From 4d290ec7139c0858aebc81a7a7eb7ce8d564e899 Mon Sep 17 00:00:00 2001 From: Haodong Duan Date: Sat, 2 Apr 2022 22:15:54 +0800 Subject: [PATCH] Initial Commit --- .github/workflows/lint.yml | 27 + .gitignore | 141 +++ .pre-commit-config.yaml | 38 + LICENSE | 203 ++++ README.md | 47 + configs/posec3d/README.md | 118 ++ configs/posec3d/c3d_light_gym/joint.py | 73 ++ configs/posec3d/c3d_light_gym/limb.py | 80 ++ configs/posec3d/c3d_light_ntu60_xsub/joint.py | 73 ++ configs/posec3d/c3d_light_ntu60_xsub/limb.py | 79 ++ configs/posec3d/slowonly_r50_gym/joint.py | 79 ++ configs/posec3d/slowonly_r50_gym/limb.py | 86 ++ .../slowonly_r50_hmdb51_k400p/s1_joint.py | 81 ++ .../posec3d/slowonly_r50_ntu120_xset/joint.py | 79 ++ .../posec3d/slowonly_r50_ntu120_xset/limb.py | 85 ++ .../posec3d/slowonly_r50_ntu120_xsub/joint.py | 85 ++ .../posec3d/slowonly_r50_ntu120_xsub/limb.py | 91 ++ .../posec3d/slowonly_r50_ntu60_xsub/joint.py | 79 ++ .../posec3d/slowonly_r50_ntu60_xsub/limb.py | 85 ++ 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create mode 100644 pyskl/core/hooks.py create mode 100644 pyskl/datasets/__init__.py create mode 100644 pyskl/datasets/base.py create mode 100644 pyskl/datasets/builder.py create mode 100644 pyskl/datasets/dataset_wrappers.py create mode 100644 pyskl/datasets/pipelines/__init__.py create mode 100644 pyskl/datasets/pipelines/augmentations.py create mode 100644 pyskl/datasets/pipelines/compose.py create mode 100644 pyskl/datasets/pipelines/formatting.py create mode 100644 pyskl/datasets/pipelines/loading.py create mode 100644 pyskl/datasets/pipelines/pose_related.py create mode 100644 pyskl/datasets/pipelines/posec3d_related.py create mode 100644 pyskl/datasets/pipelines/sampling.py create mode 100644 pyskl/datasets/pose_dataset.py create mode 100644 pyskl/datasets/samplers/__init__.py create mode 100644 pyskl/datasets/samplers/distributed_sampler.py create mode 100644 pyskl/datasets/video_dataset.py create mode 100644 pyskl/models/__init__.py create mode 100644 pyskl/models/builder.py create mode 100644 pyskl/models/cnns/__init__.py create mode 100644 pyskl/models/cnns/c3d.py create mode 100644 pyskl/models/cnns/resnet.py create mode 100644 pyskl/models/cnns/resnet3d.py create mode 100644 pyskl/models/cnns/resnet3d_slowfast.py create mode 100644 pyskl/models/cnns/resnet3d_slowonly.py create mode 100644 pyskl/models/cnns/x3d.py create mode 100644 pyskl/models/gcns/__init__.py create mode 100644 pyskl/models/gcns/stgcn.py create mode 100644 pyskl/models/gcns/utils/__init__.py create mode 100644 pyskl/models/gcns/utils/gcn.py create mode 100644 pyskl/models/gcns/utils/tcn.py create mode 100644 pyskl/models/heads/__init__.py create mode 100644 pyskl/models/heads/base.py create mode 100644 pyskl/models/heads/simple_head.py create mode 100644 pyskl/models/losses/__init__.py create mode 100644 pyskl/models/losses/base.py create mode 100644 pyskl/models/losses/cross_entropy_loss.py create mode 100644 pyskl/models/recognizers/__init__.py create mode 100644 pyskl/models/recognizers/base.py create mode 100644 pyskl/models/recognizers/recognizer2d.py create mode 100644 pyskl/models/recognizers/recognizer3d.py create mode 100644 pyskl/models/recognizers/recognizergcn.py create mode 100644 pyskl/smp.py create mode 100644 pyskl/utils/__init__.py create mode 100644 pyskl/utils/collect_env.py create mode 100644 pyskl/utils/graph.py create mode 100644 pyskl/utils/misc.py create mode 100644 pyskl/utils/visualize.py create mode 100644 pyskl/version.py create mode 100644 requirements.txt create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 tools/data_list.md create mode 100644 tools/dist_test.sh create mode 100644 tools/dist_train.sh create mode 100644 tools/slurm_test.sh create mode 100644 tools/slurm_train.sh create mode 100644 tools/test.py create mode 100644 tools/train.py diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 00000000..be297e79 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,27 @@ +name: lint + +on: [push, pull_request] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + - name: Install pre-commit hook + run: | + # markdownlint requires ruby >= 2.7 + sudo apt-add-repository ppa:brightbox/ruby-ng -y + sudo apt-get update + sudo apt-get install -y ruby2.7 + pip install pre-commit + pre-commit install + - name: Linting + run: pre-commit run --all-files diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..86ffaaed --- /dev/null +++ b/.gitignore @@ -0,0 +1,141 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +**/*.pyc + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +# custom +/data +.vscode +.idea +*.pkl +*.pkl.json +*.log.json +benchlist.txt +work_dirs/ + +# Pytorch +*.pth + +# Profile +*.prof + +# lmdb +*.mdb + +# unignore some data file in tests/data +!tests/data/**/*.pkl +!tests/data/**/*.pkl.json +!tests/data/**/*.log.json +!tests/data/**/*.pth + +# avoid soft links created by MIM +pyskl/configs/* +pyskl/tools/* +*.ipynb + +# unignore ipython notebook files in demo +!demo/*.ipynb +pyskl/.mim +.VScodeCounter +tmp/* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..f535789b --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,38 @@ +exclude: > + (?x)^( + ^tests/data/ + ^configs/ + )$ +repos: + - repo: https://gitlab.com/pycqa/flake8.git + rev: 3.8.3 + hooks: + - id: flake8 + args: ["--max-line-length=120"] + exclude: ^configs/ + - repo: https://github.com/asottile/seed-isort-config + rev: v2.2.0 + hooks: + - id: seed-isort-config + - repo: https://github.com/timothycrosley/isort + rev: 4.3.21 + hooks: + - id: isort + - repo: https://github.com/pre-commit/mirrors-yapf + rev: v0.30.0 + hooks: + - id: yapf + args: ["--style={column_limit=120}"] + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.1.0 + hooks: + - id: trailing-whitespace + - id: check-yaml + - id: end-of-file-fixer + - id: requirements-txt-fixer + - id: double-quote-string-fixer + - id: check-merge-conflict + - id: fix-encoding-pragma + args: ["--remove"] + - id: mixed-line-ending + args: ["--fix=lf"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..04adf5cb --- /dev/null +++ b/LICENSE @@ -0,0 +1,203 @@ +Copyright 2018-2019 Open-MMLab. 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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 2018-2019 Open-MMLab. + + 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/README.md b/README.md new file mode 100644 index 00000000..b6ccd028 --- /dev/null +++ b/README.md @@ -0,0 +1,47 @@ +# PYSKL + +PYSKL is a toolbox focusing on action recognition based on **SK**e**L**eton data with **PY**Torch. Various algorithms will be supported for skeleton-based action recognition. We build this project based on the OpenSource Project [MMAction2](https://github.com/open-mmlab/mmaction2). + +This repo is the official implementation of [PoseConv3D](https://arxiv.org/abs/2104.13586) and [STGCN++](). + +## Supported Algorithms + +- [x] ST-GCN (AAAI 2018): https://arxiv.org/abs/1801.07455 [[MODELZOO](/configs/stgcn/README.md)] +- [x] ST-GCN++ (PYSKL): [Tech Report Coming Soon]() [[MODELZOO](/configs/stgcn++/README.md)] +- [x] PoseConv3D (CVPR 2022 Oral): https://arxiv.org/abs/2104.13586 [[MODELZOO](/configs/posec3d/README.md)] + +## Installation +```shell +git clone https://github.com/kennymckormick/pyskl.git +cd pyskl +pip install -r requirements.txt +pip install -e . +``` + +## Data Preparation +For data pre-processing, we estimate 2D skeletons with a two-stage pose estimator (Faster-RCNN + HRNet). For 3D skeletons, we follow the pre-processing procedure of [CTR-GCN](https://github.com/Uason-Chen/CTR-GCN). Currently, we do not provide the pre-processing scripts. Instead, we directly provide the [processed skeleton data](/tools/data_list.md) as pickle files, which can be directly used in training and evaluation. You can use [vis_skeleton](/demo/vis_skeleton.ipynb) to visualize the provided skeleton data. + + +## Training & Testing +You can use following commands for training and testing. Basically, we support distribued training on a single server with multiple GPUs. +```shell +# Training +bash tools/dist_train.sh {config_name} {num_gpus} {other_options} +# Testing +bash tools/dist_test.sh {config_name} {checkpoint} {num_gpus} --out {output_file} --eval top_k_accuracy mean_class_accuracy +``` +For specific examples, please go to the README for each specific algorithm we supported. + +## Citation + +If you use PYSKL in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry and the BibTex entry corresponding to the specific algorithm you used. + +```BibTeX +% Tech Report Coming Soon! +@misc{duan2022pyskl, + title={PYSKL: a toolbox for skeleton-based video understanding}, + author={PYSKL Contributors}, + howpublished = {\url{https://github.com/kennymckormick/pyskl}}, + year={2022} +} +``` diff --git a/configs/posec3d/README.md b/configs/posec3d/README.md new file mode 100644 index 00000000..9528cb7f --- /dev/null +++ b/configs/posec3d/README.md @@ -0,0 +1,118 @@ +# PoseC3D + +## Introduction + +PoseC3D is the first framework that formats 2D human skeletons as 3D voxels and processes human skeletons with 3D ConvNets. We release multiple PoseC3D variants instantiated with different backbones and trained on different datasets. + +
+ +
+ + +## Citation + +```BibTeX +@misc{duan2021revisiting, + title={Revisiting Skeleton-based Action Recognition}, + author={Haodong Duan and Yue Zhao and Kai Chen and Dian Shao and Dahua Lin and Bo Dai}, + year={2021}, + eprint={2104.13586}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Model Zoo + +We release numerous weights trained on various datasets and with multiple 3D backbones. The accuracy of each modality links to the weight file. + +| Dataset | Backbone | Annotation | Pretrain | GPUs | Joint Top-1 | Limb Top-1 | Two-Stream Top1 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| NTURGB+D XSub | SlowOnly-R50 | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/slowonly_r50_ntu60_xsub/joint.py): [93.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu60_xsub/joint.pth) | [limb_config](/configs/posec3d/slowonly_r50_ntu60_xsub/limb.py): [93.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu60_xsub/limb.pth) | 94.1 | +| NTURGB+D XSub | C3D-light | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/c3d_light_ntu60_xsub/joint.py): [92.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/c3d_light_ntu60_xsub/joint.pth) | [limb_config](/configs/posec3d/c3d_light_ntu60_xsub/limb.py): [92.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/c3d_light_ntu60_xsub/limb.pth) | 93.3 | +| NTURGB+D XSub | X3D-Shallow | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/x3d_shallow_ntu60_xsub/joint.py): [92.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/x3d_shallow_ntu60_xsub/joint.pth) | [limb_config](/configs/posec3d/x3d_shallow_ntu60_xsub/limb.py): [91.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/x3d_shallow_ntu60_xsub/limb.pth) | 92.4 | +| NTURGB+D XView | SlowOnly-R50 | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/slowonly_r50_ntu60_xview/joint.py): [96.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu60_xview/joint.pth) | [limb_config](/configs/posec3d/slowonly_r50_ntu60_xview/limb.py): [96.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu60_xview/limb.pth) | 96.9 | +| NTURGB+D 120 XSub | SlowOnly-R50 | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/slowonly_r50_ntu120_xsub/joint.py): [85.9](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu120_xsub/joint.pth) | [limb_config](/configs/posec3d/slowonly_r50_ntu120_xsub/limb.py): [85.9](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu120_xsub/limb.pth) | 86.7 | +| NTURGB+D 120 XSet | SlowOnly-R50 | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/slowonly_r50_ntu120_xset/joint.py): [89.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu120_xset/joint.pth) | [limb_config](/configs/posec3d/slowonly_r50_ntu120_xset/limb.py): [89.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ntu120_xset/limb.pth) | 90.3 | +| FineGYM¹ | SlowOnly-R50 | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/slowonly_r50_gym/joint.py): [93.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_gym/joint.pth) | [limb_config](/configs/posec3d/slowonly_r50_gym/limb.py): [93.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_gym/limb.pth) | 94.1 | +| FineGYM¹ | C3D-light | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/c3d_light_gym/joint.py): [91.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/c3d_light_gym/joint.pth) | [limb_config](/configs/posec3d/c3d_light_gym/limb.py): [91.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/c3d_light_gym/limb.pth) | 92.1 | +| FineGYM¹ | X3D-shallow | HRNet 2D Pose | None | 8 | [joint_config](/configs/posec3d/x3d_shallow_gym/joint.py): [91.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/x3d_shallow_gym/joint.pth) | [limb_config](/configs/posec3d/x3d_shallow_gym/limb.py): [90.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/x3d_shallow_gym/limb.pth) | 91.8 | +| UCF101² | SlowOnly-R50 | HRNet 2D Pose | Kinetics-400 | 8 | [joint_config](/configs/posec3d/slowonly_r50_ucf101_k400p/s1_joint.py): [86.9](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_ucf101_k400p/s1_joint.pth) | | | +| HMDB51² | SlowOnly-R50 | HRNet 2D Pose | Kinetics-400 | 8 | [joint_config](/configs/posec3d/slowonly_r50_hmdb51_k400p/s1_joint.py): [69.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/posec3d/slowonly_r50_hmdb51_k400p/s1_joint.pth) | | | + +**Note** + +1. For FineGYM, we report the mean class Top-1 accuracy instead of the Top-1 accuracy. + +2. For UCF101 and HMDB51, we provide the checkpoints trained on the official split 1. + +3. We use linear scaling learning rate (`Initial LR` ∝ `Batch Size`). If you change the training batch size, remember to change the initial LR proportionally. + +4. Though optimized, multi-clip testing may consumes large amounts of time. For faster inference, you may change the test_pipeline to disable the multi-clip testing, this may lead to a small drop in recognition performance. Below is the guide: + + ```python + test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), # Change `num_clips=10` to `num_clips=1` + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), # Change `double=True` to `double=False` + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) + ] + ``` + +## Demonstration of heatmap volumes + + + + + + + + + +
+
+ Pose Estimation Results +
+ +
+
+ +
 +
+ Keypoint Heatmap Volume Visualization +
+ +
+
+ +
 +
+ Limb Heatmap Volume Visualization +
+ +
+
+ +
+ +## Training & Testing + +You can use the following command to train a model. + +```shell +bash tools/dist_train.sh ${CONFIG_FILE} ${NUM_GPUS} [optional arguments] +# For example: train PoseC3D on FineGYM (HRNet 2D skeleton, Joint Modality) with 8 GPUs, with validation, and test the last and the best (with best validation metric) checkpoint. +bash tools/dist_train.sh configs/posec3d/slowonly_r50_gym/joint.py 8 --validate --test-last --test-best +``` + +You can use the following command to test a model. + +```shell +bash tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${NUM_GPUS} [optional arguments] +# For example: test PoseC3D on FineGYM (HRNet 2D skeleton, Joint Modality) with metrics `top_k_accuracy` and `mean_class_accuracy`, and dump the result to `result.pkl`. +bash tools/dist_test.sh configs/posec3d/slowonly_r50_gym/joint.py checkpoints/SOME_CHECKPOINT.pth 8 --eval top_k_accuracy mean_class_accuracy --out result.pkl +``` diff --git a/configs/posec3d/c3d_light_gym/joint.py b/configs/posec3d/c3d_light_gym/joint.py new file mode 100644 index 00000000..d8037abd --- /dev/null +++ b/configs/posec3d/c3d_light_gym/joint.py @@ -0,0 +1,73 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='C3D', + in_channels=17, + base_channels=32, + num_stages=3, + temporal_downsample=False), + cls_head=dict( + type='I3DHead', + in_channels=256, + num_classes=99, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/c3d_light_gym/joint' diff --git a/configs/posec3d/c3d_light_gym/limb.py b/configs/posec3d/c3d_light_gym/limb.py new file mode 100644 index 00000000..33d692e5 --- /dev/null +++ b/configs/posec3d/c3d_light_gym/limb.py @@ -0,0 +1,80 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='C3D', + in_channels=17, + base_channels=32, + num_stages=3, + temporal_downsample=False), + cls_head=dict( + type='I3DHead', + in_channels=256, + num_classes=99, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/posec3d/c3d_light_gym/limb' diff --git a/configs/posec3d/c3d_light_ntu60_xsub/joint.py b/configs/posec3d/c3d_light_ntu60_xsub/joint.py new file mode 100644 index 00000000..d4f51309 --- /dev/null +++ b/configs/posec3d/c3d_light_ntu60_xsub/joint.py @@ -0,0 +1,73 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='C3D', + in_channels=17, + base_channels=32, + num_stages=3, + temporal_downsample=False), + cls_head=dict( + type='I3DHead', + in_channels=256, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xsub_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/c3d_light_ntu60_xsub/joint' diff --git a/configs/posec3d/c3d_light_ntu60_xsub/limb.py b/configs/posec3d/c3d_light_ntu60_xsub/limb.py new file mode 100644 index 00000000..def2f596 --- /dev/null +++ b/configs/posec3d/c3d_light_ntu60_xsub/limb.py @@ -0,0 +1,79 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='C3D', + in_channels=17, + base_channels=32, + num_stages=3, + temporal_downsample=False), + cls_head=dict( + type='I3DHead', + in_channels=256, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xsub_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/c3d_light_ntu60_xsub/limb' diff --git a/configs/posec3d/slowonly_r50_gym/joint.py b/configs/posec3d/slowonly_r50_gym/joint.py new file mode 100644 index 00000000..84bcb135 --- /dev/null +++ b/configs/posec3d/slowonly_r50_gym/joint.py @@ -0,0 +1,79 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=99, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_gym/joint' diff --git a/configs/posec3d/slowonly_r50_gym/limb.py b/configs/posec3d/slowonly_r50_gym/limb.py new file mode 100644 index 00000000..60bec9f8 --- /dev/null +++ b/configs/posec3d/slowonly_r50_gym/limb.py @@ -0,0 +1,86 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=99, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_gym/limb' diff --git a/configs/posec3d/slowonly_r50_hmdb51_k400p/s1_joint.py b/configs/posec3d/slowonly_r50_hmdb51_k400p/s1_joint.py new file mode 100644 index 00000000..713afdba --- /dev/null +++ b/configs/posec3d/slowonly_r50_hmdb51_k400p/s1_joint.py @@ -0,0 +1,81 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(3, 4, 6), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=51, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/hmdb51/hmdb51_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(48, 48), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train1', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='test1', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='test1', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[9, 11]) +total_epochs = 12 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_hmdb51_k400p/s1_joint' +load_from = 'https://download.openmmlab.com/mmaction/skeleton/posec3d/k400_posec3d-041f49c6.pth' # noqa: E501 +find_unused_parameters = True diff --git a/configs/posec3d/slowonly_r50_ntu120_xset/joint.py b/configs/posec3d/slowonly_r50_ntu120_xset/joint.py new file mode 100644 index 00000000..f7bb2d3b --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu120_xset/joint.py @@ -0,0 +1,79 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=120, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xset_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xset_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xset_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu120_xset/joint' diff --git a/configs/posec3d/slowonly_r50_ntu120_xset/limb.py b/configs/posec3d/slowonly_r50_ntu120_xset/limb.py new file mode 100644 index 00000000..860dc8b9 --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu120_xset/limb.py @@ -0,0 +1,85 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=120, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xset_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xset_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xset_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu120_xset/limb' diff --git a/configs/posec3d/slowonly_r50_ntu120_xsub/joint.py b/configs/posec3d/slowonly_r50_ntu120_xsub/joint.py new file mode 100644 index 00000000..998f379c --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu120_xsub/joint.py @@ -0,0 +1,85 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=120, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +class_prob = [1] * 60 + [2] * 60 +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict( + type=dataset_type, + ann_file=ann_file, + split='xsub_train', + pipeline=train_pipeline, + class_prob=class_prob)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu120_xsub/joint' diff --git a/configs/posec3d/slowonly_r50_ntu120_xsub/limb.py b/configs/posec3d/slowonly_r50_ntu120_xsub/limb.py new file mode 100644 index 00000000..091df9b1 --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu120_xsub/limb.py @@ -0,0 +1,91 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=120, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +class_prob = [1] * 60 + [2] * 60 +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict( + type=dataset_type, + ann_file=ann_file, + split='xsub_train', + pipeline=train_pipeline, + class_prob=class_prob)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu120_xsub/limb' diff --git a/configs/posec3d/slowonly_r50_ntu60_xsub/joint.py b/configs/posec3d/slowonly_r50_ntu60_xsub/joint.py new file mode 100644 index 00000000..6428de92 --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu60_xsub/joint.py @@ -0,0 +1,79 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xsub_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu60_xsub/joint' diff --git a/configs/posec3d/slowonly_r50_ntu60_xsub/limb.py b/configs/posec3d/slowonly_r50_ntu60_xsub/limb.py new file mode 100644 index 00000000..d38d81b0 --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu60_xsub/limb.py @@ -0,0 +1,85 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xsub_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu60_xsub/limb' diff --git a/configs/posec3d/slowonly_r50_ntu60_xview/joint.py b/configs/posec3d/slowonly_r50_ntu60_xview/joint.py new file mode 100644 index 00000000..63ed9ae8 --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu60_xview/joint.py @@ -0,0 +1,79 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xview_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xview_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xview_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu60_xview/joint' diff --git a/configs/posec3d/slowonly_r50_ntu60_xview/limb.py b/configs/posec3d/slowonly_r50_ntu60_xview/limb.py new file mode 100644 index 00000000..09d0a5aa --- /dev/null +++ b/configs/posec3d/slowonly_r50_ntu60_xview/limb.py @@ -0,0 +1,85 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(4, 6, 3), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xview_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xview_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xview_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ntu60_xview/limb' diff --git a/configs/posec3d/slowonly_r50_ucf101_k400p/s1_joint.py b/configs/posec3d/slowonly_r50_ucf101_k400p/s1_joint.py new file mode 100644 index 00000000..d487daf1 --- /dev/null +++ b/configs/posec3d/slowonly_r50_ucf101_k400p/s1_joint.py @@ -0,0 +1,81 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='ResNet3dSlowOnly', + in_channels=17, + base_channels=32, + num_stages=3, + out_indices=(2, ), + stage_blocks=(3, 4, 6), + conv1_stride=(1, 1), + pool1_stride=(1, 1), + inflate=(0, 1, 1), + spatial_strides=(2, 2, 2), + temporal_strides=(1, 1, 2)), + cls_head=dict( + type='I3DHead', + in_channels=512, + num_classes=101, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/ucf101/ucf101_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(48, 48), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train1', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='test1', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='test1', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[9, 11]) +total_epochs = 12 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/slowonly_r50_ucf101_k400p/s1_joint' +load_from = 'https://download.openmmlab.com/mmaction/skeleton/posec3d/k400_posec3d-041f49c6.pth' # noqa: E501 +find_unused_parameters = True diff --git a/configs/posec3d/x3d_shallow_gym/joint.py b/configs/posec3d/x3d_shallow_gym/joint.py new file mode 100644 index 00000000..bfa4ec86 --- /dev/null +++ b/configs/posec3d/x3d_shallow_gym/joint.py @@ -0,0 +1,77 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='X3D', + gamma_d=1, + in_channels=17, + base_channels=24, + num_stages=3, + se_ratio=None, + use_swish=False, + stage_blocks=(2, 5, 3), + spatial_strides=(2, 2, 2)), + cls_head=dict( + type='I3DHead', + in_channels=216, + num_classes=99, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/x3d_shallow_gym/joint' diff --git a/configs/posec3d/x3d_shallow_gym/limb.py b/configs/posec3d/x3d_shallow_gym/limb.py new file mode 100644 index 00000000..db4313a0 --- /dev/null +++ b/configs/posec3d/x3d_shallow_gym/limb.py @@ -0,0 +1,84 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='X3D', + gamma_d=1, + in_channels=17, + base_channels=24, + num_stages=3, + se_ratio=None, + use_swish=False, + stage_blocks=(2, 5, 3), + spatial_strides=(2, 2, 2)), + cls_head=dict( + type='I3DHead', + in_channels=216, + num_classes=99, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/gym/gym_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/posec3d/x3d_shallow_gym/limb' diff --git a/configs/posec3d/x3d_shallow_ntu60_xsub/joint.py b/configs/posec3d/x3d_shallow_ntu60_xsub/joint.py new file mode 100644 index 00000000..70129504 --- /dev/null +++ b/configs/posec3d/x3d_shallow_ntu60_xsub/joint.py @@ -0,0 +1,77 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='X3D', + gamma_d=1, + in_channels=17, + base_channels=24, + num_stages=3, + se_ratio=None, + use_swish=False, + stage_blocks=(2, 5, 3), + spatial_strides=(2, 2, 2)), + cls_head=dict( + type='I3DHead', + in_channels=216, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=True, with_limb=False, double=True, left_kp=left_kp, right_kp=right_kp), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xsub_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/x3d_shallow_ntu60_xsub/joint' diff --git a/configs/posec3d/x3d_shallow_ntu60_xsub/limb.py b/configs/posec3d/x3d_shallow_ntu60_xsub/limb.py new file mode 100644 index 00000000..4a53cc5c --- /dev/null +++ b/configs/posec3d/x3d_shallow_ntu60_xsub/limb.py @@ -0,0 +1,83 @@ +model = dict( + type='Recognizer3D', + backbone=dict( + type='X3D', + gamma_d=1, + in_channels=17, + base_channels=24, + num_stages=3, + se_ratio=None, + use_swish=False, + stage_blocks=(2, 5, 3), + spatial_strides=(2, 2, 2)), + cls_head=dict( + type='I3DHead', + in_channels=216, + num_classes=60, + dropout=0.5), + test_cfg=dict(average_clips='prob')) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +left_kp = [1, 3, 5, 7, 9, 11, 13, 15] +right_kp = [2, 4, 6, 8, 10, 12, 14, 16] +skeletons = [[0, 5], [0, 6], [5, 7], [7, 9], [6, 8], [8, 10], [5, 11], + [11, 13], [13, 15], [6, 12], [12, 14], [14, 16], [0, 1], [0, 2], + [1, 3], [2, 4], [11, 12]] +left_limb = [0, 2, 3, 6, 7, 8, 12, 14] +right_limb = [1, 4, 5, 9, 10, 11, 13, 15] +train_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(-1, 64)), + dict(type='RandomResizedCrop', area_range=(0.56, 1.0)), + dict(type='Resize', scale=(56, 56), keep_ratio=False), + dict(type='Flip', flip_ratio=0.5, left_kp=left_kp, right_kp=right_kp), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs', 'label']) +] +val_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +test_pipeline = [ + dict(type='UniformSampleFrames', clip_len=48, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True), + dict(type='Resize', scale=(64, 64), keep_ratio=False), + dict(type='GeneratePoseTarget', with_kp=False, with_limb=True, skeletons=skeletons, + double=True, left_kp=left_kp, right_kp=right_kp, left_limb=left_limb, right_limb=right_limb), + dict(type='FormatShape', input_format='NCTHW_Heatmap'), + dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['imgs']) +] +data = dict( + videos_per_gpu=32, + workers_per_gpu=4, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=10, + dataset=dict(type=dataset_type, ann_file=ann_file, split='xsub_train', pipeline=train_pipeline)), + val=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=val_pipeline), + test=dict(type=dataset_type, ann_file=ann_file, split='xsub_val', pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003) # this lr is used for 8 gpus +optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2)) +# learning policy +lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0) +total_epochs = 24 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5)) +log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')]) +log_level = 'INFO' +work_dir = './work_dirs/posec3d/x3d_shallow_ntu60_xsub/limb' diff --git a/configs/stgcn++/README.md b/configs/stgcn++/README.md new file mode 100644 index 00000000..bedeb8f9 --- /dev/null +++ b/configs/stgcn++/README.md @@ -0,0 +1,55 @@ +# STGCN++ + +## Introduction + +STGCN++ is a variant of STGCN we developed in PYSKL with some modifications in the architecture of the spatial module and the temporal module. We provide STGCN++ trained on NTURGB+D with 2D skeletons (HRNet) and 3D skeletons with **PYSKL** training setting. We provide checkpoints for four modalities: Joint, Bone, Joint Motion, and Bone Motion. We will describe the architecture of STGCN++ in the upcoming tech report. + +## Citation + +```BibTeX +@misc{duan2022pyskl, + title={PYSKL: a toolbox for skeleton-based video understanding}, + author={PYSKL Contributors}, + howpublished = {\url{https://github.com/kennymckormick/pyskl}}, + year={2022} +} +``` + +## Model Zoo + +We release numerous checkpoints trained with various modalities, annotations on NTURGB+D and NTURGB+D 120. The accuracy of each modality links to the weight file. + +| Dataset | Annotation | GPUs | Joint Top1 | Bone Top1 | Joint Motion Top1 | Bone-Motion Top1 | Two-Stream Top1 | Four Stream Top1 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| NTURGB+D XSub | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/j.py): [89.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_3dkp/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/b.py): [90.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_3dkp/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/jm.py): [87.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/bm.py): [87.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_3dkp/bm.pth) | 91.4 | 92.1 | +| NTURGB+D XSub | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/j.py): [89.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_hrnet/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/b.py): [92.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_hrnet/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/jm.py): [84.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/bm.py): [88.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xsub_hrnet/bm.pth) | 92.8 | 93.2 | +| NTURGB+D XView | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu60_xview_3dkp/j.py): [95.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_3dkp/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu60_xview_3dkp/b.py): [95.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_3dkp/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu60_xview_3dkp/jm.py): [94.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu60_xview_3dkp/bm.py): [93.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_3dkp/bm.pth) | 96.7 | 97.0 | +| NTURGB+D XView | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu60_xview_hrnet/j.py): [97.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_hrnet/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu60_xview_hrnet/b.py): [97.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_hrnet/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu60_xview_hrnet/jm.py): [93.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu60_xview_hrnet/bm.py): [95.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu60_xview_hrnet/bm.pth) | 98.4 | 98.5 | +| NTURGB+D 120 XSub | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/j.py): [83.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_3dkp/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/b.py): [85.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_3dkp/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/jm.py): [80.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/bm.py): [81.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_3dkp/bm.pth) | 87.0 | 87.5 | +| NTURGB+D 120 XSub | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/j.py): [84.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_hrnet/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/b.py): [84.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_hrnet/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/jm.py): [76.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/bm.py): [81.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xsub_hrnet/bm.pth) | 86.4 | 86.4 | +| NTURGB+D 120 XSet | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu120_xset_3dkp/j.py): [85.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_3dkp/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu120_xset_3dkp/b.py): [87.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_3dkp/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu120_xset_3dkp/jm.py): [84.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu120_xset_3dkp/bm.py): [83.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_3dkp/bm.pth) | 89.1 | 89.8 | +| NTURGB+D 120 XSet | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn++/stgcn++_ntu120_xset_hrnet/j.py): [88.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_hrnet/j.pth) | [bone_config](/configs/stgcn++/stgcn++_ntu120_xset_hrnet/b.py): [88.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_hrnet/b.pth) | [joint_motion_config](/configs/stgcn++/stgcn++_ntu120_xset_hrnet/jm.py): [82.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn++/stgcn++_ntu120_xset_hrnet/bm.py): [84.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn++/stgcn++_ntu120_xset_hrnet/bm.pth) | 90.0 | 90.3 | + +**Note** + +1. We use the linear-scaling learning rate (**Initial LR ∝ Batch Size**). If you change the training batch size, remember to change the initial LR proportionally. +2. For Two-Stream results, we adopt the **1 (Joint):1 (Bone)** fusion. For Four-Stream results, we adopt the **2 (Joint):2 (Bone):1 (Joint Motion):1 (Bone Motion)** fusion. + + +## Training & Testing + +You can use the following command to train a model. + +```shell +bash tools/dist_train.sh ${CONFIG_FILE} ${NUM_GPUS} [optional arguments] +# For example: train STGCN++ on NTURGB+D XSub (3D skeleton, Joint Modality) with 8 GPUs, with validation, and test the last and the best (with best validation metric) checkpoint. +bash tools/dist_train.sh configs/stgcn++/stgcn++_ntu60_xsub_3dkp/j.py 8 --validate --test-last --test-best +``` + +You can use the following command to test a model. + +```shell +bash tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${NUM_GPUS} [optional arguments] +# For example: test STGCN++ on NTURGB+D XSub (3D skeleton, Joint Modality) with metrics `top_k_accuracy`, and dump the result to `result.pkl`. +bash tools/dist_test.sh configs/stgcn++/stgcn++_ntu60_xsub_3dkp/j.py checkpoints/SOME_CHECKPOINT.pth 8 --eval top_k_accuracy --out result.pkl +``` diff --git a/configs/stgcn++/stgcn++_ntu120_xset_3dkp/b.py b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/b.py new file mode 100644 index 00000000..0c66a4e5 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_3dkp/b' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_3dkp/bm.py b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/bm.py new file mode 100644 index 00000000..e322a3a8 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_3dkp/bm' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_3dkp/j.py b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/j.py new file mode 100644 index 00000000..312fac2e --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_3dkp/j' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_3dkp/jm.py b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/jm.py new file mode 100644 index 00000000..75db3fb3 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_3dkp/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_3dkp/jm' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_hrnet/b.py b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/b.py new file mode 100644 index 00000000..dfb679e9 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_hrnet/b' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_hrnet/bm.py b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/bm.py new file mode 100644 index 00000000..622eec46 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_hrnet/bm' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_hrnet/j.py b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/j.py new file mode 100644 index 00000000..56ddaed5 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_hrnet/j' diff --git a/configs/stgcn++/stgcn++_ntu120_xset_hrnet/jm.py b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/jm.py new file mode 100644 index 00000000..899bc0c2 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xset_hrnet/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xset_hrnet/jm' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/b.py b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/b.py new file mode 100644 index 00000000..24430f47 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_3dkp/b' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/bm.py b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/bm.py new file mode 100644 index 00000000..dbbf4437 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_3dkp/bm' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/j.py b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/j.py new file mode 100644 index 00000000..14e33433 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_3dkp/j' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/jm.py b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/jm.py new file mode 100644 index 00000000..30013625 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_3dkp/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_3dkp/jm' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/b.py b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/b.py new file mode 100644 index 00000000..88d7b468 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_hrnet/b' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/bm.py b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/bm.py new file mode 100644 index 00000000..1e3e47a4 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_hrnet/bm' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/j.py b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/j.py new file mode 100644 index 00000000..4cd84071 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_hrnet/j' diff --git a/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/jm.py b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/jm.py new file mode 100644 index 00000000..21ae77f2 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu120_xsub_hrnet/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu120_xsub_hrnet/jm' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/b.py b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/b.py new file mode 100644 index 00000000..a2d44a7c --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_3dkp/b' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/bm.py b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/bm.py new file mode 100644 index 00000000..740b2dd7 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_3dkp/bm' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/j.py b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/j.py new file mode 100644 index 00000000..f2eca778 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_3dkp/j' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/jm.py b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/jm.py new file mode 100644 index 00000000..f8c6258b --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_3dkp/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_3dkp/jm' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/b.py b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/b.py new file mode 100644 index 00000000..d67c0c1c --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_hrnet/b' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/bm.py b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/bm.py new file mode 100644 index 00000000..7379fa37 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_hrnet/bm' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/j.py b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/j.py new file mode 100644 index 00000000..f4532265 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_hrnet/j' diff --git a/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/jm.py b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/jm.py new file mode 100644 index 00000000..a4519522 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xsub_hrnet/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xsub_hrnet/jm' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_3dkp/b.py b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/b.py new file mode 100644 index 00000000..6e531695 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_3dkp/b' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_3dkp/bm.py b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/bm.py new file mode 100644 index 00000000..a7eb023a --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_3dkp/bm' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_3dkp/j.py b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/j.py new file mode 100644 index 00000000..ac847d70 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_3dkp/j' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_3dkp/jm.py b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/jm.py new file mode 100644 index 00000000..e53d8879 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_3dkp/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='nturgb+d', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_3dkp/jm' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_hrnet/b.py b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/b.py new file mode 100644 index 00000000..8ed196e3 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/b.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_hrnet/b' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_hrnet/bm.py b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/bm.py new file mode 100644 index 00000000..f8a9cf97 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/bm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_hrnet/bm' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_hrnet/j.py b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/j.py new file mode 100644 index 00000000..af521ea8 --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/j.