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| [01;33mThis stage is deprecated. Please consider moving to a new stage (2024 | |
| or newer)[0m | |
| The following modules were not unloaded: | |
| (Use "module --force purge" to unload all): | |
| 1) Stages/2023 | |
| [01;33mThis stage is deprecated. Please consider moving to a new stage (2024 | |
| or newer)[0m | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:401] [c10d] The server socket cannot be initialized on [::]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| [W socket.cpp:558] [c10d] The client socket cannot be initialized to connect to [jwb0088i.juwels]:7010 (errno: 97 - Address family not supported by protocol). | |
| 2024-05-23 18:18:25,400 - mmseg - INFO - Multi-processing start method is `None` | |
| 2024-05-23 18:18:25,440 - mmseg - INFO - OpenCV num_threads is `8 | |
| 2024-05-23 18:18:26,303 - mmseg - INFO - Environment info: | |
| ------------------------------------------------------------ | |
| sys.platform: linux | |
| Python: 3.10.4 (main, Oct 4 2022, 08:48:24) [GCC 11.3.0] | |
| CUDA available: True | |
| GPU 0,1,2,3: NVIDIA A100-SXM4-40GB | |
| CUDA_HOME: /p/software/juwelsbooster/stages/2023/software/CUDA/11.7 | |
| NVCC: Cuda compilation tools, release 11.7, V11.7.64 | |
| GCC: gcc (GCC) 11.3.0 | |
| PyTorch: 1.12.0 | |
| PyTorch compiling details: PyTorch built with: | |
| - GCC 11.3 | |
| - C++ Version: 201402 | |
| - Intel(R) oneAPI Math Kernel Library Version 2022.1-Product Build 20220311 for Intel(R) 64 architecture applications | |
| - Intel(R) MKL-DNN v2.6.0 (Git Hash N/A) | |
| - OpenMP 201511 (a.k.a. OpenMP 4.5) | |
| - LAPACK is enabled (usually provided by MKL) | |
| - NNPACK is enabled | |
| - CPU capability usage: AVX2 | |
| - CUDA Runtime 11.7 | |
| - NVCC architecture flags: -gencode;arch=compute_80,code=sm_80 | |
| - CuDNN 8.6 (built against CUDA 11.8) | |
| - Magma 2.6.2 | |
| - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.6.0, CXX_COMPILER=/p/software/juwelsbooster/stages/2023/software/GCCcore/11.3.0/bin/g++, CXX_FLAGS=-O2 -ftree-vectorize -march=native -fno-math-errno -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=1, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=ON, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, | |
| TorchVision: 0.13.1 | |
| OpenCV: 4.7.0 | |
| MMCV: 1.7.2 | |
| MMCV Compiler: GCC 11.3 | |
| MMCV CUDA Compiler: not available | |
| MMSegmentation: 0.30.0+38deb36 | |
| ------------------------------------------------------------ | |
| 2024-05-23 18:18:26,303 - mmseg - INFO - Distributed training: True | |
| 2024-05-23 18:18:26,957 - mmseg - INFO - Config: | |
| custom_imports = dict(imports=['geospatial_fm']) | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| cudnn_benchmark = True | |
| dataset_type = 'GeospatialDataset' | |
| data_root = '/p/project/training2411/strube1/HDCRS-school-2024' | |
| num_frames = 1 | |
| img_size = 224 | |
| num_workers = 4 | |
| samples_per_gpu = 4 | |
| img_norm_cfg = dict( | |
| means=[ | |
| 0.033349706741586264, 0.05701185520536176, 0.05889748132001316, | |
| 0.2323245113436119, 0.1972854853760658, 0.11944914225186566 | |
| ], | |
| stds=[ | |
| 0.02269135568823774, 0.026807560223070237, 0.04004109844362779, | |
| 0.07791732423672691, 0.08708738838140137, 0.07241979477437814 | |
| ]) | |
| bands = [0, 1, 2, 3, 4, 5] | |
| tile_size = 224 | |
| orig_nsize = 512 | |
| crop_size = (224, 224) | |
| img_suffix = '_merged.tif' | |
| seg_map_suffix = '.mask.tif' | |
| ignore_index = -1 | |
| image_nodata = -9999 | |
| image_nodata_replace = 0 | |
| image_to_float32 = True | |
| pretrained_weights_path = '/p/project/training2411/strube1/HDCRS-school-2024/models/Prithvi_100M.pt' | |
| num_layers = 12 | |
| patch_size = 16 | |
| embed_dim = 768 | |
| num_heads = 12 | |
| tubelet_size = 1 | |
| output_embed_dim = 768 | |
| max_intervals = 10000 | |
| evaluation_interval = 1000 | |
| experiment = 'burn_scars' | |
| project_dir = 'v1' | |
| work_dir = 'v1/burn_scars' | |
| save_path = 'v1/burn_scars' | |
| train_pipeline = [ | |
| dict(type='LoadGeospatialImageFromFile', to_float32=True), | |
| dict(type='LoadGeospatialAnnotations', reduce_zero_label=False), | |
| dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='ToTensor', keys=['img', 'gt_semantic_seg']), | |
| dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)), | |
| dict( | |
| type='TorchNormalize', | |
| means=[ | |
| 0.033349706741586264, 0.05701185520536176, 0.05889748132001316, | |
| 0.2323245113436119, 0.1972854853760658, 0.11944914225186566 | |
| ], | |
| stds=[ | |
| 0.02269135568823774, 0.026807560223070237, 0.04004109844362779, | |
| 0.07791732423672691, 0.08708738838140137, 0.07241979477437814 | |
| ]), | |
| dict(type='TorchRandomCrop', crop_size=(224, 224)), | |
| dict(type='Reshape', keys=['img'], new_shape=(6, 1, 224, 224)), | |
| dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, 224, 224)), | |
| dict( | |
| type='CastTensor', | |
| keys=['gt_semantic_seg'], | |
| new_type='torch.LongTensor'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadGeospatialImageFromFile', to_float32=True), | |
| dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]), | |
| dict(type='ToTensor', keys=['img']), | |
| dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)), | |
| dict( | |
| type='TorchNormalize', | |
| means=[ | |
| 0.033349706741586264, 0.05701185520536176, 0.05889748132001316, | |
| 0.2323245113436119, 0.1972854853760658, 0.11944914225186566 | |
| ], | |
| stds=[ | |
| 0.02269135568823774, 0.026807560223070237, 0.04004109844362779, | |
| 0.07791732423672691, 0.08708738838140137, 0.07241979477437814 | |
| ]), | |
| dict( | |
| type='Reshape', | |
| keys=['img'], | |
| new_shape=(6, 1, -1, -1), | |
| look_up=dict({ | |
| '2': 1, | |
| '3': 2 | |
| })), | |
| dict(type='CastTensor', keys=['img'], new_type='torch.FloatTensor'), | |
| dict( | |
| type='CollectTestList', | |
| keys=['img'], | |
| meta_keys=[ | |
| 'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', 'filename', | |
| 'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape', | |
| 'scale_factor', 'img_norm_cfg' | |
| ]) | |
| ] | |
| CLASSES = ('Unburnt land', 'Burn scar') | |
| data = dict( | |
| samples_per_gpu=4, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='GeospatialDataset', | |
| CLASSES=('Unburnt land', 'Burn scar'), | |
| data_root='/p/project/training2411/strube1/HDCRS-school-2024', | |
| img_dir='training', | |
| ann_dir='training', | |
| img_suffix='_merged.tif', | |
| seg_map_suffix='.mask.tif', | |
| pipeline=[ | |
| dict(type='LoadGeospatialImageFromFile', to_float32=True), | |
| dict(type='LoadGeospatialAnnotations', reduce_zero_label=False), | |
| dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='ToTensor', keys=['img', 'gt_semantic_seg']), | |
| dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)), | |
| dict( | |
| type='TorchNormalize', | |
| means=[ | |
| 0.033349706741586264, 0.05701185520536176, | |
| 0.05889748132001316, 0.2323245113436119, | |
| 0.1972854853760658, 0.11944914225186566 | |
| ], | |
| stds=[ | |
| 0.02269135568823774, 0.026807560223070237, | |
| 0.04004109844362779, 0.07791732423672691, | |
| 0.08708738838140137, 0.07241979477437814 | |
| ]), | |
| dict(type='TorchRandomCrop', crop_size=(224, 224)), | |
| dict(type='Reshape', keys=['img'], new_shape=(6, 1, 224, 224)), | |
| dict( | |
| type='Reshape', | |
| keys=['gt_semantic_seg'], | |
| new_shape=(1, 224, 224)), | |
| dict( | |
| type='CastTensor', | |
| keys=['gt_semantic_seg'], | |
| new_type='torch.LongTensor'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ], | |
| ignore_index=-1), | |
| val=dict( | |
| type='GeospatialDataset', | |
| CLASSES=('Unburnt land', 'Burn scar'), | |
| data_root='/p/project/training2411/strube1/HDCRS-school-2024', | |
| img_dir='validation', | |
| ann_dir='validation', | |
| img_suffix='_merged.tif', | |
| seg_map_suffix='.mask.tif', | |
| pipeline=[ | |
| dict(type='LoadGeospatialImageFromFile', to_float32=True), | |
| dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]), | |
| dict(type='ToTensor', keys=['img']), | |
| dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)), | |
| dict( | |
| type='TorchNormalize', | |
| means=[ | |
| 0.033349706741586264, 0.05701185520536176, | |
| 0.05889748132001316, 0.2323245113436119, | |
| 0.1972854853760658, 0.11944914225186566 | |
| ], | |
