_qvel_raw[:6] - right_arm_qvel = right_qvel_raw[:6] - left_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(left_qvel_raw[6])] - right_gripper_qvel = [PUPPET_GRIPPER_VELOCITY_NORMALIZE_FN(right_qvel_raw[6])] - return np.concatenate([left_arm_qvel, left_gripper_qvel, right_arm_qvel, right_gripper_qvel]) - - @staticmethod - def get_env_state(physics): - raise NotImplementedError - - def get_observation(self, physics): - obs = collections.OrderedDict() - obs['qpos'] = self.get_qpos(physics) - obs['qvel'] = self.get_qvel(physics) - obs['env_state'] = self.get_env_state(physics) - obs['images'] = dict() - obs['images']['top'] = physics.render(height=480, width=640, camera_id='top') - obs['images']['angle'] = physics.render(height=480, width=640, camera_id='angle') - obs['images']['vis'] = physics.render(height=480, width=640, camera_id='front_close') - - return obs - - def get_reward(self, physics): - # return whether left gripper is holding the box - raise NotImplementedError - - -class TransferCubeTask(BimanualViperXTask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside - # reset qpos, control and box position - with physics.reset_context(): - physics.named.data.qpos[:16] = START_ARM_POSE - np.copyto(physics.data.ctrl, START_ARM_POSE) - assert BOX_POSE[0] is not None - physics.named.data.qpos[-7:] = BOX_POSE[0] - # print(f"{BOX_POSE=}") - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether left gripper is holding the box - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_left_gripper = ("red_box", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - touch_right_gripper = ("red_box", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_table = ("red_box", "table") in all_contact_pairs - - reward = 0 - if touch_right_gripper: - reward = 1 - if touch_right_gripper and not touch_table: # lifted - reward = 2 - if touch_left_gripper: # attempted transfer - reward = 3 - if touch_left_gripper and not touch_table: # successful transfer - reward = 4 - return reward - - -class InsertionTask(BimanualViperXTask): - def __init__(self, random=None): - super().__init__(random=random) - self.max_reward = 4 - - def initialize_episode(self, physics): - """Sets the state of the environment at the start of each episode.""" - # TODO Notice: this function does not randomize the env configuration. Instead, set BOX_POSE from outside - # reset qpos, control and box position - with physics.reset_context(): - physics.named.data.qpos[:16] = START_ARM_POSE - np.copyto(physics.data.ctrl, START_ARM_POSE) - assert BOX_POSE[0] is not None - physics.named.data.qpos[-7*2:] = BOX_POSE[0] # two objects - # print(f"{BOX_POSE=}") - super().initialize_episode(physics) - - @staticmethod - def get_env_state(physics): - env_state = physics.data.qpos.copy()[16:] - return env_state - - def get_reward(self, physics): - # return whether peg touches the pin - all_contact_pairs = [] - for i_contact in range(physics.data.ncon): - id_geom_1 = physics.data.contact[i_contact].geom1 - id_geom_2 = physics.data.contact[i_contact].geom2 - name_geom_1 = physics.model.id2name(id_geom_1, 'geom') - name_geom_2 = physics.model.id2name(id_geom_2, 'geom') - contact_pair = (name_geom_1, name_geom_2) - all_contact_pairs.append(contact_pair) - - touch_right_gripper = ("red_peg", "vx300s_right/10_right_gripper_finger") in all_contact_pairs - touch_left_gripper = ("socket-1", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-2", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-3", "vx300s_left/10_left_gripper_finger") in all_contact_pairs or \ - ("socket-4", "vx300s_left/10_left_gripper_finger") in all_contact_pairs - - peg_touch_table = ("red_peg", "table") in all_contact_pairs - socket_touch_table = ("socket-1", "table") in all_contact_pairs or \ - ("socket-2", "table") in all_contact_pairs or \ - ("socket-3", "table") in all_contact_pairs or \ - ("socket-4", "table") in all_contact_pairs - peg_touch_socket = ("red_peg", "socket-1") in all_contact_pairs or \ - ("red_peg", "socket-2") in all_contact_pairs or \ - ("red_peg", "socket-3") in all_contact_pairs or \ - ("red_peg", "socket-4") in all_contact_pairs - pin_touched = ("red_peg", "pin") in all_contact_pairs - - reward = 0 - if touch_left_gripper and touch_right_gripper: # touch both - reward = 1 - if touch_left_gripper and touch_right_gripper and (not peg_touch_table) and (not socket_touch_table): # grasp both - reward = 2 - if peg_touch_socket and (not peg_touch_table) and (not socket_touch_table): # peg and socket touching - reward = 3 - if pin_touched: # successful insertion - reward = 4 - return reward - - -def get_action(master_bot_left, master_bot_right): - action = np.zeros(14) - # arm action - action[:6] = master_bot_left.dxl.joint_states.position[:6] - action[7:7+6] = master_bot_right.dxl.joint_states.position[:6] - # gripper action - left_gripper_pos = master_bot_left.dxl.joint_states.position[7] - right_gripper_pos = master_bot_right.dxl.joint_states.position[7] - normalized_left_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(left_gripper_pos) - normalized_right_pos = MASTER_GRIPPER_POSITION_NORMALIZE_FN(right_gripper_pos) - action[6] = normalized_left_pos - action[7+6] = normalized_right_pos - return action - -def test_sim_teleop(): - """ Testing teleoperation in sim with ALOHA. Requires hardware and ALOHA repo to work. """ - from interbotix_xs_modules.arm import InterbotixManipulatorXS - - BOX_POSE[0] = [0.2, 0.5, 0.05, 1, 0, 0, 0] - - # source of data - master_bot_left = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", - robot_name=f'master_left', init_node=True) - master_bot_right = InterbotixManipulatorXS(robot_model="wx250s", group_name="arm", gripper_name="gripper", - robot_name=f'master_right', init_node=False) - - # setup the environment - env = make_sim_env('sim_transfer_cube') - ts = env.reset() - episode = [ts] - # setup plotting - ax = plt.subplot() - plt_img = ax.imshow(ts.observation['images']['angle']) - plt.ion() - - for t in range(1000): - action = get_action(master_bot_left, master_bot_right) - ts = env.step(action) - episode.append(ts) - - plt_img.set_data(ts.observation['images']['angle']) - plt.pause(0.02) - - -if __name__ == '__main__': - test_sim_teleop() - diff --git a/act/utils.py b/act/utils.py deleted file mode 100644 index 673cbb10..00000000 --- a/act/utils.py +++ /dev/null @@ -1,189 +0,0 @@ -import numpy as np -import torch -import os -import h5py -from torch.utils.data import TensorDataset, DataLoader - -import IPython -e = IPython.embed - -class EpisodicDataset(torch.utils.data.Dataset): - def __init__(self, episode_ids, dataset_dir, camera_names, norm_stats): - super(EpisodicDataset).__init__() - self.episode_ids = episode_ids - self.dataset_dir = dataset_dir - self.camera_names = camera_names - self.norm_stats = norm_stats - self.is_sim = None - self.__getitem__(0) # initialize self.is_sim - - def __len__(self): - return len(self.episode_ids) - - def __getitem__(self, index): - sample_full_episode = False # hardcode - - episode_id = self.episode_ids[index] - dataset_path = os.path.join(self.dataset_dir, f'episode_{episode_id}.hdf5') - with h5py.File(dataset_path, 'r') as root: - is_sim = root.attrs['sim'] - original_action_shape = root['/action'].shape - episode_len = original_action_shape[0] - if sample_full_episode: - start_ts = 0 - else: - start_ts = np.random.choice(episode_len) - # get observation at start_ts only - qpos = root['/observations/qpos'][start_ts] - qvel = root['/observations/qvel'][start_ts] - image_dict = dict() - for cam_name in self.camera_names: - image_dict[cam_name] = root[f'/observations/images/{cam_name}'][start_ts] - # get all actions after and including start_ts - if is_sim: - action = root['/action'][start_ts:] - action_len = episode_len - start_ts - else: - action = root['/action'][max(0, start_ts - 1):] # hack, to make timesteps more aligned - action_len = episode_len - max(0, start_ts - 1) # hack, to make timesteps more aligned - - self.is_sim = is_sim - padded_action = np.zeros(original_action_shape, dtype=np.float32) - padded_action[:action_len] = action - is_pad = np.zeros(episode_len) - is_pad[action_len:] = 1 - - # new axis for different cameras - all_cam_images = [] - for cam_name in self.camera_names: - all_cam_images.append(image_dict[cam_name]) - all_cam_images = np.stack(all_cam_images, axis=0) - - # construct observations - image_data = torch.from_numpy(all_cam_images) - qpos_data = torch.from_numpy(qpos).float() - action_data = torch.from_numpy(padded_action).float() - is_pad = torch.from_numpy(is_pad).bool() - - # channel last - image_data = torch.einsum('k h w c -> k c h w', image_data) - - # normalize image and change dtype to float - image_data = image_data / 255.0 - action_data = (action_data - self.norm_stats["action_mean"]) / self.norm_stats["action_std"] - qpos_data = (qpos_data - self.norm_stats["qpos_mean"]) / self.norm_stats["qpos_std"] - - return image_data, qpos_data, action_data, is_pad - - -def get_norm_stats(dataset_dir, num_episodes): - all_qpos_data = [] - all_action_data = [] - for episode_idx in range(num_episodes): - dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}.hdf5') - with h5py.File(dataset_path, 'r') as root: - qpos = root['/observations/qpos'][()] - qvel = root['/observations/qvel'][()] - action = root['/action'][()] - all_qpos_data.append(torch.from_numpy(qpos)) - all_action_data.append(torch.from_numpy(action)) - all_qpos_data = torch.stack(all_qpos_data) - all_action_data = torch.stack(all_action_data) - all_action_data = all_action_data - - # normalize action data - action_mean = all_action_data.mean(dim=[0, 1], keepdim=True) - action_std = all_action_data.std(dim=[0, 1], keepdim=True) - action_std = torch.clip(action_std, 1e-2, 10) # clipping - - # normalize qpos data - qpos_mean = all_qpos_data.mean(dim=[0, 1], keepdim=True) - qpos_std = all_qpos_data.std(dim=[0, 1], keepdim=True) - qpos_std = torch.clip(qpos_std, 1e-2, 10) # clipping - - stats = {"action_mean": action_mean.numpy().squeeze(), "action_std": action_std.numpy().squeeze(), - "qpos_mean": qpos_mean.numpy().squeeze(), "qpos_std": qpos_std.numpy().squeeze(), - "example_qpos": qpos} - - return stats - - -def load_data(dataset_dir, num_episodes, camera_names, batch_size_train, batch_size_val): - print(f'\nData from: {dataset_dir}\n') - # obtain train test split - train_ratio = 0.8 - shuffled_indices = np.random.permutation(num_episodes) - train_indices = shuffled_indices[:int(train_ratio * num_episodes)] - val_indices = shuffled_indices[int(train_ratio * num_episodes):] - - # obtain normalization stats for qpos and action - norm_stats = get_norm_stats(dataset_dir, num_episodes) - - # construct dataset and dataloader - train_dataset = EpisodicDataset(train_indices, dataset_dir, camera_names, norm_stats) - val_dataset = EpisodicDataset(val_indices, dataset_dir, camera_names, norm_stats) - train_dataloader = DataLoader(train_dataset, batch_size=batch_size_train, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) - val_dataloader = DataLoader(val_dataset, batch_size=batch_size_val, shuffle=True, pin_memory=True, num_workers=1, prefetch_factor=1) - - return train_dataloader, val_dataloader, norm_stats, train_dataset.is_sim - - -### env utils - -def sample_box_pose(): - x_range = [0.0, 0.2] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - cube_quat = np.array([1, 0, 0, 0]) - return np.concatenate([cube_position, cube_quat]) - -def sample_insertion_pose(): - # Peg - x_range = [0.1, 0.2] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - peg_quat = np.array([1, 0, 0, 0]) - peg_pose = np.concatenate([peg_position, peg_quat]) - - # Socket - x_range = [-0.2, -0.1] - y_range = [0.4, 0.6] - z_range = [0.05, 0.05] - - ranges = np.vstack([x_range, y_range, z_range]) - socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1]) - - socket_quat = np.array([1, 0, 0, 0]) - socket_pose = np.concatenate([socket_position, socket_quat]) - - return peg_pose, socket_pose - -### helper functions - -def compute_dict_mean(epoch_dicts): - result = {k: None for k in epoch_dicts[0]} - num_items = len(epoch_dicts) - for k in result: - value_sum = 0 - for epoch_dict in epoch_dicts: - value_sum += epoch_dict[k] - result[k] = value_sum / num_items - return result - -def detach_dict(d): - new_d = dict() - for k, v in d.items(): - new_d[k] = v.detach() - return new_d - -def set_seed(seed): - torch.manual_seed(seed) - np.random.seed(seed) diff --git a/act/visualize_episodes.py b/act/visualize_episodes.py deleted file mode 100644 index 4e55e471..00000000 --- a/act/visualize_episodes.py +++ /dev/null @@ -1,147 +0,0 @@ -import os -import numpy as np -import cv2 -import h5py -import argparse - -import matplotlib.pyplot as plt -from constants import DT - -import IPython -e = IPython.embed - -JOINT_NAMES = ["waist", "shoulder", "elbow", "forearm_roll", "wrist_angle", "wrist_rotate"] -STATE_NAMES = JOINT_NAMES + ["gripper"] - -def load_hdf5(dataset_dir, dataset_name): - dataset_path = os.path.join(dataset_dir, dataset_name + '.hdf5') - if not os.path.isfile(dataset_path): - print(f'Dataset does not exist at \n{dataset_path}\n') - exit() - - with h5py.File(dataset_path, 'r') as root: - is_sim = root.attrs['sim'] - qpos = root['/observations/qpos'][()] - qvel = root['/observations/qvel'][()] - action = root['/action'][()] - image_dict = dict() - for cam_name in root[f'/observations/images/'].keys(): - image_dict[cam_name] = root[f'/observations/images/{cam_name}'][()] - - return qpos, qvel, action, image_dict - -def main(args): - dataset_dir = args['dataset_dir'] - episode_idx = args['episode_idx'] - dataset_name = f'episode_{episode_idx}' - - qpos, qvel, action, image_dict = load_hdf5(dataset_dir, dataset_name) - save_videos(image_dict, DT, video_path=os.path.join(dataset_dir, dataset_name + '_video.mp4')) - visualize_joints(qpos, action, plot_path=os.path.join(dataset_dir, dataset_name + '_qpos.png')) - # visualize_timestamp(t_list, dataset_path) # TODO addn timestamp back - - -def save_videos(video, dt, video_path=None): - if isinstance(video, list): - cam_names = list(video[0].keys()) - h, w, _ = video[0][cam_names[0]].shape - w = w * len(cam_names) - fps = int(1/dt) - out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - for ts, image_dict in enumerate(video): - images = [] - for cam_name in cam_names: - image = image_dict[cam_name] - image = image[:, :, [2, 1, 0]] # swap B and R channel - images.append(image) - images = np.concatenate(images, axis=1) - out.write(images) - out.release() - print(f'Saved video to: {video_path}') - elif isinstance(video, dict): - cam_names = list(video.keys()) - all_cam_videos = [] - for cam_name in cam_names: - all_cam_videos.append(video[cam_name]) - all_cam_videos = np.concatenate(all_cam_videos, axis=2) # width dimension - - n_frames, h, w, _ = all_cam_videos.shape - fps = int(1 / dt) - out = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - for t in range(n_frames): - image = all_cam_videos[t] - image = image[:, :, [2, 1, 0]] # swap B and R channel - out.write(image) - out.release() - print(f'Saved video to: {video_path}') - - -def visualize_joints(qpos_list, command_list, plot_path=None, ylim=None, label_overwrite=None): - if label_overwrite: - label1, label2 = label_overwrite - else: - label1, label2 = 'State', 'Command' - - qpos = np.array(qpos_list) # ts, dim - command = np.array(command_list) - num_ts, num_dim = qpos.shape - h, w = 2, num_dim - num_figs = num_dim - fig, axs = plt.subplots(num_figs, 1, figsize=(w, h * num_figs)) - - # plot joint state - all_names = [name + '_left' for name in STATE_NAMES] + [name + '_right' for name in STATE_NAMES] - for dim_idx in range(num_dim): - ax = axs[dim_idx] - ax.plot(qpos[:, dim_idx], label=label1) - ax.set_title(f'Joint {dim_idx}: {all_names[dim_idx]}') - ax.legend() - - # plot arm command - for dim_idx in range(num_dim): - ax = axs[dim_idx] - ax.plot(command[:, dim_idx], label=label2) - ax.legend() - - if ylim: - for dim_idx in range(num_dim): - ax = axs[dim_idx] - ax.set_ylim(ylim) - - plt.tight_layout() - plt.savefig(plot_path) - print(f'Saved qpos plot to: {plot_path}') - plt.close() - -def visualize_timestamp(t_list, dataset_path): - plot_path = dataset_path.replace('.pkl', '_timestamp.png') - h, w = 4, 10 - fig, axs = plt.subplots(2, 1, figsize=(w, h*2)) - # process t_list - t_float = [] - for secs, nsecs in t_list: - t_float.append(secs + nsecs * 10E-10) - t_float = np.array(t_float) - - ax = axs[0] - ax.plot(np.arange(len(t_float)), t_float) - ax.set_title(f'Camera frame timestamps') - ax.set_xlabel('timestep') - ax.set_ylabel('time (sec)') - - ax = axs[1] - ax.plot(np.arange(len(t_float)-1), t_float[:-1] - t_float[1:]) - ax.set_title(f'dt') - ax.set_xlabel('timestep') - ax.set_ylabel('time (sec)') - - plt.tight_layout() - plt.savefig(plot_path) - print(f'Saved timestamp plot to: {plot_path}') - plt.close() - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--dataset_dir', action='store', type=str, help='Dataset dir.', required=True) - parser.add_argument('--episode_idx', action='store', type=int, help='Episode index.', required=False) - main(vars(parser.parse_args())) diff --git a/docs/api/robomimic.rst b/docs/api/robomimic.rst index 0d8ad0a7..2dc96c06 100644 --- a/docs/api/robomimic.rst +++ b/docs/api/robomimic.rst @@ -24,6 +24,14 @@ robomimic.macros module :undoc-members: :show-inheritance: +robomimic.macros\_private module +-------------------------------- + +.. automodule:: robomimic.macros_private + :members: + :undoc-members: + :show-inheritance: + Module contents --------------- diff --git a/docs/datasets/mimicgen.md b/docs/datasets/mimicgen.md new file mode 100644 index 00000000..3c47d2b7 --- /dev/null +++ b/docs/datasets/mimicgen.md @@ -0,0 +1,37 @@ +# MimicGen (CoRL 2023) + +## Overview + +The [MimicGen paper](https://arxiv.org/abs/2310.17596) released a large collection of task demonstrations across several different environments. + +The datasets contain over 48,000 task demonstrations across 12 tasks, grouped into the following categories: +- **source**: 120 human demonstrations across 12 tasks used to automatically generate the other datasets +- **core**: 26,000 task demonstrations across 12 tasks (26 task variants) +- **object**: 2000 task demonstrations on the Mug Cleanup task with different mugs +- **robot**: 16,000 task demonstrations across 4 different robot arms on 2 tasks (4 task variants) +- **large_interpolation**: 6000 task demonstrations across 6 tasks that pose significant challenges for modern imitation learning methods + +See [this link](https://github.com/NVlabs/mimicgen_environments#dataset-types) for more information on the datasets. + +

+ +

+ +## Downloading + +Please see [this link](https://github.com/NVlabs/mimicgen_environments#downloading-and-using-datasets) for instructions on downloading and using these datasets. + +## Postprocessing + +No postprocessing is needed for these datasets. + +## Citation + +```bibtex +@inproceedings{mandlekar2023mimicgen, + title={MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations}, + author={Mandlekar, Ajay and Nasiriany, Soroush and Wen, Bowen and Akinola, Iretiayo and Narang, Yashraj and Fan, Linxi and Zhu, Yuke and Fox, Dieter}, + booktitle={7th Annual Conference on Robot Learning}, + year={2023} +} +``` \ No newline at end of file diff --git a/docs/datasets/overview.md b/docs/datasets/overview.md index 0f260e59..2bf39b92 100644 --- a/docs/datasets/overview.md +++ b/docs/datasets/overview.md @@ -20,6 +20,7 @@ Datasets capture recorded environment data and are used as inputs to a given off | **Dataset** |
**Task Types**
| **Downloading** | **Postprocessing** | | ----------------------------- | :-------------: | :-------------: | :-------------: | | [**robomimic v0.1**](robomimic_v0.1.html)| Sim + Real Robot Manipulation | [Link](robomimic_v0.1.html#downloading) | [Link](robomimic_v0.1.html#postprocessing) | +| [**MimicGen**](mimicgen.html) | Sim Robot Manipulation | [Link](mimicgen.html#downloading) | [Link](mimicgen.html#postprocessing) | | [**D4RL**](d4rl.html) | Sim Locomotion | [Link](d4rl.html#downloading) | [Link](d4rl.html#postprocessing) | | [**MOMART**](momart.html) | Sim Mobile Manipulation | [Link](momart.html#downloading) | [Link](momart.html#postprocessing) | | [**RoboTurk Pilot**](roboturk_pilot.html) | Sim Robot Manipulation | [Link](roboturk_pilot.html#downloading) | [Link](roboturk_pilot.html#postprocessing) | @@ -42,7 +43,7 @@ python train.py --dataset --config

Create Your Own Environment Wrapper!

-If you want to generate your own dataset in a custom environment platform that is not listed above, please see [THIS PAGE](../modules/environments.md#implement-an-environment-wrapper). +If you want to generate your own dataset in a custom environment platform that is not listed above, please see [this page](../modules/environments.md#implement-an-environment-wrapper). diff --git a/docs/datasets/robomimic_v0.1.md b/docs/datasets/robomimic_v0.1.md index 90fec860..9fc887ba 100644 --- a/docs/datasets/robomimic_v0.1.md +++ b/docs/datasets/robomimic_v0.1.md @@ -157,11 +157,11 @@ These datasets were generated by [training](https://github.com/ARISE-Initiative/ This is a diagnostic dataset to test the ability of algorithms to learn from mixed quality human data. A single experienced operator collected 2 demonstrations for each of 100 task initializations on the Can task, resulting in 200 total demonstrations. Each pair of demonstrations consists of a "good" trajectory, where the can is picked up and placed in the correct bin, and a "bad" trajectory, where the can is picked up, and tossed outside of the robot workspace. Since the task initializations are identical, and the first part of each trajectory leading up to the can grasp is similar, there is a strong expectation for algorithms that deal with suboptimal data, to be able to filter the good trajectories from the bad ones, and achieve near-perfect performance. -## Reproduce [Study](https://arise-initiative.github.io/robomimic-web/study/) Results +## Reproduce [Study](https://robomimic.github.io/study/) Results ### Running Experiments -After downloading the appropriate datasets you're interested in using by running the `download_datasets.py` script, the `generate_paper_configs.py` script can be used to generate all training config json files necessary to reproduce the experiments in the [study](https://arise-initiative.github.io/robomimic-web/study/). A few examples are below. +After downloading the appropriate datasets you're interested in using by running the `download_datasets.py` script, the `generate_paper_configs.py` script can be used to generate all training config json files necessary to reproduce the experiments in the [study](https://robomimic.github.io/study/). A few examples are below. ```sh # Assume datasets already exist in robomimic/../datasets folder. Configs will be generated under robomimic/exps/paper, and training results will be at /tmp/experiment_results when launching training runs. @@ -224,13 +224,13 @@ $ python generate_paper_configs.py --output_dir /tmp/experiment_results # the training command can be found in robomimic/exps/paper/core.sh # Training results can be viewed at /tmp/experiment_results (--output_dir when generating paper configs). -$ python train.py --config ../exps/paper/core/lift/ph/low_dim/bc.json +$ python train.py --config ../exps/paper/core/lift/ph/low_dim/bc_rnn.json ``` ## Citation -```sh -@inproceedings{mandlekar2021matters, +```bibtex +@inproceedings{robomimic2021, title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation}, author={Mandlekar, Ajay and Xu, Danfei and Wong, Josiah and Nasiriany, Soroush and Wang, Chen and Kulkarni, Rohun and Fei-Fei, Li and Savarese, Silvio and Zhu, Yuke and Mart{\'\i}n-Mart{\'\i}n, Roberto}, booktitle={5th Annual Conference on Robot Learning}, diff --git a/docs/datasets/robosuite.md b/docs/datasets/robosuite.md index 774b17ee..73f57b0f 100644 --- a/docs/datasets/robosuite.md +++ b/docs/datasets/robosuite.md @@ -64,6 +64,9 @@ $ python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name low # For including image observations $ python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name image.hdf5 --done_mode 2 --camera_names agentview robot0_eye_in_hand --camera_height 84 --camera_width 84 +# For including depth observations too +python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name depth.hdf5 --done_mode 2 --camera_names agentview robot0_eye_in_hand --camera_height 84 --camera_width 84 --depth + # Using dense rewards $ python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name image_dense.hdf5 --done_mode 2 --dense --camera_names agentview robot0_eye_in_hand --camera_height 84 --camera_width 84 diff --git a/docs/images/mimicgen_mosaic.gif b/docs/images/mimicgen_mosaic.gif new file mode 100644 index 0000000000000000000000000000000000000000..f1e5ddf61c668850ea1dac7485095208cb8f70f5 GIT binary patch literal 8967711 zcmeEN_fr#H*xk*hhd}6|8l**P=%FVBkgAa;phBnuf(ArEMBNmslz^asEg&igYQTm) zQ~^q$E1OiTlq@qDm*3eYf($>-?Yw8>5Ez?#vBA|_z8<{QBp_(nHQq8TbENp2ucIxVm zIJC2~ldHA4ha26~$;#8q)63M*i{az#<>t-sX88KDC|ExtaV1TCWq^M`>W&~MgHU^O z_NqWOd-WO?J$$8Gcz}0!uunvwd!({zq_%#fmrl-BpSSBZ}Pcbw}@gS$HG))P%N!8I$wKPqQV5G*bN==SWO-S*prDL^? 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zihTlFaHKda&^2VSL<*&PZ*2AgtnbCtgJNE5@T^MIhj?;jUd)PY-*8gNDw^>u-{EKd z`xYK0jD+#k6hf_S<9-czwEOnaIA-Da Yt>Bv5sxR3JH9lj}?jwV@WdL~mACFjE>;M1& literal 0 HcmV?d00001 diff --git a/docs/index.rst b/docs/index.rst index dc2dc316..6c15c41a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -21,6 +21,7 @@ Welcome to robomimic's documentation! datasets/overview datasets/robomimic_v0.1 + datasets/mimicgen datasets/robosuite datasets/d4rl datasets/momart @@ -41,6 +42,7 @@ Welcome to robomimic's documentation! tutorials/hyperparam_scan tutorials/reproducing_experiments tutorials/dataset_contents + tutorials/training_transformers tutorials/using_pretrained_models tutorials/observations tutorials/pretrained_representations diff --git a/docs/introduction/getting_started.md b/docs/introduction/getting_started.md index 122bf9c9..d995e3af 100644 --- a/docs/introduction/getting_started.md +++ b/docs/introduction/getting_started.md @@ -39,7 +39,7 @@ Make sure to add the `--debug` flag to your experiments as a sanity check that y

      diff --git a/docs/introduction/installation.md b/docs/introduction/installation.md index 95e673f8..bc7b022a 100644 --- a/docs/introduction/installation.md +++ b/docs/introduction/installation.md @@ -177,6 +177,7 @@ $ cd /docs $ make clean $ make apidoc $ make html +$ cp -r images _build/html/ ``` There should be a generated `_build` folder - navigate to `_build/html/` and open `index.html` in a web browser to view the documentation. \ No newline at end of file diff --git a/docs/miscellaneous/contributing.md b/docs/miscellaneous/contributing.md index 3833c349..fc691595 100644 --- a/docs/miscellaneous/contributing.md +++ b/docs/miscellaneous/contributing.md @@ -2,7 +2,7 @@ Our team wholeheartedly welcomes the community to contribute to robomimic. Contributions from members of the community will help ensure the long-term success of this project. Before you plan to make contributions, here are important resources to get started with: -- Read the robomimic [documentation](https://arise-initiative.github.io/robomimic-web/docs/overview.html) and [paper](https://arxiv.org/abs/2108.03298) +- Read the robomimic [documentation](https://robomimic.github.io/docs/introduction/overview.html) and [paper](https://arxiv.org/abs/2108.03298) - Check our latest status from existing [issues](https://github.com/ARISE-Initiative/robomimic/issues), [pull requests](https://github.com/ARISE-Initiative/robomimic/pulls), and [branches](https://github.com/ARISE-Initiative/robomimic/branches) and avoid duplicate efforts - Join our [ARISE Slack](https://ariseinitiative.slack.com) workspace for technical discussions. Please [email us](mailto:yukez@cs.utexas.edu) to be added to the workspace. diff --git a/docs/miscellaneous/references.md b/docs/miscellaneous/references.md index 1dd2dc45..351c3490 100644 --- a/docs/miscellaneous/references.md +++ b/docs/miscellaneous/references.md @@ -4,37 +4,47 @@ A list of projects and papers that use **robomimic**. If you would like to add y ## 2023 -- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://arxiv.org/abs/2302.12422) Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar -- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://arxiv.org/abs/2303.04137) Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, Shuran Song -- [ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments](https://arxiv.org/abs/2301.04195) Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Pooria Poorsarvi Tehrani, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg -- [PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining](https://arxiv.org/abs/2303.08789) Garrett Thomas, Ching-An Cheng, Ricky Loynd, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov -- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://arxiv.org/abs/2304.08742) Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn -- [Mind the Gap: Offline Policy Optimization for Imperfect Rewards](https://arxiv.org/abs/2302.01667) Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Qing-Shan Jia, Ya-Qin Zhang +- [Imitating Task and Motion Planning with Visuomotor Transformers](https://arxiv.org/abs/2305.16309). Murtaza Dalal, Ajay Mandlekar\*, Caelan Garrett\*, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox +- [Data Quality in Imitation Learning](https://arxiv.org/abs/2306.02437). Suneel Belkhale, Yuchen Cui, Dorsa Sadigh +- [Coherent Soft Imitation Learning](https://arxiv.org/abs/2305.16498). Joe Watson, Sandy H. Huang, Nicolas Heess +- [Inverse Preference Learning: Preference-based RL without a Reward Function](https://arxiv.org/abs/2305.15363). Joey Hejna, Dorsa Sadigh +- [Sequence Modeling is a Robust Contender for Offline Reinforcement Learning](https://arxiv.org/abs/2305.14550). Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang +- [Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks](https://arxiv.org/abs/2305.12171). Eley Ng, Ziang Liu, Monroe Kennedy III +- [Zero-shot Preference Learning for Offline RL via Optimal Transport](https://arxiv.org/abs/2306.03615). Runze Liu, Yali Du, Fengshuo Bai, Jiafei Lyu, Xiu Li +- [Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers](https://arxiv.org/abs/2306.09331). Dominick Reilly, Aman Chadha, Srijan Das +- [Get Back Here: Robust Imitation by Return-to-Distribution Planning](https://arxiv.org/abs/2305.01400). Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Leonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi +- [Preference Transformer: Modeling Human Preferences using Transformers for RL](https://arxiv.org/abs/2303.00957). Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee +- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://arxiv.org/abs/2302.12422). Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar +- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://arxiv.org/abs/2303.04137). Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, Shuran Song +- [ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments](https://arxiv.org/abs/2301.04195). Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Pooria Poorsarvi Tehrani, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg +- [PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining](https://arxiv.org/abs/2303.08789). Garrett Thomas, Ching-An Cheng, Ricky Loynd, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov +- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://arxiv.org/abs/2304.08742). Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn +- [Mind the Gap: Offline Policy Optimization for Imperfect Rewards](https://arxiv.org/abs/2302.01667). Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Qing-Shan Jia, Ya-Qin Zhang ## 2022 -- [Learning and Retrieval from Prior Data for Skill-based Imitation Learning](https://arxiv.org/abs/2210.11435) Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu -- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://arxiv.org/abs/2210.11339) Yifeng Zhu, Abhishek Joshi, Peter Stone, Yuke Zhu -- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://arxiv.org/abs/2211.08416) Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu -- [Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data](https://arxiv.org/abs/2210.08642) Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill -- [Eliciting Compatible Demonstrations for Multi-Human Imitation Learning](https://arxiv.org/abs/2210.08073) Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh -- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://arxiv.org/abs/2209.07682) Yilun Hao, Ruinan Wang, Zhangjie Cao, Zihan Wang, Yuchen Cui, Dorsa Sadigh -- [Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL](https://arxiv.org/abs/2206.00695) Wonjoon Goo, Scott Niekum -- [HEETR: Pretraining for Robotic Manipulation on Heteromodal Data](https://openreview.net/forum?id=1_XARk3k-M) Garrett Thomas, Andrey Kolobov, Ching-An Cheng, Vibhav Vineet, Mihai Jalobeanu -- [Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots](https://proceedings.mlr.press/v162/shankar22a.html) Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh -- [Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems](https://arxiv.org/abs/2209.09174) Alexander Ororbia, Ankur Mali -- [Imitation Learning by Estimating Expertise of Demonstrators](https://arxiv.org/abs/2202.01288) Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani +- [Learning and Retrieval from Prior Data for Skill-based Imitation Learning](https://arxiv.org/abs/2210.11435). Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu +- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://arxiv.org/abs/2210.11339). Yifeng Zhu, Abhishek Joshi, Peter Stone, Yuke Zhu +- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://arxiv.org/abs/2211.08416). Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu +- [Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data](https://arxiv.org/abs/2210.08642). Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill +- [Eliciting Compatible Demonstrations for Multi-Human Imitation Learning](https://arxiv.org/abs/2210.08073). Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh +- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://arxiv.org/abs/2209.07682). Yilun Hao, Ruinan Wang, Zhangjie Cao, Zihan Wang, Yuchen Cui, Dorsa Sadigh +- [Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL](https://arxiv.org/abs/2206.00695). Wonjoon Goo, Scott Niekum +- [HEETR: Pretraining for Robotic Manipulation on Heteromodal Data](https://openreview.net/forum?id=1_XARk3k-M). Garrett Thomas, Andrey Kolobov, Ching-An Cheng, Vibhav Vineet, Mihai Jalobeanu +- [Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots](https://proceedings.mlr.press/v162/shankar22a.html). Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh +- [Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems](https://arxiv.org/abs/2209.09174). Alexander Ororbia, Ankur Mali +- [Imitation Learning by Estimating Expertise of Demonstrators](https://arxiv.org/abs/2202.01288). Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani ## 2021 -- [RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning](https://arxiv.org/abs/2111.02767) Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev -- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://arxiv.org/abs/2112.05251) Josiah Wong, Albert Tung, Andrey Kurenkov, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Roberto Martín-Martín -- [Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control](https://arxiv.org/abs/2103.00375) Chen Wang, Rui Wang, Danfei Xu, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese -- [Deep Affordance Foresight: Planning Through What Can Be Done in the Future](https://arxiv.org/abs/2011.08424) Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, Li Fei-Fei -- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://arxiv.org/abs/2012.06738) Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese -- [Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations](https://arxiv.org/abs/2003.06085) Ajay Mandlekar\*, Danfei Xu\*, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei +- [RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning](https://arxiv.org/abs/2111.02767). Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev +- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://arxiv.org/abs/2112.05251). Josiah Wong, Albert Tung, Andrey Kurenkov, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Roberto Martín-Martín +- [Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control](https://arxiv.org/abs/2103.00375). Chen Wang, Rui Wang, Danfei Xu, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese +- [Deep Affordance Foresight: Planning Through What Can Be Done in the Future](https://arxiv.org/abs/2011.08424). Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, Li Fei-Fei +- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://arxiv.org/abs/2012.06738). Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese +- [Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations](https://arxiv.org/abs/2003.06085). Ajay Mandlekar\*, Danfei Xu\*, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei ## 2020 -- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://arxiv.org/abs/2012.06733) Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese +- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://arxiv.org/abs/2012.06733). Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese - [IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data](https://arxiv.org/abs/1911.05321). Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox diff --git a/docs/miscellaneous/team.md b/docs/miscellaneous/team.md index 31179139..16b784e0 100644 --- a/docs/miscellaneous/team.md +++ b/docs/miscellaneous/team.md @@ -4,11 +4,12 @@ These people contributed directly to the codebase and helped build it. -- [Ajay Mandlekar](http://web.stanford.edu/~amandlek/) -- [Danfei Xu](https://cs.stanford.edu/~danfei/) +- [Ajay Mandlekar](https://ai.stanford.edu/~amandlek/) +- [Danfei Xu](https://faculty.cc.gatech.edu/~danfei/) - [Josiah Wong](https://www.jowo.me/about) - [Soroush Nasiriany](http://snasiriany.me/) - [Chen Wang](http://www.chenwangjeremy.net/) +- [Matthew Bronars](https://bronars.github.io/) ## Mentors diff --git a/docs/miscellaneous/troubleshooting.md b/docs/miscellaneous/troubleshooting.md index e5e1e8ca..189de425 100644 --- a/docs/miscellaneous/troubleshooting.md +++ b/docs/miscellaneous/troubleshooting.md @@ -4,7 +4,7 @@ This section will be populated with common mistakes and links to issues and fixes as we are made aware of them. -If you're still having problems, please submit an issue on the github page. +If you're still having problems, please submit an issue on [the github page](https://github.com/ARISE-Initiative/robomimic/issues). ## Known Issues @@ -12,4 +12,4 @@ This section contains known issues that are either minor, or that will be patche - `PrintLogger` breaks if you use `embed()` with a new-ish IPython installation. The current workaround is to use an old version. Known working version is `ipython==5.8.0` -- The `test_scripts` tests will fail if `robosuite` is not on the `offline_study` branch with the following error: `No "site" with name gripper0_ee_x exists.`. This is because the test hdf5 was collected on that branch -- switching to that branch should make the test pass. \ No newline at end of file +- On robomimic v0.2, the `test_scripts` tests will fail if `robosuite` is not on the `offline_study` branch with the following error: `No "site" with name gripper0_ee_x exists.`. This is because the test hdf5 was collected on that branch -- switching to that branch should make the test pass. \ No newline at end of file diff --git a/docs/model_zoo/robomimic_v0.1.md b/docs/model_zoo/robomimic_v0.1.md index 8021a094..d5ea8551 100644 --- a/docs/model_zoo/robomimic_v0.1.md +++ b/docs/model_zoo/robomimic_v0.1.md @@ -10,9 +10,16 @@ See the ["Using Pretrained Models"](../tutorials/using_pretrained_models.html) t

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-

Warning: use correct robosuite branch!

+

Warning: use correct robomimic and robosuite versions!

-When using these trained models, please make sure that robosuite is on the `offline_study` branch of robosuite. +When using these trained models, please make sure that robosuite is on the [`offline_study` branch](https://github.com/ARISE-Initiative/robosuite/tree/offline_study), and that robomimic is on the [`v0.1` branch](https://github.com/ARISE-Initiative/robomimic/tree/v0.1). + +
+ +
+

Troubleshooting download issues

+ +Some users (eg. using Chrome) may experience issues with the dataset download links. If you are experiencing these issues, follow these steps: (1) right click on the download link, (2) select the option to copy the linked url, and (3) copy paste the link in a new tab.
diff --git a/docs/modules/dataset.md b/docs/modules/dataset.md index 8a7871ee..4c2510e2 100644 --- a/docs/modules/dataset.md +++ b/docs/modules/dataset.md @@ -1,6 +1,6 @@ # SequenceDataset -The `robomimic.utils.dataset.SequenceDataset` class extends PyTorch's default `torch.utils.data.Dataset` to interface with our demonstration [datasets](../introduction/datasets.html). The class supports accessing demonstration sub-sequences (as opposed to individual states) by index and both on-demand fetching and in-memory caching. This page walks through the key concepts of the `SequenceDataset` interface. Please refer to the official PyTorch [documentation](https://pytorch.org/docs/stable/data.html) and a short [example](../introduction/examples.html#train-loop-example) on how to use the `Dataset` and `DataLoader` interfaces to build a training pipeline. Also, please see the [released datasets section](../introduction/results.html#downloading-released-datasets) for guidance on downloading datasets that work out of the box with the repository. +The `robomimic.utils.dataset.SequenceDataset` class extends PyTorch's default `torch.utils.data.Dataset` to interface with our demonstration [datasets](../datasets/overview.html). The class supports accessing demonstration sub-sequences (as opposed to individual states) by index and both on-demand fetching and in-memory caching. This page walks through the key concepts of the `SequenceDataset` interface. Please refer to the official PyTorch [documentation](https://pytorch.org/docs/stable/data.html) and a short [example](https://github.com/ARISE-Initiative/robomimic/blob/master/examples/simple_train_loop.py) on how to use the `Dataset` and `DataLoader` interfaces to build a training pipeline. Here is a sample dataset object: @@ -19,8 +19,10 @@ dataset = SequenceDataset( "rewards", "dones", ), - seq_length=10, # length-10 temporal sequences + seq_length=10, # length of sub-sequence to fetch: (s_{t}, a_{t}), (s_{t+1}, a_{t+1}), ..., (s_{t+9}, a_{t+9}) + frame_stack=1, # length of sub-sequence to prepend pad_seq_length=True, # pad last obs per trajectory to ensure all sequences are sampled + pad_frame_stack=True, # pad first obs per trajectory to ensure all sequences are sampled hdf5_cache_mode="all", # cache dataset in memory to avoid repeated file i/o hdf5_normalize_obs=False, filter_by_attribute=None, # can optionally provide a filter key here @@ -28,15 +30,19 @@ dataset = SequenceDataset( ``` - `hdf5_path` - - The absolute / relative path to the hdf5 file containing training demonstrations. See [datasets](../introduction/datasets.html) page for the expected data structure. + - The absolute / relative path to the hdf5 file containing training demonstrations. See the [datasets page](../datasets/overview.html#dataset-structure) for the expected data structure. - `obs_keys` - A list of strings specifying which observation modalities to read from the dataset. This is typically read from the config file: our implementation pools observation keys from `config.observation.modalities.obs.low_dim` and `config.observation.modalities.obs.rgb`. - `dataset_keys` - Keys of non-observation data to read from a demonstration. Typically include `actions`, `rewards`, `dones`. - `seq_length` - - Length of the demonstration sub-sequence to fetch. + - Length of demonstration sub-sequence to fetch. For example, if `seq_length = 10` at time `t`, the data loader will fetch ${(s_{t}, a_{t}), (s_{t+1}, a_{t+1}), ..., (s_{t+9}, a_{t+9})}$ +- `frame_stack` + - Length of sub-sequence to stack at the beginning of fetched demonstration. For example, if `frame_stack = 10` at time `t`, the data loader will fetch ${(s_{t-1}, a_{t-1}), (s_{t-2}, a_{t-2}), ..., (s_{t-9}, a_{t-9})}$. Note that the actual length of the fetched sequence is `frame_stack - 1`. This term is useful when training a model to predict `seq_length` actions from `frame_stack` observations. If training a transformer, this should be the same as context length. - `pad_seq_length` - Whether to allow fetching subsequence that ends beyond the sequence. For example, given a demo of length 10 and `seq_length=10`, setting `pad_seq_length=True` allows the dataset object to access subsequence at `__get_item(index=5)__` by repeating the last frame 5 times. +- `pad_frame_stack` + - Whether to allow fetching subsequence that starts before the first time step. For example, given a demo of length 10 and `frame_stack=10`, setting `pad_frame_stack=True` allows the dataset object to access subsequence at `__get_item(index=5)__` by repeating the first frame 5 times. - `hdf5_cache_mode` - Optionally cache the dataset in memory for faster access. The dataset supports three caching modes: `["all", "low_dim", or None]`. - `all`: Load the entire dataset into the RAM. This mode minimizes data loading time but incurs the largest memory footprint. Recommended if the dataset is small or when working with low-dimensional observation data. @@ -45,4 +51,4 @@ dataset = SequenceDataset( - `hdf5_normalize_obs` - If `True`, normalize observations by computing the mean observation and std of each observation (in each dimension and modality), and normalizing unit mean and variance in each dimension. - `filter_by_attribute` - - if provided, use the provided filter key to look up a subset of demonstrations to load. See the documentation on [filter keys](../introduction/datasets.html#filter-keys-and-train-valid-splits) for more information. + - if provided, use the provided filter key to look up a subset of demonstrations to load. See the documentation on [filter keys](../datasets/overview.html#filter-keys) for more information. diff --git a/docs/modules/environments.md b/docs/modules/environments.md index a87fd17a..59c4808a 100644 --- a/docs/modules/environments.md +++ b/docs/modules/environments.md @@ -51,7 +51,7 @@ def create_env_from_metadata( - `type` is an enum defined in `robomimicenvs.env_base.EnvType` that specifies the type of environment. `type` is mainly used by the `robomimic.utils.env_utils.get_env_class()` function to look up the correct environment wrapper class. - `env_kwargs` specifies the keyword args that are required to initialize an environment. The `env_kwargs` will be passed to the constructor of the wrapped environment as keyword arguments through the `robomimic.utils.env_utils.create_env()` helper function. -Although it is possible to manually specify the `env_meta` dictionary, the **robomimic** training pipeline reads the `env_meta` from the hdf5 dataset as an attribute. Please refer to the [Dataset section](../introduction/datasets.html) for more details on where the metadata is stored, and the `robomimic.utils.file_utils.get_env_metadata_from_dataset` function to see how it is loaded from the dataset at run-time. +Although it is possible to manually specify the `env_meta` dictionary, the **robomimic** training pipeline reads the `env_meta` from the hdf5 dataset as an attribute. Please refer to the [Dataset section](../datasets/overview.html#dataset-structure) for more details on where the metadata is stored, and the `robomimic.utils.file_utils.get_env_metadata_from_dataset` function to see how it is loaded from the dataset at run-time. ## Initialize an Environment from a Dataset diff --git a/docs/modules/models.md b/docs/modules/models.md index fb3ed9e9..b9c18af2 100644 --- a/docs/modules/models.md +++ b/docs/modules/models.md @@ -43,7 +43,7 @@ config.observation.encoder.rgb.core_class = "MyCustomRGBEncoder" config.observation.encoder.rgb.core_kwargs = ... ``` -Any corresponding keyword arguments that should be passed to the encoder constructor should be specified in `core_kwargs` in the config. For more information on creating your own custom encoder, please see our [example script](../introduction/examples.html#custom-observation-modalities-example). +Any corresponding keyword arguments that should be passed to the encoder constructor should be specified in `core_kwargs` in the config. For more information on creating your own custom encoder, please see our [example script](https://github.com/ARISE-Initiative/robomimic/blob/master/examples/simple_obs_nets.py). Below, we provide descriptions of specific EncoderCore-based classes used to encode RGB and depth observations (`VisualCore`) and range scanner observations (`ScanCore`). @@ -196,13 +196,13 @@ These networks take an observation dictionary as input (and possibly additional ### ValueNetwork - A basic value network that predicts values from observations. Can optionally be goal conditioned on future observations. ### DistributionalActionValueNetwork -- Distributional Q (action-value) network that outputs a categorical distribution over a discrete grid of value atoms. See the [paper](https://arxiv.org/pdf/1707.06887.pdf) for more details. +- Distributional Q (action-value) network that outputs a categorical distribution over a discrete grid of value atoms. See the [paper](https://arxiv.org/abs/1707.06887) for more details. ## Implemented VAEs The library implements a general VAE architecture and a number of prior distributions. See `robomimic/models/vae_nets.py` for complete implementations. ### VAE -A general Variational Autoencoder (VAE) implementation, as described in https://arxiv.org/abs/1312.6114. +A general Variational Autoencoder (VAE) implementation, as described in this [paper](https://arxiv.org/abs/1312.6114). Models a distribution p(X) or a conditional distribution p(X | Y), where each variable can consist of multiple modalities. The target variable X, whose distribution is modeled, is specified through the `input_shapes` argument, which is a map between modalities (strings) and expected shapes. In this way, a variable that consists of multiple kinds of data (e.g. image and flat-dimensional) can be modeled as well. A separate `output_shapes` argument is used to specify the expected reconstructions - this allows for asymmetric reconstruction (for example, reconstructing low-resolution images). diff --git a/docs/tutorials/configs.md b/docs/tutorials/configs.md index fa67a28a..519257de 100644 --- a/docs/tutorials/configs.md +++ b/docs/tutorials/configs.md @@ -19,10 +19,10 @@ Please see the [Config documentation](../modules/configs.html) for more informat The preferred way to specify training parameters is to pass a config json to the main training script `train.py` via the `--config` argument. The dataset can be specified by setting the `data` attribute of the `train` section of the config json, or specified via the `--dataset` argument. The example below runs a default template json for the BC algorithm. **This is the preferred way to launch training runs.** ```sh -$ python train.py --config ../exps/templates/bc.json --dataset ../../tests/assets/test.hdf5 +$ python train.py --config ../exps/templates/bc.json --dataset ../../tests/assets/test_v141.hdf5 ``` -Please see the [hyperparameter helper docs](./advanced.html#using-the-hyperparameter-helper-to-launch-runs) to see how to easily generate json configs for launching training runs. +Please see the [hyperparameter helper tutorial](../tutorials/hyperparam_scan.html) to see how to easily generate json configs for launching training runs. #### 2. Constructing a config object in code diff --git a/docs/tutorials/dataset_contents.md b/docs/tutorials/dataset_contents.md index 1519af4c..0cfb2584 100644 --- a/docs/tutorials/dataset_contents.md +++ b/docs/tutorials/dataset_contents.md @@ -18,10 +18,10 @@ The repository offers a simple utility script (`get_dataset_info.py`) to view th - the [environment metadata](../modules/environments.html#initialize-an-environment-from-a-dataset) in the dataset, which is used to construct the same simulator environment that the data was collected on - the dataset structure for the first demonstration -Pass the `--verbose` argument to print the list of demonstration keys under each filter key, and the dataset structure for all demonstrations. An example, using the small hdf5 dataset packaged with the repository in `tests/assets/test.hdf5` is shown below. +Pass the `--verbose` argument to print the list of demonstration keys under each filter key, and the dataset structure for all demonstrations. An example, using the small hdf5 dataset packaged with the repository in `tests/assets/test_v141.hdf5` is shown below. ```sh -$ python get_dataset_info.py --dataset ../../tests/assets/test.hdf5 +$ python get_dataset_info.py --dataset ../../tests/assets/test_v141.hdf5 ```
@@ -36,7 +36,7 @@ Any user wishing to write custom code that works with robomimic datasets should

Note: These examples are compatible with any robomimic dataset.