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_hrnet/j' diff --git a/configs/stgcn++/stgcn++_ntu60_xview_hrnet/jm.py b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/jm.py new file mode 100644 index 00000000..cf436d8b --- /dev/null +++ b/configs/stgcn++/stgcn++_ntu60_xview_hrnet/jm.py @@ -0,0 +1,63 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + gcn_adaptive='init', + gcn_with_res=True, + tcn_type='mstcn', + graph_cfg=dict(layout='coco', mode='spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn++/stgcn++_ntu60_xview_hrnet/jm' diff --git a/configs/stgcn/README.md b/configs/stgcn/README.md new file mode 100644 index 00000000..7b7eccf5 --- /dev/null +++ b/configs/stgcn/README.md @@ -0,0 +1,66 @@ +# STGCN + +## Introduction + +STGCN is one of the first algorithms that adopt Graph Convolution Neural Networks for skeleton processing. We provide STGCN trained on NTURGB+D with 2D skeletons (HRNet) and 3D skeletons in both the original training setting and the **PYSKL** training setting. We provide checkpoints for four modalities: Joint, Bone, Joint Motion, and Bone Motion. The accuracy of each modality links to the weight file. + +## Citation + +```BibTeX +@inproceedings{yan2018spatial, + title={Spatial temporal graph convolutional networks for skeleton-based action recognition}, + author={Yan, Sijie and Xiong, Yuanjun and Lin, Dahua}, + booktitle={Thirty-second AAAI conference on artificial intelligence}, + year={2018} +} +# If you use the STGCN with PYSKL practices in your work +@misc{duan2022pyskl, + title={PYSKL: a toolbox for skeleton-based video understanding}, + author={PYSKL Contributors}, + howpublished = {\url{https://github.com/kennymckormick/pyskl}}, + year={2022} +} +``` + +## Model Zoo + +We release numerous checkpoints trained with various modalities, annotations on NTURGB+D and NTURGB+D 120. The accuracy of each modality links to the weight file. + +| Dataset | Practice | Annotation | GPUs | Joint Top1 | Bone Top1 | Joint Motion Top1 | Bone-Motion Top1 | Two-Stream Top1 | Four Stream Top1 | +| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| NTURGB+D XSub | Vanilla | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/j.py): [81.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/j.pth) | [bone_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/b.py): [81.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/jm.py): [79.9](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/bm.py): [81.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/bm.pth) | 84.3 | 86.6 | +| NTURGB+D XSub | Vanilla | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/j.py): [85.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/j.pth) | [bone_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/b.py): [85.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/jm.py): [81.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/bm.py): [83.9](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/bm.pth) | 88.8 | 90.1 | +| NTURGB+D XSub | PYSKL | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.py): [87.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/b.py): [88.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/jm.py): [85.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/bm.py): [86.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/bm.pth) | 90.0 | 90.7 | +| NTURGB+D XSub | PYSKL | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/j.py): [89.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/b.py): [91.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/jm.py): [86.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/bm.py): [87.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/bm.pth) | 92.0 | 92.4 | +| NTURGB+D XView | Vanilla | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/j.py): [90.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_3dkp/j.pth) | [bone_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/b.py): [87.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_3dkp/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/jm.py): [88.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/bm.py): [88.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_3dkp/bm.pth) | 91.4 | 93.2 | +| NTURGB+D XView | Vanilla | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/j.py): [92.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_hrnet/j.pth) | [bone_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/b.py): [90.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_hrnet/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/jm.py): [92.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/bm.py): [86.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_vanilla_ntu60_xview_hrnet/bm.pth) | 93.8 | 95.1 | +| NTURGB+D XView | PYSKL | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/j.py): [95.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_3dkp/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/b.py): [95.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_3dkp/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/jm.py): [93.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/bm.py): [92.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_3dkp/bm.pth) | 96.2 | 96.5 | +| NTURGB+D XView | PYSKL | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/j.py): [98.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_hrnet/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/b.py): [96.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_hrnet/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/jm.py): [95.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/bm.py): [95.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu60_xview_hrnet/bm.pth) | 98.2 | 98.3 | +| NTURGB+D 120 XSub | PYSKL | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/j.py): [82.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/b.py): [83.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/jm.py): [80.3](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/bm.py): [80.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/bm.pth) | 85.6 | 86.2 | +| NTURGB+D 120 XSub | PYSKL | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/j.py): [80.1](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/b.py): [83.4](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/jm.py): [78.6](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/bm.py): [79.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/bm.pth) | 84.0 | 84.7 | +| NTURGB+D 120 XSet | PYSKL | Official 3D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/j.py): [84.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_3dkp/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/b.py): [85.8](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_3dkp/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/jm.py): [82.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_3dkp/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/bm.py): [83.0](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_3dkp/bm.pth) | 87.5 | 88.4 | +| NTURGB+D 120 XSet | PYSKL | HRNet 2D Skeleton | 8 | [joint_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/j.py): [84.2](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_hrnet/j.pth) | [bone_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/b.py): [87.7](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_hrnet/b.pth) | [joint_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/jm.py): [82.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_hrnet/jm.pth) | [bone_motion_config](/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/bm.py): [83.5](http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcn/stgcn_pyskl_ntu120_xset_hrnet/bm.pth) | 88.3 | 89.0 | + +**Note** + +1. We use the linear-scaling learning rate (**Initial LR ∝ Batch Size**). If you change the training batch size, remember to change the initial LR proportionally. +2. For Two-Stream results, we adopt the **1 (Joint):1 (Bone)** fusion. For Four-Stream results, we adopt the **2 (Joint):2 (Bone):1 (Joint Motion):1 (Bone Motion)** fusion. + + +## Training & Testing + +You can use the following command to train a model. + +```shell +bash tools/dist_train.sh ${CONFIG_FILE} ${NUM_GPUS} [optional arguments] +# For example: train STGCN on NTURGB+D XSub (3D skeleton, Joint Modality) with 8 GPUs, with validation, with PYSKL practice, and test the last and the best (with best validation metric) checkpoint. +bash tools/dist_train.sh configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.py 8 --validate --test-last --test-best +``` + +You can use the following command to test a model. + +```shell +bash tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${NUM_GPUS} [optional arguments] +# For example: test STGCN on NTURGB+D XSub (3D skeleton, Joint Modality) with metrics `top_k_accuracy`, and dump the result to `result.pkl`. +bash tools/dist_test.sh configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.py checkpoints/SOME_CHECKPOINT.pth 8 --eval top_k_accuracy --out result.pkl +``` diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/b.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/b.py new file mode 100644 index 00000000..55b48a28 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/bm.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/bm.py new file mode 100644 index 00000000..7b8ae202 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/j.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/j.py new file mode 100644 index 00000000..031f84aa --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/jm.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/jm.py new file mode 100644 index 00000000..9225bba0 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_3dkp/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/b.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/b.py new file mode 100644 index 00000000..e88904a1 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/bm.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/bm.py new file mode 100644 index 00000000..45c9717c --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/j.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/j.py new file mode 100644 index 00000000..f7b309b8 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/jm.py b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/jm.py new file mode 100644 index 00000000..9ba990af --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xset_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xset_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xset_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xset_hrnet/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/b.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/b.py new file mode 100644 index 00000000..1bf1fbb2 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/bm.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/bm.py new file mode 100644 index 00000000..d4c48d6e --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/j.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/j.py new file mode 100644 index 00000000..3b9afd16 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/jm.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/jm.py new file mode 100644 index 00000000..01574cb0 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_3dkp/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/b.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/b.py new file mode 100644 index 00000000..70d599c6 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/bm.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/bm.py new file mode 100644 index 00000000..5767889f --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/j.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/j.py new file mode 100644 index 00000000..3d9f9afe --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/jm.py b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/jm.py new file mode 100644 index 00000000..32173bf9 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=120, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu120_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu120_xsub_hrnet/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/b.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/b.py new file mode 100644 index 00000000..721c6a8c --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/bm.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/bm.py new file mode 100644 index 00000000..27b05a72 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.py new file mode 100644 index 00000000..1d4e594a --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/jm.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/jm.py new file mode 100644 index 00000000..d0fbbb1c --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_3dkp/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/b.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/b.py new file mode 100644 index 00000000..0f6486da --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/bm.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/bm.py new file mode 100644 index 00000000..409efaa7 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/j.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/j.py new file mode 100644 index 00000000..9115cc07 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/jm.py b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/jm.py new file mode 100644 index 00000000..00d9bccf --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xsub_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xsub_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xsub_hrnet/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/b.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/b.py new file mode 100644 index 00000000..fe730cf9 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/bm.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/bm.py new file mode 100644 index 00000000..d5d8a01a --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/j.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/j.py new file mode 100644 index 00000000..ec6418c3 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/jm.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/jm.py new file mode 100644 index 00000000..cc032eb7 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +train_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_3dkp/jm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/b.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/b.py new file mode 100644 index 00000000..db67f705 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/b.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/b' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/bm.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/bm.py new file mode 100644 index 00000000..02837741 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/bm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/bm' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/j.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/j.py new file mode 100644 index 00000000..577190e1 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/j.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/j' diff --git a/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/jm.py b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/jm.py new file mode 100644 index 00000000..f3abafc2 --- /dev/null +++ b/configs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/jm.py @@ -0,0 +1,60 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +train_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +val_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=1, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +test_pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='UniformSample', clip_len=100, num_clips=10, test_mode=True), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=train_pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=val_pipeline, split='xview_val'), + test=dict(type=dataset_type, ann_file=ann_file, pipeline=test_pipeline, split='xview_val')) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0, by_epoch=False) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_pyskl_ntu60_xview_hrnet/jm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/b.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/b.py new file mode 100644 index 00000000..6450da5c --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/b.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/b' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/bm.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/bm.py new file mode 100644 index 00000000..29ff054e --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/bm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/bm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/j.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/j.py new file mode 100644 index 00000000..f4061032 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/j.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/j' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/jm.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/jm.py new file mode 100644 index 00000000..7ccb1ef4 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/jm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_3dkp/jm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/b.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/b.py new file mode 100644 index 00000000..c7f719b9 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/b.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/b' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/bm.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/bm.py new file mode 100644 index 00000000..a3c3df85 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/bm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/bm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/j.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/j.py new file mode 100644 index 00000000..748bcc13 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/j.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/j' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/jm.py b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/jm.py new file mode 100644 index 00000000..c4595538 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/jm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xsub_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xsub_hrnet/jm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/b.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/b.py new file mode 100644 index 00000000..c34cebf6 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/b.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['b']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/b' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/bm.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/bm.py new file mode 100644 index 00000000..72aea45b --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/bm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['bm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/bm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/j.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/j.py new file mode 100644 index 00000000..311e4686 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/j.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['j']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/j' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/jm.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/jm.py new file mode 100644 index 00000000..843ad830 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/jm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='nturgb+d', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_3danno.pkl' +pipeline = [ + dict(type='PreNormalize3D'), + dict(type='GenSkeFeat', dataset='nturgb+d', feats=['jm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_3dkp/jm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/b.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/b.py new file mode 100644 index 00000000..76f5cf0d --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/b.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['b']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/b' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/bm.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/bm.py new file mode 100644 index 00000000..bca45534 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/bm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['bm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/bm' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/j.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/j.py new file mode 100644 index 00000000..d3073e80 --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/j.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['j']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/j' diff --git a/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/jm.py b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/jm.py new file mode 100644 index 00000000..138e1a8e --- /dev/null +++ b/configs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/jm.py @@ -0,0 +1,43 @@ +model = dict( + type='RecognizerGCN', + backbone=dict( + type='STGCN', + tcn_dropout=0.5, + graph_cfg=dict(layout='coco', mode='stgcn_spatial')), + cls_head=dict(type='GCNHead', num_classes=60, in_channels=256)) + +dataset_type = 'PoseDataset' +ann_file = 'data/nturgbd/ntu60_hrnet.pkl' +pipeline = [ + dict(type='PreNormalize2D'), + dict(type='GenSkeFeat', dataset='coco', feats=['jm']), + dict(type='PadTo', length=300, mode='zero'), + dict(type='PoseDecode'), + dict(type='FormatGCNInput', num_person=2), + dict(type='Collect', keys=['keypoint', 'label'], meta_keys=[]), + dict(type='ToTensor', keys=['keypoint']) +] +data = dict( + videos_per_gpu=16, + workers_per_gpu=2, + test_dataloader=dict(videos_per_gpu=1), + train=dict( + type='RepeatDataset', + times=5, + dataset=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_train')), + val=dict(type=dataset_type, ann_file=ann_file, pipeline=pipeline, split='xview_val')) +data['test'] = data['val'] + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[2, 10]) +total_epochs = 16 +checkpoint_config = dict(interval=1) +evaluation = dict(interval=1, metrics=['top_k_accuracy']) +log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) + +# runtime settings +log_level = 'INFO' +work_dir = './work_dirs/stgcn/stgcn_vanilla_ntu60_xview_hrnet/jm' diff --git a/demo/demo_posec3d.py b/demo/demo_posec3d.py new file mode 100644 index 00000000..18d06e23 --- /dev/null +++ b/demo/demo_posec3d.py @@ -0,0 +1,255 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp +import shutil +import warnings + +import cv2 +import mmcv +import numpy as np +import torch + +from pyskl.apis import inference_recognizer, init_recognizer + +try: + from mmdet.apis import inference_detector, init_detector +except (ImportError, ModuleNotFoundError): + def inference_detector(*args, **kwargs): + pass + + def init_detector(*args, **kwargs): + pass + warnings.warn( + 'Failed to import `inference_detector` and `init_detector` from `mmdet.apis`. ' + 'Make sure you can successfully import these if you want to use related features. ' + ) + +try: + from mmpose.apis import init_pose_model, inference_top_down_pose_model, vis_pose_result +except (ImportError, ModuleNotFoundError): + def init_pose_model(*args, **kwargs): + pass + + def inference_top_down_pose_model(*args, **kwargs): + pass + + def vis_pose_result(*args, **kwargs): + pass + + warnings.warn( + 'Failed to import `init_pose_model`, `inference_top_down_pose_model`, `vis_pose_result` from ' + '`mmpose.apis`. Make sure you can successfully import these if you want to use related features. ' + ) + + +try: + import moviepy.editor as mpy +except ImportError: + raise ImportError('Please install moviepy to enable output file') + +FONTFACE = cv2.FONT_HERSHEY_DUPLEX +FONTSCALE = 0.75 +FONTCOLOR = (255, 255, 255) # BGR, white +THICKNESS = 1 +LINETYPE = 1 + + +def parse_args(): + parser = argparse.ArgumentParser(description='PoseC3D demo') + parser.add_argument('video', help='video file/url') + parser.add_argument('out_filename', help='output filename') + parser.add_argument( + '--config', + default=('configs/posec3d/slowonly_r50_u48_240e_ntu120_xsub_keypoint.py'), + help='posec3d config file path') + parser.add_argument( + '--checkpoint', + default=('https://download.openmmlab.com/mmaction/skeleton/posec3d/' + 'slowonly_r50_u48_240e_ntu120_xsub_keypoint/' + 'slowonly_r50_u48_240e_ntu120_xsub_keypoint-6736b03f.pth'), + help='posec3d checkpoint file/url') + parser.add_argument( + '--det-config', + default='demo/faster_rcnn_r50_fpn_2x_coco.py', + help='human detection config file path (from mmdet)') + parser.add_argument( + '--det-checkpoint', + default=('http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/' + 'faster_rcnn_r50_fpn_2x_coco/' + 'faster_rcnn_r50_fpn_2x_coco_' + 'bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'), + help='human detection checkpoint file/url') + parser.add_argument( + '--pose-config', + default='demo/hrnet_w32_coco_256x192.py', + help='human pose estimation config file path (from mmpose)') + parser.add_argument( + '--pose-checkpoint', + default=('https://download.openmmlab.com/mmpose/top_down/hrnet/' + 'hrnet_w32_coco_256x192-c78dce93_20200708.pth'), + help='human pose estimation checkpoint file/url') + parser.add_argument( + '--det-score-thr', + type=float, + default=0.9, + help='the threshold of human detection score') + parser.add_argument( + '--label-map', + default='tools/data/skeleton/label_map_ntu120.txt', + help='label map file') + parser.add_argument( + '--device', type=str, default='cuda:0', help='CPU/CUDA device option') + parser.add_argument( + '--short-side', + type=int, + default=480, + help='specify the short-side length of the image') + args = parser.parse_args() + return args + + +def frame_extraction(video_path, short_side): + """Extract frames given video_path. + + Args: + video_path (str): The video_path. + """ + # Load the video, extract frames into ./tmp/video_name + target_dir = osp.join('./tmp', osp.basename(osp.splitext(video_path)[0])) + os.makedirs(target_dir, exist_ok=True) + # Should be able to handle videos up to several hours + frame_tmpl = osp.join(target_dir, 'img_{:06d}.jpg') + vid = cv2.VideoCapture(video_path) + frames = [] + frame_paths = [] + flag, frame = vid.read() + cnt = 0 + new_h, new_w = None, None + while flag: + if new_h is None: + h, w, _ = frame.shape + new_w, new_h = mmcv.rescale_size((w, h), (short_side, np.Inf)) + + frame = mmcv.imresize(frame, (new_w, new_h)) + + frames.append(frame) + frame_path = frame_tmpl.format(cnt + 1) + frame_paths.append(frame_path) + + cv2.imwrite(frame_path, frame) + cnt += 1 + flag, frame = vid.read() + + return frame_paths, frames + + +def detection_inference(args, frame_paths): + """Detect human boxes given frame paths. + + Args: + args (argparse.Namespace): The arguments. + frame_paths (list[str]): The paths of frames to do detection inference. + + Returns: + list[np.ndarray]: The human detection results. + """ + model = init_detector(args.det_config, args.det_checkpoint, args.device) + assert model.CLASSES[0] == 'person', ('We require you to use a detector ' + 'trained on COCO') + results = [] + print('Performing Human Detection for each frame') + prog_bar = mmcv.ProgressBar(len(frame_paths)) + for frame_path in frame_paths: + result = inference_detector(model, frame_path) + # We only keep human detections with score larger than det_score_thr + result = result[0][result[0][:, 4] >= args.det_score_thr] + results.append(result) + prog_bar.update() + return results + + +def pose_inference(args, frame_paths, det_results): + model = init_pose_model(args.pose_config, args.pose_checkpoint, + args.device) + ret = [] + print('Performing Human Pose Estimation for each frame') + prog_bar = mmcv.ProgressBar(len(frame_paths)) + for f, d in zip(frame_paths, det_results): + # Align input format + d = [dict(bbox=x) for x in list(d)] + pose = inference_top_down_pose_model(model, f, d, format='xyxy')[0] + ret.append(pose) + prog_bar.update() + return ret + + +def main(): + args = parse_args() + + frame_paths, original_frames = frame_extraction(args.video, + args.short_side) + num_frame = len(frame_paths) + h, w, _ = original_frames[0].shape + + # Get clip_len, frame_interval and calculate center index of each clip + config = mmcv.Config.fromfile(args.config) + + model = init_recognizer(config, args.checkpoint, args.device) + + # Load label_map + label_map = [x.strip() for x in open(args.label_map).readlines()] + + # Get Human detection results + det_results = detection_inference(args, frame_paths) + torch.cuda.empty_cache() + + pose_results = pose_inference(args, frame_paths, det_results) + torch.cuda.empty_cache() + + fake_anno = dict( + frame_dir='', + label=-1, + img_shape=(h, w), + original_shape=(h, w), + start_index=0, + modality='Pose', + total_frames=num_frame) + num_person = max([len(x) for x in pose_results]) + # Current PoseC3D models are trained on COCO-keypoints (17 keypoints) + num_keypoint = 17 + keypoint = np.zeros((num_person, num_frame, num_keypoint, 2), + dtype=np.float16) + keypoint_score = np.zeros((num_person, num_frame, num_keypoint), + dtype=np.float16) + for i, poses in enumerate(pose_results): + for j, pose in enumerate(poses): + pose = pose['keypoints'] + keypoint[j, i] = pose[:, :2] + keypoint_score[j, i] = pose[:, 2] + fake_anno['keypoint'] = keypoint + fake_anno['keypoint_score'] = keypoint_score + + results = inference_recognizer(model, fake_anno) + + action_label = label_map[results[0][0]] + + pose_model = init_pose_model(args.pose_config, args.pose_checkpoint, + args.device) + vis_frames = [ + vis_pose_result(pose_model, frame_paths[i], pose_results[i]) + for i in range(num_frame) + ] + for frame in vis_frames: + cv2.putText(frame, action_label, (10, 30), FONTFACE, FONTSCALE, + FONTCOLOR, THICKNESS, LINETYPE) + + vid = mpy.ImageSequenceClip([x[:, :, ::-1] for x in vis_frames], fps=24) + vid.write_videofile(args.out_filename, remove_temp=True) + + tmp_frame_dir = osp.dirname(frame_paths[0]) + shutil.rmtree(tmp_frame_dir) + + +if __name__ == '__main__': + main() diff --git a/demo/faster_rcnn_r50_fpn_2x_coco.py b/demo/faster_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 00000000..33fc5645 --- /dev/null +++ b/demo/faster_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,181 @@ +# model config +model = dict( + type='FasterRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) + +# dataset config +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') +# Schedule +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 22]) +total_epochs = 24 +# runtime +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + ]) +# yapf:enable +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] diff --git a/demo/hrnet_w32_coco_256x192.py b/demo/hrnet_w32_coco_256x192.py new file mode 100644 index 00000000..6ef3b6ef --- /dev/null +++ b/demo/hrnet_w32_coco_256x192.py @@ -0,0 +1,172 @@ +log_level = 'INFO' +load_from = None +resume_from = None +dist_params = dict(backend='nccl') +workflow = [('train', 1)] +checkpoint_config = dict(interval=10) +evaluation = dict(interval=10, metric='mAP', key_indicator='AP') + +optimizer = dict( + type='Adam', + lr=5e-4, +) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[170, 200]) +total_epochs = 210 +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) + +channel_cfg = dict( + num_output_channels=17, + dataset_joints=17, + dataset_channel=[ + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], + ], + inference_channel=[ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 + ]) + +# model settings +model = dict( + type='TopDown', + pretrained='https://download.openmmlab.com/mmpose/' + 'pretrain_models/hrnet_w32-36af842e.pth', + backbone=dict( + type='HRNet', + in_channels=3, + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + ), + keypoint_head=dict( + type='TopdownHeatmapSimpleHead', + in_channels=32, + out_channels=channel_cfg['num_output_channels'], + num_deconv_layers=0, + extra=dict(final_conv_kernel=1, ), + loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), + train_cfg=dict(), + test_cfg=dict( + flip_test=True, + post_process='default', + shift_heatmap=True, + modulate_kernel=11)) + +data_cfg = dict( + image_size=[192, 256], + heatmap_size=[48, 64], + num_output_channels=channel_cfg['num_output_channels'], + num_joints=channel_cfg['dataset_joints'], + dataset_channel=channel_cfg['dataset_channel'], + inference_channel=channel_cfg['inference_channel'], + soft_nms=False, + nms_thr=1.0, + oks_thr=0.9, + vis_thr=0.2, + use_gt_bbox=False, + det_bbox_thr=0.0, + bbox_file='data/coco/person_detection_results/' + 'COCO_val2017_detections_AP_H_56_person.json', +) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='TopDownRandomFlip', flip_prob=0.5), + dict( + type='TopDownHalfBodyTransform', + num_joints_half_body=8, + prob_half_body=0.3), + dict( + type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), + dict(type='TopDownAffine'), + dict(type='ToTensor'), + dict( + type='NormalizeTensor', + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + dict(type='TopDownGenerateTarget', sigma=2), + dict( + type='Collect', + keys=['img', 'target', 'target_weight'], + meta_keys=[ + 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', + 'rotation', 'bbox_score', 'flip_pairs' + ]), +] + +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='TopDownAffine'), + dict(type='ToTensor'), + dict( + type='NormalizeTensor', + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + dict( + type='Collect', + keys=['img'], + meta_keys=[ + 'image_file', 'center', 'scale', 'rotation', 'bbox_score', + 'flip_pairs' + ]), +] + +test_pipeline = val_pipeline + +data_root = 'data/coco' +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + val_dataloader=dict(samples_per_gpu=32), + test_dataloader=dict(samples_per_gpu=32), + train=dict( + type='TopDownCocoDataset', + ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', + img_prefix=f'{data_root}/train2017/', + data_cfg=data_cfg, + pipeline=train_pipeline), + val=dict( + type='TopDownCocoDataset', + ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', + img_prefix=f'{data_root}/val2017/', + data_cfg=data_cfg, + pipeline=val_pipeline), + test=dict( + type='TopDownCocoDataset', + ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', + img_prefix=f'{data_root}/val2017/', + data_cfg=data_cfg, + pipeline=val_pipeline), +) diff --git a/demo/ntu_sample.avi b/demo/ntu_sample.avi new file mode 100644 index 0000000000000000000000000000000000000000..42f8e03b1e3a294e026b340ac8bb3b94e66effb0 GIT binary 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+ ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download annotations\n", + "download_file('http://download.openmmlab.com/mmaction/pyskl/demo/annotations/ntu60_samples_hrnet.pkl', 'ntu60_2d.pkl')\n", + "download_file('http://download.openmmlab.com/mmaction/pyskl/demo/annotations/ntu60_samples_3danno.pkl', 'ntu60_3d.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize 2D Skeletons without video\n", + "annotations = load('ntu60_2d.pkl')\n", + "index = 0\n", + "anno = annotations[index]\n", + "vid = Vis2DPose(anno, thre=0.2, out_shape=(540, 960), layout='coco', fps=12, video=None)\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize 2D Skeletons with the original RGB Video\n", + "annotations = load('ntu60_2d.pkl')\n", + "index = 0\n", + "anno = annotations[index]\n", + "frame_dir = anno['frame_dir']\n", + "video_url = f\"http://download.openmmlab.com/mmaction/pyskl/demo/nturgbd/{frame_dir}.avi\"\n", + "download_file(video_url, frame_dir + '.avi')\n", + "vid = Vis2DPose(anno, thre=0.2, out_shape=(540, 960), layout='coco', fps=12, video=frame_dir + '.avi')\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize 3D Skeletons\n", + "from pyskl.datasets.pipelines import PreNormalize3D\n", + "annotations = load('ntu60_3d.pkl')\n", + "index = 0\n", + "anno = annotations[index]\n", + "anno = PreNormalize3D()(anno) # * Need Pre-Normalization before Visualization\n", + "vid = Vis3DPose(anno, layout='nturgb+d', fps=12, angle=(30, 45), fig_size=(8, 8), dpi=80)\n", + "vid = vid.vis()\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean directories\n", + "os.remove('ntu60_3d.pkl')\n", + "os.remove('ntu60_2d.pkl')\n", + "for f in glob.glob(\"S*.avi\"):\n", + " os.remove(f)\n", + "os.remove('__temp__.mp4')" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "8516388d98336f8f25b57d66898c29467d9fc2c465b9ee1e16d04a4702b7bff1" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/demo/visualize_heatmap_volume.ipynb b/demo/visualize_heatmap_volume.ipynb new file mode 100644 index 00000000..39d4ff37 --- /dev/null +++ b/demo/visualize_heatmap_volume.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "id": "speaking-algebra", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import os.path as osp\n", + "import decord\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import urllib\n", + "import moviepy.editor as mpy\n", + "import random as rd\n", + "from mmpose.apis import vis_pose_result\n", + "from mmpose.models import TopDown\n", + "from mmcv import load, dump\n", + "\n", + "# We assume the annotation is already prepared\n", + "gym_train_ann_file = '../data/skeleton/gym_train.pkl'\n", + "gym_val_ann_file = '../data/skeleton/gym_val.pkl'\n", + "ntu60_xsub_train_ann_file = '../data/skeleton/ntu60_xsub_train.pkl'\n", + "ntu60_xsub_val_ann_file = '../data/skeleton/ntu60_xsub_val.pkl'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "alive-consolidation", + "metadata": {}, + "outputs": [], + "source": [ + "FONTFACE = cv2.FONT_HERSHEY_DUPLEX\n", + "FONTSCALE = 0.6\n", + "FONTCOLOR = (255, 255, 255)\n", + "BGBLUE = (0, 119, 182)\n", + "THICKNESS = 1\n", + "LINETYPE = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ranging-conjunction", + "metadata": {}, + "outputs": [], + "source": [ + "def add_label(frame, label, BGCOLOR=BGBLUE):\n", + " threshold = 30\n", + " def split_label(label):\n", + " label = label.split()\n", + " lines, cline = [], ''\n", + " for word in label:\n", + " if len(cline) + len(word) < threshold:\n", + " cline = cline + ' ' + word\n", + " else:\n", + " lines.append(cline)\n", + " cline = word\n", + " if cline != '':\n", + " lines += [cline]\n", + " return lines\n", + " \n", + " if len(label) > 30:\n", + " label = split_label(label)\n", + " else:\n", + " label = [label]\n", + " label = ['Action: '] + label\n", + " \n", + " sizes = []\n", + " for line in label:\n", + " sizes.append(cv2.getTextSize(line, FONTFACE, FONTSCALE, THICKNESS)[0])\n", + " box_width = max([x[0] for x in sizes]) + 10\n", + " text_height = sizes[0][1]\n", + " box_height = len(sizes) * (text_height + 6)\n", + " \n", + " cv2.rectangle(frame, (0, 0), (box_width, box_height), BGCOLOR, -1)\n", + " for i, line in enumerate(label):\n", + " location = (5, (text_height + 6) * i + text_height + 3)\n", + " cv2.putText(frame, line, location, FONTFACE, FONTSCALE, FONTCOLOR, THICKNESS, LINETYPE)\n", + " return frame\n", + " \n", + "\n", + "def vis_skeleton(vid_path, anno, category_name=None, ratio=0.5):\n", + " vid = decord.VideoReader(vid_path)\n", + " frames = [x.asnumpy() for x in vid]\n", + " \n", + " h, w, _ = frames[0].shape\n", + " new_shape = (int(w * ratio), int(h * ratio))\n", + " frames = [cv2.resize(f, new_shape) for f in frames]\n", + " \n", + " assert len(frames) == anno['total_frames']\n", + " # The shape is N x T x K x 3\n", + " kps = np.concatenate([anno['keypoint'], anno['keypoint_score'][..., None]], axis=-1)\n", + " kps[..., :2] *= ratio\n", + " # Convert to T x N x K x 3\n", + " kps = kps.transpose([1, 0, 2, 3])\n", + " vis_frames = []\n", + "\n", + " # we need an instance of TopDown model, so build a minimal one\n", + " model = TopDown(backbone=dict(type='ShuffleNetV1'))\n", + "\n", + " for f, kp in zip(frames, kps):\n", + " bbox = np.zeros([0, 4], dtype=np.float32)\n", + " result = [dict(bbox=bbox, keypoints=k) for k in kp]\n", + " vis_frame = vis_pose_result(model, f, result)\n", + " \n", + " if category_name is not None:\n", + " vis_frame = add_label(vis_frame, category_name)\n", + " \n", + " vis_frames.append(vis_frame)\n", + " return vis_frames" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "applied-humanity", + "metadata": {}, + "outputs": [], + "source": [ + "keypoint_pipeline = [\n", + " dict(type='PoseDecode'),\n", + " dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),\n", + " dict(type='Resize', scale=(-1, 64)),\n", + " dict(type='CenterCrop', crop_size=64),\n", + " dict(type='GeneratePoseTarget', with_kp=True, with_limb=False)\n", + "]\n", + "\n", + "limb_pipeline = [\n", + " dict(type='PoseDecode'),\n", + " dict(type='PoseCompact', hw_ratio=1., allow_imgpad=True),\n", + " dict(type='Resize', scale=(-1, 64)),\n", + " dict(type='CenterCrop', crop_size=64),\n", + " dict(type='GeneratePoseTarget', with_kp=False, with_limb=True)\n", + "]\n", + "\n", + "from pyskl.datasets.pipelines import Compose\n", + "def get_pseudo_heatmap(anno, flag='keypoint'):\n", + " assert flag in ['keypoint', 'limb']\n", + " pipeline = Compose(keypoint_pipeline if flag == 'keypoint' else limb_pipeline)\n", + " return pipeline(anno)['imgs']\n", + "\n", + "def vis_heatmaps(heatmaps, channel=-1, ratio=8):\n", + " # if channel is -1, draw all keypoints / limbs on the same map\n", + " import matplotlib.cm as cm\n", + " h, w, _ = heatmaps[0].shape\n", + " newh, neww = int(h * ratio), int(w * ratio)\n", + " \n", + " if channel == -1:\n", + " heatmaps = [np.max(x, axis=-1) for x in heatmaps]\n", + " cmap = cm.viridis\n", + " heatmaps = [(cmap(x)[..., :3] * 255).astype(np.uint8) for x in heatmaps]\n", + " heatmaps = [cv2.resize(x, (neww, newh)) for x in heatmaps]\n", + " return heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "automatic-commons", + "metadata": {}, + "outputs": [], + "source": [ + "# Load GYM annotations\n", + "lines = list(urllib.request.urlopen('https://sdolivia.github.io/FineGym/resources/dataset/gym99_categories.txt'))\n", + "gym_categories = [x.decode().strip().split('; ')[-1] for x in lines]\n", + "gym_annos = load(gym_train_ann_file) + load(gym_val_ann_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "numerous-bristol", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-04-25 22:18:53-- https://download.openmmlab.com/mmaction/posec3d/gym_samples.tar\n", + "Resolving download.openmmlab.com (download.openmmlab.com)... 124.160.145.22\n", + "Connecting to download.openmmlab.com (download.openmmlab.com)|124.160.145.22|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 36300800 (35M) [application/x-tar]\n", + "Saving to: ‘gym_samples.tar’\n", + "\n", + "100%[======================================>] 36,300,800 11.5MB/s in 3.0s \n", + "\n", + "2021-04-25 22:18:58 (11.5 MB/s) - ‘gym_samples.tar’ saved [36300800/36300800]\n", + "\n" + ] + } + ], + "source": [ + "# download sample videos of GYM\n", + "!wget https://download.openmmlab.com/mmaction/posec3d/gym_samples.tar\n", + "!tar -xf gym_samples.tar\n", + "!rm gym_samples.tar" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "ranging-harrison", + "metadata": {}, + "outputs": [], + "source": [ + "gym_root = 'gym_samples/'\n", + "gym_vids = os.listdir(gym_root)\n", + "# visualize pose of which video? index in 0 - 50.\n", + "idx = 1\n", + "vid = gym_vids[idx]\n", + "\n", + "frame_dir = vid.split('.')