| stds=[ | |
| 0.02269135568823774, 0.026807560223070237, | |
| 0.04004109844362779, 0.07791732423672691, | |
| 0.08708738838140137, 0.07241979477437814 | |
| ]), | |
| dict( | |
| type='Reshape', | |
| keys=['img'], | |
| new_shape=(6, 1, -1, -1), | |
| look_up=dict({ | |
| '2': 1, | |
| '3': 2 | |
| })), | |
| dict( | |
| type='CastTensor', keys=['img'], new_type='torch.FloatTensor'), | |
| dict( | |
| type='CollectTestList', | |
| keys=['img'], | |
| meta_keys=[ | |
| 'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', | |
| 'filename', 'ori_filename', 'img', 'img_shape', | |
| 'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg' | |
| ]) | |
| ], | |
| ignore_index=-1), | |
| test=dict( | |
| type='GeospatialDataset', | |
| CLASSES=('Unburnt land', 'Burn scar'), | |
| data_root='/p/project/training2411/strube1/HDCRS-school-2024', | |
| img_dir='validation', | |
| ann_dir='validation', | |
| img_suffix='_merged.tif', | |
| seg_map_suffix='.mask.tif', | |
| pipeline=[ | |
| dict(type='LoadGeospatialImageFromFile', to_float32=True), | |
| dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]), | |
| dict(type='ToTensor', keys=['img']), | |
| dict(type='TorchPermute', keys=['img'], order=(2, 0, 1)), | |
| dict( | |
| type='TorchNormalize', | |
| means=[ | |
| 0.033349706741586264, 0.05701185520536176, | |
| 0.05889748132001316, 0.2323245113436119, | |
| 0.1972854853760658, 0.11944914225186566 | |
| ], | |
| stds=[ | |
| 0.02269135568823774, 0.026807560223070237, | |
| 0.04004109844362779, 0.07791732423672691, | |
| 0.08708738838140137, 0.07241979477437814 | |
| ]), | |
| dict( | |
| type='Reshape', | |
| keys=['img'], | |
| new_shape=(6, 1, -1, -1), | |
| look_up=dict({ | |
| '2': 1, | |
| '3': 2 | |
| })), | |
| dict( | |
| type='CastTensor', keys=['img'], new_type='torch.FloatTensor'), | |
| dict( | |
| type='CollectTestList', | |
| keys=['img'], | |
| meta_keys=[ | |
| 'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', | |
| 'filename', 'ori_filename', 'img', 'img_shape', | |
| 'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg' | |
| ]) | |
| ], | |
| ignore_index=-1)) | |
| optimizer = dict(type='Adam', lr=1.3e-05, betas=(0.9, 0.999)) | |
| optimizer_config = dict(grad_clip=None) | |
| lr_config = dict( | |
| policy='poly', | |
| warmup='linear', | |
| warmup_iters=1500, | |
| warmup_ratio=1e-06, | |
| power=1.0, | |
| min_lr=0.0, | |
| by_epoch=False) | |
| log_config = dict( | |
| interval=20, | |
| hooks=[ | |
| dict(type='TextLoggerHook', by_epoch=False), | |
| dict(type='TensorboardLoggerHook', by_epoch=False) | |
| ]) | |
| checkpoint_config = dict(by_epoch=True, interval=10, out_dir='v1/burn_scars') | |
| evaluation = dict( | |
| interval=1000, | |
| metric='mIoU', | |
| pre_eval=True, | |
| save_best='mIoU', | |
| by_epoch=False) | |
| loss_func = dict( | |
| type='DiceLoss', use_sigmoid=False, loss_weight=1, ignore_index=-1) | |
| runner = dict(type='IterBasedRunner', max_iters=10000) | |
| workflow = [('train', 1)] | |
| norm_cfg = dict(type='BN', requires_grad=True) | |
| model = dict( | |
| type='TemporalEncoderDecoder', | |
| frozen_backbone=False, | |
| pretrained= | |
| '/p/project/training2411/strube1/HDCRS-school-2024/models/Prithvi_100M.pt', | |
| backbone=dict( | |
| type='TemporalViTEncoder', | |
| img_size=224, | |
| patch_size=16, | |
| num_frames=1, | |
| tubelet_size=1, | |
| in_chans=6, | |
| embed_dim=768, | |
| depth=12, | |
| num_heads=12, | |
| mlp_ratio=4.0, | |
| norm_pix_loss=False), | |
| neck=dict( | |
| type='ConvTransformerTokensToEmbeddingNeck', | |
| embed_dim=768, | |
| output_embed_dim=768, | |
| drop_cls_token=True, | |
| Hp=14, | |
| Wp=14), | |
| decode_head=dict( | |
| num_classes=2, | |
| in_channels=768, | |
| type='FCNHead', | |
| in_index=-1, | |
| channels=256, | |
| num_convs=1, | |
| concat_input=False, | |
| dropout_ratio=0.1, | |
| norm_cfg=dict(type='BN', requires_grad=True), | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='DiceLoss', use_sigmoid=False, loss_weight=1, | |
| ignore_index=-1)), | |
| auxiliary_head=dict( | |
| num_classes=2, | |
| in_channels=768, | |
| type='FCNHead', | |
| in_index=-1, | |
| channels=256, | |
| num_convs=2, | |