-The examples in this section use the small hdf5 dataset packaged with the repository in `tests/assets/test.hdf5`, but you can run these examples with any robomimic hdf5 dataset. If you are using the default dataset, please make sure that robosuite is on the `offline_study` branch of robosuite -- this is necessary for the playback scripts to function properly. +The examples in this section use the small hdf5 dataset packaged with the repository in `tests/assets/test_v141.hdf5` (which requires robosuite v1.4.1), but you can run these examples with any robomimic hdf5 dataset.
@@ -44,14 +44,17 @@ Use the `playback_dataset.py` script to easily view dataset trajectories. ```sh # For the first 5 trajectories, load environment simulator states one-by-one, and render "agentview" and "robot0_eye_in_hand" cameras to video at /tmp/playback_dataset.mp4 -$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --render_image_names agentview robot0_eye_in_hand --video_path /tmp/playback_dataset.mp4 --n 5 +$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --render_image_names agentview robot0_eye_in_hand --video_path /tmp/playback_dataset.mp4 --n 5 # Directly visualize the image observations in the dataset. This is especially useful for real robot datasets where there is no simulator to use for rendering. -$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --use-obs --render_image_names agentview_image --video_path /tmp/obs_trajectory.mp4 +$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --use-obs --render_image_names agentview_image --video_path /tmp/obs_trajectory.mp4 + +# Visualize depth observations as well. +$ python playback_dataset.py --dataset /path/to/dataset.hdf5 --use-obs --render_image_names agentview_image --render_depth_names agentview_depth --video_path /tmp/obs_trajectory.mp4 # Play the dataset actions in the environment to verify that the recorded actions are reasonable. -$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --use-actions --render_image_names agentview --video_path /tmp/playback_dataset_with_actions.mp4 +$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --use-actions --render_image_names agentview --video_path /tmp/playback_dataset_with_actions.mp4 # Visualize only the initial demonstration frames. -$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --first --render_image_names agentview --video_path /tmp/dataset_task_inits.mp4 +$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --first --render_image_names agentview --video_path /tmp/dataset_task_inits.mp4 ``` diff --git a/docs/tutorials/hyperparam_scan.md b/docs/tutorials/hyperparam_scan.md index 43e7cb4e..1ca4b35e 100644 --- a/docs/tutorials/hyperparam_scan.md +++ b/docs/tutorials/hyperparam_scan.md @@ -22,7 +22,7 @@ Sections of the config that are not involved in the scan and that do not differ
-We modify `/tmp/gen_configs/base.json`, adding a base experiment name (`"bc_rnn_hyper"`) and specified the dataset path (`"/tmp/test.hdf5"`). +We modify `/tmp/gen_configs/base.json`, adding a base experiment name (`"bc_rnn_hyper"`) and specified the dataset path (`"/tmp/test_v141.hdf5"`). ```sh $ cat /tmp/gen_configs/base.json @@ -59,7 +59,7 @@ $ cat /tmp/gen_configs/base.json } }, "train": { - "data": "/tmp/test.hdf5", + "data": "/tmp/test_v141.hdf5", "output_dir": "../bc_trained_models", "num_data_workers": 0, "hdf5_cache_mode": "all", @@ -198,6 +198,21 @@ Only do this if you are sweeping over a single value! +
+

wandb logging

+ +If you would like to log and view results on wandb, enable wandb logging in the hyperparameter generator: +```python +generator.add_param( + key="experiment.logging.log_wandb", + name="", + group=-1, + values=[True], +) +``` + +
+ ### Define hyperparameter scan values Now we define our scan - we could like to sweep the following: - policy learning rate in [1e-3, 1e-4] @@ -276,3 +291,10 @@ python train.py --config /tmp/gen_configs/bc_rnn_hyper_plr_0.0001_gmm_t_rnnd_100 python train.py --config /tmp/gen_configs/bc_rnn_hyper_plr_0.0001_gmm_f_rnnd_400_mlp_1024.json python train.py --config /tmp/gen_configs/bc_rnn_hyper_plr_0.0001_gmm_f_rnnd_1000_mlp_0.json ``` + +
+

Meta information

+ +For each generated config file you will find a `meta` section that contains hyperparameter names, values, and other metadata information. This `meta` section is generated automatically, and you should NOT need to edit or modify it. + +
diff --git a/docs/tutorials/pretrained_representations.md b/docs/tutorials/pretrained_representations.md index 072de4da..72c66a51 100644 --- a/docs/tutorials/pretrained_representations.md +++ b/docs/tutorials/pretrained_representations.md @@ -26,7 +26,17 @@ config.observation.encoder.rgb.core_kwargs.pool_class = None config.observation.encoder.rgb.core_kwargs.backbone_class = 'MVPConv' # MVP backbone for image observations (unused if no image observations) config.observation.encoder.rgb.core_kwargs.backbone_kwargs.mvp_model_class = 'vitb-mae-egosoup' # MVP model class (vits-mae-hoi, vits-mae-in, vits-sup-in, vitb-mae-egosoup, vitl-256-mae-egosoup) config.observation.encoder.rgb.core_kwargs.backbone_kwargs.freeze = True # whether to freeze network during training or allow finetuning -config.observation.encoder.rgb.core_kwargs.pool_class = None # no pooling class for pretraining model +config.observation.encoder.rgb.core_kwargs.pool_class = None # no pooling class for pretraining model + +# Set data loader attributes for image observations +config.train.num_data_workers = 2 # 2 data workers for image datasets +config.train.hdf5_cache_mode = "low_dim" # only cache non-image data + +# Ensure that you are using image observation modalities, names may depend on your dataset naming convention +config.observation.modalities.obs.rgb = [ + "agentview_image", + "robot0_eye_in_hand_image" + ] ``` Alternatively, if you are using a config json, you can set the appropriate keys in your json. diff --git a/docs/tutorials/training_transformers.md b/docs/tutorials/training_transformers.md new file mode 100644 index 00000000..03af242a --- /dev/null +++ b/docs/tutorials/training_transformers.md @@ -0,0 +1,43 @@ +# Training Transformers + +This tutorial shows how to train a transformer policy network. + +
+

Note: Understand how to launch training runs and view results first!

+ +Before trying to train a transformer, it might be useful to read the following tutorials: +- [how to launch training runs](./configs.html) +- [how to view training results](./viewing_results.html) +- [how to launch multiple training runs efficiently](./hyperparam_scan.html) + +
+ +A template with tuned parameters for transformer based policy networks is defined in `robomimic/config/default_templates/bc_transformer.json`. + +#### 1. Using default configurations + +The easiest way to train a transformer policy network is to pass the default template json to the main training script `train.py` via the `--config` argument. The dataset can be specified by setting the `data` attribute of the `train` section of the config json, or specified via the `--dataset` argument. You may find that your data has different rollout horizon lengths, observation modalities, or other incompatibilities with the default template. In this scenario, we suggest defining custom parameters as described in (2). + +```sh +$ python train.py --config ../config/default_templates/bc_transformer.json --dataset /path/to/dataset.hdf5 +``` + +#### 2. Defining custom parameters + +If you want to modify the default transformer parameters, do not directly modify the default config (`config/bc_config.py`) or template (`config/default_templates/bc_transformer.json`). Instead, you can create a copy of `robomimic/config/default_templates/bc_transformer.json` and store it in a new directory on your computer. Set this as the base file for `scripts/hyperparam_helper.py` and define custom settings as described [here](./hyperparam_scan.html). This is particularly useful when running a sweep over hyperparameters; **it is the prefered way to launch multiple training runs**. + +Optionally, you can modify the default template in python code or directly set the appropriate keys in your copy of the config file. This code snippet below highlights useful parameters to tune for transformers. To see all transformer policy settings, refer to `config/bc_config.py`. + +```python +# make sure transformer is enabled +config.algo.transformer.enabled = True + +# useful config attributes to modify for transformers +config.algo.transformer.embed_dim = 512 # dimension for embeddings used by transformer +config.algo.transformer.num_layers = 6 # number of transformer blocks to stack +config.algo.transformer.num_heads = 8 # number of attention heads for each transformer block (should divide embed_dim evenly) +config.algo.transformer.context_length = 10 # length of (s, a) sub-seqeunces to feed to transformer +config.train.frame_stack = 10 # length of sub-sequence to observe: (s_{t-1}, a_{t-1}), (s_{t-2}, a_{t-2}), ..., (s_{t-9}, a_{t-9}) +config.train.seq_length = 1 # length of sub-seqeunce to predict: (s_{t}, a_{t}) +``` + diff --git a/docs/tutorials/viewing_results.md b/docs/tutorials/viewing_results.md index 7a6838f1..64acd9ff 100644 --- a/docs/tutorials/viewing_results.md +++ b/docs/tutorials/viewing_results.md @@ -95,4 +95,12 @@ You may find the following logging metrics useful: - `Timing_Stats/`: time spent by the algorithm loading data, training, performing rollouts, etc. - `Train/`: training stats - `Validation/`: validation stats -- `System/RAM Usage (MB)`: system RAM used by algorithm \ No newline at end of file +- `System/RAM Usage (MB)`: system RAM used by algorithm + + +### Viewing wandb Results +You can also view results in [wandb](https://wandb.ai), similarly to tensorboard. To do so, ensure that you have set `experiment.logging.log_wandb` to True in the experiment config. + +When first logging to wandb, you will need to specify a wandb entity name, ie. the wandb account under which results will be logged. You can do so by setting `WANDB_ENTITY` to the desired wandb account name in `robomimic/macros_private.py`. Note: if this file does not exist, run `python robomimic/scripts/setup_macros.py` to setup the private macros file. + +By default all results will be logged under a wandb project labled `default`, however you can set the project name by setting `experiment.logging.wandb_proj_name` in the configs. diff --git a/examples/notebooks/datasets.ipynb b/examples/notebooks/datasets.ipynb index f930e99d..32893b7a 100644 --- a/examples/notebooks/datasets.ipynb +++ b/examples/notebooks/datasets.ipynb @@ -22,18 +22,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "4e2b90e6", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "low_dim.hdf5: 18.6MB [00:00, 27.5MB/s] \n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import json\n", @@ -60,7 +52,7 @@ ")\n", "\n", "# enforce that the dataset exists\n", - "dataset_path = os.path.join(download_folder, \"low_dim.hdf5\")\n", + "dataset_path = os.path.join(download_folder, \"low_dim_v141.hdf5\")\n", "assert os.path.exists(dataset_path)" ] }, @@ -78,18 +70,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a35cd8e9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hdf5 file /tmp/robomimic_ds_example/low_dim.hdf5 has 200 demonstrations\n" - ] - } - ], + "outputs": [], "source": [ "# open file\n", "f = h5py.File(dataset_path, \"r\")\n", @@ -111,217 +95,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "9bda3e70", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "demo_0 has 59 samples\n", - "demo_1 has 58 samples\n", - "demo_2 has 57 samples\n", - "demo_3 has 55 samples\n", - "demo_4 has 51 samples\n", - "demo_5 has 58 samples\n", - "demo_6 has 49 samples\n", - "demo_7 has 49 samples\n", - "demo_8 has 44 samples\n", - "demo_9 has 51 samples\n", - "demo_10 has 54 samples\n", - "demo_11 has 49 samples\n", - "demo_12 has 53 samples\n", - "demo_13 has 59 samples\n", - "demo_14 has 51 samples\n", - "demo_15 has 50 samples\n", - "demo_16 has 50 samples\n", - "demo_17 has 45 samples\n", - "demo_18 has 49 samples\n", - "demo_19 has 51 samples\n", - "demo_20 has 48 samples\n", - "demo_21 has 57 samples\n", - "demo_22 has 47 samples\n", - "demo_23 has 47 samples\n", - "demo_24 has 52 samples\n", - "demo_25 has 48 samples\n", - "demo_26 has 43 samples\n", - "demo_27 has 46 samples\n", - "demo_28 has 49 samples\n", - "demo_29 has 41 samples\n", - "demo_30 has 46 samples\n", - "demo_31 has 42 samples\n", - "demo_32 has 59 samples\n", - "demo_33 has 54 samples\n", - "demo_34 has 48 samples\n", - "demo_35 has 58 samples\n", - "demo_36 has 45 samples\n", - "demo_37 has 50 samples\n", - "demo_38 has 49 samples\n", - "demo_39 has 41 samples\n", - "demo_40 has 40 samples\n", - "demo_41 has 49 samples\n", - "demo_42 has 53 samples\n", - "demo_43 has 39 samples\n", - "demo_44 has 46 samples\n", - "demo_45 has 49 samples\n", - "demo_46 has 47 samples\n", - "demo_47 has 40 samples\n", - "demo_48 has 53 samples\n", - "demo_49 has 48 samples\n", - "demo_50 has 45 samples\n", - "demo_51 has 47 samples\n", - "demo_52 has 46 samples\n", - "demo_53 has 55 samples\n", - "demo_54 has 43 samples\n", - "demo_55 has 56 samples\n", - "demo_56 has 40 samples\n", - "demo_57 has 38 samples\n", - "demo_58 has 38 samples\n", - "demo_59 has 44 samples\n", - "demo_60 has 42 samples\n", - "demo_61 has 54 samples\n", - "demo_62 has 41 samples\n", - "demo_63 has 42 samples\n", - "demo_64 has 53 samples\n", - "demo_65 has 38 samples\n", - "demo_66 has 41 samples\n", - "demo_67 has 42 samples\n", - "demo_68 has 39 samples\n", - "demo_69 has 42 samples\n", - "demo_70 has 48 samples\n", - "demo_71 has 45 samples\n", - "demo_72 has 38 samples\n", - "demo_73 has 36 samples\n", - "demo_74 has 48 samples\n", - "demo_75 has 36 samples\n", - "demo_76 has 48 samples\n", - "demo_77 has 39 samples\n", - "demo_78 has 44 samples\n", - "demo_79 has 44 samples\n", - "demo_80 has 40 samples\n", - "demo_81 has 38 samples\n", - "demo_82 has 47 samples\n", - "demo_83 has 52 samples\n", - "demo_84 has 53 samples\n", - "demo_85 has 46 samples\n", - "demo_86 has 38 samples\n", - "demo_87 has 39 samples\n", - "demo_88 has 39 samples\n", - "demo_89 has 41 samples\n", - "demo_90 has 42 samples\n", - "demo_91 has 37 samples\n", - "demo_92 has 51 samples\n", - "demo_93 has 50 samples\n", - "demo_94 has 51 samples\n", - "demo_95 has 46 samples\n", - "demo_96 has 56 samples\n", - "demo_97 has 53 samples\n", - "demo_98 has 46 samples\n", - "demo_99 has 46 samples\n", - "demo_100 has 47 samples\n", - "demo_101 has 43 samples\n", - "demo_102 has 58 samples\n", - "demo_103 has 52 samples\n", - "demo_104 has 48 samples\n", - "demo_105 has 55 samples\n", - "demo_106 has 49 samples\n", - "demo_107 has 62 samples\n", - "demo_108 has 43 samples\n", - "demo_109 has 50 samples\n", - "demo_110 has 45 samples\n", - "demo_111 has 46 samples\n", - "demo_112 has 44 samples\n", - "demo_113 has 43 samples\n", - "demo_114 has 47 samples\n", - "demo_115 has 49 samples\n", - "demo_116 has 59 samples\n", - "demo_117 has 52 samples\n", - "demo_118 has 54 samples\n", - "demo_119 has 53 samples\n", - "demo_120 has 63 samples\n", - "demo_121 has 53 samples\n", - "demo_122 has 60 samples\n", - "demo_123 has 51 samples\n", - "demo_124 has 47 samples\n", - "demo_125 has 55 samples\n", - "demo_126 has 56 samples\n", - "demo_127 has 58 samples\n", - "demo_128 has 55 samples\n", - "demo_129 has 53 samples\n", - "demo_130 has 50 samples\n", - "demo_131 has 47 samples\n", - "demo_132 has 46 samples\n", - "demo_133 has 43 samples\n", - "demo_134 has 45 samples\n", - "demo_135 has 54 samples\n", - "demo_136 has 53 samples\n", - "demo_137 has 57 samples\n", - "demo_138 has 50 samples\n", - "demo_139 has 48 samples\n", - "demo_140 has 49 samples\n", - "demo_141 has 54 samples\n", - "demo_142 has 55 samples\n", - "demo_143 has 49 samples\n", - "demo_144 has 51 samples\n", - "demo_145 has 45 samples\n", - "demo_146 has 50 samples\n", - "demo_147 has 51 samples\n", - "demo_148 has 50 samples\n", - "demo_149 has 58 samples\n", - "demo_150 has 46 samples\n", - "demo_151 has 46 samples\n", - "demo_152 has 45 samples\n", - "demo_153 has 42 samples\n", - "demo_154 has 49 samples\n", - "demo_155 has 45 samples\n", - "demo_156 has 63 samples\n", - "demo_157 has 41 samples\n", - "demo_158 has 42 samples\n", - "demo_159 has 45 samples\n", - "demo_160 has 43 samples\n", - "demo_161 has 46 samples\n", - "demo_162 has 52 samples\n", - "demo_163 has 55 samples\n", - "demo_164 has 44 samples\n", - "demo_165 has 42 samples\n", - "demo_166 has 51 samples\n", - "demo_167 has 64 samples\n", - "demo_168 has 57 samples\n", - "demo_169 has 52 samples\n", - "demo_170 has 48 samples\n", - "demo_171 has 45 samples\n", - "demo_172 has 53 samples\n", - "demo_173 has 39 samples\n", - "demo_174 has 47 samples\n", - "demo_175 has 52 samples\n", - "demo_176 has 63 samples\n", - "demo_177 has 50 samples\n", - "demo_178 has 47 samples\n", - "demo_179 has 48 samples\n", - "demo_180 has 55 samples\n", - "demo_181 has 52 samples\n", - "demo_182 has 55 samples\n", - "demo_183 has 53 samples\n", - "demo_184 has 44 samples\n", - "demo_185 has 59 samples\n", - "demo_186 has 45 samples\n", - "demo_187 has 43 samples\n", - "demo_188 has 44 samples\n", - "demo_189 has 52 samples\n", - "demo_190 has 51 samples\n", - "demo_191 has 40 samples\n", - "demo_192 has 49 samples\n", - "demo_193 has 42 samples\n", - "demo_194 has 36 samples\n", - "demo_195 has 54 samples\n", - "demo_196 has 42 samples\n", - "demo_197 has 40 samples\n", - "demo_198 has 45 samples\n", - "demo_199 has 49 samples\n" - ] - } - ], + "outputs": [], "source": [ "# each demonstration is named \"demo_#\" where # is a number.\n", "# Let's put the demonstration list in increasing episode order\n", @@ -343,437 +120,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "2f7b497a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "timestep 0\n", - "obs\n", - "{\n", - " \"object\": [\n", - " 0.026449414293141932,\n", - 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" \"object\": [\n", - " 0.02644979048142018,\n", - " 0.026981656585798056,\n", - " 0.8188363799238965,\n", - " 6.124550057574148e-06,\n", - " 1.1039634957752413e-05,\n", - " 0.9691094122391417,\n", - " 0.24663119622246055,\n", - " -0.12215724667074111,\n", - " -0.03828681206822328,\n", - " 0.1896874619017075\n", - " ],\n", - " \"robot0_eef_pos\": [\n", - " -0.09570745618932093,\n", - " -0.011305155482425224,\n", - " 1.008523841825604\n", - " ],\n", - " \"robot0_eef_quat\": [\n", - " 0.9980669555971526,\n", - " -0.0037167922644707474,\n", - " 0.06203175911084908,\n", - " 0.0007736031979145991\n", - " ],\n", - " \"robot0_eef_vel_ang\": [\n", - " 0.008174675780542328,\n", - " 0.2117180550504403,\n", - " 0.025337152199680114\n", - " ],\n", - " \"robot0_eef_vel_lin\": [\n", - " 0.021946528579046963,\n", - " 0.019244360508797305,\n", - " -0.03325545509870823\n", - " ],\n", - " \"robot0_gripper_qpos\": [\n", - " 0.023065368793203082,\n", - " -0.023089813685773657\n", - " ],\n", - " \"robot0_gripper_qvel\": [\n", - " 0.05386538871513393,\n", - " -0.05464789210707713\n", - " ],\n", - " \"robot0_joint_pos\": [\n", - " -0.03862546371287872,\n", - " 0.2176935517862639,\n", - " 0.012540741592900084,\n", - " -2.59203892182414,\n", - " -0.006915289916081674,\n", - " 2.933857605408448,\n", - " 0.7734679128154149\n", - " ],\n", - " \"robot0_joint_pos_cos\": [\n", - " 0.9992541295153923,\n", - " 0.9763981884673584,\n", - " 0.9999213659307244,\n", - " -0.852757695866131,\n", - " 0.9999760894779743,\n", - " -0.978500557298211,\n", - " 0.7154922211915071\n", - " ],\n", - " \"robot0_joint_pos_sin\": [\n", - " -0.03861586003749854,\n", - " 0.21597818768954657,\n", - " 0.012540412881329139,\n", - " -0.5223067222821157,\n", - " -0.0069152347999298655,\n", - " 0.20624417414096938,\n", - " 0.6986206992456232\n", - " ],\n", - " \"robot0_joint_vel\": [\n", - " 0.03587066701618021,\n", - " 0.11427360447919478,\n", - " 0.007098630969981366,\n", - " 0.13903479858802026,\n", - " -0.0045408292982859885,\n", - " -0.2366374332873465,\n", - " 0.021655490569626238\n", - " ]\n", - "}\n", - "action\n", - "[ 0.323 0.131 -0.073 0.00670891 0.12851983 -0.00825769\n", - " -1. ]\n", - "timestep 3\n", - "obs\n", - "{\n", - " \"object\": [\n", - " 0.026449610437205617,\n", - " 0.026981465550533244,\n", - " 0.8200930180165275,\n", - " 2.989477547714015e-06,\n", - " 5.389691506845857e-06,\n", - " 0.9691094122977165,\n", - " 0.24663119623840957,\n", - " -0.12084811993760973,\n", - " -0.03702038558498077,\n", - " 0.1864341070243961\n", - " ],\n", - " \"robot0_eef_pos\": [\n", - " -0.09439850950040411,\n", - " -0.010038920034447524,\n", - " 1.0065271250409236\n", - " ],\n", - " \"robot0_eef_quat\": [\n", - " 0.9983755425976737,\n", - " -0.003414240303874383,\n", - " 0.056872138948892356,\n", - " 0.00042274972047360443\n", - " ],\n", - " \"robot0_eef_vel_ang\": [\n", - " 0.015588272761144207,\n", - " 0.20338910134567467,\n", - " 0.001415554639112783\n", - " ],\n", - " \"robot0_eef_vel_lin\": [\n", - " 0.06160381269345277,\n", - " 0.025010135696567033,\n", - " -0.03656737866839496\n", - " ],\n", - " \"robot0_gripper_qpos\": [\n", - " 0.026358274928870957,\n", - " -0.02636141449910154\n", - " ],\n", - " \"robot0_gripper_qvel\": [\n", - " 0.0745201310630871,\n", - " -0.07411367614184626\n", - " ],\n", - " \"robot0_joint_pos\": [\n", - " -0.03650447151890695,\n", - " 0.22634892583028604,\n", - " 0.013167323320805235,\n", - " -2.581147494051686,\n", - " -0.006772139059673779,\n", - " 2.9212721348692297,\n", - " 0.7754134156517873\n", - " ],\n", - " \"robot0_joint_pos_cos\": [\n", - " 0.999333785766275,\n", - " 0.9744922663574513,\n", - " 0.9999133120507784,\n", - " -0.847018564469704,\n", - " 0.999977069153916,\n", - " -0.9758274525241541,\n", - " 0.7141316994350739\n", - " ],\n", - " \"robot0_joint_pos_sin\": [\n", - " -0.03649636455929419,\n", - " 0.22442108370097089,\n", - " 0.0131669428358545,\n", - " -0.5315633089705137,\n", - " -0.006772087295968501,\n", - " 0.21854240526776433,\n", - " 0.7000113683805237\n", - " ],\n", - " \"robot0_joint_vel\": [\n", - " 0.04691353138789363,\n", - " 0.1994635772259614,\n", - " 0.01143329494353153,\n", - " 0.2674340987745522,\n", - " 0.004438708485487719,\n", - " -0.27163705349779815,\n", - " 0.052193205025039595\n", - " ]\n", - "}\n", - "action\n", - "[ 0.491 0.21 -0.19 0.00824866 0.12259889 -0.02527941\n", - " -1. ]\n", - "timestep 4\n", - "obs\n", - "{\n", - " \"object\": [\n", - " 0.026449513581120028,\n", - " 0.026981362757104405,\n", - " 0.8206866047770742,\n", - " 1.3457488015772851e-06,\n", - " 2.4265647886561854e-06,\n", - " 0.969109412312331,\n", - " 0.24663119624238392,\n", - " -0.1173250875824681,\n", - " -0.03522245982820696,\n", - " 0.1830334739182844\n", - " ],\n", - " \"robot0_eef_pos\": [\n", - " -0.09087557400134808,\n", - " -0.008241097071102555,\n", - " 1.0037200786953586\n", - " ],\n", - " \"robot0_eef_quat\": [\n", - " 0.9986502568703245,\n", - " -0.0038131638489504183,\n", - " 0.05179871492175989,\n", - " -0.00013178296654296415\n", - " ],\n", - " \"robot0_eef_vel_ang\": [\n", - " 0.02512282243933508,\n", - " 0.20309719193885506,\n", - " -0.02906478800666458\n", - " ],\n", - " \"robot0_eef_vel_lin\": [\n", - " 0.10622683467941454,\n", - " 0.03716610735305083,\n", - " -0.06009354586057167\n", - " ],\n", - " \"robot0_gripper_qpos\": [\n", - " 0.030413930567981536,\n", - " -0.030375387077326593\n", - " ],\n", - " \"robot0_gripper_qvel\": [\n", - " 0.08575894495332877,\n", - " -0.0853433936999638\n", - " ],\n", - " \"robot0_joint_pos\": [\n", - " -0.033742898584622906,\n", - " 0.24158202550110155,\n", - " 0.014307614740759025,\n", - " -2.562765138816014,\n", - " -0.006403913367086805,\n", - " 2.907945086256439,\n", - " 0.7796524071820293\n", - " ],\n", - " \"robot0_joint_pos_cos\": [\n", - " 0.999430762410992,\n", - " 0.9709607078578428,\n", - " 0.9998976478262572,\n", - " -0.8371046247858968,\n", - " 0.9999794950168696,\n", - " -0.9728283562961821,\n", - " 0.7111579499359364\n", - " ],\n", - " \"robot0_joint_pos_sin\": [\n", - " -0.03373649576620538,\n", - " 0.23923900977097504,\n", - " 0.014307126598938211,\n", - " -0.5470428202271399,\n", - " -0.006403869596315117,\n", - " 0.2315275128058616,\n", - " 0.703032268279996\n", - " ],\n", - " \"robot0_joint_vel\": [\n", - " 0.06079543928279841,\n", - " 0.3522398354114109,\n", - " 0.02364828609743366,\n", - " 0.4241619255231188,\n", - " 0.0024945495675376796,\n", - " -0.2753739143424271,\n", - " 0.10996504673995737\n", - " ]\n", - "}\n", - "action\n", - "[ 0.465 0.309 -0.304 0.00568831 0.10961337 -0.04577875\n", - " -1. ]\n" - ] - } - ], + "outputs": [], "source": [ "# look at first demonstration\n", "demo_key = demos[0]\n", @@ -800,19 +150,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "552be387", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of first ten actions (10, 7)\n", - "shape of all actions (59, 7)\n" - ] - } - ], + "outputs": [], "source": [ "# we can also grab multiple timesteps at once directly, or even the full trajectory at once\n", "first_ten_actions = demo_grp[\"actions\"][:10]\n", @@ -823,18 +164,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "57976238", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "success\n" - ] - } - ], + "outputs": [], "source": [ "# the trajectory also contains the next observations under \"next_obs\", \n", "# for convenient use in a batch (offline) RL pipeline. Let's verify\n", @@ -847,25 +180,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "51ab4a38", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dones\n", - "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", - " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1]\n", - "\n", - "rewards\n", - "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1.]\n" - ] - } - ], + "outputs": [], "source": [ "# we also have \"done\" and \"reward\" information stored in each trajectory.\n", "# In this case, we have sparse rewards that indicate task completion at\n", @@ -881,217 +199,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "360df27c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " \n", - " \n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# each demonstration also contains metadata\n", "num_samples = demo_grp.attrs[\"num_samples\"] # number of samples in this trajectory\n", @@ -1109,76 +220,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "3b579caf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==== Env Meta ====\n", - "{\n", - " \"env_name\": \"Lift\",\n", - " \"type\": 1,\n", - " \"env_kwargs\": {\n", - " \"has_renderer\": false,\n", - " \"has_offscreen_renderer\": false,\n", - " \"ignore_done\": true,\n", - " \"use_object_obs\": true,\n", - " \"use_camera_obs\": false,\n", - " \"control_freq\": 20,\n", - " \"controller_configs\": {\n", - " \"type\": \"OSC_POSE\",\n", - " \"input_max\": 1,\n", - " \"input_min\": -1,\n", - " \"output_max\": [\n", - " 0.05,\n", - " 0.05,\n", - " 0.05,\n", - " 0.5,\n", - " 0.5,\n", - " 0.5\n", - " ],\n", - " \"output_min\": [\n", - " -0.05,\n", - " -0.05,\n", - " -0.05,\n", - " -0.5,\n", - " -0.5,\n", - " -0.5\n", - " ],\n", - " \"kp\": 150,\n", - " \"damping\": 1,\n", - " \"impedance_mode\": \"fixed\",\n", - " \"kp_limits\": [\n", - " 0,\n", - " 300\n", - " ],\n", - " \"damping_limits\": [\n", - " 0,\n", - " 10\n", - " ],\n", - " \"position_limits\": null,\n", - " \"orientation_limits\": null,\n", - " \"uncouple_pos_ori\": true,\n", - " \"control_delta\": true,\n", - " \"interpolation\": null,\n", - " \"ramp_ratio\": 0.2\n", - " },\n", - " \"robots\": [\n", - " \"Panda\"\n", - " ],\n", - " \"camera_depths\": false,\n", - " \"camera_heights\": 84,\n", - " \"camera_widths\": 84,\n", - " \"reward_shaping\": false\n", - " }\n", - "}\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "env_meta = json.loads(f[\"data\"].attrs[\"env_args\"])\n", "# note: we could also have used the following function:\n", @@ -1208,27 +253,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "9c98068e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO: Probing, EGL cannot run on this device\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating offscreen glfw\n", - "Created environment with name Lift\n", - "Action size is 7\n" - ] - } - ], + "outputs": [], "source": [ "import robomimic.utils.env_utils as EnvUtils\n", "\n", @@ -1242,22 +270,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "595a47d7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============= Initialized Observation Utils with Obs Spec =============\n", - "\n", - "using obs modality: low_dim with keys: ['robot0_eef_pos']\n", - "using obs modality: rgb with keys: []\n" - ] - } - ], + "outputs": [], "source": [ "import robomimic.utils.obs_utils as ObsUtils\n", "\n", @@ -1276,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "d997cf39", "metadata": {}, "outputs": [], @@ -1290,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "dfae1aaa", "metadata": {}, "outputs": [], @@ -1321,32 +337,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "926d1811", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Playing back demo key: demo_0\n", - "Creating offscreen glfw\n", - "Creating offscreen glfw\n", - "Playing back demo key: demo_1\n", - "Creating offscreen glfw\n", - "Creating offscreen glfw\n", - "Playing back demo key: demo_2\n", - "Creating offscreen glfw\n", - "Creating offscreen glfw\n", - "Playing back demo key: demo_3\n", - "Creating offscreen glfw\n", - "Creating offscreen glfw\n", - "Playing back demo key: demo_4\n", - "Creating offscreen glfw\n", - "Creating offscreen glfw\n" - ] - } - ], + "outputs": [], "source": [ "# playback the first 5 demos\n", "for ep in demos[:5]:\n", @@ -1359,40 +353,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "bc89c8d8", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# view the trajectories!\n", "from IPython.display import Video\n", "Video(video_path, embed=True)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eea881c7", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1411,7 +380,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/examples/notebooks/run_policy.ipynb b/examples/notebooks/run_policy.ipynb index 0e9f1ca2..913d74ec 100644 --- a/examples/notebooks/run_policy.ipynb +++ b/examples/notebooks/run_policy.ipynb @@ -279,7 +279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/examples/train_bc_rnn.py b/examples/train_bc_rnn.py index 73d858a1..b3ec7c51 100644 --- a/examples/train_bc_rnn.py +++ b/examples/train_bc_rnn.py @@ -62,7 +62,7 @@ def robosuite_hyperparameters(config): ## evaluation rollout config ## config.experiment.rollout.enabled = True # enable evaluation rollouts config.experiment.rollout.n = 50 # number of rollouts per evaluation - config.experiment.rollout.horizon = 400 # maximum number of env steps per rollout + config.experiment.rollout.horizon = 400 # set horizon based on length of demonstrations (can be obtained with scripts/get_dataset_info.py) config.experiment.rollout.rate = 50 # do rollouts every @rate epochs config.experiment.rollout.warmstart = 0 # number of epochs to wait before starting rollouts config.experiment.rollout.terminate_on_success = True # end rollout early after task success diff --git a/robomimic/macros.py b/robomimic/macros.py index 3b6c0503..9d54a4cc 100644 --- a/robomimic/macros.py +++ b/robomimic/macros.py @@ -9,11 +9,11 @@ VISUALIZE_RANDOMIZER = False # wandb entity (eg. username or team name) -WANDB_ENTITY = None +WANDB_ENTITY = "tri" # wandb api key (obtain from https://wandb.ai/authorize) # alternatively, set up wandb from terminal with `wandb login` -WANDB_API_KEY = None +WANDB_API_KEY = "10eb447cdaa881ddb7748e1ebb4814706c2be903" try: from robomimic.macros_private import * From 0b022fe45f62af21f3484784c96a20fad9208135 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Sat, 9 Mar 2024 06:47:44 +0000 Subject: [PATCH 14/43] clean --- .gitignore | 3 + docs/api/robomimic.rst | 8 - docs/datasets/mimicgen.md | 37 - docs/datasets/overview.md | 3 +- docs/datasets/robomimic_v0.1.md | 10 +- docs/datasets/robosuite.md | 3 - docs/images/mimicgen_mosaic.gif | Bin 8967711 -> 0 bytes docs/index.rst | 2 - docs/introduction/getting_started.md | 2 +- docs/introduction/installation.md | 1 - docs/miscellaneous/contributing.md | 2 +- docs/miscellaneous/references.md | 58 +- docs/miscellaneous/team.md | 5 +- docs/miscellaneous/troubleshooting.md | 4 +- docs/model_zoo/robomimic_v0.1.md | 11 +- docs/modules/dataset.md | 16 +- docs/modules/environments.md | 2 +- docs/modules/models.md | 6 +- docs/tutorials/configs.md | 4 +- docs/tutorials/dataset_contents.md | 17 +- docs/tutorials/hyperparam_scan.md | 26 +- docs/tutorials/pretrained_representations.md | 12 +- docs/tutorials/training_transformers.md | 43 - docs/tutorials/viewing_results.md | 10 +- examples/notebooks/datasets.ipynb | 1091 +++++++++++++++++- examples/notebooks/run_policy.ipynb | 2 +- examples/train_bc_rnn.py | 2 +- 27 files changed, 1126 insertions(+), 254 deletions(-) delete mode 100644 docs/datasets/mimicgen.md delete mode 100644 docs/images/mimicgen_mosaic.gif delete mode 100644 docs/tutorials/training_transformers.md diff --git a/.gitignore b/.gitignore index 65249c39..0bbf99ff 100644 --- a/.gitignore +++ b/.gitignore @@ -119,3 +119,6 @@ venv.bak/ .mypy_cache/ *.mp4 *.pth + +# private macros +macros_private.py diff --git a/docs/api/robomimic.rst b/docs/api/robomimic.rst index 2dc96c06..0d8ad0a7 100644 --- a/docs/api/robomimic.rst +++ b/docs/api/robomimic.rst @@ -24,14 +24,6 @@ robomimic.macros module :undoc-members: :show-inheritance: -robomimic.macros\_private module --------------------------------- - -.. automodule:: robomimic.macros_private - :members: - :undoc-members: - :show-inheritance: - Module contents --------------- diff --git a/docs/datasets/mimicgen.md b/docs/datasets/mimicgen.md deleted file mode 100644 index 3c47d2b7..00000000 --- a/docs/datasets/mimicgen.md +++ /dev/null @@ -1,37 +0,0 @@ -# MimicGen (CoRL 2023) - -## Overview - -The [MimicGen paper](https://arxiv.org/abs/2310.17596) released a large collection of task demonstrations across several different environments. - -The datasets contain over 48,000 task demonstrations across 12 tasks, grouped into the following categories: -- **source**: 120 human demonstrations across 12 tasks used to automatically generate the other datasets -- **core**: 26,000 task demonstrations across 12 tasks (26 task variants) -- **object**: 2000 task demonstrations on the Mug Cleanup task with different mugs -- **robot**: 16,000 task demonstrations across 4 different robot arms on 2 tasks (4 task variants) -- **large_interpolation**: 6000 task demonstrations across 6 tasks that pose significant challenges for modern imitation learning methods - -See [this link](https://github.com/NVlabs/mimicgen_environments#dataset-types) for more information on the datasets. - -

- -

- -## Downloading - -Please see [this link](https://github.com/NVlabs/mimicgen_environments#downloading-and-using-datasets) for instructions on downloading and using these datasets. - -## Postprocessing - -No postprocessing is needed for these datasets. - -## Citation - -```bibtex -@inproceedings{mandlekar2023mimicgen, - title={MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations}, - author={Mandlekar, Ajay and Nasiriany, Soroush and Wen, Bowen and Akinola, Iretiayo and Narang, Yashraj and Fan, Linxi and Zhu, Yuke and Fox, Dieter}, - booktitle={7th Annual Conference on Robot Learning}, - year={2023} -} -``` \ No newline at end of file diff --git a/docs/datasets/overview.md b/docs/datasets/overview.md index 2bf39b92..0f260e59 100644 --- a/docs/datasets/overview.md +++ b/docs/datasets/overview.md @@ -20,7 +20,6 @@ Datasets capture recorded environment data and are used as inputs to a given off | **Dataset** |
**Task Types**
| **Downloading** | **Postprocessing** | | ----------------------------- | :-------------: | :-------------: | :-------------: | | [**robomimic v0.1**](robomimic_v0.1.html)| Sim + Real Robot Manipulation | [Link](robomimic_v0.1.html#downloading) | [Link](robomimic_v0.1.html#postprocessing) | -| [**MimicGen**](mimicgen.html) | Sim Robot Manipulation | [Link](mimicgen.html#downloading) | [Link](mimicgen.html#postprocessing) | | [**D4RL**](d4rl.html) | Sim Locomotion | [Link](d4rl.html#downloading) | [Link](d4rl.html#postprocessing) | | [**MOMART**](momart.html) | Sim Mobile Manipulation | [Link](momart.html#downloading) | [Link](momart.html#postprocessing) | | [**RoboTurk Pilot**](roboturk_pilot.html) | Sim Robot Manipulation | [Link](roboturk_pilot.html#downloading) | [Link](roboturk_pilot.html#postprocessing) | @@ -43,7 +42,7 @@ python train.py --dataset --config

Create Your Own Environment Wrapper!

-If you want to generate your own dataset in a custom environment platform that is not listed above, please see [this page](../modules/environments.md#implement-an-environment-wrapper). +If you want to generate your own dataset in a custom environment platform that is not listed above, please see [THIS PAGE](../modules/environments.md#implement-an-environment-wrapper). diff --git a/docs/datasets/robomimic_v0.1.md b/docs/datasets/robomimic_v0.1.md index 9fc887ba..90fec860 100644 --- a/docs/datasets/robomimic_v0.1.md +++ b/docs/datasets/robomimic_v0.1.md @@ -157,11 +157,11 @@ These datasets were generated by [training](https://github.com/ARISE-Initiative/ This is a diagnostic dataset to test the ability of algorithms to learn from mixed quality human data. A single experienced operator collected 2 demonstrations for each of 100 task initializations on the Can task, resulting in 200 total demonstrations. Each pair of demonstrations consists of a "good" trajectory, where the can is picked up and placed in the correct bin, and a "bad" trajectory, where the can is picked up, and tossed outside of the robot workspace. Since the task initializations are identical, and the first part of each trajectory leading up to the can grasp is similar, there is a strong expectation for algorithms that deal with suboptimal data, to be able to filter the good trajectories from the bad ones, and achieve near-perfect performance. -## Reproduce [Study](https://robomimic.github.io/study/) Results +## Reproduce [Study](https://arise-initiative.github.io/robomimic-web/study/) Results ### Running Experiments -After downloading the appropriate datasets you're interested in using by running the `download_datasets.py` script, the `generate_paper_configs.py` script can be used to generate all training config json files necessary to reproduce the experiments in the [study](https://robomimic.github.io/study/). A few examples are below. +After downloading the appropriate datasets you're interested in using by running the `download_datasets.py` script, the `generate_paper_configs.py` script can be used to generate all training config json files necessary to reproduce the experiments in the [study](https://arise-initiative.github.io/robomimic-web/study/). A few examples are below. ```sh # Assume datasets already exist in robomimic/../datasets folder. Configs will be generated under robomimic/exps/paper, and training results will be at /tmp/experiment_results when launching training runs. @@ -224,13 +224,13 @@ $ python generate_paper_configs.py --output_dir /tmp/experiment_results # the training command can be found in robomimic/exps/paper/core.sh # Training results can be viewed at /tmp/experiment_results (--output_dir when generating paper configs). -$ python train.py --config ../exps/paper/core/lift/ph/low_dim/bc_rnn.json +$ python train.py --config ../exps/paper/core/lift/ph/low_dim/bc.json ``` ## Citation -```bibtex -@inproceedings{robomimic2021, +```sh +@inproceedings{mandlekar2021matters, title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation}, author={Mandlekar, Ajay and Xu, Danfei and Wong, Josiah and Nasiriany, Soroush and Wang, Chen and Kulkarni, Rohun and Fei-Fei, Li and Savarese, Silvio and Zhu, Yuke and Mart{\'\i}n-Mart{\'\i}n, Roberto}, booktitle={5th Annual Conference on Robot Learning}, diff --git a/docs/datasets/robosuite.md b/docs/datasets/robosuite.md index 73f57b0f..774b17ee 100644 --- a/docs/datasets/robosuite.md +++ b/docs/datasets/robosuite.md @@ -64,9 +64,6 @@ $ python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name low # For including image observations $ python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name image.hdf5 --done_mode 2 --camera_names agentview robot0_eye_in_hand --camera_height 84 --camera_width 84 -# For including depth observations too -python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name depth.hdf5 --done_mode 2 --camera_names agentview robot0_eye_in_hand --camera_height 84 --camera_width 84 --depth - # Using dense rewards $ python dataset_states_to_obs.py --dataset /path/to/demo.hdf5 --output_name image_dense.hdf5 --done_mode 2 --dense --camera_names agentview robot0_eye_in_hand --camera_height 84 --camera_width 84 diff --git a/docs/images/mimicgen_mosaic.gif b/docs/images/mimicgen_mosaic.gif deleted file mode 100644 index f1e5ddf61c668850ea1dac7485095208cb8f70f5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8967711 zcmeEN_fr#H*xk*hhd}6|8l**P=%FVBkgAa;phBnuf(ArEMBNmslz^asEg&igYQTm) zQ~^q$E1OiTlq@qDm*3eYf($>-?Yw8>5Ez?#vBA|_z8<{QBp_(nHQq8TbENp2ucIxVm zIJC2~ldHA4ha26~$;#8q)63M*i{az#<>t-sX88KDC|ExtaV1TCWq^M`>W&~MgHU^O z_NqWOd-WO?J$$8Gcz}0!uunvwd!({zq_%#fmrl-BpSSBZ}Pcbw}@gS$HG))P%N!8I$wKPqQV5G*bN==SWO-S*prDL^? 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zihTlFaHKda&^2VSL<*&PZ*2AgtnbCtgJNE5@T^MIhj?;jUd)PY-*8gNDw^>u-{EKd z`xYK0jD+#k6hf_S<9-czwEOnaIA-Da Yt>Bv5sxR3JH9lj}?jwV@WdL~mACFjE>;M1& diff --git a/docs/index.rst b/docs/index.rst index 6c15c41a..dc2dc316 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -21,7 +21,6 @@ Welcome to robomimic's documentation! datasets/overview datasets/robomimic_v0.1 - datasets/mimicgen datasets/robosuite datasets/d4rl datasets/momart @@ -42,7 +41,6 @@ Welcome to robomimic's documentation! tutorials/hyperparam_scan tutorials/reproducing_experiments tutorials/dataset_contents - tutorials/training_transformers tutorials/using_pretrained_models tutorials/observations tutorials/pretrained_representations diff --git a/docs/introduction/getting_started.md b/docs/introduction/getting_started.md index d995e3af..122bf9c9 100644 --- a/docs/introduction/getting_started.md +++ b/docs/introduction/getting_started.md @@ -39,7 +39,7 @@ Make sure to add the `--debug` flag to your experiments as a sanity check that y

      diff --git a/docs/introduction/installation.md b/docs/introduction/installation.md index bc7b022a..95e673f8 100644 --- a/docs/introduction/installation.md +++ b/docs/introduction/installation.md @@ -177,7 +177,6 @@ $ cd /docs $ make clean $ make apidoc $ make html -$ cp -r images _build/html/ ``` There should be a generated `_build` folder - navigate to `_build/html/` and open `index.html` in a web browser to view the documentation. \ No newline at end of file diff --git a/docs/miscellaneous/contributing.md b/docs/miscellaneous/contributing.md index fc691595..3833c349 100644 --- a/docs/miscellaneous/contributing.md +++ b/docs/miscellaneous/contributing.md @@ -2,7 +2,7 @@ Our team wholeheartedly welcomes the community to contribute to robomimic. Contributions from members of the community will help ensure the long-term success of this project. Before you plan to make contributions, here are important resources to get started with: -- Read the robomimic [documentation](https://robomimic.github.io/docs/introduction/overview.html) and [paper](https://arxiv.org/abs/2108.03298) +- Read the robomimic [documentation](https://arise-initiative.github.io/robomimic-web/docs/overview.html) and [paper](https://arxiv.org/abs/2108.03298) - Check our latest status from existing [issues](https://github.com/ARISE-Initiative/robomimic/issues), [pull requests](https://github.com/ARISE-Initiative/robomimic/pulls), and [branches](https://github.com/ARISE-Initiative/robomimic/branches) and avoid duplicate efforts - Join our [ARISE Slack](https://ariseinitiative.slack.com) workspace for technical discussions. Please [email us](mailto:yukez@cs.utexas.edu) to be added to the workspace. diff --git a/docs/miscellaneous/references.md b/docs/miscellaneous/references.md index 351c3490..1dd2dc45 100644 --- a/docs/miscellaneous/references.md +++ b/docs/miscellaneous/references.md @@ -4,47 +4,37 @@ A list of projects and papers that use **robomimic**. If you would like to add y ## 2023 -- [Imitating Task and Motion Planning with Visuomotor Transformers](https://arxiv.org/abs/2305.16309). Murtaza Dalal, Ajay Mandlekar\*, Caelan Garrett\*, Ankur Handa, Ruslan Salakhutdinov, Dieter Fox -- [Data Quality in Imitation Learning](https://arxiv.org/abs/2306.02437). Suneel Belkhale, Yuchen Cui, Dorsa Sadigh -- [Coherent Soft Imitation Learning](https://arxiv.org/abs/2305.16498). Joe Watson, Sandy H. Huang, Nicolas Heess -- [Inverse Preference Learning: Preference-based RL without a Reward Function](https://arxiv.org/abs/2305.15363). Joey Hejna, Dorsa Sadigh -- [Sequence Modeling is a Robust Contender for Offline Reinforcement Learning](https://arxiv.org/abs/2305.14550). Prajjwal Bhargava, Rohan Chitnis, Alborz Geramifard, Shagun Sodhani, Amy Zhang -- [Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks](https://arxiv.org/abs/2305.12171). Eley Ng, Ziang Liu, Monroe Kennedy III -- [Zero-shot Preference Learning for Offline RL via Optimal Transport](https://arxiv.org/abs/2306.03615). Runze Liu, Yali Du, Fengshuo Bai, Jiafei Lyu, Xiu Li -- [Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers](https://arxiv.org/abs/2306.09331). Dominick Reilly, Aman Chadha, Srijan Das -- [Get Back Here: Robust Imitation by Return-to-Distribution Planning](https://arxiv.org/abs/2305.01400). Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Leonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi -- [Preference Transformer: Modeling Human Preferences using Transformers for RL](https://arxiv.org/abs/2303.00957). Changyeon Kim, Jongjin Park, Jinwoo Shin, Honglak Lee, Pieter Abbeel, Kimin Lee -- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://arxiv.org/abs/2302.12422). Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar -- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://arxiv.org/abs/2303.04137). Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, Shuran Song -- [ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments](https://arxiv.org/abs/2301.04195). Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Pooria Poorsarvi Tehrani, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg -- [PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining](https://arxiv.org/abs/2303.08789). Garrett Thomas, Ching-An Cheng, Ricky Loynd, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov -- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://arxiv.org/abs/2304.08742). Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn -- [Mind the Gap: Offline Policy Optimization for Imperfect Rewards](https://arxiv.org/abs/2302.01667). Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Qing-Shan Jia, Ya-Qin Zhang +- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://arxiv.org/abs/2302.12422) Chen Wang, Linxi Fan, Jiankai Sun, Ruohan Zhang, Li Fei-Fei, Danfei Xu, Yuke Zhu, Anima Anandkumar +- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://arxiv.org/abs/2303.04137) Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, Shuran Song +- [ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments](https://arxiv.org/abs/2301.04195) Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Pooria Poorsarvi Tehrani, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, Animesh Garg +- [PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining](https://arxiv.org/abs/2303.08789) Garrett Thomas, Ching-An Cheng, Ricky Loynd, Vibhav Vineet, Mihai Jalobeanu, Andrey Kolobov +- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://arxiv.org/abs/2304.08742) Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn +- [Mind the Gap: Offline Policy Optimization for Imperfect Rewards](https://arxiv.org/abs/2302.01667) Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Qing-Shan Jia, Ya-Qin Zhang ## 2022 -- [Learning and Retrieval from Prior Data for Skill-based Imitation Learning](https://arxiv.org/abs/2210.11435). Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu -- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://arxiv.org/abs/2210.11339). Yifeng Zhu, Abhishek Joshi, Peter Stone, Yuke Zhu -- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://arxiv.org/abs/2211.08416). Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu -- [Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data](https://arxiv.org/abs/2210.08642). Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill -- [Eliciting Compatible Demonstrations for Multi-Human Imitation Learning](https://arxiv.org/abs/2210.08073). Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh -- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://arxiv.org/abs/2209.07682). Yilun Hao, Ruinan Wang, Zhangjie Cao, Zihan Wang, Yuchen Cui, Dorsa Sadigh -- [Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL](https://arxiv.org/abs/2206.00695). Wonjoon Goo, Scott Niekum -- [HEETR: Pretraining for Robotic Manipulation on Heteromodal Data](https://openreview.net/forum?id=1_XARk3k-M). Garrett Thomas, Andrey Kolobov, Ching-An Cheng, Vibhav Vineet, Mihai Jalobeanu -- [Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots](https://proceedings.mlr.press/v162/shankar22a.html). Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh -- [Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems](https://arxiv.org/abs/2209.09174). Alexander Ororbia, Ankur Mali -- [Imitation Learning by Estimating Expertise of Demonstrators](https://arxiv.org/abs/2202.01288). Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani +- [Learning and Retrieval from Prior Data for Skill-based Imitation Learning](https://arxiv.org/abs/2210.11435) Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu +- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://arxiv.org/abs/2210.11339) Yifeng Zhu, Abhishek Joshi, Peter Stone, Yuke Zhu +- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://arxiv.org/abs/2211.08416) Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu +- [Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data](https://arxiv.org/abs/2210.08642) Allen Nie, Yannis Flet-Berliac, Deon R. Jordan, William Steenbergen, Emma Brunskill +- [Eliciting Compatible Demonstrations for Multi-Human Imitation Learning](https://arxiv.org/abs/2210.08073) Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh +- [Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations](https://arxiv.org/abs/2209.07682) Yilun Hao, Ruinan Wang, Zhangjie Cao, Zihan Wang, Yuchen Cui, Dorsa Sadigh +- [Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL](https://arxiv.org/abs/2206.00695) Wonjoon Goo, Scott Niekum +- [HEETR: Pretraining for Robotic Manipulation on Heteromodal Data](https://openreview.net/forum?id=1_XARk3k-M) Garrett Thomas, Andrey Kolobov, Ching-An Cheng, Vibhav Vineet, Mihai Jalobeanu +- [Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots](https://proceedings.mlr.press/v162/shankar22a.html) Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, Jean Oh +- [Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems](https://arxiv.org/abs/2209.09174) Alexander Ororbia, Ankur Mali +- [Imitation Learning by Estimating Expertise of Demonstrators](https://arxiv.org/abs/2202.01288) Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani ## 2021 -- [RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning](https://arxiv.org/abs/2111.02767). Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev -- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://arxiv.org/abs/2112.05251). Josiah Wong, Albert Tung, Andrey Kurenkov, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Roberto Martín-Martín -- [Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control](https://arxiv.org/abs/2103.00375). Chen Wang, Rui Wang, Danfei Xu, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese -- [Deep Affordance Foresight: Planning Through What Can Be Done in the Future](https://arxiv.org/abs/2011.08424). Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, Li Fei-Fei -- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://arxiv.org/abs/2012.06738). Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese -- [Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations](https://arxiv.org/abs/2003.06085). Ajay Mandlekar\*, Danfei Xu\*, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei +- [RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning](https://arxiv.org/abs/2111.02767) Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev +- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://arxiv.org/abs/2112.05251) Josiah Wong, Albert Tung, Andrey Kurenkov, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Roberto Martín-Martín +- [Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control](https://arxiv.org/abs/2103.00375) Chen Wang, Rui Wang, Danfei Xu, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese +- [Deep Affordance Foresight: Planning Through What Can Be Done in the Future](https://arxiv.org/abs/2011.08424) Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, Li Fei-Fei +- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://arxiv.org/abs/2012.06738) Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese +- [Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations](https://arxiv.org/abs/2003.06085) Ajay Mandlekar\*, Danfei Xu\*, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei ## 2020 -- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://arxiv.org/abs/2012.06733). Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese +- [Human-in-the-Loop Imitation Learning using Remote Teleoperation](https://arxiv.org/abs/2012.06733) Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese - [IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data](https://arxiv.org/abs/1911.05321). Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox diff --git a/docs/miscellaneous/team.md b/docs/miscellaneous/team.md index 16b784e0..31179139 100644 --- a/docs/miscellaneous/team.md +++ b/docs/miscellaneous/team.md @@ -4,12 +4,11 @@ These people contributed directly to the codebase and helped build it. -- [Ajay Mandlekar](https://ai.stanford.edu/~amandlek/) -- [Danfei Xu](https://faculty.cc.gatech.edu/~danfei/) +- [Ajay Mandlekar](http://web.stanford.edu/~amandlek/) +- [Danfei Xu](https://cs.stanford.edu/~danfei/) - [Josiah Wong](https://www.jowo.me/about) - [Soroush Nasiriany](http://snasiriany.me/) - [Chen Wang](http://www.chenwangjeremy.net/) -- [Matthew Bronars](https://bronars.github.io/) ## Mentors diff --git a/docs/miscellaneous/troubleshooting.md b/docs/miscellaneous/troubleshooting.md index 189de425..e5e1e8ca 100644 --- a/docs/miscellaneous/troubleshooting.md +++ b/docs/miscellaneous/troubleshooting.md @@ -4,7 +4,7 @@ This section will be populated with common mistakes and links to issues and fixes as we are made aware of them. -If you're still having problems, please submit an issue on [the github page](https://github.com/ARISE-Initiative/robomimic/issues). +If you're still having problems, please submit an issue on the github page. ## Known Issues @@ -12,4 +12,4 @@ This section contains known issues that are either minor, or that will be patche - `PrintLogger` breaks if you use `embed()` with a new-ish IPython installation. The current workaround is to use an old version. Known working version is `ipython==5.8.0` -- On robomimic v0.2, the `test_scripts` tests will fail if `robosuite` is not on the `offline_study` branch with the following error: `No "site" with name gripper0_ee_x exists.`. This is because the test hdf5 was collected on that branch -- switching to that branch should make the test pass. \ No newline at end of file +- The `test_scripts` tests will fail if `robosuite` is not on the `offline_study` branch with the following error: `No "site" with name gripper0_ee_x exists.`. This is because the test hdf5 was collected on that branch -- switching to that branch should make the test pass. \ No newline at end of file diff --git a/docs/model_zoo/robomimic_v0.1.md b/docs/model_zoo/robomimic_v0.1.md index d5ea8551..8021a094 100644 --- a/docs/model_zoo/robomimic_v0.1.md +++ b/docs/model_zoo/robomimic_v0.1.md @@ -10,16 +10,9 @@ See the ["Using Pretrained Models"](../tutorials/using_pretrained_models.html) t

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-

Warning: use correct robomimic and robosuite versions!