[0]\n", + "vid_path = osp.join(gym_root, vid)\n", + "anno = [x for x in gym_annos if x['frame_dir'] == frame_dir][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "fitting-courage", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize Skeleton\n", + "vis_frames = vis_skeleton(vid_path, anno, gym_categories[anno['label']])\n", + "vid = mpy.ImageSequenceClip(vis_frames, fps=24)\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "orange-logging", + "metadata": {}, + "outputs": [], + "source": [ + "keypoint_heatmap = get_pseudo_heatmap(anno)\n", + "keypoint_mapvis = vis_heatmaps(keypoint_heatmap)\n", + "keypoint_mapvis = [add_label(f, gym_categories[anno['label']]) for f in keypoint_mapvis]\n", + "vid = mpy.ImageSequenceClip(keypoint_mapvis, fps=24)\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "residential-conjunction", + "metadata": {}, + "outputs": [], + "source": [ + "limb_heatmap = get_pseudo_heatmap(anno, 'limb')\n", + "limb_mapvis = vis_heatmaps(limb_heatmap)\n", + "limb_mapvis = [add_label(f, gym_categories[anno['label']]) for f in limb_mapvis]\n", + "vid = mpy.ImageSequenceClip(limb_mapvis, fps=24)\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "coupled-stranger", + "metadata": {}, + "outputs": [], + "source": [ + "# The name list of \n", + "ntu_categories = ['drink water', 'eat meal/snack', 'brushing teeth', 'brushing hair', 'drop', 'pickup', \n", + " 'throw', 'sitting down', 'standing up (from sitting position)', 'clapping', 'reading', \n", + " 'writing', 'tear up paper', 'wear jacket', 'take off jacket', 'wear a shoe', \n", + " 'take off a shoe', 'wear on glasses', 'take off glasses', 'put on a hat/cap', \n", + " 'take off a hat/cap', 'cheer up', 'hand waving', 'kicking something', \n", + " 'reach into pocket', 'hopping (one foot jumping)', 'jump up', \n", + " 'make a phone call/answer phone', 'playing with phone/tablet', 'typing on a keyboard', \n", + " 'pointing to something with finger', 'taking a selfie', 'check time (from watch)', \n", + " 'rub two hands together', 'nod head/bow', 'shake head', 'wipe face', 'salute', \n", + " 'put the palms together', 'cross hands in front (say stop)', 'sneeze/cough', \n", + " 'staggering', 'falling', 'touch head (headache)', 'touch chest (stomachache/heart pain)', \n", + " 'touch back (backache)', 'touch neck (neckache)', 'nausea or vomiting condition', \n", + " 'use a fan (with hand or paper)/feeling warm', 'punching/slapping other person', \n", + " 'kicking other person', 'pushing other person', 'pat on back of other person', \n", + " 'point finger at the other person', 'hugging other person', \n", + " 'giving something to other person', \"touch other person's pocket\", 'handshaking', \n", + " 'walking towards each other', 'walking apart from each other']\n", + "ntu_annos = load(ntu60_xsub_train_ann_file) + load(ntu60_xsub_val_ann_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "critical-review", + "metadata": {}, + "outputs": [], + "source": [ + "ntu_root = 'ntu_samples/'\n", + "ntu_vids = os.listdir(ntu_root)\n", + "# visualize pose of which video? index in 0 - 50.\n", + "idx = 20\n", + "vid = ntu_vids[idx]\n", + "\n", + "frame_dir = vid.split('.')[0]\n", + "vid_path = osp.join(ntu_root, vid)\n", + "anno = [x for x in ntu_annos if x['frame_dir'] == frame_dir.split('_')[0]][0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "seasonal-palmer", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-04-25 22:21:16-- https://download.openmmlab.com/mmaction/posec3d/ntu_samples.tar\n", + "Resolving download.openmmlab.com (download.openmmlab.com)... 124.160.145.22\n", + "Connecting to download.openmmlab.com (download.openmmlab.com)|124.160.145.22|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 121753600 (116M) [application/x-tar]\n", + "Saving to: ‘ntu_samples.tar’\n", + "\n", + "100%[======================================>] 121,753,600 14.4MB/s in 9.2s \n", + "\n", + "2021-04-25 22:21:26 (12.6 MB/s) - ‘ntu_samples.tar’ saved [121753600/121753600]\n", + "\n" + ] + } + ], + "source": [ + "# download sample videos of NTU-60\n", + "!wget https://download.openmmlab.com/mmaction/posec3d/ntu_samples.tar\n", + "!tar -xf ntu_samples.tar\n", + "!rm ntu_samples.tar" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "accompanied-invitation", + "metadata": {}, + "outputs": [], + "source": [ + "vis_frames = vis_skeleton(vid_path, anno, ntu_categories[anno['label']])\n", + "vid = mpy.ImageSequenceClip(vis_frames, fps=24)\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "respiratory-conclusion", + "metadata": {}, + "outputs": [], + "source": [ + "keypoint_heatmap = get_pseudo_heatmap(anno)\n", + "keypoint_mapvis = vis_heatmaps(keypoint_heatmap)\n", + "keypoint_mapvis = [add_label(f, gym_categories[anno['label']]) for f in keypoint_mapvis]\n", + "vid = mpy.ImageSequenceClip(keypoint_mapvis, fps=24)\n", + "vid.ipython_display()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "thirty-vancouver", + "metadata": {}, + "outputs": [], + "source": [ + "limb_heatmap = get_pseudo_heatmap(anno, 'limb')\n", + "limb_mapvis = vis_heatmaps(limb_heatmap)\n", + "limb_mapvis = [add_label(f, gym_categories[anno['label']]) for f in limb_mapvis]\n", + "vid = mpy.ImageSequenceClip(limb_mapvis, fps=24)\n", + "vid.ipython_display()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyskl/__init__.py b/pyskl/__init__.py new file mode 100644 index 00000000..72a2a34d --- /dev/null +++ b/pyskl/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import mmcv +from mmcv import digit_version + +from .version import __version__ + +mmcv_minimum_version = '1.3.6' +mmcv_maximum_version = '1.5.0' +mmcv_version = digit_version(mmcv.__version__) + +assert (digit_version(mmcv_minimum_version) <= mmcv_version + <= digit_version(mmcv_maximum_version)), \ + f'MMCV=={mmcv.__version__} is used but incompatible. ' \ + f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.' + +__all__ = ['__version__'] diff --git a/pyskl/apis/__init__.py b/pyskl/apis/__init__.py new file mode 100644 index 00000000..777f6eb8 --- /dev/null +++ b/pyskl/apis/__init__.py @@ -0,0 +1,10 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmcv.engine import multi_gpu_test, single_gpu_test + +from .inference import inference_recognizer, init_recognizer +from .train import init_random_seed, train_model + +__all__ = [ + 'train_model', 'init_recognizer', 'inference_recognizer', 'multi_gpu_test', + 'single_gpu_test', 'init_random_seed' +] diff --git a/pyskl/apis/inference.py b/pyskl/apis/inference.py new file mode 100644 index 00000000..2c005438 --- /dev/null +++ b/pyskl/apis/inference.py @@ -0,0 +1,182 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os +import os.path as osp +import re +import warnings +from operator import itemgetter + +import mmcv +import numpy as np +import torch +from mmcv.parallel import collate, scatter +from mmcv.runner import load_checkpoint + +from pyskl.core import OutputHook +from pyskl.datasets.pipelines import Compose +from pyskl.models import build_recognizer + + +def init_recognizer(config, checkpoint=None, device='cuda:0', **kwargs): + """Initialize a recognizer from config file. + + Args: + config (str | :obj:`mmcv.Config`): Config file path or the config + object. + checkpoint (str | None, optional): Checkpoint path/url. If set to None, + the model will not load any weights. Default: None. + device (str | :obj:`torch.device`): The desired device of returned + tensor. Default: 'cuda:0'. + + Returns: + nn.Module: The constructed recognizer. + """ + if 'use_frames' in kwargs: + warnings.warn('The argument `use_frames` is deprecated PR #1191. ' + 'Now you can use models trained with frames or videos ' + 'arbitrarily. ') + + if isinstance(config, str): + config = mmcv.Config.fromfile(config) + elif not isinstance(config, mmcv.Config): + raise TypeError('config must be a filename or Config object, ' + f'but got {type(config)}') + + # pretrained model is unnecessary since we directly load checkpoint later + config.model.backbone.pretrained = None + model = build_recognizer(config.model) + + if checkpoint is not None: + load_checkpoint(model, checkpoint, map_location='cpu') + model.cfg = config + model.to(device) + model.eval() + return model + + +def inference_recognizer(model, video, outputs=None, as_tensor=True, **kwargs): + """Inference a video with the recognizer. + + Args: + model (nn.Module): The loaded recognizer. + video (str | dict | ndarray): The video file path / url or the + rawframes directory path / results dictionary (the input of + pipeline) / a 4D array T x H x W x 3 (The input video). + outputs (list(str) | tuple(str) | str | None) : Names of layers whose + outputs need to be returned, default: None. + as_tensor (bool): Same as that in ``OutputHook``. Default: True. + + Returns: + dict[tuple(str, float)]: Top-5 recognition result dict. + dict[torch.tensor | np.ndarray]: + Output feature maps from layers specified in `outputs`. + """ + if 'use_frames' in kwargs: + warnings.warn('The argument `use_frames` is deprecated PR #1191. ' + 'Now you can use models trained with frames or videos ' + 'arbitrarily. ') + if 'label_path' in kwargs: + warnings.warn('The argument `use_frames` is deprecated PR #1191. ' + 'Now the label file is not needed in ' + 'inference_recognizer. ') + + input_flag = None + if isinstance(video, dict): + input_flag = 'dict' + elif isinstance(video, np.ndarray): + assert len(video.shape) == 4, 'The shape should be T x H x W x C' + input_flag = 'array' + elif isinstance(video, str) and video.startswith('http'): + input_flag = 'video' + elif isinstance(video, str) and osp.exists(video): + if osp.isfile(video): + input_flag = 'video' + if osp.isdir(video): + input_flag = 'rawframes' + else: + raise RuntimeError('The type of argument video is not supported: ' + f'{type(video)}') + + if isinstance(outputs, str): + outputs = (outputs, ) + assert outputs is None or isinstance(outputs, (tuple, list)) + + cfg = model.cfg + device = next(model.parameters()).device # model device + # build the data pipeline + test_pipeline = cfg.data.test.pipeline + # Alter data pipelines & prepare inputs + if input_flag == 'dict': + data = video + if input_flag == 'array': + modality_map = {2: 'Flow', 3: 'RGB'} + modality = modality_map.get(video.shape[-1]) + data = dict( + total_frames=video.shape[0], + label=-1, + start_index=0, + array=video, + modality=modality) + for i in range(len(test_pipeline)): + if 'Decode' in test_pipeline[i]['type']: + test_pipeline[i] = dict(type='ArrayDecode') + if input_flag == 'video': + data = dict(filename=video, label=-1, start_index=0, modality='RGB') + if 'Init' not in test_pipeline[0]['type']: + test_pipeline = [dict(type='OpenCVInit')] + test_pipeline + else: + test_pipeline[0] = dict(type='OpenCVInit') + for i in range(len(test_pipeline)): + if 'Decode' in test_pipeline[i]['type']: + test_pipeline[i] = dict(type='OpenCVDecode') + if input_flag == 'rawframes': + filename_tmpl = cfg.data.test.get('filename_tmpl', 'img_{:05}.jpg') + modality = cfg.data.test.get('modality', 'RGB') + start_index = cfg.data.test.get('start_index', 1) + + # count the number of frames that match the format of `filename_tmpl` + # RGB pattern example: img_{:05}.jpg -> ^img_\d+.jpg$ + # Flow patteren example: {}_{:05d}.jpg -> ^x_\d+.jpg$ + pattern = f'^{filename_tmpl}$' + if modality == 'Flow': + pattern = pattern.replace('{}', 'x') + pattern = pattern.replace( + pattern[pattern.find('{'):pattern.find('}') + 1], '\\d+') + total_frames = len( + list( + filter(lambda x: re.match(pattern, x) is not None, + os.listdir(video)))) + data = dict( + frame_dir=video, + total_frames=total_frames, + label=-1, + start_index=start_index, + filename_tmpl=filename_tmpl, + modality=modality) + if 'Init' in test_pipeline[0]['type']: + test_pipeline = test_pipeline[1:] + for i in range(len(test_pipeline)): + if 'Decode' in test_pipeline[i]['type']: + test_pipeline[i] = dict(type='RawFrameDecode') + + test_pipeline = Compose(test_pipeline) + data = test_pipeline(data) + data = collate([data], samples_per_gpu=1) + + if next(model.parameters()).is_cuda: + # scatter to specified GPU + data = scatter(data, [device])[0] + + # forward the model + with OutputHook(model, outputs=outputs, as_tensor=as_tensor) as h: + with torch.no_grad(): + scores = model(return_loss=False, **data)[0] + returned_features = h.layer_outputs if outputs else None + + num_classes = scores.shape[-1] + score_tuples = tuple(zip(range(num_classes), scores)) + score_sorted = sorted(score_tuples, key=itemgetter(1), reverse=True) + + top5_label = score_sorted[:5] + if outputs: + return top5_label, returned_features + return top5_label diff --git a/pyskl/apis/train.py b/pyskl/apis/train.py new file mode 100644 index 00000000..615cc777 --- /dev/null +++ b/pyskl/apis/train.py @@ -0,0 +1,215 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os +import os.path as osp +import time + +import numpy as np +import torch +import torch.distributed as dist +from mmcv.engine import multi_gpu_test +from mmcv.parallel import MMDistributedDataParallel +from mmcv.runner import DistSamplerSeedHook, EpochBasedRunner, OptimizerHook, build_optimizer, get_dist_info + +from ..core import DistEvalHook +from ..datasets import build_dataloader, build_dataset +from ..utils import get_root_logger + + +def init_random_seed(seed=None, device='cuda'): + """Initialize random seed. + + If the seed is not set, the seed will be automatically randomized, + and then broadcast to all processes to prevent some potential bugs. + Args: + seed (int, Optional): The seed. Default to None. + device (str): The device where the seed will be put on. + Default to 'cuda'. + Returns: + int: Seed to be used. + """ + if seed is not None: + return seed + + # Make sure all ranks share the same random seed to prevent + # some potential bugs. Please refer to + # https://github.com/open-mmlab/mmdetection/issues/6339 + rank, world_size = get_dist_info() + seed = np.random.randint(2**31) + + if world_size == 1: + return seed + + if rank == 0: + random_num = torch.tensor(seed, dtype=torch.int32, device=device) + else: + random_num = torch.tensor(0, dtype=torch.int32, device=device) + + dist.broadcast(random_num, src=0) + return random_num.item() + + +def train_model(model, + dataset, + cfg, + validate=False, + test=dict(test_best=False, test_last=False), + timestamp=None, + meta=None): + """Train model entry function. + + Args: + model (nn.Module): The model to be trained. + dataset (:obj:`Dataset`): Train dataset. + cfg (dict): The config dict for training. + validate (bool): Whether to do evaluation. Default: False. + test (dict): The testing option, with two keys: test_last & test_best. + The value is True or False, indicating whether to test the + corresponding checkpoint. + Default: dict(test_best=False, test_last=False). + timestamp (str | None): Local time for runner. Default: None. + meta (dict | None): Meta dict to record some important information. + Default: None + """ + logger = get_root_logger(log_level=cfg.log_level) + + # prepare data loaders + dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] + + dataloader_setting = dict( + videos_per_gpu=cfg.data.get('videos_per_gpu', 1), + workers_per_gpu=cfg.data.get('workers_per_gpu', 1), + persistent_workers=cfg.data.get('persistent_workers', False), + seed=cfg.seed) + dataloader_setting = dict(dataloader_setting, + **cfg.data.get('train_dataloader', {})) + + data_loaders = [ + build_dataloader(ds, **dataloader_setting) for ds in dataset + ] + + # put model on gpus + find_unused_parameters = cfg.get('find_unused_parameters', False) + # Sets the `find_unused_parameters` parameter in + # torch.nn.parallel.DistributedDataParallel + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False, + find_unused_parameters=find_unused_parameters) + + # build runner + optimizer = build_optimizer(model, cfg.optimizer) + + Runner = EpochBasedRunner + runner = Runner( + model, + optimizer=optimizer, + work_dir=cfg.work_dir, + logger=logger, + meta=meta) + # an ugly workaround to make .log and .log.json filenames the same + runner.timestamp = timestamp + + if 'type' not in cfg.optimizer_config: + optimizer_config = OptimizerHook(**cfg.optimizer_config) + else: + optimizer_config = cfg.optimizer_config + + # register hooks + runner.register_training_hooks(cfg.lr_config, optimizer_config, + cfg.checkpoint_config, cfg.log_config, + cfg.get('momentum_config', None)) + runner.register_hook(DistSamplerSeedHook()) + + eval_hook = None + if validate: + eval_cfg = cfg.get('evaluation', {}) + val_dataset = build_dataset(cfg.data.val, dict(test_mode=True)) + dataloader_setting = dict( + videos_per_gpu=cfg.data.get('videos_per_gpu', 1), + workers_per_gpu=cfg.data.get('workers_per_gpu', 1), + persistent_workers=cfg.data.get('persistent_workers', False), + shuffle=False) + dataloader_setting = dict(dataloader_setting, + **cfg.data.get('val_dataloader', {})) + val_dataloader = build_dataloader(val_dataset, **dataloader_setting) + eval_hook = DistEvalHook(val_dataloader, **eval_cfg) + runner.register_hook(eval_hook) + + if cfg.get('resume_from', None): + runner.resume(cfg.resume_from) + elif cfg.get('load_from', None): + runner.load_checkpoint(cfg.load_from) + + runner.run(data_loaders, cfg.workflow, cfg.total_epochs) + + dist.barrier() + time.sleep(2) + + if test['test_last'] or test['test_best']: + best_ckpt_path = None + if test['test_best']: + assert eval_hook is not None + best_ckpt_path = None + ckpt_paths = [x for x in os.listdir(cfg.work_dir) if 'best' in x] + ckpt_paths = [x for x in ckpt_paths if x.endswith('.pth')] + if len(ckpt_paths) == 0: + logger.info('Warning: test_best set, but no ckpt found') + test['test_best'] = False + if not test['test_last']: + return + elif len(ckpt_paths) > 1: + epoch_ids = [ + int(x.split('epoch_')[-1][:-4]) for x in ckpt_paths + ] + best_ckpt_path = ckpt_paths[np.argmax(epoch_ids)] + else: + best_ckpt_path = ckpt_paths[0] + if best_ckpt_path: + best_ckpt_path = osp.join(cfg.work_dir, best_ckpt_path) + + test_dataset = build_dataset(cfg.data.test, dict(test_mode=True)) + gpu_collect = cfg.get('evaluation', {}).get('gpu_collect', False) + tmpdir = cfg.get('evaluation', {}).get('tmpdir', + osp.join(cfg.work_dir, 'tmp')) + dataloader_setting = dict( + videos_per_gpu=cfg.data.get('videos_per_gpu', 1), + workers_per_gpu=cfg.data.get('workers_per_gpu', 1), + persistent_workers=cfg.data.get('persistent_workers', False), + shuffle=False) + dataloader_setting = dict(dataloader_setting, + **cfg.data.get('test_dataloader', {})) + + test_dataloader = build_dataloader(test_dataset, **dataloader_setting) + + names, ckpts = [], [] + + if test['test_last']: + names.append('last') + ckpts.append(None) + if test['test_best']: + names.append('best') + ckpts.append(best_ckpt_path) + + for name, ckpt in zip(names, ckpts): + if ckpt is not None: + runner.load_checkpoint(ckpt) + + outputs = multi_gpu_test(runner.model, test_dataloader, tmpdir, + gpu_collect) + rank, _ = get_dist_info() + if rank == 0: + out = osp.join(cfg.work_dir, f'{name}_pred.pkl') + test_dataset.dump_results(outputs, out) + + eval_cfg = cfg.get('evaluation', {}) + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', + 'save_best', 'rule', 'by_epoch', 'broadcast_bn_buffers' + ]: + eval_cfg.pop(key, None) + + eval_res = test_dataset.evaluate(outputs, **eval_cfg) + logger.info(f'Testing results of the {name} checkpoint') + for metric_name, val in eval_res.items(): + logger.info(f'{metric_name}: {val:.04f}') diff --git a/pyskl/core/__init__.py b/pyskl/core/__init__.py new file mode 100644 index 00000000..fdba2bb4 --- /dev/null +++ b/pyskl/core/__init__.py @@ -0,0 +1,3 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .evaluation import * # noqa: F401, F403 +from .hooks import * # noqa: F401, F403 diff --git a/pyskl/core/evaluation.py b/pyskl/core/evaluation.py new file mode 100644 index 00000000..8f355199 --- /dev/null +++ b/pyskl/core/evaluation.py @@ -0,0 +1,191 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np +from mmcv.runner import DistEvalHook as BasicDistEvalHook + + +class DistEvalHook(BasicDistEvalHook): + greater_keys = [ + 'acc', 'top', 'AR@', 'auc', 'precision', 'mAP@', 'Recall@' + ] + less_keys = ['loss'] + + def __init__(self, *args, save_best='auto', **kwargs): + super().__init__(*args, save_best=save_best, **kwargs) + + +def confusion_matrix(y_pred, y_real, normalize=None): + """Compute confusion matrix. + + Args: + y_pred (list[int] | np.ndarray[int]): Prediction labels. + y_real (list[int] | np.ndarray[int]): Ground truth labels. + normalize (str | None): Normalizes confusion matrix over the true + (rows), predicted (columns) conditions or all the population. + If None, confusion matrix will not be normalized. Options are + "true", "pred", "all", None. Default: None. + + Returns: + np.ndarray: Confusion matrix. + """ + if normalize not in ['true', 'pred', 'all', None]: + raise ValueError("normalize must be one of {'true', 'pred', " + "'all', None}") + + if isinstance(y_pred, list): + y_pred = np.array(y_pred) + if not isinstance(y_pred, np.ndarray): + raise TypeError( + f'y_pred must be list or np.ndarray, but got {type(y_pred)}') + if not y_pred.dtype == np.int64: + raise TypeError( + f'y_pred dtype must be np.int64, but got {y_pred.dtype}') + + if isinstance(y_real, list): + y_real = np.array(y_real) + if not isinstance(y_real, np.ndarray): + raise TypeError( + f'y_real must be list or np.ndarray, but got {type(y_real)}') + if not y_real.dtype == np.int64: + raise TypeError( + f'y_real dtype must be np.int64, but got {y_real.dtype}') + + label_set = np.unique(np.concatenate((y_pred, y_real))) + num_labels = len(label_set) + max_label = label_set[-1] + label_map = np.zeros(max_label + 1, dtype=np.int64) + for i, label in enumerate(label_set): + label_map[label] = i + + y_pred_mapped = label_map[y_pred] + y_real_mapped = label_map[y_real] + + confusion_mat = np.bincount( + num_labels * y_real_mapped + y_pred_mapped, + minlength=num_labels**2).reshape(num_labels, num_labels) + + with np.errstate(all='ignore'): + if normalize == 'true': + confusion_mat = ( + confusion_mat / confusion_mat.sum(axis=1, keepdims=True)) + elif normalize == 'pred': + confusion_mat = ( + confusion_mat / confusion_mat.sum(axis=0, keepdims=True)) + elif normalize == 'all': + confusion_mat = (confusion_mat / confusion_mat.sum()) + confusion_mat = np.nan_to_num(confusion_mat) + + return confusion_mat + + +def mean_class_accuracy(scores, labels): + """Calculate mean class accuracy. + + Args: + scores (list[np.ndarray]): Prediction scores for each class. + labels (list[int]): Ground truth labels. + + Returns: + np.ndarray: Mean class accuracy. + """ + pred = np.argmax(scores, axis=1) + cf_mat = confusion_matrix(pred, labels).astype(float) + + cls_cnt = cf_mat.sum(axis=1) + cls_hit = np.diag(cf_mat) + + mean_class_acc = np.mean( + [hit / cnt if cnt else 0.0 for cnt, hit in zip(cls_cnt, cls_hit)]) + + return mean_class_acc + + +def top_k_accuracy(scores, labels, topk=(1, )): + """Calculate top k accuracy score. + + Args: + scores (list[np.ndarray]): Prediction scores for each class. + labels (list[int]): Ground truth labels. + topk (tuple[int]): K value for top_k_accuracy. Default: (1, ). + + Returns: + list[float]: Top k accuracy score for each k. + """ + res = [] + labels = np.array(labels)[:, np.newaxis] + for k in topk: + max_k_preds = np.argsort(scores, axis=1)[:, -k:][:, ::-1] + match_array = np.logical_or.reduce(max_k_preds == labels, axis=1) + topk_acc_score = match_array.sum() / match_array.shape[0] + res.append(topk_acc_score) + + return res + + +def mean_average_precision(scores, labels): + """Mean average precision for multi-label recognition. + + Args: + scores (list[np.ndarray]): Prediction scores of different classes for + each sample. + labels (list[np.ndarray]): Ground truth many-hot vector for each + sample. + + Returns: + np.float: The mean average precision. + """ + results = [] + scores = np.stack(scores).T + labels = np.stack(labels).T + + for score, label in zip(scores, labels): + precision, recall, _ = binary_precision_recall_curve(score, label) + ap = -np.sum(np.diff(recall) * np.array(precision)[:-1]) + results.append(ap) + results = [x for x in results if not np.isnan(x)] + if results == []: + return np.nan + return np.mean(results) + + +def binary_precision_recall_curve(y_score, y_true): + """Calculate the binary precision recall curve at step thresholds. + + Args: + y_score (np.ndarray): Prediction scores for each class. + Shape should be (num_classes, ). + y_true (np.ndarray): Ground truth many-hot vector. + Shape should be (num_classes, ). + + Returns: + precision (np.ndarray): The precision of different thresholds. + recall (np.ndarray): The recall of different thresholds. + thresholds (np.ndarray): Different thresholds at which precision and + recall are tested. + """ + assert isinstance(y_score, np.ndarray) + assert isinstance(y_true, np.ndarray) + assert y_score.shape == y_true.shape + + # make y_true a boolean vector + y_true = (y_true == 1) + # sort scores and corresponding truth values + desc_score_indices = np.argsort(y_score, kind='mergesort')[::-1] + y_score = y_score[desc_score_indices] + y_true = y_true[desc_score_indices] + # There may be ties in values, therefore find the `distinct_value_inds` + distinct_value_inds = np.where(np.diff(y_score))[0] + threshold_inds = np.r_[distinct_value_inds, y_true.size - 1] + # accumulate the true positives with decreasing threshold + tps = np.cumsum(y_true)[threshold_inds] + fps = 1 + threshold_inds - tps + thresholds = y_score[threshold_inds] + + precision = tps / (tps + fps) + precision[np.isnan(precision)] = 0 + recall = tps / tps[-1] + # stop when full recall attained + # and reverse the outputs so recall is decreasing + last_ind = tps.searchsorted(tps[-1]) + sl = slice(last_ind, None, -1) + + return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl] diff --git a/pyskl/core/hooks.py b/pyskl/core/hooks.py new file mode 100644 index 00000000..fb30beba --- /dev/null +++ b/pyskl/core/hooks.py @@ -0,0 +1,68 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import functools +import warnings + +import torch + + +class OutputHook: + """Output feature map of some layers. + + Args: + module (nn.Module): The whole module to get layers. + outputs (tuple[str] | list[str]): Layer name to output. Default: None. + as_tensor (bool): Determine to return a tensor or a numpy array. + Default: False. + """ + + def __init__(self, module, outputs=None, as_tensor=False): + self.outputs = outputs + self.as_tensor = as_tensor + self.layer_outputs = {} + self.handles = [] + self.register(module) + + def register(self, module): + + def hook_wrapper(name): + + def hook(model, input, output): + if not isinstance(output, torch.Tensor): + warnings.warn(f'Directly return the output from {name}, ' + f'since it is not a tensor') + self.layer_outputs[name] = output + elif self.as_tensor: + self.layer_outputs[name] = output + else: + self.layer_outputs[name] = output.detach().cpu().numpy() + + return hook + + if isinstance(self.outputs, (list, tuple)): + for name in self.outputs: + try: + layer = rgetattr(module, name) + h = layer.register_forward_hook(hook_wrapper(name)) + except AttributeError: + raise AttributeError(f'Module {name} not found') + self.handles.append(h) + + def remove(self): + for h in self.handles: + h.remove() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.remove() + + +# using wonder's beautiful simplification: +# https://stackoverflow.com/questions/31174295/getattr-and-setattr-on-nested-objects +def rgetattr(obj, attr, *args): + + def _getattr(obj, attr): + return getattr(obj, attr, *args) + + return functools.reduce(_getattr, [obj] + attr.split('.')) diff --git a/pyskl/datasets/__init__.py b/pyskl/datasets/__init__.py new file mode 100644 index 00000000..a2f6ed8f --- /dev/null +++ b/pyskl/datasets/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .base import BaseDataset +from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset +from .dataset_wrappers import ConcatDataset, RepeatDataset +from .pose_dataset import PoseDataset +from .video_dataset import VideoDataset + +__all__ = [ + 'VideoDataset', 'build_dataloader', 'build_dataset', 'RepeatDataset', + 'BaseDataset', 'DATASETS', 'PIPELINES', 'PoseDataset', 'ConcatDataset' +] diff --git a/pyskl/datasets/base.py b/pyskl/datasets/base.py new file mode 100644 index 00000000..a03e4e03 --- /dev/null +++ b/pyskl/datasets/base.py @@ -0,0 +1,308 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import copy +import os.path as osp +import warnings +from abc import ABCMeta, abstractmethod +from collections import OrderedDict, defaultdict + +import mmcv +import numpy as np +import torch +from mmcv.utils import print_log +from torch.utils.data import Dataset + +from ..core import mean_average_precision, mean_class_accuracy, top_k_accuracy +from .pipelines import Compose + + +class BaseDataset(Dataset, metaclass=ABCMeta): + """Base class for datasets. + + All datasets to process video should subclass it. + All subclasses should overwrite: + + - Methods:`load_annotations`, supporting to load information from an + annotation file. + - Methods:`prepare_train_frames`, providing train data. + - Methods:`prepare_test_frames`, providing test data. + + Args: + ann_file (str): Path to the annotation file. + pipeline (list[dict | callable]): A sequence of data transforms. + data_prefix (str): Path to a directory where videos are held. Default: ''. + test_mode (bool): Store True when building test or validation dataset. + Default: False. + multi_class (bool): Determines whether the dataset is a multi-class + dataset. Default: False. + num_classes (int | None): Number of classes of the dataset, used in + multi-class datasets. Default: None. + start_index (int): Specify a start index for frames in consideration of + different filename format. However, when taking videos as input, + it should be set to 0, since frames loaded from videos count + from 0. Default: 1. + modality (str): Modality of data. Support 'RGB', 'Flow', 'Audio'. + Default: 'RGB'. + sample_by_class (bool): Sampling by class, should be set `True` when + performing inter-class data balancing. Only compatible with + `multi_class == False`. Only applies for training. Default: False. + power (float): We support sampling data with the probability + proportional to the power of its label frequency (freq ^ power) + when sampling data. `power == 1` indicates uniformly sampling all + data; `power == 0` indicates uniformly sampling all classes. + Default: 0. + dynamic_length (bool): If the dataset length is dynamic (used by + ClassSpecificDistributedSampler). Default: False. + """ + + def __init__(self, + ann_file, + pipeline, + data_prefix='', + test_mode=False, + multi_class=False, + num_classes=None, + start_index=1, + modality='RGB', + memcached=False, + mc_cfg=('localhost', 11211), + sample_by_class=False, + power=0, + dynamic_length=False): + super().__init__() + + self.ann_file = ann_file + self.data_prefix = data_prefix + self.test_mode = test_mode + self.multi_class = multi_class + self.num_classes = num_classes + self.start_index = start_index + self.modality = modality + self.sample_by_class = sample_by_class + self.power = power + self.dynamic_length = dynamic_length + # Note: Currently, memcached only works for PoseDataset + self.memcached = memcached + self.mc_cfg = mc_cfg + self.cli = None + + assert not (self.multi_class and self.sample_by_class) + + self.pipeline = Compose(pipeline) + self.video_infos = self.load_annotations() + if self.memcached: + for item in self.video_infos: + assert 'key' in item and isinstance(item['key'], str) + + if self.sample_by_class: + self.video_infos_by_class = self.parse_by_class() + + class_prob = [] + for _, samples in self.video_infos_by_class.items(): + class_prob.append(len(samples) / len(self.video_infos)) + class_prob = [x**self.power for x in class_prob] + + summ = sum(class_prob) + class_prob = [x / summ for x in class_prob] + + self.class_prob = dict(zip(self.video_infos_by_class, class_prob)) + + @abstractmethod + def load_annotations(self): + """Load the annotation according to ann_file into video_infos.""" + + # json annotations already looks like video_infos, so for each dataset, + # this func should be the same + def load_json_annotations(self): + """Load json annotation file to get video information.""" + video_infos = mmcv.load(self.ann_file) + num_videos = len(video_infos) + path_key = 'frame_dir' if 'frame_dir' in video_infos[0] else 'filename' + for i in range(num_videos): + path_value = video_infos[i][path_key] + path_value = osp.join(self.data_prefix, path_value) + video_infos[i][path_key] = path_value + if self.multi_class: + assert self.num_classes is not None + else: + assert len(video_infos[i]['label']) == 1 + video_infos[i]['label'] = video_infos[i]['label'][0] + return video_infos + + def parse_by_class(self): + video_infos_by_class = defaultdict(list) + for item in self.video_infos: + label = item['label'] + video_infos_by_class[label].append(item) + return video_infos_by_class + + @staticmethod + def label2array(num, label): + arr = np.zeros(num, dtype=np.float32) + arr[label] = 1. + return arr + + def evaluate(self, + results, + metrics='top_k_accuracy', + metric_options=dict(top_k_accuracy=dict(topk=(1, 5))), + logger=None, + **deprecated_kwargs): + """Perform evaluation for common datasets. + + Args: + results (list): Output results. + metrics (str | sequence[str]): Metrics to be performed. + Defaults: 'top_k_accuracy'. + metric_options (dict): Dict for metric options. Options are + ``topk`` for ``top_k_accuracy``. + Default: ``dict(top_k_accuracy=dict(topk=(1, 5)))``. + logger (logging.Logger | None): Logger for recording. + Default: None. + deprecated_kwargs (dict): Used for containing deprecated arguments. + See 'https://github.com/open-mmlab/mmaction2/pull/286'. + + Returns: + dict: Evaluation results dict. + """ + if not isinstance(results, list): + raise TypeError(f'results must be a list, but got {type(results)}') + assert len(results) == len(self), ( + f'The length of results is not equal to the dataset len: ' + f'{len(results)} != {len(self)}') + + if isinstance(results[0], list) or isinstance(results[0], tuple): + num_results = len(results[0]) + eval_results = dict() + for i in range(num_results): + eval_results_cur = self.evaluate( + [x[i] for x in results], metrics, metric_options, logger, **deprecated_kwargs) + eval_results.update({f'{k}_{i}': v for k, v in eval_results_cur.items()}) + return eval_results + + # Protect ``metric_options`` since it uses mutable value as default + metric_options = copy.deepcopy(metric_options) + if deprecated_kwargs != {}: + warnings.warn( + 'Option arguments for metrics has been changed to ' + "`metric_options`, See 'https://github.com/open-mmlab/mmaction2/pull/286' " # noqa: E501 + 'for more details') + metric_options['top_k_accuracy'] = dict( + metric_options['top_k_accuracy'], **deprecated_kwargs) + + metrics = metrics if isinstance(metrics, (list, tuple)) else [metrics] + allowed_metrics = ['top_k_accuracy', 'mean_class_accuracy', 'mean_average_precision'] + + for metric in metrics: + if metric not in allowed_metrics: + raise KeyError(f'metric {metric} is not supported') + + eval_results = OrderedDict() + gt_labels = [ann['label'] for ann in self.video_infos] + + for metric in metrics: + msg = f'Evaluating {metric} ...' + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + if metric == 'top_k_accuracy': + topk = metric_options.setdefault('top_k_accuracy', + {}).setdefault( + 'topk', (1, 5)) + if not isinstance(topk, (int, tuple)): + raise TypeError('topk must be int or tuple of int, ' + f'but got {type(topk)}') + if isinstance(topk, int): + topk = (topk, ) + + top_k_acc = top_k_accuracy(results, gt_labels, topk) + log_msg = [] + for k, acc in zip(topk, top_k_acc): + eval_results[f'top{k}_acc'] = acc + log_msg.append(f'\ntop{k}_acc\t{acc:.4f}') + log_msg = ''.join(log_msg) + print_log(log_msg, logger=logger) + continue + + if metric == 'mean_class_accuracy': + mean_acc = mean_class_accuracy(results, gt_labels) + eval_results['mean_class_accuracy'] = mean_acc + log_msg = f'\nmean_acc\t{mean_acc:.4f}' + print_log(log_msg, logger=logger) + continue + + if metric == 'mean_average_precision': + gt_labels_arrays = [ + self.label2array(self.num_classes, label) + for label in gt_labels + ] + mAP = mean_average_precision(results, gt_labels_arrays) + eval_results['mean_average_precision'] = mAP + log_msg = f'\nmean_average_precision\t{mAP:.4f}' + print_log(log_msg, logger=logger) + continue + + return eval_results + + @staticmethod + def dump_results(results, out): + """Dump data to json/yaml/pickle strings or files.""" + return mmcv.dump(results, out) + + def prepare_train_frames(self, idx): + """Prepare the frames for training given the index.""" + results = copy.deepcopy(self.video_infos[idx]) + if self.memcached and 'key' in results: + if self.cli is None: + from pymemcache.client.base import Client + from pymemcache import serde + self.cli = Client(self.mc_cfg, serde=serde.pickle_serde) + key = results.pop('key') + pack = self.cli.get(key) + for k in pack: + results[k] = pack[k] + + results['modality'] = self.modality + results['start_index'] = self.start_index + + # prepare tensor in getitem + # If HVU, type(results['label']) is dict + if self.multi_class and isinstance(results['label'], list): + onehot = torch.zeros(self.num_classes) + onehot[results['label']] = 1. + results['label'] = onehot + + return self.pipeline(results) + + def prepare_test_frames(self, idx): + """Prepare the frames for testing given the index.""" + results = copy.deepcopy(self.video_infos[idx]) + if self.memcached and 'key' in results: + if self.cli is None: + from pymemcache.client.base import Client + from pymemcache import serde + self.cli = Client(self.mc_cfg, serde=serde.pickle_serde) + key = results.pop('key') + pack = self.cli.get(key) + for k in pack: + results[k] = pack[k] + + results['modality'] = self.modality + results['start_index'] = self.start_index + + # prepare tensor in getitem + # If HVU, type(results['label']) is dict + if self.multi_class and isinstance(results['label'], list): + onehot = torch.zeros(self.num_classes) + onehot[results['label']] = 1. + results['label'] = onehot + + return self.pipeline(results) + + def __len__(self): + """Get the size of the dataset.""" + return len(self.video_infos) + + def __getitem__(self, idx): + """Get the sample for either training or testing given index.""" + return self.prepare_test_frames(idx) if self.test_mode else self.prepare_train_frames(idx) diff --git a/pyskl/datasets/builder.py b/pyskl/datasets/builder.py new file mode 100644 index 00000000..de38e334 --- /dev/null +++ b/pyskl/datasets/builder.py @@ -0,0 +1,124 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import platform +import random +from functools import partial + +import numpy as np +import torch +from mmcv.parallel import collate +from mmcv.runner import get_dist_info +from mmcv.utils import Registry, build_from_cfg, digit_version +from torch.utils.data import DataLoader + +from .samplers import ClassSpecificDistributedSampler, DistributedSampler + +if platform.system() != 'Windows': + # https://github.com/pytorch/pytorch/issues/973 + import resource + rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) + hard_limit = rlimit[1] + soft_limit = min(4096, hard_limit) + resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit)) + +DATASETS = Registry('dataset') +PIPELINES = Registry('pipeline') + + +def build_dataset(cfg, default_args=None): + """Build a dataset from config dict. + + Args: + cfg (dict): Config dict. It should at least contain the key "type". + default_args (dict | None, optional): Default initialization arguments. + Default: None. + + Returns: + Dataset: The constructed dataset. + """ + dataset = build_from_cfg(cfg, DATASETS, default_args) + return dataset + + +def build_dataloader(dataset, + videos_per_gpu, + workers_per_gpu, + shuffle=True, + seed=None, + drop_last=False, + pin_memory=True, + persistent_workers=False, + **kwargs): + """Build PyTorch DataLoader. + + In distributed training, each GPU/process has a dataloader. + + Args: + dataset (:obj:`Dataset`): A PyTorch dataset. + videos_per_gpu (int): Number of videos on each GPU, i.e., + batch size of each GPU. + workers_per_gpu (int): How many subprocesses to use for data + loading for each GPU. + shuffle (bool): Whether to shuffle the data at every epoch. + Default: True. + seed (int | None): Seed to be used. Default: None. + drop_last (bool): Whether to drop the last incomplete batch in epoch. + Default: False + pin_memory (bool): Whether to use pin_memory in DataLoader. + Default: True + persistent_workers (bool): If True, the data loader will not shutdown + the worker processes after a dataset has been consumed once. + This allows to maintain the workers Dataset instances alive. + The argument also has effect in PyTorch>=1.8.0. + Default: False + kwargs (dict, optional): Any keyword argument to be used to initialize + DataLoader. + + Returns: + DataLoader: A PyTorch dataloader. + """ + rank, world_size = get_dist_info() + + if hasattr(dataset, 'class_prob') and dataset.class_prob is not None: + sampler = ClassSpecificDistributedSampler( + dataset, + world_size, + rank, + class_prob=dataset.class_prob, + shuffle=shuffle, + seed=seed) + else: + sampler = DistributedSampler( + dataset, world_size, rank, shuffle=shuffle, seed=seed) + shuffle = False + batch_size = videos_per_gpu + num_workers = workers_per_gpu + + init_fn = partial( + worker_init_fn, num_workers=num_workers, rank=rank, + seed=seed) if seed is not None else None + + if digit_version(torch.