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log.txt
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2021-08-08 23:32:53,667 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:32:53,667 core INFO: Collecting env info (might take some time)
2021-08-08 23:32:56,992 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:33:29,038 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:33:55,828 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:33:55,828 core INFO: Collecting env info (might take some time)
2021-08-08 23:33:58,726 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:34:02,802 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:34:02,842 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:35:00,477 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:35:00,477 core INFO: Collecting env info (might take some time)
2021-08-08 23:35:03,557 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:35:07,960 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:35:08,013 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:36:38,384 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:36:38,384 core INFO: Collecting env info (might take some time)
2021-08-08 23:36:41,401 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:36:45,578 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:36:45,626 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:40:28,268 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:40:28,269 core INFO: Collecting env info (might take some time)
2021-08-08 23:40:31,351 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:40:35,183 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:40:35,227 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:42:34,397 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:42:34,397 core INFO: Collecting env info (might take some time)
2021-08-08 23:42:37,380 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:42:41,304 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:42:41,359 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:43:40,841 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:43:40,841 core INFO: Collecting env info (might take some time)
2021-08-08 23:43:43,861 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:43:48,008 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:43:48,081 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:45:06,152 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ('custom_dataset_val',)
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:45:06,152 core INFO: Collecting env info (might take some time)
2021-08-08 23:45:09,164 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:45:13,308 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:45:13,369 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:54:53,545 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ()
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:54:53,546 core INFO: Collecting env info (might take some time)
2021-08-08 23:54:56,541 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:55:00,599 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:55:00,647 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).
2021-08-08 23:57:05,190 core INFO: DATALOADER:
NUM_WORKERS: 8
SIZE_DIVISIBILITY: 32
DATASETS:
IGNORE_CHANNELS: (0,)
NUM_CLASS: 2
TEST: ('custom_dataset_test',)
TRAIN: ('custom_dataset_train',)
VAL: ()
INPUT:
SIZE: 512
MODEL:
ACTIVATION: softmax2d
ARCHITECTURE: Unet
DEVICE: cuda
ENCODER: resnet50
ENCODER_WEIGHTS: imagenet
GPU_NUM: 0
LOSS: DiceLoss
METRICS: ('Dice',)
WEIGHT:
OUTPUT_DIR: .
PATHS_CATALOG: /home/kolton/semantic-segmentation-framework-pytorch/core/config/paths_catalog.py
SOLVER:
BASE_LR: 0.001
BIAS_LR_FACTOR: 2
CHECKPOINT_PERIOD: 1
DCONV_OFFSETS_LR_FACTOR: 1.0
GAMMA: 0.1
IMS_PER_BATCH_TEST: 1
IMS_PER_BATCH_TRAIN: 8
IMS_PER_BATCH_VAL: 1
MAX_EPOCH: 80
MOMENTUM: 0.9
OPTIMIZER: Adam
WEIGHT_DECAY: 0.0001
WEIGHT_DECAY_BIAS: 0
2021-08-08 23:57:05,191 core INFO: Collecting env info (might take some time)
2021-08-08 23:57:08,266 core INFO:
PyTorch version: 1.9.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.27
Python version: 3.8 (64-bit runtime)
Python platform: Linux-5.10.43.3-microsoft-standard-WSL2-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Nvidia driver version: 471.11
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Versions of relevant libraries:
[pip3] efficientnet-pytorch==0.6.3
[pip3] numpy==1.21.1
[pip3] segmentation-models-pytorch==0.2.0
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0
[conda] blas 1.0 mkl
[conda] mkl 2021.2.0 h06a4308_296
[conda] mkl-service 2.3.0 py38h27cfd23_1
[conda] mkl_fft 1.3.0 py38h42c9631_2
[conda] mkl_random 1.2.1 py38ha9443f7_2
[conda] numpy 1.20.2 py38h2d18471_0
[conda] numpy-base 1.20.2 py38hfae3a4d_0
[conda] numpydoc 1.1.0 pyhd3eb1b0_1
Pillow (8.3.1)
2021-08-08 23:57:12,270 core.utils.checkpoint INFO: No checkpoint found. Initializing model from scratch
2021-08-08 23:57:12,319 core.inference INFO: Start evaluation on custom_dataset_test dataset(18334 images).