+

Warning: use correct robosuite branch!

-When using these trained models, please make sure that robosuite is on the [`offline_study` branch](https://github.com/ARISE-Initiative/robosuite/tree/offline_study), and that robomimic is on the [`v0.1` branch](https://github.com/ARISE-Initiative/robomimic/tree/v0.1). - -
- -
-

Troubleshooting download issues

- -Some users (eg. using Chrome) may experience issues with the dataset download links. If you are experiencing these issues, follow these steps: (1) right click on the download link, (2) select the option to copy the linked url, and (3) copy paste the link in a new tab. +When using these trained models, please make sure that robosuite is on the `offline_study` branch of robosuite.
diff --git a/docs/modules/dataset.md b/docs/modules/dataset.md index 4c2510e2..8a7871ee 100644 --- a/docs/modules/dataset.md +++ b/docs/modules/dataset.md @@ -1,6 +1,6 @@ # SequenceDataset -The `robomimic.utils.dataset.SequenceDataset` class extends PyTorch's default `torch.utils.data.Dataset` to interface with our demonstration [datasets](../datasets/overview.html). The class supports accessing demonstration sub-sequences (as opposed to individual states) by index and both on-demand fetching and in-memory caching. This page walks through the key concepts of the `SequenceDataset` interface. Please refer to the official PyTorch [documentation](https://pytorch.org/docs/stable/data.html) and a short [example](https://github.com/ARISE-Initiative/robomimic/blob/master/examples/simple_train_loop.py) on how to use the `Dataset` and `DataLoader` interfaces to build a training pipeline. +The `robomimic.utils.dataset.SequenceDataset` class extends PyTorch's default `torch.utils.data.Dataset` to interface with our demonstration [datasets](../introduction/datasets.html). The class supports accessing demonstration sub-sequences (as opposed to individual states) by index and both on-demand fetching and in-memory caching. This page walks through the key concepts of the `SequenceDataset` interface. Please refer to the official PyTorch [documentation](https://pytorch.org/docs/stable/data.html) and a short [example](../introduction/examples.html#train-loop-example) on how to use the `Dataset` and `DataLoader` interfaces to build a training pipeline. Also, please see the [released datasets section](../introduction/results.html#downloading-released-datasets) for guidance on downloading datasets that work out of the box with the repository. Here is a sample dataset object: @@ -19,10 +19,8 @@ dataset = SequenceDataset( "rewards", "dones", ), - seq_length=10, # length of sub-sequence to fetch: (s_{t}, a_{t}), (s_{t+1}, a_{t+1}), ..., (s_{t+9}, a_{t+9}) - frame_stack=1, # length of sub-sequence to prepend + seq_length=10, # length-10 temporal sequences pad_seq_length=True, # pad last obs per trajectory to ensure all sequences are sampled - pad_frame_stack=True, # pad first obs per trajectory to ensure all sequences are sampled hdf5_cache_mode="all", # cache dataset in memory to avoid repeated file i/o hdf5_normalize_obs=False, filter_by_attribute=None, # can optionally provide a filter key here @@ -30,19 +28,15 @@ dataset = SequenceDataset( ``` - `hdf5_path` - - The absolute / relative path to the hdf5 file containing training demonstrations. See the [datasets page](../datasets/overview.html#dataset-structure) for the expected data structure. + - The absolute / relative path to the hdf5 file containing training demonstrations. See [datasets](../introduction/datasets.html) page for the expected data structure. - `obs_keys` - A list of strings specifying which observation modalities to read from the dataset. This is typically read from the config file: our implementation pools observation keys from `config.observation.modalities.obs.low_dim` and `config.observation.modalities.obs.rgb`. - `dataset_keys` - Keys of non-observation data to read from a demonstration. Typically include `actions`, `rewards`, `dones`. - `seq_length` - - Length of demonstration sub-sequence to fetch. For example, if `seq_length = 10` at time `t`, the data loader will fetch ${(s_{t}, a_{t}), (s_{t+1}, a_{t+1}), ..., (s_{t+9}, a_{t+9})}$ -- `frame_stack` - - Length of sub-sequence to stack at the beginning of fetched demonstration. For example, if `frame_stack = 10` at time `t`, the data loader will fetch ${(s_{t-1}, a_{t-1}), (s_{t-2}, a_{t-2}), ..., (s_{t-9}, a_{t-9})}$. Note that the actual length of the fetched sequence is `frame_stack - 1`. This term is useful when training a model to predict `seq_length` actions from `frame_stack` observations. If training a transformer, this should be the same as context length. + - Length of the demonstration sub-sequence to fetch. - `pad_seq_length` - Whether to allow fetching subsequence that ends beyond the sequence. For example, given a demo of length 10 and `seq_length=10`, setting `pad_seq_length=True` allows the dataset object to access subsequence at `__get_item(index=5)__` by repeating the last frame 5 times. -- `pad_frame_stack` - - Whether to allow fetching subsequence that starts before the first time step. For example, given a demo of length 10 and `frame_stack=10`, setting `pad_frame_stack=True` allows the dataset object to access subsequence at `__get_item(index=5)__` by repeating the first frame 5 times. - `hdf5_cache_mode` - Optionally cache the dataset in memory for faster access. The dataset supports three caching modes: `["all", "low_dim", or None]`. - `all`: Load the entire dataset into the RAM. This mode minimizes data loading time but incurs the largest memory footprint. Recommended if the dataset is small or when working with low-dimensional observation data. @@ -51,4 +45,4 @@ dataset = SequenceDataset( - `hdf5_normalize_obs` - If `True`, normalize observations by computing the mean observation and std of each observation (in each dimension and modality), and normalizing unit mean and variance in each dimension. - `filter_by_attribute` - - if provided, use the provided filter key to look up a subset of demonstrations to load. See the documentation on [filter keys](../datasets/overview.html#filter-keys) for more information. + - if provided, use the provided filter key to look up a subset of demonstrations to load. See the documentation on [filter keys](../introduction/datasets.html#filter-keys-and-train-valid-splits) for more information. diff --git a/docs/modules/environments.md b/docs/modules/environments.md index 59c4808a..a87fd17a 100644 --- a/docs/modules/environments.md +++ b/docs/modules/environments.md @@ -51,7 +51,7 @@ def create_env_from_metadata( - `type` is an enum defined in `robomimicenvs.env_base.EnvType` that specifies the type of environment. `type` is mainly used by the `robomimic.utils.env_utils.get_env_class()` function to look up the correct environment wrapper class. - `env_kwargs` specifies the keyword args that are required to initialize an environment. The `env_kwargs` will be passed to the constructor of the wrapped environment as keyword arguments through the `robomimic.utils.env_utils.create_env()` helper function. -Although it is possible to manually specify the `env_meta` dictionary, the **robomimic** training pipeline reads the `env_meta` from the hdf5 dataset as an attribute. Please refer to the [Dataset section](../datasets/overview.html#dataset-structure) for more details on where the metadata is stored, and the `robomimic.utils.file_utils.get_env_metadata_from_dataset` function to see how it is loaded from the dataset at run-time. +Although it is possible to manually specify the `env_meta` dictionary, the **robomimic** training pipeline reads the `env_meta` from the hdf5 dataset as an attribute. Please refer to the [Dataset section](../introduction/datasets.html) for more details on where the metadata is stored, and the `robomimic.utils.file_utils.get_env_metadata_from_dataset` function to see how it is loaded from the dataset at run-time. ## Initialize an Environment from a Dataset diff --git a/docs/modules/models.md b/docs/modules/models.md index b9c18af2..fb3ed9e9 100644 --- a/docs/modules/models.md +++ b/docs/modules/models.md @@ -43,7 +43,7 @@ config.observation.encoder.rgb.core_class = "MyCustomRGBEncoder" config.observation.encoder.rgb.core_kwargs = ... ``` -Any corresponding keyword arguments that should be passed to the encoder constructor should be specified in `core_kwargs` in the config. For more information on creating your own custom encoder, please see our [example script](https://github.com/ARISE-Initiative/robomimic/blob/master/examples/simple_obs_nets.py). +Any corresponding keyword arguments that should be passed to the encoder constructor should be specified in `core_kwargs` in the config. For more information on creating your own custom encoder, please see our [example script](../introduction/examples.html#custom-observation-modalities-example). Below, we provide descriptions of specific EncoderCore-based classes used to encode RGB and depth observations (`VisualCore`) and range scanner observations (`ScanCore`). @@ -196,13 +196,13 @@ These networks take an observation dictionary as input (and possibly additional ### ValueNetwork - A basic value network that predicts values from observations. Can optionally be goal conditioned on future observations. ### DistributionalActionValueNetwork -- Distributional Q (action-value) network that outputs a categorical distribution over a discrete grid of value atoms. See the [paper](https://arxiv.org/abs/1707.06887) for more details. +- Distributional Q (action-value) network that outputs a categorical distribution over a discrete grid of value atoms. See the [paper](https://arxiv.org/pdf/1707.06887.pdf) for more details. ## Implemented VAEs The library implements a general VAE architecture and a number of prior distributions. See `robomimic/models/vae_nets.py` for complete implementations. ### VAE -A general Variational Autoencoder (VAE) implementation, as described in this [paper](https://arxiv.org/abs/1312.6114). +A general Variational Autoencoder (VAE) implementation, as described in https://arxiv.org/abs/1312.6114. Models a distribution p(X) or a conditional distribution p(X | Y), where each variable can consist of multiple modalities. The target variable X, whose distribution is modeled, is specified through the `input_shapes` argument, which is a map between modalities (strings) and expected shapes. In this way, a variable that consists of multiple kinds of data (e.g. image and flat-dimensional) can be modeled as well. A separate `output_shapes` argument is used to specify the expected reconstructions - this allows for asymmetric reconstruction (for example, reconstructing low-resolution images). diff --git a/docs/tutorials/configs.md b/docs/tutorials/configs.md index 519257de..fa67a28a 100644 --- a/docs/tutorials/configs.md +++ b/docs/tutorials/configs.md @@ -19,10 +19,10 @@ Please see the [Config documentation](../modules/configs.html) for more informat The preferred way to specify training parameters is to pass a config json to the main training script `train.py` via the `--config` argument. The dataset can be specified by setting the `data` attribute of the `train` section of the config json, or specified via the `--dataset` argument. The example below runs a default template json for the BC algorithm. **This is the preferred way to launch training runs.** ```sh -$ python train.py --config ../exps/templates/bc.json --dataset ../../tests/assets/test_v141.hdf5 +$ python train.py --config ../exps/templates/bc.json --dataset ../../tests/assets/test.hdf5 ``` -Please see the [hyperparameter helper tutorial](../tutorials/hyperparam_scan.html) to see how to easily generate json configs for launching training runs. +Please see the [hyperparameter helper docs](./advanced.html#using-the-hyperparameter-helper-to-launch-runs) to see how to easily generate json configs for launching training runs. #### 2. Constructing a config object in code diff --git a/docs/tutorials/dataset_contents.md b/docs/tutorials/dataset_contents.md index 0cfb2584..1519af4c 100644 --- a/docs/tutorials/dataset_contents.md +++ b/docs/tutorials/dataset_contents.md @@ -18,10 +18,10 @@ The repository offers a simple utility script (`get_dataset_info.py`) to view th - the [environment metadata](../modules/environments.html#initialize-an-environment-from-a-dataset) in the dataset, which is used to construct the same simulator environment that the data was collected on - the dataset structure for the first demonstration -Pass the `--verbose` argument to print the list of demonstration keys under each filter key, and the dataset structure for all demonstrations. An example, using the small hdf5 dataset packaged with the repository in `tests/assets/test_v141.hdf5` is shown below. +Pass the `--verbose` argument to print the list of demonstration keys under each filter key, and the dataset structure for all demonstrations. An example, using the small hdf5 dataset packaged with the repository in `tests/assets/test.hdf5` is shown below. ```sh -$ python get_dataset_info.py --dataset ../../tests/assets/test_v141.hdf5 +$ python get_dataset_info.py --dataset ../../tests/assets/test.hdf5 ```
@@ -36,7 +36,7 @@ Any user wishing to write custom code that works with robomimic datasets should

Note: These examples are compatible with any robomimic dataset.

-The examples in this section use the small hdf5 dataset packaged with the repository in `tests/assets/test_v141.hdf5` (which requires robosuite v1.4.1), but you can run these examples with any robomimic hdf5 dataset. +The examples in this section use the small hdf5 dataset packaged with the repository in `tests/assets/test.hdf5`, but you can run these examples with any robomimic hdf5 dataset. If you are using the default dataset, please make sure that robosuite is on the `offline_study` branch of robosuite -- this is necessary for the playback scripts to function properly.
@@ -44,17 +44,14 @@ Use the `playback_dataset.py` script to easily view dataset trajectories. ```sh # For the first 5 trajectories, load environment simulator states one-by-one, and render "agentview" and "robot0_eye_in_hand" cameras to video at /tmp/playback_dataset.mp4 -$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --render_image_names agentview robot0_eye_in_hand --video_path /tmp/playback_dataset.mp4 --n 5 +$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --render_image_names agentview robot0_eye_in_hand --video_path /tmp/playback_dataset.mp4 --n 5 # Directly visualize the image observations in the dataset. This is especially useful for real robot datasets where there is no simulator to use for rendering. -$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --use-obs --render_image_names agentview_image --video_path /tmp/obs_trajectory.mp4 - -# Visualize depth observations as well. -$ python playback_dataset.py --dataset /path/to/dataset.hdf5 --use-obs --render_image_names agentview_image --render_depth_names agentview_depth --video_path /tmp/obs_trajectory.mp4 +$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --use-obs --render_image_names agentview_image --video_path /tmp/obs_trajectory.mp4 # Play the dataset actions in the environment to verify that the recorded actions are reasonable. -$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --use-actions --render_image_names agentview --video_path /tmp/playback_dataset_with_actions.mp4 +$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --use-actions --render_image_names agentview --video_path /tmp/playback_dataset_with_actions.mp4 # Visualize only the initial demonstration frames. -$ python playback_dataset.py --dataset ../../tests/assets/test_v141.hdf5 --first --render_image_names agentview --video_path /tmp/dataset_task_inits.mp4 +$ python playback_dataset.py --dataset ../../tests/assets/test.hdf5 --first --render_image_names agentview --video_path /tmp/dataset_task_inits.mp4 ``` diff --git a/docs/tutorials/hyperparam_scan.md b/docs/tutorials/hyperparam_scan.md index 1ca4b35e..43e7cb4e 100644 --- a/docs/tutorials/hyperparam_scan.md +++ b/docs/tutorials/hyperparam_scan.md @@ -22,7 +22,7 @@ Sections of the config that are not involved in the scan and that do not differ
-We modify `/tmp/gen_configs/base.json`, adding a base experiment name (`"bc_rnn_hyper"`) and specified the dataset path (`"/tmp/test_v141.hdf5"`). +We modify `/tmp/gen_configs/base.json`, adding a base experiment name (`"bc_rnn_hyper"`) and specified the dataset path (`"/tmp/test.hdf5"`). ```sh $ cat /tmp/gen_configs/base.json @@ -59,7 +59,7 @@ $ cat /tmp/gen_configs/base.json } }, "train": { - "data": "/tmp/test_v141.hdf5", + "data": "/tmp/test.hdf5", "output_dir": "../bc_trained_models", "num_data_workers": 0, "hdf5_cache_mode": "all", @@ -198,21 +198,6 @@ Only do this if you are sweeping over a single value! -
-

wandb logging

- -If you would like to log and view results on wandb, enable wandb logging in the hyperparameter generator: -```python -generator.add_param( - key="experiment.logging.log_wandb", - name="", - group=-1, - values=[True], -) -``` - -
- ### Define hyperparameter scan values Now we define our scan - we could like to sweep the following: - policy learning rate in [1e-3, 1e-4] @@ -291,10 +276,3 @@ python train.py --config /tmp/gen_configs/bc_rnn_hyper_plr_0.0001_gmm_t_rnnd_100 python train.py --config /tmp/gen_configs/bc_rnn_hyper_plr_0.0001_gmm_f_rnnd_400_mlp_1024.json python train.py --config /tmp/gen_configs/bc_rnn_hyper_plr_0.0001_gmm_f_rnnd_1000_mlp_0.json ``` - -
-

Meta information

- -For each generated config file you will find a `meta` section that contains hyperparameter names, values, and other metadata information. This `meta` section is generated automatically, and you should NOT need to edit or modify it. - -
diff --git a/docs/tutorials/pretrained_representations.md b/docs/tutorials/pretrained_representations.md index 72c66a51..072de4da 100644 --- a/docs/tutorials/pretrained_representations.md +++ b/docs/tutorials/pretrained_representations.md @@ -26,17 +26,7 @@ config.observation.encoder.rgb.core_kwargs.pool_class = None config.observation.encoder.rgb.core_kwargs.backbone_class = 'MVPConv' # MVP backbone for image observations (unused if no image observations) config.observation.encoder.rgb.core_kwargs.backbone_kwargs.mvp_model_class = 'vitb-mae-egosoup' # MVP model class (vits-mae-hoi, vits-mae-in, vits-sup-in, vitb-mae-egosoup, vitl-256-mae-egosoup) config.observation.encoder.rgb.core_kwargs.backbone_kwargs.freeze = True # whether to freeze network during training or allow finetuning -config.observation.encoder.rgb.core_kwargs.pool_class = None # no pooling class for pretraining model - -# Set data loader attributes for image observations -config.train.num_data_workers = 2 # 2 data workers for image datasets -config.train.hdf5_cache_mode = "low_dim" # only cache non-image data - -# Ensure that you are using image observation modalities, names may depend on your dataset naming convention -config.observation.modalities.obs.rgb = [ - "agentview_image", - "robot0_eye_in_hand_image" - ] +config.observation.encoder.rgb.core_kwargs.pool_class = None # no pooling class for pretraining model ``` Alternatively, if you are using a config json, you can set the appropriate keys in your json. diff --git a/docs/tutorials/training_transformers.md b/docs/tutorials/training_transformers.md deleted file mode 100644 index 03af242a..00000000 --- a/docs/tutorials/training_transformers.md +++ /dev/null @@ -1,43 +0,0 @@ -# Training Transformers - -This tutorial shows how to train a transformer policy network. - -
-

Note: Understand how to launch training runs and view results first!