__version__) >= digit_version('1.8.0'): + kwargs['persistent_workers'] = persistent_workers + + data_loader = DataLoader( + dataset, + batch_size=batch_size, + sampler=sampler, + num_workers=num_workers, + collate_fn=partial(collate, samples_per_gpu=videos_per_gpu), + pin_memory=pin_memory, + shuffle=shuffle, + worker_init_fn=init_fn, + drop_last=drop_last, + **kwargs) + + return data_loader + + +def worker_init_fn(worker_id, num_workers, rank, seed): + """Init the random seed for various workers.""" + # The seed of each worker equals to + # num_worker * rank + worker_id + user_seed + worker_seed = num_workers * rank + worker_id + seed + np.random.seed(worker_seed) + random.seed(worker_seed) diff --git a/pyskl/datasets/dataset_wrappers.py b/pyskl/datasets/dataset_wrappers.py new file mode 100644 index 00000000..5de7584c --- /dev/null +++ b/pyskl/datasets/dataset_wrappers.py @@ -0,0 +1,73 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np + +from .builder import DATASETS, build_dataset + + +@DATASETS.register_module() +class RepeatDataset: + """A wrapper of repeated dataset. + + The length of repeated dataset will be ``times`` larger than the original + dataset. This is useful when the data loading time is long but the dataset + is small. Using RepeatDataset can reduce the data loading time between + epochs. + + Args: + dataset (dict): The config of the dataset to be repeated. + times (int): Repeat times. + test_mode (bool): Store True when building test or validation dataset. + Default: False. + """ + + def __init__(self, dataset, times, test_mode=False): + dataset['test_mode'] = test_mode + self.dataset = build_dataset(dataset) + self.times = times + if hasattr(dataset, 'class_prob'): + self.class_prob = dataset.class_prob + + self._ori_len = len(self.dataset) + + def __getitem__(self, idx): + """Get data.""" + return self.dataset[idx % self._ori_len] + + def __len__(self): + """Length after repetition.""" + return self.times * self._ori_len + + +@DATASETS.register_module() +class ConcatDataset: + """A wrapper of concatenated dataset. + + The length of concatenated dataset will be the sum of lengths of all + datasets. This is useful when you want to train a model with multiple data + sources. + + Args: + datasets (list[dict]): The configs of the datasets. + test_mode (bool): Store True when building test or validation dataset. + Default: False. + """ + + def __init__(self, datasets, test_mode=False): + + for item in datasets: + item['test_mode'] = test_mode + + datasets = [build_dataset(cfg) for cfg in datasets] + self.datasets = datasets + self.lens = [len(x) for x in self.datasets] + self.cumsum = np.cumsum(self.lens) + + def __getitem__(self, idx): + """Get data.""" + dataset_idx = np.searchsorted(self.cumsum, idx, side='right') + item_idx = idx if dataset_idx == 0 else idx - self.cumsum[dataset_idx] + return self.datasets[dataset_idx][item_idx] + + def __len__(self): + """Length after repetition.""" + return sum(self.lens) diff --git a/pyskl/datasets/pipelines/__init__.py b/pyskl/datasets/pipelines/__init__.py new file mode 100644 index 00000000..3776b3c2 --- /dev/null +++ b/pyskl/datasets/pipelines/__init__.py @@ -0,0 +1,8 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .augmentations import * # noqa: F401, F403 +from .compose import Compose # noqa: F401, F403 +from .formatting import * # noqa: F401, F403 +from .loading import * # noqa: F401, F403 +from .pose_related import * # noqa: F401, F403 +from .posec3d_related import * # noqa: F401, F403 +from .sampling import * # noqa: F401, F403 diff --git a/pyskl/datasets/pipelines/augmentations.py b/pyskl/datasets/pipelines/augmentations.py new file mode 100644 index 00000000..2b653ef7 --- /dev/null +++ b/pyskl/datasets/pipelines/augmentations.py @@ -0,0 +1,1004 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import random +import warnings +from collections.abc import Sequence + +import cv2 +import mmcv +import numpy as np +from torch.nn.modules.utils import _pair + +from ..builder import PIPELINES + + +def _combine_quadruple(a, b): + return (a[0] + a[2] * b[0], a[1] + a[3] * b[1], a[2] * b[2], a[3] * b[3]) + + +def _flip_quadruple(a): + return (1 - a[0] - a[2], a[1], a[2], a[3]) + + +@PIPELINES.register_module() +class PoseCompact: + """Convert the coordinates of keypoints to make it more compact. + Specifically, it first find a tight bounding box that surrounds all joints + in each frame, then we expand the tight box by a given padding ratio. For + example, if 'padding == 0.25', then the expanded box has unchanged center, + and 1.25x width and height. + + Required keys in results are "img_shape", "keypoint", add or modified keys + are "img_shape", "keypoint", "crop_quadruple". + + Args: + padding (float): The padding size. Default: 0.25. + threshold (int): The threshold for the tight bounding box. If the width + or height of the tight bounding box is smaller than the threshold, + we do not perform the compact operation. Default: 10. + hw_ratio (float | tuple[float] | None): The hw_ratio of the expanded + box. Float indicates the specific ratio and tuple indicates a + ratio range. If set as None, it means there is no requirement on + hw_ratio. Default: None. + allow_imgpad (bool): Whether to allow expanding the box outside the + image to meet the hw_ratio requirement. Default: True. + + Returns: + type: Description of returned object. + """ + + def __init__(self, + padding=0.25, + threshold=10, + hw_ratio=None, + allow_imgpad=True): + + self.padding = padding + self.threshold = threshold + if hw_ratio is not None: + hw_ratio = _pair(hw_ratio) + + self.hw_ratio = hw_ratio + + self.allow_imgpad = allow_imgpad + assert self.padding >= 0 + + def __call__(self, results): + img_shape = results['img_shape'] + h, w = img_shape + kp = results['keypoint'] + + # Make NaN zero + kp[np.isnan(kp)] = 0. + kp_x = kp[..., 0] + kp_y = kp[..., 1] + + min_x = np.min(kp_x[kp_x != 0], initial=np.Inf) + min_y = np.min(kp_y[kp_y != 0], initial=np.Inf) + max_x = np.max(kp_x[kp_x != 0], initial=-np.Inf) + max_y = np.max(kp_y[kp_y != 0], initial=-np.Inf) + + # The compact area is too small + if max_x - min_x < self.threshold or max_y - min_y < self.threshold: + return results + + center = ((max_x + min_x) / 2, (max_y + min_y) / 2) + half_width = (max_x - min_x) / 2 * (1 + self.padding) + half_height = (max_y - min_y) / 2 * (1 + self.padding) + + if self.hw_ratio is not None: + half_height = max(self.hw_ratio[0] * half_width, half_height) + half_width = max(1 / self.hw_ratio[1] * half_height, half_width) + + min_x, max_x = center[0] - half_width, center[0] + half_width + min_y, max_y = center[1] - half_height, center[1] + half_height + + # hot update + if not self.allow_imgpad: + min_x, min_y = int(max(0, min_x)), int(max(0, min_y)) + max_x, max_y = int(min(w, max_x)), int(min(h, max_y)) + else: + min_x, min_y = int(min_x), int(min_y) + max_x, max_y = int(max_x), int(max_y) + + kp_x[kp_x != 0] -= min_x + kp_y[kp_y != 0] -= min_y + + new_shape = (max_y - min_y, max_x - min_x) + results['img_shape'] = new_shape + + # the order is x, y, w, h (in [0, 1]), a tuple + crop_quadruple = results.get('crop_quadruple', (0., 0., 1., 1.)) + new_crop_quadruple = (min_x / w, min_y / h, (max_x - min_x) / w, + (max_y - min_y) / h) + crop_quadruple = _combine_quadruple(crop_quadruple, new_crop_quadruple) + results['crop_quadruple'] = crop_quadruple + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(padding={self.padding}, ' + f'threshold={self.threshold}, ' + f'hw_ratio={self.hw_ratio}, ' + f'allow_imgpad={self.allow_imgpad})') + return repr_str + + +@PIPELINES.register_module() +class RandomCrop: + """Vanilla square random crop that specifics the output size. + + Required keys in results are "img_shape", "keypoint" (optional), "imgs" + (optional), added or modified keys are "keypoint", "imgs". + + Args: + size (int): The output size of the images. + """ + + def __init__(self, size): + if not isinstance(size, int): + raise TypeError(f'Size must be an int, but got {type(size)}') + self.size = size + + @staticmethod + def _crop_kps(kps, crop_bbox): + return kps - crop_bbox[:2] + + @staticmethod + def _crop_imgs(imgs, crop_bbox): + x1, y1, x2, y2 = crop_bbox + return [img[y1:y2, x1:x2] for img in imgs] + + @staticmethod + def _box_crop(box, crop_bbox): + """Crop the bounding boxes according to the crop_bbox. + + Args: + box (np.ndarray): The bounding boxes. + crop_bbox(np.ndarray): The bbox used to crop the original image. + """ + + x1, y1, x2, y2 = crop_bbox + img_w, img_h = x2 - x1, y2 - y1 + + box_ = box.copy() + box_[..., 0::2] = np.clip(box[..., 0::2] - x1, 0, img_w - 1) + box_[..., 1::2] = np.clip(box[..., 1::2] - y1, 0, img_h - 1) + return box_ + + def _all_box_crop(self, results, crop_bbox): + """Crop the gt_bboxes and proposals in results according to crop_bbox. + + Args: + results (dict): All information about the sample, which contain + 'gt_bboxes' and 'proposals' (optional). + crop_bbox(np.ndarray): The bbox used to crop the original image. + """ + results['gt_bboxes'] = self._box_crop(results['gt_bboxes'], crop_bbox) + if 'proposals' in results and results['proposals'] is not None: + assert results['proposals'].shape[1] == 4 + results['proposals'] = self._box_crop(results['proposals'], + crop_bbox) + return results + + def __call__(self, results): + """Performs the RandomCrop augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + img_h, img_w = results['img_shape'] + assert self.size <= img_h and self.size <= img_w + + y_offset = 0 + x_offset = 0 + if img_h > self.size: + y_offset = int(np.random.randint(0, img_h - self.size)) + if img_w > self.size: + x_offset = int(np.random.randint(0, img_w - self.size)) + + if 'crop_quadruple' not in results: + results['crop_quadruple'] = np.array( + [0, 0, 1, 1], # x, y, w, h + dtype=np.float32) + + x_ratio, y_ratio = x_offset / img_w, y_offset / img_h + w_ratio, h_ratio = self.size / img_w, self.size / img_h + + old_crop_quadruple = results['crop_quadruple'] + old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1] + old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3] + new_crop_quadruple = [ + old_x_ratio + x_ratio * old_w_ratio, + old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio, + h_ratio * old_h_ratio + ] + results['crop_quadruple'] = np.array( + new_crop_quadruple, dtype=np.float32) + + new_h, new_w = self.size, self.size + + crop_bbox = np.array( + [x_offset, y_offset, x_offset + new_w, y_offset + new_h]) + results['crop_bbox'] = crop_bbox + + results['img_shape'] = (new_h, new_w) + + if 'keypoint' in results: + results['keypoint'] = self._crop_kps(results['keypoint'], crop_bbox) + if 'imgs' in results: + results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox) + + # Process entity boxes + if 'gt_bboxes' in results: + results = self._all_box_crop(results, results['crop_bbox']) + + return results + + def __repr__(self): + repr_str = f'{self.__class__.__name__}(size={self.size})' + return repr_str + + +@PIPELINES.register_module() +class RandomResizedCrop(RandomCrop): + """Random crop that specifics the area and height-weight ratio range. + + Required keys in results are "img_shape", "crop_bbox", "imgs" (optional), + "keypoint" (optional), added or modified keys are "imgs", "keypoint", + "crop_bbox". + + Args: + area_range (Tuple[float]): The candidate area scales range of + output cropped images. Default: (0.08, 1.0). + aspect_ratio_range (Tuple[float]): The candidate aspect ratio range of + output cropped images. Default: (3 / 4, 4 / 3). + """ + + def __init__(self, + area_range=(0.08, 1.0), + aspect_ratio_range=(3 / 4, 4 / 3)): + self.area_range = area_range + self.aspect_ratio_range = aspect_ratio_range + if not mmcv.is_tuple_of(self.area_range, float): + raise TypeError(f'Area_range must be a tuple of float, ' + f'but got {type(area_range)}') + if not mmcv.is_tuple_of(self.aspect_ratio_range, float): + raise TypeError(f'Aspect_ratio_range must be a tuple of float, ' + f'but got {type(aspect_ratio_range)}') + + @staticmethod + def get_crop_bbox(img_shape, + area_range, + aspect_ratio_range, + max_attempts=10): + """Get a crop bbox given the area range and aspect ratio range. + + Args: + img_shape (Tuple[int]): Image shape + area_range (Tuple[float]): The candidate area scales range of + output cropped images. Default: (0.08, 1.0). + aspect_ratio_range (Tuple[float]): The candidate aspect + ratio range of output cropped images. Default: (3 / 4, 4 / 3). + max_attempts (int): The maximum of attempts. Default: 10. + max_attempts (int): Max attempts times to generate random candidate + bounding box. If it doesn't qualified one, the center bounding + box will be used. + Returns: + (list[int]) A random crop bbox within the area range and aspect + ratio range. + """ + assert 0 < area_range[0] <= area_range[1] <= 1 + assert 0 < aspect_ratio_range[0] <= aspect_ratio_range[1] + + img_h, img_w = img_shape + area = img_h * img_w + + min_ar, max_ar = aspect_ratio_range + aspect_ratios = np.exp( + np.random.uniform( + np.log(min_ar), np.log(max_ar), size=max_attempts)) + target_areas = np.random.uniform(*area_range, size=max_attempts) * area + candidate_crop_w = np.round(np.sqrt(target_areas * + aspect_ratios)).astype(np.int32) + candidate_crop_h = np.round(np.sqrt(target_areas / + aspect_ratios)).astype(np.int32) + + for i in range(max_attempts): + crop_w = candidate_crop_w[i] + crop_h = candidate_crop_h[i] + if crop_h <= img_h and crop_w <= img_w: + x_offset = random.randint(0, img_w - crop_w) + y_offset = random.randint(0, img_h - crop_h) + return x_offset, y_offset, x_offset + crop_w, y_offset + crop_h + + # Fallback + crop_size = min(img_h, img_w) + x_offset = (img_w - crop_size) // 2 + y_offset = (img_h - crop_size) // 2 + return x_offset, y_offset, x_offset + crop_size, y_offset + crop_size + + def __call__(self, results): + """Performs the RandomResizeCrop augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + img_h, img_w = results['img_shape'] + + left, top, right, bottom = self.get_crop_bbox( + (img_h, img_w), self.area_range, self.aspect_ratio_range) + new_h, new_w = bottom - top, right - left + + if 'crop_quadruple' not in results: + results['crop_quadruple'] = np.array( + [0, 0, 1, 1], # x, y, w, h + dtype=np.float32) + + x_ratio, y_ratio = left / img_w, top / img_h + w_ratio, h_ratio = new_w / img_w, new_h / img_h + + old_crop_quadruple = results['crop_quadruple'] + old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1] + old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3] + new_crop_quadruple = [ + old_x_ratio + x_ratio * old_w_ratio, + old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio, + h_ratio * old_h_ratio + ] + results['crop_quadruple'] = np.array( + new_crop_quadruple, dtype=np.float32) + + crop_bbox = np.array([left, top, right, bottom]) + results['crop_bbox'] = crop_bbox + results['img_shape'] = (new_h, new_w) + + if 'keypoint' in results: + results['keypoint'] = self._crop_kps(results['keypoint'], crop_bbox) + if 'imgs' in results: + results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox) + + if 'gt_bboxes' in results: + results = self._all_box_crop(results, results['crop_bbox']) + + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'area_range={self.area_range}, ' + f'aspect_ratio_range={self.aspect_ratio_range})') + return repr_str + + +@PIPELINES.register_module() +class Resize: + """Resize images to a specific size. + + Required keys are "img_shape", "modality", "imgs" (optional), "keypoint" + (optional), added or modified keys are "imgs", "img_shape", "keep_ratio", + "scale_factor", "resize_size". + + Args: + scale (float | Tuple[int]): If keep_ratio is True, it serves as scaling + factor or maximum size: + If it is a float number, the image will be rescaled by this + factor, else if it is a tuple of 2 integers, the image will + be rescaled as large as possible within the scale. + Otherwise, it serves as (w, h) of output size. + keep_ratio (bool): If set to True, Images will be resized without + changing the aspect ratio. Otherwise, it will resize images to a + given size. Default: True. + interpolation (str): Algorithm used for interpolation: + "nearest" | "bilinear". Default: "bilinear". + """ + + def __init__(self, + scale, + keep_ratio=True, + interpolation='bilinear'): + if isinstance(scale, float): + if scale <= 0: + raise ValueError(f'Invalid scale {scale}, must be positive.') + elif isinstance(scale, tuple): + max_long_edge = max(scale) + max_short_edge = min(scale) + if max_short_edge == -1: + # assign np.inf to long edge for rescaling short edge later. + scale = (np.inf, max_long_edge) + else: + raise TypeError( + f'Scale must be float or tuple of int, but got {type(scale)}') + self.scale = scale + self.keep_ratio = keep_ratio + self.interpolation = interpolation + + def _resize_imgs(self, imgs, new_w, new_h): + return [ + mmcv.imresize( + img, (new_w, new_h), interpolation=self.interpolation) + for img in imgs + ] + + @staticmethod + def _resize_kps(kps, scale_factor): + return kps * scale_factor + + @staticmethod + def _box_resize(box, scale_factor): + """Rescale the bounding boxes according to the scale_factor. + + Args: + box (np.ndarray): The bounding boxes. + scale_factor (np.ndarray): The scale factor used for rescaling. + """ + assert len(scale_factor) == 2 + scale_factor = np.concatenate([scale_factor, scale_factor]) + return box * scale_factor + + def __call__(self, results): + """Performs the Resize augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + if 'scale_factor' not in results: + results['scale_factor'] = np.array([1, 1], dtype=np.float32) + img_h, img_w = results['img_shape'] + + if self.keep_ratio: + new_w, new_h = mmcv.rescale_size((img_w, img_h), self.scale) + else: + new_w, new_h = self.scale + + self.scale_factor = np.array([new_w / img_w, new_h / img_h], + dtype=np.float32) + + results['img_shape'] = (new_h, new_w) + results['keep_ratio'] = self.keep_ratio + results['scale_factor'] = results['scale_factor'] * self.scale_factor + + if 'imgs' in results: + results['imgs'] = self._resize_imgs(results['imgs'], new_w, new_h) + if 'keypoint' in results: + results['keypoint'] = self._resize_kps(results['keypoint'], self.scale_factor) + + if 'gt_bboxes' in results: + results['gt_bboxes'] = self._box_resize(results['gt_bboxes'], self.scale_factor) + if 'proposals' in results and results['proposals'] is not None: + assert results['proposals'].shape[1] == 4 + results['proposals'] = self._box_resize( + results['proposals'], self.scale_factor) + + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'scale={self.scale}, keep_ratio={self.keep_ratio}, ' + f'interpolation={self.interpolation})') + return repr_str + + +@PIPELINES.register_module() +class Flip: + """Flip the input images with a probability. + + Reverse the order of elements in the given imgs with a specific direction. + The shape of the imgs is preserved, but the elements are reordered. + + Required keys are "img_shape", "modality", "imgs" (optional), "keypoint" + (optional), added or modified keys are "imgs", "keypoint", "flip_direction". + The Flip augmentation should be placed after any cropping / reshaping + augmentations, to make sure crop_quadruple is calculated properly. + + Args: + flip_ratio (float): Probability of implementing flip. Default: 0.5. + direction (str): Flip imgs horizontally or vertically. Options are + "horizontal" | "vertical". Default: "horizontal". + flip_label_map (Dict[int, int] | None): Transform the label of the + flipped image with the specific label. Default: None. + left_kp (list[int]): Indexes of left keypoints, used to flip keypoints. + Default: None. + right_kp (list[ind]): Indexes of right keypoints, used to flip + keypoints. Default: None. + """ + _directions = ['horizontal', 'vertical'] + + def __init__(self, + flip_ratio=0.5, + direction='horizontal', + flip_label_map=None, + left_kp=None, + right_kp=None): + if direction not in self._directions: + raise ValueError(f'Direction {direction} is not supported. ' + f'Currently support ones are {self._directions}') + self.flip_ratio = flip_ratio + self.direction = direction + self.flip_label_map = flip_label_map + self.left_kp = left_kp + self.right_kp = right_kp + + def _flip_imgs(self, imgs, modality): + _ = [mmcv.imflip_(img, self.direction) for img in imgs] + lt = len(imgs) + if modality == 'Flow': + # The 1st frame of each 2 frames is flow-x + for i in range(0, lt, 2): + imgs[i] = mmcv.iminvert(imgs[i]) + return imgs + + def _flip_kps(self, kps, kpscores, img_width): + kp_x = kps[..., 0] + kp_x[kp_x != 0] = img_width - kp_x[kp_x != 0] + new_order = list(range(kps.shape[2])) + if self.left_kp is not None and self.right_kp is not None: + for left, right in zip(self.left_kp, self.right_kp): + new_order[left] = right + new_order[right] = left + kps = kps[:, :, new_order] + if kpscores is not None: + kpscores = kpscores[:, :, new_order] + return kps, kpscores + + @staticmethod + def _box_flip(box, img_width): + """Flip the bounding boxes given the width of the image. + + Args: + box (np.ndarray): The bounding boxes. + img_width (int): The img width. + """ + box_ = box.copy() + box_[..., 0::4] = img_width - box[..., 2::4] + box_[..., 2::4] = img_width - box[..., 0::4] + return box_ + + def __call__(self, results): + """Performs the Flip augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + if 'keypoint' in results: + assert self.direction == 'horizontal', ( + 'Only horizontal flips are' + 'supported for human keypoints') + + modality = results['modality'] + if modality == 'Flow': + assert self.direction == 'horizontal' + + flip = np.random.rand() < self.flip_ratio + + results['flip'] = flip + results['flip_direction'] = self.direction + img_width = results['img_shape'][1] + + if self.flip_label_map is not None and flip: + results['label'] = self.flip_label_map.get(results['label'], + results['label']) + + if flip: + if 'imgs' in results: + results['imgs'] = self._flip_imgs(results['imgs'], modality) + if 'keypoint' in results: + kp = results['keypoint'] + kpscore = results.get('keypoint_score', None) + kp, kpscore = self._flip_kps(kp, kpscore, img_width) + results['keypoint'] = kp + if 'keypoint_score' in results: + results['keypoint_score'] = kpscore + + if 'gt_bboxes' in results and flip: + assert self.direction == 'horizontal' + width = results['img_shape'][1] + results['gt_bboxes'] = self._box_flip(results['gt_bboxes'], width) + if 'proposals' in results and results['proposals'] is not None: + assert results['proposals'].shape[1] == 4 + results['proposals'] = self._box_flip(results['proposals'], + width) + + return results + + def __repr__(self): + repr_str = ( + f'{self.__class__.__name__}(' + f'flip_ratio={self.flip_ratio}, direction={self.direction}, ' + f'flip_label_map={self.flip_label_map})') + return repr_str + + +@PIPELINES.register_module() +class Normalize: + """Normalize images with the given mean and std value. + + Required keys are "imgs", "img_shape", "modality", added or modified + keys are "imgs" and "img_norm_cfg". If modality is 'Flow', additional + keys "scale_factor" is required + + Args: + mean (Sequence[float]): Mean values of different channels. + std (Sequence[float]): Std values of different channels. + to_bgr (bool): Whether to convert channels from RGB to BGR. + Default: False. + adjust_magnitude (bool): Indicate whether to adjust the flow magnitude + on 'scale_factor' when modality is 'Flow'. Default: False. + """ + + def __init__(self, mean, std, to_bgr=False, adjust_magnitude=False): + if not isinstance(mean, Sequence): + raise TypeError( + f'Mean must be list, tuple or np.ndarray, but got {type(mean)}' + ) + + if not isinstance(std, Sequence): + raise TypeError( + f'Std must be list, tuple or np.ndarray, but got {type(std)}') + + self.mean = np.array(mean, dtype=np.float32) + self.std = np.array(std, dtype=np.float32) + self.to_bgr = to_bgr + self.adjust_magnitude = adjust_magnitude + + def __call__(self, results): + modality = results['modality'] + + if modality == 'RGB': + n = len(results['imgs']) + h, w, c = results['imgs'][0].shape + imgs = np.empty((n, h, w, c), dtype=np.float32) + for i, img in enumerate(results['imgs']): + imgs[i] = img + + for img in imgs: + mmcv.imnormalize_(img, self.mean, self.std, self.to_bgr) + + results['imgs'] = imgs + results['img_norm_cfg'] = dict( + mean=self.mean, std=self.std, to_bgr=self.to_bgr) + return results + if modality == 'Flow': + num_imgs = len(results['imgs']) + assert num_imgs % 2 == 0 + assert self.mean.shape[0] == 2 + assert self.std.shape[0] == 2 + n = num_imgs // 2 + h, w = results['imgs'][0].shape + x_flow = np.empty((n, h, w), dtype=np.float32) + y_flow = np.empty((n, h, w), dtype=np.float32) + for i in range(n): + x_flow[i] = results['imgs'][2 * i] + y_flow[i] = results['imgs'][2 * i + 1] + x_flow = (x_flow - self.mean[0]) / self.std[0] + y_flow = (y_flow - self.mean[1]) / self.std[1] + if self.adjust_magnitude: + x_flow = x_flow * results['scale_factor'][0] + y_flow = y_flow * results['scale_factor'][1] + imgs = np.stack([x_flow, y_flow], axis=-1) + results['imgs'] = imgs + args = dict( + mean=self.mean, + std=self.std, + to_bgr=self.to_bgr, + adjust_magnitude=self.adjust_magnitude) + results['img_norm_cfg'] = args + return results + raise NotImplementedError + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'mean={self.mean}, ' + f'std={self.std}, ' + f'to_bgr={self.to_bgr}, ' + f'adjust_magnitude={self.adjust_magnitude})') + return repr_str + + +@PIPELINES.register_module() +class ColorJitter: + """Perform ColorJitter to each img. + + Required keys are "imgs", added or modified keys are "imgs". + + Args: + brightness (float | tuple[float]): The jitter range for brightness, if + set as a float, the range will be (1 - brightness, 1 + brightness). + Default: 0.5. + contrast (float | tuple[float]): The jitter range for contrast, if set + as a float, the range will be (1 - contrast, 1 + contrast). + Default: 0.5. + saturation (float | tuple[float]): The jitter range for saturation, if + set as a float, the range will be (1 - saturation, 1 + saturation). + Default: 0.5. + hue (float | tuple[float]): The jitter range for hue, if set as a + float, the range will be (-hue, hue). Default: 0.1. + """ + + @staticmethod + def check_input(val, max, base): + if isinstance(val, tuple): + assert base - max <= val[0] <= val[1] <= base + max + return val + assert val <= max + return (base - val, base + val) + + @staticmethod + def rgb_to_grayscale(img): + return 0.2989 * img[..., 0] + 0.587 * img[..., 1] + 0.114 * img[..., 2] + + @staticmethod + def adjust_contrast(img, factor): + val = np.mean(ColorJitter.rgb_to_grayscale(img)) + return factor * img + (1 - factor) * val + + @staticmethod + def adjust_saturation(img, factor): + gray = np.stack([ColorJitter.rgb_to_grayscale(img)] * 3, axis=-1) + return factor * img + (1 - factor) * gray + + @staticmethod + def adjust_hue(img, factor): + img = np.clip(img, 0, 255).astype(np.uint8) + hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) + offset = int(factor * 255) + hsv[..., 0] = (hsv[..., 0] + offset) % 180 + img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB) + return img.astype(np.float32) + + def __init__(self, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1): + self.brightness = self.check_input(brightness, 1, 1) + self.contrast = self.check_input(contrast, 1, 1) + self.saturation = self.check_input(saturation, 1, 1) + self.hue = self.check_input(hue, 0.5, 0) + self.fn_idx = np.random.permutation(4) + + def __call__(self, results): + imgs = results['imgs'] + num_clips, clip_len = 1, len(imgs) + + new_imgs = [] + for i in range(num_clips): + b = np.random.uniform( + low=self.brightness[0], high=self.brightness[1]) + c = np.random.uniform(low=self.contrast[0], high=self.contrast[1]) + s = np.random.uniform( + low=self.saturation[0], high=self.saturation[1]) + h = np.random.uniform(low=self.hue[0], high=self.hue[1]) + start, end = i * clip_len, (i + 1) * clip_len + + for img in imgs[start:end]: + img = img.astype(np.float32) + for fn_id in self.fn_idx: + if fn_id == 0 and b != 1: + img *= b + if fn_id == 1 and c != 1: + img = self.adjust_contrast(img, c) + if fn_id == 2 and s != 1: + img = self.adjust_saturation(img, s) + if fn_id == 3 and h != 0: + img = self.adjust_hue(img, h) + img = np.clip(img, 0, 255).astype(np.uint8) + new_imgs.append(img) + results['imgs'] = new_imgs + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'brightness={self.brightness}, ' + f'contrast={self.contrast}, ' + f'saturation={self.saturation}, ' + f'hue={self.hue})') + return repr_str + + +@PIPELINES.register_module() +class CenterCrop(RandomCrop): + """Crop the center area from images. + + Required keys are "img_shape", "imgs" (optional), "keypoint" (optional), + added or modified keys are "imgs", "keypoint", "crop_bbox", "img_shape". + + Args: + crop_size (int | tuple[int]): (w, h) of crop size. + """ + + def __init__(self, crop_size): + self.crop_size = _pair(crop_size) + if not mmcv.is_tuple_of(self.crop_size, int): + raise TypeError(f'Crop_size must be int or tuple of int, ' + f'but got {type(crop_size)}') + + def __call__(self, results): + """Performs the CenterCrop augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + img_h, img_w = results['img_shape'] + crop_w, crop_h = self.crop_size + + left = (img_w - crop_w) // 2 + top = (img_h - crop_h) // 2 + right = left + crop_w + bottom = top + crop_h + new_h, new_w = bottom - top, right - left + + crop_bbox = np.array([left, top, right, bottom]) + results['crop_bbox'] = crop_bbox + results['img_shape'] = (new_h, new_w) + + if 'crop_quadruple' not in results: + results['crop_quadruple'] = np.array( + [0, 0, 1, 1], # x, y, w, h + dtype=np.float32) + + x_ratio, y_ratio = left / img_w, top / img_h + w_ratio, h_ratio = new_w / img_w, new_h / img_h + + old_crop_quadruple = results['crop_quadruple'] + old_x_ratio, old_y_ratio = old_crop_quadruple[0], old_crop_quadruple[1] + old_w_ratio, old_h_ratio = old_crop_quadruple[2], old_crop_quadruple[3] + new_crop_quadruple = [ + old_x_ratio + x_ratio * old_w_ratio, + old_y_ratio + y_ratio * old_h_ratio, w_ratio * old_w_ratio, + h_ratio * old_h_ratio + ] + results['crop_quadruple'] = np.array( + new_crop_quadruple, dtype=np.float32) + + if 'keypoint' in results: + results['keypoint'] = self._crop_kps(results['keypoint'], crop_bbox) + if 'imgs' in results: + results['imgs'] = self._crop_imgs(results['imgs'], crop_bbox) + + if 'gt_bboxes' in results: + results = self._all_box_crop(results, results['crop_bbox']) + + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(crop_size={self.crop_size})') + return repr_str + + +@PIPELINES.register_module() +class ThreeCrop: + """Crop images into three crops. + + Crop the images equally into three crops with equal intervals along the + shorter side. Required keys are "imgs", "img_shape", added or modified keys + are "imgs", "crop_bbox" and "img_shape". + + Args: + crop_size(int | tuple[int]): (w, h) of crop size. + """ + + def __init__(self, crop_size): + self.crop_size = _pair(crop_size) + if not mmcv.is_tuple_of(self.crop_size, int): + raise TypeError(f'Crop_size must be int or tuple of int, ' + f'but got {type(crop_size)}') + + def __call__(self, results): + """Performs the ThreeCrop augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + if 'gt_bboxes' in results or 'proposals' in results: + warnings.warn('ThreeCrop cannot process bounding boxes') + + imgs = results['imgs'] + img_h, img_w = results['imgs'][0].shape[:2] + crop_w, crop_h = self.crop_size + assert crop_h == img_h or crop_w == img_w + + if crop_h == img_h: + w_step = (img_w - crop_w) // 2 + offsets = [ + (0, 0), # left + (2 * w_step, 0), # right + (w_step, 0), # middle + ] + elif crop_w == img_w: + h_step = (img_h - crop_h) // 2 + offsets = [ + (0, 0), # top + (0, 2 * h_step), # down + (0, h_step), # middle + ] + + cropped = [] + crop_bboxes = [] + for x_offset, y_offset in offsets: + bbox = [x_offset, y_offset, x_offset + crop_w, y_offset + crop_h] + crop = [ + img[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w] + for img in imgs + ] + cropped.extend(crop) + crop_bboxes.extend([bbox for _ in range(len(imgs))]) + + crop_bboxes = np.array(crop_bboxes) + results['imgs'] = cropped + results['crop_bbox'] = crop_bboxes + results['img_shape'] = results['imgs'][0].shape[:2] + + return results + + def __repr__(self): + repr_str = f'{self.__class__.__name__}(crop_size={self.crop_size})' + return repr_str + + +@PIPELINES.register_module() +class TenCrop: + """Crop the images into 10 crops (corner + center + flip). + + Crop the four corners and the center part of the image with the same + given crop_size, and flip it horizontally. Required keys are "imgs", + "img_shape", added or modified keys are "imgs", "crop_bbox" and "img_shape". + + Args: + crop_size(int | tuple[int]): (w, h) of crop size. + """ + + def __init__(self, crop_size): + self.crop_size = _pair(crop_size) + if not mmcv.is_tuple_of(self.crop_size, int): + raise TypeError(f'Crop_size must be int or tuple of int, ' + f'but got {type(crop_size)}') + + def __call__(self, results): + """Performs the TenCrop augmentation. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + if 'gt_bboxes' in results or 'proposals' in results: + warnings.warn('TenCrop cannot process bounding boxes') + + imgs = results['imgs'] + + img_h, img_w = results['imgs'][0].shape[:2] + crop_w, crop_h = self.crop_size + + w_step = (img_w - crop_w) // 4 + h_step = (img_h - crop_h) // 4 + + offsets = [ + (0, 0), # upper left + (4 * w_step, 0), # upper right + (0, 4 * h_step), # lower left + (4 * w_step, 4 * h_step), # lower right + (2 * w_step, 2 * h_step), # center + ] + + img_crops = list() + crop_bboxes = list() + for x_offset, y_offsets in offsets: + crop = [ + img[y_offsets:y_offsets + crop_h, x_offset:x_offset + crop_w] + for img in imgs + ] + flip_crop = [np.flip(c, axis=1).copy() for c in crop] + bbox = [x_offset, y_offsets, x_offset + crop_w, y_offsets + crop_h] + img_crops.extend(crop) + img_crops.extend(flip_crop) + crop_bboxes.extend([bbox for _ in range(len(imgs) * 2)]) + + crop_bboxes = np.array(crop_bboxes) + results['imgs'] = img_crops + results['crop_bbox'] = crop_bboxes + results['img_shape'] = results['imgs'][0].shape[:2] + + return results + + def __repr__(self): + repr_str = f'{self.__class__.__name__}(crop_size={self.crop_size})' + return repr_str diff --git a/pyskl/datasets/pipelines/compose.py b/pyskl/datasets/pipelines/compose.py new file mode 100644 index 00000000..a60604ad --- /dev/null +++ b/pyskl/datasets/pipelines/compose.py @@ -0,0 +1,53 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from collections.abc import Sequence + +from mmcv.utils import build_from_cfg + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class Compose: + """Compose a data pipeline with a sequence of transforms. + + Args: + transforms (list[dict | callable]): + Either config dicts of transforms or transform objects. + """ + + def __init__(self, transforms): + assert isinstance(transforms, Sequence) + self.transforms = [] + for transform in transforms: + if isinstance(transform, dict): + transform = build_from_cfg(transform, PIPELINES) + self.transforms.append(transform) + elif callable(transform): + self.transforms.append(transform) + else: + raise TypeError(f'transform must be callable or a dict, ' + f'but got {type(transform)}') + + def __call__(self, data): + """Call function to apply transforms sequentially. + + Args: + data (dict): A result dict contains the data to transform. + + Returns: + dict: Transformed data. + """ + + for t in self.transforms: + data = t(data) + if data is None: + return None + return data + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string diff --git a/pyskl/datasets/pipelines/formatting.py b/pyskl/datasets/pipelines/formatting.py new file mode 100644 index 00000000..a7e05dde --- /dev/null +++ b/pyskl/datasets/pipelines/formatting.py @@ -0,0 +1,234 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from collections.abc import Sequence + +import mmcv +import numpy as np +import torch +from mmcv.parallel import DataContainer as DC + +from ..builder import PIPELINES + + +def to_tensor(data): + """Convert objects of various python types to :obj:`torch.Tensor`. + + Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`, + :class:`Sequence`, :class:`int` and :class:`float`. + """ + if isinstance(data, torch.Tensor): + return data + if isinstance(data, np.ndarray): + return torch.from_numpy(data) + if isinstance(data, Sequence) and not mmcv.is_str(data): + return torch.tensor(data) + if isinstance(data, int): + return torch.LongTensor([data]) + if isinstance(data, float): + return torch.FloatTensor([data]) + raise TypeError(f'type {type(data)} cannot be converted to tensor.') + + +@PIPELINES.register_module() +class ToTensor: + """Convert some values in results dict to `torch.Tensor` type in data + loader pipeline. + + Args: + keys (Sequence[str]): Required keys to be converted. + """ + + def __init__(self, keys): + self.keys = keys + + def __call__(self, results): + """Performs the ToTensor formatting. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + for key in self.keys: + results[key] = to_tensor(results[key]) + return results + + def __repr__(self): + return f'{self.__class__.__name__}(keys={self.keys})' + + +@PIPELINES.register_module() +class Rename: + """Rename the key in results. + + Args: + mapping (dict): The keys in results that need to be renamed. The key of + the dict is the original name, while the value is the new name. If + the original name not found in results, do nothing. + Default: dict(). + """ + + def __init__(self, mapping): + self.mapping = mapping + + def __call__(self, results): + for key, value in self.mapping.items(): + if key in results: + assert isinstance(key, str) and isinstance(value, str) + assert value not in results, ('the new name already exists in ' + 'results') + results[value] = results[key] + results.pop(key) + return results + + +@PIPELINES.register_module() +class Collect: + """Collect data from the loader relevant to the specific task. + + This keeps the items in ``keys`` as it is, and collect items in + ``meta_keys`` into a meta item called ``meta_name``.This is usually + the last stage of the data loader pipeline. + For example, when keys='imgs', meta_keys=('filename', 'label', + 'original_shape'), meta_name='img_metas', the results will be a dict with + keys 'imgs' and 'img_metas', where 'img_metas' is a DataContainer of + another dict with keys 'filename', 'label', 'original_shape'. + + Args: + keys (Sequence[str]): Required keys to be collected. + meta_name (str): The name of the key that contains meta information. + This key is always populated. Default: "img_metas". + meta_keys (Sequence[str]): Keys that are collected under meta_name. + The contents of the ``meta_name`` dictionary depends on + ``meta_keys``. + By default this includes: + + - "filename": path to the image file + - "label": label of the image file + - "original_shape": original shape of the image as a tuple + (h, w, c) + - "img_shape": shape of the image input to the network as a tuple + (h, w, c). Note that images may be zero padded on the + bottom/right, if the batch tensor is larger than this shape. + - "pad_shape": image shape after padding + - "flip_direction": a str in ("horiziontal", "vertival") to + indicate if the image is fliped horizontally or vertically. + - "img_norm_cfg": a dict of normalization information: + - mean - per channel mean subtraction + - std - per channel std divisor + - to_rgb - bool indicating if bgr was converted to rgb + nested (bool): If set as True, will apply data[x] = [data[x]] to all + items in data. The arg is added for compatibility. Default: False. + """ + + def __init__(self, + keys, + meta_keys=('filename', 'label', 'original_shape', 'img_shape', + 'pad_shape', 'flip_direction', 'img_norm_cfg'), + meta_name='img_metas', + nested=False): + self.keys = keys + self.meta_keys = meta_keys + self.meta_name = meta_name + self.nested = nested + + def __call__(self, results): + """Performs the Collect formatting. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + data = {} + for key in self.keys: + data[key] = results[key] + + if len(self.meta_keys) != 0: + meta = {} + for key in self.meta_keys: + meta[key] = results[key] + data[self.meta_name] = DC(meta, cpu_only=True) + if self.nested: + for k in data: + data[k] = [data[k]] + + return data + + def __repr__(self): + return (f'{self.__class__.__name__}(' + f'keys={self.keys}, meta_keys={self.meta_keys}, ' + f'nested={self.nested})') + + +@PIPELINES.register_module() +class FormatShape: + """Format final imgs shape to the given input_format. + + Required keys are "imgs", "num_clips" and "clip_len", added or modified + keys are "imgs" and "input_shape". + + Args: + input_format (str): Define the final imgs format. + collapse (bool): To collpase input_format N... to ... (NCTHW to CTHW, + etc.) if N is 1. Should be set as True when training and testing + detectors. Default: False. + """ + + def __init__(self, input_format, collapse=False): + self.input_format = input_format + self.collapse = collapse + if self.input_format not in ['NCTHW', 'NCHW', 'NCTHW_Heatmap']: + raise ValueError( + f'The input format {self.input_format} is invalid.') + + def __call__(self, results): + """Performs the FormatShape formatting. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + if not isinstance(results['imgs'], np.ndarray): + results['imgs'] = np.array(results['imgs']) + imgs = results['imgs'] + # [M x H x W x C] + # M = 1 * N_crops * N_clips * L + if self.collapse: + assert results['num_clips'] == 1 + + if self.input_format == 'NCTHW': + num_clips = results['num_clips'] + clip_len = results['clip_len'] + + imgs = imgs.reshape((-1, num_clips, clip_len) + imgs.shape[1:]) + # N_crops x N_clips x L x H x W x C + imgs = np.transpose(imgs, (0, 1, 5, 2, 3, 4)) + # N_crops x N_clips x C x L x H x W + imgs = imgs.reshape((-1, ) + imgs.shape[2:]) + # M' x C x L x H x W + # M' = N_crops x N_clips + elif self.input_format == 'NCTHW_Heatmap': + num_clips = results['num_clips'] + clip_len = results['clip_len'] + + imgs = imgs.reshape((-1, num_clips, clip_len) + imgs.shape[1:]) + # N_crops x N_clips x L x C x H x W + imgs = np.transpose(imgs, (0, 1, 3, 2, 4, 5)) + # N_crops x N_clips x C x L x H x W + imgs = imgs.reshape((-1, ) + imgs.shape[2:]) + # M' x C x L x H x W + # M' = N_crops x N_clips + elif self.input_format == 'NCHW': + imgs = np.transpose(imgs, (0, 3, 1, 2)) + # M x C x H x W + + if self.collapse: + assert imgs.shape[0] == 1 + imgs = imgs.squeeze(0) + + results['imgs'] = imgs + results['input_shape'] = imgs.shape + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f"(input_format='{self.input_format}')" + return repr_str diff --git a/pyskl/datasets/pipelines/loading.py b/pyskl/datasets/pipelines/loading.py new file mode 100644 index 00000000..e495c108 --- /dev/null +++ b/pyskl/datasets/pipelines/loading.py @@ -0,0 +1,166 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import io + +import numpy as np +from mmcv.fileio import FileClient + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class DecordInit: + """Using decord to initialize the video_reader. + + Decord: https://github.com/dmlc/decord + + Required keys are "filename", + added or modified keys are "video_reader" and "total_frames". + + Args: + io_backend (str): io backend where frames are store. + Default: 'disk'. + num_threads (int): Number of thread to decode the video. Default: 1. + kwargs (dict): Args for file client. + """ + + def __init__(self, io_backend='disk', num_threads=1, **kwargs): + self.io_backend = io_backend + self.num_threads = num_threads + self.kwargs = kwargs + self.file_client = None + + def __call__(self, results): + """Perform the Decord initialization. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + try: + import decord + except ImportError: + raise ImportError( + 'Please run "pip install decord" to install Decord first.') + + if self.file_client is None: + self.file_client = FileClient(self.io_backend, **self.kwargs) + + file_obj = io.BytesIO(self.file_client.get(results['filename'])) + container = decord.VideoReader(file_obj, num_threads=self.num_threads) + results['video_reader'] = container + results['total_frames'] = len(container) + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'io_backend={self.io_backend}, ' + f'num_threads={self.num_threads})') + return repr_str + + +@PIPELINES.register_module() +class DecordDecode: + """Using decord to decode the video. + + Decord: https://github.com/dmlc/decord + + Required keys are "video_reader", "filename" and "frame_inds", + added or modified keys are "imgs" and "original_shape". + + Args: + mode (str): Decoding mode. Options are 'accurate' and 'efficient'. + If set to 'accurate', it will decode videos into accurate frames. + If set to 'efficient', it will adopt fast seeking but only return + key frames, which may be duplicated and inaccurate, and more + suitable for large scene-based video datasets. Default: 'accurate'. + """ + + def __init__(self, mode='accurate'): + self.mode = mode + assert mode in ['accurate', 'efficient'] + + def __call__(self, results): + """Perform the Decord decoding. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + container = results['video_reader'] + + if results['frame_inds'].ndim != 1: + results['frame_inds'] = np.squeeze(results['frame_inds']) + + frame_inds = results['frame_inds'] + + if self.mode == 'accurate': + imgs = container.get_batch(frame_inds).asnumpy() + imgs = list(imgs) + elif self.mode == 'efficient': + # This mode is faster, however it always returns I-FRAME + container.seek(0) + imgs = list() + for idx in frame_inds: + container.seek(idx) + frame = container.next() + imgs.append(frame.asnumpy()) + + results['video_reader'] = None + del container + + results['imgs'] = imgs + results['original_shape'] = imgs[0].shape[:2] + results['img_shape'] = imgs[0].shape[:2] + + return results + + def __repr__(self): + repr_str = f'{self.__class__.__name__}(mode={self.mode})' + return repr_str + + +@PIPELINES.register_module() +class ArrayDecode: + """Load and decode frames with given indices from a 4D array. + + Required keys are "array and "frame_inds", added or modified keys are + "imgs", "img_shape" and "original_shape". + """ + + def __call__(self, results): + """Perform the ``RawFrameDecode`` to pick frames given indices. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + + modality = results['modality'] + array = results['array'] + + imgs = list() + + if results['frame_inds'].ndim != 1: + results['frame_inds'] = np.squeeze(results['frame_inds']) + + offset = results.get('offset', 0) + + for i, frame_idx in enumerate(results['frame_inds']): + + frame_idx += offset + if modality == 'RGB': + imgs.append(array[frame_idx]) + elif modality == 'Flow': + imgs.extend( + [array[frame_idx, ..., 0], array[frame_idx, ..., 1]]) + else: + raise NotImplementedError + + results['imgs'] = imgs + results['original_shape'] = imgs[0].shape[:2] + results['img_shape'] = imgs[0].shape[:2] + + return results + + def __repr__(self): + return f'{self.__class__.__name__}()' diff --git a/pyskl/datasets/pipelines/pose_related.py b/pyskl/datasets/pipelines/pose_related.py new file mode 100644 index 00000000..1ac63666 --- /dev/null +++ b/pyskl/datasets/pipelines/pose_related.py @@ -0,0 +1,354 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np + +from ..builder import PIPELINES +from .compose import Compose +from .formatting import Rename + +EPS = 1e-4 + + +@PIPELINES.register_module() +class PoseDecode: + """Load and decode pose with given indices. + + Required keys are "keypoint", "frame_inds" (optional), "keypoint_score" (optional), added or modified keys are + "keypoint", "keypoint_score" (if applicable). + """ + + @staticmethod + def _load_kp(kp, frame_inds): + return kp[:, frame_inds].astype(np.float32) + + @staticmethod + def _load_kpscore(kpscore, frame_inds): + return kpscore[:, frame_inds].astype(np.float32) + + def __call__(self, results): + + if 'frame_inds' not in results: + results['frame_inds'] = np.arange(results['total_frames']) + + if results['frame_inds'].ndim != 1: + results['frame_inds'] = np.squeeze(results['frame_inds']) + + offset = results.get('offset', 0) + frame_inds = results['frame_inds'] + offset + + if 'keypoint_score' in results: + results['keypoint_score'] = self._load_kpscore(results['keypoint_score'], frame_inds) + + if 'keypoint' in results: + results['keypoint'] = self._load_kp(results['keypoint'], frame_inds) + + return results + + def __repr__(self): + repr_str = f'{self.