| concat_input=False, | |
| dropout_ratio=0.1, | |
| norm_cfg=dict(type='BN', requires_grad=True), | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='DiceLoss', use_sigmoid=False, loss_weight=1, | |
| ignore_index=-1)), | |
| train_cfg=dict(), | |
| test_cfg=dict(mode='slide', stride=(112, 112), crop_size=(224, 224))) | |
| gpu_ids = range(0, 8) | |
| auto_resume = False | |
| 2024-05-23 18:18:30,012 - mmseg - INFO - Set random seed to 1876373295, deterministic: False | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/models/decode_heads/decode_head.py:104: UserWarning: For binary segmentation, we suggest using`out_channels = 1` to define the outputchannels of segmentor, and use `threshold`to convert seg_logist into a predictionapplying a threshold | |
| warnings.warn('For binary segmentation, we suggest using' | |
| 2024-05-23 18:18:32,535 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,536 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,537 - mmcv - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| 2024-05-23 18:18:32,538 - mmcv - INFO - | |
| backbone.cls_token - torch.Size([1, 1, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.pos_embed - torch.Size([1, 197, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.patch_embed.proj.weight - torch.Size([768, 6, 1, 16, 16]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.patch_embed.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.0.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.1.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.2.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.2.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,539 - mmcv - INFO - | |
| backbone.blocks.2.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.2.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.3.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,540 - mmcv - INFO - | |
| backbone.blocks.4.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.4.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.4.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.4.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.4.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.5.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.6.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.7.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.7.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.7.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.7.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,541 - mmcv - INFO - | |
| backbone.blocks.7.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.7.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.8.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.9.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.10.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,542 - mmcv - INFO - | |
| backbone.blocks.10.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.10.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.norm1.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.norm1.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.attn.qkv.weight - torch.Size([2304, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.attn.qkv.bias - torch.Size([2304]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.attn.proj.weight - torch.Size([768, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.attn.proj.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.norm2.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.norm2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.mlp.fc1.weight - torch.Size([3072, 768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.mlp.fc1.bias - torch.Size([3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.mlp.fc2.weight - torch.Size([768, 3072]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.blocks.11.mlp.fc2.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.norm.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| backbone.norm.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn1.0.weight - torch.Size([768, 768, 2, 2]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn1.0.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn1.1.ln.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn1.1.ln.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn1.3.weight - torch.Size([768, 768, 2, 2]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn1.3.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn2.0.weight - torch.Size([768, 768, 2, 2]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn2.0.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,543 - mmcv - INFO - | |