- -Before trying to train a transformer, it might be useful to read the following tutorials: -- [how to launch training runs](./configs.html) -- [how to view training results](./viewing_results.html) -- [how to launch multiple training runs efficiently](./hyperparam_scan.html) - -
- -A template with tuned parameters for transformer based policy networks is defined in `robomimic/config/default_templates/bc_transformer.json`. - -#### 1. Using default configurations - -The easiest way to train a transformer policy network is to pass the default template json to the main training script `train.py` via the `--config` argument. The dataset can be specified by setting the `data` attribute of the `train` section of the config json, or specified via the `--dataset` argument. You may find that your data has different rollout horizon lengths, observation modalities, or other incompatibilities with the default template. In this scenario, we suggest defining custom parameters as described in (2). - -```sh -$ python train.py --config ../config/default_templates/bc_transformer.json --dataset /path/to/dataset.hdf5 -``` - -#### 2. Defining custom parameters - -If you want to modify the default transformer parameters, do not directly modify the default config (`config/bc_config.py`) or template (`config/default_templates/bc_transformer.json`). Instead, you can create a copy of `robomimic/config/default_templates/bc_transformer.json` and store it in a new directory on your computer. Set this as the base file for `scripts/hyperparam_helper.py` and define custom settings as described [here](./hyperparam_scan.html). This is particularly useful when running a sweep over hyperparameters; **it is the prefered way to launch multiple training runs**. - -Optionally, you can modify the default template in python code or directly set the appropriate keys in your copy of the config file. This code snippet below highlights useful parameters to tune for transformers. To see all transformer policy settings, refer to `config/bc_config.py`. - -```python -# make sure transformer is enabled -config.algo.transformer.enabled = True - -# useful config attributes to modify for transformers -config.algo.transformer.embed_dim = 512 # dimension for embeddings used by transformer -config.algo.transformer.num_layers = 6 # number of transformer blocks to stack -config.algo.transformer.num_heads = 8 # number of attention heads for each transformer block (should divide embed_dim evenly) -config.algo.transformer.context_length = 10 # length of (s, a) sub-seqeunces to feed to transformer -config.train.frame_stack = 10 # length of sub-sequence to observe: (s_{t-1}, a_{t-1}), (s_{t-2}, a_{t-2}), ..., (s_{t-9}, a_{t-9}) -config.train.seq_length = 1 # length of sub-seqeunce to predict: (s_{t}, a_{t}) -``` - diff --git a/docs/tutorials/viewing_results.md b/docs/tutorials/viewing_results.md index 64acd9ff..7a6838f1 100644 --- a/docs/tutorials/viewing_results.md +++ b/docs/tutorials/viewing_results.md @@ -95,12 +95,4 @@ You may find the following logging metrics useful: - `Timing_Stats/`: time spent by the algorithm loading data, training, performing rollouts, etc. - `Train/`: training stats - `Validation/`: validation stats -- `System/RAM Usage (MB)`: system RAM used by algorithm - - -### Viewing wandb Results -You can also view results in [wandb](https://wandb.ai), similarly to tensorboard. To do so, ensure that you have set `experiment.logging.log_wandb` to True in the experiment config. - -When first logging to wandb, you will need to specify a wandb entity name, ie. the wandb account under which results will be logged. You can do so by setting `WANDB_ENTITY` to the desired wandb account name in `robomimic/macros_private.py`. Note: if this file does not exist, run `python robomimic/scripts/setup_macros.py` to setup the private macros file. - -By default all results will be logged under a wandb project labled `default`, however you can set the project name by setting `experiment.logging.wandb_proj_name` in the configs. +- `System/RAM Usage (MB)`: system RAM used by algorithm \ No newline at end of file diff --git a/examples/notebooks/datasets.ipynb b/examples/notebooks/datasets.ipynb index 32893b7a..f930e99d 100644 --- a/examples/notebooks/datasets.ipynb +++ b/examples/notebooks/datasets.ipynb @@ -22,10 +22,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4e2b90e6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "low_dim.hdf5: 18.6MB [00:00, 27.5MB/s] \n" + ] + } + ], "source": [ "import os\n", "import json\n", @@ -52,7 +60,7 @@ ")\n", "\n", "# enforce that the dataset exists\n", - "dataset_path = os.path.join(download_folder, \"low_dim_v141.hdf5\")\n", + "dataset_path = os.path.join(download_folder, \"low_dim.hdf5\")\n", "assert os.path.exists(dataset_path)" ] }, @@ -70,10 +78,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "a35cd8e9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hdf5 file /tmp/robomimic_ds_example/low_dim.hdf5 has 200 demonstrations\n" + ] + } + ], "source": [ "# open file\n", "f = h5py.File(dataset_path, \"r\")\n", @@ -95,10 +111,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "9bda3e70", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "demo_0 has 59 samples\n", + "demo_1 has 58 samples\n", + "demo_2 has 57 samples\n", + "demo_3 has 55 samples\n", + "demo_4 has 51 samples\n", + "demo_5 has 58 samples\n", + "demo_6 has 49 samples\n", + "demo_7 has 49 samples\n", + "demo_8 has 44 samples\n", + "demo_9 has 51 samples\n", + "demo_10 has 54 samples\n", + "demo_11 has 49 samples\n", + "demo_12 has 53 samples\n", + "demo_13 has 59 samples\n", + "demo_14 has 51 samples\n", + "demo_15 has 50 samples\n", + "demo_16 has 50 samples\n", + "demo_17 has 45 samples\n", + "demo_18 has 49 samples\n", + "demo_19 has 51 samples\n", + "demo_20 has 48 samples\n", + "demo_21 has 57 samples\n", + "demo_22 has 47 samples\n", + "demo_23 has 47 samples\n", + "demo_24 has 52 samples\n", + "demo_25 has 48 samples\n", + "demo_26 has 43 samples\n", + "demo_27 has 46 samples\n", + "demo_28 has 49 samples\n", + "demo_29 has 41 samples\n", + "demo_30 has 46 samples\n", + "demo_31 has 42 samples\n", + "demo_32 has 59 samples\n", + "demo_33 has 54 samples\n", + "demo_34 has 48 samples\n", + "demo_35 has 58 samples\n", + "demo_36 has 45 samples\n", + "demo_37 has 50 samples\n", + "demo_38 has 49 samples\n", + "demo_39 has 41 samples\n", + "demo_40 has 40 samples\n", + "demo_41 has 49 samples\n", + "demo_42 has 53 samples\n", + "demo_43 has 39 samples\n", + "demo_44 has 46 samples\n", + "demo_45 has 49 samples\n", + "demo_46 has 47 samples\n", + "demo_47 has 40 samples\n", + "demo_48 has 53 samples\n", + "demo_49 has 48 samples\n", + "demo_50 has 45 samples\n", + "demo_51 has 47 samples\n", + "demo_52 has 46 samples\n", + "demo_53 has 55 samples\n", + "demo_54 has 43 samples\n", + "demo_55 has 56 samples\n", + "demo_56 has 40 samples\n", + "demo_57 has 38 samples\n", + "demo_58 has 38 samples\n", + "demo_59 has 44 samples\n", + "demo_60 has 42 samples\n", + "demo_61 has 54 samples\n", + "demo_62 has 41 samples\n", + "demo_63 has 42 samples\n", + "demo_64 has 53 samples\n", + "demo_65 has 38 samples\n", + "demo_66 has 41 samples\n", + "demo_67 has 42 samples\n", + "demo_68 has 39 samples\n", + "demo_69 has 42 samples\n", + "demo_70 has 48 samples\n", + "demo_71 has 45 samples\n", + "demo_72 has 38 samples\n", + "demo_73 has 36 samples\n", + "demo_74 has 48 samples\n", + "demo_75 has 36 samples\n", + "demo_76 has 48 samples\n", + "demo_77 has 39 samples\n", + "demo_78 has 44 samples\n", + "demo_79 has 44 samples\n", + "demo_80 has 40 samples\n", + "demo_81 has 38 samples\n", + "demo_82 has 47 samples\n", + "demo_83 has 52 samples\n", + "demo_84 has 53 samples\n", + "demo_85 has 46 samples\n", + "demo_86 has 38 samples\n", + "demo_87 has 39 samples\n", + "demo_88 has 39 samples\n", + "demo_89 has 41 samples\n", + "demo_90 has 42 samples\n", + "demo_91 has 37 samples\n", + "demo_92 has 51 samples\n", + "demo_93 has 50 samples\n", + "demo_94 has 51 samples\n", + "demo_95 has 46 samples\n", + "demo_96 has 56 samples\n", + "demo_97 has 53 samples\n", + "demo_98 has 46 samples\n", + "demo_99 has 46 samples\n", + "demo_100 has 47 samples\n", + "demo_101 has 43 samples\n", + "demo_102 has 58 samples\n", + "demo_103 has 52 samples\n", + "demo_104 has 48 samples\n", + "demo_105 has 55 samples\n", + "demo_106 has 49 samples\n", + "demo_107 has 62 samples\n", + "demo_108 has 43 samples\n", + "demo_109 has 50 samples\n", + "demo_110 has 45 samples\n", + "demo_111 has 46 samples\n", + "demo_112 has 44 samples\n", + "demo_113 has 43 samples\n", + "demo_114 has 47 samples\n", + "demo_115 has 49 samples\n", + "demo_116 has 59 samples\n", + "demo_117 has 52 samples\n", + "demo_118 has 54 samples\n", + "demo_119 has 53 samples\n", + "demo_120 has 63 samples\n", + "demo_121 has 53 samples\n", + "demo_122 has 60 samples\n", + "demo_123 has 51 samples\n", + "demo_124 has 47 samples\n", + "demo_125 has 55 samples\n", + "demo_126 has 56 samples\n", + "demo_127 has 58 samples\n", + "demo_128 has 55 samples\n", + "demo_129 has 53 samples\n", + "demo_130 has 50 samples\n", + "demo_131 has 47 samples\n", + "demo_132 has 46 samples\n", + "demo_133 has 43 samples\n", + "demo_134 has 45 samples\n", + "demo_135 has 54 samples\n", + "demo_136 has 53 samples\n", + "demo_137 has 57 samples\n", + "demo_138 has 50 samples\n", + "demo_139 has 48 samples\n", + "demo_140 has 49 samples\n", + "demo_141 has 54 samples\n", + "demo_142 has 55 samples\n", + "demo_143 has 49 samples\n", + "demo_144 has 51 samples\n", + "demo_145 has 45 samples\n", + "demo_146 has 50 samples\n", + "demo_147 has 51 samples\n", + "demo_148 has 50 samples\n", + "demo_149 has 58 samples\n", + "demo_150 has 46 samples\n", + "demo_151 has 46 samples\n", + "demo_152 has 45 samples\n", + "demo_153 has 42 samples\n", + "demo_154 has 49 samples\n", + "demo_155 has 45 samples\n", + "demo_156 has 63 samples\n", + "demo_157 has 41 samples\n", + "demo_158 has 42 samples\n", + "demo_159 has 45 samples\n", + "demo_160 has 43 samples\n", + "demo_161 has 46 samples\n", + "demo_162 has 52 samples\n", + "demo_163 has 55 samples\n", + "demo_164 has 44 samples\n", + "demo_165 has 42 samples\n", + "demo_166 has 51 samples\n", + "demo_167 has 64 samples\n", + "demo_168 has 57 samples\n", + "demo_169 has 52 samples\n", + "demo_170 has 48 samples\n", + "demo_171 has 45 samples\n", + "demo_172 has 53 samples\n", + "demo_173 has 39 samples\n", + "demo_174 has 47 samples\n", + "demo_175 has 52 samples\n", + "demo_176 has 63 samples\n", + "demo_177 has 50 samples\n", + "demo_178 has 47 samples\n", + "demo_179 has 48 samples\n", + "demo_180 has 55 samples\n", + "demo_181 has 52 samples\n", + "demo_182 has 55 samples\n", + "demo_183 has 53 samples\n", + "demo_184 has 44 samples\n", + "demo_185 has 59 samples\n", + "demo_186 has 45 samples\n", + "demo_187 has 43 samples\n", + "demo_188 has 44 samples\n", + "demo_189 has 52 samples\n", + "demo_190 has 51 samples\n", + "demo_191 has 40 samples\n", + "demo_192 has 49 samples\n", + "demo_193 has 42 samples\n", + "demo_194 has 36 samples\n", + "demo_195 has 54 samples\n", + "demo_196 has 42 samples\n", + "demo_197 has 40 samples\n", + "demo_198 has 45 samples\n", + "demo_199 has 49 samples\n" + ] + } + ], "source": [ "# each demonstration is named \"demo_#\" where # is a number.\n", "# Let's put the demonstration list in increasing episode order\n", @@ -120,10 +343,437 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "2f7b497a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "timestep 0\n", + "obs\n", + "{\n", + " \"object\": [\n", + " 0.026449414293141932,\n", + " 0.026981257415918496,\n", + " 0.8314240728038541,\n", + " 0.0,\n", + " 0.0,\n", + " 0.9691094123222487,\n", + " 0.24663119621902302,\n", + " -0.11694359335499373,\n", + " -0.042210722419907185,\n", + " 0.1804238946722606\n", + " ],\n", + " \"robot0_eef_pos\": [\n", + " -0.0904941790618518,\n", + " -0.015229465003988687,\n", + " 1.0118479674761147\n", + " ],\n", + " \"robot0_eef_quat\": [\n", + " 0.9972275858407224,\n", + " -0.007232815062599316,\n", + " 0.07403510413011814,\n", + " 0.0019057232216049126\n", + " ],\n", + " \"robot0_eef_vel_ang\": [\n", + " 0.0,\n", + " 0.0,\n", + " 0.0\n", + " ],\n", + " \"robot0_eef_vel_lin\": [\n", + " 0.0,\n", + " 0.0,\n", + " 0.0\n", + " ],\n", + " \"robot0_gripper_qpos\": [\n", + " 0.020833,\n", + " -0.020833\n", + " ],\n", + " \"robot0_gripper_qvel\": [\n", + " 0.0,\n", + " 0.0\n", + " ],\n", + " \"robot0_joint_pos\": [\n", + " -0.041410389327799474,\n", + " 0.21736868939218443,\n", + " 0.007539738887367773,\n", + " -2.589845402931484,\n", + " -0.007843816214400163,\n", + " 2.9554575771696747,\n", + " 0.7738283119303786\n", + " ],\n", + " \"robot0_joint_pos_cos\": [\n", + " 0.9991427123462239,\n", + " 0.9764683001348743,\n", + " 0.9999715763034073,\n", + " -0.851609955590076,\n", + " 0.9999692374313213,\n", + " -0.9827268240955787,\n", + " 0.7152403924484989\n", + " ],\n", + " \"robot0_joint_pos_sin\": [\n", + " -0.04139855511284203,\n", + " 0.21566098124535443,\n", + " 0.0075396674514822334,\n", + " -0.5241760043533744,\n", + " -0.007843735782256875,\n", + " 0.1850621225508276,\n", + " 0.6988785166322665\n", + " ],\n", + " 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-0.0037167922644707474,\n", + " 0.06203175911084908,\n", + " 0.0007736031979145991\n", + " ],\n", + " \"robot0_eef_vel_ang\": [\n", + " 0.008174675780542328,\n", + " 0.2117180550504403,\n", + " 0.025337152199680114\n", + " ],\n", + " \"robot0_eef_vel_lin\": [\n", + " 0.021946528579046963,\n", + " 0.019244360508797305,\n", + " -0.03325545509870823\n", + " ],\n", + " \"robot0_gripper_qpos\": [\n", + " 0.023065368793203082,\n", + " -0.023089813685773657\n", + " ],\n", + " \"robot0_gripper_qvel\": [\n", + " 0.05386538871513393,\n", + " -0.05464789210707713\n", + " ],\n", + " \"robot0_joint_pos\": [\n", + " -0.03862546371287872,\n", + " 0.2176935517862639,\n", + " 0.012540741592900084,\n", + " -2.59203892182414,\n", + " -0.006915289916081674,\n", + " 2.933857605408448,\n", + " 0.7734679128154149\n", + " ],\n", + " \"robot0_joint_pos_cos\": [\n", + " 0.9992541295153923,\n", + " 0.9763981884673584,\n", + " 0.9999213659307244,\n", + " -0.852757695866131,\n", + " 0.9999760894779743,\n", + " -0.978500557298211,\n", + " 0.7154922211915071\n", + " ],\n", + " \"robot0_joint_pos_sin\": [\n", + " -0.03861586003749854,\n", + " 0.21597818768954657,\n", + " 0.012540412881329139,\n", + " -0.5223067222821157,\n", + " -0.0069152347999298655,\n", + " 0.20624417414096938,\n", + " 0.6986206992456232\n", + " ],\n", + " \"robot0_joint_vel\": [\n", + " 0.03587066701618021,\n", + " 0.11427360447919478,\n", + " 0.007098630969981366,\n", + " 0.13903479858802026,\n", + " -0.0045408292982859885,\n", + " -0.2366374332873465,\n", + " 0.021655490569626238\n", + " ]\n", + "}\n", + "action\n", + "[ 0.323 0.131 -0.073 0.00670891 0.12851983 -0.00825769\n", + " -1. ]\n", + "timestep 3\n", + "obs\n", + "{\n", + " \"object\": [\n", + " 0.026449610437205617,\n", + " 0.026981465550533244,\n", + " 0.8200930180165275,\n", + " 2.989477547714015e-06,\n", + " 5.389691506845857e-06,\n", + " 0.9691094122977165,\n", + " 0.24663119623840957,\n", + " -0.12084811993760973,\n", + " -0.03702038558498077,\n", + " 0.1864341070243961\n", + " ],\n", + " \"robot0_eef_pos\": [\n", + " -0.09439850950040411,\n", + " -0.010038920034447524,\n", + " 1.0065271250409236\n", + " ],\n", + " \"robot0_eef_quat\": [\n", + " 0.9983755425976737,\n", + " -0.003414240303874383,\n", + " 0.056872138948892356,\n", + " 0.00042274972047360443\n", + " ],\n", + " \"robot0_eef_vel_ang\": [\n", + " 0.015588272761144207,\n", + " 0.20338910134567467,\n", + " 0.001415554639112783\n", + " ],\n", + " \"robot0_eef_vel_lin\": [\n", + " 0.06160381269345277,\n", + " 0.025010135696567033,\n", + " -0.03656737866839496\n", + " ],\n", + " \"robot0_gripper_qpos\": [\n", + " 0.026358274928870957,\n", + " -0.02636141449910154\n", + " ],\n", + " \"robot0_gripper_qvel\": [\n", + " 0.0745201310630871,\n", + " -0.07411367614184626\n", + " ],\n", + " \"robot0_joint_pos\": [\n", + " -0.03650447151890695,\n", + " 0.22634892583028604,\n", + " 0.013167323320805235,\n", + " -2.581147494051686,\n", + " -0.006772139059673779,\n", + " 2.9212721348692297,\n", + " 0.7754134156517873\n", + " ],\n", + " \"robot0_joint_pos_cos\": [\n", + " 0.999333785766275,\n", + " 0.9744922663574513,\n", + " 0.9999133120507784,\n", + " -0.847018564469704,\n", + " 0.999977069153916,\n", + " -0.9758274525241541,\n", + " 0.7141316994350739\n", + " ],\n", + " \"robot0_joint_pos_sin\": [\n", + " -0.03649636455929419,\n", + " 0.22442108370097089,\n", + " 0.0131669428358545,\n", + " -0.5315633089705137,\n", + " -0.006772087295968501,\n", + " 0.21854240526776433,\n", + " 0.7000113683805237\n", + " ],\n", + " \"robot0_joint_vel\": [\n", + " 0.04691353138789363,\n", + " 0.1994635772259614,\n", + " 0.01143329494353153,\n", + " 0.2674340987745522,\n", + " 0.004438708485487719,\n", + " -0.27163705349779815,\n", + " 0.052193205025039595\n", + " ]\n", + "}\n", + "action\n", + "[ 0.491 0.21 -0.19 0.00824866 0.12259889 -0.02527941\n", + " -1. ]\n", + "timestep 4\n", + "obs\n", + "{\n", + " \"object\": [\n", + " 0.026449513581120028,\n", + " 0.026981362757104405,\n", + " 0.8206866047770742,\n", + " 1.3457488015772851e-06,\n", + " 2.4265647886561854e-06,\n", + " 0.969109412312331,\n", + " 0.24663119624238392,\n", + " -0.1173250875824681,\n", + " -0.03522245982820696,\n", + " 0.1830334739182844\n", + " ],\n", + " \"robot0_eef_pos\": [\n", + " -0.09087557400134808,\n", + " -0.008241097071102555,\n", + " 1.0037200786953586\n", + " ],\n", + " \"robot0_eef_quat\": [\n", + " 0.9986502568703245,\n", + " -0.0038131638489504183,\n", + " 0.05179871492175989,\n", + " -0.00013178296654296415\n", + " ],\n", + " \"robot0_eef_vel_ang\": [\n", + " 0.02512282243933508,\n", + " 0.20309719193885506,\n", + " -0.02906478800666458\n", + " ],\n", + " \"robot0_eef_vel_lin\": [\n", + " 0.10622683467941454,\n", + " 0.03716610735305083,\n", + " -0.06009354586057167\n", + " ],\n", + " \"robot0_gripper_qpos\": [\n", + " 0.030413930567981536,\n", + " -0.030375387077326593\n", + " ],\n", + " \"robot0_gripper_qvel\": [\n", + " 0.08575894495332877,\n", + " -0.0853433936999638\n", + " ],\n", + " \"robot0_joint_pos\": [\n", + " -0.033742898584622906,\n", + " 0.24158202550110155,\n", + " 0.014307614740759025,\n", + " -2.562765138816014,\n", + " -0.006403913367086805,\n", + " 2.907945086256439,\n", + " 0.7796524071820293\n", + " ],\n", + " \"robot0_joint_pos_cos\": [\n", + " 0.999430762410992,\n", + " 0.9709607078578428,\n", + " 0.9998976478262572,\n", + " -0.8371046247858968,\n", + " 0.9999794950168696,\n", + " -0.9728283562961821,\n", + " 0.7111579499359364\n", + " ],\n", + " \"robot0_joint_pos_sin\": [\n", + " -0.03373649576620538,\n", + " 0.23923900977097504,\n", + " 0.014307126598938211,\n", + " -0.5470428202271399,\n", + " -0.006403869596315117,\n", + " 0.2315275128058616,\n", + " 0.703032268279996\n", + " ],\n", + " \"robot0_joint_vel\": [\n", + " 0.06079543928279841,\n", + " 0.3522398354114109,\n", + " 0.02364828609743366,\n", + " 0.4241619255231188,\n", + " 0.0024945495675376796,\n", + " -0.2753739143424271,\n", + " 0.10996504673995737\n", + " ]\n", + "}\n", + "action\n", + "[ 0.465 0.309 -0.304 0.00568831 0.10961337 -0.04577875\n", + " -1. ]\n" + ] + } + ], "source": [ "# look at first demonstration\n", "demo_key = demos[0]\n", @@ -150,10 +800,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "552be387", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of first ten actions (10, 7)\n", + "shape of all actions (59, 7)\n" + ] + } + ], "source": [ "# we can also grab multiple timesteps at once directly, or even the full trajectory at once\n", "first_ten_actions = demo_grp[\"actions\"][:10]\n", @@ -164,10 +823,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "57976238", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "success\n" + ] + } + ], "source": [ "# the trajectory also contains the next observations under \"next_obs\", \n", "# for convenient use in a batch (offline) RL pipeline. Let's verify\n", @@ -180,10 +847,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "51ab4a38", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dones\n", + "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1]\n", + "\n", + "rewards\n", + "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1.]\n" + ] + } + ], "source": [ "# we also have \"done\" and \"reward\" information stored in each trajectory.\n", "# In this case, we have sparse rewards that indicate task completion at\n", @@ -199,10 +881,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "360df27c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " \n", + " \n", + "\n" + ] + } + ], "source": [ "# each demonstration also contains metadata\n", "num_samples = demo_grp.attrs[\"num_samples\"] # number of samples in this trajectory\n", @@ -220,10 +1109,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "3b579caf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "==== Env Meta ====\n", + "{\n", + " \"env_name\": \"Lift\",\n", + " \"type\": 1,\n", + " \"env_kwargs\": {\n", + " \"has_renderer\": false,\n", + " \"has_offscreen_renderer\": false,\n", + " \"ignore_done\": true,\n", + " \"use_object_obs\": true,\n", + " \"use_camera_obs\": false,\n", + " \"control_freq\": 20,\n", + " \"controller_configs\": {\n", + " \"type\": \"OSC_POSE\",\n", + " \"input_max\": 1,\n", + " \"input_min\": -1,\n", + " \"output_max\": [\n", + " 0.05,\n", + " 0.05,\n", + " 0.05,\n", + " 0.5,\n", + " 0.5,\n", + " 0.5\n", + " ],\n", + " \"output_min\": [\n", + " -0.05,\n", + " -0.05,\n", + " -0.05,\n", + " -0.5,\n", + " -0.5,\n", + " -0.5\n", + " ],\n", + " \"kp\": 150,\n", + " \"damping\": 1,\n", + " \"impedance_mode\": \"fixed\",\n", + " \"kp_limits\": [\n", + " 0,\n", + " 300\n", + " ],\n", + " \"damping_limits\": [\n", + " 0,\n", + " 10\n", + " ],\n", + " \"position_limits\": null,\n", + " \"orientation_limits\": null,\n", + " \"uncouple_pos_ori\": true,\n", + " \"control_delta\": true,\n", + " \"interpolation\": null,\n", + " \"ramp_ratio\": 0.2\n", + " },\n", + " \"robots\": [\n", + " \"Panda\"\n", + " ],\n", + " \"camera_depths\": false,\n", + " \"camera_heights\": 84,\n", + " \"camera_widths\": 84,\n", + " \"reward_shaping\": false\n", + " }\n", + "}\n", + "\n" + ] + } + ], "source": [ "env_meta = json.loads(f[\"data\"].attrs[\"env_args\"])\n", "# note: we could also have used the following function:\n", @@ -253,10 +1208,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "9c98068e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO: Probing, EGL cannot run on this device\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating offscreen glfw\n", + "Created environment with name Lift\n", + "Action size is 7\n" + ] + } + ], "source": [ "import robomimic.utils.env_utils as EnvUtils\n", "\n", @@ -270,10 +1242,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "595a47d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============= Initialized Observation Utils with Obs Spec =============\n", + "\n", + "using obs modality: low_dim with keys: ['robot0_eef_pos']\n", + "using obs modality: rgb with keys: []\n" + ] + } + ], "source": [ "import robomimic.utils.obs_utils as ObsUtils\n", "\n", @@ -292,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "d997cf39", "metadata": {}, "outputs": [], @@ -306,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "dfae1aaa", "metadata": {}, "outputs": [], @@ -337,10 +1321,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "926d1811", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Playing back demo key: demo_0\n", + "Creating offscreen glfw\n", + "Creating offscreen glfw\n", + "Playing back demo key: demo_1\n", + "Creating offscreen glfw\n", + "Creating offscreen glfw\n", + "Playing back demo key: demo_2\n", + "Creating offscreen glfw\n", + "Creating offscreen glfw\n", + "Playing back demo key: demo_3\n", + "Creating offscreen glfw\n", + "Creating offscreen glfw\n", + "Playing back demo key: demo_4\n", + "Creating offscreen glfw\n", + "Creating offscreen glfw\n" + ] + } + ], "source": [ "# playback the first 5 demos\n", "for ep in demos[:5]:\n", @@ -353,15 +1359,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "bc89c8d8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# view the trajectories!\n", "from IPython.display import Video\n", "Video(video_path, embed=True)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eea881c7", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -380,7 +1411,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/examples/notebooks/run_policy.ipynb b/examples/notebooks/run_policy.ipynb index 913d74ec..0e9f1ca2 100644 --- a/examples/notebooks/run_policy.ipynb +++ b/examples/notebooks/run_policy.ipynb @@ -279,7 +279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/examples/train_bc_rnn.py b/examples/train_bc_rnn.py index b3ec7c51..73d858a1 100644 --- a/examples/train_bc_rnn.py +++ b/examples/train_bc_rnn.py @@ -62,7 +62,7 @@ def robosuite_hyperparameters(config): ## evaluation rollout config ## config.experiment.rollout.enabled = True # enable evaluation rollouts config.experiment.rollout.n = 50 # number of rollouts per evaluation - config.experiment.rollout.horizon = 400 # set horizon based on length of demonstrations (can be obtained with scripts/get_dataset_info.py) + config.experiment.rollout.horizon = 400 # maximum number of env steps per rollout config.experiment.rollout.rate = 50 # do rollouts every @rate epochs config.experiment.rollout.warmstart = 0 # number of epochs to wait before starting rollouts config.experiment.rollout.terminate_on_success = True # end rollout early after task success From 56808a5ab9f1dc1a722fb0877fe1ff3c427991b1 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Sat, 9 Mar 2024 06:55:49 +0000 Subject: [PATCH 15/43] more cleanup --- robomimic/algo/__init__.py | 1 - robomimic/algo/act.py | 265 ------------------------------ robomimic/config/__init__.py | 1 - robomimic/config/act_config.py | 47 ------ robomimic/exps/templates/act.json | 160 ------------------ robomimic/macros.py | 4 +- 6 files changed, 2 insertions(+), 476 deletions(-) delete mode 100644 robomimic/algo/act.py delete mode 100644 robomimic/config/act_config.py delete mode 100644 robomimic/exps/templates/act.json diff --git a/robomimic/algo/__init__.py b/robomimic/algo/__init__.py index 5fd1d53f..dedba35c 100644 --- a/robomimic/algo/__init__.py +++ b/robomimic/algo/__init__.py @@ -10,4 +10,3 @@ from robomimic.algo.iris import IRIS from robomimic.algo.td3_bc import TD3_BC from robomimic.algo.diffusion_policy import DiffusionPolicyUNet -from robomimic.algo.act import ACT diff --git a/robomimic/algo/act.py b/robomimic/algo/act.py deleted file mode 100644 index ad5b6493..00000000 --- a/robomimic/algo/act.py +++ /dev/null @@ -1,265 +0,0 @@ -""" -Implementation of Action Chunking with Transformers (ACT). -""" -from collections import OrderedDict -from functools import reduce - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision.transforms as transforms - -import robomimic.utils.tensor_utils as TensorUtils - -from robomimic.algo import register_algo_factory_func, PolicyAlgo -from robomimic.algo.bc import BC_VAE - - -@register_algo_factory_func("act") -def algo_config_to_class(algo_config): - """ - Maps algo config to the BC algo class to instantiate, along with additional algo kwargs. - - Args: - algo_config (Config instance): algo config - - Returns: - algo_class: subclass of Algo - algo_kwargs (dict): dictionary of additional kwargs to pass to algorithm - """ - algo_class, algo_kwargs = ACT, {} - - return algo_class, algo_kwargs - - -class ACT(BC_VAE): - """ - BC training with a VAE policy. - """ - def _create_networks(self): - """ - Creates networks and places them into @self.nets. - """ - - self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - - self.nets = nn.ModuleDict() - self.chunk_size = self.global_config["train"]["seq_length"] - self.camera_keys = self.obs_config['modalities']['obs']['rgb'].copy() - self.proprio_keys = self.obs_config['modalities']['obs']['low_dim'].copy() - self.obs_keys = self.proprio_keys + self.camera_keys - - self.proprio_dim = 0 - for k in self.proprio_keys: - self.proprio_dim += reduce(lambda x, y: x*y, self.obs_key_shapes[k]) - - from act.detr.main import build_ACT_model_and_optimizer - policy_config = {'num_queries': self.chunk_size, - 'hidden_dim': self.algo_config.act.hidden_dim, - 'dim_feedforward': self.algo_config.act.dim_feedforward, - 'backbone': self.algo_config.act.backbone, - 'enc_layers': self.algo_config.act.enc_layers, - 'dec_layers': self.algo_config.act.dec_layers, - 'nheads': self.algo_config.act.nheads, - 'latent_dim': self.algo_config.act.latent_dim, - 'a_dim': self.ac_dim, - 'state_dim': self.proprio_dim, - 'camera_names': self.camera_keys - } - self.kl_weight = self.algo_config.act.kl_weight - model, optimizer = build_ACT_model_and_optimizer(policy_config) - self.nets["policy"] = torch.nn.parallel.DataParallel(model, device_ids=list(range(0,torch.cuda.device_count()))) - self.nets = self.nets.float().to(self.device) - - self.temporal_agg = False - self.query_frequency = self.chunk_size # TODO maybe tune - - self._step_counter = 0 - self.a_hat_store = None - - - def process_batch_for_training(self, batch): - """ - Processes input batch from a data loader to filter out - relevant information and prepare the batch for training. - Args: - batch (dict): dictionary with torch.Tensors sampled - from a data loader - Returns: - input_batch (dict): processed and filtered batch that - will be used for training - """ - - input_batch = dict() - input_batch["obs"] = {k: batch["obs"][k][:, 0, :] for k in batch["obs"] if k != 'pad_mask'} - input_batch["obs"]['pad_mask'] = batch["obs"]['pad_mask'] - input_batch["goal_obs"] = batch.get("goal_obs", None) # goals may not be present - # For the goal, only keep the camera observations - if input_batch["goal_obs"] is not None: - input_batch["goal_obs"] = {key: value for key, value in batch["goal_obs"].items() if key.startswith('camera')} - input_batch["actions"] = batch["actions"][:, :, :] - # we move to device first before float conversion because image observation modalities will be uint8 - - # this minimizes the amount of data transferred to GPU - return TensorUtils.to_float(TensorUtils.to_device(input_batch, self.device)) - - def train_on_batch(self, batch, epoch, validate=False): - """ - Update from superclass to set categorical temperature, for categorcal VAEs. - """ - - return super(BC_VAE, self).train_on_batch(batch, epoch, validate=validate) - - def _forward_training(self, batch): - """ - Internal helper function for BC algo class. Compute forward pass - and return network outputs in @predictions dict. - Args: - batch (dict): dictionary with torch.Tensors sampled - from a data loader and filtered by @process_batch_for_training - Returns: - predictions (dict): dictionary containing network outputs - """ - # TODO(Ashwin): remove this once we add in the robomimic observation encoders - for k in self.proprio_keys: - batch["obs"][k] = batch["obs"][k].reshape((batch["obs"][k].shape[0], -1)) - proprio = [batch["obs"][k] for k in self.proprio_keys] - proprio = torch.cat(proprio, axis=1) - qpos = proprio - - images = [] - goal_images = [] - for cam_name in self.camera_keys: - image = batch['obs'][cam_name] - image = self.normalize(image) - image = image.unsqueeze(axis=1) - images.append(image) - goal_image = batch['goal_obs'][cam_name] - goal_image = self.normalize(goal_image) - goal_images.append(goal_image) - - images = torch.cat(images, axis=1) - goal_images = torch.cat(goal_images, axis=1) - - # Concatenate images and goal images channel-wise - images = torch.cat((images, goal_images), axis=1) - - env_state = torch.zeros([qpos.shape[0], 10]).cuda() # this is not used - - actions = batch['actions'] - is_pad = batch['obs']['pad_mask'] == 0 # from 1.0 or 0 to False and True - is_pad = is_pad.squeeze(dim=-1) - - a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state, actions, is_pad) - total_kld, dim_wise_kld, mean_kld = self.kl_divergence(mu, logvar) - loss_dict = dict() - all_l1 = F.l1_loss(actions, a_hat, reduction='none') - l1 = (all_l1 * ~is_pad.unsqueeze(-1)).mean() - loss_dict['l1'] = l1 - loss_dict['kl'] = total_kld[0] - - - predictions = OrderedDict( - actions=actions, - kl_loss=loss_dict['kl'], - reconstruction_loss=loss_dict['l1'], - ) - - return predictions - - def get_action(self, obs_dict, goal_dict=None): - """ - Get policy action outputs. - Args: - obs_dict (dict): current observation - goal_dict (dict): (optional) goal - Returns: - action (torch.Tensor): action tensor - """ - assert not self.nets.training - - # TODO(Ashwin): remove this once we add in the robomimic observation encoders - for k in self.proprio_keys: - obs_dict[k] = obs_dict[k].reshape((obs_dict[k].shape[0], -1)) - proprio = [obs_dict[k] for k in self.proprio_keys] - proprio = torch.cat(proprio, axis=1) - qpos = proprio - - images = [] - for cam_name in self.camera_keys: - image = obs_dict[cam_name] - image = self.normalize(image) - image = image.unsqueeze(axis=1) - images.append(image) - images = torch.cat(images, axis=1) - - env_state = torch.zeros([qpos.shape[0], 10]).cuda() # not used - - if self._step_counter % self.query_frequency == 0: - a_hat, is_pad_hat, (mu, logvar) = self.nets["policy"](qpos, images, env_state) - self.a_hat_store = a_hat - - action = self.a_hat_store[:, self._step_counter % self.query_frequency, :] - self._step_counter += 1 - return action - - - def reset(self): - """ - Reset algo state to prepare for environment rollouts. - """ - self._step_counter = 0 - - def _compute_losses(self, predictions, batch): - """ - Internal helper function for BC algo class. Compute losses based on - network outputs in @predictions dict, using reference labels in @batch. - Args: - predictions (dict): dictionary containing network outputs, from @_forward_training - batch (dict): dictionary with torch.Tensors sampled - from a data loader and filtered by @process_batch_for_training - Returns: - losses (dict): dictionary of losses computed over the batch - """ - - # total loss is sum of reconstruction and KL, weighted by beta - kl_loss = predictions["kl_loss"] - recons_loss = predictions["reconstruction_loss"] - action_loss = recons_loss + self.kl_weight * kl_loss - return OrderedDict( - recons_loss=recons_loss, - kl_loss=kl_loss, - action_loss=action_loss, - ) - - def log_info(self, info): - """ - Process info dictionary from @train_on_batch to summarize - information to pass to tensorboard for logging. - Args: - info (dict): dictionary of info - Returns: - loss_log (dict): name -> summary statistic - """ - log = PolicyAlgo.log_info(self, info) - log["Loss"] = info["losses"]["action_loss"].item() - log["KL_Loss"] = info["losses"]["kl_loss"].item() - log["Reconstruction_Loss"] = info["losses"]["recons_loss"].item() - if "policy_grad_norms" in info: - log["Policy_Grad_Norms"] = info["policy_grad_norms"] - return log - - def kl_divergence(self, mu, logvar): - batch_size = mu.size(0) - assert batch_size != 0 - if mu.data.ndimension() == 4: - mu = mu.view(mu.size(0), mu.size(1)) - if logvar.data.ndimension() == 4: - logvar = logvar.view(logvar.size(0), logvar.size(1)) - - klds = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()) - total_kld = klds.sum(1).mean(0, True) - dimension_wise_kld = klds.mean(0) - mean_kld = klds.mean(1).mean(0, True) - - return total_kld, dimension_wise_kld, mean_kld - diff --git a/robomimic/config/__init__.py b/robomimic/config/__init__.py index b8b9da73..d2cba6d3 100644 --- a/robomimic/config/__init__.py +++ b/robomimic/config/__init__.py @@ -11,4 +11,3 @@ from robomimic.config.iris_config import IRISConfig from robomimic.config.td3_bc_config import TD3_BCConfig from robomimic.config.diffusion_policy_config import DiffusionPolicyConfig -from robomimic.config.act_config import ACTConfig diff --git a/robomimic/config/act_config.py b/robomimic/config/act_config.py deleted file mode 100644 index 9be3926b..00000000 --- a/robomimic/config/act_config.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Config for BC algorithm. -""" - -from robomimic.config.base_config import BaseConfig - - -class ACTConfig(BaseConfig): - ALGO_NAME = "act" - - def train_config(self): - """ - BC algorithms don't need "next_obs" from hdf5 - so save on storage and compute by disabling it. - """ - super(ACTConfig, self).train_config() - self.train.hdf5_load_next_obs = False - - def algo_config(self): - """ - This function populates the `config.algo` attribute of the config, and is given to the - `Algo` subclass (see `algo/algo.py`) for each algorithm through the `algo_config` - argument to the constructor. Any parameter that an algorithm needs to determine its - training and test-time behavior should be populated here. - """ - - # optimization parameters - self.algo.optim_params.policy.optimizer_type = "adamw" - self.algo.optim_params.policy.learning_rate.initial = 5e-5 # policy learning rate - self.algo.optim_params.policy.learning_rate.decay_factor = 1 # factor to decay LR by (if epoch schedule non-empty) - self.algo.optim_params.policy.learning_rate.epoch_schedule = [] # epochs where LR decay occurs - self.algo.optim_params.policy.learning_rate.scheduler_type = "linear" # learning rate scheduler ("multistep", "linear", etc) - self.algo.optim_params.policy.regularization.L2 = 0.0001 # L2 regularization strength - - # loss weights - self.algo.loss.l2_weight = 0.0 # L2 loss weight - self.algo.loss.l1_weight = 1.0 # L1 loss weight - self.algo.loss.cos_weight = 0.0 # cosine loss weight - - # ACT policy settings - self.algo.act.hidden_dim = 512 # length of (s, a) seqeunces to feed to transformer - should usually match train.frame_stack - self.algo.act.dim_feedforward = 3200 # dimension for embeddings used by transformer - self.algo.act.backbone = "resnet18" # number of transformer blocks to stack - self.algo.act.enc_layers = 4 # number of attention heads for each transformer block (should divide embed_dim evenly) - self.algo.act.dec_layers = 7 # dropout probability for embedding inputs in transformer - self.algo.act.nheads = 8 # dropout probability for attention outputs for each transformer block - self.algo.act.latent_dim = 32 # latent dim of VAE - self.algo.act.kl_weight = 20 # KL weight of VAE diff --git a/robomimic/exps/templates/act.json b/robomimic/exps/templates/act.json deleted file mode 100644 index 4512ecdf..00000000 --- a/robomimic/exps/templates/act.json +++ /dev/null @@ -1,160 +0,0 @@ -{ - "algo_name": "act", - "experiment": { - "name": "test", - "validate": false, - "logging": { - "terminal_output_to_txt": true, - "log_tb": true, - "log_wandb": false, - "wandb_proj_name": "debug" - }, - "mse":{}, - "save": { - "enabled": true, - "every_n_seconds": null, - "every_n_epochs": 40, - "epochs": [], - "on_best_validation": false, - "on_best_rollout_return": false, - "on_best_rollout_success_rate": true - }, - "epoch_every_n_steps": 500, - "validation_epoch_every_n_steps": 10, - "env": null, - "additional_envs": null, - "render": false, - "render_video": true, - "keep_all_videos": false, - "video_skip": 5, - "rollout": { - "enabled": true, - "n": 50, - "horizon": 400, - "rate": 40, - "warmstart": 0, - "terminate_on_success": true - } - }, - "train": { - "data": null, - "output_dir":"../act_trained_models", - "num_data_workers": 4, - "hdf5_cache_mode": "low_dim", - "hdf5_use_swmr": true, - "hdf5_load_next_obs": false, - "hdf5_normalize_obs": false, - "hdf5_filter_key": null, - "seq_length": 10, - "pad_seq_length": true, - "frame_stack": 1, - "pad_frame_stack": true, - "dataset_keys": [ - "actions" - ], - "goal_mode": null, - "cuda": true, - "batch_size": 128, - "num_epochs": 10000, - "seed": 1 - }, - "algo": { - "optim_params": { - "policy": { - "optimizer_type": "adamw", - "learning_rate": { - "initial": 0.00005, - "decay_factor": 1, - "epoch_schedule": [ - 100 - ], - "scheduler_type": "linear" - }, - "regularization": { - "L2": 0.0001 - } - } - }, - "loss": { - "l2_weight": 0.0, - "l1_weight": 1.0, - "cos_weight": 0.0 - }, - "act": { - "hidden_dim": 512, - "dim_feedforward": 3200, - "backbone": "resnet18", - "enc_layers": 4, - "dec_layers": 7, - "nheads": 8, - "latent_dim": 32, - "kl_weight": 20 - } - }, - "observation": { - "modalities": { - "obs": { - "low_dim": [ - "robot0_eef_pos", - "robot0_eef_quat", - "robot0_gripper_qpos", - "object" - ], - "rgb": [], - "depth": [], - "scan": [] - }, - "goal": { - "low_dim": [], - "rgb": [], - "depth": [], - "scan": [] - } - }, - "encoder": { - "low_dim": { - "core_class": null, - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - }, - "rgb": { - "core_class": "VisualCore", - "core_kwargs": { - "feature_dimension": 64, - "backbone_class": "ResNet18Conv", - "backbone_kwargs": { - "pretrained": false, - "input_coord_conv": false - }, - "pool_class": "SpatialSoftmax", - "pool_kwargs": { - "num_kp": 32, - "learnable_temperature": false, - "temperature": 1.0, - "noise_std": 0.0 - } - }, - "obs_randomizer_class": "CropRandomizer", - "obs_randomizer_kwargs": { - "crop_height": 76, - "crop_width": 76, - "num_crops": 1, - "pos_enc": false - } - }, - "depth": { - "core_class": "VisualCore", - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - }, - "scan": { - "core_class": "ScanCore", - "core_kwargs": {}, - "obs_randomizer_class": null, - "obs_randomizer_kwargs": {} - } - } - } -} \ No newline at end of file diff --git a/robomimic/macros.py b/robomimic/macros.py index 9d54a4cc..3b6c0503 100644 --- a/robomimic/macros.py +++ b/robomimic/macros.py @@ -9,11 +9,11 @@ VISUALIZE_RANDOMIZER = False # wandb entity (eg. username or team name) -WANDB_ENTITY = "tri" +WANDB_ENTITY = None # wandb api key (obtain from https://wandb.ai/authorize) # alternatively, set up wandb from terminal with `wandb login` -WANDB_API_KEY = "10eb447cdaa881ddb7748e1ebb4814706c2be903" +WANDB_API_KEY = None try: from robomimic.macros_private import * From 03a731d3232155b09d87b72ce38fae1f5e09a650 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Sat, 9 Mar 2024 06:59:22 +0000 Subject: [PATCH 16/43] more clean --- robomimic/scripts/config_gen/act_gen.py | 131 ---- .../scripts/config_gen/act_gen_no_define.py | 266 -------- robomimic/scripts/config_gen/diffusion_gen.py | 480 -------------- .../config_gen/diffusion_gen_no_define.py | 399 ------------ .../scripts/config_gen/droid_runs_bcxfmr.py | 456 -------------- robomimic/scripts/config_gen/voltron.py | 593 ------------------ 6 files changed, 2325 deletions(-) delete mode 100644 robomimic/scripts/config_gen/act_gen.py delete mode 100644 robomimic/scripts/config_gen/act_gen_no_define.py delete mode 100644 robomimic/scripts/config_gen/diffusion_gen.py delete mode 100644 robomimic/scripts/config_gen/diffusion_gen_no_define.py delete mode 100644 robomimic/scripts/config_gen/droid_runs_bcxfmr.py delete mode 100644 robomimic/scripts/config_gen/voltron.py diff --git a/robomimic/scripts/config_gen/act_gen.py b/robomimic/scripts/config_gen/act_gen.py deleted file mode 100644 index 57c83263..00000000 --- a/robomimic/scripts/config_gen/act_gen.py +++ /dev/null @@ -1,131 +0,0 @@ -from robomimic.scripts.config_gen.helper import * - -def make_generator_helper(args): - algo_name_short = "act" - generator = get_generator( - algo_name="act", - config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[64], - ) - - generator.add_param( - key="train.max_grad_norm", - name="", - group=-1, - values=[100.0], - ) - - if args.env == "droid": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [{"path": p} for p in scan_datasets("~/Downloads/example_pen_in_cup", postfix="trajectory_im128.h5")], - ], - value_names=[ - "pen-in-cup", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - ) - elif args.env == "kitchen": - raise NotImplementedError - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "TODO.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "~/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/act_gen_no_define.py b/robomimic/scripts/config_gen/act_gen_no_define.py deleted file mode 100644 index cf9db391..00000000 --- a/robomimic/scripts/config_gen/act_gen_no_define.py +++ /dev/null @@ -1,266 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) -evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# evaldataset = evaldataset[:200] -# broaddataset = broaddataset[:2000] -random.shuffle(evaldataset) -# random.shuffle(broaddataset) -N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -def make_generator_helper(args): - algo_name_short = "act" - generator = get_generator( - algo_name="act", - config_file=os.path.join(base_path, 'robomimic/exps/templates/act.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[2048], # 4096 works for wrist - ) - - generator.add_param( - key="train.max_grad_norm", - name="", - group=-1, - values=[100.0], - ) - - if args.env == "droid": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # broaddataset + evaldataset, - # [{"path": p["path"], "weight": (0.5 / N_EVAL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset], - # broaddataset, - evaldataset, - ], - value_names=[ - # "mixed_unbalanced", - # "mixed_balanced", - # "broad", - "eval", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - # None, # Change this to "geom" to do goal conditioning - "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - # 0.5, - 0.3 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - # "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=2498, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - ] - ], - value_names=[ - # "proprio", - "proprio-cam", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="backbone", - group=1234, - values=[ - # "ResNet18Conv", - "ResNet50Conv", - ], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - # 64, - 512, - ], - ) - elif args.env == "kitchen": - raise NotImplementedError - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "TODO.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "~/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) diff --git a/robomimic/scripts/config_gen/diffusion_gen.py b/robomimic/scripts/config_gen/diffusion_gen.py deleted file mode 100644 index 072bd266..00000000 --- a/robomimic/scripts/config_gen/diffusion_gen.py +++ /dev/null @@ -1,480 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] -broaddataset = broaddataset[:2000] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] -N_EVAL_MULTI = len(evaldataset_multitask) -print(N_BROAD, N_EVAL_MULTI) - - -def make_generator_helper(args): - algo_name_short = "diffusion_policy" - - generator = get_generator( - algo_name="diffusion_policy", - config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[8], - ) - - # use ddim by default - generator.add_param( - key="algo.ddim.enabled", - name="ddim", - group=1001, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.ddpm.enabled", - name="ddpm", - group=1001, - values=[ - False, - # True, - ], - hidename=True, - ) - - if args.env == "droid": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # broaddataset + evaldataset, - [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], - # broaddataset, - evaldataset_multitask, - ], - value_names=[ - # "mixed_unbalanced", - "mixed_balanced", - # "broad", - "eval_multi", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - # ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - # "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - None, # Change this to "geom" to do goal conditioning - # "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - 0.3, - # 0.5 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - # "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24986, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ] - ], - value_names=[ - # "proprio", - "proprio-cam", - "proprio-cam-lang", - ], - hidename=False, - ) - ## All of the following are needed for DeFiNe style encoding - generator.add_param( - key="observation.encoder.rgb.input_maps", - name="", - group=24986, - values=[ - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - }, - ], - value_names=[ - "define", - "define-lang", - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=-1, - values=["DeFiNeVisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="", - group=-1, - values=["DeFiNeImageCamEncoder"], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - 512, - # None, - # None - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.flatten", - name="flatten", - group=1234, - values=[ - True, - # False, - # False - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.fuser", - name="fuser", - group=1234, - values=[ - None, - # "transformer", - # "perceiver" - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", - name="", - group=1234, - values=[ - True, - # True, - # False, - ], - hidename=False, - ) - - - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.backbone_class", - # name="backbone", - # group=1234, - # values=[ - # # "ResNet18Conv", - # "ResNet50Conv", - # ], - # ) - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.feature_dimension", - # name="visdim", - # group=1234, - # values=[ - # # 64, - # 512, - # ], - # ) - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/diffusion_gen_no_define.py b/robomimic/scripts/config_gen/diffusion_gen_no_define.py deleted file mode 100644 index a5c81b56..00000000 --- a/robomimic/scripts/config_gen/diffusion_gen_no_define.py +++ /dev/null @@ -1,399 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] -broaddataset = broaddataset[:2000] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] -N_EVAL_MULTI = len(evaldataset_multitask) -print(N_BROAD, N_EVAL_MULTI) - -def make_generator_helper(args): - algo_name_short = "diffusion_policy" - - generator = get_generator( - algo_name="diffusion_policy", - config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[1000], - ) - - generator.add_param( - key="train.batch_size", - name="", - group=-1, - values=[16], - ) - - # use ddim by default - generator.add_param( - key="algo.ddim.enabled", - name="ddim", - group=1001, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.ddpm.enabled", - name="ddpm", - group=1001, - values=[ - False, - # True, - ], - hidename=True, - ) - - if args.env == "droid": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # broaddataset + evaldataset, - [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], - # broaddataset, - evaldataset_multitask, - ], - value_names=[ - # "mixed_unbalanced", - "mixed_balanced", - # "broad", - "eval_multi", - ], - ) - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - # ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - # "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - None, # Change this to "geom" to do goal conditioning - # "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - # 0.5, - 0.3 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - # "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - # {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24987, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ] - ], - value_names=[ - # "proprio", - "proprio-cam", - "proprio-cam-lang", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.input_maps", - name="", - group=24987, - values=[ - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "lang_emb" : "lang_fixed/language_distilbert" - }, - }, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=-1, - values=["FiLMVisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="backbone", - group=1234, - values=[ - # "ResNet18Conv", - # "ResNet50Conv", - "ResNetFiLMEncoder" - ], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - # 64, - 512, - ], - ) - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/droid_runs_bcxfmr.py b/robomimic/scripts/config_gen/droid_runs_bcxfmr.py deleted file mode 100644 index 16feace6..00000000 --- a/robomimic/scripts/config_gen/droid_runs_bcxfmr.py +++ /dev/null @@ -1,456 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] -N_BROAD_FULL = len(broaddataset_full) - -chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] -N_CHIPS = len(chips_singletask) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang_filtered.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -# evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl' in l['path']] -# N_EVAL_MULTI = len(evaldataset_multitask) - -# apple_singletask = [{'path': l['path']} for l in langs if 'TRI_1_10_multitask_food_in_bowl/apple' in l['path']] -# N_APPLE = len(apple_singletask) - -## Getting single task eval data (food in bowl) -# evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] -# evaldataset_foodinbowl = evaldataset_foodinbowl[:20] -# N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) - -## Getting single task eval data (open microwave) -# evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] -# N_EVAL_OPENMICRO = len(evaldataset_openmicro) - -## Getting single task eval data (close microwave) -# evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] -# N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) - -print(f"Broad Datapoints: {N_BROAD, N_BROAD_FULL} \ - \nSingle Task CHIPS Datapoints: {N_CHIPS} ") - # \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ - # \nSingle Task APPLE Datapoints: {N_APPLE}") - # # \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") - - -def make_generator_helper(args): - algo_name_short = "bc_xfmr" - - generator = get_generator( - algo_name="bc", - config_file=os.path.join(base_path, 'robomimic/exps/templates/bc_transformer.