__class__.__name__}()' + return repr_str + + +@PIPELINES.register_module() +class PreNormalize2D: + """Normalize the range of keypoint values. """ + + def __init__(self, img_shape=(1080, 1920)): + self.img_shape = img_shape + + def __call__(self, results): + h, w = results.get('img_shape', self.img_shape) + results['keypoint'][..., 0] = (results['keypoint'][..., 0] - (w / 2)) / (w / 2) + results['keypoint'][..., 1] = (results['keypoint'][..., 1] - (h / 2)) / (h / 2) + return results + + +@PIPELINES.register_module() +class RandomRot: + + def __init__(self, theta=0.3): + self.theta = theta + + def _rot(self, theta): + cos, sin = np.cos(theta), np.sin(theta) + rx = np.array([[1, 0, 0], [0, cos[0], sin[0]], [0, -sin[0], cos[0]]]) + ry = np.array([[cos[1], 0, -sin[1]], [0, 1, 0], [sin[1], 0, cos[1]]]) + rz = np.array([[cos[2], sin[2], 0], [-sin[2], cos[2], 0], [0, 0, 1]]) + + rot = np.matmul(rz, np.matmul(ry, rx)) + return rot + + def __call__(self, results): + theta = np.random.uniform(-self.theta, self.theta, size=3) + rot_mat = self._rot(theta) + + skeleton = results['keypoint'] + if np.all(np.isclose(skeleton, 0)): + return results + + M, T, V, C = skeleton.shape + assert M in [1, 2] and C == 3 + + results['keypoint'] = np.einsum('ab,mtvb->mtva', rot_mat, skeleton) + return results + + +@PIPELINES.register_module() +class PreNormalize3D: + """PreNormalize for NTURGB+D 3D keypoints (x, y, z). Codes adapted from https://github.com/lshiwjx/2s-AGCN. """ + + def unit_vector(self, vector): + """Returns the unit vector of the vector. """ + return vector / np.linalg.norm(vector) + + def angle_between(self, v1, v2): + """Returns the angle in radians between vectors 'v1' and 'v2'. """ + if np.abs(v1).sum() < 1e-6 or np.abs(v2).sum() < 1e-6: + return 0 + v1_u = self.unit_vector(v1) + v2_u = self.unit_vector(v2) + return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) + + def rotation_matrix(self, axis, theta): + """Return the rotation matrix associated with counterclockwise rotation + about the given axis by theta radians.""" + if np.abs(axis).sum() < 1e-6 or np.abs(theta) < 1e-6: + return np.eye(3) + axis = np.asarray(axis) + axis = axis / np.sqrt(np.dot(axis, axis)) + a = np.cos(theta / 2.0) + b, c, d = -axis * np.sin(theta / 2.0) + aa, bb, cc, dd = a * a, b * b, c * c, d * d + bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d + return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)], + [2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)], + [2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]]) + + def __init__(self, zaxis=[0, 1], xaxis=[8, 4], align_spine=True, align_center=True): + self.zaxis = zaxis + self.xaxis = xaxis + self.align_spine = align_spine + self.align_center = align_center + + def __call__(self, results): + skeleton = results['keypoint'] + total_frames = results.get('total_frames', skeleton.shape[1]) + + M, T, V, C = skeleton.shape + assert T == total_frames + if skeleton.sum() == 0: + return results + + index0 = [i for i in range(T) if not np.all(np.isclose(skeleton[0, i], 0))] + + assert M in [1, 2] + if M == 2: + index1 = [i for i in range(T) if not np.all(np.isclose(skeleton[1, i], 0))] + if len(index0) < len(index1): + skeleton = skeleton[:, np.array(index1)] + skeleton = skeleton[[1, 0]] + else: + skeleton = skeleton[:, np.array(index0)] + else: + skeleton = skeleton[:, np.array(index0)] + + T_new = skeleton.shape[1] + + if self.align_center: + if skeleton.shape[2] == 25: + main_body_center = skeleton[0, 0, 1].copy() + else: + main_body_center = skeleton[0, 0, -1].copy() + mask = ((skeleton != 0).sum(-1) > 0)[..., None] + skeleton = (skeleton - main_body_center) * mask + + if self.align_spine: + joint_bottom = skeleton[0, 0, self.zaxis[0]] + joint_top = skeleton[0, 0, self.zaxis[1]] + axis = np.cross(joint_top - joint_bottom, [0, 0, 1]) + angle = self.angle_between(joint_top - joint_bottom, [0, 0, 1]) + matrix_z = self.rotation_matrix(axis, angle) + skeleton = np.einsum('abcd,kd->abck', skeleton, matrix_z) + + joint_rshoulder = skeleton[0, 0, self.xaxis[0]] + joint_lshoulder = skeleton[0, 0, self.xaxis[1]] + axis = np.cross(joint_rshoulder - joint_lshoulder, [1, 0, 0]) + angle = self.angle_between(joint_rshoulder - joint_lshoulder, [1, 0, 0]) + matrix_x = self.rotation_matrix(axis, angle) + skeleton = np.einsum('abcd,kd->abck', skeleton, matrix_x) + + results['keypoint'] = skeleton + results['total_frames'] = T_new + results['body_center'] = main_body_center + return results + + +@PIPELINES.register_module() +class JointToBone: + + def __init__(self, dataset='nturgb+d', target='keypoint'): + self.dataset = dataset + self.target = target + if self.dataset not in ['nturgb+d', 'openpose', 'coco']: + raise ValueError( + f'The dataset type {self.dataset} is not supported') + if self.dataset == 'nturgb+d': + self.pairs = [(0, 1), (1, 20), (2, 20), (3, 2), (4, 20), (5, 4), (6, 5), (7, 6), (8, 20), (9, 8), + (10, 9), (11, 10), (12, 0), (13, 12), (14, 13), (15, 14), (16, 0), (17, 16), (18, 17), + (19, 18), (21, 22), (20, 20), (22, 7), (23, 24), (24, 11)] + elif self.dataset == 'openpose': + self.pairs = ((0, 0), (1, 0), (2, 1), (3, 2), (4, 3), (5, 1), (6, 5), (7, 6), (8, 2), (9, 8), (10, 9), + (11, 5), (12, 11), (13, 12), (14, 0), (15, 0), (16, 14), (17, 15)) + elif self.dataset == 'coco': + self.pairs = ((0, 0), (1, 0), (2, 0), (3, 1), (4, 2), (5, 0), (6, 0), (7, 5), (8, 6), (9, 7), (10, 8), + (11, 0), (12, 0), (13, 11), (14, 12), (15, 13), (16, 14)) + + def __call__(self, results): + + keypoint = results['keypoint'] + M, T, V, C = keypoint.shape + bone = np.zeros((M, T, V, C), dtype=np.float32) + + assert C in [2, 3] + for v1, v2 in self.pairs: + bone[..., v1, :] = keypoint[..., v1, :] - keypoint[..., v2, :] + if C == 3 and self.dataset in ['openpose', 'coco']: + score = (keypoint[..., v1, 2] + keypoint[..., v2, 2]) / 2 + bone[..., v1, 2] = score + + results[self.target] = bone + return results + + +@PIPELINES.register_module() +class ToMotion: + + def __init__(self, dataset='nturgb+d', source='keypoint', target='motion'): + self.dataset = dataset + self.source = source + self.target = target + + def __call__(self, results): + data = results[self.source] + M, T, V, C = data.shape + motion = np.zeros_like(data) + + assert C in [2, 3] + motion[:, :T - 1] = np.diff(data, axis=1) + if C == 3 and self.dataset in ['openpose', 'coco']: + score = (data[:, :T - 1, :, 2] + data[:, 1:, :, 2]) / 2 + motion[:, :T - 1, :, 2] = score + + results[self.target] = motion + + return results + + +@PIPELINES.register_module() +class MergeSkeFeat: + def __init__(self, feat_list=['keypoint'], target='keypoint', axis=-1): + """Merge different feats (ndarray) by concatenate them in the last axis. """ + + self.feat_list = feat_list + self.target = target + self.axis = axis + + def __call__(self, results): + feats = [] + for name in self.feat_list: + feats.append(results.pop(name)) + feats = np.concatenate(feats, axis=self.axis) + results[self.target] = feats + return results + + +@PIPELINES.register_module() +class GenSkeFeat: + def __init__(self, dataset='nturgb+d', feats=['j'], axis=-1): + self.dataset = dataset + self.feats = feats + self.axis = axis + ops = [] + if 'b' in feats or 'bm' in feats: + ops.append(JointToBone(dataset=dataset, target='b')) + ops.append(Rename({'keypoint': 'j'})) + if 'jm' in feats: + ops.append(ToMotion(dataset=dataset, source='j', target='jm')) + if 'bm' in feats: + ops.append(ToMotion(dataset=dataset, source='b', target='bm')) + ops.append(MergeSkeFeat(feat_list=feats, axis=axis)) + self.ops = Compose(ops) + + def __call__(self, results): + if 'keypoint_score' in results and 'keypoint' in results: + assert self.dataset != 'nturgb+d' + assert results['keypoint'].shape[-1] == 2, 'Only 2D keypoints have keypoint_score. ' + keypoint = results.pop('keypoint') + keypoint_score = results.pop('keypoint_score') + results['keypoint'] = np.concatenate([keypoint, keypoint_score[..., None]], -1) + return self.ops(results) + + +@PIPELINES.register_module() +class PadTo: + + def __init__(self, length, mode='loop'): + self.length = length + assert mode in ['loop', 'zero'] + self.mode = mode + + def __call__(self, results): + total_frames = results['total_frames'] + assert total_frames <= self.length + inds = np.arange(self.length) + inds = np.mod(inds, total_frames) + + keypoint = results['keypoint'][:, inds].copy() + if self.mode == 'zero': + keypoint[:, total_frames:] = 0 + results['keypoint'] = keypoint + results['total_frames'] = self.length + return results + + +@PIPELINES.register_module() +class FormatGCNInput: + """Format final skeleton shape to the given input_format. """ + + def __init__(self, num_person=2, mode='zero'): + self.num_person = num_person + assert mode in ['zero', 'loop'] + self.mode = mode + + def __call__(self, results): + """Performs the FormatShape formatting. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + keypoint = results['keypoint'] + if 'keypoint_score' in results: + keypoint = np.concatenate((keypoint, results['keypoint_score'][..., None]), axis=-1) + + # M T V C + if keypoint.shape[0] < self.num_person: + pad_dim = self.num_person - keypoint.shape[0] + pad = np.zeros((pad_dim, ) + keypoint.shape[1:], dtype=keypoint.dtype) + keypoint = np.concatenate((keypoint, pad), axis=0) + if self.mode == 'loop' and keypoint.shape[0] == 1: + for i in range(1, self.num_person): + keypoint[i] = keypoint[0] + + elif keypoint.shape[0] > self.num_person: + keypoint = keypoint[:self.num_person] + + M, T, V, C = keypoint.shape + nc = results.get('num_clips', 1) + assert T % nc == 0 + keypoint = keypoint.reshape((M, nc, T // nc, V, C)).transpose(1, 0, 2, 3, 4) + results['keypoint'] = np.ascontiguousarray(keypoint) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + f'(num_person={self.num_person}, mode={self.mode})' + return repr_str diff --git a/pyskl/datasets/pipelines/posec3d_related.py b/pyskl/datasets/pipelines/posec3d_related.py new file mode 100644 index 00000000..8c6560cb --- /dev/null +++ b/pyskl/datasets/pipelines/posec3d_related.py @@ -0,0 +1,259 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import numpy as np + +from ..builder import PIPELINES + +EPS = 1e-3 + + +@PIPELINES.register_module() +class GeneratePoseTarget: + """Generate pseudo heatmaps based on joint coordinates and confidence. + + Required keys are "keypoint", "img_shape", "keypoint_score" (optional), + added or modified keys are "imgs". + + Args: + sigma (float): The sigma of the generated gaussian map. Default: 0.6. + use_score (bool): Use the confidence score of keypoints as the maximum + of the gaussian maps. Default: True. + with_kp (bool): Generate pseudo heatmaps for keypoints. Default: True. + with_limb (bool): Generate pseudo heatmaps for limbs. At least one of + 'with_kp' and 'with_limb' should be True. Default: False. + skeletons (tuple[tuple]): The definition of human skeletons. + Default: ((0, 1), (0, 2), (1, 3), (2, 4), (0, 5), (5, 7), (7, 9), + (0, 6), (6, 8), (8, 10), (5, 11), (11, 13), (13, 15), + (6, 12), (12, 14), (14, 16), (11, 12)), + which is the definition of COCO-17p skeletons. + double (bool): Output both original heatmaps and flipped heatmaps. + Default: False. + left_kp (tuple[int]): Indexes of left keypoints, which is used when + flipping heatmaps. Default: (1, 3, 5, 7, 9, 11, 13, 15), + which is left keypoints in COCO-17p. + right_kp (tuple[int]): Indexes of right keypoints, which is used when + flipping heatmaps. Default: (2, 4, 6, 8, 10, 12, 14, 16), + which is right keypoints in COCO-17p. + """ + + def __init__(self, + sigma=0.6, + use_score=True, + with_kp=True, + with_limb=False, + skeletons=((0, 1), (0, 2), (1, 3), (2, 4), (0, 5), (5, 7), + (7, 9), (0, 6), (6, 8), (8, 10), (5, 11), (11, 13), + (13, 15), (6, 12), (12, 14), (14, 16), (11, 12)), + double=False, + left_kp=(1, 3, 5, 7, 9, 11, 13, 15), + right_kp=(2, 4, 6, 8, 10, 12, 14, 16), + left_limb=(0, 2, 4, 5, 6, 10, 11, 12), + right_limb=(1, 3, 7, 8, 9, 13, 14, 15)): + + self.sigma = sigma + self.use_score = use_score + self.with_kp = with_kp + self.with_limb = with_limb + self.double = double + + assert self.with_kp + self.with_limb == 1, ('One of "with_limb" and "with_kp" should be set as True.') + self.left_kp = left_kp + self.right_kp = right_kp + self.skeletons = skeletons + self.left_limb = left_limb + self.right_limb = right_limb + + def generate_a_heatmap(self, arr, centers, max_values): + """Generate pseudo heatmap for one keypoint in one frame. + + Args: + arr (np.ndarray): The array to store the generated heatmaps. Shape: img_h * img_w. + centers (np.ndarray): The coordinates of corresponding keypoints (of multiple persons). Shape: M * 2. + max_values (np.ndarray): The max values of each keypoint. Shape: M. + + Returns: + np.ndarray: The generated pseudo heatmap. + """ + + sigma = self.sigma + img_h, img_w = arr.shape + + for center, max_value in zip(centers, max_values): + if max_value < EPS: + continue + + mu_x, mu_y = center[0], center[1] + st_x = max(int(mu_x - 3 * sigma), 0) + ed_x = min(int(mu_x + 3 * sigma) + 1, img_w) + st_y = max(int(mu_y - 3 * sigma), 0) + ed_y = min(int(mu_y + 3 * sigma) + 1, img_h) + x = np.arange(st_x, ed_x, 1, np.float32) + y = np.arange(st_y, ed_y, 1, np.float32) + + # if the keypoint not in the heatmap coordinate system + if not (len(x) and len(y)): + continue + y = y[:, None] + + patch = np.exp(-((x - mu_x)**2 + (y - mu_y)**2) / 2 / sigma**2) + patch = patch * max_value + arr[st_y:ed_y, st_x:ed_x] = np.maximum(arr[st_y:ed_y, st_x:ed_x], patch) + + def generate_a_limb_heatmap(self, arr, starts, ends, start_values, end_values): + """Generate pseudo heatmap for one limb in one frame. + + Args: + arr (np.ndarray): The array to store the generated heatmaps. Shape: img_h * img_w. + starts (np.ndarray): The coordinates of one keypoint in the corresponding limbs. Shape: M * 2. + ends (np.ndarray): The coordinates of the other keypoint in the corresponding limbs. Shape: M * 2. + start_values (np.ndarray): The max values of one keypoint in the corresponding limbs. Shape: M. + end_values (np.ndarray): The max values of the other keypoint in the corresponding limbs. Shape: M. + + Returns: + np.ndarray: The generated pseudo heatmap. + """ + + sigma = self.sigma + img_h, img_w = arr.shape + + for start, end, start_value, end_value in zip(starts, ends, start_values, end_values): + value_coeff = min(start_value, end_value) + if value_coeff < EPS: + continue + + min_x, max_x = min(start[0], end[0]), max(start[0], end[0]) + min_y, max_y = min(start[1], end[1]), max(start[1], end[1]) + + min_x = max(int(min_x - 3 * sigma), 0) + max_x = min(int(max_x + 3 * sigma) + 1, img_w) + min_y = max(int(min_y - 3 * sigma), 0) + max_y = min(int(max_y + 3 * sigma) + 1, img_h) + + x = np.arange(min_x, max_x, 1, np.float32) + y = np.arange(min_y, max_y, 1, np.float32) + + if not (len(x) and len(y)): + continue + + y = y[:, None] + x_0 = np.zeros_like(x) + y_0 = np.zeros_like(y) + + # distance to start keypoints + d2_start = ((x - start[0])**2 + (y - start[1])**2) + + # distance to end keypoints + d2_end = ((x - end[0])**2 + (y - end[1])**2) + + # the distance between start and end keypoints. + d2_ab = ((start[0] - end[0])**2 + (start[1] - end[1])**2) + + if d2_ab < 1: + self.generate_a_heatmap(arr, start[None], start_value[None]) + continue + + coeff = (d2_start - d2_end + d2_ab) / 2. / d2_ab + + a_dominate = coeff <= 0 + b_dominate = coeff >= 1 + seg_dominate = 1 - a_dominate - b_dominate + + position = np.stack([x + y_0, y + x_0], axis=-1) + projection = start + np.stack([coeff, coeff], axis=-1) * (end - start) + d2_line = position - projection + d2_line = d2_line[:, :, 0]**2 + d2_line[:, :, 1]**2 + d2_seg = a_dominate * d2_start + b_dominate * d2_end + seg_dominate * d2_line + + patch = np.exp(-d2_seg / 2. / sigma**2) + patch = patch * value_coeff + + arr[min_y:max_y, min_x:max_x] = np.maximum(arr[min_y:max_y, min_x:max_x], patch) + + def generate_heatmap(self, arr, kps, max_values): + """Generate pseudo heatmap for all keypoints and limbs in one frame (if + needed). + + Args: + arr (np.ndarray): The array to store the generated heatmaps. Shape: V * img_h * img_w. + kps (np.ndarray): The coordinates of keypoints in this frame. Shape: M * V * 2. + max_values (np.ndarray): The confidence score of each keypoint. Shape: M * V. + + Returns: + np.ndarray: The generated pseudo heatmap. + """ + + if self.with_kp: + num_kp = kps.shape[1] + for i in range(num_kp): + self.generate_a_heatmap(arr[i], kps[:, i], max_values[:, i]) + + if self.with_limb: + for i, limb in enumerate(self.skeletons): + start_idx, end_idx = limb + starts = kps[:, start_idx] + ends = kps[:, end_idx] + + start_values = max_values[:, start_idx] + end_values = max_values[:, end_idx] + self.generate_a_limb_heatmap(arr[i], starts, ends, start_values, end_values) + + def gen_an_aug(self, results): + """Generate pseudo heatmaps for all frames. + + Args: + results (dict): The dictionary that contains all info of a sample. + + Returns: + list[np.ndarray]: The generated pseudo heatmaps. + """ + + all_kps = results['keypoint'] + kp_shape = all_kps.shape + + if 'keypoint_score' in results: + all_kpscores = results['keypoint_score'] + else: + all_kpscores = np.ones(kp_shape[:-1], dtype=np.float32) + + img_h, img_w = results['img_shape'] + num_frame = kp_shape[1] + num_c = 0 + if self.with_kp: + num_c += all_kps.shape[2] + if self.with_limb: + num_c += len(self.skeletons) + ret = np.zeros([num_frame, num_c, img_h, img_w], dtype=np.float32) + + for i in range(num_frame): + # M, V, C + kps = all_kps[:, i] + # M, C + kpscores = all_kpscores[:, i] if self.use_score else np.ones_like(all_kpscores[:, i]) + + self.generate_heatmap(ret[i], kps, kpscores) + return ret + + def __call__(self, results): + heatmap = self.gen_an_aug(results) + + if self.double: + indices = np.arange(heatmap.shape[1], dtype=np.int64) + left, right = (self.left_kp, self.right_kp) if self.with_kp else (self.left_limb, self.right_limb) + for l, r in zip(left, right): # noqa: E741 + indices[l] = r + indices[r] = l + heatmap_flip = heatmap[..., ::-1][:, indices] + heatmap = np.concatenate([heatmap, heatmap_flip]) + results['imgs'] = heatmap + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'sigma={self.sigma}, ' + f'use_score={self.use_score}, ' + f'with_kp={self.with_kp}, ' + f'with_limb={self.with_limb}, ' + f'skeletons={self.skeletons}, ' + f'double={self.double}, ' + f'left_kp={self.left_kp}, ' + f'right_kp={self.right_kp})') + return repr_str diff --git a/pyskl/datasets/pipelines/sampling.py b/pyskl/datasets/pipelines/sampling.py new file mode 100644 index 00000000..71300319 --- /dev/null +++ b/pyskl/datasets/pipelines/sampling.py @@ -0,0 +1,384 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings + +import numpy as np + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class UniformSampleFrames: + """Uniformly sample frames from the video. + + To sample an n-frame clip from the video. UniformSampleFrames basically + divide the video into n segments of equal length and randomly sample one + frame from each segment. To make the testing results reproducible, a + random seed is set during testing, to make the sampling results + deterministic. + + Required keys are "total_frames", "start_index" , added or modified keys + are "frame_inds", "clip_len", "frame_interval" and "num_clips". + + Args: + clip_len (int): Frames of each sampled output clip. + num_clips (int): Number of clips to be sampled. Default: 1. + test_mode (bool): Store True when building test or validation dataset. + Default: False. + seed (int): The random seed used during test time. Default: 255. + """ + + def __init__(self, + clip_len, + num_clips=1, + test_mode=False, + float_ok=False, + p_interval=1, + seed=255): + + self.clip_len = clip_len + self.num_clips = num_clips + self.test_mode = test_mode + self.float_ok = float_ok + self.seed = seed + self.p_interval = p_interval + if not isinstance(p_interval, tuple): + self.p_interval = (p_interval, p_interval) + + if self.float_ok: + warnings.warn('When float_ok == True, there will be no loop.') + + def _get_train_clips(self, num_frames, clip_len): + """Uniformly sample indices for training clips. + + Args: + num_frames (int): The number of frames. + clip_len (int): The length of the clip. + """ + allinds = [] + for clip_idx in range(self.num_clips): + old_num_frames = num_frames + pi = self.p_interval + ratio = np.random.rand() * (pi[1] - pi[0]) + pi[0] + num_frames = int(ratio * num_frames) + off = np.random.randint(old_num_frames - num_frames + 1) + + if self.float_ok: + interval = (num_frames - 1) / clip_len + offsets = np.arange(clip_len) * interval + inds = np.random.rand(clip_len) * interval + offsets + inds = inds.astype(np.float32) + elif num_frames < clip_len: + start = np.random.randint(0, num_frames) + inds = np.arange(start, start + clip_len) + elif clip_len <= num_frames < 2 * clip_len: + basic = np.arange(clip_len) + inds = np.random.choice( + clip_len + 1, num_frames - clip_len, replace=False) + offset = np.zeros(clip_len + 1, dtype=np.int64) + offset[inds] = 1 + offset = np.cumsum(offset) + inds = basic + offset[:-1] + else: + bids = np.array( + [i * num_frames // clip_len for i in range(clip_len + 1)]) + bsize = np.diff(bids) + bst = bids[:clip_len] + offset = np.random.randint(bsize) + inds = bst + offset + + inds = inds + off + num_frames = old_num_frames + + allinds.append(inds) + + return np.concatenate(allinds) + + def _get_test_clips(self, num_frames, clip_len): + """Uniformly sample indices for testing clips. + + Args: + num_frames (int): The number of frames. + clip_len (int): The length of the clip. + """ + np.random.seed(self.seed) + if self.float_ok: + interval = (num_frames - 1) / clip_len + offsets = np.arange(clip_len) * interval + inds = np.concatenate([ + np.random.rand(clip_len) * interval + offsets + for i in range(self.num_clips) + ]).astype(np.float32) + + all_inds = [] + + for i in range(self.num_clips): + + old_num_frames = num_frames + pi = self.p_interval + ratio = np.random.rand() * (pi[1] - pi[0]) + pi[0] + num_frames = int(ratio * num_frames) + off = np.random.randint(old_num_frames - num_frames + 1) + + if num_frames < clip_len: + start_ind = i if num_frames < self.num_clips else i * num_frames // self.num_clips + inds = np.arange(start_ind, start_ind + clip_len) + elif clip_len <= num_frames < clip_len * 2: + basic = np.arange(clip_len) + inds = np.random.choice(clip_len + 1, num_frames - clip_len, replace=False) + offset = np.zeros(clip_len + 1, dtype=np.int64) + offset[inds] = 1 + offset = np.cumsum(offset) + inds = basic + offset[:-1] + else: + bids = np.array([i * num_frames // clip_len for i in range(clip_len + 1)]) + bsize = np.diff(bids) + bst = bids[:clip_len] + offset = np.random.randint(bsize) + inds = bst + offset + + all_inds.append(inds + off) + num_frames = old_num_frames + + return np.concatenate(all_inds) + + def __call__(self, results): + num_frames = results['total_frames'] + + if self.test_mode: + inds = self._get_test_clips(num_frames, self.clip_len) + else: + inds = self._get_train_clips(num_frames, self.clip_len) + + inds = np.mod(inds, num_frames) + start_index = results['start_index'] + inds = inds + start_index + + if 'keypoint' in results: + kp = results['keypoint'] + assert num_frames == kp.shape[1] + num_person = kp.shape[0] + num_persons = [num_person] * num_frames + for i in range(num_frames): + j = num_person - 1 + while j >= 0 and np.all(np.abs(kp[j, i]) < 1e-5): + j -= 1 + num_persons[i] = j + 1 + transitional = [False] * num_frames + for i in range(1, num_frames - 1): + if num_persons[i] != num_persons[i - 1]: + transitional[i] = transitional[i - 1] = True + if num_persons[i] != num_persons[i + 1]: + transitional[i] = transitional[i + 1] = True + inds_int = inds.astype(np.int) + coeff = np.array([transitional[i] for i in inds_int]) + inds = (coeff * inds_int + (1 - coeff) * inds).astype(np.float32) + + results['frame_inds'] = inds if self.float_ok else inds.astype(np.int) + results['clip_len'] = self.clip_len + results['frame_interval'] = None + results['num_clips'] = self.num_clips + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'clip_len={self.clip_len}, ' + f'num_clips={self.num_clips}, ' + f'test_mode={self.test_mode}, ' + f'seed={self.seed})') + return repr_str + + +@PIPELINES.register_module() +class UniformSample(UniformSampleFrames): + pass + + +@PIPELINES.register_module() +class SampleFrames: + """Sample frames from the video. + + Required keys are "total_frames", "start_index" , added or modified keys + are "frame_inds", "frame_interval" and "num_clips". + + Args: + clip_len (int): Frames of each sampled output clip. + frame_interval (int): Temporal interval of adjacent sampled frames. + Default: 1. + num_clips (int): Number of clips to be sampled. Default: 1. + temporal_jitter (bool): Whether to apply temporal jittering. + Default: False. + twice_sample (bool): Whether to use twice sample when testing. + If set to True, it will sample frames with and without fixed shift, + which is commonly used for testing in TSM model. Default: False. + out_of_bound_opt (str): The way to deal with out of bounds frame + indexes. Available options are 'loop', 'repeat_last'. + Default: 'loop'. + test_mode (bool): Store True when building test or validation dataset. + Default: False. + start_index (None): This argument is deprecated and moved to dataset + class (``BaseDataset``, ``VideoDatset``, ``RawframeDataset``, etc), + see this: https://github.com/open-mmlab/mmaction2/pull/89. + keep_tail_frames (bool): Whether to keep tail frames when sampling. + Default: False. + """ + + def __init__(self, + clip_len, + frame_interval=1, + num_clips=1, + temporal_jitter=False, + twice_sample=False, + out_of_bound_opt='loop', + test_mode=False, + start_index=None, + keep_tail_frames=False): + + self.clip_len = clip_len + self.frame_interval = frame_interval + self.num_clips = num_clips + self.temporal_jitter = temporal_jitter + self.twice_sample = twice_sample + self.out_of_bound_opt = out_of_bound_opt + self.test_mode = test_mode + self.keep_tail_frames = keep_tail_frames + assert self.out_of_bound_opt in ['loop', 'repeat_last'] + + if start_index is not None: + warnings.warn('No longer support "start_index" in "SampleFrames", ' + 'it should be set in dataset class, see this pr: ' + 'https://github.com/open-mmlab/mmaction2/pull/89') + + def _get_train_clips(self, num_frames): + """Get clip offsets in train mode. + + It will calculate the average interval for selected frames, + and randomly shift them within offsets between [0, avg_interval]. + If the total number of frames is smaller than clips num or origin + frames length, it will return all zero indices. + + Args: + num_frames (int): Total number of frame in the video. + + Returns: + np.ndarray: Sampled frame indices in train mode. + """ + ori_clip_len = self.clip_len * self.frame_interval + + if self.keep_tail_frames: + avg_interval = (num_frames - ori_clip_len + 1) / float( + self.num_clips) + if num_frames > ori_clip_len - 1: + base_offsets = np.arange(self.num_clips) * avg_interval + clip_offsets = (base_offsets + np.random.uniform( + 0, avg_interval, self.num_clips)).astype(np.int) + else: + clip_offsets = np.zeros((self.num_clips, ), dtype=np.int) + else: + avg_interval = (num_frames - ori_clip_len + 1) // self.num_clips + + if avg_interval > 0: + base_offsets = np.arange(self.num_clips) * avg_interval + clip_offsets = base_offsets + np.random.randint( + avg_interval, size=self.num_clips) + elif num_frames > max(self.num_clips, ori_clip_len): + clip_offsets = np.sort( + np.random.randint( + num_frames - ori_clip_len + 1, size=self.num_clips)) + elif avg_interval == 0: + ratio = (num_frames - ori_clip_len + 1.0) / self.num_clips + clip_offsets = np.around(np.arange(self.num_clips) * ratio) + else: + clip_offsets = np.zeros((self.num_clips, ), dtype=np.int) + + return clip_offsets + + def _get_test_clips(self, num_frames): + """Get clip offsets in test mode. + + Calculate the average interval for selected frames, and shift them + fixedly by avg_interval/2. If set twice_sample True, it will sample + frames together without fixed shift. If the total number of frames is + not enough, it will return all zero indices. + + Args: + num_frames (int): Total number of frame in the video. + + Returns: + np.ndarray: Sampled frame indices in test mode. + """ + ori_clip_len = self.clip_len * self.frame_interval + avg_interval = (num_frames - ori_clip_len + 1) / float(self.num_clips) + if num_frames > ori_clip_len - 1: + base_offsets = np.arange(self.num_clips) * avg_interval + clip_offsets = (base_offsets + avg_interval / 2.0).astype(np.int) + if self.twice_sample: + clip_offsets = np.concatenate([clip_offsets, base_offsets]) + else: + clip_offsets = np.zeros((self.num_clips, ), dtype=np.int) + return clip_offsets + + def _sample_clips(self, num_frames): + """Choose clip offsets for the video in a given mode. + + Args: + num_frames (int): Total number of frame in the video. + + Returns: + np.ndarray: Sampled frame indices. + """ + if self.test_mode: + clip_offsets = self._get_test_clips(num_frames) + else: + clip_offsets = self._get_train_clips(num_frames) + + return clip_offsets + + def __call__(self, results): + """Perform the SampleFrames loading. + + Args: + results (dict): The resulting dict to be modified and passed + to the next transform in pipeline. + """ + total_frames = results['total_frames'] + + clip_offsets = self._sample_clips(total_frames) + frame_inds = clip_offsets[:, None] + np.arange( + self.clip_len)[None, :] * self.frame_interval + frame_inds = np.concatenate(frame_inds) + + if self.temporal_jitter: + perframe_offsets = np.random.randint( + self.frame_interval, size=len(frame_inds)) + frame_inds += perframe_offsets + + frame_inds = frame_inds.reshape((-1, self.clip_len)) + if self.out_of_bound_opt == 'loop': + frame_inds = np.mod(frame_inds, total_frames) + elif self.out_of_bound_opt == 'repeat_last': + safe_inds = frame_inds < total_frames + unsafe_inds = 1 - safe_inds + last_ind = np.max(safe_inds * frame_inds, axis=1) + new_inds = (safe_inds * frame_inds + (unsafe_inds.T * last_ind).T) + frame_inds = new_inds + else: + raise ValueError('Illegal out_of_bound option.') + + start_index = results['start_index'] + frame_inds = np.concatenate(frame_inds) + start_index + results['frame_inds'] = frame_inds.astype(np.int) + results['clip_len'] = self.clip_len + results['frame_interval'] = self.frame_interval + results['num_clips'] = self.num_clips + return results + + def __repr__(self): + repr_str = (f'{self.__class__.__name__}(' + f'clip_len={self.clip_len}, ' + f'frame_interval={self.frame_interval}, ' + f'num_clips={self.num_clips}, ' + f'temporal_jitter={self.temporal_jitter}, ' + f'twice_sample={self.twice_sample}, ' + f'out_of_bound_opt={self.out_of_bound_opt}, ' + f'test_mode={self.test_mode})') + return repr_str diff --git a/pyskl/datasets/pose_dataset.py b/pyskl/datasets/pose_dataset.py new file mode 100644 index 00000000..7d4e6d1a --- /dev/null +++ b/pyskl/datasets/pose_dataset.py @@ -0,0 +1,108 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp + +import mmcv +import numpy as np + +from ..utils import get_root_logger +from .base import BaseDataset +from .builder import DATASETS + + +@DATASETS.register_module() +class PoseDataset(BaseDataset): + """Pose dataset for action recognition. + + The dataset loads pose and apply specified transforms to return a + dict containing pose information. + + The ann_file is a pickle file, the json file contains a list of + annotations, the fields of an annotation include frame_dir(video_id), + total_frames, label, kp, kpscore. + + Args: + ann_file (str): Path to the annotation file. + pipeline (list[dict | callable]): A sequence of data transforms. + split (str | None): The dataset split used. For UCF101 and HMDB51, allowed choices are 'train1', 'test1', + 'train2', 'test2', 'train3', 'test3'. For NTURGB+D, allowed choices are 'xsub_train', 'xsub_val', + 'xview_train', 'xview_val'. For NTURGB+D 120, allowed choices are 'xsub_train', 'xsub_val', 'xset_train', + 'xset_val'. For FineGYM, allowed choices are 'train', 'val'. Default: None. + valid_ratio (float | None): The valid_ratio for videos in KineticsPose. For a video with n frames, it is a + valid training sample only if n * valid_ratio frames have human pose. None means not applicable (only + applicable to Kinetics Pose). Default: None. + box_thr (str | None): The threshold for human proposals. Only boxes with confidence score larger than `box_thr` + is kept. None means not applicable (only applicable to Kinetics Pose [ours]). Allowed choices are '0.5', + '0.6', '0.7', '0.8', '0.9'. Default: None. + class_prob (list | None): The class-specific multiplier, which should be a list of length 'num_classes', each + element >= 1. The goal is to resample some rare classes to improve the overall performance. None means no + resampling performed. Default: None. + **kwargs: Keyword arguments for 'BaseDataset'. + """ + + def __init__(self, + ann_file, + pipeline, + split=None, + valid_ratio=None, + box_thr=None, + class_prob=None, + **kwargs): + modality = 'Pose' + self.split = split + + super().__init__( + ann_file, pipeline, start_index=0, modality=modality, **kwargs) + + # box_thr, which should be a string + self.box_thr = box_thr + self.class_prob = class_prob + if self.box_thr is not None: + assert box_thr in ['0.5', '0.6', '0.7', '0.8', '0.9'] + + # Thresholding Training Examples + self.valid_ratio = valid_ratio + if self.valid_ratio is not None: + assert isinstance(self.valid_ratio, float) + if self.box_thr is None: + self.video_infos = [ + x for x in self.video_infos + if x['valid_frames'] / x['total_frames'] >= valid_ratio + ] + else: + key = f'valid@{self.box_thr}' + self.video_infos = [ + x for x in self.video_infos + if x[key] / x['total_frames'] >= valid_ratio + ] + if self.box_thr != '0.5': + box_thr = float(self.box_thr) + for item in self.video_infos: + inds = [ + i for i, score in enumerate(item['box_score']) + if score >= box_thr + ] + item['anno_inds'] = np.array(inds) + + logger = get_root_logger() + logger.info(f'{len(self)} videos remain after valid thresholding') + + def load_annotations(self): + """Load annotation file to get video information.""" + assert self.ann_file.endswith('.pkl') + return self.load_pkl_annotations() + + def load_pkl_annotations(self): + data = mmcv.load(self.ann_file) + + if self.split: + split, data = data['split'], data['annotations'] + identifier = 'filename' if 'filename' in data[0] else 'frame_dir' + data = [x for x in data if x[identifier] in split[self.split]] + + for item in data: + # Sometimes we may need to load anno from the file + if 'filename' in item: + item['filename'] = osp.join(self.data_prefix, item['filename']) + if 'frame_dir' in item: + item['frame_dir'] = osp.join(self.data_prefix, item['frame_dir']) + return data diff --git a/pyskl/datasets/samplers/__init__.py b/pyskl/datasets/samplers/__init__.py new file mode 100644 index 00000000..ca7f3c96 --- /dev/null +++ b/pyskl/datasets/samplers/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .distributed_sampler import ClassSpecificDistributedSampler, DistributedSampler + +__all__ = ['DistributedSampler', 'ClassSpecificDistributedSampler'] diff --git a/pyskl/datasets/samplers/distributed_sampler.py b/pyskl/datasets/samplers/distributed_sampler.py new file mode 100644 index 00000000..76655fd2 --- /dev/null +++ b/pyskl/datasets/samplers/distributed_sampler.py @@ -0,0 +1,112 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math +from collections import defaultdict + +import torch +from torch.utils.data import DistributedSampler as _DistributedSampler + + +class DistributedSampler(_DistributedSampler): + """DistributedSampler inheriting from + ``torch.utils.data.DistributedSampler``. + + In pytorch of lower versions, there is no ``shuffle`` argument. This child + class will port one to DistributedSampler. + """ + + def __init__(self, + dataset, + num_replicas=None, + rank=None, + shuffle=True, + seed=0): + super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) + # for the compatibility from PyTorch 1.3+ + self.seed = seed if seed is not None else 0 + + def __iter__(self): + # deterministically shuffle based on epoch + if self.shuffle: + g = torch.Generator() + g.manual_seed(self.epoch + self.seed) + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = torch.arange(len(self.dataset)).tolist() + + # add extra samples to make it evenly divisible + indices += indices[:(self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + return iter(indices) + + +class ClassSpecificDistributedSampler(_DistributedSampler): + """ClassSpecificDistributedSampler inheriting from 'torch.utils.data.DistributedSampler'. + + Samples are sampled with a class specific probability (class_prob). This sampler is only applicable to single class + recognition dataset. This sampler is also compatible with RepeatDataset. + """ + + def __init__(self, + dataset, + num_replicas=None, + rank=None, + class_prob=None, + shuffle=True, + seed=0): + + super().__init__(dataset, num_replicas=num_replicas, rank=rank) + self.shuffle = shuffle + if class_prob is not None: + if isinstance(class_prob, list): + class_prob = {i: n for i, n in enumerate(class_prob)} + assert isinstance(class_prob, dict) + self.class_prob = class_prob + # for the compatibility from PyTorch 1.3+ + self.seed = seed if seed is not None else 0 + + def __iter__(self): + g = torch.Generator() + g.manual_seed(self.seed + self.epoch) + + class_prob = self.class_prob + dataset_name = type(self.dataset).__name__ + dataset = self.dataset if dataset_name != 'RepeatDataset' else self.dataset.dataset + times = 1 + if dataset_name == 'RepeatDataset': + times = self.dataset.times + class_prob = {k: v * times for k, v in class_prob.items()} + + labels = [x['label'] for x in dataset.video_infos] + samples = defaultdict(list) + for i, lb in enumerate(labels): + samples[lb].append(i) + + indices = [] + for class_idx, class_indices in samples.items(): + mul = class_prob.get(class_idx, times) + for i in range(int(mul // 1)): + indices.extend(class_indices) + rem = int((mul % 1) * len(class_indices)) + inds = torch.randperm(len(class_indices), generator=g).tolist() + indices.extend([class_indices[inds[i]] for i in range(rem)]) + + if self.shuffle: + shuffle = torch.randperm(len(indices), generator=g).tolist() + indices = [indices[i] for i in shuffle] + + # reset num_samples and total_size here. + self.num_samples = math.ceil(len(indices) / self.num_replicas) + self.total_size = self.num_samples * self.num_replicas + + # add extra samples to make it evenly divisible + indices += indices[:(self.total_size - len(indices))] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + return iter(indices) diff --git a/pyskl/datasets/video_dataset.py b/pyskl/datasets/video_dataset.py new file mode 100644 index 00000000..3f8f03c3 --- /dev/null +++ b/pyskl/datasets/video_dataset.py @@ -0,0 +1,60 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp + +from .base import BaseDataset +from .builder import DATASETS + + +@DATASETS.register_module() +class VideoDataset(BaseDataset): + """Video dataset for action recognition. + + The dataset loads raw videos and apply specified transforms to return a + dict containing the frame tensors and other information. + + The ann_file is a text file with multiple lines, and each line indicates + a sample video with the filepath and label, which are split with a + whitespace. Example of a annotation file: + + .. code-block:: txt + + some/path/000.mp4 1 + some/path/001.mp4 1 + some/path/002.mp4 2 + some/path/003.mp4 2 + some/path/004.mp4 3 + some/path/005.mp4 3 + + + Args: + ann_file (str): Path to the annotation file. + pipeline (list[dict | callable]): A sequence of data transforms. + start_index (int): Specify a start index for frames in consideration of + different filename format. However, when taking videos as input, + it should be set to 0, since frames loaded from videos count + from 0. Default: 0. + **kwargs: Keyword arguments for ``BaseDataset``. + """ + + def __init__(self, ann_file, pipeline, start_index=0, **kwargs): + super().__init__(ann_file, pipeline, start_index=start_index, **kwargs) + + def load_annotations(self): + """Load annotation file to get video information.""" + if self.ann_file.endswith('.json'): + return self.load_json_annotations() + + video_infos = [] + with open(self.ann_file, 'r') as fin: + for line in fin: + line_split = line.strip().split() + if self.multi_class: + assert self.num_classes is not None + filename, label = line_split[0], line_split[1:] + label = list(map(int, label)) + else: + filename, label = line_split + label = int(label) + filename = osp.join(self.data_prefix, filename) + video_infos.append(dict(filename=filename, label=label)) + return video_infos diff --git a/pyskl/models/__init__.py b/pyskl/models/__init__.py new file mode 100644 index 00000000..4b3c9e79 --- /dev/null +++ b/pyskl/models/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .builder import * # noqa: F401, F403 +from .cnns import * # noqa: F401, F403 +from .gcns import * # noqa: F401, F403 +from .heads import * # noqa: F401, F403 +from .losses import * # noqa: F401, F403 +from .recognizers import * # noqa: F401, F403 diff --git a/pyskl/models/builder.py b/pyskl/models/builder.py new file mode 100644 index 00000000..d1a8b11b --- /dev/null +++ b/pyskl/models/builder.py @@ -0,0 +1,38 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmcv.cnn import MODELS as MMCV_MODELS +from mmcv.utils import Registry + +MODELS = Registry('models', parent=MMCV_MODELS) +BACKBONES = MODELS +HEADS = MODELS +RECOGNIZERS = MODELS +LOSSES = MODELS + + +def build_backbone(cfg): + """Build backbone.""" + return BACKBONES.build(cfg) + + +def build_head(cfg): + """Build head.""" + return HEADS.build(cfg) + + +def build_recognizer(cfg): + """Build recognizer.""" + return RECOGNIZERS.build(cfg) + + +def build_loss(cfg): + """Build loss.""" + return LOSSES.build(cfg) + + +def build_model(cfg): + """Build model.""" + args = cfg.copy() + obj_type = args.pop('type') + if obj_type in RECOGNIZERS: + return build_recognizer(cfg) + raise ValueError(f'{obj_type} is not registered') diff --git a/pyskl/models/cnns/__init__.py b/pyskl/models/cnns/__init__.py new file mode 100644 index 00000000..7420f9b4 --- /dev/null +++ b/pyskl/models/cnns/__init__.py @@ -0,0 +1,11 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .c3d import C3D +from .resnet import ResNet +from .resnet3d import ResNet3d +from .resnet3d_slowfast import ResNet3dSlowFast +from .resnet3d_slowonly import ResNet3dSlowOnly +from .x3d import X3D + +__all__ = [ + 'C3D', 'X3D', 'ResNet', 'ResNet3d', 'ResNet3dSlowFast', 'ResNet3dSlowOnly' +] diff --git a/pyskl/models/cnns/c3d.py b/pyskl/models/cnns/c3d.py new file mode 100644 index 00000000..f657190f --- /dev/null +++ b/pyskl/models/cnns/c3d.py @@ -0,0 +1,98 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn +from mmcv.cnn import ConvModule, kaiming_init +from mmcv.runner import load_checkpoint + +from ...utils import get_root_logger +from ..builder import BACKBONES + + +@BACKBONES.register_module() +class C3D(nn.Module): + """C3D backbone, without flatten and mlp. + + Args: + pretrained (str | None): Name of pretrained model. + """ + + def __init__(self, + in_channels=3, + base_channels=64, + num_stages=4, + temporal_downsample=True, + pretrained=None): + super().