| neck.fpn2.1.ln.weight - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| neck.fpn2.1.ln.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| neck.fpn2.3.weight - torch.Size([768, 768, 2, 2]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| neck.fpn2.3.bias - torch.Size([768]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| decode_head.conv_seg.weight - torch.Size([2, 256, 1, 1]): | |
| NormalInit: mean=0, std=0.01, bias=0 | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| decode_head.conv_seg.bias - torch.Size([2]): | |
| NormalInit: mean=0, std=0.01, bias=0 | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| decode_head.convs.0.conv.weight - torch.Size([256, 768, 3, 3]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| decode_head.convs.0.bn.weight - torch.Size([256]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| decode_head.convs.0.bn.bias - torch.Size([256]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.conv_seg.weight - torch.Size([2, 256, 1, 1]): | |
| NormalInit: mean=0, std=0.01, bias=0 | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.conv_seg.bias - torch.Size([2]): | |
| NormalInit: mean=0, std=0.01, bias=0 | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.convs.0.conv.weight - torch.Size([256, 768, 3, 3]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.convs.0.bn.weight - torch.Size([256]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.convs.0.bn.bias - torch.Size([256]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.convs.1.conv.weight - torch.Size([256, 256, 3, 3]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.convs.1.bn.weight - torch.Size([256]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmcv - INFO - | |
| auxiliary_head.convs.1.bn.bias - torch.Size([256]): | |
| The value is the same before and after calling `init_weights` of TemporalEncoderDecoder | |
| 2024-05-23 18:18:32,544 - mmseg - INFO - TemporalEncoderDecoder( | |
| (backbone): TemporalViTEncoder( | |
| (patch_embed): PatchEmbed( | |
| (proj): Conv3d(6, 768, kernel_size=(1, 16, 16), stride=(1, 16, 16)) | |
| (norm): Identity() | |
| ) | |
| (blocks): ModuleList( | |
| (0): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (1): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (2): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (3): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (4): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (5): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (6): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (7): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (8): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (9): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (10): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| (11): Block( | |
| (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (attn): Attention( | |
| (qkv): Linear(in_features=768, out_features=2304, bias=True) | |
| (attn_drop): Dropout(p=0.0, inplace=False) | |
| (proj): Linear(in_features=768, out_features=768, bias=True) | |
| (proj_drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| (drop_path): Identity() | |
| (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| (mlp): Mlp( | |
| (fc1): Linear(in_features=768, out_features=3072, bias=True) | |
| (act): GELU(approximate=none) | |
| (fc2): Linear(in_features=3072, out_features=768, bias=True) | |
| (drop): Dropout(p=0.0, inplace=False) | |
| ) | |
| ) | |
| ) | |
| (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) | |
| ) | |
| (neck): ConvTransformerTokensToEmbeddingNeck( | |
| (fpn1): Sequential( | |
| (0): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2)) | |
| (1): Norm2d( | |
| (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (2): GELU(approximate=none) | |