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[100000], - ) - - generator.add_param( - key="train.batch_size", - name="bz", - group=1212111, - values=[128], - hidename=False, - ) - - if args.env == "droid": - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - "wrist", - # "wrist-stereo", - "2cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=130, - values=[ - "ColorRandomizer", # jitter only - ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=130, - values=[ - {}, # jitter only - [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - ### CONDITIONING - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=24986, - values = [ - "geom", - None, # Change this to "geom" to do goal conditioning - - ] - ) - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - 0.3, - ] - ) - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24986, - values=[ - ["robot_state/cartesian_position", "robot_state/gripper_position"], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "lang_fixed/language_distilbert" - ] - ], - value_names=[ - # "proprio", - "proprio", - "proprio-lang", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", - name="", - group=2498, - values=[ - False, - # True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=-1, - values=["VisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="", - group=-1, - values=["ResNet50Conv"], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - 512, - # None, - # None - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.flatten", - name="flatten", - group=1234, - values=[ - True, - # False, - # False - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.fuser", - name="fuser", - group=1234, - values=[ - None, - # "transformer", - # "perceiver" - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", - name="", - group=1234, - values=[ - False, - ], - hidename=False, - ) - - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - chips_singletask, - [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], - [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], - ], - value_names=[ - "eval_chips", - "balanced_chips", - "full_balanced_chips", - ], - ) - - generator.add_param( - key="algo.transformer.supervise_all_steps", - name="supallsteps", - group=1, - values=[ - True, - ], - hidename=True, - ) - generator.add_param( - key="algo.transformer.pred_future_acs", - name="futureacs", - group=1, - values=[ - False, - ], - hidename=True, - ) - generator.add_param( - key="algo.transformer.causal", - name="causal", - group=1, - values=[ - False, - ], - hidename=True, - ) - generator.add_param( - key="train.frame_stack", - name="", - group=-1, - values=[2], - hidename=True, - ) - generator.add_param( - key="train.seq_length", - name="H", - group=-1, - values=[2], - hidename=False, - ) - generator.add_param( - key="algo.transformer.context_length", - name="", - group=-1, - values=[2], - hidename=True, - ) - generator.add_param( - key="algo.gmm.enabled", - name="", - group=-1, - values=[False], - hidename=True, - ) - - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/config_gen/voltron.py b/robomimic/scripts/config_gen/voltron.py deleted file mode 100644 index a649072f..00000000 --- a/robomimic/scripts/config_gen/voltron.py +++ /dev/null @@ -1,593 +0,0 @@ -from robomimic.scripts.config_gen.helper import * -import random -import json - -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] -N_BROAD = len(broaddataset) - -## Getting multitask eval data -evaldataset_multitask = [{'path': l['path']} for l in langs if 'TRI_bowls' in l['path']] -N_EVAL_MULTI = len(evaldataset_multitask) - -## Getting single task eval data (food in bowl) -evaldataset_foodinbowl = [{'path': l['path']} for l in langs if 'TRI_food_in_bowl' in l['path']] -N_EVAL_FOODINBOWL = len(evaldataset_foodinbowl) - -## Getting single task eval data (open microwave) -evaldataset_openmicro = [{'path': l['path']} for l in langs if 'TRI_microwave_open' in l['path']] -N_EVAL_OPENMICRO = len(evaldataset_openmicro) - -## Getting single task eval data (close microwave) -evaldataset_closemicro = [{'path': l['path']} for l in langs if 'TRI_microwave_close' in l['path']] -N_EVAL_CLOSEMICRO = len(evaldataset_closemicro) - -print(f"Broad Datapoints: {N_BROAD} \ - \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ - \nSingle Task Food in Bowl Datapoints: {N_EVAL_FOODINBOWL} \ - \nSingle Task Microwave Open Datapoints: {N_EVAL_OPENMICRO} \ - \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") - - -def make_generator_helper(args): - algo_name_short = "diffusion_policy" - - generator = get_generator( - algo_name="diffusion_policy", - config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), - args=args, - algo_name_short=algo_name_short, - pt=True, - ) - if args.ckpt_mode is None: - args.ckpt_mode = "off" - - generator.add_param( - key="train.num_data_workers", - name="", - group=-1, - values=[8], - ) - - generator.add_param( - key="train.num_epochs", - name="", - group=-1, - values=[100000], - ) - - generator.add_param( - key="train.batch_size", - name="bs", - group=-1, - values=[32], - ) - - # use ddim by default - generator.add_param( - key="algo.ddim.enabled", - name="ddim", - group=1001, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.ddpm.enabled", - name="ddpm", - group=1001, - values=[ - False, - # True, - ], - hidename=True, - ) - - if args.env == "droid": - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, - values=[ - # ["camera/image/hand_camera_left_image"], - # ["camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], - # [ - # "camera/image/hand_camera_left_image", "camera/image/hand_camera_right_image", - # "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_1_right_image", - # "camera/image/varied_camera_2_left_image", "camera/image/varied_camera_2_right_image", - # ], - ], - value_names=[ - # "wrist", - # "wrist-stereo", - "3cams", - # "3cams-stereo", - ] - ) - generator.add_param( - key="train.goal_mode", - name="goal_mode", - group=5678, - values = [ - None, # Change this to "geom" to do goal conditioning - # "geom" - ] - ) - - generator.add_param( - key="train.truncated_geom_factor", - name="truncated_geom_factor", - group=5555, - values = [ - 0.3, - # 0.5 - ] - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_class", - name="obsrand", - group=138, - values=[ - "CropRandomizer", # crop only - # "ColorRandomizer", # jitter only - # ["ColorRandomizer", "CropRandomizer"], # jitter, followed by crop - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.obs_randomizer_kwargs", - name="obsrandargs", - group=-1, - values=[ - {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}, # crop only - # {}, # jitter only - # [{}, {"crop_height": 116, "crop_width": 116, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - # [{}, {"crop_height": 224, "crop_width": 224, "num_crops": 1, "pos_enc": False}], # jitter, followed by crop - ], - hidename=True, - ) - - generator.add_param( - key="observation.modalities.obs.low_dim", - name="ldkeys", - group=24986, - values=[ - # ["robot_state/cartesian_position", "robot_state/gripper_position"], - # [ - # "robot_state/cartesian_position", "robot_state/gripper_position", - # "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - # "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - # "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - # "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - # "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - # "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - # ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_raw" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - [ - "robot_state/cartesian_position", "robot_state/gripper_position", - "camera/extrinsics/hand_camera_left", "camera/intrinsics/hand_camera_left", # "camera/extrinsics/hand_camera_left_gripper_offset", - "camera/extrinsics/hand_camera_right", "camera/intrinsics/hand_camera_right", # "camera/extrinsics/hand_camera_right_gripper_offset", - "camera/extrinsics/varied_camera_1_left", "camera/intrinsics/varied_camera_1_left", - "camera/extrinsics/varied_camera_1_right", "camera/intrinsics/varied_camera_1_right", - "camera/extrinsics/varied_camera_2_left", "camera/intrinsics/varied_camera_2_left", - "camera/extrinsics/varied_camera_2_right", "camera/intrinsics/varied_camera_2_right", - "lang_fixed/language_distilbert" - ], - ], - value_names=[ - # "proprio", - "proprio-cam-lang-bert2", - "proprio-cam-lang-bert", - "proprio-cam-lang-raw", - "proprio-cam-lang-bert3", - "proprio-cam-lang-bert4", - ], - hidename=False, - ) - ## All of the following are needed for DeFiNe style encoding - generator.add_param( - key="observation.encoder.rgb.input_maps", - name="", - group=24986, - values=[ - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "lang_emb" : "lang_fixed/language_raw" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "lang_emb" : "lang_fixed/language_raw" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "lang_emb" : "lang_fixed/language_raw" - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - "lang_emb" : "lang_fixed/language_distilbert" - }, - }, - { - "camera/image/hand_camera_left_image": { - "image": "camera/image/hand_camera_left_image", - "intrinsics": "camera/intrinsics/hand_camera_left", - "extrinsics": "camera/extrinsics/hand_camera_left", - }, - "camera/image/varied_camera_1_left_image": { - "image": "camera/image/varied_camera_1_left_image", - "intrinsics": "camera/intrinsics/varied_camera_1_left", - "extrinsics": "camera/extrinsics/varied_camera_1_left", - }, - "camera/image/varied_camera_2_left_image": { - "image": "camera/image/varied_camera_2_left_image", - "intrinsics": "camera/intrinsics/varied_camera_2_left", - "extrinsics": "camera/extrinsics/varied_camera_2_left", - }, - }, - ], - value_names=[ - "r3m-no-lang", - "vc1-no-lang", - "voltron-lang", - "define-film-lang", - "define-nofilm-lang", - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_class", - name="visenc", - group=24986, - values=["PretrainedVisualCore", "PretrainedVisualCore", "PretrainedVisualCore", "DeFiNeVisualCore", "DeFiNeVisualCore"], - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_class", - name="enc", - group=24986, - values=["R3MImageCamEncoder", "VC1ImageCamEncoder", "VoltronImageCamEncoder", "DeFiNeImageCamEncoder", "DeFiNeImageCamEncoder"], - hidename=False, - ) - - - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.use_cam", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.pretrained", - name="", - group=2498, - values=[ - # False, - True, - ], - hidename=True, - ) - - - generator.add_param( - key="observation.encoder.rgb.core_kwargs.feature_dimension", - name="visdim", - group=1234, - values=[ - 512, - # None, - # None - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.flatten", - name="flatten", - group=1234, - values=[ - True, - # False, - # False - ], - hidename=True, - ) - generator.add_param( - key="observation.encoder.rgb.fuser", - name="fuser", - group=1234, - values=[ - None, - # "transformer", - # "perceiver" - ], - hidename=False, - ) - generator.add_param( - key="observation.encoder.rgb.core_kwargs.backbone_kwargs.downsample", - name="", - group=1234, - values=[ - True, - # True, - # False, - ], - hidename=False, - ) - - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - broaddataset, - # evaldataset_multitask, - # evaldataset_foodinbowl, - # evaldataset_openmicro, - # evaldataset_closemicro, - # [{"path": p["path"], "weight": (0.5 / N_EVAL_MULTI if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_multitask], - # [{"path": p["path"], "weight": (0.5 / N_EVAL_FOODINBOWL if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_foodinbowl], - # [{"path": p["path"], "weight": (0.5 / N_EVAL_OPENMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_openmicro], - # [{"path": p["path"], "weight": (0.5 / N_EVAL_CLOSEMICRO if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + evaldataset_closemicro], - ], - value_names=[ - "broad", - # "eval_multi", - # "eval_foodinbowl", - # "eval_openmicro", - # "eval_closemicro", - # "balanced_broad_eval_multi", - # "balanced_broad_eval_foodinbowl", - # "balanced_broad_eval_openmicro", - # "balanced_broad_eval_closemicro", - ], - ) - - - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.backbone_class", - # name="backbone", - # group=1234, - # values=[ - # # "ResNet18Conv", - # "ResNet50Conv", - # ], - # ) - # generator.add_param( - # key="observation.encoder.rgb.core_kwargs.feature_dimension", - # name="visdim", - # group=1234, - # values=[ - # # 64, - # 512, - # ], - # ) - - elif args.env == "kitchen": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], - ], - value_names=[ - # "bowls-human-100", - "human-100", - # "mg-100", - # "mg-1000", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - "action_dict/base_mode", - # "actions", - ], - ], - value_names=[ - "abs", - ], - hidename=True, - ) - elif args.env == "square": - generator.add_param( - key="train.data", - name="ds", - group=2, - values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], - ], - value_names=[ - "square", - ], - ) - - # update env config to use absolute action control - generator.add_param( - key="experiment.env_meta_update_dict", - name="", - group=-1, - values=[ - {"env_kwargs": {"controller_configs": {"control_delta": False}}} - ], - ) - - generator.add_param( - key="train.action_keys", - name="ac_keys", - group=-1, - values=[ - [ - "action_dict/abs_pos", - "action_dict/abs_rot_6d", - "action_dict/gripper", - # "actions", - ], - ], - value_names=[ - "abs", - ], - ) - - - else: - raise ValueError - - generator.add_param( - key="train.output_dir", - name="", - group=-1, - values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( - env=args.env, - mod=args.mod, - algo_name_short=algo_name_short, - ) - ], - ) - - return generator - -if __name__ == "__main__": - parser = get_argparser() - - args = parser.parse_args() - make_generator(args, make_generator_helper) \ No newline at end of file From e1d2e41132d07a68c7e8869716e11359b44f220e Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Sat, 9 Mar 2024 08:09:31 +0000 Subject: [PATCH 17/43] clean visual core --- robomimic/models/obs_core.py | 277 ----------------------------------- 1 file changed, 277 deletions(-) diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index 4c9d35c9..3888ab7f 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -184,283 +184,6 @@ def __repr__(self): msg += textwrap.indent("\npool_net={}".format(self.pool), indent) msg = header + '(' + msg + '\n)' return msg - - -class PretrainedVisualCore(EncoderCore, BaseNets.ConvBase): - """ - Extension of VisualCore that uses a pretrained model - """ - def __init__( - self, - input_shape, - backbone_class="ResNet18Conv", - pool_class="SpatialSoftmax", - backbone_kwargs=None, - pool_kwargs=None, - flatten=True, - feature_dimension=64, - ): - """ - Args: - input_shape (tuple): shape of input (not including batch dimension) - backbone_class (str): class name for the visual backbone network. Defaults - to "ResNet18Conv". - pool_class (str): class name for the visual feature pooler (optional) - Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to - "SpatialSoftmax". - backbone_kwargs (dict): kwargs for the visual backbone network (optional) - pool_kwargs (dict): kwargs for the visual feature pooler (optional) - flatten (bool): whether to flatten the visual features - feature_dimension (int): if not None, add a Linear layer to - project output into a desired feature dimension - """ - super(PretrainedVisualCore, self).__init__(input_shape=input_shape) - self.flatten = flatten - - self.nets = nn.ModuleDict() - - if backbone_kwargs is None: - backbone_kwargs = dict() - - # add input channel dimension to visual core inputs - backbone_kwargs["input_channel"] = input_shape[0] - - # extract only relevant kwargs for this specific backbone - backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) - - # visual backbone - assert isinstance(backbone_class, str) - self.backbone = eval(backbone_class)(**backbone_kwargs) - - feat_shape = self.backbone.output_shape(input_shape) - self.feat_shape = feat_shape - # net_list = [self.backbone] - self.nets["backbone"] = self.backbone - - post_proc_list = [] - - # maybe make pool net - if pool_class is not None: - assert isinstance(pool_class, str) - # feed output shape of backbone to pool net - if pool_kwargs is None: - pool_kwargs = dict() - # extract only relevant kwargs for this specific backbone - pool_kwargs["input_shape"] = feat_shape - pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) - self.pool = eval(pool_class)(**pool_kwargs) - assert isinstance(self.pool, BaseNets.Module) - - feat_shape = self.pool.output_shape(feat_shape) - post_proc_list.append(self.pool) - else: - self.pool = None - - # flatten layer - if self.flatten: - post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) - - # maybe linear layer - self.feature_dimension = feature_dimension - if feature_dimension is not None: - assert self.flatten - post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) - - self.nets["post_proc"] = nn.Sequential(*post_proc_list) - - def output_shape(self, input_shape): - """ - Function to compute output shape from inputs to this module. - - Args: - input_shape (iterable of int): shape of input. Does not include batch dimension. - Some modules may not need this argument, if their output does not depend - on the size of the input, or if they assume fixed size input. - - Returns: - out_shape ([int]): list of integers corresponding to output shape - """ - if self.feature_dimension is not None: - # linear output - return [self.feature_dimension] - feat_shape = self.backbone.output_shape(input_shape) - if self.pool is not None: - # pool output - feat_shape = self.pool.output_shape(feat_shape) - # backbone + flat output - if self.flatten: - return [np.prod(feat_shape)] - else: - return feat_shape - - def forward(self, inputs): - """ - Forward pass through visual core. - """ - ndim = len(self.input_shape) - assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) - image = inputs["image"] - # Sometimes will be language conditioned image representation - lang_emb = inputs.get("lang_emb", None) - - x = self.nets["backbone"].forward(image=image, lang_emb=lang_emb) - # x = self.nets["backbone"].forward(inputs["image"]) - x = self.nets["post_proc"](x) - - return x - - def __repr__(self): - """Pretty print network.""" - header = '{}'.format(str(self.__class__.__name__)) - msg = '' - indent = ' ' * 2 - msg += textwrap.indent( - "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) - msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) - msg += textwrap.indent("\npool_net={}".format(self.pool), indent) - msg = header + '(' + msg + '\n)' - return msg - -class DeFiNeVisualCore(EncoderCore, BaseNets.ConvBase): - """ - Extension of VisualCore that takes in camera information - """ - def __init__( - self, - input_shape, - backbone_class="ResNet18Conv", - pool_class="SpatialSoftmax", - backbone_kwargs=None, - pool_kwargs=None, - flatten=True, - feature_dimension=64, - ): - """ - Args: - input_shape (tuple): shape of input (not including batch dimension) - backbone_class (str): class name for the visual backbone network. Defaults - to "ResNet18Conv". - pool_class (str): class name for the visual feature pooler (optional) - Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to - "SpatialSoftmax". - backbone_kwargs (dict): kwargs for the visual backbone network (optional) - pool_kwargs (dict): kwargs for the visual feature pooler (optional) - flatten (bool): whether to flatten the visual features - feature_dimension (int): if not None, add a Linear layer to - project output into a desired feature dimension - """ - super(DeFiNeVisualCore, self).__init__(input_shape=input_shape) - self.flatten = flatten - - self.nets = nn.ModuleDict() - - if backbone_kwargs is None: - backbone_kwargs = dict() - - # add input channel dimension to visual core inputs - backbone_kwargs["input_channel"] = input_shape[0] - - # extract only relevant kwargs for this specific backbone - backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) - - # visual backbone - assert isinstance(backbone_class, str) - self.backbone = eval(backbone_class)(**backbone_kwargs) - - feat_shape = self.backbone.output_shape(input_shape) - self.feat_shape = feat_shape - # net_list = [self.backbone] - self.nets["backbone"] = self.backbone - - post_proc_list = [] - - # maybe make pool net - if pool_class is not None: - assert isinstance(pool_class, str) - # feed output shape of backbone to pool net - if pool_kwargs is None: - pool_kwargs = dict() - # extract only relevant kwargs for this specific backbone - pool_kwargs["input_shape"] = feat_shape - pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) - self.pool = eval(pool_class)(**pool_kwargs) - assert isinstance(self.pool, BaseNets.Module) - - feat_shape = self.pool.output_shape(feat_shape) - post_proc_list.append(self.pool) - else: - self.pool = None - - # flatten layer - if self.flatten: - post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) - - # maybe linear layer - self.feature_dimension = feature_dimension - if feature_dimension is not None: - assert self.flatten - post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) - - self.nets["post_proc"] = nn.Sequential(*post_proc_list) - - def output_shape(self, input_shape): - """ - Function to compute output shape from inputs to this module. - - Args: - input_shape (iterable of int): shape of input. Does not include batch dimension. - Some modules may not need this argument, if their output does not depend - on the size of the input, or if they assume fixed size input. - - Returns: - out_shape ([int]): list of integers corresponding to output shape - """ - if self.feature_dimension is not None: - # linear output - return [self.feature_dimension] - feat_shape = self.backbone.output_shape(input_shape) - if self.pool is not None: - # pool output - feat_shape = self.pool.output_shape(feat_shape) - # backbone + flat output - if self.flatten: - return [np.prod(feat_shape)] - else: - return feat_shape - - def forward(self, inputs): - """ - Forward pass through visual core. - """ - ndim = len(self.input_shape) - assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) - image = inputs["image"] - intrinsics = inputs.get("intrinsics", None) - extrinsics = inputs.get("extrinsics", None) - lang_emb = inputs.get("lang_emb", None) - - # # TODO: replace with actual extrinsics - # extrinsics = TensorUtils.to_torch(np.array([[ - # [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1], - # ]] * image.shape[0]), image.device) - - x = self.nets["backbone"].forward(image=image, intrinsics=intrinsics, extrinsics=extrinsics, lang_emb=lang_emb) - # x = self.nets["backbone"].forward(inputs["image"]) - x = self.nets["post_proc"](x) - - return x - - def __repr__(self): - """Pretty print network.""" - header = '{}'.format(str(self.__class__.__name__)) - msg = '' - indent = ' ' * 2 - msg += textwrap.indent( - "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) - msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) - msg += textwrap.indent("\npool_net={}".format(self.pool), indent) - msg = header + '(' + msg + '\n)' - return msg class FiLMVisualCore(EncoderCore, BaseNets.ConvBase): From f814f8f6ddd975aec27f68c37cd5fc1f824e3fe6 Mon Sep 17 00:00:00 2001 From: Suraj Nair Date: Sat, 9 Mar 2024 20:54:06 +0000 Subject: [PATCH 18/43] some hdf5 cleanup --- README.md | 44 ++++- .../droid_runs_language_conditioned.py | 172 ++---------------- .../conversion/add_lang_to_converted_data.py | 29 +-- .../{convert_r2d2.py => convert_droid.py} | 38 +--- 4 files changed, 67 insertions(+), 216 deletions(-) rename robomimic/scripts/conversion/{convert_r2d2.py => convert_droid.py} (84%) diff --git a/README.md b/README.md index 6260ba3e..a774fd8b 100644 --- a/README.md +++ b/README.md @@ -44,5 +44,47 @@ To specify your information for Weights and Biases logging, make sure to update Make sure [DROID](https://github.com/AlexanderKhazatsky/DROID) is installed and follow the policy evaluation instructions at the bottom of the README. ------- -## TODO(Suraj): Describe debugging with HDF5 based policies and how to train lang/goal conditioned policies with them + +## Training Policies with HDF5 Format +While we recommend using RLDS, HDF5 can be useful for debugging on smaller datasets, and is supported by robomimic natively. + +### Convert from raw DROID data format to HDF5 + +First you need to make sure to install the ZED SDK, follow the instructions [here](https://www.stereolabs.com/docs/installation/linux/) for your CUDA version and the accompanying `pyzed` package. Then run +`python robomimic/scripts/conversion/convert_droid.py --folder --imsize 128` +which will populate each demo folder with an HDF5 file `trajectory_im128.h5` which contains the full observations and actions for that demo. + +### Composing a manifest file +You may want to subselect certain demos to train on. As a result, we assume that you define a manifest json file which contains a list of demos, including the path to each H5 file and the associated language instruction. For example: +``` +[ + { + "path": "/fullpathA/trajectory_im128.h5", + "lang": "Put the apple on the plate" + }, + { + "path": "/fullpathB/trajectory_im128.h5", + "lang": "Move the fork to the sink" + }, + ... +] +``` + +### Adding language embeddings to HDF5 +For the files and language specified in the above manifest JSON, run: +`python robomimic/scripts/conversion/add_lang_to_converted_data.py --manifest_file --imsize 128` +to compute DistilBERT embeddings of each language instruction and add it as an observation key to the HDF5. + +### Run training +To train policies, update `MANIFEST_PATH`, `EXP_LOG_PATH`, in `robomimic/scripts/config_gen/droid_runs_language_conditioned.py` and then run: + +`python robomimic/scripts/config_gen/droid_runs_language_conditioned.py --wandb_proj_name ` + +This will generate a python command that can be run to launch training. You can also update other training parameters within `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py`. Please see the `robomimic` documentation for more information on how `robomimic` configs are defined. The three +most important parameters in this file are: + +- `MANIFEST_PATH`: This is the manifest JSON for the training data you want to use. +- `MANIFEST_2_PATH`: You can optionally set a second manfiest for another dataset to do 50-50 co-training with. +- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. + ## TODO(Suraj): Help cleanup unnecessary files \ No newline at end of file diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py index a85f2d44..5ae87f60 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py @@ -3,84 +3,19 @@ import json import numpy as np -# fulldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/", postfix="trajectory_im128.h5")] -# import pdb; pdb.set_trace() -# with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "r") as f: broaddataset = json.load(f) -# # with open("/mnt/fsx/surajnair/datasets/droid_eval/manifest.json", "r") as f: evaldataset = json.load(f) -# evaldataset = [{"path": p} for p in scan_datasets("/mnt/fsx/surajnair/datasets/droid_eval/TRI_2/", postfix="trajectory_im128.h5")] -# # with open("/mnt/fsx/surajnair/datasets/droid_full_raw/manifest.json", "w") as f: json.dump(fulldataset, f) -# # evaldataset = evaldataset[:200] -# # broaddataset = broaddataset[:2000] -# random.shuffle(evaldataset) -# random.shuffle(broaddataset) -# N_EVAL = len(evaldataset) -# N_BROAD = len(broaddataset) +MANIFEST_PATH = "/mnt/fsx/surajnair/code/droid_cleanup/manifest_test.json" +# MANIFEST_2_PATH = "" +EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" -## Getting all OXE -with open("/mnt/fsx/surajnair/datasets/oxe_hdf5/manifest_oxe.json", 'r') as file: - oxe = json.load(file) -N_OXE = len(oxe) +## Define training Dataset +with open(MANIFEST_PATH, 'r') as file: + dataset1 = json.load(file) +N_D1 = len(dataset1) -## Getting all stanford data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang_stanford_eval.json", 'r') as file: - langs = json.load(file) -stanford_singletask_eraser = [{'path': l['path']} for l in langs if 'stanford_eval_0125' in l['path']] -N_STANFORD_ERASER = len(stanford_singletask_eraser) - -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_stanford_evgr_laundry.json", 'r') as file: - stanford_singletask_laundry = json.load(file) -N_STANFORD_LAUNDRY = len(stanford_singletask_laundry) - -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_stanford_evgr_cooking.json", 'r') as file: - stanford_singletask_cooking = json.load(file) -N_STANFORD_COOKING = len(stanford_singletask_cooking) - - -## Getting all TRI data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_tri_chips.json", 'r') as file: - langs = json.load(file) -chips_singletask = [{'path': l['path']} for l in langs if 'TRI_chips_only_1_21' in l['path']] -N_CHIPS = len(chips_singletask) - -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_tri_frenchpress.json", 'r') as file: - tri_frenchpress = json.load(file) -N_FRENCHPRESS = len(tri_frenchpress) - -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_tri_pot.json", 'r') as file: - tri_pot = json.load(file) -N_POT = len(tri_pot) - -## Getting all language labeled data -with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - -## Getting broad data -broaddataset_full = [{'path': l['path']} for l in langs if '/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/' in l['path']] -N_BROAD_FULL = len(broaddataset_full) - -with open("/mnt/fsx/surajnair/datasets/droid-data/filter_traj_20scene_10k_episodes_success.json", 'r') as file: - subset = json.load(file) - -broaddataset_filtered = [{'path': l['path']} for l in broaddataset_full if "/".join(l['path'].split("/")[7:-1])+"/" in subset] -N_BROAD_FILTERED = len(broaddataset_filtered) - -broaddataset_filtered_random = np.random.choice(broaddataset_full, N_BROAD_FILTERED).tolist() -N_BROAD_FILTERED_RANDOM = len(broaddataset_filtered_random) - -cmutoast_singletask = [{'path': l['path']} for l in langs if 'r2_d2_toaster3_cmu_rgb' in l['path']] -N_CMU_TOASTER = len(cmutoast_singletask) - -cmu_multi = [{'path': l['path']} for l in langs if 'cmu_rgb' in l['path']] -N_CMU_MULTI = len(cmu_multi) - - -print(f"Broad Datapoints: DROID {N_BROAD_FULL, N_BROAD_FILTERED, N_BROAD_FILTERED_RANDOM} OXE {N_OXE} \ - \nSingle Task TRI Datapoints: CHIPS {N_CHIPS} POT {N_POT} FRENCHPRESS {N_FRENCHPRESS} \ - \nStanford Datapoints: Eraser {N_STANFORD_ERASER} Laundry {N_STANFORD_LAUNDRY} Cooking {N_STANFORD_COOKING} \ - \nCMU Datapoints: {N_CMU_MULTI, N_CMU_TOASTER} ") - # \nMultitask Eval Datapoints: {N_EVAL_MULTI} \ - # \nSingle Task APPLE Datapoints: {N_APPLE}") - # # \nSingle Task Microwave Close Datapoints {N_EVAL_CLOSEMICRO}") +## Optionally define a co-training dataset +# with open(MANIFEST_2_PATH, 'r') as file: +# dataset2 = json.load(file) +# N_D2 = len(dataset2) def make_generator_helper(args): @@ -326,84 +261,12 @@ def make_generator_helper(args): name="ds", group=2, values=[ - # stanford_singletask_cooking, - # stanford_singletask_laundry, - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + stanford_singletask_cooking], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + stanford_singletask_laundry], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + stanford_singletask_cooking], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + stanford_singletask_laundry], - # stanford_singletask_eraser, - # cmutoast_singletask, - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + stanford_singletask_eraser], - # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED)} for p in broaddataset_filtered + cmutoast_singletask], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + stanford_singletask_eraser], - # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FILTERED_RANDOM)} for p in broaddataset_filtered_random + cmutoast_singletask], - # stanford_singletask_cooking, - # stanford_singletask_laundry, - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + stanford_singletask_cooking], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + stanford_singletask_laundry], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_COOKING if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_cooking], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_LAUNDRY if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_laundry], - # tri_pot, - # tri_frenchpress, - # [{"path": p["path"], "weight": (0.5 / N_POT if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + tri_pot], - # [{"path": p["path"], "weight": (0.5 / N_FRENCHPRESS if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + tri_frenchpress], - # [{"path": p["path"], "weight": (0.5 / N_POT if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + tri_pot], - # [{"path": p["path"], "weight": (0.5 / N_FRENCHPRESS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + tri_frenchpress], - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + stanford_singletask_eraser], - # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + chips_singletask], - # [{"path": p["path"], "weight": (0.5 / N_CMU_MULTI if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + cmu_multi], - # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_OXE)} for p in oxe + cmutoast_singletask], - # stanford_singletask_eraser, - # [{"path": p["path"], "weight": (0.5 / N_STANFORD_ERASER if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + stanford_singletask_eraser], - # cmu_multi, - cmutoast_singletask, - # [{"path": p["path"], "weight": (0.5 / N_CMU_MULTI if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + cmu_multi], - # [{"path": p["path"], "weight": (0.5 / N_CMU_TOASTER if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + cmutoast_singletask], - # broaddataset_full, - # chips_singletask, - # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD)} for p in broaddataset + chips_singletask], - # [{"path": p["path"], "weight": (0.5 / N_CHIPS if "eval" in p["path"] else 0.5 / N_BROAD_FULL)} for p in broaddataset_full + chips_singletask], - ], + dataset1, + # [{"path": p["path"], "weight": (0.5 / N_D1} for p in dataset1] + [{"path": p["path"], "weight": (0.5 / N_D2} for p in dataset2], + ], value_names=[ - # "stanford_cooking", - # "stanford_laundry", - # "filtereddroid_stanford_cooking", - # "filtereddroid_stanford_laundry", - # "filteredrandomdroid_stanford_cooking", - # "filteredrandomdroid_stanford_laundry", - # "stanford_eraser", - # "cmu_toast", - # "filtereddroid_stanford_eraser", - # "filtereddroid_cmu_toast", - # "filteredrandomdroid_stanford_eraser", - # "filteredrandomdroid_cmu_toast", - # "stanford_cooking", - # "stanford_laundry", - # "oxe_balanced_stanford_cooking", - # "oxe_balanced_stanford_laundry", - # "fulldroid_balanced_stanford_cooking", - # "fulldroid_balanced_stanford_laundry", - # "tri_pot", - # "tri_frenchpress", - # "oxe_balanced_tri_pot", - # "oxe_balanced_tri_frenchpress", - # "fulldroid_balanced_tri_pot", - # "fulldroid_balanced_tri_frenchpress", - # "oxe_balanced_stanford_eraser", - # "oxe_balanced_chips", - # "oxe_balanced_cmu_multi", - # "oxe_balanced_cmu_toast", - # "stanford_eraser", - # "fulldroid_balanced_stanford_eraser" - # "cmu_multi", - "cmu_toast_hdf5", - # "fulldroid_balanced_cmu_multi", - # "fulldroid_balanced_cmu_toast", - # "broad", - # "eval_chips", - # "balanced_chips", - # "full_balanced_chips", + "justdataset1", + # "dataset1and2cotrain" ], ) @@ -514,7 +377,8 @@ def make_generator_helper(args): name="", group=-1, values=[ - "/mnt/fsx/surajnair/expdata/{env}/{mod}/{algo_name_short}".format( + "{exp_log_path}/{env}/{mod}/{algo_name_short}".format( + exp_log_path=EXP_LOG_PATH, env=args.env, mod=args.mod, algo_name_short=algo_name_short, diff --git a/robomimic/scripts/conversion/add_lang_to_converted_data.py b/robomimic/scripts/conversion/add_lang_to_converted_data.py index b900285f..5e9fa1ff 100644 --- a/robomimic/scripts/conversion/add_lang_to_converted_data.py +++ b/robomimic/scripts/conversion/add_lang_to_converted_data.py @@ -51,10 +51,9 @@ def add_lang(path, raw_lang, args): parser = argparse.ArgumentParser() parser.add_argument( - "--folder", + "--manifest_file", type=str, - help="folder containing hdf5's to add camera images to", - default="~/datasets/droid/success" + help="manifest file path", ) parser.add_argument( @@ -66,32 +65,12 @@ def add_lang(path, raw_lang, args): args = parser.parse_args() - with open("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json", 'r') as file: - langs = json.load(file) - langs = [l for l in langs if "TRI_chips_only_1_21" in l["path"]] - datasets = langs - print(datasets) - - # datasets = [] - # for root, dirs, files in os.walk(os.path.expanduser(args.folder)): - # for f in files: - # if f == f"trajectory_im{args.imsize}.h5": - # datasets.append(os.path.join(root, f)) + with open(args.manifest_file, 'r') as file: + datasets = json.load(file) print("adding lang to datasets...") random.shuffle(datasets) for item in tqdm(datasets): d, l = item['path'], item['lang'] d = os.path.expanduser(d) - # try: add_lang(d, l, args) - # except: - # print(d, "Failed") - - - -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/AUTOLab/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/CLVR/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/GuptaLab/success --imsize 128 - -# python robomimic/scripts/conversion/add_lang_to_converted_data.py --folder /mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval/TRI_1_10_multitask_food_in_bowl/ --imsize 256 \ No newline at end of file diff --git a/robomimic/scripts/conversion/convert_r2d2.py b/robomimic/scripts/conversion/convert_droid.py similarity index 84% rename from robomimic/scripts/conversion/convert_r2d2.py rename to robomimic/scripts/conversion/convert_droid.py index 471036f5..75711a35 100644 --- a/robomimic/scripts/conversion/convert_r2d2.py +++ b/robomimic/scripts/conversion/convert_droid.py @@ -30,10 +30,6 @@ from droid.camera_utils.camera_readers.zed_camera import ZedCamera, standard_params -DISNEY_IMAGE_NAME_TO_CAM_KEY_MAPPING = {'hand_camera_left_image': '16522755_left', 'hand_camera_right_image': '16522755_right', 'varied_camera_1_left_image': '29431508_left', 'varied_camera_1_right_image': '29431508_right', 'varied_camera_2_left_image': '29513368_left', 'varied_camera_2_right_image': '29513368_right'} -DISNEY_CAM_ID_TO_TYPE = {'16522755': 'hand_camera', '29431508': 'varied_camera', '29513368': 'varied_camera'} - - def get_cam_instrinsics(svo_path): """ utility function to get camera intrinsics @@ -45,13 +41,12 @@ def get_cam_instrinsics(svo_path): def convert_dataset(path, args): output_path = os.path.join(os.path.dirname(path), "trajectory_im{}.h5".format(args.imsize)) recording_folderpath = os.path.join(os.path.dirname(path), "recordings", "SVO") + if os.path.exists(output_path): # dataset already exists, skip f = h5py.File(output_path) if "observation/camera/image/hand_camera_left_image" in f.keys(): # print("Skipping finished") - if os.path.exists(recording_folderpath): - shutil.rmtree(recording_folderpath) return f.close() @@ -103,8 +98,6 @@ def convert_dataset(path, args): else: raise ValueError - if "TRI_" in path: - CAM_ID_TO_TYPE = DISNEY_CAM_ID_TO_TYPE # sort the camera ids: important to maintain consistency of cams between train and eval! hand_cam_ids = sorted(hand_cam_ids) @@ -121,9 +114,6 @@ def convert_dataset(path, args): cam_key = "{}_{}".format(varied_cam_ids[i], side) IMAGE_NAME_TO_CAM_KEY_MAPPING[cam_name] = cam_key - if "TRI_" in path: - IMAGE_NAME_TO_CAM_KEY_MAPPING = DISNEY_IMAGE_NAME_TO_CAM_KEY_MAPPING - cam_data = {cam_name: [] for cam_name in IMAGE_NAME_TO_CAM_KEY_MAPPING.keys()} traj_reader = TrajectoryReader(path, read_images=False) @@ -260,8 +250,6 @@ def convert_dataset(path, args): f.close() camera_reader.disable_cameras() del camera_reader - # print("Deleting", recording_folderpath) - # shutil.rmtree(recording_folderpath) def remove_timesteps(f, timesteps_to_remove): total_timesteps = f["action/cartesian_position"].shape[0] @@ -309,12 +297,6 @@ def remove_timesteps_for_group(g): args = parser.parse_args() - # langs = json.load("/mnt/fsx/surajnair/datasets/droid-data/manifest_lang.json") - # with open("/mnt/fsx/surajnair/datasets/droid-data/manifest.json", 'r') as file: - # files = json.load(file) - # # files = [os.path.join(os.path.dirname(l["path"]), "trajectory.h5") for l in langs if ("eval" in l["path"])] - # datasets = files - datasets = [] j = os.walk(os.path.expanduser(args.folder)) import pdb; pdb.set_trace() @@ -325,8 +307,6 @@ def remove_timesteps_for_group(g): datasets.append(os.path.join(root, f)) print(len(datasets)) - - print("converting datasets...") random.shuffle(datasets) failed = 0 @@ -335,19 +315,5 @@ def remove_timesteps_for_group(g): try: convert_dataset(d, args) except Exception as e: - print(d, "Failed", failed, e) - traceback.print_exc() failed += 1 - # try: - # output_path = os.path.join(os.path.dirname(d), "trajectory_im{}.h5".format(args.imsize)) - # os.remove(output_path) - # except: - # pass - - -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/AUTOLab/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/CLVR/success --imsize 128 -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --folder /mnt/fsx/surajnair/datasets/droid_full_raw/GuptaLab/success --imsize 128 - -# /opt/conda/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --imsize 256 -# /mnt/fsx/surajnair/mambaforge/envs/zed/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/convert_droid.py --imsize 128 \ No newline at end of file + print(f"{failed} Failed") From b21c4d7da6101ae55b239cc6a955ea1a36fc5f8d Mon Sep 17 00:00:00 2001 From: Suraj Nair Date: Sat, 9 Mar 2024 21:10:01 +0000 Subject: [PATCH 19/43] Cleanup and removing unused files --- README.md | 4 +- oxe_conversion.sh | 38 - robomimic/models/obs_core.py | 144 +- .../conversion/get_eval_lang_annotations.py | 51 - robomimic/scripts/conversion/oxe_to_h5.py | 136 - .../robosuite_add_absolute_actions.py | 298 -- robomimic/scripts/filter_dataset_size.py | 81 - robomimic/scripts/plot_model_predictions.py | 213 -- robomimic/utils/camera_utils.py | 2471 ----------------- 9 files changed, 2 insertions(+), 3434 deletions(-) delete mode 100644 oxe_conversion.sh delete mode 100644 robomimic/scripts/conversion/get_eval_lang_annotations.py delete mode 100644 robomimic/scripts/conversion/oxe_to_h5.py delete mode 100644 robomimic/scripts/conversion/robosuite_add_absolute_actions.py delete mode 100644 robomimic/scripts/filter_dataset_size.py delete mode 100644 robomimic/scripts/plot_model_predictions.py delete mode 100644 robomimic/utils/camera_utils.py diff --git a/README.md b/README.md index a774fd8b..564e7d38 100644 --- a/README.md +++ b/README.md @@ -85,6 +85,4 @@ most important parameters in this file are: - `MANIFEST_PATH`: This is the manifest JSON for the training data you want to use. - `MANIFEST_2_PATH`: You can optionally set a second manfiest for another dataset to do 50-50 co-training with. -- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. - -## TODO(Suraj): Help cleanup unnecessary files \ No newline at end of file +- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. \ No newline at end of file diff --git a/oxe_conversion.sh b/oxe_conversion.sh deleted file mode 100644 index 86b50bb5..00000000 --- a/oxe_conversion.sh +++ /dev/null @@ -1,38 +0,0 @@ -#!/bin/bash -datasets=( - "austin_buds_dataset_converted_externally_to_rlds" - "jaco_play" - "austin_sailor_dataset_converted_externally_to_rlds" - "kuka" - "austin_sirius_dataset_converted_externally_to_rlds" - # "language_table" - "bc_z" - "nyu_door_opening_surprising_effectiveness" - "berkeley_autolab_ur5" - "nyu_franka_play_dataset_converted_externally_to_rlds" - "berkeley_cable_routing" - "ppgm" - "berkeley_fanuc_manipulation" - "roboturk" - "bridge_dataset" - "stanford_hydra_dataset_converted_externally_to_rlds" - # "bridge_orig" - "taco_play" - "cmu_stretch" - "toto" - "dlr_edan_shared_control_converted_externally_to_rlds" - "ucsd_kitchen_dataset_converted_externally_to_rlds" - "fractal20220817_data" - "utaustin_mutex" - "furniture_bench_dataset_converted_externally_to_rlds" - "viola" - "iamlab_cmu_pickup_insert_converted_externally_to_rlds" -) - -for ((i=0; i<${#datasets[@]}; i++)); do - # Get the current command - d="${datasets[$i]}" - - /mnt/fsx/surajnair/mambaforge/envs/octo/bin/python /mnt/fsx/surajnair/code/robomimic/robomimic/scripts/conversion/oxe_to_h5.py --out_dir=/mnt/fsx/surajnair/datasets/oxe_hdf5/$d --dataset=$d; - done -done diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index 3888ab7f..5fcdd2f0 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -19,8 +19,6 @@ # NOTE: this is required for the backbone classes to be found by the `eval` call in the core networks from robomimic.models.base_nets import * -from robomimic.utils.camera_utils import DeFiNeImageCamEncoder, VoltronImageCamEncoder, R3MImageCamEncoder, VC1ImageCamEncoder -from robomimic.utils.camera_utils import ResNetFiLMEncoder from robomimic.utils.vis_utils import visualize_image_randomizer from robomimic.macros import VISUALIZE_RANDOMIZER @@ -186,139 +184,6 @@ def __repr__(self): return msg -class FiLMVisualCore(EncoderCore, BaseNets.ConvBase): - """ - Extension of VisualCore that takes in camera information - """ - def __init__( - self, - input_shape, - backbone_class="ResNet18Conv", - pool_class="SpatialSoftmax", - backbone_kwargs=None, - pool_kwargs=None, - flatten=True, - feature_dimension=64, - ): - """ - Args: - input_shape (tuple): shape of input (not including batch dimension) - backbone_class (str): class name for the visual backbone network. Defaults - to "ResNet18Conv". - pool_class (str): class name for the visual feature pooler (optional) - Common options are "SpatialSoftmax" and "SpatialMeanPool". Defaults to - "SpatialSoftmax". - backbone_kwargs (dict): kwargs for the visual backbone network (optional) - pool_kwargs (dict): kwargs for the visual feature pooler (optional) - flatten (bool): whether to flatten the visual features - feature_dimension (int): if not None, add a Linear layer to - project output into a desired feature dimension - """ - super(FiLMVisualCore, self).__init__(input_shape=input_shape) - self.flatten = flatten - - self.nets = nn.ModuleDict() - - if backbone_kwargs is None: - backbone_kwargs = dict() - - # add input channel dimension to visual core inputs - backbone_kwargs["input_channel"] = input_shape[0] - - # extract only relevant kwargs for this specific backbone - backbone_kwargs = extract_class_init_kwargs_from_dict(cls=eval(backbone_class), dic=backbone_kwargs, copy=True) - - # visual backbone - assert isinstance(backbone_class, str) - self.backbone = eval(backbone_class)(**backbone_kwargs) - - feat_shape = self.backbone.output_shape(input_shape) - self.feat_shape = feat_shape - # net_list = [self.backbone] - self.nets["backbone"] = self.backbone - - post_proc_list = [] - - # maybe make pool net - if pool_class is not None: - assert isinstance(pool_class, str) - # feed output shape of backbone to pool net - if pool_kwargs is None: - pool_kwargs = dict() - # extract only relevant kwargs for this specific backbone - pool_kwargs["input_shape"] = feat_shape - pool_kwargs = extract_class_init_kwargs_from_dict(cls=eval(pool_class), dic=pool_kwargs, copy=True) - self.pool = eval(pool_class)(**pool_kwargs) - assert isinstance(self.pool, BaseNets.Module) - - feat_shape = self.pool.output_shape(feat_shape) - post_proc_list.append(self.pool) - else: - self.pool = None - - # flatten layer - if self.flatten: - post_proc_list.append(torch.nn.Flatten(start_dim=1, end_dim=-1)) - - # maybe linear layer - self.feature_dimension = feature_dimension - if feature_dimension is not None: - assert self.flatten - post_proc_list.append(torch.nn.Linear(int(np.prod(feat_shape)), self.feature_dimension)) - - self.nets["post_proc"] = nn.Sequential(*post_proc_list) - - def output_shape(self, input_shape): - """ - Function to compute output shape from inputs to this module. - - Args: - input_shape (iterable of int): shape of input. Does not include batch dimension. - Some modules may not need this argument, if their output does not depend - on the size of the input, or if they assume fixed size input. - - Returns: - out_shape ([int]): list of integers corresponding to output shape - """ - if self.feature_dimension is not None: - # linear output - return [self.feature_dimension] - feat_shape = self.backbone.output_shape(input_shape) - if self.pool is not None: - # pool output - feat_shape = self.pool.output_shape(feat_shape) - # backbone + flat output - if self.flatten: - return [np.prod(feat_shape)] - else: - return feat_shape - - def forward(self, inputs): - """ - Forward pass through visual core. - """ - ndim = len(self.input_shape) - assert tuple(inputs["image"].shape)[-ndim:] == tuple(self.input_shape) - image = inputs["image"] - lang_emb = inputs.get("lang_emb", None) - - x = self.nets["backbone"].forward(image=image, lang_emb=lang_emb) - # x = self.nets["backbone"].forward(inputs["image"]) - x = self.nets["post_proc"](x) - - return x - - def __repr__(self): - """Pretty print network.""" - header = '{}'.format(str(self.__class__.__name__)) - msg = '' - indent = ' ' * 2 - msg += textwrap.indent( - "\ninput_shape={}\noutput_shape={}".format(self.input_shape, self.output_shape(self.input_shape)), indent) - msg += textwrap.indent("\nbackbone_net={}".format(self.backbone), indent) - msg += textwrap.indent("\npool_net={}".format(self.pool), indent) - msg = header + '(' + msg + '\n)' - return msg """ @@ -833,14 +698,7 @@ def _forward_in(self, inputs): # Create lambda to aggregate all color randomizings at once transform = self.get_batch_transform(N=self.num_samples) - ## Special case when image is frame stacked - ## Apply processsing to each image individually - if inputs.shape[1] == 6: - im1 = transform(inputs[:, :3]) - im2 = transform(inputs[:, 3:]) - out = torch.cat([im1, im2], 1) - else: - out = transform(inputs) + out = transform(inputs) return out diff --git a/robomimic/scripts/conversion/get_eval_lang_annotations.py b/robomimic/scripts/conversion/get_eval_lang_annotations.py deleted file mode 100644 index 797f33da..00000000 --- a/robomimic/scripts/conversion/get_eval_lang_annotations.py +++ /dev/null @@ -1,51 +0,0 @@ -import random -import json -import numpy as np -import os -import csv - -EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval/" -RAW_ANNOTATIONS = os.path.join(EVAL_DATA_ROOT_DIR, "tri-multi-task-eval-lang-annotations.csv") -METADATA_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval-json/" - -def read_csv(file_path): - data_list = [] - with open(file_path, 'r') as csvfile: - csv_reader = csv.reader(csvfile) - for row in csv_reader: - data_list.append(row) - return data_list - -# Update CSV to have dummy language annotations for single task data -single_task_evals = { - "TRI_food_in_bowl": "Put the food item in the bowl", - "TRI_microwave_close": "Close the microwave", - "TRI_microwave_open": "Open the microwave"} - -with open(RAW_ANNOTATIONS, 'a', newline='') as csvfile: - # Create a CSV writer object - csv_writer = csv.writer(csvfile) - - for task in single_task_evals.keys(): - for traj_folder in os.listdir(os.path.join(EVAL_DATA_ROOT_DIR, task)): - new_line = [os.path.join(task, traj_folder, "recordings"), single_task_evals[task]] - csv_writer.writerow(new_line) - -csv_data = read_csv(RAW_ANNOTATIONS) - -aggregated_annotations_eval = {} -for _, annotation_info in enumerate(csv_data): - path = annotation_info[0].split("/recordings")[0] - lang = annotation_info[1] - hdf5_path = os.path.join(path, "trajectory.h5") - uuid = path.replace("/", "-") - uuid_annotations = f"pilot/{uuid}.mp4" - aggregated_annotations_eval[uuid_annotations] = { - "language_instruction1": lang - } - metadata_path = os.path.join(METADATA_DIR, f"metadata_{uuid}.json") - with open(metadata_path, 'w') as metadata_path: - json.dump({"uuid": uuid, "hdf5_path": hdf5_path, "lab": ""}, metadata_path) - -with open(os.path.join(METADATA_DIR, "aggregated_annotations_eval.json"), 'w') as annotations_path: - json.dump(aggregated_annotations_eval, annotations_path, indent=4) \ No newline at end of file diff --git a/robomimic/scripts/conversion/oxe_to_h5.py b/robomimic/scripts/conversion/oxe_to_h5.py deleted file mode 100644 index c05cfa1d..00000000 --- a/robomimic/scripts/conversion/oxe_to_h5.py +++ /dev/null @@ -1,136 +0,0 @@ -from absl import app, flags, logging -import dlimp as dl -import h5py -import os -import tqdm -import tensorflow as tf -from functools import partial -from typing import Callable -import torch - -from octo.data.oxe import make_oxe_dataset_kwargs -from octo.data.dataset import make_dataset_from_rlds, apply_frame_transforms -from octo.data import obs_transforms -from octo.data.utils.data_utils import NormalizationType - -from transformers import AutoTokenizer, AutoModel -tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') -model = AutoModel.from_pretrained("distilbert-base-uncased", torch_dtype=torch.float16) -model.to('cuda') - - -FLAGS = flags.FLAGS -flags.DEFINE_string("out_dir", None, "Path of output directory.") -flags.DEFINE_string("dataset", None, "Name of dataset.") - -DATA_DIR = "/mnt/fsx/surajnair/datasets/openx_processed" - - -import tensorflow_graphics.geometry.transformation as tfg - -def euler_to_rmat(euler): - return tfg.rotation_matrix_3d.from_euler(euler) - -def quat_to_rmat(quat): - return tfg.rotation_matrix_3d.from_quaternion(quat) - -def mat_to_rot6d(mat): - r6 = mat[..., :2, :] - r6_0, r6_1 = r6[..., 0, :], r6[..., 1, :] - r6_flat = tf.concat([r6_0, r6_1], axis=-1) - return r6_flat - -def main(_): - - if FLAGS.dataset == "bridge_orig": - ds = "bridge_dataset" - # get dataset specific config - dataset_kwargs = make_oxe_dataset_kwargs( - FLAGS.dataset, - DATA_DIR, - load_camera_views=("primary", "secondary", "wrist"), - load_proprio=True, - load_language=True, - action_proprio_normalization_type=NormalizationType.BOUNDS, - ) - - # create RLDS dataset - ds, _ = make_dataset_from_rlds( - **dataset_kwargs, - train=True, - ) - - def apply_obs_transform(fn: Callable[[dict], dict], frame: dict) -> dict: - # observation is chunked -- apply fn along first axis - frame["observation"] = fn(frame["observation"]) - return frame - - def apply_6d_transform(frame: dict) -> dict: - # observation is chunked -- apply fn along first axis - if frame["observation"]["proprio"][6] == 0: - euler_rot = frame["observation"]["proprio"][3:6][:] - r6 = mat_to_rot6d(euler_to_rmat(euler_rot)) - else: - quat_rot = frame["observation"]["proprio"][3:7][:] - mat = quat_to_rmat(quat_rot) - r6 = mat_to_rot6d(mat) - euler_rot = tfg.euler.from_rotation_matrix(mat) - - frame["observation"]["proprio_rot_6d"] = r6 - frame["observation"]["proprio"] = tf.concat([frame["observation"]["proprio"][:3], euler_rot, tf.zeros_like(euler_rot)[:1], frame["observation"]["proprio"][7:]], axis=-1) - return frame - - - # decode + resize images (and depth images) - ds = ds.frame_map( - partial( - apply_obs_transform, - partial( - obs_transforms.decode_and_resize, - resize_size={ - "primary": (128, 128), - "secondary": (128, 128), - "wrist": (128, 128), - }, - depth_resize_size=None, - ), - ), - tf.data.AUTOTUNE, - ) - - ds = ds.frame_map( - partial( - apply_6d_transform, - ), - tf.data.AUTOTUNE, - ) - - - # iterate over dataset and dump to h5: - os.makedirs(FLAGS.out_dir, exist_ok=True) - print(f"Converting {FLAGS.dataset}...") - for i, episode in enumerate(tqdm.tqdm(ds.iterator())): - with h5py.File(os.path.join(FLAGS.out_dir, f"episode_{i}.h5"), "w") as F: - F["observation/camera/image/varied_camera_1_left_image"] = episode["observation"]["image_primary"][:-1] - F["observation/camera/image/varied_camera_2_left_image"] = episode["observation"]["image_secondary"][:-1] - F["observation/camera/image/hand_camera_left_image"] = episode["observation"]["image_wrist"][:-1] - - F["action/abs_pos"] = episode["observation"]["proprio"][1:, :3] - # F["action/abs_rot_euler"] = episode["observation"]["proprio"][1:, 3:6] - F["action/gripper_position"] = episode["observation"]["proprio"][1:, 7:] - F["action/abs_rot_6d"] = episode["observation"]["proprio_rot_6d"][1:, :] - - F["observation/robot_state/cartesian_position"] = episode["observation"]["proprio"][:-1, :6] - F["observation/robot_state/gripper_position"] = episode["observation"]["proprio"][:-1, 7] - - H = F["observation/robot_state/gripper_position"].shape[0] - l = episode["task"]["language_instruction"][0:1].tolist()[0].decode("utf-8") - encoded_input = tokenizer([l], return_tensors='pt').to('cuda') - outputs = model(**encoded_input) - encoded_lang = outputs.last_hidden_state.sum(1).squeeze().unsqueeze(0).repeat(H, 1) - F["observation/lang_fixed/language_raw"] = [l] * H - F["observation/lang_fixed/language_distilbert"] = encoded_lang.cpu().detach().numpy() - - -if __name__ == "__main__": - app.run(main) diff --git a/robomimic/scripts/conversion/robosuite_add_absolute_actions.py b/robomimic/scripts/conversion/robosuite_add_absolute_actions.py deleted file mode 100644 index 8d634f20..00000000 --- a/robomimic/scripts/conversion/robosuite_add_absolute_actions.py +++ /dev/null @@ -1,298 +0,0 @@ -import multiprocessing -import os -import pathlib -import h5py -from tqdm import tqdm -import collections -import pickle -import argparse -import numpy as np -import copy - -import h5py -import robomimic.utils.obs_utils as ObsUtils -import robomimic.utils.file_utils as FileUtils -import robomimic.utils.env_utils as EnvUtils -from scipy.spatial.transform import Rotation - -from robomimic.config import config_factory - -""" -copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/common/robomimic_util.py -""" -class RobomimicAbsoluteActionConverter: - def __init__(self, dataset_path, algo_name='bc'): - # default BC config - config = config_factory(algo_name=algo_name) - - # read config to set up metadata for observation modalities (e.g. detecting rgb observations) - # must ran before create dataset - ObsUtils.initialize_obs_utils_with_config(config) - - env_meta = FileUtils.get_env_metadata_from_dataset(dataset_path) - abs_env_meta = copy.deepcopy(env_meta) - abs_env_meta['env_kwargs']['controller_configs']['control_delta'] = False - - env = EnvUtils.create_env_from_metadata( - env_meta=env_meta, - render=False, - render_offscreen=False, - use_image_obs=False, - ) - assert len(env.env.robots) in (1, 2) - - abs_env = EnvUtils.create_env_from_metadata( - env_meta=abs_env_meta, - render=False, - render_offscreen=False, - use_image_obs=False, - ) - assert not abs_env.env.robots[0].controller.use_delta - - self.env = env - self.abs_env = abs_env - self.file = h5py.File(dataset_path, 'r') - - def get_demo_keys(self): - return list(self.file['data'].keys()) - - def convert_actions(self, - states: np.ndarray, - actions: np.ndarray) -> np.ndarray: - """ - Given state and delta action sequence - generate equivalent goal position and orientation for each step - keep the original gripper action intact. - """ - env = self.env - d_a = len(env.env.robots[0].action_limits[0]) - - # in case of multi robot - # reshape (N,14) to (N,2,7) - # or (N,7) to (N,1,7) - stacked_actions = actions.reshape(*actions.shape[:-1], -1, d_a) - - # generate abs actions - action_goal_pos = np.zeros( - stacked_actions.shape[:-1]+(3,), - dtype=stacked_actions.dtype) - action_goal_ori = np.zeros( - stacked_actions.shape[:-1]+(3,), - dtype=stacked_actions.dtype) - action_remainder = stacked_actions[...,6:] - for i in range(len(states)): - _ = env.reset_to({'states': states[i]}) - - # taken from robot_env.py L#454 - for idx, robot in enumerate(env.env.robots): - # run controller goal generator - robot.control(stacked_actions[i,idx], policy_step=True) - - # read pos and ori from robots - controller = robot.controller - action_goal_pos[i,idx] = controller.goal_pos - action_goal_ori[i,idx] = Rotation.from_matrix( - controller.goal_ori).as_rotvec() - - stacked_abs_actions = np.concatenate([ - action_goal_pos, - action_goal_ori, - action_remainder - ], axis=-1) - abs_actions = stacked_abs_actions.reshape(actions.shape) - return abs_actions - - def convert_demo(self, demo_key): - file = self.file - demo = file["data/{}".format(demo_key)] - # input - states = demo['states'][:] - actions = demo['actions'][:] - - # generate abs actions - abs_actions = self.convert_actions(states, actions) - return abs_actions - - def convert_and_eval_demo(self, demo_key): - env = self.env - abs_env = self.abs_env - file = self.file - # first step have high error for some reason, not representative - eval_skip_steps = 1 - - demo = file["data/{}".format(demo_key)] - # input - states = demo['states'][:] - actions = demo['actions'][:] - - # generate abs actions - abs_actions = self.convert_actions(states, actions) - - # verify - robot0_eef_pos = demo['obs']['robot0_eef_pos'][:] - robot0_eef_quat = demo['obs']['robot0_eef_quat'][:] - - delta_error_info = self.evaluate_rollout_error( - env, states, actions, robot0_eef_pos, robot0_eef_quat, - metric_skip_steps=eval_skip_steps) - abs_error_info = self.evaluate_rollout_error( - abs_env, states, abs_actions, robot0_eef_pos, robot0_eef_quat, - metric_skip_steps=eval_skip_steps) - - info = { - 'delta_max_error': delta_error_info, - 'abs_max_error': abs_error_info - } - return abs_actions, info - - @staticmethod - def evaluate_rollout_error(env, - states, actions, - robot0_eef_pos, - robot0_eef_quat, - metric_skip_steps=1): - # first step have high error for some reason, not representative - - # evaluate abs actions - rollout_next_states = list() - rollout_next_eef_pos = list() - rollout_next_eef_quat = list() - obs = env.reset_to({'states': states[0]}) - for i in range(len(states)): - obs = env.reset_to({'states': states[i]}) - obs, reward, done, info = env.step(actions[i]) - obs = env.get_observation() - rollout_next_states.append(env.get_state()['states']) - rollout_next_eef_pos.append(obs['robot0_eef_pos']) - rollout_next_eef_quat.append(obs['robot0_eef_quat']) - rollout_next_states = np.array(rollout_next_states) - rollout_next_eef_pos = np.array(rollout_next_eef_pos) - rollout_next_eef_quat = np.array(rollout_next_eef_quat) - - next_state_diff = states[1:] - rollout_next_states[:-1] - max_next_state_diff = np.max(np.abs(next_state_diff[metric_skip_steps:])) - - next_eef_pos_diff = robot0_eef_pos[1:] - rollout_next_eef_pos[:-1] - next_eef_pos_dist = np.linalg.norm(next_eef_pos_diff, axis=-1) - max_next_eef_pos_dist = next_eef_pos_dist[metric_skip_steps:].max() - - next_eef_rot_diff = Rotation.from_quat(robot0_eef_quat[1:]) \ - * Rotation.from_quat(rollout_next_eef_quat[:-1]).inv() - next_eef_rot_dist = next_eef_rot_diff.magnitude() - max_next_eef_rot_dist = next_eef_rot_dist[metric_skip_steps:].max() - - info = { - 'state': max_next_state_diff, - 'pos': max_next_eef_pos_dist, - 'rot': max_next_eef_rot_dist - } - return info - -""" -copied/adapted from https://github.com/columbia-ai-robotics/diffusion_policy/blob/main/diffusion_policy/scripts/robomimic_dataset_conversion.py -""" -def worker(x): - path, demo_key, do_eval = x - converter = RobomimicAbsoluteActionConverter(path) - if do_eval: - abs_actions, info = converter.convert_and_eval_demo(demo_key) - else: - abs_actions = converter.convert_demo(demo_key) - info = dict() - return abs_actions, info - - -def add_absolute_actions_to_dataset(dataset, eval_dir, num_workers): - # process inputs - dataset = pathlib.Path(dataset).expanduser() - assert dataset.is_file() - - do_eval = False - if eval_dir is not None: - eval_dir = pathlib.Path(eval_dir).expanduser() - assert eval_dir.parent.exists() - do_eval = True - - converter = RobomimicAbsoluteActionConverter(dataset) - demo_keys = converter.get_demo_keys() - del converter - - # run - with multiprocessing.Pool(num_workers) as pool: - results = pool.map(worker, [(dataset, demo_key, do_eval) for demo_key in demo_keys]) - - # modify action - with h5py.File(dataset, 'r+') as out_file: - for i in tqdm(range(len(results)), desc="Writing to output"): - abs_actions, info = results[i] - demo = out_file["data/{}".format(demo_keys[i])] - if "actions_abs" not in demo: - demo.create_dataset("actions_abs", data=np.array(abs_actions)) - else: - demo['actions_abs'][:] = abs_actions - - # save eval - if do_eval: - eval_dir.mkdir(parents=False, exist_ok=True) - - print("Writing error_stats.pkl") - infos = [info for _, info in results] - pickle.dump(infos, eval_dir.joinpath('error_stats.pkl').open('wb')) - - print("Generating visualization") - metrics = ['pos', 'rot'] - metrics_dicts = dict() - for m in metrics: - metrics_dicts[m] = collections.defaultdict(list) - - for i in range(len(infos)): - info = infos[i] - for k, v in info.items(): - for m in metrics: - metrics_dicts[m][k].append(v[m]) - - from matplotlib import pyplot as plt - plt.switch_backend('PDF') - - fig, ax = plt.subplots(1, len(metrics)) - for i in range(len(metrics)): - axis = ax[i] - data = metrics_dicts[metrics[i]] - for key, value in data.items(): - axis.plot(value, label=key) - axis.legend() - axis.set_title(metrics[i]) - fig.set_size_inches(10,4) - fig.savefig(str(eval_dir.joinpath('error_stats.pdf'))) - fig.savefig(str(eval_dir.joinpath('error_stats.png'))) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--dataset", - type=str, - required=True - ) - - parser.add_argument( - "--eval_dir", - type=str, - help="directory to output evaluation metrics", - ) - - parser.add_argument( - "--num_workers", - type=int, - default=10, - ) - - args = parser.parse_args() - - - add_absolute_actions_to_dataset( - dataset=args.dataset, - eval_dir=args.eval_dir, - num_workers=args.num_workers, - ) \ No newline at end of file diff --git a/robomimic/scripts/filter_dataset_size.py b/robomimic/scripts/filter_dataset_size.py deleted file mode 100644 index a1509dd1..00000000 --- a/robomimic/scripts/filter_dataset_size.py +++ /dev/null @@ -1,81 +0,0 @@ -import argparse -import h5py -import numpy as np - -from robomimic.utils.file_utils import create_hdf5_filter_key - - -def filter_dataset_size(hdf5_path, num_demos, input_filter_key=None, output_filter_key=None): - # retrieve number of demos - f = h5py.File(hdf5_path, "r") - if input_filter_key is not None: - print("using filter key: {}".format(input_filter_key)) - demos = sorted([elem.decode("utf-8") for elem in np.array(f["mask/{}".format(input_filter_key)])]) - else: - demos = sorted(list(f["data"].keys())) - f.close() - - # get random split - total_num_demos = len(demos) - mask = np.zeros(total_num_demos) - mask[:num_demos] = 1. - np.random.shuffle(mask) - mask = mask.astype(int) - subset_inds = mask.nonzero()[0] - subset_keys = [demos[i] for i in subset_inds] - - # pass mask to generate split - if output_filter_key is not None: - name = output_filter_key - else: - name = "{}_demos".format(num_demos) - - if input_filter_key is not None: - name = "{}_{}".format(input_filter_key, name) - - subset_lengths = create_hdf5_filter_key(hdf5_path=hdf5_path, demo_keys=subset_keys, key_name=name) - - print("Total number of subset samples: {}".format(np.sum(subset_lengths))) - print("Average number of subset samples {}".format(np.mean(subset_lengths))) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "--dataset", - type=str, - required=True, - help="path to hdf5 dataset", - ) - parser.add_argument( - "--input_filter_key", - type=str, - default=None, - help="if provided, split the subset of trajectories in the file that correspond to\ - this filter key into a training and validation set of trajectories, instead of\ - splitting the full set of trajectories", - ) - parser.add_argument( - "--num_demos", - type=int, - nargs='+', - required=True, - ) - parser.add_argument( - "--output_filter_key", - type=str, - required=False, - help="(optional) use custom name for output filter key name" - ) - args = parser.parse_args() - - # seed to make sure results are consistent - np.random.seed(0) - - for n in args.num_demos: - filter_dataset_size( - args.dataset, - input_filter_key=args.input_filter_key, - num_demos=n, - output_filter_key=args.output_filter_key, - ) \ No newline at end of file diff --git a/robomimic/scripts/plot_model_predictions.py b/robomimic/scripts/plot_model_predictions.py deleted file mode 100644 index 6a9fa772..00000000 --- a/robomimic/scripts/plot_model_predictions.py +++ /dev/null @@ -1,213 +0,0 @@ -import json -import os -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec -from copy import deepcopy -import random -from sklearn.metrics import mean_squared_error -import re -import robomimic.utils.file_utils as FileUtils -import robomimic.utils.torch_utils as TorchUtils -import robomimic.utils.tensor_utils as TensorUtils -import robomimic.utils.train_utils as TrainUtils -from robomimic.config import config_factory -import robomimic.utils.obs_utils as ObsUtils -import torch -from torch.utils.data import DataLoader - -""" -TODO: track rotation magnitude seperately (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.transform.Rotation.magnitude.html) -""" - -# the configs of the models to be plotted -model_config_mapping = { - # "bottle_less_obs": { - # "model":"/home/zehan/expdata/droid/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/models/model_epoch_60.pth", - # 'folder':"/home/zehan/expdata/droid/im/bc_xfmr/google_bc_baseline/bottle_less_obs/20230815225106/test_inference_figures/", - # # "action_names": ['x', 'y', 'z', 'roll', 'pitch', 'yaw', "gripper_action" , 'terminate'], - # "action_names": None, - # "trajectory_name_regex": r'(\d+_trajectory_im\d+)' - # }, - "droid_wire": { - # "model": "/home/soroushn/expdata/droid/im/diffusion_policy/debug/ds_pen-in-cup_cams_3cams/20230830160945/models/model_epoch_2.pth", - "model": "/home/soroushn/expdata/droid/im/bc_xfmr/debug/ds_pen-in-cup_cams_3cams_predfuture_True_ac_keys_rel/20230830161631/models/model_epoch_2.pth", - "folder": "/home/soroushn/tmp/model_predictions", - # "action_names": ['x', 'y', 'z', 'r', 'p', 'y', "gripper_pos"], # use custom names - "action_names": None, # use default names, see line 71 - "trajectory_name_regex": r'(\w+_\w+_\d{2}_\d{2}:\d{2}:\d{2}_\d{4})' # the name of the figure files need to be custom defined (the part of the names of the trajectories that uniquely identifies them) - } -} - -NUM_SAMPLES = 2 - -# loop through each model -for model_name in model_config_mapping: - ckpt_path = model_config_mapping[model_name]['model'] - saving_folder = model_config_mapping[model_name]['folder'] - # can custom-define or using default action_names - action_names = model_config_mapping[model_name]['action_names'] - trajectory_name_regex = model_config_mapping[model_name]['trajectory_name_regex'] - accuracy_thresholds = np.logspace(-3,-5, num=3).tolist() - - - - device = TorchUtils.get_torch_device(try_to_use_cuda=True) - - ckpt_dict = FileUtils.maybe_dict_from_checkpoint(ckpt_path=ckpt_path) - config = json.loads(ckpt_dict["config"]) - config["train"]["shuffled_obs_key_groups"] = None - ckpt_dict["config"] = json.dumps(config) - policy, _ = FileUtils.policy_from_checkpoint(ckpt_dict=ckpt_dict, device=device, verbose=True) - shape_meta = ckpt_dict['shape_metadata'] - ext_cfg = json.loads(ckpt_dict["config"]) - config = config_factory(ext_cfg["algo_name"]) - with config.values_unlocked(): - config.update(ext_cfg) - - - frame_stack = config.train.frame_stack - device = TorchUtils.get_torch_device(try_to_use_cuda=config.train.cuda) - - trainset, validset = TrainUtils.load_data_for_training(config, obs_keys=shape_meta["all_obs_keys"]) - # trainset.datasets is a list - # the trajectories to plot is randomly sampled from the training and validation sets - training_sampled_data = random.sample(trainset.datasets, NUM_SAMPLES) - # validation_sampled_data = random.sample(validset.datasets, NUM_SAMPLES) - - inference_datasets_mapping = {"training": training_sampled_data} #, "validation": validation_sampled_data} - - - if action_names == None: - # TODO - action_keys = config.train.action_keys # Need to adjust. For Robomimic datasets, there is no `action_keys`, it is config.train.dataset_keys - modified_action_keys = [element.replace('action/', '') for element in action_keys] - action_names = [] - for i, action_key in enumerate(action_keys): - if isinstance(training_sampled_data[0].__getitem__(0)[action_key][frame_stack-1], np.ndarray): - action_names.extend([f'{modified_action_keys[i]}_{j+1}' for j in range(len(training_sampled_data[0].__getitem__(0)[action_key][frame_stack-1]))]) - else: - action_names.append(modified_action_keys[i]) - - # loop through training and validation sets - for inference_key in inference_datasets_mapping: - mse_training_per_traj = [] - data_name = [] - actual_actions_all_traj = [] # (NxT, D) - predicted_actions_all_traj = [] # (NxT, D) - - # loop through each trajectory - for d in inference_datasets_mapping[inference_key]: - hdf5_path = d.hdf5_path - mse_for_one_traj = [] - traj_length = len(d) - action_dim = len(action_names) - actual_actions = [[] for _ in range(action_dim)] # (T, D) - predicted_actions = [[] for _ in range(action_dim)] # (T, D) - - image_keys = [item for item in d.__getitem__(0)['obs'].keys() if "image" in item] - images = {key: [] for key in image_keys} - - dataloader = DataLoader( - dataset=d, - sampler=None, - batch_size=1, - shuffle=False, - num_workers=1, - drop_last=True, - ) - - model = policy.policy - - model.reset() - - # loop through each timestep - for batch in iter(dataloader): - batch = model.process_batch_for_training(batch) - - for image_key in image_keys: - im = batch["obs"][image_key][0][-1] - im = TensorUtils.to_numpy(im).astype(np.uint32) - images[image_key].append(im) - - batch = model.postprocess_batch_for_training(batch, obs_normalization_stats=None) # ignore obs_normalization for now - # model_output = model.nets["policy"](batch["obs"]) - - model_output = model.get_action(batch["obs"]) - - actual_action = TensorUtils.to_numpy( - batch["actions"][0][0] - ) - predicted_action = TensorUtils.to_numpy( - model_output[0] - ) - - actual_actions_all_traj.append(actual_action) - predicted_actions_all_traj.append(predicted_action) - - for dim in range(action_dim): - actual_actions[dim].append(actual_action[dim]) - predicted_actions[dim].append(predicted_action[dim]) - - # Plot - fig, axs = plt.subplots(len(images) + action_dim, 1, figsize=(30, (len(images) + action_dim) * 3)) - for i, image_key in enumerate(image_keys): - interval = int(traj_length/15) # plot `5` images - images[image_key] = images[image_key][::interval] - combined_images = np.concatenate(images[image_key], axis=1) - axs[i].imshow(combined_images) - if i == 0: - axs[i].set_title(hdf5_path + '\n' + image_key, fontsize=30) - else: - axs[i].set_title(image_key, fontsize=30) - axs[i].axis("off") - for dim in range(action_dim): - mse = mean_squared_error(actual_actions[dim], predicted_actions[dim]) - mse_for_one_traj.append(mse) - axs[len(images)+dim].plot(range(traj_length), actual_actions[dim], label='Actual Action', color='blue') - axs[len(images)+dim].plot(range(traj_length), predicted_actions[dim], label='Predicted Action', color='red') - # axs[len(images)+dim].set_xlabel('Timestep') - # axs[len(images)+dim].set_ylabel('Action Dimension {}'.format(dim + 1)) - axs[len(images)+dim].set_title(action_names[dim], fontsize=30) - axs[len(images)+dim].xaxis.set_tick_params(labelsize=24) - axs[len(images)+dim].yaxis.set_tick_params(labelsize=24) - axs[len(images)+dim].legend(fontsize=20) - plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05, wspace=0.3, hspace=0.6) - - # Save inference figures - save_path = saving_folder + inference_key+"/" #remember to add / at the end - # data_content = re.search(trajectory_name_regex, hdf5_path).group(1) - data_content = "test" - filename = "comparison_figure_"+data_content +".png" - if not os.path.exists(save_path): - os.makedirs(save_path) - print(save_path + filename) - # Save the figure with the specified path and filename - plt.savefig(save_path + filename) - mse_training_per_traj.append(mse_for_one_traj) - data_name.append(hdf5_path) - - # log MSE information - accuracy_thresholds = np.logspace(-3,-5, num=3).tolist() - mse = torch.nn.functional.mse_loss(torch.tensor(predicted_actions_all_traj), torch.tensor(actual_actions_all_traj), reduction='none') # (NxT, D) - step_log = {} - step_log[f'{inference_key}_action_mse_error'] = mse.mean().item() # average MSE across all timesteps averaged across all action dimensions (D,) - - # compute percentage of timesteps that have MSE less than the accuracy thresholds - for accuracy_threshold in accuracy_thresholds: - step_log[f'{inference_key}_action_accuracy@{accuracy_threshold}'] = (torch.less(mse,accuracy_threshold).float().mean().item()) - - - average_mse_per_dimension = np.mean(mse_training_per_traj, axis=0) # (D,) - txt_path = saving_folder+inference_key+"/" +"output.txt" - list_str = '\n'.join(['{} {}'.format(desc, ' '.join(map(str, sublist))) for desc, sublist in zip(data_name, mse_training_per_traj)]) - - # save MSE information - with open(txt_path, "w+") as txt_file: - txt_file.write(f"MSE per trajectory:\n{list_str}\n") - txt_file.write("\n") - txt_file.write(f"Average MSE across trajectories per dimension: {average_mse_per_dimension}\n") - txt_file.write("\n") - txt_file.write(f"MSE log: {step_log}\n") - - diff --git a/robomimic/utils/camera_utils.py b/robomimic/utils/camera_utils.py deleted file mode 100644 index e4f1ba1f..00000000 --- a/robomimic/utils/camera_utils.py +++ /dev/null @@ -1,2471 +0,0 @@ -""" -Almost all the code in this file has been copied from TRI's VIDAR -https://github.com/TRI-ML/vidar -""" - - -from abc import ABC -import abc -import torch -import torch.nn as nn -import torch.nn.functional as tfn -import os -import numpy as np -from collections import OrderedDict -import torchvision -from einops import rearrange - -def is_int(data): - """Checks if data is an integer.""" - return isinstance(data, int) - -def is_tensor(data): - """Checks if data is a torch tensor.""" - return type(data) == torch.Tensor - -def to_global_pose_broken(pose, zero_origin=False): - """Get global pose coordinates from current and context poses""" - tgt = 0 if 0 in pose else (0, 0) - base = None if zero_origin else pose[tgt].T.clone() - pose[tgt].T = torch.eye( - 4, device=pose[tgt].device, dtype=pose[tgt].dtype).repeat(pose[tgt].T.shape[0], 1, 1) - - keys = pose.keys() - # steps = sorted(set([key[0] for key in keys])) - steps = {key[1]: [key2[0] for key2 in keys if key2[1] == key[1]] for key in keys} - cams = sorted(set([key[1] for key in keys])) - - for cam in cams: - if cam != tgt[1]: - pose[(tgt[0], cam)].T = (pose[(tgt[0], cam)] * pose[tgt]).T.float() - for cam in cams: - for step in steps[cam]: - if step != tgt[0]: - pose[(step, cam)] = (pose[(step, cam)] * pose[(tgt[0], cam)]) - # for step in steps: - # if step != tgt[0]: - # for cam in cams: - # pose[(step, cam)] = (pose[(step, cam)] * pose[(tgt[0], cam)]) - if not zero_origin: - for key in keys: - pose[key].T = pose[key].T @ base - return pose - -def to_global_pose(pose, zero_origin=False): - """Get global pose coordinates from current and context poses""" - tgt = 0 if 0 in pose else (0, 0) - base = None if zero_origin else pose[tgt].T[[0]].clone() - pose[tgt].T[[0]] = torch.eye(4, device=pose[tgt].device, dtype=pose[tgt].dtype) - for b in range(1, len(pose[tgt])): - pose[tgt].T[[b]] = (pose[tgt][b] * pose[tgt][0]).T.float() - for key in pose.keys(): - if key != tgt: - pose[key] = pose[key] * pose[tgt] - if not zero_origin: - for key in pose.keys(): - for b in range(len(pose[key])): - pose[key].T[[b]] = pose[key].T[[b]] @ base - return pose - -def from_dict_sample(T, to_global=False, zero_origin=False, to_matrix=False, broken=False): - """Helper function to convert sample poses to Pose objects""" - pose = {key: Pose(val) for key, val in T.items()} - if to_global: - to_global_pose_fn = to_global_pose_broken if broken else to_global_pose - pose = to_global_pose_fn(pose, zero_origin=zero_origin) - if to_matrix: - pose = {key: val.T for key, val in pose.items()} - return pose - - -def pose_vec2mat(vec, mode='euler', dtype=None): - """Convert Euler parameters to transformation matrix.""" - if mode is None: - return vec - trans, rot = vec[:, :3].unsqueeze(-1), vec[:, 3:] - if mode == 'euler': - rot_mat = euler2mat(rot, dtype) - else: - raise ValueError('Rotation mode not supported {}'.format(mode)) - mat = torch.cat([rot_mat, trans], dim=2) # [B,3,4] - return mat - - -def euler2mat(angle, dtype=None): - """Convert euler angles to rotation matrix""" - B = angle.size(0) - if dtype is not None: - angle = angle.to(dtype) - x, y, z = angle[:, 0], angle[:, 1], angle[:, 2] - - cosz = torch.cos(z) - sinz = torch.sin(z) - - zeros = z.detach() * 0 - ones = zeros.detach() + 1 - zmat = torch.stack([ cosz, -sinz, zeros, - sinz, cosz, zeros, - zeros, zeros, ones], dim=1).view(B, 3, 3) - - cosy = torch.cos(y) - siny = torch.sin(y) - - ymat = torch.stack([ cosy, zeros, siny, - zeros, ones, zeros, - -siny, zeros, cosy], dim=1).view(B, 3, 3) - - cosx = torch.cos(x) - sinx = torch.sin(x) - - xmat = torch.stack([ ones, zeros, zeros, - zeros, cosx, -sinx, - zeros, sinx, cosx], dim=1).view(B, 3, 3) - - rot_mat = xmat.bmm(ymat).bmm(zmat) - return rot_mat - -def is_tuple(data): - """Checks if data is a tuple.""" - return isinstance(data, tuple) - -def is_list(data): - """Checks if data is a list.""" - return isinstance(data, list) or isinstance(data, torch.nn.ModuleList) - -def is_seq(data): - """Checks if data is a list or tuple.""" - return is_tuple(data) or is_list(data) - -def is_dict(data): - """Checks if data is a dictionary.""" - return isinstance(data, dict) or isinstance(data, torch.nn.ModuleDict) - -def iterate1(func): - """Decorator to iterate over a list (first argument)""" - def inner(var, *args, **kwargs): - if is_seq(var): - return [func(v, *args, **kwargs) for v in var] - elif is_dict(var): - return {key: func(val, *args, **kwargs) for key, val in var.items()} - else: - return func(var, *args, **kwargs) - return inner - -@iterate1 -def invert_pose(T): - """Inverts a [B,4,4] torch.tensor pose""" - Tinv = torch.eye(4, device=T.device, dtype=T.dtype).repeat([len(T), 1, 1]) - Tinv[:, :3, :3] = torch.transpose(T[:, :3, :3], -2, -1) - Tinv[:, :3, -1] = torch.bmm(-1. * Tinv[:, :3, :3], T[:, :3, -1].unsqueeze(-1)).squeeze(-1) - return Tinv - # return torch.linalg.inv(T) - -def from_dict_batch(T, **kwargs): - """Helper function to convert a dicionary of tensor poses to Pose objects""" - pose_batch = [from_dict_sample({key: val[b] for key, val in T.items()}, **kwargs) - for b in range(T[0].shape[0])] - return {key: torch.stack([v[key] for v in pose_batch], 0) for key in pose_batch[0]} - - -def align_corners(value=None): - """Get the align_corners value from the environment variable""" - if value is not None: - return value - if os.getenv('ALIGN_CORNERS') is not None: - return True if os.getenv('ALIGN_CORNERS') == 'True' else \ - False if os.getenv('ALIGN_CORNERS') == 'False' else None - return True - - -@iterate1 -def interpolate(tensor, size, scale_factor, mode): - """Helper function to interpolate tensors""" - if size is None and scale_factor is None: - return tensor - if is_tensor(size): - size = size.shape[-2:] - return tfn.interpolate( - tensor, size=size, scale_factor=scale_factor, - recompute_scale_factor=False, mode=mode, - align_corners=None if mode == 'nearest' else align_corners(), - ) - - -def interpolate_nearest(tensor, size=None, scale_factor=None): - """Helper function to interpolate tensors using nearest neighbor interpolation""" - if size is not None and is_tensor(size): - size = size.shape[-2:] - return interpolate(tensor.float(), size, scale_factor, mode='nearest') - - -def masked_average(loss, mask, eps=1e-7): - """Mask average for loss given mask""" - return (loss * mask).sum() / (mask.sum() + eps) - - -def multiply_mask(data, mask): - """Multiply data with masks""" - return data if (data is None or mask is None) else data * mask - - -def multiply_args(*args): - """Multiply all arguments""" - valids = [v for v in args if v is not None] - return None if not valids else reduce((lambda x, y: x * y), valids) - - -def grid_sample(tensor, grid, padding_mode, mode): - """Helper function for grid sampling""" - return tfn.grid_sample( - tensor, grid, - padding_mode=padding_mode, mode=mode, - align_corners=align_corners(), - ) - - -def grid_sample_volume(tensor, grid, padding_mode, mode): - """Helper function for multi-layer grid sampling""" - b, d, h, w, _ = grid.shape - return grid_sample( - tensor, grid.reshape(b, d, h * w, 2), - padding_mode=padding_mode, mode=mode - ).reshape(b, 3, d, h, w) - - -def pixel_grid(hw, b=None, with_ones=False, device=None, normalize=False, shake=False): - """Helper function to generate a pixel grid given [H,W] or [B,H,W]""" - if is_tensor(hw): - b, hw, device = hw.shape[0], hw.shape[-2:], hw.device - if is_tensor(device): - device = device.device - if align_corners(): - hi, hf = 0, hw[0] - 1 - wi, wf = 0, hw[1] - 1 - else: - hi, hf = 0.5, hw[0] - 0.5 - wi, wf = 0.5, hw[1] - 0.5 - yy, xx = torch.meshgrid([torch.linspace(hi, hf, hw[0], device=device), - torch.linspace(wi, wf, hw[1], device=device)], indexing='ij') - if with_ones: - grid = torch.stack([xx, yy, torch.ones(hw, device=device)], 0) - else: - grid = torch.stack([xx, yy], 0) - if b is not None: - grid = grid.unsqueeze(0).repeat(b, 1, 1, 1) - if shake: - if align_corners(): - rand = torch.rand((b, 2, *hw), device=device) - else: - rand = torch.rand((b, 2, *hw), device=device) - 0.5 - grid[:, :2, :, :] += rand - if normalize: - grid = norm_pixel_grid(grid) - return grid - - -def norm_pixel_grid(grid, hw=None, in_place=False): - """Normalize a pixel grid from [W,H] to [-1,+1].""" - if hw is None: - hw = grid.shape[-2:] - if not in_place: - grid = grid.clone() - if align_corners(): - grid[:, 0] = 2.0 * grid[:, 0] / (hw[1] - 1) - 1.0 - grid[:, 1] = 2.0 * grid[:, 1] / (hw[0] - 1) - 1.0 - else: - grid[:, 0] = 2.0 * grid[:, 0] / hw[1] - 1.0 - grid[:, 1] = 2.0 * grid[:, 1] / hw[0] - 1.0 - return grid - - -def unnorm_pixel_grid(grid, hw=None, in_place=False): - """Unnormalize a pixel grid from [-1,+1] to [W,H].""" - if hw is None: - hw = grid.shape[-2:] - if not in_place: - grid = grid.clone() - if align_corners(): - grid[:, 0] = 0.5 * (hw[1] - 1) * (grid[:, 0] + 1) - grid[:, 1] = 0.5 * (hw[0] - 1) * (grid[:, 1] + 1) - else: - grid[:, 0] = 0.5 * hw[1] * (grid[:, 0] + 1) - grid[:, 1] = 0.5 * hw[0] * (grid[:, 1] + 1) - return grid - - -def match_scales(image, targets, num_scales, mode='bilinear'): - """Match scales of image given target scales and resolutions - - Parameters - ---------- - image : torch.Tensor - Input image to be scaled - targets : list of torch.Tensor - Targes to match scale - num_scales : int - Number of scales to match - mode : str, optional - Interpolation mode, by default 'bilinear' - - Returns - ------- - _type_ - _description_ - """ - # For all scales - images = [] - image_shape = image.shape[-2:] - for i in range(num_scales): - target_shape = targets[i].shape - # If image shape is equal to target shape - if same_shape(image_shape, target_shape): - images.append(image) - else: - # Otherwise, interpolate - images.append(interpolate_image(image, target_shape, mode=mode)) - # Return scaled images - return images - - -def cat_channel_ones(tensor, n=1): - """ - Concatenate tensor with an extra channel of ones - - Parameters - ---------- - tensor : torch.Tensor - Tensor to be concatenated - n : int - Which channel will be concatenated - - Returns - ------- - cat_tensor : torch.Tensor - Concatenated tensor - """ - # Get tensor shape with 1 channel - shape = list(tensor.shape) - shape[n] = 1 - # Return concatenation of tensor with ones - return torch.cat([tensor, torch.ones(shape, - device=tensor.device, dtype=tensor.dtype)], n) - - -def same_shape(shape1, shape2): - """Checks if two shapes are the same""" - if len(shape1) != len(shape2): - return False - for i in range(len(shape1)): - if shape1[i] != shape2[i]: - return False - return True - - -def interpolate_image(image, shape=None, scale_factor=None, - mode='bilinear', recompute_scale_factor=False): - """ - Interpolate an image to a different resolution - - Parameters - ---------- - image : torch.Tensor - Image to be interpolated [B,?,h,w] - shape : torch.Tensor or tuple - Output shape [H,W] - scale_factor : float - Scale factor for output shape - mode : str - Interpolation mode - recompute_scale_factor : bool - True if scale factor is recomputed - - Returns - ------- - image : torch.Tensor - Interpolated image [B,?,H,W] - """ - assert shape is not None or scale_factor is not None, 'Invalid option for interpolate_image' - # Take last two dimensions as shape - if shape is not None: - if is_tensor(shape): - shape = shape.shape - if len(shape) > 2: - shape = shape[-2:] - # If the shapes are the same, do nothing - if same_shape(image.shape[-2:], shape): - return image - # Interpolate image to match the shape - return interpolate(image, size=shape, scale_factor=scale_factor, mode=mode) - - -def check_assert(pred, gt, atol=1e-5, rtol=1e-5): - """Check if two dictionaries are equal""" - for key in gt.keys(): - if key in pred.keys(): - # assert key in pred and key in gt - if is_dict(pred[key]): - check_assert(pred[key], gt[key]) - elif is_seq(pred[key]): - for val1, val2 in zip(pred[key], gt[key]): - if is_tensor(val1): - assert torch.allclose(val1, val2, atol=atol, rtol=rtol), \ - f'Assert error in {key} : {val1.mean().item()} x {val2.mean().item()}' - else: - assert val1 == val2, \ - f'Assert error in {key} : {val1} x {val2}' - else: - if is_tensor(pred[key]): - assert torch.allclose(pred[key], gt[key], atol=atol, rtol=rtol), \ - f'Assert error in {key} : {pred[key].mean().item()} x {gt[key].mean().item()}' - else: - assert pred[key] == gt[key], \ - f'Assert error in {key} : {pred[key]} x {gt[key]}' - - -def interleave(data, b): - """Interleave data across multiple batches""" - data_interleave = data.unsqueeze(1).expand(-1, b, *data.shape[1:]) - return data_interleave.reshape(-1, *data.shape[1:]) - -def invert_intrinsics(K): - """Invert camera intrinsics""" - - Kinv = K.clone() - Kinv[:, 0, 0] = 1. / K[:, 0, 0] - Kinv[:, 1, 1] = 1. / K[:, 1, 1] - Kinv[:, 0, 2] = -1. * K[:, 0, 2] / K[:, 0, 0] - Kinv[:, 1, 2] = -1. * K[:, 1, 2] / K[:, 1, 1] - return Kinv - - -def scale_intrinsics(K, ratio): - """Scale intrinsics given x_scale and y_scale factors""" - - if is_seq(ratio): - ratio_h, ratio_w = ratio - else: - ratio_h = ratio_w = ratio - - K = K.clone() - - K[..., 0, 0] *= ratio_w - K[..., 1, 1] *= ratio_h - - K[..., 0, 2] = K[..., 0, 2] * ratio_w - K[..., 1, 2] = K[..., 1, 2] * ratio_h - - return K - -class Pose: - """ - Pose class, that encapsulates a [4,4] transformation matrix - for a specific reference frame - """ - def __init__(self, T=1): - """ - Initializes a Pose object. - - Parameters - ---------- - T : int or torch.Tensor - Transformation matrix [B,4,4] - """ - if is_int(T): - T = torch.eye(4).repeat(T, 1, 1) - self.T = T if T.dim() == 3 else T.unsqueeze(0) - - def __len__(self): - """Batch size of the transformation matrix""" - return len(self.T) - - def __getitem__(self, idx): - """Return batch-wise pose""" - if not is_tensor(idx): - idx = [idx] - return Pose(self.T[idx]) - - def __mul__(self, data): - """Transforms the input (Pose or 3D points) using this object""" - if isinstance(data, Pose): - return Pose(self.T.bmm(data.T)) - elif isinstance(data, torch.Tensor): - return self.T[:, :3, :3].bmm(data) + self.T[:, :3, -1].unsqueeze(-1) - else: - raise NotImplementedError() - - def detach(self): - """Detach pose from graph""" - return Pose(self.T.detach()) - - def clone(self): - """Clone pose""" - return type(self)( - T=self.T.clone() - ) - - @property - def shape(self): - """Return pose shape""" - return self.T.shape - - @property - def device(self): - """Return pose device""" - return self.T.device - - @property - def dtype(self): - """Return pose type""" - return self.T.dtype - - @classmethod - def identity(cls, N=1, device=None, dtype=torch.float): - """Initializes as a [4,4] identity matrix""" - return cls(torch.eye(4, device=device, dtype=dtype).repeat([N,1,1])) - - @staticmethod - def from_dict(T, to_global=False, zero_origin=False, to_matrix=False, broken=False): - if T is None: - return None - tgt = (0, 0) if broken else 0 - if T[tgt].dim() == 3: - return from_dict_sample( - T, to_global=to_global, zero_origin=zero_origin, to_matrix=to_matrix, broken=broken) - elif T[tgt].dim() == 4: - return from_dict_batch( - T, to_global=to_global, zero_origin=zero_origin, to_matrix=True, broken=broken) - - @classmethod - def from_vec(cls, vec, mode): - """Initializes from a [B,6] batch vector""" - mat = pose_vec2mat(vec, mode) - pose = torch.eye(4, device=vec.device, dtype=vec.dtype).repeat([len(vec), 1, 1]) - pose[:, :3, :3] = mat[:, :3, :3] - pose[:, :3, -1] = mat[:, :3, -1] - return cls(pose) - - def repeat(self, *args, **kwargs): - """Repeats the transformation matrix multiple times""" - self.T = self.T.repeat(*args, **kwargs) - return self - - def inverse(self): - """Returns a new Pose that is the inverse of this one""" - return Pose(invert_pose(self.T)) - - def to(self, *args, **kwargs): - """Moves object to a specific device""" - self.T = self.T.to(*args, **kwargs) - return self - - def cuda(self, *args, **kwargs): - """Moves pose to GPU""" - self.to('cuda') - return self - - def translate(self, xyz): - """Translate pose by a vector""" - self.T[:, :3, -1] = self.T[:, :3, -1] + xyz.to(self.device) - return self - - def rotate(self, rpw): - """Rotate pose by a vector""" - rot = euler2mat(rpw) - T = invert_pose(self.T).clone() - T[:, :3, :3] = T[:, :3, :3] @ rot.to(self.device) - self.T = invert_pose(T) - return self - - def rotateRoll(self, r): - """Rotate pose by a roll angle""" - return self.rotate(torch.tensor([[0, 0, r]])) - - def rotatePitch(self, p): - """Rotate pose by a pitch angle""" - return self.rotate(torch.tensor([[p, 0, 0]])) - - def rotateYaw(self, w): - """Rotate pose by a yaw angle""" - return self.rotate(torch.tensor([[0, w, 0]])) - - def translateForward(self, t): - """Translate pose on its z axis (forward)""" - return self.translate(torch.tensor([[0, 0, -t]])) - - def translateBackward(self, t): - """Translate pose on its z axis (backward)""" - return self.translate(torch.tensor([[0, 0, +t]])) - - def translateLeft(self, t): - """Translate pose on its x axis (left)""" - return self.translate(torch.tensor([[+t, 0, 0]])) - - def translateRight(self, t): - """Translate pose on its x axis (right)""" - return self.translate(torch.tensor([[-t, 0, 0]])) - - def translateUp(self, t): - """Translate pose on its y axis (up)""" - return self.translate(torch.tensor([[0, +t, 0]])) - - def translateDown(self, t): - """Translate pose on its y axis (down)""" - return self.translate(torch.tensor([[0, -t, 0]])) - - -class CameraBase(nn.Module, ABC): - """Base camera class - - Parameters - ---------- - hw : tuple - Camera resolution - Twc : torch.Tensor, optional - Camera pose (world to camera), by default None - Tcw : torch.Tensor, optional - Camera pose (camera to world), by default None - """ - def __init__(self, hw, Twc=None, Tcw=None): - super().__init__() - assert Twc is None or Tcw is None - - # Pose - - if Twc is None and Tcw is None: - self._Twc = torch.eye( - 4, dtype=self._K.dtype, device=self._K.device).unsqueeze(0).repeat(self._K.shape[0], 1, 1) - else: - self._Twc = invert_pose(Tcw) if Tcw is not None else Twc - if is_tensor(self._Twc): - self._Twc = Pose(self._Twc) - - # Resolution - - self._hw = hw - if is_tensor(self._hw): - self._hw = self._hw.shape[-2:] - - def __getitem__(self, idx): - """Return batch-wise pose""" - if is_seq(idx): - return type(self).from_list([self.__getitem__(i) for i in idx]) - else: - if not is_tensor(idx): - idx = [idx] - return type(self)( - K=self._K[idx], - Twc=self._Twc[idx] if self._Twc is not None else None, - hw=self._hw, - ) - - def __len__(self): - """Return length as intrinsics batch""" - return self._K.shape[0] - - def __eq__(self, cam): - """Check camera equality""" - if not isinstance(cam, type(self)): - return False - if self._hw[0] != cam.hw[0] or self._hw[1] != cam.hw[1]: - return False - if not torch.allclose(self._K, cam.K): - return False - if not torch.allclose(self._Twc.T, cam.Twc.T): - return False - return True - - def clone(self): - """Clone camera""" - return type(self)( - K=self.K.clone(), - Twc=self.Twc.clone(), - hw=[v for v in self._hw], - ) - - @property - def pose(self): - """Return camera pose (world to camera)""" - return self._Twc.T - - @property - def K(self): - """Return camera intrinsics""" - return self._K - - @K.setter - def K(self, K): - """Set camera intrinsics""" - self._K = K - - @property - def batch_size(self): - """Return batch size""" - return self._Twc.T.shape[0] - - @property - def b(self): - """Return batch size""" - return self._Twc.T.shape[0] - - @property - def bhw(self): - """Return batch size and resolution""" - return self.b, self.hw - - @property - def bdhw(self): - """Return batch size, device, and resolution""" - return self.b, self.device, self.hw - - @property - def hw(self): - """Return camera resolution""" - return self._hw - - @hw.setter - def hw(self, hw): - """Set camera resolution""" - self._hw = hw - - @property - def wh(self): - """Return camera resolution""" - return self._hw[::-1] - - @property - def n_pixels(self): - """Return number of pixels""" - return self._hw[0] * self._hw[1] - - @property - def Tcw(self): - """Return camera pose (camera to world)""" - return None if self._Twc is None else self._Twc.inverse() - - @Tcw.setter - def Tcw(self, Tcw): - """Set camera pose (camera to world)""" - self._Twc = Tcw.inverse() - - @property - def Twc(self): - """Return camera pose (world to camera)""" - return self._Twc - - @Twc.setter - def Twc(self, Twc): - """Set camera pose (world to camera)""" - self._Twc = Twc - - @property - def dtype(self): - """Get camera data type""" - return self._K.dtype - - @property - def device(self): - """Get camera device""" - return self._K.device - - def detach_pose(self): - """Detach camera pose""" - return type(self)(K=self._K, hw=self._hw, - Twc=self._Twc.detach() if self._Twc is not None else None) - - def detach_K(self): - """Detach camera intrinsics""" - return type(self)(K=self._K.detach(), hw=self._hw, Twc=self._Twc) - - def detach(self): - """Detach camera""" - return type(self)(K=self._K.detach(), hw=self._hw, - Twc=self._Twc.detach() if self._Twc is not None else None) - - def inverted_pose(self): - """Return camera with inverted pose""" - return type(self)(K=self._K, hw=self._hw, - Twc=self._Twc.inverse() if self._Twc is not None else None) - - def no_translation(self): - """Return camera with no translation""" - Twc = self.pose.clone() - Twc[:, :-1, -1] = 0 - return type(self)(K=self._K, hw=self._hw, Twc=Twc) - - def no_pose(self): - """Return camera with no pose""" - return type(self)(K=self._K, hw=self._hw) - - def interpolate(self, rgb): - """Interpolate RGB image""" - if rgb.dim() == 5: - rgb = rearrange(rgb, 'b n c h w -> (b n) c h w') - return interpolate(rgb, scale_factor=None, size=self.hw, mode='bilinear') - - def interleave_K(self, b): - """Interleave intrinsics for multiple batches""" - return type(self)( - K=interleave(self._K, b), - Twc=self._Twc, - hw=self._hw, - ) - - def interleave_Twc(self, b): - """Interleave pose for multiple batches""" - return type(self)( - K=self._K, - Twc=interleave(self._Twc, b), - hw=self._hw, - ) - - def interleave(self, b): - """Interleave camera for multiple batches""" - return type(self)( - K=interleave(self._K, b), - Twc=interleave(self._Twc, b), - hw=self._hw, - ) - - def repeat_bidir(self): - """Repeat camera for bidirectional training""" - return type(self)( - K=self._K.repeat(2, 1, 1), - Twc=torch.cat([self._Twc.T, self.Tcw.T], 0), - hw=self.hw, - ) - - def Pwc(self, from_world=True): - """Return camera projection matrix (world to camera)""" - return self._K[:, :3] if not from_world or self._Twc is None else \ - torch.matmul(self._K, self._Twc.T)[:, :3] - - def to_world(self, points): - """Moves pointcloud to world coordinates""" - if points.dim() > 3: - points = points.reshape(points.shape[0], 3, -1) - return points if self.Tcw is None else self.Tcw * points - - def from_world(self, points): - """Moves pointcloud to camera coordinates""" - if points.dim() > 3: - shape = points.shape - points = points.reshape(points.shape[0], 3, -1) - else: - shape = None - local_points = points if self._Twc is None else \ - torch.matmul(self._Twc.T, cat_channel_ones(points, 1))[:, :3] - return local_points if shape is None else local_points.view(shape) - - def from_world2(self, points): - """Moves pointcloud to camera coordinates""" - if points.dim() > 3: - points = points.reshape(points.shape[0], 3, -1) - return points if self._Twc is None else \ - torch.matmul(self._Twc.T[:, :3, :3], points[:, :3]) + self._Twc.T[:, :3, 3:] - - def to(self, *args, **kwargs): - """Moves camera to device""" - self._K = self._K.to(*args, **kwargs) - if self._Twc is not None: - self._Twc = self._Twc.to(*args, **kwargs) - return self - - def cuda(self, *args, **kwargs): - """Moves camera to GPU""" - return self.to('cuda') - - def relative_to(self, cam): - """Returns camera relative to another camera""" - return type(self)(K=self._K, hw=self._hw, Twc=self._Twc * cam.Twc.inverse()) - - def global_from(self, cam): - """Returns global camera from one relative to another camera""" - return type(self)(K=self._K, hw=self._hw, Twc=self._Twc * cam.Twc) - - def pixel_grid(self, shake=False): - """Returns pixel grid""" - return pixel_grid( - b=self.batch_size, hw=self.hw, with_ones=True, - shake=shake, device=self.device).view(self.batch_size, 3, -1) - - def reconstruct_depth_map(self, depth, to_world=False, grid=None, scene_flow=None, world_scene_flow=None): - """Reconstruct 3D pointcloud from z-buffer depth map""" - if depth is None: - return None - b, _, h, w = depth.shape - if grid is None: - grid = pixel_grid(depth, with_ones=True, device=depth.device).view(b, 3, -1) - points = self.lift(grid) * depth.view(depth.shape[0], 1, -1) - if scene_flow is not None: - points = points + scene_flow.view(b, 3, -1) - if to_world and self.Tcw is not None: - points = self.Tcw * points - if world_scene_flow is not None: - points = points + world_scene_flow.view(b, 3, -1) - return points.view(b, 3, h, w) - - def reconstruct_euclidean(self, depth, to_world=False, grid=None, scene_flow=None, world_scene_flow=None): - """Reconstruct 3D pointcloud from euclidean depth map""" - if depth is None: - return None - b, _, h, w = depth.shape - rays = self.get_viewdirs(normalize=True, to_world=False, grid=grid).view(b, 3, -1) - points = rays * depth.view(depth.shape[0], 1, -1) - if scene_flow is not None: - points = points + scene_flow.view(b, 3, -1) - if to_world and self.Tcw is not None: - points = self.Tcw * points - if world_scene_flow is not None: - points = points + world_scene_flow.view(b, 3, -1) - return points.view(b, 3, h, w) - - def reconstruct_volume(self, depth, euclidean=False, **kwargs): - """Reconstruct 3D pointcloud from depth volume""" - if euclidean: - return torch.stack([self.reconstruct_euclidean(depth[:, :, i], **kwargs) - for i in range(depth.shape[2])], 2) - else: - return torch.stack([self.reconstruct_depth_map(depth[:, :, i], **kwargs) - for i in range(depth.shape[2])], 2) - - def reconstruct_cost_volume(self, volume, to_world=True, flatten=True): - """Reconstruct 3D pointcloud from cost volume""" - c, d, h, w = volume.shape - grid = pixel_grid((h, w), with_ones=True, device=volume.device).view(3, -1).repeat(1, d) - points = torch.stack([ - (volume.view(c, -1) * torch.matmul(invK[:3, :3].unsqueeze(0), grid)).view(3, d * h * w) - for invK in self.invK], 0) - if to_world and self.Tcw is not None: - points = self.Tcw * points - if flatten: - return points.view(-1, 3, d, h * w).permute(0, 2, 1, 3) - else: - return points.view(-1, 3, d, h, w) - - def project_points(self, points, from_world=True, normalize=True, - return_z=False, return_e=False, flag_invalid=True): - """Projects 3D points to 2D image plane""" - - is_depth_map = points.dim() == 4 - hw = self._hw if not is_depth_map else points.shape[-2:] - return_depth = return_z or return_e - - if is_depth_map: - points = points.reshape(points.shape[0], 3, -1) - b, _, n = points.shape - - coords, depth = self.unlift(points, from_world=from_world, euclidean=return_e) - - if not is_depth_map: - if normalize: - coords = norm_pixel_grid(coords, hw=self._hw, in_place=True) - if flag_invalid: - invalid = (coords[:, 0] < -1) | (coords[:, 0] > 1) | \ - (coords[:, 1] < -1) | (coords[:, 1] > 1) | (depth < 0) - coords[invalid.unsqueeze(1).repeat(1, 2, 1)] = -2 - if return_depth: - return coords.permute(0, 2, 1), depth - else: - return coords.permute(0, 2, 1) - - coords = coords.view(b, 2, *hw) - depth = depth.view(b, 1, *hw) - - if normalize: - coords = norm_pixel_grid(coords, hw=self._hw, in_place=True) - if flag_invalid: - invalid = (coords[:, 0] < -1) | (coords[:, 0] > 1) | \ - (coords[:, 1] < -1) | (coords[:, 1] > 1) | (depth[:, 0] < 0) - coords[invalid.unsqueeze(1).repeat(1, 2, 1, 1)] = -2 - - if return_depth: - return coords.permute(0, 2, 3, 1), depth - else: - return coords.permute(0, 2, 3, 1) - - def project_cost_volume(self, points, from_world=True, normalize=True): - """Projects 3D points from a cost volume to 2D image plane""" - - if points.dim() == 4: - points = points.permute(0, 2, 1, 3).reshape(points.shape[0], 3, -1) - b, _, n = points.shape - - points = torch.matmul(self.Pwc(from_world), cat_channel_ones(points, 1)) - - coords = points[:, :2] / (points[:, 2].unsqueeze(1) + 1e-7) - coords = coords.view(b, 2, -1, *self._hw).permute(0, 2, 3, 4, 1) - - if normalize: - coords[..., 0] /= self._hw[1] - 1 - coords[..., 1] /= self._hw[0] - 1 - return 2 * coords - 1 - else: - return coords - - def create_radial_volume(self, bins, to_world=True): - """Create a volume of radial depth bins""" - ones = torch.ones((1, *self.hw), device=self.device) - volume = torch.stack([depth * ones for depth in bins], 1).unsqueeze(0) - return self.reconstruct_volume(volume, to_world=to_world) - - def project_volume(self, volume, from_world=True): - """Project a volume to 2D image plane""" - b, c, d, h, w = volume.shape - return self.project_points(volume.view(b, c, -1), from_world=from_world).view(b, d, h, w, 2) - - def coords_from_cost_volume(self, volume, ref_cam=None): - """Project a cost volume to 2D image plane""" - if ref_cam is None: - return self.project_cost_volume(self.reconstruct_cost_volume(volume, to_world=False), from_world=True) - else: - return ref_cam.project_cost_volume(self.reconstruct_cost_volume(volume, to_world=True), from_world=True) - - def z2e(self, z_depth): - """Convert z-buffer depth to euclidean depth""" - points = self.reconstruct_depth_map(z_depth, to_world=False) - return self.project_points(points, from_world=False, return_e=True)[1] - - def e2z(self, e_depth): - """Convert euclidean depth to z-buffer depth""" - points = self.reconstruct_euclidean(e_depth, to_world=False) - return self.project_points(points, from_world=False, return_z=True)[1] - - def control(self, draw, tvel=0.2, rvel=0.1): - """Control camera with keyboard (requires camviz)""" - change = False - if draw.UP: - self.Twc.translateForward(tvel) - change = True - if draw.DOWN: - self.Twc.translateBackward(tvel) - change = True - if draw.LEFT: - self.Twc.translateLeft(tvel) - change = True - if draw.RIGHT: - self.Twc.translateRight(tvel) - change = True - if draw.KEY_Z: - self.Twc.translateUp(tvel) - change = True - if draw.KEY_X: - self.Twc.translateDown(tvel) - change = True - if draw.KEY_A: - self.Twc.rotateYaw(-rvel) - change = True - if draw.KEY_D: - self.Twc.rotateYaw(+rvel) - change = True - if draw.KEY_W: - self.Twc.rotatePitch(+rvel) - change = True - if draw.KEY_S: - self.Twc.rotatePitch(-rvel) - change = True - if draw.KEY_Q: - self.Twc.rotateRoll(-rvel) - change = True - if draw.KEY_E: - self.Twc.rotateRoll(+rvel) - change = True - return change - - -class CameraNerf(CameraBase): - """Camera class with NeRF functionalities""" - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - # Matrix to convert from vidar to NeRF coordinate system - self.convert_matrix = torch.tensor( - [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], - dtype=torch.float32, - ).unsqueeze(0) - - @staticmethod - def from_list(cams): - """Get a CameraNerf object from a list of cameras""" - K = torch.cat([cam.K for cam in cams], 0) - Twc = torch.cat([cam.Twc.T for cam in cams], 0) - return CameraNerf(K=K, Twc=Twc, hw=cams[0].hw) - - @staticmethod - def from_dict(K, hw, Twc=None): - """Get a CameraNerf object from a dictionary of cameras""" - return {key: CameraNerf(K=K[0], hw=hw[0], Twc=val) for key, val in Twc.items()} - - def switch(self): - """Convert from vidar to NeRF coordinate system""" - T = self.convert_matrix.to(self.device) - Twc = T @ self.Twc.T @ T - return type(self)(K=self.K, Twc=Twc, hw=self.hw) - - def bwd(self): - """Go from NeRF to vidar coordinate system""" - T = self.convert_matrix.to(self.device) - Tcw = T @ self.Twc.T @ T - return type(self)(K=self.K, Tcw=Tcw, hw=self.hw) - - def fwd(self): - """Go to vidar from NeRF coordinate system""" - T = self.convert_matrix.to(self.device) - Twc = T @ self.Tcw.T @ T - return type(self)(K=self.K, Twc=Twc, hw=self.hw) - - def look_at(self, at, up=torch.Tensor([0, 1, 0])): - """Point a camera to a particular 3D point""" - - eps = 1e-5 - eye = self.Tcw.T[:, :3, -1] - - at = at.unsqueeze(0) - up = up.unsqueeze(0).to(at.device) - up /= up.norm(dim=-1, keepdim=True) + eps - - z_axis = at - eye - z_axis /= z_axis.norm(dim=-1, keepdim=True) + eps - - up = up.expand(z_axis.shape) - x_axis = torch.cross(up, z_axis) - x_axis /= x_axis.norm(dim=-1, keepdim=True) + eps - - y_axis = torch.cross(z_axis, x_axis) - y_axis /= y_axis.norm(dim=-1, keepdim=True) + eps - - R = torch.stack((x_axis, y_axis, z_axis), dim=-1) - - Tcw = self.Tcw - Tcw.T[:, :3, :3] = R - self.Twc = Tcw.inverse() - - def get_origin(self, flatten=False): - """Get camera origin""" - orig = self.Tcw.T[:, :3, -1].view(len(self), 3, 1, 1).repeat(1, 1, *self.hw) - if flatten: - orig = orig.reshape(len(self), 3, -1).permute(0, 2, 1) - return orig - - def get_viewdirs(self, normalize=False, flatten=False, to_world=False): - """Get camera viewing rays""" - ones = torch.ones((len(self), 1, *self.hw), dtype=self.dtype, device=self.device) - rays = self.reconstruct_depth_map(ones, to_world=False) - if normalize: - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - if to_world: - rays = self.to_world(rays).reshape(len(self), 3, *self.hw) - if flatten: - rays = rays.reshape(len(self), 3, -1).permute(0, 2, 1) - return rays - - def get_render_rays(self, near=None, far=None, n_rays=None, gt=None): - """Get camera render rays""" - b = len(self) - - ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) - - rays = self.reconstruct_depth_map(ones, to_world=False) - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - - rays[:, 1] = - rays[:, 1] - rays[:, 2] = - rays[:, 2] - - orig = self.pose[:, :3, -1].view(b, 3, 1, 1).repeat(1, 1, *self.hw) - rays = self.no_translation().inverted_pose().to_world(rays).reshape(b, 3, *self.hw) - - info = [orig, rays] - if near is not None: - info = info + [near * ones] - if far is not None: - info = info + [far * ones] - if gt is not None: - info = info + [gt] - - rays = torch.cat(info, 1) - rays = rays.permute(0, 2, 3, 1).reshape(b, -1, rays.shape[1]) - - if n_rays is not None: - idx = torch.randint(0, self.n_pixels, (n_rays,)) - rays = rays[:, idx, :] - - return rays - - def get_plucker(self): - """Get camera plucker rays""" - b = len(self) - ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) - rays = self.reconstruct_depth_map(ones, to_world=False) - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - orig = self.Tcw.T[:, :3, -1].view(b, 3, 1, 1).repeat(1, 1, *self.hw) - - orig = orig.view(1, 3, -1).permute(0, 2, 1) - rays = rays.view(1, 3, -1).permute(0, 2, 1) - - cross = torch.cross(orig, rays, dim=-1) - plucker = torch.cat((rays, cross), dim=-1) - - return plucker - - def project_pointcloud(self, pcl_src, rgb_src, thr=1): - """Project pointcloud to the image plane""" - - if rgb_src.dim() == 4: - rgb_src = rgb_src.view(*rgb_src.shape[:2], -1) - - # Get projected coordinates and depth values - uv_all, z_all = self.project_points(pcl_src, return_z=True, from_world=True) - - rgbs_tgt, depths_tgt = [], [] - - b = pcl_src.shape[0] - for i in range(b): - uv, z = uv_all[i].reshape(-1, 2), z_all[i].reshape(-1, 1) - - # Remove out-of-bounds coordinates and points behind the camera - idx = (uv[:, 0] >= -1) & (uv[:, 0] <= 1) & \ - (uv[:, 1] >= -1) & (uv[:, 1] <= 1) & (z[:, 0] > 0.0) - - # Unormalize and stack coordinates for scatter operation - uv = (unnorm_pixel_grid(uv[idx], self.hw)).round().long() - uv = uv[:, 0] + uv[:, 1] * self.hw[1] - - # Min scatter operation (only keep the closest depth) - depth_tgt = 1e10 * torch.ones((self.hw[0] * self.hw[1], 1), device=pcl_src.device) - depth_tgt, argmin = scatter_min(src=z[idx], index=uv.unsqueeze(1), dim=0, out=depth_tgt) - depth_tgt[depth_tgt == 1e10] = 0. - - num_valid = (depth_tgt > 0).sum() - if num_valid > thr: - - # Substitute invalid values with zero - invalid = argmin == argmin.max() - argmin[invalid] = 0 - rgb_tgt = rgb_src[i].permute(1, 0)[idx][argmin] - rgb_tgt[invalid] = -1 - - else: - - rgb_tgt = -1 * torch.ones(1, self.n_pixels, 3, device=self.device, dtype=self.dtype) - - # Reshape outputs - rgb_tgt = rgb_tgt.reshape(1, self.hw[0], self.hw[1], 3).permute(0, 3, 1, 2) - depth_tgt = depth_tgt.reshape(1, 1, self.hw[0], self.hw[1]) - - rgbs_tgt.append(rgb_tgt) - depths_tgt.append(depth_tgt) - - rgb_tgt = torch.cat(rgbs_tgt, 0) - depth_tgt = torch.cat(depths_tgt, 0) - - return rgb_tgt, depth_tgt - - def reconstruct_depth_map_rays(self, depth, to_world=False): - """Reconstruct 3D points from depth map""" - if depth is None: - return None - b, _, h, w = depth.shape - rays = self.get_viewdirs(normalize=True, to_world=False) - points = (rays * depth).view(b, 3, -1) - if to_world and self.Tcw is not None: - points = self.Tcw * points - return points.view(b, 3, h, w) - - -class CameraPinhole(CameraBase): - """Pinhole camera model""" - def __init__(self, K, *args, **kwargs): - # Intrinsics - if same_shape(K.shape[-2:], (3, 3)): - self._K = torch.eye(4, dtype=K.dtype, device=K.device).repeat(K.shape[0], 1, 1) - self._K[:, :3, :3] = K - else: - self._K = K - super().__init__(*args, **kwargs) - - self.convert_matrix = torch.tensor( - [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]], - dtype=torch.float32, - ).unsqueeze(0) - - @staticmethod - def from_list(cams): - """Create a single camera from a list of cameras""" - K = torch.cat([cam.K for cam in cams], 0) - Twc = torch.cat([cam.Twc.T for cam in cams], 0) - return CameraPinhole(K=K, Twc=Twc, hw=cams[0].hw) - - @staticmethod - def from_dict(K, hw, Twc=None, broken=False): - """Create a single camera from a dictionary of cameras""" - if broken: - return {key: CameraPinhole( - K=K[key] if key in K else K[(0, key[1])], - hw=hw[key], Twc=val - ) for key, val in Twc.items()} - else: - return {key: CameraPinhole( - K=K[key] if key in K else K[0], - hw=hw[key], Twc=val - ) for key, val in Twc.items()} - - @property - def fx(self): - """Focal length in x direction""" - return self._K[:, 0, 0] - - @property - def fy(self): - """Focal length in y direction""" - return self._K[:, 1, 1] - - @property - def cx(self): - """Principal point in x direction""" - return self._K[:, 0, 2] - - @property - def cy(self): - """Principal point in y direction""" - return self._K[:, 1, 2] - - @property - def fxy(self): - """Focal length in x and y direction""" - return torch.tensor([self.fx, self.fy], dtype=self.dtype, device=self.device) - - @property - def cxy(self): - """Principal point in x and y direction""" - return torch.tensor([self.cx, self.cy], dtype=self.dtype, device=self.device) - - @property - def invK(self): - """Inverse of camera intrinsics""" - return invert_intrinsics(self._K) - - def offset_start(self, start): - """Offset the principal point""" - new_cam = self.clone() - if is_seq(start): - new_cam.K[:, 0, 2] -= start[1] - new_cam.K[:, 1, 2] -= start[0] - else: - start = start.to(self.device) - new_cam.K[:, 0, 2] -= start[:, 1] - new_cam.K[:, 1, 2] -= start[:, 0] - return new_cam - - def scaled(self, scale_factor): - """Scale the camera intrinsics""" - if scale_factor is None or scale_factor == 1: - return self - if is_seq(scale_factor): - if len(scale_factor) == 4: - scale_factor = scale_factor[-2:] - scale_factor = [float(scale_factor[i]) / float(self._hw[i]) for i in range(2)] - else: - scale_factor = [scale_factor] * 2 - return type(self)( - K=scale_intrinsics(self._K, scale_factor), - hw=[int(self._hw[i] * scale_factor[i]) for i in range(len(self._hw))], - Twc=self._Twc - ) - - def lift(self, grid): - """Lift a grid of points to 3D""" - return torch.matmul(self.invK[:, :3, :3], grid) - - def unlift(self, points, from_world=True, euclidean=False): - """Unlift a grid of points to 2D""" - projected = torch.matmul(self.Pwc(from_world), cat_channel_ones(points, 1)) - coords = projected[:, :2] / (projected[:, 2].unsqueeze(1) + 1e-7) - if not euclidean: - depth = projected[:, 2] - else: - points = self.from_world(points) if from_world else points - depth = torch.linalg.vector_norm(points, dim=1, keepdim=True) - return coords, depth - - def switch(self): - """Switch the camera from world to camera coordinates""" - T = self.convert_matrix.to(self.device) - Twc = T @ self.Twc.T @ T - return type(self)(K=self.K, Twc=Twc, hw=self.hw) - - def bwd(self): - """Switch the camera from world to camera coordinates""" - T = self.convert_matrix.to(self.device) - Tcw = T @ self.Twc.T @ T - return type(self)(K=self.K, Tcw=Tcw, hw=self.hw) - - def fwd(self): - """Switch the camera from world to camera coordinates""" - T = self.convert_matrix.to(self.device) - Twc = T @ self.Tcw.T @ T - return type(self)(K=self.K, Twc=Twc, hw=self.hw) - - def up(self): - """Get the up vector of the camera""" - up = self.clone() - up.Twc.translateUp(1) - return up.get_center() - self.get_center() - - def forward(self): - """Get the forward vector of the camera""" - forward = self.clone() - forward.Twc.translateForward(1) - return forward.get_center() - self.get_center() - - def look_at(self, at, up=None): - """Look at a point""" - - if up is None: - up = self.up() - - eps = 1e-5 - eye = self.get_center() - - at = at.unsqueeze(0) - up = up.unsqueeze(0).to(at.device) - up /= up.norm(dim=-1, keepdim=True) + eps - - z_axis = at - eye - z_axis /= z_axis.norm(dim=-1, keepdim=True) + eps - - up = up.expand(z_axis.shape) - x_axis = torch.cross(up, z_axis) - x_axis /= x_axis.norm(dim=-1, keepdim=True) + eps - - y_axis = torch.cross(z_axis, x_axis) - y_axis /= y_axis.norm(dim=-1, keepdim=True) + eps - - R = torch.stack((x_axis, y_axis, z_axis), dim=-1) - - Tcw = self.Tcw - Tcw.T[:, :3, :3] = R - self.Twc = Tcw.inverse() - - def get_center(self): - """Get the center of the camera""" - return self.Tcw.T[:, :3, -1] - - def get_origin(self, flatten=False): - """Get the origin of the camera""" - orig = self.get_center().view(len(self), 3, 1, 1).repeat(1, 1, *self.hw) - if flatten: - orig = orig.reshape(len(self), 3, -1).permute(0, 2, 1) - return orig - - def get_viewdirs(self, normalize=None, to_world=None, flatten=False, reflect=False, grid=None): - """Get the view directions of the camera""" - - ones = torch.ones((len(self), 1, *self.hw), dtype=self.dtype, device=self.device) - rays = self.reconstruct_depth_map(ones, to_world=False, grid=grid) - - if reflect: - rays[:, 1] = - rays[:, 1] - rays[:, 2] = - rays[:, 2] - - if normalize is True or normalize == 'unit': - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - if normalize == 'plane': - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - rays = rays / rays[:, [2]] - - if to_world: - # rays = self.to_world(rays).reshape(len(self), 3, *self.hw) - rays = self.no_translation().to_world(rays).reshape(len(self), 3, *self.hw) - - if flatten: - rays = rays.reshape(len(self), 3, -1).permute(0, 2, 1) - - return rays - - def get_render_rays(self, near=None, far=None, n_rays=None, gt=None): - """Get the rays for rendering""" - - b = len(self) - - ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) - - rays = self.reconstruct_depth_map(ones, to_world=False) - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - - rays[:, 1] = - rays[:, 1] - rays[:, 2] = - rays[:, 2] - - orig = self.pose[:, :3, -1].view(b, 3, 1, 1).repeat(1, 1, *self.hw) - rays = self.no_translation().inverted_pose().to_world(rays).reshape(b, 3, *self.hw) - - info = [orig, rays] - if near is not None: - info = info + [near * ones] - if far is not None: - info = info + [far * ones] - if gt is not None: - info = info + [gt] - - rays = torch.cat(info, 1) - rays = rays.permute(0, 2, 3, 1).reshape(b, -1, rays.shape[1]) - - if n_rays is not None: - idx = torch.randint(0, self.n_pixels, (n_rays,)) - rays = rays[:, idx, :] - - return rays - - def get_plucker(self): - """Get the plucker coordinates of the camera""" - - b = len(self) - ones = torch.ones((b, 1, *self.hw), dtype=self.dtype, device=self.device) - rays = self.reconstruct_depth_map(ones, to_world=False) - rays = rays / torch.norm(rays, dim=1).unsqueeze(1) - orig = self.get_center().view(b, 3, 1, 1).repeat(1, 1, *self.hw) - - orig = orig.view(1, 3, -1).permute(0, 2, 1) - rays = rays.view(1, 3, -1).permute(0, 2, 1) - - cross = torch.cross(orig, rays, dim=-1) - plucker = torch.cat((rays, cross), dim=-1) - - return plucker - - def project_pointcloud(self, pcl_src, rgb_src=None, thr=1): - """Project a pointcloud to the image plane""" - - if rgb_src is not None and rgb_src.dim() == 4: - rgb_src = rgb_src.view(*rgb_src.shape[:2], -1) - - # Get projected coordinates and depth values - uv_all, z_all = self.project_points(pcl_src, return_z=True, from_world=True) - - rgbs_tgt, depths_tgt = [], [] - - b = pcl_src.shape[0] - for i in range(b): - uv, z = uv_all[i].reshape(-1, 2), z_all[i].reshape(-1, 1) - - # Remove out-of-bounds coordinates and points behind the camera - idx = (uv[:, 0] >= -1) & (uv[:, 0] <= 1) & \ - (uv[:, 1] >= -1) & (uv[:, 1] <= 1) & (z[:, 0] > 0.0) - - # Unormalize and stack coordinates for scatter operation - uv = (unnorm_pixel_grid(uv[idx], self.hw)).round().long() - uv = uv[:, 0] + uv[:, 1] * self.hw[1] - - # Min scatter operation (only keep the closest depth) - depth_tgt = 1e10 * torch.ones((self.hw[0] * self.hw[1], 1), device=pcl_src.device) - depth_tgt, argmin = scatter_min(src=z[idx], index=uv.unsqueeze(1), dim=0, out=depth_tgt) - depth_tgt[depth_tgt == 1e10] = 0. - - num_valid = (depth_tgt > 0).sum() - if num_valid > thr: - - # Substitute invalid values with zero - invalid = argmin == argmin.max() - argmin[invalid] = 0 - if rgb_src is not None: - rgb_tgt = rgb_src[i].permute(1, 0)[idx][argmin] - rgb_tgt[invalid] = 0 - - else: - - if rgb_src is not None: - rgb_tgt = -1 * torch.ones(1, self.n_pixels, 3, device=self.device, dtype=self.dtype) - - # Reshape outputs - depth_tgt = depth_tgt.reshape(1, 1, self.hw[0], self.hw[1]) - depths_tgt.append(depth_tgt) - - if rgb_src is not None: - rgb_tgt = rgb_tgt.reshape(1, self.hw[0], self.hw[1], 3).permute(0, 3, 1, 2) - rgbs_tgt.append(rgb_tgt) - - if rgb_src is not None: - rgb_tgt = torch.cat(rgbs_tgt, 0) - else: - rgb_tgt = None - - depth_tgt = torch.cat(depths_tgt, 0) - - return rgb_tgt, depth_tgt - - def reconstruct_depth_map_rays(self, depth, to_world=False): - """Reconstruct a depth map from a depth image""" - if depth is None: - return None - b, _, h, w = depth.shape - rays = self.get_viewdirs(normalize=True, to_world=False) - points = (rays * depth).view(b, 3, -1) - if to_world and self.Tcw is not None: - points = self.Tcw * points - return points.view(b, 3, h, w) - - def to_ndc_rays(self, rays_o, rays_d, near=1.0): - """Transform rays from camera coordinates to NDC coordinates""" - H, W = self.hw - focal = self.fy[0].item() - - t = - (near + rays_o[..., 2]) / rays_d[..., 2] - rays_o = rays_o + t[..., None] * rays_d - - o0 = -1. / (W / (2. * focal)) * rays_o[..., 0] / rays_o[..., 2] - o1 = -1. / (H / (2. * focal)) * rays_o[..., 1] / rays_o[..., 2] - o2 = 1. + 2. * near / rays_o[..., 2] - - d0 = -1. / (W / (2. * focal)) * (rays_d[..., 0] / rays_d[..., 2] - rays_o[..., 0] / rays_o[..., 2]) - d1 = -1. / (H / (2. * focal)) * (rays_d[..., 1] / rays_d[..., 2] - rays_o[..., 1] / rays_o[..., 2]) - d2 = -2. * near / rays_o[..., 2] - - rays_o = torch.stack([o0, o1, o2], -1) - rays_d = torch.stack([d0, d1, d2], -1) - - return rays_o, rays_d - - def from_ndc(self, xyz_ndc): - """Transform points from NDC coordinates to camera coordinates""" - wh = self.wh - fx = fy = self.fy[0].item() - - z_e = 2. / (xyz_ndc[..., 2:3] - 1. + 1e-6) - x_e = - xyz_ndc[..., 0:1] * z_e * wh[0] / (2. * fx) - y_e = - xyz_ndc[..., 1:2] * z_e * wh[1] / (2. * fy) - - return torch.cat([x_e, y_e, z_e], -1) - - -class PerceiverAbstractPositionEncoding(nn.Module, metaclass=abc.ABCMeta): - """Perceiver abstract position encoding.""" - - @property - @abc.abstractmethod - def num_dimensions(self) -> int: - raise NotImplementedError - - @abc.abstractmethod - def output_size(self, *args, **kwargs) -> int: - raise NotImplementedError - - @abc.abstractmethod - def forward(self, batch_size, pos): - raise NotImplementedError - - -class PerceiverTrainablePositionEncoding(PerceiverAbstractPositionEncoding): - """Trainable position encoding.""" - - def __init__(self, index_dims, num_channels=128): - super().