__init__() + conv_cfg = dict(type='Conv3d') + norm_cfg = dict(type='BN3d') + act_cfg = dict(type='ReLU') + self.pretrained = pretrained + self.in_channels = in_channels + self.base_channels = base_channels + assert num_stages in [3, 4] + self.num_stages = num_stages + self.temporal_downsample = temporal_downsample + pool_kernel, pool_stride = 2, 2 + if not self.temporal_downsample: + pool_kernel, pool_stride = (1, 2, 2), (1, 2, 2) + + c3d_conv_param = dict(kernel_size=3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg) + + self.conv1a = ConvModule(self.in_channels, self.base_channels, **c3d_conv_param) + self.pool1 = nn.AvgPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)) + + self.conv2a = ConvModule(self.base_channels, self.base_channels * 2, **c3d_conv_param) + self.pool2 = nn.AvgPool3d(kernel_size=pool_kernel, stride=pool_stride) + + self.conv3a = ConvModule(self.base_channels * 2, self.base_channels * 4, **c3d_conv_param) + self.conv3b = ConvModule(self.base_channels * 4, self.base_channels * 4, **c3d_conv_param) + self.pool3 = nn.AvgPool3d(kernel_size=pool_kernel, stride=pool_stride) + + self.conv4a = ConvModule(self.base_channels * 4, self.base_channels * 8, **c3d_conv_param) + self.conv4b = ConvModule(self.base_channels * 8, self.base_channels * 8, **c3d_conv_param) + + if self.num_stages == 4: + self.pool4 = nn.AvgPool3d(kernel_size=pool_kernel, stride=pool_stride) + self.conv5a = ConvModule(self.base_channels * 8, self.base_channels * 8, **c3d_conv_param) + self.conv5b = ConvModule(self.base_channels * 8, self.base_channels * 8, **c3d_conv_param) + + def init_weights(self): + """Initiate the parameters either from existing checkpoint or from + scratch.""" + for m in self.modules(): + if isinstance(m, nn.Conv3d): + kaiming_init(m) + if isinstance(self.pretrained, str): + logger = get_root_logger() + logger.info(f'load model from: {self.pretrained}') + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. The size of x is (num_batches, 3, 16, 112, 112). + + Returns: + torch.Tensor: The feature of the input samples extracted by the backbone. + """ + x = self.conv1a(x) + + x = self.pool1(x) + x = self.conv2a(x) + + x = self.pool2(x) + x = self.conv3a(x) + x = self.conv3b(x) + + x = self.pool3(x) + x = self.conv4a(x) + x = self.conv4b(x) + + if self.num_stages == 3: + return x + + x = self.pool4(x) + x = self.conv5a(x) + x = self.conv5b(x) + + return x diff --git a/pyskl/models/cnns/resnet.py b/pyskl/models/cnns/resnet.py new file mode 100644 index 00000000..b11978ba --- /dev/null +++ b/pyskl/models/cnns/resnet.py @@ -0,0 +1,477 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch.nn as nn +from mmcv.cnn import ConvModule, constant_init, kaiming_init +from mmcv.runner import _load_checkpoint, load_checkpoint +from mmcv.utils import _BatchNorm + +from ...utils import get_root_logger +from ..builder import BACKBONES + + +class BasicBlock(nn.Module): + """Basic block for ResNet. + + Args: + inplanes (int): Number of channels for the input in first conv2d layer. + planes (int): Number of channels produced by some norm/conv2d layers. + stride (int): Stride in the conv layer. Default: 1. + downsample (nn.Module | None): Downsample layer. Default: None. + conv_cfg (dict): Config for norm layers. Default: dict(type='Conv'). + norm_cfg (dict): Config for norm layers. required keys are 'type' and 'requires_grad'. + Default: dict(type='BN2d', requires_grad=True). + act_cfg (dict): Config for activate layers. Default: dict(type='ReLU', inplace=True). + """ + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + downsample=None, + conv_cfg=dict(type='Conv'), + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='ReLU', inplace=True)): + super().__init__() + self.conv1 = ConvModule( + inplanes, + planes, + kernel_size=3, + stride=stride, + padding=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + self.conv2 = ConvModule( + planes, + planes, + kernel_size=3, + stride=1, + padding=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.norm_cfg = norm_cfg + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + torch.Tensor: The output of the module. + """ + identity = x + + out = self.conv1(x) + out = self.conv2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out = out + identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block for ResNet. + + Args: + inplanes (int): Number of channels for the input feature in first conv layer. + planes (int): Number of channels produced by some norm layes and conv layers. + stride (int): Spatial stride in the conv layer. Default: 1. + downsample (nn.Module | None): Downsample layer. Default: None. + conv_cfg (dict): Config for norm layers. Default: dict(type='Conv'). + norm_cfg (dict): Config for norm layers. required keys are 'type' and 'requires_grad'. + Default: dict(type='BN2d', requires_grad=True). + act_cfg (dict): Config for activate layers. Default: dict(type='ReLU', inplace=True). + """ + + expansion = 4 + + def __init__(self, + inplanes, + planes, + stride=1, + downsample=None, + conv_cfg=dict(type='Conv'), + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='ReLU', inplace=True)): + super().__init__() + self.inplanes = inplanes + self.planes = planes + self.conv1_stride = 1 + self.conv2_stride = stride + self.conv1 = ConvModule( + inplanes, + planes, + kernel_size=1, + stride=self.conv1_stride, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.conv2 = ConvModule( + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + self.conv3 = ConvModule( + planes, + planes * self.expansion, + kernel_size=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.norm_cfg = norm_cfg + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + torch.Tensor: The output of the module. + """ + + def _inner_forward(x): + """Forward wrapper for utilizing checkpoint.""" + identity = x + + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out = out + identity + + return out + + out = _inner_forward(x) + out = self.relu(out) + + return out + + +def make_res_layer(block, + inplanes, + planes, + blocks, + stride=1, + conv_cfg=None, + norm_cfg=None, + act_cfg=None): + """Build residual layer for ResNet. + + Args: + block: (nn.Module): Residual module to be built. + inplanes (int): Number of channels for the input feature in each block. + planes (int): Number of channels for the output feature in each block. + blocks (int): Number of residual blocks. + stride (int): Stride in the conv layer. Default: 1. + conv_cfg (dict | None): Config for norm layers. Default: None. + norm_cfg (dict | None): Config for norm layers. Default: None. + act_cfg (dict | None): Config for activate layers. Default: None. + + Returns: + nn.Module: A residual layer for the given config. + """ + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = ConvModule( + inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + layers = [] + layers.append( + block( + inplanes, + planes, + stride, + downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + inplanes, + planes, + 1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + + return nn.Sequential(*layers) + + +@BACKBONES.register_module() +class ResNet(nn.Module): + """ResNet backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. Default: 50. + pretrained (str | None): Name of pretrained model. Default: None. + in_channels (int): Channel num of input features. Default: 3. + num_stages (int): Resnet stages. Default: 4. + strides (Sequence[int]): Strides of the first block of each stage. + out_indices (Sequence[int]): Indices of output feature. Default: (3, ). + frozen_stages (int): Stages to be frozen (all param fixed). -1 means not freezing any parameters. Default: -1. + conv_cfg (dict): Config for norm layers. Default: dict(type='Conv'). + norm_cfg (dict): Config for norm layers. required keys are 'type' and 'requires_grad'. + Default: dict(type='BN2d', requires_grad=True). + act_cfg (dict): Config for activate layers. Default: dict(type='ReLU', inplace=True). + norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze running stats (mean and var). + Default: False. + """ + + arch_settings = { + 18: (BasicBlock, (2, 2, 2, 2)), + 34: (BasicBlock, (3, 4, 6, 3)), + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, + depth=50, + pretrained=None, + torchvision_pretrain=True, + in_channels=3, + num_stages=4, + out_indices=(3, ), + strides=(1, 2, 2, 2), + frozen_stages=-1, + conv_cfg=dict(type='Conv'), + norm_cfg=dict(type='BN2d', requires_grad=True), + act_cfg=dict(type='ReLU', inplace=True), + norm_eval=False): + super().__init__() + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for resnet') + self.depth = depth + self.in_channels = in_channels + self.pretrained = pretrained + self.torchvision_pretrain = torchvision_pretrain + self.num_stages = num_stages + assert 1 <= num_stages <= 4 + self.out_indices = out_indices + assert max(out_indices) < num_stages + self.strides = strides + assert len(strides) == num_stages + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.norm_eval = norm_eval + + self.block, stage_blocks = self.arch_settings[depth] + self.stage_blocks = stage_blocks[:num_stages] + self.inplanes = 64 + + self._make_stem_layer() + + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = strides[i] + planes = 64 * 2**i + res_layer = make_res_layer( + self.block, + self.inplanes, + planes, + num_blocks, + stride=stride, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.inplanes = planes * self.block.expansion + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self.feat_dim = self.block.expansion * 64 * 2**( + len(self.stage_blocks) - 1) + + def _make_stem_layer(self): + """Construct the stem layers consists of a conv+norm+act module and a + pooling layer.""" + self.conv1 = ConvModule( + self.in_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + @staticmethod + def _load_conv_params(conv, state_dict_tv, module_name_tv, loaded_param_names): + """Load the conv parameters of resnet from torchvision. + + Args: + conv (nn.Module): The destination conv module. + state_dict_tv (OrderedDict): The state dict of pretrained torchvision model. + module_name_tv (str): The name of corresponding conv module in the torchvision model. + loaded_param_names (list[str]): List of parameters that have been loaded. + """ + + weight_tv_name = module_name_tv + '.weight' + if conv.weight.data.shape == state_dict_tv[weight_tv_name].shape: + conv.weight.data.copy_(state_dict_tv[weight_tv_name]) + loaded_param_names.append(weight_tv_name) + + if getattr(conv, 'bias') is not None: + bias_tv_name = module_name_tv + '.bias' + if conv.bias.data.shape == state_dict_tv[bias_tv_name].shape: + conv.bias.data.copy_(state_dict_tv[bias_tv_name]) + loaded_param_names.append(bias_tv_name) + + @staticmethod + def _load_bn_params(bn, state_dict_tv, module_name_tv, loaded_param_names): + """Load the bn parameters of resnet from torchvision. + + Args: + bn (nn.Module): The destination bn module. + state_dict_tv (OrderedDict): The state dict of pretrained torchvision model. + module_name_tv (str): The name of corresponding bn module in the torchvision model. + loaded_param_names (list[str]): List of parameters that have been loaded. + """ + + for param_name, param in bn.named_parameters(): + param_tv_name = f'{module_name_tv}.{param_name}' + param_tv = state_dict_tv[param_tv_name] + if param.data.shape == param_tv.shape: + param.data.copy_(param_tv) + loaded_param_names.append(param_tv_name) + + for param_name, param in bn.named_buffers(): + param_tv_name = f'{module_name_tv}.{param_name}' + # some buffers like num_batches_tracked may not exist + if param_tv_name in state_dict_tv: + param_tv = state_dict_tv[param_tv_name] + if param.data.shape == param_tv.shape: + param.data.copy_(param_tv) + loaded_param_names.append(param_tv_name) + + def _load_torchvision_checkpoint(self, logger=None): + """Initiate the parameters from torchvision pretrained checkpoint.""" + state_dict_torchvision = _load_checkpoint(self.pretrained) + if 'state_dict' in state_dict_torchvision: + state_dict_torchvision = state_dict_torchvision['state_dict'] + + loaded_param_names = [] + for name, module in self.named_modules(): + if isinstance(module, ConvModule): + # we use a ConvModule to wrap conv+bn+relu layers, thus the name mapping is needed + if 'downsample' in name: + # layer{X}.{Y}.downsample.conv->layer{X}.{Y}.downsample.0 + original_conv_name = name + '.0' + # layer{X}.{Y}.downsample.bn->layer{X}.{Y}.downsample.1 + original_bn_name = name + '.1' + else: + # layer{X}.{Y}.conv{n}.conv->layer{X}.{Y}.conv{n} + original_conv_name = name + # layer{X}.{Y}.conv{n}.bn->layer{X}.{Y}.bn{n} + original_bn_name = name.replace('conv', 'bn') + self._load_conv_params(module.conv, state_dict_torchvision, original_conv_name, loaded_param_names) + self._load_bn_params(module.bn, state_dict_torchvision, original_bn_name, loaded_param_names) + + # check if any parameters in the 2d checkpoint are not loaded + remaining_names = set(state_dict_torchvision.keys()) - set(loaded_param_names) + if remaining_names: + logger.info(f'These parameters in pretrained checkpoint are not loaded: {remaining_names}') + + def init_weights(self): + """Initiate the parameters either from existing checkpoint or from + scratch.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, nn.BatchNorm2d): + constant_init(m, 1) + + if isinstance(self.pretrained, str): + logger = get_root_logger() + if self.torchvision_pretrain: + self._load_torchvision_checkpoint(logger) + else: + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + torch.Tensor: The feature of the input samples extracted + by the backbone. + """ + x = self.conv1(x) + x = self.maxpool(x) + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + if len(outs) == 1: + return outs[0] + + return tuple(outs) + + def _freeze_stages(self): + """Prevent all the parameters from being optimized before self.frozen_stages.""" + if self.frozen_stages >= 0: + self.conv1.bn.eval() + for m in self.conv1.modules(): + for param in m.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = getattr(self, f'layer{i}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def train(self, mode=True): + """Set the optimization status when training.""" + super().train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() diff --git a/pyskl/models/cnns/resnet3d.py b/pyskl/models/cnns/resnet3d.py new file mode 100644 index 00000000..4901f917 --- /dev/null +++ b/pyskl/models/cnns/resnet3d.py @@ -0,0 +1,625 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings + +import torch.nn as nn +from mmcv.cnn import ConvModule, build_activation_layer, constant_init, kaiming_init +from mmcv.runner import _load_checkpoint, load_checkpoint +from mmcv.utils import _BatchNorm +from torch.nn.modules.utils import _ntuple, _triple + +from ...utils import get_root_logger +from ..builder import BACKBONES + + +class BasicBlock3d(nn.Module): + """BasicBlock 3d block for ResNet3D. + + Args: + inplanes (int): Number of channels for the input in first conv3d layer. + planes (int): Number of channels produced by some norm/conv3d layers. + stride (tuple): Stride is a two element tuple (temporal, spatial). Default: (1, 1). + downsample (nn.Module | None): Downsample layer. Default: None. + inflate (bool): Whether to inflate kernel. Default: True. + conv_cfg (dict): Config dict for convolution layer. Default: 'dict(type='Conv3d')'. + norm_cfg (dict): Config for norm layers. required keys are 'type'. Default: 'dict(type='BN3d')'. + act_cfg (dict): Config dict for activation layer. Default: 'dict(type='ReLU')'. + """ + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=(1, 1), + downsample=None, + inflate=True, + inflate_style='3x3x3', + conv_cfg=dict(type='Conv3d'), + norm_cfg=dict(type='BN3d'), + act_cfg=dict(type='ReLU')): + super().__init__() + assert inflate_style == '3x3x3' + + self.inplanes = inplanes + self.planes = planes + self.stride = stride + self.inflate = inflate + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + + self.conv1 = ConvModule( + inplanes, + planes, + 3 if self.inflate else (1, 3, 3), + stride=(self.stride[0], self.stride[1], self.stride[1]), + padding=1 if self.inflate else (0, 1, 1), + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.conv2 = ConvModule( + planes, + planes * self.expansion, + 3 if self.inflate else (1, 3, 3), + stride=1, + padding=1 if self.inflate else (0, 1, 1), + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=None) + + self.downsample = downsample + self.relu = build_activation_layer(self.act_cfg) + + def forward(self, x): + """Defines the computation performed at every call.""" + + def _inner_forward(x): + """Forward wrapper for utilizing checkpoint.""" + identity = x + + out = self.conv1(x) + out = self.conv2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out = out + identity + return out + + out = _inner_forward(x) + out = self.relu(out) + + return out + + +class Bottleneck3d(nn.Module): + """Bottleneck 3d block for ResNet3D. + + Args: + inplanes (int): Number of channels for the input in first conv3d layer. + planes (int): Number of channels produced by some norm/conv3d layers. + stride (tuple): Stride is a two element tuple (temporal, spatial). Default: (1, 1). + downsample (nn.Module | None): Downsample layer. Default: None. + inflate (bool): Whether to inflate kernel. Default: True. + inflate_style (str): '3x1x1' or '3x3x3'. which determines the kernel sizes and padding strides + for conv1 and conv2 in each block. Default: '3x1x1'. + conv_cfg (dict): Config dict for convolution layer. Default: 'dict(type='Conv3d')'. + norm_cfg (dict): Config for norm layers. required keys are 'type'. Default: 'dict(type='BN3d')'. + act_cfg (dict): Config dict for activation layer. Default: 'dict(type='ReLU')'. + """ + expansion = 4 + + def __init__(self, + inplanes, + planes, + stride=(1, 1), + downsample=None, + inflate=True, + inflate_style='3x1x1', + conv_cfg=dict(type='Conv3d'), + norm_cfg=dict(type='BN3d'), + act_cfg=dict(type='ReLU')): + super().__init__() + assert inflate_style in ['3x1x1', '3x3x3'] + + self.inplanes = inplanes + self.planes = planes + self.stride = stride + self.inflate = inflate + self.inflate_style = inflate_style + self.norm_cfg = norm_cfg + self.conv_cfg = conv_cfg + self.act_cfg = act_cfg + + mode = 'no_inflate' if not self.inflate else self.inflate_style + conv1_kernel_size = {'no_inflate': 1, '3x1x1': (3, 1, 1), '3x3x3': 1} + conv1_padding = {'no_inflate': 0, '3x1x1': (1, 0, 0), '3x3x3': 0} + conv2_kernel_size = {'no_inflate': (1, 3, 3), '3x1x1': (1, 3, 3), '3x3x3': 3} + conv2_padding = {'no_inflate': (0, 1, 1), '3x1x1': (0, 1, 1), '3x3x3': 1} + + self.conv1 = ConvModule( + inplanes, + planes, + conv1_kernel_size[mode], + stride=1, + padding=conv1_padding[mode], + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.conv2 = ConvModule( + planes, + planes, + conv2_kernel_size[mode], + stride=(self.stride[0], self.stride[1], self.stride[1]), + padding=conv2_padding[mode], + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.conv3 = ConvModule( + planes, + planes * self.expansion, + 1, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + # No activation in the third ConvModule for bottleneck + act_cfg=None) + + self.downsample = downsample + self.relu = build_activation_layer(self.act_cfg) + + def forward(self, x): + """Defines the computation performed at every call.""" + + def _inner_forward(x): + """Forward wrapper for utilizing checkpoint.""" + identity = x + + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out = out + identity + return out + + out = _inner_forward(x) + out = self.relu(out) + + return out + + +@BACKBONES.register_module() +class ResNet3d(nn.Module): + """ResNet 3d backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. Default: 50. + pretrained (str | None): Name of pretrained model. + stage_blocks (tuple | None): Set number of stages for each res layer. Default: None. + pretrained2d (bool): Whether to load pretrained 2D model. Default: True. + in_channels (int): Channel num of input features. Default: 3. + base_channels (int): Channel num of stem output features. Default: 64. + out_indices (tuple[int]): Indices of output feature. Default: (3, ). + num_stages (int): Resnet stages. Default: 4. + spatial_strides (tuple[int]): Spatial strides of residual blocks of each stage. Default: (1, 2, 2, 2). + temporal_strides (tuple[int]): Temporal strides of residual blocks of each stage. Default: (1, 1, 1, 1). + conv1_kernel (tuple[int]): Kernel size of the first conv layer. Default: (3, 7, 7). + conv1_stride (tuple[int]): Stride of the first conv layer. Default: (1, 2). + pool1_stride (tuple[int]): Stride of the first pooling layer. Default: (1, 2). + advanced (bool): Flag indicating if an advanced design for downsample is adopted. Default: False. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means not freezing any parameters. Default: -1. + inflate (tuple[int]): Inflate Dims of each block. Default: (1, 1, 1, 1). + inflate_style (str): '3x1x1' or '3x3x3'. which determines the kernel sizes and padding strides + for conv1 and conv2 in each block. Default: '3x1x1'. + conv_cfg (dict): Config for conv layers. required keys are 'type'. Default: 'dict(type='Conv3d')'. + norm_cfg (dict): Config for norm layers. required keys are 'type' and 'requires_grad'. + Default: 'dict(type='BN3d', requires_grad=True)'. + act_cfg (dict): Config dict for activation layer. Default: 'dict(type='ReLU', inplace=True)'. + norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze running stats (mean and var). + Default: False. + zero_init_residual (bool): Whether to use zero initialization for residual block. Default: True. + """ + + arch_settings = { + 18: (BasicBlock3d, (2, 2, 2, 2)), + 34: (BasicBlock3d, (3, 4, 6, 3)), + 50: (Bottleneck3d, (3, 4, 6, 3)), + 101: (Bottleneck3d, (3, 4, 23, 3)), + 152: (Bottleneck3d, (3, 8, 36, 3)) + } + + def __init__(self, + depth=50, + pretrained=None, + stage_blocks=None, + pretrained2d=True, + in_channels=3, + num_stages=4, + base_channels=64, + out_indices=(3, ), + spatial_strides=(1, 2, 2, 2), + temporal_strides=(1, 1, 1, 1), + conv1_kernel=(3, 7, 7), + conv1_stride=(1, 2), + pool1_stride=(1, 2), + advanced=False, + frozen_stages=-1, + inflate=(1, 1, 1, 1), + inflate_style='3x1x1', + conv_cfg=dict(type='Conv3d'), + norm_cfg=dict(type='BN3d', requires_grad=True), + act_cfg=dict(type='ReLU', inplace=True), + norm_eval=False, + zero_init_residual=True): + super().__init__() + if depth not in self.arch_settings: + raise KeyError(f'invalid depth {depth} for resnet') + self.depth = depth + self.pretrained = pretrained + self.pretrained2d = pretrained2d + self.in_channels = in_channels + self.base_channels = base_channels + self.num_stages = num_stages + assert 1 <= num_stages <= 4 + self.stage_blocks = stage_blocks + self.out_indices = out_indices + assert max(out_indices) < num_stages + self.spatial_strides = spatial_strides + self.temporal_strides = temporal_strides + assert len(spatial_strides) == len(temporal_strides) == num_stages + if self.stage_blocks is not None: + assert len(self.stage_blocks) == num_stages + + self.conv1_kernel = conv1_kernel + self.conv1_stride = conv1_stride + self.pool1_stride = pool1_stride + self.advanced = advanced + self.frozen_stages = frozen_stages + self.stage_inflations = _ntuple(num_stages)(inflate) + self.inflate_style = inflate_style + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.norm_eval = norm_eval + self.zero_init_residual = zero_init_residual + + self.block, stage_blocks = self.arch_settings[depth] + + if self.stage_blocks is None: + self.stage_blocks = stage_blocks[:num_stages] + + self.inplanes = self.base_channels + + self._make_stem_layer() + + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + spatial_stride = spatial_strides[i] + temporal_stride = temporal_strides[i] + planes = self.base_channels * 2**i + res_layer = self.make_res_layer( + self.block, + self.inplanes, + planes, + num_blocks, + stride=(temporal_stride, spatial_stride), + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + act_cfg=self.act_cfg, + advanced=self.advanced, + inflate=self.stage_inflations[i], + inflate_style=self.inflate_style) + self.inplanes = planes * self.block.expansion + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self.feat_dim = self.block.expansion * self.base_channels * 2 ** (len(self.stage_blocks) - 1) + + @staticmethod + def make_res_layer(block, + inplanes, + planes, + blocks, + stride=(1, 1), + inflate=1, + inflate_style='3x1x1', + advanced=False, + norm_cfg=None, + act_cfg=None, + conv_cfg=None): + """Build residual layer for ResNet3D. + + Args: + block (nn.Module): Residual module to be built. + inplanes (int): Number of channels for the input feature in each block. + planes (int): Number of channels for the output feature in each block. + blocks (int): Number of residual blocks. + stride (tuple[int]): Stride (temporal, spatial) in residual and conv layers. Default: (1, 1). + inflate (int | tuple[int]): Determine whether to inflate for each block. Default: 1. + inflate_style (str): '3x1x1' or '3x3x3'. which determines the kernel sizes and padding strides + for conv1 and conv2 in each block. Default: '3x1x1'. + conv_cfg (dict | None): Config for norm layers. Default: None. + norm_cfg (dict | None): Config for norm layers. Default: None. + act_cfg (dict | None): Config for activate layers. Default: None. + + Returns: + nn.Module: A residual layer for the given config. + """ + inflate = inflate if not isinstance(inflate, int) else (inflate, ) * blocks + assert len(inflate) == blocks + downsample = None + if stride[1] != 1 or inplanes != planes * block.expansion: + if advanced: + conv = ConvModule( + inplanes, + planes * block.expansion, + kernel_size=1, + stride=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + pool = nn.AvgPool3d( + kernel_size=(stride[0], stride[1], stride[1]), + stride=(stride[0], stride[1], stride[1]), + ceil_mode=True) + downsample = nn.Sequential(conv, pool) + else: + downsample = ConvModule( + inplanes, + planes * block.expansion, + kernel_size=1, + stride=(stride[0], stride[1], stride[1]), + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + layers = [] + layers.append( + block( + inplanes, + planes, + stride=stride, + downsample=downsample, + inflate=(inflate[0] == 1), + inflate_style=inflate_style, + norm_cfg=norm_cfg, + conv_cfg=conv_cfg, + act_cfg=act_cfg)) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append( + block( + inplanes, + planes, + stride=(1, 1), + inflate=(inflate[i] == 1), + inflate_style=inflate_style, + norm_cfg=norm_cfg, + conv_cfg=conv_cfg, + act_cfg=act_cfg)) + + return nn.Sequential(*layers) + + @staticmethod + def _inflate_conv_params(conv3d, state_dict_2d, module_name_2d, inflated_param_names): + """Inflate a conv module from 2d to 3d. + + Args: + conv3d (nn.Module): The destination conv3d module. + state_dict_2d (OrderedDict): The state dict of pretrained 2d model. + module_name_2d (str): The name of corresponding conv module in the 2d model. + inflated_param_names (list[str]): List of parameters that have been inflated. + """ + weight_2d_name = module_name_2d + '.weight' + + conv2d_weight = state_dict_2d[weight_2d_name] + kernel_t = conv3d.weight.data.shape[2] + + new_weight = conv2d_weight.data.unsqueeze(2).expand_as(conv3d.weight) / kernel_t + conv3d.weight.data.copy_(new_weight) + inflated_param_names.append(weight_2d_name) + + if getattr(conv3d, 'bias') is not None: + bias_2d_name = module_name_2d + '.bias' + conv3d.bias.data.copy_(state_dict_2d[bias_2d_name]) + inflated_param_names.append(bias_2d_name) + + @staticmethod + def _inflate_bn_params(bn3d, state_dict_2d, module_name_2d, + inflated_param_names): + """Inflate a norm module from 2d to 3d. + + Args: + bn3d (nn.Module): The destination bn3d module. + state_dict_2d (OrderedDict): The state dict of pretrained 2d model. + module_name_2d (str): The name of corresponding bn module in the 2d model. + inflated_param_names (list[str]): List of parameters that have been inflated. + """ + for param_name, param in bn3d.named_parameters(): + param_2d_name = f'{module_name_2d}.{param_name}' + param_2d = state_dict_2d[param_2d_name] + if param.data.shape != param_2d.shape: + warnings.warn(f'The parameter of {module_name_2d} is not loaded due to incompatible shapes. ') + return + + param.data.copy_(param_2d) + inflated_param_names.append(param_2d_name) + + for param_name, param in bn3d.named_buffers(): + param_2d_name = f'{module_name_2d}.{param_name}' + # some buffers like num_batches_tracked may not exist in old checkpoints + if param_2d_name in state_dict_2d: + param_2d = state_dict_2d[param_2d_name] + param.data.copy_(param_2d) + inflated_param_names.append(param_2d_name) + + @staticmethod + def _inflate_weights(self, logger): + """Inflate the resnet2d parameters to resnet3d. + + The differences between resnet3d and resnet2d mainly lie in an extra + axis of conv kernel. To utilize the pretrained parameters in 2d model, + the weight of conv2d models should be inflated to fit in the shapes of + the 3d counterpart. + + Args: + logger (logging.Logger): The logger used to print + debugging information. + """ + + state_dict_r2d = _load_checkpoint(self.pretrained) + if 'state_dict' in state_dict_r2d: + state_dict_r2d = state_dict_r2d['state_dict'] + + inflated_param_names = [] + for name, module in self.named_modules(): + if isinstance(module, ConvModule): + # we use a ConvModule to wrap conv+bn+relu layers, thus the name mapping is needed + if 'downsample' in name: + # layer{X}.{Y}.downsample.conv->layer{X}.{Y}.downsample.0 + original_conv_name = name + '.0' + # layer{X}.{Y}.downsample.bn->layer{X}.{Y}.downsample.1 + original_bn_name = name + '.1' + else: + # layer{X}.{Y}.conv{n}.conv->layer{X}.{Y}.conv{n} + original_conv_name = name + # layer{X}.{Y}.conv{n}.bn->layer{X}.{Y}.bn{n} + original_bn_name = name.replace('conv', 'bn') + if original_conv_name + '.weight' not in state_dict_r2d: + logger.warning(f'Module not exist in the state_dict_r2d: {original_conv_name}') + else: + shape_2d = state_dict_r2d[original_conv_name + '.weight'].shape + shape_3d = module.conv.weight.data.shape + if shape_2d != shape_3d[:2] + shape_3d[3:]: + logger.warning(f'Weight shape mismatch for: {original_conv_name}: ' + f'3d weight shape: {shape_3d}; 2d weight shape: {shape_2d}.') + else: + self._inflate_conv_params( + module.conv, state_dict_r2d, original_conv_name, inflated_param_names + ) + + if original_bn_name + '.weight' not in state_dict_r2d: + logger.warning(f'Module not exist in the state_dict_r2d: {original_bn_name}') + else: + self._inflate_bn_params(module.bn, state_dict_r2d, original_bn_name, inflated_param_names) + + # check if any parameters in the 2d checkpoint are not loaded + remaining_names = set(state_dict_r2d.keys()) - set(inflated_param_names) + if remaining_names: + logger.info(f'These parameters in the 2d checkpoint are not loaded: {remaining_names}') + + def inflate_weights(self, logger): + self._inflate_weights(self, logger) + + def _make_stem_layer(self): + """Construct the stem layers consists of a conv+norm+act module and a + pooling layer.""" + self.conv1 = ConvModule( + self.in_channels, + self.base_channels, + kernel_size=self.conv1_kernel, + stride=(self.conv1_stride[0], self.conv1_stride[1], self.conv1_stride[1]), + padding=tuple([(k - 1) // 2 for k in _triple(self.conv1_kernel)]), + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.maxpool = nn.MaxPool3d( + kernel_size=(1, 3, 3), + stride=(self.pool1_stride[0], self.pool1_stride[1], self.pool1_stride[1]), + padding=(0, 1, 1)) + + def _freeze_stages(self): + """Prevent all the parameters from being optimized before + 'self.frozen_stages'.""" + if self.frozen_stages >= 0: + self.conv1.eval() + for param in self.conv1.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = getattr(self, f'layer{i}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + @staticmethod + def _init_weights(self, pretrained=None): + """Initiate the parameters either from existing checkpoint or from + scratch. + + Args: + pretrained (str | None): The path of the pretrained weight. Will override the original 'pretrained' if set. + The arg is added to be compatible with mmdet. Default: None. + """ + for m in self.modules(): + if isinstance(m, nn.Conv3d): + kaiming_init(m) + elif isinstance(m, _BatchNorm): + constant_init(m, 1) + + if self.zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck3d): + constant_init(m.conv3.bn, 0) + elif isinstance(m, BasicBlock3d): + constant_init(m.conv2.bn, 0) + + if pretrained: + self.pretrained = pretrained + if isinstance(self.pretrained, str): + logger = get_root_logger() + logger.info(f'load model from: {self.pretrained}') + + if self.pretrained2d: + self.inflate_weights(logger) + else: + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + + def init_weights(self, pretrained=None): + self._init_weights(self, pretrained) + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + torch.Tensor: The feature of the input + samples extracted by the backbone. + """ + x = self.conv1(x) + x = self.maxpool(x) + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + if len(outs) == 1: + return outs[0] + + return tuple(outs) + + def train(self, mode=True): + """Set the optimization status when training.""" + super().train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() diff --git a/pyskl/models/cnns/resnet3d_slowfast.py b/pyskl/models/cnns/resnet3d_slowfast.py new file mode 100644 index 00000000..04f6b717 --- /dev/null +++ b/pyskl/models/cnns/resnet3d_slowfast.py @@ -0,0 +1,327 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import warnings + +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule, kaiming_init +from mmcv.runner import _load_checkpoint, load_checkpoint +from mmcv.utils import print_log + +from ...utils import get_root_logger +from ..builder import BACKBONES +from .resnet3d import ResNet3d + + +class ResNet3dPathway(ResNet3d): + """A pathway of Slowfast based on ResNet3d. + + Args: + lateral (bool): Determines whether to enable the lateral connection from another pathway. Default: False. + speed_ratio (int): Speed ratio indicating the ratio between time dimension of the fast and slow pathway, + corresponding to the 'alpha' in the paper. Default: 8. + channel_ratio (int): Reduce the channel number of fast pathway by 'channel_ratio', + corresponding to 'beta' in the paper. Default: 8. + fusion_kernel (int): The kernel size of lateral fusion. Default: 7. + **kwargs (keyword arguments): Keywords arguments for ResNet3d. + """ + + def __init__(self, + lateral=False, + speed_ratio=8, + channel_ratio=8, + fusion_kernel=7, + **kwargs): + self.lateral = lateral + self.speed_ratio = speed_ratio + self.channel_ratio = channel_ratio + self.fusion_kernel = fusion_kernel + super().__init__(**kwargs) + self.inplanes = self.base_channels + if self.lateral: + self.conv1_lateral = ConvModule( + self.inplanes // self.channel_ratio, + self.inplanes * 2 // self.channel_ratio, + kernel_size=(fusion_kernel, 1, 1), + stride=(self.speed_ratio, 1, 1), + padding=((fusion_kernel - 1) // 2, 0, 0), + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=None, + act_cfg=None) + + self.lateral_connections = [] + for i in range(len(self.stage_blocks)): + planes = self.base_channels * 2**i + self.inplanes = planes * self.block.expansion + + if lateral and i != self.num_stages - 1: + # no lateral connection needed in final stage + lateral_name = f'layer{(i + 1)}_lateral' + setattr( + self, lateral_name, + ConvModule( + self.inplanes // self.channel_ratio, + self.inplanes * 2 // self.channel_ratio, + kernel_size=(fusion_kernel, 1, 1), + stride=(self.speed_ratio, 1, 1), + padding=((fusion_kernel - 1) // 2, 0, 0), + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=None, + act_cfg=None)) + self.lateral_connections.append(lateral_name) + + def make_res_layer(self, + block, + inplanes, + planes, + blocks, + **kwargs): + """ + Build residual layer for Slowfast. Basically, it's the same as ResNet3d make_res_layer. + However, the inplanes used will be: self.lateral * inplanes * (2 // self.channel_ratio) + inplanes. + + Args: + block (nn.Module): Residual module to be built. + inplanes (int): Number of channels for the input feature in each block. + planes (int): Number of channels for the output feature in each block. + blocks (int): Number of residual blocks. + + Returns: + nn.Module: A residual layer for the given config. + """ + lateral_inplanes = inplanes * 2 // self.channel_ratio if self.lateral else 0 + return super().make_res_layer(block, inplanes + lateral_inplanes, planes, blocks) + + def inflate_weights(self, logger): + """Inflate the resnet2d parameters to resnet3d pathway. + + The differences between resnet3d and resnet2d mainly lie in an extra + axis of conv kernel. To utilize the pretrained parameters in 2d model, + the weight of conv2d models should be inflated to fit in the shapes of + the 3d counterpart. For pathway the 'lateral_connection' part should + not be inflated from 2d weights. + + Args: + logger (logging.Logger): The logger used to print debugging information. + """ + + state_dict_r2d = _load_checkpoint(self.pretrained) + if 'state_dict' in state_dict_r2d: + state_dict_r2d = state_dict_r2d['state_dict'] + + inflated_param_names = [] + for name, module in self.named_modules(): + if 'lateral' in name: + continue + if isinstance(module, ConvModule): + # we use a ConvModule to wrap conv+bn+relu layers, thus the name mapping is needed + if 'downsample' in name: + # layer{X}.{Y}.downsample.conv->layer{X}.{Y}.downsample.0 + original_conv_name = name + '.0' + # layer{X}.{Y}.downsample.bn->layer{X}.{Y}.downsample.1 + original_bn_name = name + '.1' + else: + # layer{X}.{Y}.conv{n}.conv->layer{X}.{Y}.conv{n} + original_conv_name = name + # layer{X}.{Y}.conv{n}.bn->layer{X}.{Y}.bn{n} + original_bn_name = name.replace('conv', 'bn') + if original_conv_name + '.weight' not in state_dict_r2d: + logger.warning(f'Module not exist in the state_dict_r2d: {original_conv_name}') + else: + self._inflate_conv_params(module.conv, state_dict_r2d, original_conv_name, inflated_param_names) + if original_bn_name + '.weight' not in state_dict_r2d: + logger.warning(f'Module not exist in the state_dict_r2d: {original_bn_name}') + else: + self._inflate_bn_params(module.bn, state_dict_r2d, original_bn_name, inflated_param_names) + + # check if any parameters in the 2d checkpoint are not loaded + remaining_names = set(state_dict_r2d.keys()) - set(inflated_param_names) + if remaining_names: + logger.info(f'These parameters in the 2d checkpoint are not loaded: {remaining_names}') + + def _inflate_conv_params(self, conv3d, state_dict_2d, module_name_2d, inflated_param_names): + """Inflate a conv module from 2d to 3d. + + The differences of conv modules betweene 2d and 3d in Pathway + mainly lie in the inplanes due to lateral connections. To fit the + shapes of the lateral connection counterpart, it will expand + parameters by concatting conv2d parameters and extra zero paddings. + + Args: + conv3d (nn.Module): The destination conv3d module. + state_dict_2d (OrderedDict): The state dict of pretrained 2d model. + module_name_2d (str): The name of corresponding conv module in the 2d model. + inflated_param_names (list[str]): List of parameters that have been inflated. + """ + weight_2d_name = module_name_2d + '.weight' + conv2d_weight = state_dict_2d[weight_2d_name] + old_shape = conv2d_weight.shape + new_shape = conv3d.weight.data.shape + kernel_t = new_shape[2] + + if new_shape[1] != old_shape[1]: + if new_shape[1] < old_shape[1]: + warnings.warn(f'The parameter of {module_name_2d} is not' + 'loaded due to incompatible shapes. ') + return + # Inplanes may be different due to lateral connections + new_channels = new_shape[1] - old_shape[1] + pad_shape = old_shape + pad_shape = pad_shape[:1] + (new_channels, ) + pad_shape[2:] + # Expand parameters by concat extra channels + conv2d_weight = torch.cat( + (conv2d_weight, torch.zeros(pad_shape).type_as(conv2d_weight).to(conv2d_weight.device)), dim=1) + + new_weight = conv2d_weight.data.unsqueeze(2).expand_as(conv3d.weight) / kernel_t + conv3d.weight.data.copy_(new_weight) + inflated_param_names.append(weight_2d_name) + + if getattr(conv3d, 'bias') is not None: + bias_2d_name = module_name_2d + '.bias' + conv3d.bias.data.copy_(state_dict_2d[bias_2d_name]) + inflated_param_names.append(bias_2d_name) + + def _freeze_stages(self): + """Prevent all the parameters from being optimized before'self.frozen_stages'. """ + if self.frozen_stages >= 0: + self.conv1.eval() + for param in self.conv1.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = getattr(self, f'layer{i}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + if i != len(self.res_layers) and self.lateral: + # No fusion needed in the final stage + lateral_name = self.lateral_connections[i - 1] + conv_lateral = getattr(self, lateral_name) + conv_lateral.eval() + for param in conv_lateral.parameters(): + param.requires_grad = False + + def init_weights(self, pretrained=None): + """Initiate the parameters either from existing checkpoint or from + scratch.""" + if pretrained: + self.pretrained = pretrained + + super().init_weights() + for module_name in self.lateral_connections: + layer = getattr(self, module_name) + for m in layer.modules(): + if isinstance(m, (nn.Conv3d, nn.Conv2d)): + kaiming_init(m) + + +@BACKBONES.register_module() +class ResNet3dSlowFast(nn.Module): + """Slowfast backbone. + + This module is proposed in `SlowFast Networks for Video Recognition + `_ + + Args: + pretrained (str): The file path to a pretrained model. + resample_rate (int): A large temporal stride 'resample_rate' on input frames. The actual resample rate is + calculated by multipling the 'interval' in 'SampleFrames' in the pipeline with 'resample_rate', equivalent + to the :math:`\\tau` in the paper, i.e. it processes only one out of 'resample_rate * interval' frames. + Default: 8. + speed_ratio (int): Speed ratio indicating the ratio between time dimension of the fast and slow pathway, + corresponding to the :math:`\\alpha` in the paper. Default: 8. + channel_ratio (int): Reduce the channel number of fast pathway by 'channel_ratio', corresponding to + :math:`\\beta` in the paper. Default: 8. + slow_pathway (dict): Configuration of slow branch. + Default: dict(lateral=True, depth=50, conv1_kernel=(1, 7, 7), inflate=(0, 0, 1, 1)) + fast_pathway (dict): Configuration of fast branch. + Default: dict(lateral=False, depth=50, base_channels=8, conv1_kernel=(5, 7, 7)) + """ + + def __init__(self, + pretrained=None, + resample_rate=8, + speed_ratio=8, + channel_ratio=8, + slow_pathway=dict( + depth=50, + lateral=True, + conv1_kernel=(1, 7, 7), + inflate=(0, 0, 1, 1)), + fast_pathway=dict( + depth=50, + lateral=False, + base_channels=8, + conv1_kernel=(5, 7, 7))): + super().__init__() + self.pretrained = pretrained + self.resample_rate = resample_rate + self.speed_ratio = speed_ratio + self.channel_ratio = channel_ratio + + if slow_pathway['lateral']: + slow_pathway['speed_ratio'] = speed_ratio + slow_pathway['channel_ratio'] = channel_ratio + + self.slow_path = ResNet3dPathway(**slow_pathway) + self.fast_path = ResNet3dPathway(**fast_pathway) + + def init_weights(self, pretrained=None): + """Initiate the parameters either from existing checkpoint or from + scratch.""" + if pretrained: + self.pretrained = pretrained + + if isinstance(self.pretrained, str): + logger = get_root_logger() + msg = f'load model from: {self.pretrained}' + print_log(msg, logger=logger) + # Directly load 3D model. + load_checkpoint(self, self.pretrained, strict=True, logger=logger) + elif self.pretrained is None: + # Init two branch separately. + self.fast_path.init_weights() + self.slow_path.init_weights() + else: + raise TypeError('pretrained must be a str or None') + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + tuple[torch.Tensor]: The feature of the input samples extracted by the backbone. + """ + x_slow = nn.functional.interpolate( + x, mode='nearest', scale_factor=(1.0 / self.resample_rate, 1.0, 1.0)) + x_slow = self.slow_path.conv1(x_slow) + x_slow = self.slow_path.maxpool(x_slow) + + x_fast = nn.functional.interpolate( + x, mode='nearest', scale_factor=(1.0 / (self.resample_rate // self.speed_ratio), 1.0, 1.0)) + x_fast = self.fast_path.conv1(x_fast) + x_fast = self.fast_path.maxpool(x_fast) + + if self.slow_path.lateral: + x_fast_lateral = self.slow_path.conv1_lateral(x_fast) + x_slow = torch.cat((x_slow, x_fast_lateral), dim=1) + + for i, layer_name in enumerate(self.slow_path.res_layers): + res_layer = getattr(self.slow_path, layer_name) + x_slow = res_layer(x_slow) + res_layer_fast = getattr(self.fast_path, layer_name) + x_fast = res_layer_fast(x_fast) + if (i != len(self.slow_path.res_layers) - 1 and self.slow_path.lateral): + # No fusion needed in the final stage + lateral_name = self.slow_path.lateral_connections[i] + conv_lateral = getattr(self.slow_path, lateral_name) + x_fast_lateral = conv_lateral(x_fast) + x_slow = torch.cat((x_slow, x_fast_lateral), dim=1) + + out = (x_slow, x_fast) + + return out diff --git a/pyskl/models/cnns/resnet3d_slowonly.py b/pyskl/models/cnns/resnet3d_slowonly.py new file mode 100644 index 00000000..dc86d7c3 --- /dev/null +++ b/pyskl/models/cnns/resnet3d_slowonly.py @@ -0,0 +1,17 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from ..builder import BACKBONES +from .resnet3d import ResNet3d + + +@BACKBONES.register_module() +class ResNet3dSlowOnly(ResNet3d): + """SlowOnly backbone based on ResNet3d. + + Args: + conv1_kernel (tuple[int]): Kernel size of the first conv layer. Default: (1, 7, 7). + inflate (tuple[int]): Inflate Dims of each block. Default: (0, 0, 1, 1). + **kwargs (keyword arguments): Other keywords arguments for 'ResNet3d'. + """ + + def __init__(self, conv1_kernel=(1, 7, 7), inflate=(0, 0, 1, 1), **kwargs): + super().__init__(conv1_kernel=conv1_kernel, inflate=inflate, **kwargs) diff --git a/pyskl/models/cnns/x3d.py b/pyskl/models/cnns/x3d.py new file mode 100644 index 00000000..6ee33cf4 --- /dev/null +++ b/pyskl/models/cnns/x3d.py @@ -0,0 +1,503 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +import torch.nn as nn +from mmcv.cnn import ConvModule, Swish, build_activation_layer, constant_init, kaiming_init +from mmcv.runner import load_checkpoint +from mmcv.utils import _BatchNorm + +from ...utils import get_root_logger +from ..builder import BACKBONES + + +class SEModule(nn.Module): + + def __init__(self, channels, reduction): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool3d(1) + self.bottleneck = self._round_width(channels, reduction) + self.fc1 = nn.Conv3d( + channels, self.bottleneck, kernel_size=1, padding=0) + self.relu = nn.ReLU() + self.fc2 = nn.Conv3d( + self.bottleneck, channels, kernel_size=1, padding=0) + self.sigmoid = nn.Sigmoid() + + @staticmethod + def _round_width(width, multiplier, min_width=8, divisor=8): + width *= multiplier + min_width = min_width or divisor + width_out = max(min_width, + int(width + divisor / 2) // divisor * divisor) + if width_out < 0.9 * width: + width_out += divisor + return int(width_out) + + def forward(self, x): + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return module_input * x + + +class BlockX3D(nn.Module): + """BlockX3D 3d building block for X3D. + + Args: + inplanes (int): Number of channels for the input in first conv3d layer. + planes (int): Number of channels produced by some norm/conv3d layers. + outplanes (int): Number of channels produced by final the conv3d layer. + spatial_stride (int): Spatial stride in the conv3d layer. Default: 1. + downsample (nn.Module | None): Downsample layer. Default: None. + se_ratio (float | None): The reduction ratio of squeeze and excitation + unit. If set as None, it means not using SE unit. Default: None. + use_swish (bool): Whether to use swish as the activation function + before and after the 3x3x3 conv. Default: True. + conv_cfg (dict): Config dict for convolution layer. + Default: ``dict(type='Conv3d')``. + norm_cfg (dict): Config for norm layers. required keys are ``type``, + Default: ``dict(type='BN3d')``. + act_cfg (dict): Config dict for activation layer. + Default: ``dict(type='ReLU')``. + """ + + def __init__(self, + inplanes, + planes, + outplanes, + spatial_stride=1, + downsample=None, + se_ratio=None, + use_swish=True, + conv_cfg=dict(type='Conv3d'), + norm_cfg=dict(type='BN3d'), + act_cfg=dict(type='ReLU')): + super().__init__() + + self.inplanes = inplanes + self.planes = planes + self.outplanes = outplanes + self.spatial_stride = spatial_stride + self.downsample = downsample + self.se_ratio = se_ratio + self.use_swish = use_swish + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.act_cfg_swish = dict(type='Swish') + + self.conv1 = ConvModule( + in_channels=inplanes, + out_channels=planes, + kernel_size=1, + stride=1, + padding=0, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + # Here we use the channel-wise conv + self.conv2 = ConvModule( + in_channels=planes, + out_channels=planes, + kernel_size=3, + stride=(1, self.spatial_stride, self.spatial_stride), + padding=1, + groups=planes, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=None) + + self.swish = Swish() if self.use_swish else nn.Identity() + + self.conv3 = ConvModule( + in_channels=planes, + out_channels=outplanes, + kernel_size=1, + stride=1, + padding=0, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=None) + + if self.se_ratio is not None: + self.se_module = SEModule(planes, self.se_ratio) + + self.relu = build_activation_layer(self.act_cfg) + + def forward(self, x): + """Defines the computation performed at every call.""" + + def _inner_forward(x): + """Forward wrapper for utilizing checkpoint.""" + identity = x + + out = self.conv1(x) + out = self.conv2(out) + if self.se_ratio is not None: + out = self.se_module(out) + + out = self.swish(out) + + out = self.conv3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out = out + identity + return out + + out = _inner_forward(x) + out = self.relu(out) + return out + + +# We do not support initialize with 2D pretrain weight for X3D +@BACKBONES.register_module() +class X3D(nn.Module): + """X3D backbone. https://arxiv.org/pdf/2004.04730.pdf. + + Args: + gamma_w (float): Global channel width expansion factor. Default: 1. + gamma_b (float): Bottleneck channel width expansion factor. Default: 1. + gamma_d (float): Network depth expansion factor. Default: 1. + pretrained (str | None): Name of pretrained model. Default: None. + in_channels (int): Channel num of input features. Default: 3. + num_stages (int): Resnet stages. Default: 4. + spatial_strides (Sequence[int]): + Spatial strides of residual blocks of each stage. + Default: ``(1, 2, 2, 2)``. + frozen_stages (int): Stages to be frozen (all param fixed). If set to + -1, it means not freezing any parameters. Default: -1. + se_style (str): The style of inserting SE modules into BlockX3D, 'half' + denotes insert into half of the blocks, while 'all' denotes insert + into all blocks. Default: 'half'. + se_ratio (float | None): The reduction ratio of squeeze and excitation + unit. If set as None, it means not using SE unit. Default: 1 / 16. + use_swish (bool): Whether to use swish as the activation function + before and after the 3x3x3 conv. Default: True. + conv_cfg (dict): Config for conv layers. required keys are ``type`` + Default: ``dict(type='Conv3d')``. + norm_cfg (dict): Config for norm layers. required keys are ``type`` and + ``requires_grad``. + Default: ``dict(type='BN3d', requires_grad=True)``. + act_cfg (dict): Config dict for activation layer. + Default: ``dict(type='ReLU', inplace=True)``. + norm_eval (bool): Whether to set BN layers to eval mode, namely, freeze + running stats (mean and var). Default: False. + zero_init_residual (bool): + Whether to use zero initialization for residual block, + Default: True. + kwargs (dict, optional): Key arguments for "make_res_layer". + """ + + def __init__(self, + gamma_w=1.0, + gamma_b=2.25, + gamma_d=2.2, + pretrained=None, + in_channels=3, + base_channels=24, + num_stages=4, + stage_blocks=(1, 2, 5, 3), + spatial_strides=(2, 2, 2, 2), + frozen_stages=-1, + se_style='half', + se_ratio=1 / 16, + use_swish=True, + conv_cfg=dict(type='Conv3d'), + norm_cfg=dict(type='BN3d', requires_grad=True), + act_cfg=dict(type='ReLU', inplace=True), + norm_eval=False, + zero_init_residual=True, + **kwargs): + super().__init__() + self.gamma_w = gamma_w + self.gamma_b = gamma_b + self.gamma_d = gamma_d + + self.pretrained = pretrained + self.in_channels = in_channels + # Hard coded, can be changed by gamma_w + self.base_channels = base_channels + self.stage_blocks = stage_blocks + + # apply parameters gamma_w and gamma_d + self.base_channels = self._round_width(self.base_channels, + self.gamma_w) + + self.stage_blocks = [ + self._round_repeats(x, self.gamma_d) for x in self.stage_blocks + ] + + self.num_stages = num_stages + assert 1 <= num_stages <= 4 + self.spatial_strides = spatial_strides + assert len(spatial_strides) == num_stages + self.frozen_stages = frozen_stages + + self.se_style = se_style + assert self.se_style in ['all', 'half'] + self.se_ratio = se_ratio + assert (self.se_ratio is None) or (self.se_ratio > 0) + self.use_swish = use_swish + + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.norm_eval = norm_eval + self.zero_init_residual = zero_init_residual + + self.block = BlockX3D + self.stage_blocks = self.stage_blocks[:num_stages] + self.layer_inplanes = self.base_channels + self._make_stem_layer() + + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + spatial_stride = spatial_strides[i] + inplanes = self.base_channels * 2**i + planes = int(inplanes * self.gamma_b) + + res_layer = self.make_res_layer( + self.block, + self.layer_inplanes, + inplanes, + planes, + num_blocks, + spatial_stride=spatial_stride, + se_style=self.se_style, + se_ratio=self.se_ratio, + use_swish=self.use_swish, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + act_cfg=self.act_cfg, + **kwargs) + self.layer_inplanes = inplanes + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self.feat_dim = self.base_channels * 2**(len(self.stage_blocks) - 1) + self.conv5 = ConvModule( + self.feat_dim, + int(self.feat_dim * self.gamma_b), + kernel_size=1, + stride=1, + padding=0, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.feat_dim = int(self.feat_dim * self.gamma_b) + + @staticmethod + def _round_width(width, multiplier, min_depth=8, divisor=8): + """Round width of filters based on width multiplier.""" + if not multiplier: + return width + + width *= multiplier + min_depth = min_depth or divisor + new_filters = max(min_depth, + int(width + divisor / 2) // divisor * divisor) + if new_filters < 0.9 * width: + new_filters += divisor + return int(new_filters) + + @staticmethod + def _round_repeats(repeats, multiplier): + """Round number of layers based on depth multiplier.""" + if not multiplier: + return repeats + return int(math.ceil(multiplier * repeats)) + + # the module is parameterized with gamma_b + # no temporal_stride + def make_res_layer(self, + block, + layer_inplanes, + inplanes, + planes, + blocks, + spatial_stride=1, + se_style='half', + se_ratio=None, + use_swish=True, + norm_cfg=None, + act_cfg=None, + conv_cfg=None, + **kwargs): + """Build residual layer for ResNet3D. + + Args: + block (nn.Module): Residual module to be built. + layer_inplanes (int): Number of channels for the input feature + of the res layer. + inplanes (int): Number of channels for the input feature in each + block, which equals to base_channels * gamma_w. + planes (int): Number of channels for the output feature in each + block, which equals to base_channel * gamma_w * gamma_b. + blocks (int): Number of residual blocks. + spatial_stride (int): Spatial strides in residual and conv layers. + Default: 1. + se_style (str): The style of inserting SE modules into BlockX3D, + 'half' denotes insert into half of the blocks, while 'all' + denotes insert into all blocks. Default: 'half'. + se_ratio (float | None): The reduction ratio of squeeze and + excitation unit. If set as None, it means not using SE unit. + Default: None. + use_swish (bool): Whether to use swish as the activation function + before and after the 3x3x3 conv. Default: True. + conv_cfg (dict | None): Config for norm layers. Default: None. + norm_cfg (dict | None): Config for norm layers. Default: None. + act_cfg (dict | None): Config for activate layers. Default: None. + + Returns: + nn.Module: A residual layer for the given config. + """ + downsample = None + if spatial_stride != 1 or layer_inplanes != inplanes: + downsample = ConvModule( + layer_inplanes, + inplanes, + kernel_size=1, + stride=(1, spatial_stride, spatial_stride), + padding=0, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + use_se = [False] * blocks + if self.se_style == 'all': + use_se = [True] * blocks + elif self.se_style == 'half': + use_se = [i % 2 == 0 for i in range(blocks)] + else: + raise NotImplementedError + + layers = [] + layers.append( + block( + layer_inplanes, + planes, + inplanes, + spatial_stride=spatial_stride, + downsample=downsample, + se_ratio=se_ratio if use_se[0] else None, + use_swish=use_swish, + norm_cfg=norm_cfg, + conv_cfg=conv_cfg, + act_cfg=act_cfg, + **kwargs)) + + for i in range(1, blocks): + layers.append( + block( + inplanes, + planes, + inplanes, + spatial_stride=1, + se_ratio=se_ratio if use_se[i] else None, + use_swish=use_swish, + norm_cfg=norm_cfg, + conv_cfg=conv_cfg, + act_cfg=act_cfg, + **kwargs)) + + return nn.Sequential(*layers) + + def _make_stem_layer(self): + """Construct the stem layers consists of a conv+norm+act module and a + pooling layer.""" + self.conv1_s = ConvModule( + self.in_channels, + self.base_channels, + kernel_size=(1, 3, 3), + stride=(1, 2, 2), + padding=(0, 1, 1), + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=None, + act_cfg=None) + self.conv1_t = ConvModule( + self.base_channels, + self.base_channels, + kernel_size=(5, 1, 1), + stride=(1, 1, 1), + padding=(2, 0, 0), + groups=self.base_channels, + bias=False, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + def _freeze_stages(self): + """Prevent all the parameters from being optimized before + ``self.frozen_stages``.""" + if self.frozen_stages >= 0: + self.conv1_s.eval() + self.conv1_t.eval() + for param in self.conv1_s.parameters(): + param.requires_grad = False + for param in self.conv1_t.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = getattr(self, f'layer{i}') + m.eval() + for param in m.parameters(): + param.requires_grad = False + + def init_weights(self): + """Initiate the parameters either from existing checkpoint or from + scratch.""" + for m in self.modules(): + if isinstance(m, nn.Conv3d): + kaiming_init(m) + elif isinstance(m, _BatchNorm): + constant_init(m, 1) + + if self.zero_init_residual: + for m in self.modules(): + if isinstance(m, BlockX3D): + constant_init(m.conv3.bn, 0) + + if isinstance(self.pretrained, str): + logger = get_root_logger() + logger.info(f'load model from: {self.pretrained}') + + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + torch.Tensor: The feature of the input + samples extracted by the backbone. + """ + x = self.conv1_s(x) + x = self.conv1_t(x) + for layer_name in self.res_layers: + res_layer = getattr(self, layer_name) + x = res_layer(x) + x = self.conv5(x) + return x + + def train(self, mode=True): + """Set the optimization status when training.""" + super().train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + if isinstance(m, _BatchNorm): + m.eval() diff --git a/pyskl/models/gcns/__init__.py b/pyskl/models/gcns/__init__.py new file mode 100644 index 00000000..82248441 --- /dev/null +++ b/pyskl/models/gcns/__init__.py @@ -0,0 +1,4 @@ +from .stgcn import STGCN +from .utils import mstcn, unit_gcn, unit_tcn + +__all__ = ['unit_gcn', 'unit_tcn', 'mstcn', 'STGCN'] diff --git a/pyskl/models/gcns/stgcn.py b/pyskl/models/gcns/stgcn.py new file mode 100644 index 00000000..42c77c5f --- /dev/null +++ b/pyskl/models/gcns/stgcn.py @@ -0,0 +1,145 @@ +import copy as cp + +import torch +import torch.nn as nn +from mmcv.runner import load_checkpoint + +from ...utils import Graph +from ..builder import BACKBONES +from .utils import mstcn, unit_gcn, unit_tcn + +EPS = 1e-4 + + +class STGCNBlock(nn.Module): + + def __init__(self, + in_channels, + out_channels, + A, + stride=1, + residual=True, + **kwargs): + super().__init__() + # prepare kwargs for gcn and tcn + common_args = ['norm'] + for arg in common_args: + if arg in kwargs: + value = kwargs.pop(arg) + kwargs['tcn_' + arg] = value + kwargs['gcn_' + arg] = value + + gcn_kwargs = {k[4:]: v for k, v in kwargs.items() if k[:4] == 'gcn_'} + tcn_kwargs = {k[4:]: v for k, v in kwargs.items() if k[:4] == 'tcn_'} + kwargs = {k: v for k, v in kwargs.items() if k[:4] not in ['gcn_', 'tcn_']} + assert len(kwargs) == 0, f'Invalid arguments: {kwargs}' + + tcn_type = tcn_kwargs.pop('type', 'unit_tcn') + assert tcn_type in ['unit_tcn', 'mstcn'] + gcn_type = gcn_kwargs.pop('type', 'unit_gcn') + assert gcn_type in ['unit_gcn'] + + self.gcn = unit_gcn(in_channels, out_channels, A, **gcn_kwargs) + + if tcn_type == 'unit_tcn': + self.tcn = unit_tcn(out_channels, out_channels, 9, stride=stride, **tcn_kwargs) + elif tcn_type == 'mstcn': + self.tcn = mstcn(out_channels, out_channels, stride=stride, **tcn_kwargs) + self.relu = nn.ReLU() + + if not residual: + self.residual = lambda x: 0 + elif (in_channels == out_channels) and (stride == 1): + self.residual = lambda x: x + else: + self.residual = unit_tcn(in_channels, out_channels, kernel_size=1, stride=stride) + + def forward(self, x, A=None): + """Defines the computation performed at every call.""" + res = self.residual(x) + x = self.tcn(self.gcn(x, A)) + res + return self.relu(x) + + +@BACKBONES.register_module() +class STGCN(nn.Module): + + def __init__(self, + graph_cfg, + in_channels=3, + base_channels=64, + ch_ratio=2, + num_stages=10, + inflate_stages=[5, 8], + down_stages=[5, 8], + data_bn_type='VC', + num_person=2, # * Only used when data_bn_type == 'MVC' + pretrained=None, + **kwargs): + super().__init__() + + self.graph = Graph(**graph_cfg) + A = torch.tensor(self.graph.A, dtype=torch.float32, requires_grad=False) + self.data_bn_type = data_bn_type + self.kwargs = kwargs + + if data_bn_type == 'MVC': + self.data_bn = nn.BatchNorm1d(num_person * in_channels * A.size(1)) + elif data_bn_type == 'VC': + self.data_bn = nn.BatchNorm1d(in_channels * A.size(1)) + else: + self.data_bn = nn.Identity() + + lw_kwargs = [cp.deepcopy(kwargs) for i in range(num_stages)] + for k, v in kwargs.items(): + if isinstance(v, tuple) and len(v) == num_stages: + for i in range(num_stages): + lw_kwargs[i][k] = v[i] + lw_kwargs[0].pop('tcn_dropout', None) + + self.in_channels = in_channels + self.base_channels = base_channels + self.ch_ratio = ch_ratio + self.inflate_stages = inflate_stages + self.down_stages = down_stages + + modules = [] + if self.in_channels != self.base_channels: + modules = [STGCNBlock(in_channels, base_channels, A.clone(), 1, residual=False, **lw_kwargs[0])] + + inflate_times = 0 + for i in range(2, num_stages + 1): + stride = 1 + (i in down_stages) + in_channels = base_channels + res_channels = 0 + if i in inflate_stages: + inflate_times += 1 + out_channels = int(self.base_channels * self.ch_ratio ** inflate_times + EPS) + base_channels = out_channels + modules.append(STGCNBlock(in_channels + res_channels, out_channels, A.clone(), stride, **lw_kwargs[i - 1])) + + if self.in_channels == self.base_channels: + num_stages -= 1 + + self.num_stages = num_stages + self.gcn = nn.ModuleList(modules) + self.pretrained = pretrained + + def init_weights(self): + if isinstance(self.pretrained, str): + load_checkpoint(self, self.pretrained, strict=False) + + def forward(self, x): + N, M, T, V, C = x.size() + x = x.permute(0, 1, 3, 4, 2).contiguous() + if self.data_bn_type == 'MVC': + x = self.data_bn(x.view(N, M * V * C, T)) + else: + x = self.data_bn(x.view(N * M, V * C, T)) + x = x.view(N, M, V, C, T).permute(0, 1, 3, 4, 2).contiguous().view(N * M, C, T, V) + + for i in range(self.num_stages): + x = self.gcn[i](x) + + x = x.reshape((N, M) + x.shape[1:]) + return x diff --git a/pyskl/models/gcns/utils/__init__.py b/pyskl/models/gcns/utils/__init__.py new file mode 100644 index 00000000..78d29052 --- /dev/null +++ b/pyskl/models/gcns/utils/__init__.py @@ -0,0 +1,4 @@ +from .gcn import unit_gcn +from .tcn import mstcn, unit_tcn + +__all__ = ['unit_gcn', 'unit_tcn', 'mstcn'] diff --git a/pyskl/models/gcns/utils/gcn.py b/pyskl/models/gcns/utils/gcn.py new file mode 100644 index 00000000..7042ed0c --- /dev/null +++ b/pyskl/models/gcns/utils/gcn.py @@ -0,0 +1,79 @@ +import torch +import torch.nn as nn +from mmcv.cnn import build_activation_layer, build_norm_layer + +EPS = 1e-4 + + +class unit_gcn(nn.Module): + + def __init__(self, + in_channels, + out_channels, + A, + adaptive='importance', + conv_pos='pre', + with_res=False, + norm='BN', + act='ReLU'): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.num_subsets = A.size(0) + + assert adaptive in [None, 'init', 'offset', 'importance'] + self.adaptive = adaptive + assert conv_pos in ['pre', 'post'] + self.conv_pos = conv_pos + self.with_res = with_res + + self.norm_cfg = norm if isinstance(norm, dict) else dict(type=norm) + self.act_cfg = act if isinstance(act, dict) else dict(type=act) + self.bn = build_norm_layer(self.norm_cfg, out_channels)[1] + self.act = build_activation_layer(self.act_cfg) + + if self.adaptive == 'init': + self.A = nn.Parameter(A.clone()) + else: + self.register_buffer('A', A) + + if self.adaptive in ['offset', 'importance']: + self.PA = nn.Parameter(A.clone()) + if self.adaptive == 'offset': + nn.init.uniform_(self.PA, -1e-6, 1e-6) + elif self.adaptive == 'importance': + nn.init.constant_(self.PA, 1) + + if self.conv_pos == 'pre': + self.conv = nn.Conv2d(in_channels, out_channels * A.size(0), 1) + elif self.conv_pos == 'post': + self.conv = nn.Conv2d(A.size(0) * in_channels, out_channels, 1) + + if self.with_res: + if in_channels != out_channels: + self.down = nn.Sequential( + nn.Conv2d(in_channels, out_channels, 1), + build_norm_layer(self.norm_cfg, out_channels)[1]) + else: + self.down = lambda x: x + + def forward(self, x, A=None): + """Defines the computation performed at every call.""" + n, c, t, v = x.shape + res = self.down(x) if self.with_res else 0 + + A_switch = {None: self.A, 'init': self.A} + if hasattr(self, 'PA'): + A_switch.update({'offset': self.A + self.PA, 'importance': self.A * self.PA}) + A = A_switch[self.adaptive] + + if self.conv_pos == 'pre': + x = self.conv(x) + x = x.view(n, self.num_subsets, -1, t, v) + x = torch.einsum('nkctv,kvw->nctw', (x, A)).contiguous() + elif self.conv_pos == 'post': + x = torch.einsum('nctv,kvw->nkctw', (x, A)).contiguous() + x = x.view(n, -1, t, v) + x = self.conv(x) + + return self.act(self.bn(x) + res) diff --git a/pyskl/models/gcns/utils/tcn.py b/pyskl/models/gcns/utils/tcn.py new file mode 100644 index 00000000..3aea8d1c --- /dev/null +++ b/pyskl/models/gcns/utils/tcn.py @@ -0,0 +1,105 @@ +import torch +import torch.nn as nn +from mmcv.cnn import build_norm_layer + + +class unit_tcn(nn.Module): + + def __init__(self, in_channels, out_channels, kernel_size=9, stride=1, dilation=1, norm='BN', dropout=0): + + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.norm_cfg = norm if isinstance(norm, dict) else dict(type=norm) + pad = (kernel_size + (kernel_size - 1) * (dilation - 1) - 1) // 2 + + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=(kernel_size, 1), + padding=(pad, 0), + stride=(stride, 1), + dilation=(dilation, 1)) + self.bn = build_norm_layer(self.norm_cfg, out_channels)[1] if norm is not None else nn.Identity() + self.drop = nn.Dropout(dropout, inplace=True) + self.stride = stride + + def forward(self, x): + return self.drop(self.bn(self.conv(x))) + + +class mstcn(nn.Module): + + def __init__(self, + in_channels, + out_channels, + mid_channels=None, + dropout=0., + ms_cfg=[(3, 1), (3, 2), (3, 3), (3, 4), ('max', 3), '1x1'], + stride=1): + + super().__init__() + # Multiple branches of temporal convolution + self.ms_cfg = ms_cfg + num_branches = len(ms_cfg) + self.num_branches = num_branches + self.in_channels = in_channels + self.out_channels = out_channels + self.act = nn.ReLU() + + if mid_channels is None: + mid_channels = out_channels // num_branches + rem_mid_channels = out_channels - mid_channels * (num_branches - 1) + else: + assert isinstance(mid_channels, float) and mid_channels > 0 + mid_channels = int(out_channels * mid_channels) + rem_mid_channels = mid_channels + + self.mid_channels = mid_channels + self.rem_mid_channels = rem_mid_channels + + branches = [] + for i, cfg in enumerate(ms_cfg): + branch_c = rem_mid_channels if i == 0 else mid_channels + if cfg == '1x1': + branches.append(nn.Conv2d(in_channels, branch_c, kernel_size=1, stride=(stride, 1))) + continue + assert isinstance(cfg, tuple) + if cfg[0] == 'max': + branches.append( + nn.Sequential( + nn.Conv2d(in_channels, branch_c, kernel_size=1), nn.BatchNorm2d(branch_c), self.act, + nn.MaxPool2d(kernel_size=(cfg[1], 1), stride=(stride, 1), padding=(1, 0)))) + continue + assert isinstance(cfg[0], int) and isinstance(cfg[1], int) + branch = nn.Sequential( + nn.Conv2d(in_channels, branch_c, kernel_size=1), nn.BatchNorm2d(branch_c), self.act, + unit_tcn(branch_c, branch_c, kernel_size=cfg[0], stride=stride, dilation=cfg[1], norm=None)) + branches.append(branch) + + self.branches = nn.ModuleList(branches) + tin_channels = mid_channels * (num_branches - 1) + rem_mid_channels + + self.transform = nn.Sequential( + nn.BatchNorm2d(tin_channels), self.act, nn.Conv2d(tin_channels, out_channels, kernel_size=1)) + + self.bn = nn.BatchNorm2d(out_channels) + self.drop = nn.Dropout(dropout, inplace=True) + + def inner_forward(self, x): + N, C, T, V = x.shape + + branch_outs = [] + for tempconv in self.branches: + out = tempconv(x) + branch_outs.append(out) + + feat = torch.cat(branch_outs, dim=1) + feat = self.transform(feat) + return feat + + def forward(self, x): + out = self.inner_forward(x) + out = self.bn(out) + return self.drop(out) diff --git a/pyskl/models/heads/__init__.py b/pyskl/models/heads/__init__.py new file mode 100644 index 00000000..1958f674 --- /dev/null +++ b/pyskl/models/heads/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# flake8: noqa: F401 +from .base import BaseHead +from .simple_head import GCNHead, I3DHead, SimpleHead, SlowFastHead, TSNHead diff --git a/pyskl/models/heads/base.py b/pyskl/models/heads/base.py new file mode 100644 index 00000000..3c862664 --- /dev/null +++ b/pyskl/models/heads/base.py @@ -0,0 +1,88 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from abc import ABCMeta, abstractmethod + +import torch +import torch.nn as nn + +from ...core import top_k_accuracy +from ..builder import build_loss + + +class BaseHead(nn.Module, metaclass=ABCMeta): + """Base class for head. + + All Head should subclass it. + All subclass should overwrite: + - Methods:``init_weights``, initializing weights in some modules. + - Methods:``forward``, supporting to forward both for training and testing. + + Args: + num_classes (int): Number of classes to be classified. + in_channels (int): Number of channels in input feature. + loss_cls (dict): Config for building loss. + Default: dict(type='CrossEntropyLoss', loss_weight=1.0). + multi_class (bool): Determines whether it is a multi-class + recognition task. Default: False. + label_smooth_eps (float): Epsilon used in label smooth. + Reference: arxiv.org/abs/1906.02629. Default: 0. + """ + + def __init__(self, + num_classes, + in_channels, + loss_cls=dict(type='CrossEntropyLoss', loss_weight=1.0), + multi_class=False, + label_smooth_eps=0.0): + super().__init__() + self.num_classes = num_classes + self.in_channels = in_channels + self.loss_cls = build_loss(loss_cls) + self.multi_class = multi_class + self.label_smooth_eps = label_smooth_eps + + @abstractmethod + def init_weights(self): + """Initiate the parameters either from existing checkpoint or from + scratch.""" + + @abstractmethod + def forward(self, x): + """Defines the computation performed at every call.""" + + def loss(self, cls_score, label, **kwargs): + """Calculate the loss given output ``cls_score``, target ``label``. + + Args: + cls_score (torch.Tensor): The output of the model. + label (torch.Tensor): The target output of the model. + + Returns: + dict: A dict containing field 'loss_cls'(mandatory) + and 'top1_acc', 'top5_acc'(optional). + """ + losses = dict() + if label.shape == torch.Size([]): + label = label.unsqueeze(0) + elif label.dim() == 1 and label.size()[0] == self.num_classes \ + and cls_score.size()[0] == 1: + label = label.unsqueeze(0) + + if not self.multi_class and cls_score.size() != label.size(): + top_k_acc = top_k_accuracy(cls_score.detach().cpu().numpy(), + label.detach().cpu().numpy(), (1, 5)) + losses['top1_acc'] = torch.tensor( + top_k_acc[0], device=cls_score.device) + losses['top5_acc'] = torch.tensor( + top_k_acc[1], device=cls_score.device) + + elif self.multi_class and self.label_smooth_eps != 0: + label = ((1 - self.label_smooth_eps) * label + self.label_smooth_eps / self.num_classes) + + loss_cls = self.loss_cls(cls_score, label, **kwargs) + # loss_cls may be dictionary or single tensor + if isinstance(loss_cls, dict): + losses.update(loss_cls) + else: + losses['loss_cls'] = loss_cls + + return losses diff --git a/pyskl/models/heads/simple_head.py b/pyskl/models/heads/simple_head.py new file mode 100644 index 00000000..2c6e73d6 --- /dev/null +++ b/pyskl/models/heads/simple_head.py @@ -0,0 +1,156 @@ +import torch +import torch.nn as nn +from mmcv.cnn import normal_init + +from ..builder import HEADS +from .base import BaseHead + + +@HEADS.register_module() +class SimpleHead(BaseHead): + """ A simple classification head. + + Args: + num_classes (int): Number of classes to be classified. + in_channels (int): Number of channels in input feature. + loss_cls (dict): Config for building loss. Default: dict(type='CrossEntropyLoss') + dropout (float): Probability of dropout layer. Default: 0.5. + init_std (float): Std value for Initiation. Default: 0.01. + kwargs (dict, optional): Any keyword argument to be used to initialize + the head. + """ + + def __init__(self, + num_classes, + in_channels, + loss_cls=dict(type='CrossEntropyLoss'), + dropout=0.5, + init_std=0.01, + mode='3D', + **kwargs): + super().__init__(num_classes, in_channels, loss_cls, **kwargs) + + self.dropout_ratio = dropout + self.init_std = init_std + if self.dropout_ratio != 0: + self.dropout = nn.Dropout(p=self.dropout_ratio) + else: + self.dropout = None + assert mode in ['3D', 'GCN', '2D'] + self.mode = mode + + self.in_c = in_channels + self.fc_cls = nn.Linear(self.in_c, num_classes) + + def init_weights(self): + """Initiate the parameters from scratch.""" + normal_init(self.fc_cls, std=self.init_std) + + def forward(self, x): + """Defines the computation performed at every call. + + Args: + x (torch.Tensor): The input data. + + Returns: + torch.Tensor: The classification scores for input samples. + """ + + if isinstance(x, list): + for item in x: + assert len(item.shape) == 2 + x = [item.mean(dim=0) for item in x] + x = torch.stack(x) + + if len(x.shape) != 2: + if self.mode == '2D': + assert len(x.shape) == 5 + N, S, C, H, W = x.shape + pool = nn.AdaptiveAvgPool2d(1) + x = x.reshape(N * S, C, H, W) + x = pool(x) + x = x.reshape(N, S, C) + x = x.mean(dim=1) + if self.mode == '3D': + pool = nn.AdaptiveAvgPool3d(1) + if isinstance(x, tuple) or isinstance(x, list): + x = torch.cat(x, dim=1) + x = pool(x) + x = x.view(x.shape[:2]) + if self.mode == 'GCN': + pool = nn.AdaptiveAvgPool2d(1) + N, M, C, T, V = x.shape + x = x.reshape(N * M, C, T, V) + + x = pool(x) + x = x.reshape(N, M, C) + x = x.mean(dim=1) + + assert x.shape[1] == self.in_c + if self.dropout is not None: + x = self.dropout(x) + + cls_score = self.fc_cls(x) + return cls_score + + +@HEADS.register_module() +class I3DHead(SimpleHead): + + def __init__(self, + num_classes, + in_channels, + loss_cls=dict(type='CrossEntropyLoss'), + dropout=0.5, + init_std=0.01, + **kwargs): + super().__init__(num_classes, + in_channels, + loss_cls=loss_cls, + dropout=dropout, + init_std=init_std, + mode='3D', + **kwargs) + + +@HEADS.register_module() +class SlowFastHead(I3DHead): + pass + + +@HEADS.register_module() +class GCNHead(SimpleHead): + + def __init__(self, + num_classes, + in_channels, + loss_cls=dict(type='CrossEntropyLoss'), + dropout=0., + init_std=0.01, + **kwargs): + super().__init__(num_classes, + in_channels, + loss_cls=loss_cls, + dropout=dropout, + init_std=init_std, + mode='GCN', + **kwargs) + + +@HEADS.register_module() +class TSNHead(BaseHead): + + def __init__(self, + num_classes, + in_channels, + loss_cls=dict(type='CrossEntropyLoss'), + dropout=0.5, + init_std=0.01, + **kwargs): + super().__init__(num_classes, + in_channels, + loss_cls=loss_cls, + dropout=dropout, + init_std=init_std, + mode='2D', + **kwargs) diff --git a/pyskl/models/losses/__init__.py b/pyskl/models/losses/__init__.py new file mode 100644 index 00000000..e398ae5a --- /dev/null +++ b/pyskl/models/losses/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# flake8: noqa: F401 + +from .base import BaseWeightedLoss +from .cross_entropy_loss import BCELossWithLogits, CrossEntropyLoss diff --git a/pyskl/models/losses/base.py b/pyskl/models/losses/base.py new file mode 100644 index 00000000..9e1df07d --- /dev/null +++ b/pyskl/models/losses/base.py @@ -0,0 +1,45 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from abc import ABCMeta, abstractmethod + +import torch.nn as nn + + +class BaseWeightedLoss(nn.Module, metaclass=ABCMeta): + """Base class for loss. + + All subclass should overwrite the ``_forward()`` method which returns the + normal loss without loss weights. + + Args: + loss_weight (float): Factor scalar multiplied on the loss. + Default: 1.0. + """ + + def __init__(self, loss_weight=1.0): + super().__init__() + self.loss_weight = loss_weight + + @abstractmethod + def _forward(self, *args, **kwargs): + pass + + def forward(self, *args, **kwargs): + """Defines the computation performed at every call. + + Args: + *args: The positional arguments for the corresponding + loss. + **kwargs: The keyword arguments for the corresponding + loss. + + Returns: + torch.Tensor: The calculated loss. + """ + ret = self._forward(*args, **kwargs) + if isinstance(ret, dict): + for k in ret: + if 'loss' in k: + ret[k] *= self.loss_weight + else: + ret *= self.loss_weight + return ret diff --git a/pyskl/models/losses/cross_entropy_loss.py b/pyskl/models/losses/cross_entropy_loss.py new file mode 100644 index 00000000..5c84c6b4 --- /dev/null +++ b/pyskl/models/losses/cross_entropy_loss.py @@ -0,0 +1,121 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +import torch.nn.functional as F + +from ..builder import LOSSES +from .base import BaseWeightedLoss + + +@LOSSES.register_module() +class CrossEntropyLoss(BaseWeightedLoss): + """Cross Entropy Loss. + + Support two kinds of labels and their corresponding loss type. It's worth + mentioning that loss type will be detected by the shape of ``cls_score`` + and ``label``. + 1) Hard label: This label is an integer array and all of the elements are + in the range [0, num_classes - 1]. This label's shape should be + ``cls_score``'s shape with the `num_classes` dimension removed. + 2) Soft label(probablity distribution over classes): This label is a + probability distribution and all of the elements are in the range + [0, 1]. This label's shape must be the same as ``cls_score``. For now, + only 2-dim soft label is supported. + + Args: + loss_weight (float): Factor scalar multiplied on the loss. + Default: 1.0. + class_weight (list[float] | None): Loss weight for each class. If set + as None, use the same weight 1 for all classes. Only applies + to CrossEntropyLoss and BCELossWithLogits (should not be set when + using other losses). Default: None. + """ + + def __init__(self, loss_weight=1.0, class_weight=None): + super().__init__(loss_weight=loss_weight) + self.class_weight = None + if class_weight is not None: + self.class_weight = torch.Tensor(class_weight) + + def _forward(self, cls_score, label, **kwargs): + """Forward function. + + Args: + cls_score (torch.Tensor): The class score. + label (torch.Tensor): The ground truth label. + kwargs: Any keyword argument to be used to calculate + CrossEntropy loss. + + Returns: + torch.Tensor: The returned CrossEntropy loss. + """ + if cls_score.size() == label.size(): + # calculate loss for soft label + + assert cls_score.dim() == 2, 'Only support 2-dim soft label' + assert len(kwargs) == 0, \ + ('For now, no extra args are supported for soft label, ' + f'but get {kwargs}') + + lsm = F.log_softmax(cls_score, 1) + if self.class_weight is not None: + self.class_weight = self.class_weight.to(cls_score.device) + lsm = lsm * self.class_weight.unsqueeze(0) + loss_cls = -(label * lsm).sum(1) + + # default reduction 'mean' + if self.class_weight is not None: + # Use weighted average as pytorch CrossEntropyLoss does. + # For more information, please visit https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html # noqa + loss_cls = loss_cls.sum() / torch.sum( + self.class_weight.unsqueeze(0) * label) + else: + loss_cls = loss_cls.mean() + else: + # calculate loss for hard label + + if self.class_weight is not None: + assert 'weight' not in kwargs, \ + "The key 'weight' already exists." + kwargs['weight'] = self.class_weight.to(cls_score.device) + loss_cls = F.cross_entropy(cls_score, label, **kwargs) + + return loss_cls + + +@LOSSES.register_module() +class BCELossWithLogits(BaseWeightedLoss): + """Binary Cross Entropy Loss with logits. + + Args: + loss_weight (float): Factor scalar multiplied on the loss. + Default: 1.0. + class_weight (list[float] | None): Loss weight for each class. If set + as None, use the same weight 1 for all classes. Only applies + to CrossEntropyLoss and BCELossWithLogits (should not be set when + using other losses). Default: None. + """ + + def __init__(self, loss_weight=1.0, class_weight=None): + super().__init__(loss_weight=loss_weight) + self.class_weight = None + if class_weight is not None: + self.class_weight = torch.Tensor(class_weight) + + def _forward(self, cls_score, label, **kwargs): + """Forward function. + + Args: + cls_score (torch.Tensor): The class score. + label (torch.Tensor): The ground truth label. + kwargs: Any keyword argument to be used to calculate + bce loss with logits. + + Returns: + torch.Tensor: The returned bce loss with logits. + """ + if self.class_weight is not None: + assert 'weight' not in kwargs, "The key 'weight' already exists." + kwargs['weight'] = self.class_weight.to(cls_score.device) + loss_cls = F.binary_cross_entropy_with_logits(cls_score, label, + **kwargs) + return loss_cls diff --git a/pyskl/models/recognizers/__init__.py b/pyskl/models/recognizers/__init__.py new file mode 100644 index 00000000..0a11c08e --- /dev/null +++ b/pyskl/models/recognizers/__init__.py @@ -0,0 +1,6 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .recognizer2d import Recognizer2D +from .recognizer3d import Recognizer3D +from .recognizergcn import RecognizerGCN + +__all__ = ['Recognizer2D', 'Recognizer3D', 'RecognizerGCN'] diff --git a/pyskl/models/recognizers/base.py b/pyskl/models/recognizers/base.py new file mode 100644 index 00000000..49b98adb --- /dev/null +++ b/pyskl/models/recognizers/base.py @@ -0,0 +1,196 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import functools +from abc import ABCMeta, abstractmethod +from collections import OrderedDict + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F + +from .. import builder + + +def rgetattr(obj, attr, *args): + def _getattr(obj, attr): + return getattr(obj, attr, *args) + + return functools.reduce(_getattr, [obj] + attr.split('.')) + + +class BaseRecognizer(nn.Module, metaclass=ABCMeta): + """Base class for recognizers. + + All recognizers should subclass it. + All subclass should overwrite: + + - Methods:``forward_train``, supporting to forward when training. + - Methods:``forward_test``, supporting to forward when testing. + + Args: + backbone (dict): Backbone modules to extract feature. + cls_head (dict | None): Classification head to process feature. Default: None. + train_cfg (dict): Config for training. Default: {}. + test_cfg (dict): Config for testing. Default: {}. + """ + + def __init__(self, + backbone, + cls_head=None, + train_cfg=dict(), + test_cfg=dict()): + super().