__init__() - self._num_channels = num_channels - self._index_dims = index_dims - index_dim = np.prod(index_dims) - self.position_embeddings = nn.Parameter( - torch.randn(index_dim, num_channels)) - - @property - def num_dimensions(self) -> int: - if isinstance(self._index_dims, int): - return 1 - return len(self._index_dims) - - def output_size(self, *args, **kwargs) -> int: - return self._num_channels - - def forward(self, batch_size): - position_embeddings = self.position_embeddings - - if batch_size is not None: - position_embeddings = position_embeddings.expand( - batch_size, -1, -1) - return position_embeddings - -def build_linear_positions(index_dims, output_range=(-1.0, 1.0)): - """ - Generate an array of position indices for an N-D input array. - - Args: - index_dims (`List[int]`): - The shape of the index dimensions of the input array. - output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`): - The min and max values taken by each input index dimension. - - Returns: - `torch.FloatTensor` of shape `(index_dims[0], index_dims[1], .., index_dims[-1], N)`. - """ - - def _linspace(n_xels_per_dim): - return torch.linspace(start=output_range[0], end=output_range[1], steps=n_xels_per_dim, dtype=torch.float32) - - dim_ranges = [_linspace(n_xels_per_dim) for n_xels_per_dim in index_dims] - array_index_grid = torch.meshgrid(*dim_ranges) - - return torch.stack(array_index_grid, dim=-1) - -def _check_or_build_spatial_positions(pos, index_dims, batch_size): - """ - Checks or builds spatial position features (x, y, ...). - - Args: - pos (`torch.FloatTensor`): - None, or an array of position features. If None, position features are built. Otherwise, their size is checked. - index_dims (`List[int]`): - An iterable giving the spatial/index size of the data to be featurized. - batch_size (`int`): - The batch size of the data to be featurized. - - Returns: - `torch.FloatTensor` of shape `(batch_size, prod(index_dims))` an array of position features. - """ - if pos is None: - pos = build_linear_positions(index_dims) - pos = torch.broadcast_to(pos[None], (batch_size,) + pos.shape) - pos = torch.reshape(pos, [batch_size, np.prod(index_dims), -1]) - # else: - # # Just a warning label: you probably don't want your spatial features to - # # have a different spatial layout than your pos coordinate system. - # # But feel free to override if you think it'll work! - # if pos.shape[-1] != len(index_dims): - # raise ValueError("Spatial features have the wrong number of dimensions.") - return pos - -def generate_fourier_features(pos, num_bands, max_resolution=(224, 224), concat_pos=True, sine_only=False): - """ - Generate a Fourier frequency position encoding with linear spacing. - - Args: - pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`): - The Tensor containing the position of n points in d dimensional space. - num_bands (`int`): - The number of frequency bands (K) to use. - max_resolution (`Tuple[int]`, *optional*, defaults to (224, 224)): - The maximum resolution (i.e. the number of pixels per dim). A tuple representing resolution for each dimension. - concat_pos (`bool`, *optional*, defaults to `True`): - Whether to concatenate the input position encoding to the Fourier features. - sine_only (`bool`, *optional*, defaults to `False`): - Whether to use a single phase (sin) or two (sin/cos) for each frequency band. - - Returns: - `torch.FloatTensor` of shape `(batch_size, sequence_length, n_channels)`: The Fourier position embeddings. If - `concat_pos` is `True` and `sine_only` is `False`, output dimensions are ordered as: [dim_1, dim_2, ..., dim_d, - sin(pi*f_1*dim_1), ..., sin(pi*f_K*dim_1), ..., sin(pi*f_1*dim_d), ..., sin(pi*f_K*dim_d), cos(pi*f_1*dim_1), - ..., cos(pi*f_K*dim_1), ..., cos(pi*f_1*dim_d), ..., cos(pi*f_K*dim_d)], where dim_i is pos[:, i] and f_k is the - kth frequency band. - """ - - batch_size = pos.shape[0] - device = pos.device - - min_freq = 1.0 - # Nyquist frequency at the target resolution: - freq_bands = torch.stack( - [torch.linspace(start=min_freq, end=res / 2, steps=num_bands, device=device) for res in max_resolution], dim=0 - ) - - # Get frequency bands for each spatial dimension. - # Output is size [n, d * num_bands] - per_pos_features = pos[0, :, :][:, :, None] * freq_bands[None, :, :] - per_pos_features = torch.reshape( - per_pos_features, [-1, np.prod(per_pos_features.shape[1:])]) - - if sine_only: - # Output is size [n, d * num_bands] - per_pos_features = torch.sin(np.pi * (per_pos_features)) - else: - # Output is size [n, 2 * d * num_bands] - per_pos_features = torch.cat( - [torch.sin(np.pi * per_pos_features), torch.cos(np.pi * per_pos_features)], dim=-1 - ) - # Concatenate the raw input positions. - if concat_pos: - # Adds d bands to the encoding. - per_pos_features = torch.cat( - [pos, per_pos_features.expand(batch_size, -1, -1)], dim=-1) - return per_pos_features - -class PerceiverFourierPositionEncoding(PerceiverAbstractPositionEncoding): - """Fourier (Sinusoidal) position encoding.""" - - def __init__(self, num_bands, max_resolution, concat_pos=True, sine_only=False): - super().__init__() - self.num_bands = num_bands - self.max_resolution = max_resolution - self.concat_pos = concat_pos - self.sine_only = sine_only - - @property - def num_dimensions(self) -> int: - return len(self.max_resolution) - - def output_size(self): - """Returns size of positional encodings last dimension.""" - num_dims = len(self.max_resolution) - encoding_size = self.num_bands * num_dims - if not self.sine_only: - encoding_size *= 2 - if self.concat_pos: - encoding_size += self.num_dimensions - - return encoding_size - - def forward(self, index_dims, batch_size, device, pos=None): - pos = _check_or_build_spatial_positions(pos, index_dims, batch_size) - fourier_pos_enc = generate_fourier_features( - pos, - num_bands=self.num_bands, - max_resolution=self.max_resolution, - concat_pos=self.concat_pos, - sine_only=self.sine_only, - ).to(device) - return fourier_pos_enc - - -def build_position_encoding( - position_encoding_type, - out_channels=None, - project_pos_dim=-1, - trainable_position_encoding_kwargs=None, - fourier_position_encoding_kwargs=None, -): - """ - Builds the position encoding. - - Args: - - - out_channels: refers to the number of channels of the position encodings. - - project_pos_dim: if specified, will project the position encodings to this dimension. - - """ - - if position_encoding_type == "trainable": - if not trainable_position_encoding_kwargs: - raise ValueError( - "Make sure to pass trainable_position_encoding_kwargs") - output_pos_enc = PerceiverTrainablePositionEncoding( - **trainable_position_encoding_kwargs) - elif position_encoding_type == "fourier": - # We don't use the index_dims argument, as this is only known during the forward pass - if not fourier_position_encoding_kwargs: - raise ValueError( - "Make sure to pass fourier_position_encoding_kwargs") - output_pos_enc = PerceiverFourierPositionEncoding( - **fourier_position_encoding_kwargs) - else: - raise ValueError( - f"Unknown position encoding type: {position_encoding_type}.") - - # Optionally, project the position encoding to a target dimension: - positions_projection = nn.Linear( - out_channels, project_pos_dim) if project_pos_dim > 0 else nn.Identity() - - return output_pos_enc, positions_projection - -import torchvision.models as models -RESNET_VERSIONS = { # general resblock - 18: models.resnet18, - 34: models.resnet34, - 50: models.resnet50, - 101: models.resnet101, - 152: models.resnet152 -} - -RESNET_WEIGHTS = 'IMAGENET1K_V1' - -class ResNetMultiInput(models.ResNet, ABC): - def __init__(self, block_type, block_channels, num_input_rgb): - """Multi-input ResNet model - - Parameters - ---------- - block_type : nn.Module - Residual block type - block_channels : int - Number of channels in each block - num_input_rgb : int - Number of input images - """ - super().__init__(block_type, block_channels) - - self.inplanes = 64 - self.conv1 = nn.Conv2d( - num_input_rgb * 3, 64, kernel_size=7, stride=2, padding=3, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.relu = nn.ReLU(inplace=True) - - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(block_type, 64, block_channels[0]) - self.layer2 = self._make_layer(block_type, 128, block_channels[1], stride=2) - self.layer3 = self._make_layer(block_type, 256, block_channels[2], stride=2) - self.layer4 = self._make_layer(block_type, 512, block_channels[3], stride=2) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - -def resnet_multi_input(num_layers, num_input_rgb, pretrained=True): - """Generates a multi-input ResNet model - - Parameters - ---------- - num_layers : int - Number of layers in the ResNet - num_input_rgb : int - Number of input images - pretrained : bool, optional - True is pre-trained ImageNet weights are used, by default True - - Returns - ------- - nn.Module - Multi-input ResNet model - """ - assert num_layers in [18, 50], 'Can only run with 18 or 50 layer resnet' - - block_channels = { - 18: [2, 2, 2, 2], - 50: [3, 4, 6, 3] - }[num_layers] - - block_type = { - 18: models.resnet.BasicBlock, - 50: models.resnet.Bottleneck - }[num_layers] - - model = ResNetMultiInput(block_type, block_channels, num_input_rgb) - - if pretrained: - loaded = RESNET_VERSIONS[num_layers](weights=RESNET_WEIGHTS).state_dict() - loaded['conv1.weight'] = torch.cat( - [loaded['conv1.weight']] * num_input_rgb, 1) / num_input_rgb - model.load_state_dict(loaded) - - return model - -''' -appends spatial co-ordinates to the feature map -the range of the spatial co-ordinates can be defined as [min_val, max_val] - -Arguments: - feature_maps : input feature maps (N,C,H,W) - min_val : minimum value of spatial co-ordinate - max_val : maximum value of spatial co-ordinate - -Returns: - feature_maps : input feature maps concatenated with spatial co-ordinates (N,C+2,H,W) -''' -def append_spatial_location(feature_maps, min_val=-1, max_val=1): - batch_size, channels, height, width = feature_maps.data.shape - - # arange spatial co-ordiantes for height - h_array = Variable(torch.stack([torch.linspace(min_val, max_val, height)]*width, dim=1).cuda()) - # arange spatial co-ordinates for width - w_array = Variable(torch.stack([torch.linspace(min_val, max_val, width)]*height, dim=0).cuda()) - # stack the h_array and w_array to get the spatial co-ordinates at each location - spatial_array = torch.stack([h_array, w_array], dim=0) - # expand the spatial co-ordinates across the batch size - spatial_array = torch.stack([spatial_array]*batch_size, dim=0) - # concatenate feature maps with spatial co-ordinates - feature_maps = torch.cat([feature_maps, spatial_array], dim=1) - - return feature_maps - - -class DynamicFC(nn.Module): - - def __init__(self): - super(DynamicFC, self).__init__() - - self.in_planes = None - self.out_planes = None - self.activation = None - self.use_bias = None - - self.activation = None - self.linear = None - - ''' - Arguments: - embedding : input to the MLP (N,*,C) - out_planes : total ch dddannels in the output - activation : 'relu' or 'tanh' - use_bias : True / False - - Returns: - out : output of the MLP (N,*,out_planes) - ''' - def forward(self, embedding, out_planes=1, activation=None, use_bias=True): - - self.in_planes = embedding.data.shape[-1] - self.out_planes = out_planes - self.use_bias = use_bias - - self.linear = nn.Linear(self.in_planes, self.out_planes, bias=use_bias).cuda() - if activation == 'relu': - self.activation = nn.ReLU(inplace=True).cuda() - elif activation == 'tanh': - self.activation = nn.Tanh().cuda() - - for m in self.modules(): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform(m.weight) - if self.use_bias: - nn.init.constant(m.bias, 0.1) - - out = self.linear(embedding) - if self.activation is not None: - out = self.activation(out) - - return out - - -''' -A very basic FiLM layer with a linear transformation from context to FiLM parameters -''' -class FilmLayer(nn.Module): - - def __init__(self, feature_size): - super(FilmLayer, self).__init__() - - self.batch_size = None - self.channels = None - self.height = None - self.width = None - self.feature_size = feature_size - - # self.fc = DynamicFC().cuda() - self.fc = nn.Linear(768, self.feature_size * 2).cuda() # Hardcode language embedding shape for now - - ''' - Arguments: - feature_maps : input feature maps (N,C,H,W) - context : context embedding (N,L) - - Return: - output : feature maps modulated with betas and gammas (FiLM parameters) - ''' - def forward(self, feature_maps, context): - self.batch_size, self.channels, self.height, self.width = feature_maps.data.shape - # FiLM parameters needed for each channel in the feature map - # hence, feature_size defined to be same as no. of channels - # self.feature_size = feature_maps.data.shape[1] - - # import pdb; pdb.set_trace() - - # linear transformation of context to FiLM parameters - # film_params = self.fc(context, out_planes=2*self.feature_size, activation=None) - film_params = self.fc(context) - - # stack the FiLM parameters across the spatial dimension - film_params = torch.stack([film_params]*self.height, dim=2) - film_params = torch.stack([film_params]*self.width, dim=3) - - # slice the film_params to get betas and gammas - gammas = film_params[:, :self.feature_size, :, :] - betas = film_params[:, self.feature_size:, :, :] - - # modulate the feature map with FiLM parameters - output = (1 + gammas) * feature_maps + betas - - return output - -''' -Modualted ResNet block with FiLM layer -''' -class FilmResBlock(nn.Module): - - ''' - Arguments: - in_channels : no. of channels in the input - feature_size : feature size required - spatial_location : whether to append spatial co-ordinates - ''' - def __init__(self, in_channels, feature_size, spatial_location=True): - super(FilmResBlock, self).__init__() - - self.spatial_location = spatial_location - self.feature_size = feature_size - self.in_channels = in_channels - # add 2 channels for spatial location - if spatial_location: - self.in_channels += 2 - - # modulated resnet block with FiLM layer - self.conv1 = nn.Conv2d(self.in_channels, self.feature_size, kernel_size=1) - self.relu1 = nn.ReLU() - self.conv2 = nn.Conv2d(self.feature_size, self.feature_size, kernel_size=3, padding=1) - self.bn2 = nn.BatchNorm2d(self.feature_size) - self.film_layer = FilmLayer(self.feature_size).cuda() - self.relu2 = nn.ReLU() - - ''' - Arguments: - feature_maps : input feature maps (N,C,H,W) - context : context embedding (N,L) - - Returns: - out : input feature maps modulated with FiLM parameters computed using context embedding - ''' - def forward(self, feature_maps, context): - - if self.spatial_location: - feature_maps = append_spatial_location(feature_maps) - - conv1 = self.conv1(feature_maps) - out1 = self.relu1(conv1) - - conv2 = self.conv2(out1) - bn = self.bn2(conv2) - film_out = self.film_layer(bn, context) - out2 = self.relu2(film_out) - - # residual connection - out = out1 + out2 - - return out - - -class ResNetFiLMFeaturizer(nn.Module, ABC): - """ - Single-frame depth encoder - - Parameters - ---------- - cfg : Config - Configuration with parameters - """ - def __init__(self, version=18, num_rgb_in=1, pretrained=False): - super().__init__() - - self.num_ch_enc = np.array([64, 64, 128, 256, 512]) - # features = [] - - assert version in RESNET_VERSIONS, f'Invalid ResNet version: {version}' - - if num_rgb_in > 1: - self.encoder = resnet_multi_input( - version, num_rgb_in, pretrained) - else: - self.encoder = RESNET_VERSIONS[version]( - weights=None if not pretrained else RESNET_WEIGHTS) - - if version > 34: - self.num_ch_enc[1:] *= 4 - - self.film1 = FilmLayer(self.num_ch_enc[1]).cuda() - self.film2 = FilmLayer(self.num_ch_enc[2]).cuda() - self.film3 = FilmLayer(self.num_ch_enc[3]).cuda() - self.film4 = FilmLayer(self.num_ch_enc[4]).cuda() - - def forward(self, input_image, context): - """Network forward pass""" - - features = [] - x = (input_image - 0.45) / 0.225 - x = self.encoder.conv1(x) - x = self.encoder.bn1(x) - - features.clear() - features.append(self.encoder.relu(x)) - if context is None: - features.append(self.encoder.layer1(self.encoder.maxpool(features[-1]))) - features.append(self.encoder.layer2(features[-1])) - features.append(self.encoder.layer3(features[-1])) - features.append(self.encoder.layer4(features[-1])) - else: - features.append(self.film1(self.encoder.layer1(self.encoder.maxpool(features[-1])), context)) - features.append(self.film2(self.encoder.layer2(features[-1]), context)) - features.append(self.film3(self.encoder.layer3(features[-1]), context)) - features.append(self.film4(self.encoder.layer4(features[-1]), context)) - - return features - - -class ResNetFiLMEncoder(nn.Module, ABC): - """ - Single-frame depth encoder - - Parameters - ---------- - cfg : Config - Configuration with parameters - """ - def __init__(self, pretrained=False, input_channel=3): - super().__init__() - self.resnet_film_featurizer = ResNetFiLMFeaturizer(version=50, pretrained=pretrained, num_rgb_in=int(input_channel / 3)) - - def forward(self, image, lang_emb): - """Network forward pass""" - film_features = self.resnet_film_featurizer(image, lang_emb)[-1] - film_features_flat = film_features.view(*film_features.shape[:-2], -1).permute(0, 2, 1) - return film_features_flat - - def output_shape(self, input_shape): - return ( (input_shape[1] // 29) * (input_shape[2] // 29), 2048) - - - -class DeFiNeImageCamEncoder(nn.Module, ABC): - def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): - super().__init__() - - """ - pulled these from here: - https://github.com/TRI-ML/vidar/blob/6fdbee65bff2cc94a42aaedd121a2179ac48d441/configs/papers/define/hub_define_temporal.yaml#L21 - """ - num_bands_orig = 20 - num_bands_dirs = 10 - max_resolution_orig = 60 - max_resolution_dirs = 60 - - # whether to use camera information (intrinsics + extrinsics) in the encoder - self.use_cam = use_cam - self.downsample = downsample - - self.image_feature_encoder = ResNetFiLMFeaturizer(version=50, pretrained=pretrained, num_rgb_in=int(input_channel / 3)) - - # Fourier encoding for camera origin - self._fourier_encoding_orig, _ = build_position_encoding( - position_encoding_type='fourier', - fourier_position_encoding_kwargs={ - 'num_bands': num_bands_orig, - 'max_resolution': [max_resolution_orig] * 3, - 'concat_pos': True, - 'sine_only': False, - } - ) - - # Fourier encoding for camera viewing rays - self._fourier_encoding_dirs, _ = build_position_encoding( - position_encoding_type='fourier', - fourier_position_encoding_kwargs={ - 'num_bands': num_bands_dirs, - 'max_resolution': [num_bands_dirs] * 3, - 'concat_pos': True, - 'sine_only': False, - } - ) - - if self.downsample: - # Adding in another Conv2 layer that is applied [H/4, W/4, features] to - # reduce size - self.num_cam_channels = 186 - self.num_image_channels = 3840 # ResNet50 - if self.use_cam: - num_channels = self.num_image_channels + self.num_cam_channels - else: - num_channels = self.num_image_channels - self.finalconv = torch.nn.Conv2d(num_channels, 1024, 2, stride=2) - - - def get_rgb_feat(self, rgb, lang=None, rgb_feat_type="resnet_all"): - """Exract image features""" - if rgb_feat_type == 'convnet': - return { - 'feat': self.image_feature_encoder(rgb, lang) - } - elif rgb_feat_type == 'resnet': - return { - 'feat': self.image_feature_encoder(rgb, lang)[1] - } - elif rgb_feat_type.startswith('resnet_all'): - all_feats = self.image_feature_encoder(rgb, lang) - feats = all_feats[1:] - for i in range(1, len(feats)): - feats[i] = interpolate( - feats[i], size=feats[0], scale_factor=None, mode='bilinear') - if rgb_feat_type.endswith('rgb'): - feats = feats + [interpolate( - rgb, size=feats[0], scale_factor=None, mode='bilinear')] - feat = torch.cat(feats, 1) - return { - 'all_feats': all_feats, - 'feat': feat - } - - - def embeddings(self, data, sources, downsample): - """Compute embeddings for encoder and decoder data""" - if 'rgb' in sources: - assert 'rgb' in data[0].keys() - b = [datum['rgb'].shape[0] for datum in data] - - rgb = torch.cat([datum['rgb'] for datum in data], 0) - - if 'lang' in sources: - lang = torch.cat([datum['lang'] for datum in data], 0) - output_feats = self.get_rgb_feat(rgb, lang) - else: - output_feats = self.get_rgb_feat(rgb) - - feats = torch.split(output_feats['feat'], b) - for i in range(len(data)): - data[i]['feat'] = feats[i] - - encodings = [] - for datum in data: - - encoding = OrderedDict() - - # Camera embeddings - if 'cam' in sources: - assert 'cam' in data[0].keys() - - cam = datum['cam'].scaled(1. / downsample) - orig = cam.get_origin(flatten=True) - - to_world = True # should it be True or False? idk - - if to_world: - dirs = cam.get_viewdirs(normalize=True, flatten=True, to_world=True) - else: - dirs = cam.no_translation().get_viewdirs(normalize=True, flatten=True, to_world=True) - - orig_encodings = self._fourier_encoding_orig( - index_dims=None, pos=orig, batch_size=orig.shape[0], device=orig.device) - dirs_encodings = self._fourier_encoding_dirs( - index_dims=None, pos=dirs, batch_size=dirs.shape[0], device=dirs.device) - - encoding['cam'] = torch.cat([orig_encodings, dirs_encodings], -1) - - # Image embeddings - if 'rgb' in sources: - rgb = datum['feat'] - rgb_flat = rgb.view(*rgb.shape[:-2], -1).permute(0, 2, 1) - encoding['rgb'] = rgb_flat - - # Adding in another Conv2 layer that is applied [H/4, W/4, features] to - # reduce size - if self.downsample: - stacked = torch.cat([val for val in encoding.values()], -1) - st_sh = stacked.shape - stacked = stacked.reshape(st_sh[0], int(np.sqrt(st_sh[1])), int(np.sqrt(st_sh[1])), st_sh[-1]) - stacked = self.finalconv(stacked.permute(0, 3, 1, 2)) - encoding['all'] = stacked.reshape(stacked.shape[0], stacked.shape[1], -1).permute(0, 2, 1) - else: - encoding['all'] = torch.cat([val for val in encoding.values()], -1) - encodings.append(encoding) - - return encodings - - def forward(self, image, extrinsics, intrinsics, lang_emb): - if extrinsics is None or intrinsics is None: - sources = ["rgb"] # camera info not present - data = [{ - "rgb": image, - }] - else: - sources = ["rgb", "cam"] - cam = CameraPinhole( - K=intrinsics, - Twc=Pose(extrinsics).to(image.device), - hw=image.shape[-2:], - ) - data = [{ - "cam": cam, - "rgb": image, - }] - if lang_emb is not None: - sources += ["lang"] - data[0]["lang"] = lang_emb - - emb = self.embeddings( - data, - sources=sources, - downsample=4, - ) - result = torch.cat([emb[i]["all"] for i in range(len(emb))]) - return result - - def output_shape(self, input_shape): - if self.downsample: - # num_cam_channels = 186 - # num_image_channels = 3840 # ResNet18: 512 + 256 + 128 + 64 - num_sptial_feats = int(np.floor((input_shape[1] / 8))) * int(np.floor(input_shape[1] / 8)) - # Updated to have the shapes outputted by the final conv2. - return (num_sptial_feats, 1024) - else: - num_cam_channels = 186 - num_image_channels = 3840 # ResNet18: 512 + 256 + 128 + 64 - num_sptial_feats = int(input_shape[1] / 4 * input_shape[2] / 4) - if self.use_cam: - num_channels = num_image_channels + num_cam_channels - else: - num_channels = num_image_channels - return (num_sptial_feats, num_channels) - - -class VoltronImageCamEncoder(nn.Module, ABC): - def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): - super().__init__() - - from vtx import load - from torchvision import transforms as T - - from torchvision.transforms.functional import InterpolationMode - - device = "cuda" if torch.cuda.is_available() else "cpu" - self.device = device - self.img_transform, self.ctcn = load("ctcn-base+x7", "/mnt/fsx/x-voltron/ctcn-base+x7/ctcn-base+x7.pt", device=device, freeze_backbone=False) - - def forward(self, image, lang_emb): - all_langs = [] - for b in range(len(lang_emb[0])): - for h in range(len(lang_emb)): - all_langs.append(lang_emb[h][b]) - processed_images = torch.stack([self.img_transform(i) for i in image]).to(self.device) - embedding: torch.Tensor = self.ctcn.embed(processed_images, all_langs) - return embedding - - def output_shape(self, input_shape): - return (768) - - -class R3MImageCamEncoder(nn.Module, ABC): - def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): - super().__init__() - - from r3m import load_r3m - from torchvision import transforms as T - - device = "cuda" if torch.cuda.is_available() else "cpu" - self.device = device - - self.r3m = load_r3m("resnet50") # resnet18, resnet34 - self.r3m.train() - self.r3m.to(self.device) - - self.transforms = T.Compose([ - T.ToPILImage(), - T.Resize(256), - T.CenterCrop(224), - T.ToTensor() - ]) - - def forward(self, image, lang_emb): - processed_images = torch.stack([self.transforms(i) for i in image]).to(self.device) - embedding: torch.Tensor = self.r3m(processed_images * 255.0) - return embedding - - def output_shape(self, input_shape): - return (2048,) - -class VC1ImageCamEncoder(nn.Module, ABC): - def __init__(self, pretrained=False, input_coord_conv=False, use_cam=True, downsample=False, input_channel=3): - super().__init__() - - from vc_models.models.vit import model_utils - self.model, self.embd_size, self.model_transforms, self.model_info = model_utils.load_model(model_utils.VC1_BASE_NAME) - self.model.train() - - device = "cuda" if torch.cuda.is_available() else "cpu" - self.device = device - - def forward(self, image, lang_emb): - processed_images = self.model_transforms(image) - embedding: torch.Tensor = self.model(processed_images) - return embedding - - def output_shape(self, input_shape): - return (768,) \ No newline at end of file From a804476261fb9016baf6580f12dd217e103021f1 Mon Sep 17 00:00:00 2001 From: Karl Pertsch Date: Sat, 9 Mar 2024 15:10:38 -0800 Subject: [PATCH 20/43] Update README.md --- README.md | 45 ++++++++++++++++++++++++++++++++++----------- 1 file changed, 34 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 564e7d38..11f42f6e 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,36 @@ # DROID Policy Learning and Evaluation -This repository contains all code for the policy learning and evaluation experiments in DROID(TODO(Karl/Sasha): add link to DROID website), a project aimed at collecting a large-scale dataset in-the-wild robot manipulation dataset for the robot learning resaerch community. This codebase is built as a fork of the popular repository for imitation learning algorithm development, `robomimic`, about which further information can be found here: [**[Homepage]**](https://robomimic.github.io/)   [**[Documentation]**](https://robomimic.github.io/docs/introduction/overview.html)   [**[Study Paper]**](https://arxiv.org/abs/2108.03298)   [**[Study Website]**](https://robomimic.github.io/study/)   [**[ARISE Initiative]**](https://github.com/ARISE-Initiative). +This repository contains code for training and evaluating policies on the DROID(TODO(Karl/Sasha): add link to DROID website) dataset. DROID is a large-scale, in-the-wild robot manipulation dataset. This codebase is built as a fork of [`robomimic`](https://robomimic.github.io/), a popular repository for imitation learning algorithm development. For more information about DROID, please see the following links: [**[Homepage]**](XXX)   [**[Documentation]**](XXX)   [**[Paper]**](XXX)   [**[Dataset Visualizer]**](XXX). ------- ## Installation Create a python3 conda environment (tested with Python 3.10) and run the following: 1. Create python 3.10 conda environment: `conda create --name droid_policy_learning python=3.10` -2. Install [octo](https://github.com/octo-models/octo) -3. Clone the repo -4. Switch to the `r2d2` branch -5. Run `pip install -e .` in `robomimic` +2. Activate the conda environment: `conda activate droid_policy_learning` +3. Install [octo](https://github.com/octo-models/octo) (used for data loading) +4. Run `pip install -e .` in `robomimic` -If you are running policy evaluation on a DROID robot station, then you also need to have DROID installed in the same conda environment. To do that, please follow the instructions [here](https://github.com/AlexanderKhazatsky/DROID). +With this you are all set up for training policies on DROID. If you want to evaluate your policies on a real robot DROID setup, +please install the DROID robot controller in the same conda environment (follow the instructions [here](https://github.com/AlexanderKhazatsky/DROID)). ------- ## Preparing Datasets -We provide all DROID datasets in RLDS, which makes it easy to co-train with various other robot-learning datasets (such as those in the [Open X-Embodiment](https://robotics-transformer-x.github.io/)). +We provide all DROID datasets in RLDS format, which makes it easy to co-train with various other robot-learning datasets (such as those in the [Open X-Embodiment](https://robotics-transformer-x.github.io/)). -To prepare datasets, first download the desired training split of DROID here(TODO(Karl/Sasha): insert link and details based on what the splits are). -If you want to additionally train DROID policies on task-specific data collected in your own DROID hardware platform, follow the instructions [here](https://github.com/kpertsch/droid_dataset_builder?tab=readme-ov-file) to convert the data to an RLDS format that can be used for DROID policy learning. Make sure -that all datasets you want to train on are under the same root directory `DATA_PATH`. +To download the DROID dataset from the Google cloud bucket, install the [gsutil package](https://cloud.google.com/storage/docs/gsutil_install) and run the following command (Note: the full dataset is XXX TB in size): +``` +gsutil -m cp -r XXX +``` + +We also provide a small (2GB) example dataset with 100 DROID trajectories that uses the same format as the full RLDS dataset and can be used for code prototyping and debugging: +``` +gsutil -m cp -r XXX +``` + +For good performance of DROID policies in your target setting, it is helpful to include a small number of demonstrations in your target domain into the training mix ("co-training"). +Please follow the instructions [here](XXX) for collecting a small teleoperated dataset in your target domain and converting it to the RLDS training format. +Make sure that all datasets you want to train on are under the same root directory `DATA_PATH`. ------- ## Training @@ -35,6 +45,7 @@ most important parameters in this file are: - `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. - `EXP_NAMES`: This defines the name of each experiment (as will be logged in `wandb`), the RLDS datasets corresponding to that experiment, and the desired sample weights between those datasets. See `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py` for a template on how this should be formatted. +During training, we use a [_shuffle buffer_](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) to ensure that training samples are properly randomized. It is important to use a large enough shuffle buffer size. The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce this based on your RAM availability. For best results, we recommend using `shuffle_buffer_size >= 100000` if possible. All polices were trained on a single NVIDIA A100 GPU. To specify your information for Weights and Biases logging, make sure to update the `WANDB_ENTITY` and `WANDB_API_KEY` values in `robomimic/macros.py`. @@ -85,4 +96,16 @@ most important parameters in this file are: - `MANIFEST_PATH`: This is the manifest JSON for the training data you want to use. - `MANIFEST_2_PATH`: You can optionally set a second manfiest for another dataset to do 50-50 co-training with. -- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. \ No newline at end of file +- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. + +------------ +## Citation + +``` +@misc{droid_2024, + title={DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset}, + author = {XXX}, + howpublished = {\url{XXX}}, + year = {2024}, +} +``` From d551ceda17aba650b60984953c6f426a283ab191 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Sun, 10 Mar 2024 23:50:09 +0000 Subject: [PATCH 21/43] fix readmes --- README.md | 58 +++++++++++++++++++------------------------------- README_hdf5.md | 38 +++++++++++++++++++++++++++++++++ 2 files changed, 60 insertions(+), 36 deletions(-) create mode 100644 README_hdf5.md diff --git a/README.md b/README.md index 564e7d38..acd64841 100644 --- a/README.md +++ b/README.md @@ -44,45 +44,31 @@ To specify your information for Weights and Biases logging, make sure to update Make sure [DROID](https://github.com/AlexanderKhazatsky/DROID) is installed and follow the policy evaluation instructions at the bottom of the README. ------- +## Code Structure + +| | File | Description | +|---------------------------|---------------------------------------------------------|-------------------------------------------------------------------------------| +| Hyperparameters | [droid_runs_language_conditioned_rlds.py](robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py) | Generates a config based on defined hyperparameters | +| Training Loop | [train.py](robomimic/scripts/train.py) | Main training script. | +| Datasets | [dataset.py](octo/data/dataset.py) | Functions for creating datasets and computing dataset statistics, | +| RLDS Data Processing | [rlds_utils.py](robomimic/utils/rlds_utils.py) | Processing to convert RLDS dataset into dataset compatible for DROID training | +| General Algorithm Class | [algo.py](robomimic/algo/algo.py) | Defines a high level template for all algorithms (eg. diffusion policy) to extend | +| Diffusion Policy | [diffusion_policy.py](robomimic/algo/diffusion_policy.py) | Implementation of diffusion policy | +| Observation Processing | [obs_nets.py](robomimic/models/obs_nets.py) | General observation pre-processing/encoding | +| Visualization | [vis_utils.py](robomimic/utils/vis_utils.py) | Utilities for generating trajectory visualizations | -## Training Policies with HDF5 Format -While we recommend using RLDS, HDF5 can be useful for debugging on smaller datasets, and is supported by robomimic natively. - -### Convert from raw DROID data format to HDF5 - -First you need to make sure to install the ZED SDK, follow the instructions [here](https://www.stereolabs.com/docs/installation/linux/) for your CUDA version and the accompanying `pyzed` package. Then run -`python robomimic/scripts/conversion/convert_droid.py --folder --imsize 128` -which will populate each demo folder with an HDF5 file `trajectory_im128.h5` which contains the full observations and actions for that demo. +------- -### Composing a manifest file -You may want to subselect certain demos to train on. As a result, we assume that you define a manifest json file which contains a list of demos, including the path to each H5 file and the associated language instruction. For example: -``` -[ - { - "path": "/fullpathA/trajectory_im128.h5", - "lang": "Put the apple on the plate" - }, - { - "path": "/fullpathB/trajectory_im128.h5", - "lang": "Move the fork to the sink" - }, - ... -] +## Evaluating Trained Policies +To evaluate policies, make sure that you additionally install DROID (https://github.com/AlexanderKhazatsky/DROID) in your conda environment and then run: +```python +python scripts/evaluation/evaluate_policy.py ``` +from the DROID root directory. Make sure to use the appropriate command line arguments for the model checkpoint path and whether to do goal or language conditioning, and then follow +all resulting prompts in the terminal. To replicate experiments from the paper, use the language conditioning mode. -### Adding language embeddings to HDF5 -For the files and language specified in the above manifest JSON, run: -`python robomimic/scripts/conversion/add_lang_to_converted_data.py --manifest_file --imsize 128` -to compute DistilBERT embeddings of each language instruction and add it as an observation key to the HDF5. - -### Run training -To train policies, update `MANIFEST_PATH`, `EXP_LOG_PATH`, in `robomimic/scripts/config_gen/droid_runs_language_conditioned.py` and then run: - -`python robomimic/scripts/config_gen/droid_runs_language_conditioned.py --wandb_proj_name ` +------- -This will generate a python command that can be run to launch training. You can also update other training parameters within `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py`. Please see the `robomimic` documentation for more information on how `robomimic` configs are defined. The three -most important parameters in this file are: +## Training Policies with HDF5 Format +Natively, robomimic uses HDF5 files to store and load data. While we mainly support RLDS as the data format for training with DROID, [here](https://github.com/ashwin-balakrishna96/robomimic/tree/r2d2/README_hdf5.md) are instructions for how to run training with the HDF5 data format. -- `MANIFEST_PATH`: This is the manifest JSON for the training data you want to use. -- `MANIFEST_2_PATH`: You can optionally set a second manfiest for another dataset to do 50-50 co-training with. -- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. \ No newline at end of file diff --git a/README_hdf5.md b/README_hdf5.md new file mode 100644 index 00000000..00a3d6d9 --- /dev/null +++ b/README_hdf5.md @@ -0,0 +1,38 @@ +### Convert from raw DROID data format to HDF5 + +First you need to make sure to install the ZED SDK, follow the instructions [here](https://www.stereolabs.com/docs/installation/linux/) for your CUDA version and the accompanying `pyzed` package. Then run +`python robomimic/scripts/conversion/convert_droid.py --folder --imsize 128` +which will populate each demo folder with an HDF5 file `trajectory_im128.h5` which contains the full observations and actions for that demo. + +### Composing a manifest file +You may want to subselect certain demos to train on. As a result, we assume that you define a manifest json file which contains a list of demos, including the path to each H5 file and the associated language instruction. For example: +``` +[ + { + "path": "/fullpathA/trajectory_im128.h5", + "lang": "Put the apple on the plate" + }, + { + "path": "/fullpathB/trajectory_im128.h5", + "lang": "Move the fork to the sink" + }, + ... +] +``` + +### Adding language embeddings to HDF5 +For the files and language specified in the above manifest JSON, run: +`python robomimic/scripts/conversion/add_lang_to_converted_data.py --manifest_file --imsize 128` +to compute DistilBERT embeddings of each language instruction and add it as an observation key to the HDF5. + +### Run training +To train policies, update `MANIFEST_PATH`, `EXP_LOG_PATH`, in `robomimic/scripts/config_gen/droid_runs_language_conditioned.py` and then run: + +`python robomimic/scripts/config_gen/droid_runs_language_conditioned.py --wandb_proj_name ` + +This will generate a python command that can be run to launch training. You can also update other training parameters within `robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py`. Please see the `robomimic` documentation for more information on how `robomimic` configs are defined. The three +most important parameters in this file are: + +- `MANIFEST_PATH`: This is the manifest JSON for the training data you want to use. +- `MANIFEST_2_PATH`: You can optionally set a second manfiest for another dataset to do 50-50 co-training with. +- `EXP_LOG_PATH`: This is the path at which experimental data (eg. policy checkpoints) will be stored. \ No newline at end of file From 3b6f36c2d59ed06760c93a949a0ccd64583e2f9f Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 06:36:28 +0000 Subject: [PATCH 22/43] clean more, gotta do bc transformer stuff --- robomimic/models/obs_core.py | 4 +- .../get_lang_annotations_in_manifest.py | 48 --------- .../scripts/conversion/set_dataset_attr.py | 98 ------------------- 3 files changed, 1 insertion(+), 149 deletions(-) delete mode 100644 robomimic/scripts/conversion/get_lang_annotations_in_manifest.py delete mode 100644 robomimic/scripts/conversion/set_dataset_attr.py diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index 5fcdd2f0..b3c4da23 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -698,9 +698,7 @@ def _forward_in(self, inputs): # Create lambda to aggregate all color randomizings at once transform = self.get_batch_transform(N=self.num_samples) - out = transform(inputs) - - return out + return transform(inputs) def _forward_out(self, inputs): """ diff --git a/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py b/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py deleted file mode 100644 index 7b6fc722..00000000 --- a/robomimic/scripts/conversion/get_lang_annotations_in_manifest.py +++ /dev/null @@ -1,48 +0,0 @@ -import random -import json -import numpy as np -import os - -DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads/" -ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-json/" -AGGREGATED_ANNOTATIONS_PATH = os.path.join(ANNOTATIONS_DIR, "aggregated-annotations.json") - -EVAL_DATA_ROOT_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval/" -EVAL_ANNOTATIONS_DIR = "/mnt/fsx/surajnair/datasets/droid-data/lab-uploads-eval-json/" -AGGREGATED_ANNOTATIONS_EVAL_PATH = os.path.join(EVAL_ANNOTATIONS_DIR, "aggregated_annotations_eval.json") - -with open(AGGREGATED_ANNOTATIONS_PATH, "r") as a_file: - ANNOTATIONS = json.load(a_file) - -with open(AGGREGATED_ANNOTATIONS_EVAL_PATH, "r") as a_file: - ANNOTATIONS_EVAL = json.load(a_file) - -def fill_hdf5_to_lang_map(broaddataset_lang, annotations, data_root, annotations_dir): - print("ANNOTATIONS DIR: ", annotations_dir) - for i, uuid in enumerate(list(annotations.keys())): - if i % 1000 == 0: - print("I: ", i) - ann = annotations[uuid] - lang_keys = [l_key for l_key in ann.keys() if "language_instruction" in l_key] - lang_annotations = [ann[l_key] for l_key in lang_keys] - # Select random annotation - lang_annotation = np.random.choice(lang_annotations) - # Get metadata file - uuid = uuid.split("/")[-1].split(".")[0] - metadata_path = os.path.join(annotations_dir, f"metadata_{uuid}.json") - if not os.path.exists(metadata_path) or lang_annotation in [""]: - continue - with open(metadata_path, "r") as m_file: - metadata = json.load(m_file) - hdf5_path = os.path.join(data_root, metadata["lab"], metadata["hdf5_path"]).replace('trajectory', 'trajectory_im128') - broaddataset_lang.append({"path": hdf5_path, "lang": lang_annotation}) - -broaddataset_lang = [] - -# Get hdf5 to lang for broad data -fill_hdf5_to_lang_map(broaddataset_lang, ANNOTATIONS, DATA_ROOT_DIR, ANNOTATIONS_DIR) -# Get hdf5 to lang for eval data -fill_hdf5_to_lang_map(broaddataset_lang, ANNOTATIONS_EVAL, EVAL_DATA_ROOT_DIR, EVAL_ANNOTATIONS_DIR) - -# Save broaddataset_lang to a new manifest file called manifest_lang.json -json.dump(broaddataset_lang, open(os.path.join(DATA_ROOT_DIR.split("lab-uploads")[0], "manifest_lang.json"), "w"), indent=4) \ No newline at end of file diff --git a/robomimic/scripts/conversion/set_dataset_attr.py b/robomimic/scripts/conversion/set_dataset_attr.py deleted file mode 100644 index 4f148d08..00000000 --- a/robomimic/scripts/conversion/set_dataset_attr.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -Example: -python robomimic/scripts/set_dataset_attr.py --glob 'datasets/**/*_abs.hdf5' --env_args env_kwargs.controller_configs.control_delta=false absolute_actions=true -""" -import argparse -import pathlib -import json -import sys -import tqdm -import h5py - -def update_env_args_dict(env_args_dict: dict, key: tuple, value): - if key is None: - return env_args_dict - elif len(key) == 0: - return env_args_dict - elif len(key) == 1: - env_args_dict[key[0]] = value - return env_args_dict - else: - this_key = key[0] - if this_key not in env_args_dict: - env_args_dict[this_key] = dict() - update_env_args_dict(env_args_dict[this_key], key[1:], value) - return env_args_dict - -def main(): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--glob", - type=str, - required=True - ) - - parser.add_argument( - "--env_args", - type=str, - default=None - ) - - parser.add_argument( - 'attrs', - nargs='*' - ) - - args = parser.parse_args() - - # parse attrs to set - # format: key=value - # values are parsed with json - attrs_dict = dict() - for attr_arg in args.attrs: - key, svalue = attr_arg.split("=") - value = json.loads(svalue) - attrs_dict[key] = value - - # parse env_args update - env_args_key = None - env_args_value = None - if args.env_args is not None: - key, svalue = args.env_args.split('=') - env_args_key = key.split('.') - env_args_value = json.loads(svalue) - - # find files - file_paths = list(pathlib.Path.cwd().glob(args.glob)) - - # confirm with the user - print("Found matching files:") - for f in file_paths: - print(f) - print("Are you sure to modify these files with the following attributes:") - print(json.dumps(attrs_dict, indent=2)) - if env_args_key is not None: - print("env_args."+'.'