__init__() + # record the source of the backbone + self.backbone = builder.build_backbone(backbone) + self.cls_head = builder.build_head(cls_head) if cls_head else None + + if train_cfg is None: + train_cfg = dict() + if test_cfg is None: + test_cfg = dict() + + assert isinstance(train_cfg, dict) + assert isinstance(test_cfg, dict) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + # max_testing_views should be int + self.max_testing_views = test_cfg.get('max_testing_views', None) + self.init_weights() + + @property + def with_cls_head(self): + """bool: whether the recognizer has a cls_head""" + return hasattr(self, 'cls_head') and self.cls_head is not None + + def init_weights(self): + """Initialize the model network weights.""" + self.backbone.init_weights() + if self.with_cls_head: + self.cls_head.init_weights() + + def extract_feat(self, imgs): + """Extract features through a backbone. + + Args: + imgs (torch.Tensor): The input images. + + Returns: + torch.tensor: The extracted features. + """ + x = self.backbone(imgs) + return x + + def average_clip(self, cls_score): + """Averaging class score over multiple clips. + + Using different averaging types ('score' or 'prob' or None, which defined in test_cfg) to computed the final + averaged class score. Only called in test mode. By default, we use 'prob' mode. + + Args: + cls_score (torch.Tensor): Class score to be averaged. + + Returns: + torch.Tensor: Averaged class score. + """ + assert len(cls_score.shape) == 3 # * (Batch, NumSegs, Dim) + average_clips = self.test_cfg.get('average_clips', 'prob') + if average_clips not in ['score', 'prob', None]: + raise ValueError(f'{average_clips} is not supported. Supported: ["score", "prob", None]') + + if average_clips is None: + return cls_score + + if average_clips == 'prob': + return F.softmax(cls_score, dim=2).mean(dim=1) + elif average_clips == 'score': + return cls_score.mean(dim=1) + + @abstractmethod + def forward_train(self, imgs, label, **kwargs): + """Defines the computation performed at every call when training.""" + + @abstractmethod + def forward_test(self, imgs, **kwargs): + """Defines the computation performed at every call when evaluation and + testing.""" + + def _parse_losses(self, losses): + """Parse the ra w outputs (losses) of the network. + + Args: + losses (dict): Raw output of the network, which usually contain + losses and other necessary information. + + Returns: + tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor + which may be a weighted sum of all losses, log_vars contains + all the variables to be sent to the logger. + """ + log_vars = OrderedDict() + for loss_name, loss_value in losses.items(): + if isinstance(loss_value, torch.Tensor): + log_vars[loss_name] = loss_value.mean() + elif isinstance(loss_value, list): + log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value) + else: + raise TypeError(f'{loss_name} is not a tensor or list of tensors') + + loss = sum(_value for _key, _value in log_vars.items() if 'loss' in _key) + + log_vars['loss'] = loss + for loss_name, loss_value in log_vars.items(): + # reduce loss when distributed training + if dist.is_available() and dist.is_initialized(): + loss_value = loss_value.data.clone() + dist.all_reduce(loss_value.div_(dist.get_world_size())) + log_vars[loss_name] = loss_value.item() + + return loss, log_vars + + def forward(self, imgs, label=None, return_loss=True, **kwargs): + """Define the computation performed at every call.""" + if return_loss: + if label is None: + raise ValueError('Label should not be None.') + return self.forward_train(imgs, label, **kwargs) + + return self.forward_test(imgs, **kwargs) + + def train_step(self, data_batch, optimizer, **kwargs): + """The iteration step during training. + + This method defines an iteration step during training, except for the + back propagation and optimizer updating, which are done in an optimizer + hook. Note that in some complicated cases or models, the whole process + including back propagation and optimizer updating is also defined in + this method, such as GAN. + + Args: + data_batch (dict): The output of dataloader. + optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of + runner is passed to ``train_step()``. This argument is unused + and reserved. + + Returns: + dict: It should contain at least 3 keys: ``loss``, ``log_vars``, + ``num_samples``. + ``loss`` is a tensor for back propagation, which can be a + weighted sum of multiple losses. + ``log_vars`` contains all the variables to be sent to the + logger. + ``num_samples`` indicates the batch size (when the model is + DDP, it means the batch size on each GPU), which is used for + averaging the logs. + """ + losses = self(**data_batch, return_loss=True) + + loss, log_vars = self._parse_losses(losses) + + outputs = dict( + loss=loss, + log_vars=log_vars, + num_samples=len(next(iter(data_batch.values())))) + + return outputs diff --git a/pyskl/models/recognizers/recognizer2d.py b/pyskl/models/recognizers/recognizer2d.py new file mode 100644 index 00000000..995069d1 --- /dev/null +++ b/pyskl/models/recognizers/recognizer2d.py @@ -0,0 +1,58 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from torch import nn + +from ..builder import RECOGNIZERS +from .base import BaseRecognizer + + +@RECOGNIZERS.register_module() +class Recognizer2D(BaseRecognizer): + """2D recognizer model framework.""" + + def forward_train(self, imgs, label, **kwargs): + """Defines the computation performed at every call when training.""" + + assert self.with_cls_head + batches = imgs.shape[0] + imgs = imgs.reshape((-1, ) + imgs.shape[2:]) + num_segs = imgs.shape[0] // batches + + losses = dict() + + x = self.extract_feat(imgs) + x = x.reshape((batches, num_segs) + x.shape[1:]) + + cls_score = self.cls_head(x) + gt_label = label.squeeze() + loss_cls = self.cls_head.loss(cls_score, gt_label, **kwargs) + losses.update(loss_cls) + + return losses + + def forward_test(self, imgs, **kwargs): + """Defines the computation performed at every call when evaluation and testing.""" + batches = imgs.shape[0] + imgs = imgs.reshape((-1, ) + imgs.shape[2:]) + + x = self.extract_feat(imgs) + assert 'num_segs' in self.test_cfg + num_segs = self.test_cfg['num_segs'] + assert x.shape[0] % (batches * num_segs) == 0 + num_crops = x.shape[0] // (batches * num_segs) + + if self.test_cfg.get('feat_ext', False): + # perform spatial pooling + avg_pool = nn.AdaptiveAvgPool2d(1) + x = avg_pool(x) + # squeeze dimensions + x = x.reshape((batches, num_crops, num_segs, -1)) + # temporal average pooling + x = x.mean(axis=1).mean(axis=1) + return x.cpu().numpy() + + x = x.reshape((batches * num_crops, num_segs) + x.shape[1:]) + cls_score = self.cls_head(x) + cls_score = cls_score.reshape(batches, num_crops, cls_score.shape[-1]) + # calculate num_crops automatically + cls_score = self.average_clip(cls_score) + return cls_score.cpu().numpy() diff --git a/pyskl/models/recognizers/recognizer3d.py b/pyskl/models/recognizers/recognizer3d.py new file mode 100644 index 00000000..c416d6a0 --- /dev/null +++ b/pyskl/models/recognizers/recognizer3d.py @@ -0,0 +1,85 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from torch import nn + +from ..builder import RECOGNIZERS +from .base import BaseRecognizer + + +@RECOGNIZERS.register_module() +class Recognizer3D(BaseRecognizer): + """3D recognizer model framework.""" + + def forward_train(self, imgs, label, **kwargs): + """Defines the computation performed at every call when training.""" + + assert self.with_cls_head + imgs = imgs.reshape((-1, ) + imgs.shape[2:]) + losses = dict() + + x = self.extract_feat(imgs) + + cls_score = self.cls_head(x) + gt_label = label.squeeze() + loss_cls = self.cls_head.loss(cls_score, gt_label, **kwargs) + losses.update(loss_cls) + + return losses + + def forward_test(self, imgs, **kwargs): + """Defines the computation performed at every call when evaluation, testing.""" + batches = imgs.shape[0] + num_segs = imgs.shape[1] + imgs = imgs.reshape((-1, ) + imgs.shape[2:]) + + if self.max_testing_views is not None: + total_views = imgs.shape[0] + assert num_segs == total_views, ( + 'max_testing_views is only compatible ' + 'with batch_size == 1') + view_ptr = 0 + feats = [] + while view_ptr < total_views: + batch_imgs = imgs[view_ptr:view_ptr + self.max_testing_views] + x = self.extract_feat(batch_imgs) + feats.append(x) + view_ptr += self.max_testing_views + # should consider the case that feat is a tuple + if isinstance(feats[0], tuple): + len_tuple = len(feats[0]) + feat = [ + torch.cat([x[i] for x in feats]) for i in range(len_tuple) + ] + feat = tuple(feat) + else: + feat = torch.cat(feats) + else: + feat = self.extract_feat(imgs) + + if self.test_cfg.get('feat_ext', False): + feat_dim = len(feat[0].size()) if isinstance(feat, tuple) else len(feat.size()) + assert feat_dim in [5, 2], ( + 'Got feature of unknown architecture, ' + 'only 3D-CNN-like ([N, in_channels, T, H, W]), and ' + 'transformer-like ([N, in_channels]) features are supported.') + if feat_dim == 5: # 3D-CNN architecture + # perform spatio-temporal pooling + avg_pool = nn.AdaptiveAvgPool3d(1) + if isinstance(feat, tuple): + feat = [avg_pool(x) for x in feat] + # concat them + feat = torch.cat(feat, axis=1) + else: + feat = avg_pool(feat) + # squeeze dimensions + feat = feat.reshape((batches, num_segs, -1)) + # temporal average pooling + feat = feat.mean(axis=1) + return feat.cpu().numpy() + + # should have cls_head if not extracting features + assert self.with_cls_head + cls_score = self.cls_head(feat) + cls_score = cls_score.reshape(batches, num_segs, cls_score.shape[-1]) + cls_score = self.average_clip(cls_score) + return cls_score.cpu().numpy() diff --git a/pyskl/models/recognizers/recognizergcn.py b/pyskl/models/recognizers/recognizergcn.py new file mode 100644 index 00000000..ae905629 --- /dev/null +++ b/pyskl/models/recognizers/recognizergcn.py @@ -0,0 +1,96 @@ +import numpy as np +import torch + +from ..builder import RECOGNIZERS +from .base import BaseRecognizer + + +@RECOGNIZERS.register_module() +class RecognizerGCN(BaseRecognizer): + """GCN-based recognizer for skeleton-based action recognition. """ + + def forward_train(self, keypoint, label, **kwargs): + """Defines the computation performed at every call when training.""" + assert self.with_cls_head + assert keypoint.shape[1] == 1 + keypoint = keypoint[:, 0] + + losses = dict() + x = self.extract_feat(keypoint) + cls_score = self.cls_head(x) + gt_label = label.squeeze(-1) + loss = self.cls_head.loss(cls_score, gt_label) + losses.update(loss) + + return losses + + def forward_test(self, keypoint, **kwargs): + """Defines the computation performed at every call when evaluation and + testing.""" + assert self.with_cls_head or self.feat_ext + bs, nc = keypoint.shape[:2] + keypoint = keypoint.reshape((bs * nc, ) + keypoint.shape[2:]) + + x = self.extract_feat(keypoint) + feat_ext = self.test_cfg.get('feat_ext', False) + pool_opt = self.test_cfg.get('pool_opt', 'all') + score_ext = self.test_cfg.get('score_ext', False) + if feat_ext or score_ext: + assert bs == 1 + assert isinstance(pool_opt, str) + dim_idx = dict(n=0, m=1, t=3, v=4) + + if pool_opt == 'all': + pool_opt == 'nmtv' + if pool_opt != 'none': + for digit in pool_opt: + assert digit in dim_idx + + if isinstance(x, tuple) or isinstance(x, list): + x = torch.cat(x, dim=2) + assert len(x.shape) == 5, 'The shape is N, M, C, T, V' + if pool_opt != 'none': + for d in pool_opt: + x = x.mean(dim_idx[d], keepdim=True) + + if score_ext: + w = self.cls_head.fc_cls.weight + b = self.cls_head.fc_cls.bias + x = torch.einsum('nmctv,oc->nmotv', x, w) + if b is not None: + x = x + b[..., None, None] + x = x[None] + return x.data.cpu().numpy().astype(np.float16) + + cls_score = self.cls_head(x) + cls_score = cls_score.reshape(bs, nc, cls_score.shape[-1]) + # harmless patch + if 'average_clips' not in self.test_cfg: + self.test_cfg['average_clips'] = 'prob' + + cls_score = self.average_clip(cls_score) + if isinstance(cls_score, tuple) or isinstance(cls_score, list): + cls_score = [x.data.cpu().numpy() for x in cls_score] + return [[x[i] for x in cls_score] for i in range(bs)] + + return cls_score.data.cpu().numpy() + + def forward(self, keypoint, label=None, return_loss=True, **kwargs): + """Define the computation performed at every call.""" + if return_loss: + if label is None: + raise ValueError('Label should not be None.') + return self.forward_train(keypoint, label, **kwargs) + + return self.forward_test(keypoint, **kwargs) + + def extract_feat(self, keypoint): + """Extract features through a backbone. + + Args: + keypoint (torch.Tensor): The input keypoints. + + Returns: + torch.tensor: The extracted features. + """ + return self.backbone(keypoint) diff --git a/pyskl/smp.py b/pyskl/smp.py new file mode 100644 index 00000000..751b641a --- /dev/null +++ b/pyskl/smp.py @@ -0,0 +1,105 @@ +# flake8: noqa: F401, F403 +import collections +import json +import multiprocessing as mp +import os +import os.path as osp +import pickle +import random as rd +import subprocess +import sys +from collections import defaultdict +from functools import reduce + +import cv2 +import numpy as np +import requests +from tqdm import tqdm + + +def mrlines(fname, sp='\n'): + f = open(fname).read().split(sp) + while f != [] and f[-1] == '': + f = f[:-1] + return f + +def mwlines(lines, fname): + with open(fname, 'w') as fout: + fout.write('\n'.join(lines)) + +def default_set(self, args, name, default): + if hasattr(args, name): + val = getattr(args, name) + setattr(self, name, val) + else: + setattr(self, name, default) + +def youtube_dl(url, output_name): + cmd = 'youtube-dl -f best -f mp4 "{}" -o {}'.format(url, output_name) + os.system(cmd) + +def run_command(cmd): + return subprocess.check_output(cmd) + +def ls(dirname='.', full=True): + if not full: + return os.listdir(dirname) + return [osp.join(dirname, x) for x in os.listdir(dirname)] + +def add(x, y): + return x + y + +def lpkl(pth): + return pickle.load(open(pth, 'rb')) + +def ljson(pth): + return json.load(open(pth, 'r')) + +def intop(pred, label, n): + pred = [np.argsort(x)[-n:] for x in pred] + hit = [(l in p) for l, p in zip(label, pred)] + return hit + +def comb(scores, coeffs): + ret = [x * coeffs[0] for x in scores[0]] + for i in range(1, len(scores)): + ret = [x + y for x, y in zip(ret, [x * coeffs[i] for x in scores[i]])] + return ret + +def top1(score, label): + return np.mean(intop(score, label, 1)) + +def load_label(ann, split=None): + if ann.endswith('.txt'): + lines = mrlines(ann) + return [int(x.split()[-1]) for x in lines] + elif ann.endswith('.pkl'): + data = lpkl(ann) + if split is not None: + split, annotations = set(data['split'][split]), data['annotations'] + key_name = 'frame_dir' if 'frame_dir' in annotations[0] else 'filename' + data = [x for x in annotations if x[key_name] in split] + return [x['label'] for x in data] + else: + raise NotImplemented + +def mean_acc(pred, label, with_class_acc=False): + hits = defaultdict(list) + for p, g in zip(pred, label): + hits[g].append(np.argmax(p) == g) + class_acc = [np.mean(x) for x in hits.values()] + return np.mean(class_acc), class_acc if with_class_acc else np.mean(class_acc) + +def match_dict(s, d): + values = [] + for k, v in d.items(): + if k in s: + values.append(v) + assert len(values) == 1 + return values[0] + +def download_file(url, filename=None): + if filename is None: + filename = url.split('/')[-1] + response = requests.get(url) + open(filename, 'wb').write(response.content) diff --git a/pyskl/utils/__init__.py b/pyskl/utils/__init__.py new file mode 100644 index 00000000..fa45efd3 --- /dev/null +++ b/pyskl/utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from .collect_env import collect_env # noqa: F401, F403 +from .graph import Graph # noqa: F401, F403 +from .misc import get_root_logger, mc_off, mc_on, test_port # noqa: F401, F403 + +try: + from .visualize import Vis3DPose, Vis2DPose # noqa: F401, F403 +except ImportError: + pass diff --git a/pyskl/utils/collect_env.py b/pyskl/utils/collect_env.py new file mode 100644 index 00000000..432450f5 --- /dev/null +++ b/pyskl/utils/collect_env.py @@ -0,0 +1,17 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from mmcv.utils import collect_env as collect_basic_env +from mmcv.utils import get_git_hash + +import pyskl + + +def collect_env(): + env_info = collect_basic_env() + env_info['pyskl'] = ( + pyskl.__version__ + '+' + get_git_hash(digits=7)) + return env_info + + +if __name__ == '__main__': + for name, val in collect_env().items(): + print(f'{name}: {val}') diff --git a/pyskl/utils/graph.py b/pyskl/utils/graph.py new file mode 100644 index 00000000..e5c37462 --- /dev/null +++ b/pyskl/utils/graph.py @@ -0,0 +1,149 @@ +import numpy as np +import torch + + +def k_adjacency(A, k, with_self=False, self_factor=1): + # A is a 2D square array + if isinstance(A, torch.Tensor): + A = A.data.cpu().numpy() + assert isinstance(A, np.ndarray) + Iden = np.eye(len(A), dtype=A.dtype) + if k == 0: + return Iden + Ak = np.minimum(np.linalg.matrix_power(A + Iden, k), 1) - np.minimum(np.linalg.matrix_power(A + Iden, k - 1), 1) + if with_self: + Ak += (self_factor * Iden) + return Ak + + +def edge2mat(link, num_node): + A = np.zeros((num_node, num_node)) + for i, j in link: + A[j, i] = 1 + return A + + +def normalize_digraph(A, dim=0): + # A is a 2D square array + Dl = np.sum(A, dim) + h, w = A.shape + Dn = np.zeros((w, w)) + + for i in range(w): + if Dl[i] > 0: + Dn[i, i] = Dl[i] ** (-1) + + AD = np.dot(A, Dn) + return AD + + +def get_hop_distance(num_node, edge, max_hop=1): + A = np.eye(num_node) + + for i, j in edge: + A[i, j] = 1 + A[j, i] = 1 + + # compute hop steps + hop_dis = np.zeros((num_node, num_node)) + np.inf + transfer_mat = [ + np.linalg.matrix_power(A, d) for d in range(max_hop + 1) + ] + arrive_mat = (np.stack(transfer_mat) > 0) + for d in range(max_hop, -1, -1): + hop_dis[arrive_mat[d]] = d + return hop_dis + + +class Graph: + """The Graph to model the skeletons. + + Args: + layout (str): must be one of the following candidates: 'openpose', 'nturgb+d', 'coco'. Default: 'coco'. + mode (str): must be one of the following candidates: 'stgcn_spatial', 'spatial'. Default: 'spatial'. + max_hop (int): the maximal distance between two connected nodes. + Default: 1 + """ + + def __init__(self, + layout='coco', + mode='spatial', + max_hop=1): + + self.max_hop = max_hop + self.layout = layout + self.mode = mode + + assert layout in ['openpose', 'nturgb+d', 'coco'] + + self.get_layout(layout) + self.hop_dis = get_hop_distance(self.num_node, self.inward, max_hop) + + assert hasattr(self, mode), f'Do Not Exist This Mode: {mode}' + self.A = getattr(self, mode)() + + def __str__(self): + return self.A + + def get_layout(self, layout): + if layout == 'openpose': + self.num_node = 18 + self.inward = [ + (4, 3), (3, 2), (7, 6), (6, 5), (13, 12), (12, 11), (10, 9), + (9, 8), (11, 5), (8, 2), (5, 1), (2, 1), (0, 1), (15, 0), + (14, 0), (17, 15), (16, 14) + ] + self.center = 1 + elif layout == 'nturgb+d': + self.num_node = 25 + neighbor_base = [ + (1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5), (7, 6), + (8, 7), (9, 21), (10, 9), (11, 10), (12, 11), (13, 1), + (14, 13), (15, 14), (16, 15), (17, 1), (18, 17), (19, 18), + (20, 19), (22, 8), (23, 8), (24, 12), (25, 12) + ] + self.inward = [(i - 1, j - 1) for (i, j) in neighbor_base] + self.center = 21 - 1 + elif layout == 'coco': + self.num_node = 17 + self.inward = [ + (15, 13), (13, 11), (16, 14), (14, 12), (11, 5), (12, 6), + (9, 7), (7, 5), (10, 8), (8, 6), (5, 0), (6, 0), + (1, 0), (3, 1), (2, 0), (4, 2) + ] + self.center = 0 + else: + raise ValueError(f'Do Not Exist This Layout: {layout}') + self.self_link = [(i, i) for i in range(self.num_node)] + self.outward = [(j, i) for (i, j) in self.inward] + self.neighbor = self.inward + self.outward + + def stgcn_spatial(self): + adj = np.zeros((self.num_node, self.num_node)) + adj[self.hop_dis <= self.max_hop] = 1 + normalize_adj = normalize_digraph(adj) + hop_dis = self.hop_dis + center = self.center + + A = [] + for hop in range(self.max_hop + 1): + a_close = np.zeros((self.num_node, self.num_node)) + a_further = np.zeros((self.num_node, self.num_node)) + for i in range(self.num_node): + for j in range(self.num_node): + if hop_dis[j, i] == hop: + if hop_dis[j, center] >= hop_dis[i, center]: + a_close[j, i] = normalize_adj[j, i] + else: + a_further[j, i] = normalize_adj[j, i] + A.append(a_close) + if hop > 0: + A.append(a_further) + return np.stack(A) + + def spatial(self): + Iden = edge2mat(self.self_link, self.num_node) + In = normalize_digraph(edge2mat(self.inward, self.num_node)) + Out = normalize_digraph(edge2mat(self.outward, self.num_node)) + A = np.stack((Iden, In, Out)) + return A diff --git a/pyskl/utils/misc.py b/pyskl/utils/misc.py new file mode 100644 index 00000000..4e328633 --- /dev/null +++ b/pyskl/utils/misc.py @@ -0,0 +1,48 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import logging +import os +import socket + +from mmcv.utils import get_logger + + +def mc_on(port=22077, launcher='pytorch', size=24000): + # size is mb, allocate 24GB memory by default. + mc_exe = 'memcached' if launcher == 'pytorch' else '/mnt/lustre/share/memcached/bin/memcached' + os.system(f'{mc_exe} -p {port} -m {size}m -d') + + +def mc_off(): + os.system('killall memcached') + + +def test_port(ip, port): + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + sock.settimeout(2) + assert isinstance(ip, str) + if isinstance(port, str): + port = int(port) + assert 1 <= port <= 65535 + result = sock.connect_ex((ip, port)) + return result == 0 + + +def get_root_logger(log_file=None, log_level=logging.INFO): + """Use ``get_logger`` method in mmcv to get the root logger. + + The logger will be initialized if it has not been initialized. By default a + StreamHandler will be added. If ``log_file`` is specified, a FileHandler + will also be added. The name of the root logger is the top-level package + name, e.g., "pyskl". + + Args: + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the root logger. + log_level (int): The root logger level. Note that only the process of + rank 0 is affected, while other processes will set the level to + "Error" and be silent most of the time. + + Returns: + :obj:`logging.Logger`: The root logger. + """ + return get_logger(__name__.split('.')[0], log_file, log_level) diff --git a/pyskl/utils/visualize.py b/pyskl/utils/visualize.py new file mode 100644 index 00000000..de181350 --- /dev/null +++ b/pyskl/utils/visualize.py @@ -0,0 +1,143 @@ +import io + +import cv2 +import decord +import matplotlib.pyplot as plt +import moviepy.editor as mpy +import numpy as np +from mmcv import load +from tqdm import tqdm + + +class Vis3DPose: + + def __init__(self, item, layout='nturgb+d', fps=12, angle=(30, 45), fig_size=(8, 8), dpi=80): + kp = item['keypoint'] + self.kp = kp + assert self.kp.shape[-1] == 3 + self.layout = layout + self.fps = fps + self.angle = angle # For 3D data only + self.colors = ('#3498db', '#000000', '#e74c3c') # l, m, r + self.fig_size = fig_size + self.dpi = dpi + + assert layout == 'nturgb+d' + if self.layout == 'nturgb+d': + self.num_joint = 25 + self.links = np.array([ + (1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5), + (7, 6), (8, 7), (9, 21), (10, 9), (11, 10), (12, 11), + (13, 1), (14, 13), (15, 14), (16, 15), (17, 1), (18, 17), + (19, 18), (20, 19), (22, 8), (23, 8), (24, 12), (25, 12)], dtype=np.int) - 1 + self.left = np.array([5, 6, 7, 8, 13, 14, 15, 16, 22, 23], dtype=np.int) - 1 + self.right = np.array([9, 10, 11, 12, 17, 18, 19, 20, 24, 25], dtype=np.int) - 1 + self.num_link = len(self.links) + self.limb_tag = [1] * self.num_link + + for i, link in enumerate(self.links): + if link[0] in self.left or link[1] in self.left: + self.limb_tag[i] = 0 + elif link[0] in self.right or link[1] in self.right: + self.limb_tag[i] = 2 + + assert len(kp.shape) == 4 and kp.shape[3] == 3 and kp.shape[2] == self.num_joint + x, y, z = kp[..., 0], kp[..., 1], kp[..., 2] + + min_x, max_x = min(x[x != 0]), max(x[x != 0]) + min_y, max_y = min(y[y != 0]), max(y[y != 0]) + min_z, max_z = min(z[z != 0]), max(z[z != 0]) + max_axis = max(max_x - min_x, max_y - min_y, max_z - min_z) + mid_x, mid_y, mid_z = (min_x + max_x) / 2, (min_y + max_y) / 2, (min_z + max_z) / 2 + self.min_x, self.max_x = mid_x - max_axis / 2, mid_x + max_axis / 2 + self.min_y, self.max_y = mid_y - max_axis / 2, mid_y + max_axis / 2 + self.min_z, self.max_z = mid_z - max_axis / 2, mid_z + max_axis / 2 + + self.images = [] + + def get_img(self, dpi=80): + buf = io.BytesIO() + plt.savefig(buf, format='png', dpi=dpi) + buf.seek(0) + img = np.frombuffer(buf.getvalue(), dtype=np.uint8) + buf.close() + return cv2.imdecode(img, -1) + + def vis(self): + self.images = [] + plt.figure(figsize=self.fig_size) + for t in range(self.kp.shape[1]): + ax = plt.gca(projection='3d') + ax.set_xlim3d([self.min_x, self.max_x]) + ax.set_ylim3d([self.min_y, self.max_y]) + ax.set_zlim3d([self.min_z, self.max_z]) + ax.view_init(*self.angle) + ax.set_aspect('auto') + for i in range(self.num_link): + for m in range(self.kp.shape[0]): + link = self.links[i] + color = self.colors[self.limb_tag[i]] + j1, j2 = self.kp[m, t, link[0]], self.kp[m, t, link[1]] + if not ((np.allclose(j1, 0) or np.allclose(j2, 0)) and link[0] != 1 and link[1] != 1): + ax.plot([j1[0], j2[0]], [j1[1], j2[1]], [j1[2], j2[2]], lw=1, c=color) + self.images.append(self.get_img(dpi=self.dpi)) + ax.cla() + return mpy.ImageSequenceClip(self.images, fps=self.fps) + + +def Vis2DPose(item, thre=0.2, out_shape=(540, 960), layout='coco', fps=24, video=None): + if isinstance(item, str): + item = load(item) + + assert layout == 'coco' + + kp = item['keypoint'] + if 'keypoint_score' in item: + kpscore = item['keypoint_score'] + kp = np.concatenate([kp, kpscore[..., None]], -1) + + assert kp.shape[-1] == 3 + img_shape = item.get('img_shape', out_shape) + kp[..., 0] *= out_shape[1] / img_shape[1] + kp[..., 1] *= out_shape[0] / img_shape[0] + + total_frames = item.get('total_frames', kp.shape[1]) + assert total_frames == kp.shape[1] + + if video is None: + frames = [np.ones([out_shape[0], out_shape[1], 3], dtype=np.uint8) * 255 for i in range(total_frames)] + else: + vid = decord.VideoReader(video) + frames = [x.asnumpy() for x in vid] + frames = [cv2.resize(x, (out_shape[1], out_shape[0])) for x in frames] + if len(frames) != total_frames: + frames = [frames[int(i / total_frames * len(frames))] for i in range(total_frames)] + + if layout == 'coco': + edges = [ + (0, 1, 'f'), (0, 2, 'f'), (1, 3, 'f'), (2, 4, 'f'), (0, 5, 't'), (0, 6, 't'), + (5, 7, 'ru'), (6, 8, 'lu'), (7, 9, 'ru'), (8, 10, 'lu'), (5, 11, 't'), (6, 12, 't'), + (11, 13, 'ld'), (12, 14, 'rd'), (13, 15, 'ld'), (14, 16, 'rd') + ] + color_map = { + 'ru': ((0, 0x96, 0xc7), (0x3, 0x4, 0x5e)), + 'rd': ((0xca, 0xf0, 0xf8), (0x48, 0xca, 0xe4)), + 'lu': ((0x9d, 0x2, 0x8), (0x3, 0x7, 0x1e)), + 'ld': ((0xff, 0xba, 0x8), (0xe8, 0x5d, 0x4)), + 't': ((0xee, 0x8b, 0x98), (0xd9, 0x4, 0x29)), + 'f': ((0x8d, 0x99, 0xae), (0x2b, 0x2d, 0x42))} + + for i in tqdm(range(total_frames)): + for m in range(kp.shape[0]): + ske = kp[m, i] + for e in edges: + st, ed, co = e + co_tup = color_map[co] + j1, j2 = ske[st], ske[ed] + j1x, j1y, j2x, j2y = int(j1[0]), int(j1[1]), int(j2[0]), int(j2[1]) + conf = min(j1[2], j2[2]) + if conf > thre: + color = [x + (y - x) * (conf - thre) / 0.8 for x, y in zip(co_tup[0], co_tup[1])] + color = tuple([int(x) for x in color]) + frames[i] = cv2.line(frames[i], (j1x, j1y), (j2x, j2y), color, thickness=2) + return mpy.ImageSequenceClip(frames, fps=fps) diff --git a/pyskl/version.py b/pyskl/version.py new file mode 100644 index 00000000..275595c2 --- /dev/null +++ b/pyskl/version.py @@ -0,0 +1,18 @@ +# Copyright (c) Open-MMLab. All rights reserved. + +__version__ = '0.1.0' + + +def parse_version_info(version_str): + version_info = [] + for x in version_str.split('.'): + if x.isdigit(): + version_info.append(int(x)) + elif x.find('rc') != -1: + patch_version = x.split('rc') + version_info.append(int(patch_version[0])) + version_info.append(f'rc{patch_version[1]}') + return tuple(version_info) + + +version_info = parse_version_info(__version__) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..a3f953ee --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +decord>=0.6.0 +matplotlib +mmcv +moviepy +numpy>=1.19.5 +opencv-contrib-python +opencv-python +scipy +torch>=1.5 +tqdm diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..7309a8d5 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,24 @@ +[bdist_wheel] +universal=1 + +[aliases] +test=pytest + +[tool:pytest] +addopts=tests/ + +[yapf] +based_on_style = pep8 +blank_line_before_nested_class_or_def = true +split_before_expression_after_opening_paren = true +split_penalty_import_names=0 +SPLIT_PENALTY_AFTER_OPENING_BRACKET=800 + +[isort] +line_length = 119 +multi_line_output = 0 +known_standard_library = pkg_resources,setuptools +known_first_party = pyskl +known_third_party = cv2,decord,matplotlib,mmcv,moviepy,numpy,requests,torch,tqdm +no_lines_before = STDLIB,LOCALFOLDER +default_section = THIRDPARTY diff --git a/setup.py b/setup.py new file mode 100644 index 00000000..c4b0b357 --- /dev/null +++ b/setup.py @@ -0,0 +1,127 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# test doublepush +import re +import sys +from os.path import exists +from setuptools import find_packages, setup + + +def readme(): + with open('README.md', encoding='utf-8') as f: + content = f.read() + return content + + +version_file = 'pyskl/version.py' + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +def parse_requirements(fname='requirements.txt', with_version=True): + """Parse the package dependencies listed in a requirements file but strips + specific versioning information. + + Args: + fname (str): path to requirements file + with_version (bool, default=False): if True include version specs + + Returns: + List[str]: list of requirements items + + CommandLine: + python -c "import setup; print(setup.parse_requirements())" + """ + + require_fpath = fname + + def parse_line(line): + """Parse information from a line in a requirements text file.""" + if line.startswith('-r '): + # Allow specifying requirements in other files + target = line.split(' ')[1] + for info in parse_require_file(target): + yield info + else: + info = {'line': line} + if line.startswith('-e '): + info['package'] = line.split('#egg=')[1] + elif '@git+' in line: + info['package'] = line + else: + # Remove versioning from the package + pat = '(' + '|'.join(['>=', '==', '>']) + ')' + parts = re.split(pat, line, maxsplit=1) + parts = [p.strip() for p in parts] + + info['package'] = parts[0] + if len(parts) > 1: + op, rest = parts[1:] + if ';' in rest: + # Handle platform specific dependencies + # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies + version, platform_deps = map(str.strip, + rest.split(';')) + info['platform_deps'] = platform_deps + else: + version = rest # NOQA + info['version'] = (op, version) + yield info + + def parse_require_file(fpath): + with open(fpath, 'r') as f: + for line in f.readlines(): + line = line.strip() + if line and not line.startswith('#'): + for info in parse_line(line): + yield info + + def gen_packages_items(): + if exists(require_fpath): + for info in parse_require_file(require_fpath): + parts = [info['package']] + if with_version and 'version' in info: + parts.extend(info['version']) + if not sys.version.startswith('3.4'): + # apparently package_deps are broken in 3.4 + platform_deps = info.get('platform_deps') + if platform_deps is not None: + parts.append(';' + platform_deps) + item = ''.join(parts) + yield item + + packages = list(gen_packages_items()) + return packages + + +if __name__ == '__main__': + setup( + name='pyskl', + version=get_version(), + description='A Toolbox for skeleton-based action recognition', + long_description=readme(), + long_description_content_type='text/markdown', + author='Haodong Duan', + author_email='dhd.efz@gmail.com', + maintainer='Haodong Duan', + maintainer_email='dhd.efz@gmail.com', + packages=find_packages(exclude=('configs', 'tools', 'demo')), + keywords='computer vision, video understanding', + include_package_data=True, + classifiers=[ + 'Development Status :: 4 - Beta', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3.9', + ], + url='https://github.com/kennymckormick/pyskl', + license='Apache License 2.0', + install_requires=parse_requirements('requirement.txt'), + zip_safe=False) diff --git a/tools/data_list.md b/tools/data_list.md new file mode 100644 index 00000000..23e6a321 --- /dev/null +++ b/tools/data_list.md @@ -0,0 +1,59 @@ +# Download the Pre-processed Skeletons + +We provide links to the pre-processed skeleton annotations, you can directly download them and use them for training & testing. + +- NTURGB+D [2D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/nturgbd/ntu60_hrnet.pkl +- NTURGB+D [3D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/nturgbd/ntu60_3danno.pkl +- NTURGB+D 120 [2D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/nturgbd/ntu120_hrnet.pkl +- NTURGB+D 120 [3D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/nturgbd/ntu120_3danno.pkl +- GYM [2D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/gym/gym_hrnet.pkl +- UCF101 [2D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/ucf101/ucf101_hrnet.pkl +- HMDB51 [2D Skeleton]: https://download.openmmlab.com/mmaction/pyskl/data/hmdb51/hmdb51_hrnet.pkl + +Here are the BibTex items for each dataset: + +```BibTex +% NTURGB+D +@inproceedings{shahroudy2016ntu, + title={Ntu rgb+ d: A large scale dataset for 3d human activity analysis}, + author={Shahroudy, Amir and Liu, Jun and Ng, Tian-Tsong and Wang, Gang}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={1010--1019}, + year={2016} +} +% NTURGB+D 120 +@article{liu2019ntu, + title={Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding}, + author={Liu, Jun and Shahroudy, Amir and Perez, Mauricio and Wang, Gang and Duan, Ling-Yu and Kot, Alex C}, + journal={IEEE transactions on pattern analysis and machine intelligence}, + volume={42}, + number={10}, + pages={2684--2701}, + year={2019}, + publisher={IEEE} +} +% GYM +@inproceedings{shao2020finegym, + title={Finegym: A hierarchical video dataset for fine-grained action understanding}, + author={Shao, Dian and Zhao, Yue and Dai, Bo and Lin, Dahua}, + booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, + pages={2616--2625}, + year={2020} +} +% UCF101 +@article{soomro2012ucf101, + title={UCF101: A dataset of 101 human actions classes from videos in the wild}, + author={Soomro, Khurram and Zamir, Amir Roshan and Shah, Mubarak}, + journal={arXiv preprint arXiv:1212.0402}, + year={2012} +} +% HMDB51 +@inproceedings{kuehne2011hmdb, + title={HMDB: a large video database for human motion recognition}, + author={Kuehne, Hildegard and Jhuang, Hueihan and Garrote, Est{\'\i}baliz and Poggio, Tomaso and Serre, Thomas}, + booktitle={2011 International conference on computer vision}, + pages={2556--2563}, + year={2011}, + organization={IEEE} +} +``` diff --git a/tools/dist_test.sh b/tools/dist_test.sh new file mode 100644 index 00000000..bf321bc3 --- /dev/null +++ b/tools/dist_test.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash + +CONFIG=$1 +CHECKPOINT=$2 +GPUS=$3 +PORT=${PORT:-29500} + +MKL_SERVICE_FORCE_INTEL=1 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +# Arguments starting from the forth one are captured by ${@:4} +python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ + $(dirname "$0")/test.py $CONFIG -C $CHECKPOINT --launcher pytorch ${@:4} diff --git a/tools/dist_train.sh b/tools/dist_train.sh new file mode 100644 index 00000000..be416939 --- /dev/null +++ b/tools/dist_train.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +CONFIG=$1 +GPUS=$2 +PORT=${PORT:-29500} + +MKL_SERVICE_FORCE_INTEL=1 PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ + $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} +# Any arguments from the third one are captured by ${@:3} diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh new file mode 100644 index 00000000..fdea5da7 --- /dev/null +++ b/tools/slurm_test.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +set -x + +PARTITION=$1 +JOB_NAME=$2 +CONFIG=$3 +CHECKPOINT=$4 +GPUS=${GPUS:-8} +GPUS_PER_NODE=${GPUS_PER_NODE:-8} +CPUS_PER_TASK=${CPUS_PER_TASK:-5} +PY_ARGS=${@:5} # Arguments starting from the fifth one are captured +SRUN_ARGS=${SRUN_ARGS:-""} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +srun -p ${PARTITION} \ + --job-name=${JOB_NAME} \ + --gres=gpu:${GPUS_PER_NODE} \ + --ntasks=${GPUS} \ + --ntasks-per-node=${GPUS_PER_NODE} \ + --cpus-per-task=${CPUS_PER_TASK} \ + --kill-on-bad-exit=1 \ + ${SRUN_ARGS} \ + python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh new file mode 100644 index 00000000..2cff8aae --- /dev/null +++ b/tools/slurm_train.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +export MASTER_PORT=$((12000 + $RANDOM % 20000)) +set -x + +PARTITION=$1 +JOB_NAME=$2 +CONFIG=$3 +GPUS=${GPUS:-8} +GPUS_PER_NODE=${GPUS_PER_NODE:-8} +CPUS_PER_TASK=${CPUS_PER_TASK:-5} +SRUN_ARGS=${SRUN_ARGS:-""} +PY_ARGS=${@:4} # Any arguments from the forth one are captured by this + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +srun -p ${PARTITION} \ + --job-name=${JOB_NAME} \ + --gres=gpu:${GPUS_PER_NODE} \ + --ntasks=${GPUS} \ + --ntasks-per-node=${GPUS_PER_NODE} \ + --cpus-per-task=${CPUS_PER_TASK} \ + --kill-on-bad-exit=1 \ + ${SRUN_ARGS} \ + python -u tools/train.py ${CONFIG} --launcher="slurm" ${PY_ARGS} diff --git a/tools/test.py b/tools/test.py new file mode 100644 index 00000000..ffa48b6c --- /dev/null +++ b/tools/test.py @@ -0,0 +1,221 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp +import time +import warnings + +import mmcv +import torch +import torch.distributed as dist +from mmcv import Config, DictAction, load +from mmcv.cnn import fuse_conv_bn +from mmcv.engine import multi_gpu_test +from mmcv.fileio.io import file_handlers +from mmcv.parallel import MMDistributedDataParallel +from mmcv.runner import get_dist_info, init_dist, load_checkpoint + +from pyskl.datasets import build_dataloader, build_dataset +from pyskl.models import build_model +from pyskl.utils import mc_off, mc_on, test_port + + +def parse_args(): + parser = argparse.ArgumentParser( + description='pyskl test (and eval) a model') + parser.add_argument('config', help='test config file path') + parser.add_argument('-C', '--checkpoint', help='checkpoint file', default=None) + parser.add_argument( + '--out', + default=None, + help='output result file in pkl/yaml/json format') + parser.add_argument( + '--fuse-conv-bn', + action='store_true', + help='Whether to fuse conv and bn, this will slightly increase' + 'the inference speed') + parser.add_argument( + '--eval', + type=str, + nargs='+', + default=['top_k_accuracy', 'mean_class_accuracy'], + help='evaluation metrics, which depends on the dataset, e.g.,' + ' "top_k_accuracy", "mean_class_accuracy" for video dataset') + parser.add_argument( + '--gpu-collect', + action='store_true', + help='whether to use gpu to collect results') + parser.add_argument( + '--tmpdir', + help='tmp directory used for collecting results from multiple ' + 'workers, available when gpu-collect is not specified') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + default={}, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function (deprecate), ' + 'change to --eval-options instead.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + default={}, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + parser.add_argument( + '--average-clips', + choices=['score', 'prob', None], + default=None, + help='average type when averaging test clips') + parser.add_argument( + '--launcher', + choices=['pytorch', 'slurm'], + default='pytorch', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + if args.options and args.eval_options: + raise ValueError( + '--options and --eval-options cannot be both ' + 'specified, --options is deprecated in favor of --eval-options') + if args.options: + warnings.warn('--options is deprecated in favor of --eval-options') + args.eval_options = args.options + return args + + +def inference_pytorch(args, cfg, data_loader): + """Get predictions by pytorch models.""" + if args.average_clips is not None: + # You can set average_clips during testing, it will override the + # original setting + if cfg.model.get('test_cfg') is None and cfg.get('test_cfg') is None: + cfg.model.setdefault('test_cfg', + dict(average_clips=args.average_clips)) + else: + if cfg.model.get('test_cfg') is not None: + cfg.model.test_cfg.average_clips = args.average_clips + else: + cfg.test_cfg.average_clips = args.average_clips + + # build the model and load checkpoint + model = build_model(cfg.model) + + if args.checkpoint is None: + work_dir = cfg.work_dir + args.checkpoint = osp.join(work_dir, 'latest.pth') + + load_checkpoint(model, args.checkpoint, map_location='cpu') + + if args.fuse_conv_bn: + model = fuse_conv_bn(model) + + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False) + outputs = multi_gpu_test(model, data_loader, args.tmpdir, args.gpu_collect) + + return outputs + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + + out = osp.join(cfg.work_dir, 'result.pkl') if args.out is None else args.out + + # Load eval_config from cfg + eval_cfg = cfg.get('evaluation', {}) + keys = ['interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', 'rule', 'by_epoch', 'broadcast_bn_buffers'] + for key in keys: + eval_cfg.pop(key, None) + if args.eval: + eval_cfg['metrics'] = args.eval + + dataset_type = cfg.data.test.type + + mmcv.mkdir_or_exist(osp.dirname(out)) + _, suffix = osp.splitext(out) + if dataset_type == 'AVADataset': + assert suffix[1:] == 'csv', ('For AVADataset, the format of the output file should be csv') + else: + assert suffix[1:] in file_handlers, ('The format of the output file should be json, pickle or yaml') + + # set cudnn benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.data.test.test_mode = True + + if not hasattr(cfg, 'dist_params'): + cfg.dist_params = dict(backend='nccl') + + init_dist(args.launcher, **cfg.dist_params) + rank, world_size = get_dist_info() + cfg.gpu_ids = range(world_size) + + # build the dataloader + dataset = build_dataset(cfg.data.test, dict(test_mode=True)) + dataloader_setting = dict( + videos_per_gpu=cfg.data.get('videos_per_gpu', 1), + workers_per_gpu=cfg.data.get('workers_per_gpu', 1), + shuffle=False) + dataloader_setting = dict(dataloader_setting, **cfg.data.get('test_dataloader', {})) + data_loader = build_dataloader(dataset, **dataloader_setting) + + so_keys, cli = set(), None + if rank == 0: + mc_list = cfg.get('mc_list', None) + if mc_list is not None: + mc_cfg = cfg.get('mc_cfg', ('localhost', 11211)) + assert isinstance(mc_cfg, tuple) and mc_cfg[0] == 'localhost' + if not test_port(mc_cfg[0], mc_cfg[1]): + mc_on(port=mc_cfg[1], launcher=args.launcher) + retry = 3 + while not test_port(mc_cfg[0], mc_cfg[1]) and retry > 0: + time.sleep(5) + retry -= 1 + assert retry >= 0, 'Failed to launch memcached. ' + assert isinstance(mc_list, list) + from pymemcache.client.base import Client + from pymemcache import serde + cli = Client(mc_cfg, serde=serde.pickle_serde) + for data_file in mc_list: + assert osp.exists(data_file) + kv_dict = load(data_file) + if isinstance(kv_dict, list): + assert ('frame_dir' in kv_dict[0]) != ('filename' in kv_dict[0]) + key = 'frame_dir' if 'frame_dir' in kv_dict[0] else 'filename' + kv_dict = {x[key]: x for x in kv_dict} + for k, v in kv_dict.items(): + assert k not in so_keys + cli.set(k, v) + so_keys.add(k) + dist.barrier() + + outputs = inference_pytorch(args, cfg, data_loader) + + rank, _ = get_dist_info() + if rank == 0: + print(f'\nwriting results to {out}') + dataset.dump_results(outputs, out=out) + if eval_cfg: + eval_res = dataset.evaluate(outputs, **eval_cfg) + for name, val in eval_res.items(): + print(f'{name}: {val:.04f}') + + dist.barrier() + if rank == 0 and cli is not None: + for k in so_keys: + cli.delete(k) + mc_off() + + +if __name__ == '__main__': + main() diff --git a/tools/train.py b/tools/train.py new file mode 100644 index 00000000..548db17d --- /dev/null +++ b/tools/train.py @@ -0,0 +1,178 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp +import time + +import mmcv +import torch +import torch.distributed as dist +from mmcv import Config, load +from mmcv.runner import get_dist_info, init_dist, set_random_seed +from mmcv.utils import get_git_hash + +from pyskl import __version__ +from pyskl.apis import init_random_seed, train_model +from pyskl.datasets import build_dataset +from pyskl.models import build_model +from pyskl.utils import collect_env, get_root_logger, mc_off, mc_on, test_port + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a recognizer') + parser.add_argument('config', help='train config file path') + parser.add_argument('--work-dir', help='the dir to save logs and models') + parser.add_argument( + '--validate', + action='store_true', + help='whether to evaluate the checkpoint during training') + parser.add_argument( + '--test-last', + action='store_true', + help='whether to test the checkpoint after training') + parser.add_argument( + '--test-best', + action='store_true', + help=('whether to test the best checkpoint (if applicable) after ' + 'training')) + parser.add_argument('--seed', type=int, default=None, help='random seed') + parser.add_argument( + '--deterministic', + action='store_true', + help='whether to set deterministic options for CUDNN backend.') + parser.add_argument( + '--launcher', + choices=['pytorch', 'slurm'], + default='pytorch', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + + # work_dir is determined in this priority: + # CLI > config file > default (base filename) + if args.work_dir is not None: + # update configs according to CLI args if args.work_dir is not None + cfg.work_dir = args.work_dir + elif cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) + + if not hasattr(cfg, 'dist_params'): + cfg.dist_params = dict(backend='nccl') + + init_dist(args.launcher, **cfg.dist_params) + rank, world_size = get_dist_info() + cfg.gpu_ids = range(world_size) + + # create work_dir + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # dump config + cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) + # init logger before other steps + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + log_file = osp.join(cfg.work_dir, f'{timestamp}.log') + logger = get_root_logger(log_file=log_file, log_level=cfg.log_level) + + # init the meta dict to record some important information such as + # environment info and seed, which will be logged + meta = dict() + # log env info + env_info_dict = collect_env() + env_info = '\n'.join([f'{k}: {v}' for k, v in env_info_dict.items()]) + dash_line = '-' * 60 + '\n' + logger.info('Environment info:\n' + dash_line + env_info + '\n' + + dash_line) + meta['env_info'] = env_info + + # log some basic info + logger.info(f'Config: {cfg.pretty_text}') + + # set random seeds + seed = init_random_seed(args.seed) + logger.info(f'Set random seed to {seed}, deterministic: {args.deterministic}') + set_random_seed(seed, deterministic=args.deterministic) + + cfg.seed = seed + meta['seed'] = seed + meta['config_name'] = osp.basename(args.config) + meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\')) + + model = build_model(cfg.model) + + datasets = [build_dataset(cfg.data.train)] + + cfg.workflow = cfg.get('workflow', [('train', 1)]) + assert len(cfg.workflow) == 1 + if cfg.checkpoint_config is not None: + # save pyskl version, config file content and class names in + # checkpoints as meta data + cfg.checkpoint_config.meta = dict( + pyskl_version=__version__ + get_git_hash(digits=7), + tag=['nturgb+d graph changed', 'scale A > 1 in training, unchanged in testing'], + config=cfg.pretty_text) + + test_option = dict(test_last=args.test_last, test_best=args.test_best) + + # We need to write the keys into memcache + so_keys, cli = set(), None + if rank == 0: + mc_list = cfg.get('mc_list', None) + if mc_list is not None: + mc_cfg = cfg.get('mc_cfg', ('localhost', 11211)) + assert isinstance(mc_cfg, tuple) and mc_cfg[0] == 'localhost' + if not test_port(mc_cfg[0], mc_cfg[1]): + mc_on(port=mc_cfg[1], launcher=args.launcher) + retry = 3 + while not test_port(mc_cfg[0], mc_cfg[1]) and retry > 0: + time.sleep(5) + retry -= 1 + assert retry >= 0, 'Failed to launch memcached. ' + assert isinstance(mc_list, list) + from pymemcache.client.base import Client + from pymemcache import serde + cli = Client(mc_cfg, serde=serde.pickle_serde) + for data_file in mc_list: + assert osp.exists(data_file) + kv_dict = load(data_file) + if isinstance(kv_dict, list): + assert ('frame_dir' in kv_dict[0]) != ('filename' in kv_dict[0]) + key = 'frame_dir' if 'frame_dir' in kv_dict[0] else 'filename' + kv_dict = {x[key]: x for x in kv_dict} + for k, v in kv_dict.items(): + assert k not in so_keys + cli.set(k, v) + so_keys.add(k) + dist.barrier() + + train_model( + model, + datasets, + cfg, + validate=args.validate, + test=test_option, + timestamp=timestamp, + meta=meta) + + dist.barrier() + if rank == 0 and cli is not None: + for k in so_keys: + cli.delete(k) + mc_off() + + +if __name__ == '__main__': + main()