.join(env_args_key)+'='+str(env_args_value)) - result = input("[y/n]?") - if 'y' not in result: - sys.exit(0) - - # execute - for file_path in tqdm.tqdm(file_paths): - with h5py.File(str(file_path), mode='r+') as file: - # update env_args - if env_args_key is not None: - env_args = file['data'].attrs['env_args'] - env_args_dict = json.loads(env_args) - env_args_dict = update_env_args_dict( - env_args_dict=env_args_dict, - key=env_args_key, value=env_args_value) - env_args = json.dumps(env_args_dict) - file['data'].attrs['env_args'] = env_args - - # update other attrs - file['data'].attrs.update(attrs_dict) - -if __name__ == "__main__": - main() From 0d8c41bf56d0f7e3040bfb356f1df982ffd0338d Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 07:16:59 +0000 Subject: [PATCH 23/43] remove unnecessary files --- robomimic/algo/bc.py | 20 ++--- robomimic/config/bc_config.py | 2 - robomimic/envs/env_robosuite.py | 7 +- robomimic/exps/templates/bc.json | 1 - robomimic/exps/templates/bc_transformer.json | 3 +- robomimic/models/obs_core.py | 7 +- robomimic/scripts/config_gen/bc_xfmr_gen.py | 34 +++++--- .../scripts/conversion/extract_action_dict.py | 81 ------------------- robomimic/scripts/dataset_states_to_obs.py | 8 +- robomimic/scripts/playback_dataset.py | 6 +- robomimic/scripts/run_trained_agent.py | 3 +- 11 files changed, 49 insertions(+), 123 deletions(-) delete mode 100644 robomimic/scripts/conversion/extract_action_dict.py diff --git a/robomimic/algo/bc.py b/robomimic/algo/bc.py index e8a6e955..9f4cac04 100644 --- a/robomimic/algo/bc.py +++ b/robomimic/algo/bc.py @@ -684,14 +684,13 @@ def _create_networks(self): assert self.algo_config.transformer.enabled self.nets = nn.ModuleDict() - self.nets["policy"] = torch.nn.parallel.DataParallel( - PolicyNets.TransformerActorNetwork( - obs_shapes=self.obs_shapes, - goal_shapes=self.goal_shapes, - ac_dim=self.ac_dim, - encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), - **BaseNets.transformer_args_from_config(self.algo_config.transformer), - ), device_ids=list(range(0,torch.cuda.device_count()))) + self.nets["policy"] = PolicyNets.TransformerActorNetwork( + obs_shapes=self.obs_shapes, + goal_shapes=self.goal_shapes, + ac_dim=self.ac_dim, + encoder_kwargs=ObsUtils.obs_encoder_kwargs_from_config(self.obs_config.encoder), + **BaseNets.transformer_args_from_config(self.algo_config.transformer), + ) self._set_params_from_config() self.nets = self.nets.float().to(self.device) @@ -777,7 +776,7 @@ def get_action(self, obs_dict, goal_dict=None): """ assert not self.nets.training - output = self.nets["policy"](obs_dict=obs_dict, actions=None, goal_dict=goal_dict) + output = self.nets["policy"](obs_dict, actions=None, goal_dict=goal_dict) if self.supervise_all_steps: if self.algo_config.transformer.pred_future_acs: @@ -802,6 +801,9 @@ def _create_networks(self): assert self.algo_config.gmm.enabled assert self.algo_config.transformer.enabled + if self.algo_config.language_conditioned: + self.obs_shapes["lang_emb"] = [768] # clip is 768-dim embedding + self.nets = nn.ModuleDict() self.nets["policy"] = PolicyNets.TransformerGMMActorNetwork( obs_shapes=self.obs_shapes, diff --git a/robomimic/config/bc_config.py b/robomimic/config/bc_config.py index 22376a66..1f701c68 100644 --- a/robomimic/config/bc_config.py +++ b/robomimic/config/bc_config.py @@ -104,5 +104,3 @@ def algo_config(self): self.algo.transformer.activation = "gelu" # activation function for MLP in Transformer Block self.algo.transformer.supervise_all_steps = False # if true, supervise all intermediate actions, otherwise only final one self.algo.transformer.nn_parameter_for_timesteps = True # if true, use nn.Parameter otherwise use nn.Embedding - self.algo.transformer.pred_future_acs = False # shift action prediction forward to predict future actions instead of past actions - self.algo.transformer.causal = True # whether the transformer is causal diff --git a/robomimic/envs/env_robosuite.py b/robomimic/envs/env_robosuite.py index 2792de4f..dc66390a 100644 --- a/robomimic/envs/env_robosuite.py +++ b/robomimic/envs/env_robosuite.py @@ -7,6 +7,7 @@ import numpy as np from copy import deepcopy +import mujoco_py import robosuite import robomimic.utils.obs_utils as ObsUtils @@ -79,7 +80,6 @@ def __init__( self._env_name = env_name self._init_kwargs = deepcopy(kwargs) self.env = robosuite.make(self._env_name, **kwargs) - self.base_env = self.env # for mimicgen if self._is_v1: # Make sure joint position observations and eef vel observations are active @@ -200,8 +200,7 @@ def get_observation(self, di=None): # ensures that we don't accidentally add robot wrist images a second time pf = robot.robot_model.naming_prefix for k in di: - if k.startswith(pf) and (k not in ret) and \ - (not k.endswith("proprio-state")): + if k.startswith(pf) and (k not in ret) and (not k.endswith("proprio-state")): ret[k] = np.array(di[k]) else: # minimal proprioception for older versions of robosuite @@ -375,7 +374,7 @@ def rollout_exceptions(self): that the entire training run doesn't crash because of a bad policy that causes unstable simulation computations. """ - return (Exception) + return (mujoco_py.builder.MujocoException) def __repr__(self): """ diff --git a/robomimic/exps/templates/bc.json b/robomimic/exps/templates/bc.json index a2c18105..82ad783f 100644 --- a/robomimic/exps/templates/bc.json +++ b/robomimic/exps/templates/bc.json @@ -9,7 +9,6 @@ "log_wandb": false, "wandb_proj_name": "debug" }, - "mse":{}, "save": { "enabled": true, "every_n_seconds": null, diff --git a/robomimic/exps/templates/bc_transformer.json b/robomimic/exps/templates/bc_transformer.json index 0bb5c3d9..c28696cb 100644 --- a/robomimic/exps/templates/bc_transformer.json +++ b/robomimic/exps/templates/bc_transformer.json @@ -9,7 +9,6 @@ "log_wandb": false, "wandb_proj_name": "debug" }, - "mse":{}, "save": { "enabled": true, "every_n_seconds": null, @@ -65,7 +64,7 @@ "learning_rate": { "initial": 0.0001, "decay_factor": 0.1, - "epoch_schedule": [], + "epoch_schedule": [100], "scheduler_type": "linear" }, "regularization": { diff --git a/robomimic/models/obs_core.py b/robomimic/models/obs_core.py index b3c4da23..3566f829 100644 --- a/robomimic/models/obs_core.py +++ b/robomimic/models/obs_core.py @@ -182,8 +182,6 @@ def __repr__(self): msg += textwrap.indent("\npool_net={}".format(self.pool), indent) msg = header + '(' + msg + '\n)' return msg - - """ @@ -668,10 +666,7 @@ def get_batch_transform(self, N): each sub-set of samples along batch dimension, assumed to be the FIRST dimension in the inputted tensor Note: This function will MULTIPLY the first dimension by N """ - return Lambda(lambda x: - torch.stack([ - self.get_transform()(x_) for x_ in x for _ in range(N)]) - ) + return Lambda(lambda x: torch.stack([self.get_transform()(x_) for x_ in x for _ in range(N)])) def output_shape_in(self, input_shape=None): # outputs are same shape as inputs diff --git a/robomimic/scripts/config_gen/bc_xfmr_gen.py b/robomimic/scripts/config_gen/bc_xfmr_gen.py index f3d43332..cd174048 100644 --- a/robomimic/scripts/config_gen/bc_xfmr_gen.py +++ b/robomimic/scripts/config_gen/bc_xfmr_gen.py @@ -13,7 +13,7 @@ def make_generator_helper(args): if args.ckpt_mode is None: args.ckpt_mode = "off" - if args.env == "droid": + if args.env == "r2d2": generator.add_param( key="train.data", name="ds", @@ -44,16 +44,30 @@ def make_generator_helper(args): name="ds", group=2, values=[ - [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/bowls/20230816_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/human_demos/pnp_table_to_cab/all/20230806_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "100_demos"}], - # [{"path": "~/datasets/kitchen/prior/mimicgen/pnp_table_to_cab/viraj_mg_2023-08-10-20-31-14/demo_im84.hdf5", "filter_key": "1000_demos"}], + [ + { + "path": "/home/aaronl/tmp/v2_demos/KitchenPnPCounterToCab_im84.hdf5", + "filter_key": "100_demos", + "lang": "pick and place the object from the counter to the cabinet", + }, + { + "path": "/home/aaronl/tmp/v2_demos/KitchenPnPCabToCounter_im84.hdf5", + "filter_key": "100_demos", + "lang": "pick and place the object from the cabinet to the counter", + }, + ], ], value_names=[ - "bowls-human-100", - # "human-100", - # "mg-100", - # "mg-1000", + "pnp-multi-task" + ], + ) + generator.add_param( + key="algo.language_conditioned", + name="langcond", + group=145892, + values=[ + True, + False, ], ) elif args.env == "square": @@ -82,7 +96,7 @@ def make_generator_helper(args): True, # False, ], - # hidename=True, + hidename=True, ) generator.add_param( key="algo.transformer.supervise_all_steps", diff --git a/robomimic/scripts/conversion/extract_action_dict.py b/robomimic/scripts/conversion/extract_action_dict.py deleted file mode 100644 index 2d3a50e3..00000000 --- a/robomimic/scripts/conversion/extract_action_dict.py +++ /dev/null @@ -1,81 +0,0 @@ -import argparse -import pathlib -import sys -import tqdm -import h5py -import numpy as np -import torch -import os - -import robomimic.utils.torch_utils as TorchUtils - -def extract_action_dict(dataset): - # find files - f = h5py.File(os.path.expanduser(dataset), mode="r+") - - SPECS = [ - dict( - key="actions", - is_absolute=False, - ), - dict( - key="actions_abs", - is_absolute=True, - ) - ] - - # execute - for spec in SPECS: - input_action_key = spec["key"] - is_absolute = spec["is_absolute"] - - if is_absolute: - prefix = "abs_" - else: - prefix = "rel_" - - for demo in f["data"].values(): - in_action = demo[str(input_action_key)][:] - in_pos = in_action[:,:3].astype(np.float32) - in_rot = in_action[:,3:6].astype(np.float32) - in_grip = in_action[:,6:7].astype(np.float32) - - rot_6d = TorchUtils.axis_angle_to_rot_6d( - axis_angle=torch.from_numpy(in_rot) - ) - rot_6d = rot_6d.numpy().astype(np.float32) # convert to numpy - - this_action_dict = { - prefix + "pos": in_pos, - prefix + "rot_axis_angle": in_rot, - prefix + "rot_6d": rot_6d, - "gripper": in_grip - } - - # special case: 8 dim actions mean there is a mobile base mode in the action space - if in_action.shape[1] == 8: - this_action_dict["base_mode"] = in_action[:,7:8].astype(np.float32) - - action_dict_group = demo.require_group("action_dict") - for key, data in this_action_dict.items(): - if key in action_dict_group: - del action_dict_group[key] - action_dict_group.create_dataset(key, data=data) - - f.close() - - -def main(): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--dataset", - type=str, - required=True - ) - - args = parser.parse_args() - extract_action_dict(args.dataset) - -if __name__ == "__main__": - main() diff --git a/robomimic/scripts/dataset_states_to_obs.py b/robomimic/scripts/dataset_states_to_obs.py index 3550e9f4..34185c67 100644 --- a/robomimic/scripts/dataset_states_to_obs.py +++ b/robomimic/scripts/dataset_states_to_obs.py @@ -81,7 +81,10 @@ def extract_trajectory( assert states.shape[0] == actions.shape[0] # load the initial state - env.reset() + ## this reset call doesn't seem necessary. + ## seems ok to remove but haven't fully tested it. + ## removing for now + # env.reset() obs = env.reset_to(initial_state) traj = dict( @@ -200,6 +203,7 @@ def dataset_states_to_obs(args): initial_state = dict(states=states[0]) if is_robosuite_env: initial_state["model"] = f["data/{}".format(ep)].attrs["model_file"] + initial_state["ep_meta"] = f["data/{}".format(ep)].attrs.get("ep_meta", None) # extract obs, rewards, dones actions = f["data/{}/actions".format(ep)][()] @@ -253,6 +257,8 @@ def dataset_states_to_obs(args): # episode metadata if is_robosuite_env: ep_data_grp.attrs["model_file"] = traj["initial_state_dict"]["model"] # model xml for this episode + if "ep_meta" in f["data/{}".format(ep)].attrs: + ep_data_grp.attrs["ep_meta"] = f["data/{}".format(ep)].attrs["ep_meta"] ep_data_grp.attrs["num_samples"] = traj["actions"].shape[0] # number of transitions in this episode total_samples += traj["actions"].shape[0] print("ep {}: wrote {} transitions to group {}".format(ind, ep_data_grp.attrs["num_samples"], ep)) diff --git a/robomimic/scripts/playback_dataset.py b/robomimic/scripts/playback_dataset.py index d214d57a..94799140 100644 --- a/robomimic/scripts/playback_dataset.py +++ b/robomimic/scripts/playback_dataset.py @@ -61,7 +61,6 @@ import argparse import imageio import numpy as np -import random import robomimic import robomimic.utils.obs_utils as ObsUtils @@ -234,12 +233,9 @@ def playback_dataset(args): demos = list(f["data"].keys()) inds = np.argsort([int(elem[5:]) for elem in demos]) demos = [demos[i] for i in inds] - + # maybe reduce the number of demonstrations to playback if args.n is not None: - # if not args.dont_shuffle_demos: - # random.shuffle(demos) - random.shuffle(demos) demos = demos[:args.n] # maybe dump video diff --git a/robomimic/scripts/run_trained_agent.py b/robomimic/scripts/run_trained_agent.py index 2ec09dfe..95bddb21 100644 --- a/robomimic/scripts/run_trained_agent.py +++ b/robomimic/scripts/run_trained_agent.py @@ -66,7 +66,6 @@ import robomimic.utils.tensor_utils as TensorUtils import robomimic.utils.obs_utils as ObsUtils from robomimic.envs.env_base import EnvBase -from robomimic.envs.wrappers import EnvWrapper from robomimic.algo import RolloutPolicy @@ -92,7 +91,7 @@ def rollout(policy, env, horizon, render=False, video_writer=None, video_skip=5, stats (dict): some statistics for the rollout - such as return, horizon, and task success traj (dict): dictionary that corresponds to the rollout trajectory """ - assert isinstance(env, EnvBase) or isinstance(env, EnvWrapper) + assert isinstance(env, EnvBase) assert isinstance(policy, RolloutPolicy) assert not (render and (video_writer is not None)) From c96fa71a82e94d226eee7e88dd6ac29d4fefb5a2 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 07:23:38 +0000 Subject: [PATCH 24/43] clean config --- .../scripts/config_gen/droid_runs_language_conditioned.py | 1 + .../config_gen/droid_runs_language_conditioned_rlds.py | 1 + robomimic/scripts/config_gen/helper.py | 4 ++-- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py index 5ae87f60..6d62cff0 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py @@ -25,6 +25,7 @@ def make_generator_helper(args): algo_name="diffusion_policy", config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), args=args, + exp_log_path=EXP_LOG_PATH, algo_name_short=algo_name_short, pt=True, ) diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py index 2fde524e..724fc662 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py @@ -21,6 +21,7 @@ def make_generator_helper(args): algo_name="diffusion_policy", config_file=os.path.join(base_path, 'robomimic/exps/templates/diffusion_policy.json'), args=args, + exp_log_path=EXP_LOG_PATH, algo_name_short=algo_name_short, pt=True, ) diff --git a/robomimic/scripts/config_gen/helper.py b/robomimic/scripts/config_gen/helper.py index c84e1df8..5cfdfb38 100644 --- a/robomimic/scripts/config_gen/helper.py +++ b/robomimic/scripts/config_gen/helper.py @@ -17,7 +17,7 @@ def scan_datasets(folder, postfix=".h5"): return dataset_paths -def get_generator(algo_name, config_file, args, algo_name_short=None, pt=False): +def get_generator(algo_name, config_file, args, exp_log_path='', algo_name_short=None, pt=False): if args.wandb_proj_name is None: strings = [ algo_name_short if (algo_name_short is not None) else algo_name, @@ -32,7 +32,7 @@ def get_generator(algo_name, config_file, args, algo_name_short=None, pt=False): else: curr_time = datetime.datetime.fromtimestamp(time.time()).strftime('%m-%d-%y-%H-%M-%S') generated_config_dir=os.path.join( - '/mnt/fsx/surajnair/', 'tmp/autogen_configs/ril', algo_name, args.env, args.mod, args.name, curr_time, "json", + exp_log_path, 'tmp/autogen_configs/ril', algo_name, args.env, args.mod, args.name, curr_time, "json", ) generator = HyperparamUtils.ConfigGenerator( From 77239a850c99b53490837ebf211cfddfd9248731 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 07:26:16 +0000 Subject: [PATCH 25/43] clean configs --- .../scripts/config_gen/droid_runs_language_conditioned.py | 4 ++++ .../config_gen/droid_runs_language_conditioned_rlds.py | 5 +++++ 2 files changed, 9 insertions(+) diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py index 6d62cff0..4ce66d81 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py @@ -3,6 +3,9 @@ import json import numpy as np +############################################################################# +# *************** Replace with your paths/config information **************** + MANIFEST_PATH = "/mnt/fsx/surajnair/code/droid_cleanup/manifest_test.json" # MANIFEST_2_PATH = "" EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" @@ -17,6 +20,7 @@ # dataset2 = json.load(file) # N_D2 = len(dataset2) +############################################################################# def make_generator_helper(args): algo_name_short = "diffusion_policy" diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py index 724fc662..eefc859c 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py @@ -4,6 +4,9 @@ import numpy as np from collections import OrderedDict +############################################################################# +# *************** Replace with your paths/config information **************** + DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" EXP_NAMES = OrderedDict( @@ -14,6 +17,8 @@ "sample_weights": [1]}) ]) +############################################################################# + def make_generator_helper(args): algo_name_short = "diffusion_policy" From fe27799917c3514e681461ed98bee4151738767f Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 07:59:08 +0000 Subject: [PATCH 26/43] clean --- robomimic/algo/bc.py | 29 +-- robomimic/scripts/config_gen/bc_xfmr_gen.py | 182 ++++++++---------- .../scripts/conversion/convert_robosuite.py | 54 ++---- robomimic/scripts/dataset_states_to_obs.py | 35 +--- 4 files changed, 93 insertions(+), 207 deletions(-) diff --git a/robomimic/algo/bc.py b/robomimic/algo/bc.py index 9f4cac04..4788c5cc 100644 --- a/robomimic/algo/bc.py +++ b/robomimic/algo/bc.py @@ -207,7 +207,6 @@ def _train_step(self, losses): net=self.nets["policy"], optim=self.optimizers["policy"], loss=losses["action_loss"], - max_grad_norm=self.global_config.train.max_grad_norm, ) info["policy_grad_norms"] = policy_grad_norms return info @@ -701,9 +700,6 @@ def _set_params_from_config(self): """ self.context_length = self.algo_config.transformer.context_length self.supervise_all_steps = self.algo_config.transformer.supervise_all_steps - self.pred_future_acs = self.algo_config.transformer.pred_future_acs - if self.pred_future_acs: - assert self.supervise_all_steps is True def process_batch_for_training(self, batch): """ @@ -723,18 +719,11 @@ def process_batch_for_training(self, batch): if self.supervise_all_steps: # supervision on entire sequence (instead of just current timestep) - if self.pred_future_acs: - ac_start = h - 1 - else: - ac_start = 0 - input_batch["actions"] = batch["actions"][:, ac_start:ac_start+h, :] + input_batch["actions"] = batch["actions"][:, :h, :] else: # just use current timestep input_batch["actions"] = batch["actions"][:, h-1, :] - if self.pred_future_acs: - assert input_batch["actions"].shape[1] == h - input_batch = TensorUtils.to_device(TensorUtils.to_float(input_batch), self.device) return input_batch @@ -776,19 +765,7 @@ def get_action(self, obs_dict, goal_dict=None): """ assert not self.nets.training - output = self.nets["policy"](obs_dict, actions=None, goal_dict=goal_dict) - - if self.supervise_all_steps: - if self.algo_config.transformer.pred_future_acs: - output = output[:, 0, :] - else: - output = output[:, -1, :] - else: - output = output[:, -1, :] - - return output - - + return self.nets["policy"](obs_dict, actions=None, goal_dict=goal_dict)[:, -1, :] class BC_Transformer_GMM(BC_Transformer): """ @@ -801,8 +778,6 @@ def _create_networks(self): assert self.algo_config.gmm.enabled assert self.algo_config.transformer.enabled - if self.algo_config.language_conditioned: - self.obs_shapes["lang_emb"] = [768] # clip is 768-dim embedding self.nets = nn.ModuleDict() self.nets["policy"] = PolicyNets.TransformerGMMActorNetwork( diff --git a/robomimic/scripts/config_gen/bc_xfmr_gen.py b/robomimic/scripts/config_gen/bc_xfmr_gen.py index cd174048..e67856f3 100644 --- a/robomimic/scripts/config_gen/bc_xfmr_gen.py +++ b/robomimic/scripts/config_gen/bc_xfmr_gen.py @@ -13,139 +13,101 @@ def make_generator_helper(args): if args.ckpt_mode is None: args.ckpt_mode = "off" + generator.add_param( + key="train.num_data_workers", + name="", + group=-1, + values=[4], + ) + generator.add_param( + key="experiment.save.every_n_epochs", + name="", + group=-1, + values=[ + 100 + ], + ) + + # run rollouts at epoch 0 only + generator.add_param( + key="experiment.rollout.warmstart", + name="", + group=-1, + values=[ + -1, + ], + ) + generator.add_param( + key="train.num_epochs", + name="", + group=-1, + values=[40], + ) + generator.add_param( + key="experiment.rollout.rate", + name="", + group=-1, + values=[10], + ) + if args.env == "r2d2": generator.add_param( key="train.data", name="ds", group=2, values=[ - [{"path": p} for p in scan_datasets("~/Downloads/example_pen_in_cup", postfix="trajectory_im128.h5")], + [{"path": p} for p in scan_datasets("~/code/r2d2/data/success/2023-05-23_t2c-cans", postfix="trajectory_im84.h5")], + [{"path": p} for p in scan_datasets("~/code/r2d2/data/success/2023-05-23_t2c-cans", postfix="trajectory_im128.h5")], ], value_names=[ - "pen-in-cup", + "pnp-t2c-cans-84", + "pnp-t2c-cans-128", ], ) generator.add_param( - key="observation.modalities.obs.rgb", - name="cams", - group=130, + key="observation.encoder.rgb.obs_randomizer_kwargs.crop_height", + name="", + group=2, values=[ - # ["camera/image/hand_camera_left_image"], - ["camera/image/hand_camera_left_image", "camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"], + 76, + 116 ], - value_names=[ - # "wrist", - "3cams", - ] ) - elif args.env == "kitchen": generator.add_param( - key="train.data", - name="ds", + key="observation.encoder.rgb.obs_randomizer_kwargs.crop_width", + name="", group=2, values=[ - [ - { - "path": "/home/aaronl/tmp/v2_demos/KitchenPnPCounterToCab_im84.hdf5", - "filter_key": "100_demos", - "lang": "pick and place the object from the counter to the cabinet", - }, - { - "path": "/home/aaronl/tmp/v2_demos/KitchenPnPCabToCounter_im84.hdf5", - "filter_key": "100_demos", - "lang": "pick and place the object from the cabinet to the counter", - }, - ], - ], - value_names=[ - "pnp-multi-task" + 76, + 116 ], ) + else: + raise ValueError + + if "experiment.ckpt_path" in generator.parameters: generator.add_param( - key="algo.language_conditioned", - name="langcond", - group=145892, + key="algo.optim_params.policy.learning_rate.initial", + name="lrinit", + group=110, values=[ - True, - False, + 1e-5, ], + hidename=True, ) - elif args.env == "square": generator.add_param( - key="train.data", - name="ds", - group=2, + key="algo.optim_params.policy.learning_rate.lr_scheduler_type", + name="lrsch", + group=111, values=[ - [ - {"path": "~/datasets/square/ph/square_ph_abs_tmp.hdf5"}, # replace with your own path - ], + # "linear", + None, ], value_names=[ - "square", + "none" ], + hidename=True, ) - else: - raise ValueError - - # change default settings: predict 10 steps into future - generator.add_param( - key="algo.transformer.pred_future_acs", - name="predfuture", - group=1, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.transformer.supervise_all_steps", - name="supallsteps", - group=1, - values=[ - True, - # False, - ], - hidename=True, - ) - generator.add_param( - key="algo.transformer.causal", - name="causal", - group=1, - values=[ - False, - # True, - ], - hidename=True, - ) - generator.add_param( - key="train.seq_length", - name="", - group=-1, - values=[10], - hidename=True, - ) - - generator.add_param( - key="algo.gmm.min_std", - name="mindstd", - group=271314, - values=[ - 0.03, - #0.0001, - ], - hidename=True, - ) - generator.add_param( - key="train.max_grad_norm", - name="maxgradnorm", - group=18371, - values=[ - # None, - 100.0, - ], - hidename=True, - ) generator.add_param( key="train.output_dir", @@ -160,10 +122,20 @@ def make_generator_helper(args): ], ) + generator.add_param( + key="experiment.rollout.enabled", + name="", + group=-1, + values=[ + True + ], + hidename=False, + ) + return generator if __name__ == "__main__": parser = get_argparser() args = parser.parse_args() - make_generator(args, make_generator_helper) + make_generator(args, make_generator_helper) \ No newline at end of file diff --git a/robomimic/scripts/conversion/convert_robosuite.py b/robomimic/scripts/conversion/convert_robosuite.py index d3c748ee..88258698 100644 --- a/robomimic/scripts/conversion/convert_robosuite.py +++ b/robomimic/scripts/conversion/convert_robosuite.py @@ -22,9 +22,6 @@ import robomimic.envs.env_base as EB from robomimic.scripts.split_train_val import split_train_val_from_hdf5 -from robomimic.scripts.conversion.robosuite_add_absolute_actions import add_absolute_actions_to_dataset -from robomimic.scripts.conversion.extract_action_dict import extract_action_dict -from robomimic.scripts.filter_dataset_size import filter_dataset_size if __name__ == "__main__": @@ -34,33 +31,22 @@ type=str, help="path to input hdf5 dataset", ) - parser.add_argument( - "--filter_num_demos", - type=int, - nargs='+', - help="Num demos to filter by (can be list)", - ) args = parser.parse_args() f = h5py.File(args.dataset, "a") # edit mode # store env meta - env_name = f["data"].attrs.get("env", None) - if "env_info" in f["data"].attrs: - env_info = json.loads(f["data"].attrs["env_info"]) - if env_name is not None and env_info is not None: - env_meta = dict( - type=EB.EnvType.ROBOSUITE_TYPE, - env_name=env_name, - env_version=f["data"].attrs["repository_version"], - env_kwargs=env_info, - ) - if "env_args" in f["data"].attrs: - del f["data"].attrs["env_args"] - f["data"].attrs["env_args"] = json.dumps(env_meta, indent=4) - else: - # assume env_args already present - assert "env_args" in f["data"].attrs + env_name = f["data"].attrs["env"] + env_info = json.loads(f["data"].attrs["env_info"]) + env_meta = dict( + type=EB.EnvType.ROBOSUITE_TYPE, + env_name=env_name, + env_version=f["data"].attrs["repository_version"], + env_kwargs=env_info, + ) + if "env_args" in f["data"].attrs: + del f["data"].attrs["env_args"] + f["data"].attrs["env_args"] = json.dumps(env_meta, indent=4) print("====== Stored env meta ======") print(f["data"].attrs["env_args"]) @@ -87,21 +73,3 @@ # create 90-10 train-validation split in the dataset split_train_val_from_hdf5(hdf5_path=args.dataset, val_ratio=0.1) - - # add absolute actions to dataset - add_absolute_actions_to_dataset( - dataset=args.dataset, - eval_dir=None, - num_workers=10, - ) - - # extract corresponding action keys into action_dict - extract_action_dict(dataset=args.dataset) - - # create filter keys according to number of demos - if args.filter_num_demos is not None: - for n in args.filter_num_demos: - filter_dataset_size( - args.dataset, - num_demos=n, - ) \ No newline at end of file diff --git a/robomimic/scripts/dataset_states_to_obs.py b/robomimic/scripts/dataset_states_to_obs.py index 34185c67..6665ab17 100644 --- a/robomimic/scripts/dataset_states_to_obs.py +++ b/robomimic/scripts/dataset_states_to_obs.py @@ -61,7 +61,6 @@ def extract_trajectory( initial_state, states, actions, - actions_abs, done_mode, ): """ @@ -81,10 +80,7 @@ def extract_trajectory( assert states.shape[0] == actions.shape[0] # load the initial state - ## this reset call doesn't seem necessary. - ## seems ok to remove but haven't fully tested it. - ## removing for now - # env.reset() + env.reset() obs = env.reset_to(initial_state) traj = dict( @@ -96,9 +92,6 @@ def extract_trajectory( states=np.array(states), initial_state_dict=initial_state, ) - if actions_abs is not None: - traj["actions_abs"] = np.array(actions_abs) - traj_len = states.shape[0] # iteration variable @t is over "next obs" indices for t in range(1, traj_len + 1): @@ -181,14 +174,7 @@ def dataset_states_to_obs(args): demos = demos[:args.n] # output file in same directory as input file - output_name = args.output_name - if output_name is None: - if len(args.camera_names) == 0: - output_name = os.path.basename(args.dataset)[:-5] + "_ld.hdf5" - else: - output_name = os.path.basename(args.dataset)[:-5] + "_im{}.hdf5".format(args.camera_width) - - output_path = os.path.join(os.path.dirname(args.dataset), output_name) + output_path = os.path.join(os.path.dirname(args.dataset), args.output_name) f_out = h5py.File(output_path, "w") data_grp = f_out.create_group("data") print("input file: {}".format(args.dataset)) @@ -203,20 +189,14 @@ def dataset_states_to_obs(args): initial_state = dict(states=states[0]) if is_robosuite_env: initial_state["model"] = f["data/{}".format(ep)].attrs["model_file"] - initial_state["ep_meta"] = f["data/{}".format(ep)].attrs.get("ep_meta", None) # extract obs, rewards, dones actions = f["data/{}/actions".format(ep)][()] - if "data/{}/actions_abs".format(ep) in f: - actions_abs = f["data/{}/actions_abs".format(ep)][()] - else: - actions_abs = None traj = extract_trajectory( env=env, initial_state=initial_state, states=states, actions=actions, - actions_abs=actions_abs, done_mode=args.done_mode, ) @@ -235,8 +215,6 @@ def dataset_states_to_obs(args): ep_data_grp.create_dataset("states", data=np.array(traj["states"])) ep_data_grp.create_dataset("rewards", data=np.array(traj["rewards"])) ep_data_grp.create_dataset("dones", data=np.array(traj["dones"])) - if "actions_abs" in traj: - ep_data_grp.create_dataset("actions_abs", data=np.array(traj["actions_abs"])) for k in traj["obs"]: if args.compress: ep_data_grp.create_dataset("obs/{}".format(k), data=np.array(traj["obs"][k]), compression="gzip") @@ -248,17 +226,9 @@ def dataset_states_to_obs(args): else: ep_data_grp.create_dataset("next_obs/{}".format(k), data=np.array(traj["next_obs"][k])) - # copy action dict (if applicable) - if "data/{}/action_dict".format(ep) in f: - action_dict = f["data/{}/action_dict".format(ep)] - for k in action_dict: - ep_data_grp.create_dataset("action_dict/{}".format(k), data=np.array(action_dict[k][()])) - # episode metadata if is_robosuite_env: ep_data_grp.attrs["model_file"] = traj["initial_state_dict"]["model"] # model xml for this episode - if "ep_meta" in f["data/{}".format(ep)].attrs: - ep_data_grp.attrs["ep_meta"] = f["data/{}".format(ep)].attrs["ep_meta"] ep_data_grp.attrs["num_samples"] = traj["actions"].shape[0] # number of transitions in this episode total_samples += traj["actions"].shape[0] print("ep {}: wrote {} transitions to group {}".format(ind, ep_data_grp.attrs["num_samples"], ep)) @@ -289,6 +259,7 @@ def dataset_states_to_obs(args): parser.add_argument( "--output_name", type=str, + required=True, help="name of output hdf5 dataset", ) From 1269b0f7ced756080a5fb134b083364a42514c46 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 08:00:43 +0000 Subject: [PATCH 27/43] minor fix --- robomimic/algo/bc.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/robomimic/algo/bc.py b/robomimic/algo/bc.py index 4788c5cc..e9ede7a0 100644 --- a/robomimic/algo/bc.py +++ b/robomimic/algo/bc.py @@ -671,7 +671,6 @@ def log_info(self, info): log["Policy_Grad_Norms"] = info["policy_grad_norms"] return log - class BC_Transformer(BC): """ BC training with a Transformer policy. @@ -778,7 +777,6 @@ def _create_networks(self): assert self.algo_config.gmm.enabled assert self.algo_config.transformer.enabled - self.nets = nn.ModuleDict() self.nets["policy"] = PolicyNets.TransformerGMMActorNetwork( obs_shapes=self.obs_shapes, From c38876ac11cada37f02025ad9f6060c3b655bb19 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 08:01:24 +0000 Subject: [PATCH 28/43] small fix --- robomimic/algo/bc.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/robomimic/algo/bc.py b/robomimic/algo/bc.py index e9ede7a0..c5674f05 100644 --- a/robomimic/algo/bc.py +++ b/robomimic/algo/bc.py @@ -671,6 +671,7 @@ def log_info(self, info): log["Policy_Grad_Norms"] = info["policy_grad_norms"] return log + class BC_Transformer(BC): """ BC training with a Transformer policy. @@ -766,6 +767,7 @@ def get_action(self, obs_dict, goal_dict=None): return self.nets["policy"](obs_dict, actions=None, goal_dict=goal_dict)[:, -1, :] + class BC_Transformer_GMM(BC_Transformer): """ BC training with a Transformer GMM policy. From 8f2a0f9f2787db70fcab68563a0d64e57c8895e5 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 08:14:23 +0000 Subject: [PATCH 29/43] clean up install instructions --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 72ae8175..c7165f2f 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ Create a python3 conda environment (tested with Python 3.10) and run the followi 1. Create python 3.10 conda environment: `conda create --name droid_policy_learning python=3.10` 2. Activate the conda environment: `conda activate droid_policy_learning` 3. Install [octo](https://github.com/octo-models/octo) (used for data loading) -4. Run `pip install -e .` in `robomimic` +4. Run `pip install -e .` in `robomimic`. Make sure you are on the `r2d2` branch. With this you are all set up for training policies on DROID. If you want to evaluate your policies on a real robot DROID setup, please install the DROID robot controller in the same conda environment (follow the instructions [here](https://github.com/AlexanderKhazatsky/DROID)). From d55376cbb147ac24044c603bf9b1f7a721fee849 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Mon, 11 Mar 2024 21:43:24 +0000 Subject: [PATCH 30/43] octo dataloader fixes --- robomimic/scripts/train.py | 11 ++++++++--- robomimic/utils/action_utils.py | 2 +- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index b534aac4..44147de2 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -46,7 +46,8 @@ from robomimic.utils.log_utils import PrintLogger, DataLogger, flush_warnings from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, DROID_TO_RLDS_OBS_KEY_MAP, DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset -from octo.data.dataset import make_interleaved_dataset +from octo.data.dataset import make_dataset_from_rlds, make_interleaved_dataset +from octo.data.utils.data_utils import combine_dataset_statistics def train(config, device): @@ -96,7 +97,7 @@ def train(config, device): "image_obs_keys": {"primary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, "state_obs_keys": [DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], "language_key": "language_instruction", - "keys_to_normalize": {"action": "action"}, + "norm_skip_keys": ["proprio"], "action_proprio_normalization_type": "bounds", "absolute_action_mask": [True] * ac_dim, "action_normalization_mask": [True] * ac_dim, @@ -108,6 +109,10 @@ def train(config, device): dataset_kwargs_list = [ {"name": d_name, **BASE_DATASET_KWARGS} for d_name in dataset_names ] + # Compute combined normalization stats + combined_dataset_statistics = combine_dataset_statistics( + [make_dataset_from_rlds(**dataset_kwargs, train=train)[1] for dataset_kwargs in dataset_kwargs_list] + ) dataset = make_interleaved_dataset( dataset_kwargs_list, @@ -116,7 +121,7 @@ def train(config, device): shuffle_buffer_size=config.train.shuffle_buffer_size, batch_size=None, # batching will be handles in PyTorch Dataloader object balance_weights=False, - do_combined_normalization=True, + dataset_statistics=combined_dataset_statistics, traj_transform_kwargs=dict( # NOTE(Ashwin): window_size and future_action_window_size may break if # not using diffusion policy diff --git a/robomimic/utils/action_utils.py b/robomimic/utils/action_utils.py index a191194b..4d6e0a1d 100644 --- a/robomimic/utils/action_utils.py +++ b/robomimic/utils/action_utils.py @@ -43,7 +43,7 @@ def get_action_stats_dict(rlds_dataset_stats, action_keys, action_shapes): end_idx = start_idx + this_act_dim action_stats[key] = dict() for sub_key in rlds_dataset_stats.keys(): - action = rlds_dataset_stats[sub_key] + action = np.array(rlds_dataset_stats[sub_key]) action_stats[key][sub_key] = action[...,start_idx:end_idx].reshape( action.shape[:-1]+tuple(this_act_shape)) start_idx = end_idx From 51e12215691332772b9a62d49e6570fc9e0878e2 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 01:07:43 +0000 Subject: [PATCH 31/43] clean readme --- README.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/README.md b/README.md index c7165f2f..9476d88d 100644 --- a/README.md +++ b/README.md @@ -50,10 +50,6 @@ The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce To specify your information for Weights and Biases logging, make sure to update the `WANDB_ENTITY` and `WANDB_API_KEY` values in `robomimic/macros.py`. -------- -## Evaluation on a DROID robot station -Make sure [DROID](https://github.com/AlexanderKhazatsky/DROID) is installed and follow the policy evaluation instructions at the bottom of the README. - ------- ## Code Structure @@ -71,7 +67,7 @@ Make sure [DROID](https://github.com/AlexanderKhazatsky/DROID) is installed and ------- ## Evaluating Trained Policies -To evaluate policies, make sure that you additionally install DROID (https://github.com/AlexanderKhazatsky/DROID) in your conda environment and then run: +To evaluate policies, make sure that you additionally install [DROID](https://github.com/AlexanderKhazatsky/DROID) in your conda environment and then run: ```python python scripts/evaluation/evaluate_policy.py ``` From f31a7420e9a7f7481cef90890a46ccaf65c626d5 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 04:01:41 +0000 Subject: [PATCH 32/43] fix tiny bug --- robomimic/scripts/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index 44147de2..a8d253a8 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -111,7 +111,7 @@ def train(config, device): ] # Compute combined normalization stats combined_dataset_statistics = combine_dataset_statistics( - [make_dataset_from_rlds(**dataset_kwargs, train=train)[1] for dataset_kwargs in dataset_kwargs_list] + [make_dataset_from_rlds(**dataset_kwargs, train=True)[1] for dataset_kwargs in dataset_kwargs_list] ) dataset = make_interleaved_dataset( From 50d17ebf8895580c5d5b04fa5a2872507598e8ea Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 05:15:51 +0000 Subject: [PATCH 33/43] add dataloader example --- README.md | 2 + examples/droid_dataloader.py | 142 +++++++++++++++++++++++++++++++++++ robomimic/scripts/train.py | 17 +++-- 3 files changed, 153 insertions(+), 8 deletions(-) create mode 100644 examples/droid_dataloader.py diff --git a/README.md b/README.md index 9476d88d..6af50737 100644 --- a/README.md +++ b/README.md @@ -50,6 +50,8 @@ The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce To specify your information for Weights and Biases logging, make sure to update the `WANDB_ENTITY` and `WANDB_API_KEY` values in `robomimic/macros.py`. +We also provide a stand-alone example to load data from DROID [here](examples/droid_dataloader.py) + ------- ## Code Structure diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py new file mode 100644 index 00000000..2aef3c93 --- /dev/null +++ b/examples/droid_dataloader.py @@ -0,0 +1,142 @@ +import tqdm +import torch +from torch.utils.data import DataLoader +import tensorflow as tf + +from robomimic.utils.rlds_utils import droid_dataset_transform, robomimic_transform, DROID_TO_RLDS_OBS_KEY_MAP, DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP, TorchRLDSDataset +import robomimic.utils.action_utils as ActionUtils +from robomimic.utils.dataset import action_stats_to_normalization_stats + +from octo.data.dataset import make_dataset_from_rlds, make_interleaved_dataset +from octo.data.utils.data_utils import combine_dataset_statistics + +tf.config.set_visible_devices([], "GPU") + +# ------------------------------ Get Dataset Information ------------------------------ +DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" +DATASET_NAMES = ["r2_d2", "r2_d2_cmu_toaster"] +sample_weights = [1, 1] + +# ------------------------------ Get Observation Information ------------------------------ +obs_modalities = ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"] +obs_low_dim_modalities = ["robot_state/cartesian_position", "robot_state/gripper_position"] + +# ------------------------------ Get Action Information ------------------------------ +action_keys = [ + "action/abs_pos", + "action/abs_rot_6d", + "action/gripper_position"] +action_shapes=[ + (1, 3), + (1, 6), + (1, 1)] + +ac_dim = sum([ac_comp[1] for ac_comp in action_shapes]) + +action_config = { + "action/cartesian_position":{ + "normalization": "min_max", + }, + "action/abs_pos":{ + "normalization": "min_max", + }, + "action/abs_rot_6d":{ + "normalization": "min_max", + "format": "rot_6d", + "convert_at_runtime": "rot_euler", + }, + "action/abs_rot_euler":{ + "normalization": "min_max", + "format": "rot_euler", + }, + "action/gripper_position":{ + "normalization": "min_max", + }, + "action/cartesian_velocity":{ + "normalization": None, + }, + "action/rel_pos":{ + "normalization": None, + }, + "action/rel_rot_6d":{ + "format": "rot_6d", + "normalization": None, + "convert_at_runtime": "rot_euler", + }, + "action/rel_rot_euler":{ + "format": "rot_euler", + "normalization": None, + }, + "action/gripper_velocity":{ + "normalization": None, + }, +} + +is_abs_action = [action_config[k]["normalization"] != None for k in action_config.keys()] + +# ------------------------------ Construct Dataset ------------------------------ +BASE_DATASET_KWARGS = { + "data_dir": DATA_PATH, + "image_obs_keys": {"primary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, + "state_obs_keys": [DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP[obs_key] for obs_key in obs_low_dim_modalities], + "language_key": "language_instruction", + "norm_skip_keys": ["proprio"], + "action_proprio_normalization_type": "bounds", + "absolute_action_mask": is_abs_action, + "action_normalization_mask": is_abs_action, + "standardize_fn": droid_dataset_transform, + } + +# you can add more datasets here & the sampling weights below if you want to mix +dataset_kwargs_list = [ + {"name": d_name, **BASE_DATASET_KWARGS} for d_name in DATASET_NAMES +] +# Compute combined normalization stats +combined_dataset_statistics = combine_dataset_statistics( + [make_dataset_from_rlds(**dataset_kwargs, train=True)[1] for dataset_kwargs in dataset_kwargs_list] +) + +dataset = make_interleaved_dataset( + dataset_kwargs_list, + sample_weights, + train=True, + shuffle_buffer_size=100000, + batch_size=None, # batching will be handled in PyTorch Dataloader object + balance_weights=False, + dataset_statistics=combined_dataset_statistics, + traj_transform_kwargs=dict( + window_size=2, + future_action_window_size=15, + subsample_length=100, + skip_unlabeled=True, # skip all trajectories without language + ), + frame_transform_kwargs=dict( + image_augment_kwargs=dict( + ), + resize_size=dict( + primary=[128, 128], + secondary=[128, 128], + ), + num_parallel_calls=200, + ), + traj_transform_threads=48, + traj_read_threads=48, +) + +rlds_dataset_stats = dataset.dataset_statistics +action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats["action"], action_keys, action_shapes) +action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) +dataset = dataset.map(robomimic_transform, num_parallel_calls=48) + +# ------------------------------ Create Dataloader ------------------------------ + +pytorch_dataset = TorchRLDSDataset(dataset) +train_loader = DataLoader( + pytorch_dataset, + batch_size=128, + num_workers=0, # important to keep this to 0 so PyTorch does not mess with the parallelism +) + +for i, sample in tqdm.tqdm(enumerate(train_loader)): + if i == 5000: + break \ No newline at end of file diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index a8d253a8..af84b648 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -91,16 +91,18 @@ def train(config, device): # NOTE: Must be 2 cam for now, can clean this up later assert(len(obs_modalities) == 2) ac_dim = sum([ac_comp[1] for ac_comp in config.train.action_shapes]) + action_config = config.train.action_config + is_abs_action = [action_config[k]["normalization"] != None for k in action_config.keys()] BASE_DATASET_KWARGS = { "data_dir": config.train.data_path, "image_obs_keys": {"primary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[0]], "secondary": DROID_TO_RLDS_OBS_KEY_MAP[obs_modalities[1]]}, - "state_obs_keys": [DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP[a] for a in config.observation.modalities.obs.low_dim], + "state_obs_keys": [DROID_TO_RLDS_LOW_DIM_OBS_KEY_MAP[obs_key] for obs_key in config.observation.modalities.obs.low_dim], "language_key": "language_instruction", "norm_skip_keys": ["proprio"], "action_proprio_normalization_type": "bounds", - "absolute_action_mask": [True] * ac_dim, - "action_normalization_mask": [True] * ac_dim, + "absolute_action_mask": is_abs_action, + "action_normalization_mask": is_abs_action, "standardize_fn": droid_dataset_transform, } @@ -119,7 +121,7 @@ def train(config, device): config.train.sample_weights, train=True, shuffle_buffer_size=config.train.shuffle_buffer_size, - batch_size=None, # batching will be handles in PyTorch Dataloader object + batch_size=None, # batching will be handled in PyTorch Dataloader object balance_weights=False, dataset_statistics=combined_dataset_statistics, traj_transform_kwargs=dict( @@ -142,11 +144,10 @@ def train(config, device): traj_transform_threads=config.train.traj_transform_threads, traj_read_threads=config.train.traj_read_threads, ) - # Note: If we have statistics for multiple datasets separately, use the last one (assumes last one is the dataset from the target domain) - # rlds_dataset_stats will only be a list if do_combined_normalization is set to False - rlds_dataset_stats = dataset.dataset_statistics[-1] if isinstance(dataset.dataset_statistics, list) else dataset.dataset_statistics + # Note: If we have separated statistics for multiple datasets, use the first one (assumed to be DROID) + # Otherwise, use the combined dataset statistics. + rlds_dataset_stats = dataset.dataset_statistics[0] if isinstance(dataset.dataset_statistics, list) else dataset.dataset_statistics action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats["action"], config.train.action_keys, config.train.action_shapes) - action_config = config.train.action_config action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) dataset = dataset.map(robomimic_transform, num_parallel_calls=config.train.traj_transform_threads) From e2df06b5d38e0154d8b5b548f51c02b5ea5ab210 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 05:16:33 +0000 Subject: [PATCH 34/43] fix --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6af50737..4864f722 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ The default `shuffle_buffer_size` is set to `500000`, but you may need to reduce To specify your information for Weights and Biases logging, make sure to update the `WANDB_ENTITY` and `WANDB_API_KEY` values in `robomimic/macros.py`. -We also provide a stand-alone example to load data from DROID [here](examples/droid_dataloader.py) +We also provide a stand-alone example to load data from DROID [here](examples/droid_dataloader.py). ------- ## Code Structure From 14c1c42d13a2341d2354121419454c2cbde2dd81 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 20:15:24 +0000 Subject: [PATCH 35/43] add in filter support --- robomimic/scripts/train.py | 8 +++++++- robomimic/utils/rlds_utils.py | 7 +++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index af84b648..42d3a3bd 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -48,6 +48,7 @@ from octo.data.dataset import make_dataset_from_rlds, make_interleaved_dataset from octo.data.utils.data_utils import combine_dataset_statistics +from octo.utils.spec import ModuleSpec def train(config, device): @@ -104,7 +105,12 @@ def train(config, device): "absolute_action_mask": is_abs_action, "action_normalization_mask": is_abs_action, "standardize_fn": droid_dataset_transform, - } + "filter_functions": [ + ModuleSpec.create( + "robomimic.utils.rlds_utils:filter_success" + ), + ] + } # you can add more datasets here & the sampling weights below if you want to mix dataset_names = config.train.dataset_names diff --git a/robomimic/utils/rlds_utils.py b/robomimic/utils/rlds_utils.py index ff16f53a..785d531e 100644 --- a/robomimic/utils/rlds_utils.py +++ b/robomimic/utils/rlds_utils.py @@ -5,6 +5,13 @@ import torch import tensorflow_graphics.geometry.transformation as tfg +def filter_success(trajectory: dict[str, any]): + # only keep trajectories that have "success" in the file path + return tf.strings.regex_full_match( + trajectory['traj_metadata']['episode_metadata']['file_path'][0], + ".*/success/.*" + ) + def euler_to_rmat(euler): return tfg.rotation_matrix_3d.from_euler(euler) From 713de7028d6ba23b6de0b655c5bfc0f254eb21c9 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 23:50:03 +0000 Subject: [PATCH 36/43] fix --- README.md | 2 +- examples/droid_dataloader.py | 14 +++++++++----- robomimic/scripts/config_gen/bc_xfmr_gen.py | 6 +++--- .../droid_runs_language_conditioned_rlds.py | 11 +++++++---- robomimic/scripts/train.py | 12 +++++------- 5 files changed, 25 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 4864f722..0ca6097c 100644 --- a/README.md +++ b/README.md @@ -79,7 +79,7 @@ all resulting prompts in the terminal. To replicate experiments from the paper, ------- ## Training Policies with HDF5 Format -Natively, robomimic uses HDF5 files to store and load data. While we mainly support RLDS as the data format for training with DROID, [here](https://github.com/ashwin-balakrishna96/robomimic/tree/r2d2/README_hdf5.md) are instructions for how to run training with the HDF5 data format. +Natively, robomimic uses HDF5 files to store and load data. While we mainly support RLDS as the data format for training with DROID, [here](robomimic/README_hdf5.md) are instructions for how to run training with the HDF5 data format. ------------ ## Citation diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py index 2aef3c93..fcddff1a 100644 --- a/examples/droid_dataloader.py +++ b/examples/droid_dataloader.py @@ -9,13 +9,14 @@ from octo.data.dataset import make_dataset_from_rlds, make_interleaved_dataset from octo.data.utils.data_utils import combine_dataset_statistics +from octo.utils.spec import ModuleSpec tf.config.set_visible_devices([], "GPU") # ------------------------------ Get Dataset Information ------------------------------ -DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" -DATASET_NAMES = ["r2_d2", "r2_d2_cmu_toaster"] -sample_weights = [1, 1] +DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid" +DATASET_NAMES = ["droid"] # You can add additional co-training datasets here +sample_weights = [1] # Add to this if you add additional co-training datasets # ------------------------------ Get Observation Information ------------------------------ obs_modalities = ["camera/image/varied_camera_1_left_image", "camera/image/varied_camera_2_left_image"] @@ -87,9 +88,12 @@ "standardize_fn": droid_dataset_transform, } -# you can add more datasets here & the sampling weights below if you want to mix +filter_functions = [[ModuleSpec.create( + "robomimic.utils.rlds_utils:filter_success" + )] if d_name == "droid" else [] \ + for d_name in DATASET_NAMES] dataset_kwargs_list = [ - {"name": d_name, **BASE_DATASET_KWARGS} for d_name in DATASET_NAMES + {"name": d_name, "filter_functions": f_functions, **BASE_DATASET_KWARGS} for d_name, f_functions in zip(DATASET_NAMES, filter_functions) ] # Compute combined normalization stats combined_dataset_statistics = combine_dataset_statistics( diff --git a/robomimic/scripts/config_gen/bc_xfmr_gen.py b/robomimic/scripts/config_gen/bc_xfmr_gen.py index e67856f3..e04a438e 100644 --- a/robomimic/scripts/config_gen/bc_xfmr_gen.py +++ b/robomimic/scripts/config_gen/bc_xfmr_gen.py @@ -50,14 +50,14 @@ def make_generator_helper(args): values=[10], ) - if args.env == "r2d2": + if args.env == "droid": generator.add_param( key="train.data", name="ds", group=2, values=[ - [{"path": p} for p in scan_datasets("~/code/r2d2/data/success/2023-05-23_t2c-cans", postfix="trajectory_im84.h5")], - [{"path": p} for p in scan_datasets("~/code/r2d2/data/success/2023-05-23_t2c-cans", postfix="trajectory_im128.h5")], + [{"path": p} for p in scan_datasets("~/code/droid/data/success/2023-05-23_t2c-cans", postfix="trajectory_im84.h5")], + [{"path": p} for p in scan_datasets("~/code/droid/data/success/2023-05-23_t2c-cans", postfix="trajectory_im128.h5")], ], value_names=[ "pnp-t2c-cans-84", diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py index eefc859c..e6a17265 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py @@ -7,13 +7,16 @@ ############################################################################# # *************** Replace with your paths/config information **************** -DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_r2d2" +# Note: Assumes naming of dataset in "datasets" for the full DROID dataset is +# droid + +DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid" EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" EXP_NAMES = OrderedDict( [ - ("r2_d2__r2_d2_cmu_toaster", {"datasets": ["r2_d2", "r2_d2_cmu_toaster"], + ("droid_droid_cmu_toaster", {"datasets": ["droid", "droid_cmu_toaster"], "sample_weights": [1, 1]}), - ("r2_d2_cmu_toaster", {"datasets": ["r2_d2_cmu_toaster"], + ("droid_cmu_toaster", {"datasets": ["droid"], "sample_weights": [1]}) ]) @@ -60,7 +63,7 @@ def make_generator_helper(args): key="train.shuffle_buffer_size", name="", group=-1, - values=[500000], + values=[500], ) generator.add_param( diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index 42d3a3bd..bf79252e 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -105,17 +105,15 @@ def train(config, device): "absolute_action_mask": is_abs_action, "action_normalization_mask": is_abs_action, "standardize_fn": droid_dataset_transform, - "filter_functions": [ - ModuleSpec.create( - "robomimic.utils.rlds_utils:filter_success" - ), - ] } - # you can add more datasets here & the sampling weights below if you want to mix dataset_names = config.train.dataset_names + filter_functions = [[ModuleSpec.create( + "robomimic.utils.rlds_utils:filter_success" + )] if d_name == "droid" else [] \ + for d_name in dataset_names] dataset_kwargs_list = [ - {"name": d_name, **BASE_DATASET_KWARGS} for d_name in dataset_names + {"name": d_name, "filter_functions": f_functions, **BASE_DATASET_KWARGS} for d_name, f_functions in zip(dataset_names, filter_functions) ] # Compute combined normalization stats combined_dataset_statistics = combine_dataset_statistics( From 1e062968a20d9cc8dab6bca1c1f443381acf31c4 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Tue, 12 Mar 2024 23:52:11 +0000 Subject: [PATCH 37/43] fix shuffle buffer size --- .../scripts/config_gen/droid_runs_language_conditioned_rlds.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py index e6a17265..a779f683 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py @@ -63,7 +63,7 @@ def make_generator_helper(args): key="train.shuffle_buffer_size", name="", group=-1, - values=[500], + values=[500000], ) generator.add_param( From 223292189a0b9e832929607dfdac6da0ad4685dd Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 13 Mar 2024 00:14:01 +0000 Subject: [PATCH 38/43] full clean --- examples/droid_dataloader.py | 4 ++-- .../config_gen/droid_runs_language_conditioned.py | 4 ++-- .../droid_runs_language_conditioned_rlds.py | 13 +++++++------ 3 files changed, 11 insertions(+), 10 deletions(-) diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py index fcddff1a..26b9e31f 100644 --- a/examples/droid_dataloader.py +++ b/examples/droid_dataloader.py @@ -14,7 +14,7 @@ tf.config.set_visible_devices([], "GPU") # ------------------------------ Get Dataset Information ------------------------------ -DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid" +DATA_PATH = "" # UPDATE WITH PATH TO RLDS DATASETS DATASET_NAMES = ["droid"] # You can add additional co-training datasets here sample_weights = [1] # Add to this if you add additional co-training datasets @@ -141,6 +141,6 @@ num_workers=0, # important to keep this to 0 so PyTorch does not mess with the parallelism ) -for i, sample in tqdm.tqdm(enumerate(train_loader)): +for i, sample in tqdm.tqdm(enumerate(train_loader)):i) if i == 5000: break \ No newline at end of file diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py index 4ce66d81..6a392aeb 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned.py @@ -6,9 +6,9 @@ ############################################################################# # *************** Replace with your paths/config information **************** -MANIFEST_PATH = "/mnt/fsx/surajnair/code/droid_cleanup/manifest_test.json" +MANIFEST_PATH = "" # UPDATE WITH PATH TO MANIFEST # MANIFEST_2_PATH = "" -EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" +EXP_LOG_PATH = "" # UPDATE WITH PATH TO DESIRED LOGGING DIRECTORY ## Define training Dataset with open(MANIFEST_PATH, 'r') as file: diff --git a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py index a779f683..c6040497 100644 --- a/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py +++ b/robomimic/scripts/config_gen/droid_runs_language_conditioned_rlds.py @@ -10,14 +10,15 @@ # Note: Assumes naming of dataset in "datasets" for the full DROID dataset is # droid -DATA_PATH = "/mnt/fsx/ashwinbalakrishna/datasets/rlds_droid" -EXP_LOG_PATH = "/mnt/fsx/surajnair/expdata" +DATA_PATH = "" # UPDATE WITH PATH TO RLDS DATASETS +EXP_LOG_PATH = "" # UPDATE WITH PATH TO DESIRED LOGGING DIRECTORY EXP_NAMES = OrderedDict( [ - ("droid_droid_cmu_toaster", {"datasets": ["droid", "droid_cmu_toaster"], - "sample_weights": [1, 1]}), - ("droid_cmu_toaster", {"datasets": ["droid"], - "sample_weights": [1]}) + # Note: you can add co-training dataset here appending + # a new dataset to "datasets" and adjusting "sample_weights" + # accordingly + ("droid", {"datasets": ["droid"], + "sample_weights": [1]}) ]) ############################################################################# From cb5a9350abd2d5d91f2913d6719affee89679b63 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 13 Mar 2024 00:21:50 +0000 Subject: [PATCH 39/43] clean out norm stuff --- examples/droid_dataloader.py | 60 +++--------------------------------- 1 file changed, 4 insertions(+), 56 deletions(-) diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py index 26b9e31f..4836a47c 100644 --- a/examples/droid_dataloader.py +++ b/examples/droid_dataloader.py @@ -14,7 +14,7 @@ tf.config.set_visible_devices([], "GPU") # ------------------------------ Get Dataset Information ------------------------------ -DATA_PATH = "" # UPDATE WITH PATH TO RLDS DATASETS +DATA_PATH = "" DATASET_NAMES = ["droid"] # You can add additional co-training datasets here sample_weights = [1] # Add to this if you add additional co-training datasets @@ -23,57 +23,7 @@ obs_low_dim_modalities = ["robot_state/cartesian_position", "robot_state/gripper_position"] # ------------------------------ Get Action Information ------------------------------ -action_keys = [ - "action/abs_pos", - "action/abs_rot_6d", - "action/gripper_position"] -action_shapes=[ - (1, 3), - (1, 6), - (1, 1)] - -ac_dim = sum([ac_comp[1] for ac_comp in action_shapes]) - -action_config = { - "action/cartesian_position":{ - "normalization": "min_max", - }, - "action/abs_pos":{ - "normalization": "min_max", - }, - "action/abs_rot_6d":{ - "normalization": "min_max", - "format": "rot_6d", - "convert_at_runtime": "rot_euler", - }, - "action/abs_rot_euler":{ - "normalization": "min_max", - "format": "rot_euler", - }, - "action/gripper_position":{ - "normalization": "min_max", - }, - "action/cartesian_velocity":{ - "normalization": None, - }, - "action/rel_pos":{ - "normalization": None, - }, - "action/rel_rot_6d":{ - "format": "rot_6d", - "normalization": None, - "convert_at_runtime": "rot_euler", - }, - "action/rel_rot_euler":{ - "format": "rot_euler", - "normalization": None, - }, - "action/gripper_velocity":{ - "normalization": None, - }, -} - -is_abs_action = [action_config[k]["normalization"] != None for k in action_config.keys()] +is_abs_action = [True] * 5 + [False] * 5 # ------------------------------ Construct Dataset ------------------------------ BASE_DATASET_KWARGS = { @@ -95,7 +45,8 @@ dataset_kwargs_list = [ {"name": d_name, "filter_functions": f_functions, **BASE_DATASET_KWARGS} for d_name, f_functions in zip(DATASET_NAMES, filter_functions) ] -# Compute combined normalization stats + +# Compute combined normalization stats. Note: can also set this to None to normalize each dataset separately. combined_dataset_statistics = combine_dataset_statistics( [make_dataset_from_rlds(**dataset_kwargs, train=True)[1] for dataset_kwargs in dataset_kwargs_list] ) @@ -127,9 +78,6 @@ traj_read_threads=48, ) -rlds_dataset_stats = dataset.dataset_statistics -action_stats = ActionUtils.get_action_stats_dict(rlds_dataset_stats["action"], action_keys, action_shapes) -action_normalization_stats = action_stats_to_normalization_stats(action_stats, action_config) dataset = dataset.map(robomimic_transform, num_parallel_calls=48) # ------------------------------ Create Dataloader ------------------------------ From 0725feb9c57fc830a3a0f21101d58db8624d0e66 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 13 Mar 2024 00:23:01 +0000 Subject: [PATCH 40/43] fix comment --- examples/droid_dataloader.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py index 4836a47c..36c03d82 100644 --- a/examples/droid_dataloader.py +++ b/examples/droid_dataloader.py @@ -14,7 +14,7 @@ tf.config.set_visible_devices([], "GPU") # ------------------------------ Get Dataset Information ------------------------------ -DATA_PATH = "" +DATA_PATH = "" # UPDATE WITH PATH TO RLDS DATASETS DATASET_NAMES = ["droid"] # You can add additional co-training datasets here sample_weights = [1] # Add to this if you add additional co-training datasets @@ -46,7 +46,7 @@ {"name": d_name, "filter_functions": f_functions, **BASE_DATASET_KWARGS} for d_name, f_functions in zip(DATASET_NAMES, filter_functions) ] -# Compute combined normalization stats. Note: can also set this to None to normalize each dataset separately. +# Compute combined normalization stats. Note: can also set this to None to normalize each dataset separately combined_dataset_statistics = combine_dataset_statistics( [make_dataset_from_rlds(**dataset_kwargs, train=True)[1] for dataset_kwargs in dataset_kwargs_list] ) From 4ffb38330ebe6548e394b93f23cf3ded77ea3d11 Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 13 Mar 2024 00:27:24 +0000 Subject: [PATCH 41/43] fix typo --- examples/droid_dataloader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py index 36c03d82..1b281009 100644 --- a/examples/droid_dataloader.py +++ b/examples/droid_dataloader.py @@ -89,6 +89,6 @@ num_workers=0, # important to keep this to 0 so PyTorch does not mess with the parallelism ) -for i, sample in tqdm.tqdm(enumerate(train_loader)):i) +for i, sample in tqdm.tqdm(enumerate(train_loader)): if i == 5000: break \ No newline at end of file From eae359b25169b2e6ea3bf0f89883b09c8e23cc3a Mon Sep 17 00:00:00 2001 From: Ashwin Balakrishna Date: Wed, 13 Mar 2024 04:36:27 +0000 Subject: [PATCH 42/43] fix abs action --- examples/droid_dataloader.py | 2 +- robomimic/scripts/train.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/droid_dataloader.py b/examples/droid_dataloader.py index 1b281009..ae5c5bfc 100644 --- a/examples/droid_dataloader.py +++ b/examples/droid_dataloader.py @@ -23,7 +23,7 @@ obs_low_dim_modalities = ["robot_state/cartesian_position", "robot_state/gripper_position"] # ------------------------------ Get Action Information ------------------------------ -is_abs_action = [True] * 5 + [False] * 5 +is_abs_action = [True] * 10 # ------------------------------ Construct Dataset ------------------------------ BASE_DATASET_KWARGS = { diff --git a/robomimic/scripts/train.py b/robomimic/scripts/train.py index bf79252e..ccd68dfc 100644 --- a/robomimic/scripts/train.py +++ b/robomimic/scripts/train.py @@ -93,7 +93,7 @@ def train(config, device): assert(len(obs_modalities) == 2) ac_dim = sum([ac_comp[1] for ac_comp in config.train.action_shapes]) action_config = config.train.action_config - is_abs_action = [action_config[k]["normalization"] != None for k in action_config.keys()] + is_abs_action = [True] * ac_dim BASE_DATASET_KWARGS = { "data_dir": config.train.data_path, From 58e48fdbc022d902bc3052522f2537b3f44827ee Mon Sep 17 00:00:00 2001 From: Karl Pertsch Date: Wed, 13 Mar 2024 14:29:01 -0700 Subject: [PATCH 43/43] Update README.md --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 0ca6097c..db106708 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # DROID Policy Learning and Evaluation -This repository contains code for training and evaluating policies on the DROID(TODO(Karl/Sasha): add link to DROID website) dataset. DROID is a large-scale, in-the-wild robot manipulation dataset. This codebase is built as a fork of [`robomimic`](https://robomimic.github.io/), a popular repository for imitation learning algorithm development. For more information about DROID, please see the following links: [**[Homepage]**](XXX)   [**[Documentation]**](XXX)   [**[Paper]**](XXX)   [**[Dataset Visualizer]**](XXX). +This repository contains code for training and evaluating policies on the DROID(TODO(Karl/Sasha): add link to DROID website) dataset. DROID is a large-scale, in-the-wild robot manipulation dataset. This codebase is built as a fork of [`robomimic`](https://robomimic.github.io/), a popular repository for imitation learning algorithm development. For more information about DROID, please see the following links: + +[**[Homepage]**](XXX)   [**[Documentation]**](XXX)   [**[Paper]**](XXX)   [**[Dataset Visualizer]**](XXX). ------- ## Installation

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