| (3): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2)) | |
| ) | |
| (fpn2): Sequential( | |
| (0): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2)) | |
| (1): Norm2d( | |
| (ln): LayerNorm((768,), eps=1e-06, elementwise_affine=True) | |
| ) | |
| (2): GELU(approximate=none) | |
| (3): ConvTranspose2d(768, 768, kernel_size=(2, 2), stride=(2, 2)) | |
| ) | |
| ) | |
| (decode_head): FCNHead( | |
| input_transform=None, ignore_index=255, align_corners=False | |
| (loss_decode): DiceLoss() | |
| (conv_seg): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1)) | |
| (dropout): Dropout2d(p=0.1, inplace=False) | |
| (convs): Sequential( | |
| (0): ConvModule( | |
| (conv): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| ) | |
| ) | |
| init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| (auxiliary_head): FCNHead( | |
| input_transform=None, ignore_index=255, align_corners=False | |
| (loss_decode): DiceLoss() | |
| (conv_seg): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1)) | |
| (dropout): Dropout2d(p=0.1, inplace=False) | |
| (convs): Sequential( | |
| (0): ConvModule( | |
| (conv): Conv2d(768, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| (1): ConvModule( | |
| (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) | |
| (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (activate): ReLU(inplace=True) | |
| ) | |
| ) | |
| ) | |
| init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} | |
| ) | |
| 2024-05-23 18:18:32,547 - mmseg - INFO - Loaded 0 images | |
| 2024-05-23 18:18:33,432 - mmseg - INFO - Loaded 0 images | |
| 2024-05-23 18:18:33,433 - mmseg - INFO - Start running, host: strube1@jwb0088.juwels, work_dir: /p/project/training2411/strube1/HDCRS-school-2024/v1/burn_scars | |
| 2024-05-23 18:18:33,433 - mmseg - INFO - Hooks will be executed in the following order: | |
| before_run: | |
| (VERY_HIGH ) PolyLrUpdaterHook | |
| (NORMAL ) CheckpointHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| before_train_epoch: | |
| (VERY_HIGH ) PolyLrUpdaterHook | |
| (LOW ) IterTimerHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| before_train_iter: | |
| (VERY_HIGH ) PolyLrUpdaterHook | |
| (LOW ) IterTimerHook | |
| (LOW ) DistEvalHook | |
| -------------------- | |
| after_train_iter: | |
| (ABOVE_NORMAL) OptimizerHook | |
| (NORMAL ) CheckpointHook | |
| (LOW ) IterTimerHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| after_train_epoch: | |
| (NORMAL ) CheckpointHook | |
| (LOW ) DistEvalHook | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| before_val_epoch: | |
| (LOW ) IterTimerHook | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| before_val_iter: | |
| (LOW ) IterTimerHook | |
| -------------------- | |
| after_val_iter: | |
| (LOW ) IterTimerHook | |
| -------------------- | |
| after_val_epoch: | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| after_run: | |
| (VERY_LOW ) TextLoggerHook | |
| (VERY_LOW ) TensorboardLoggerHook | |
| -------------------- | |
| 2024-05-23 18:18:33,433 - mmseg - INFO - workflow: [('train', 1)], max: 10000 iters | |
| 2024-05-23 18:18:33,433 - mmseg - INFO - Checkpoints will be saved to v1/burn_scars/burn_scars by HardDiskBackend. | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/__init__.py:20: UserWarning: On January 1, 2023, MMCV will release v2.0.0, in which it will remove components related to the training process and add a data transformation module. In addition, it will rename the package names mmcv to mmcv-lite and mmcv-full to mmcv. See https://github.com/open-mmlab/mmcv/blob/master/docs/en/compatibility.md for more details. | |
| warnings.warn( | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| /p/software/juwelsbooster/stages/2023/software/SciPy-bundle/2022.05-gcccoremkl-11.3.0-2022.1.0/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4 | |
| warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 34, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 244, in <module> | |
| main() | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/train.py", line 234, in main | |
| train_segmentor( | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmseg/apis/train.py", line 194, in train_segmentor | |
| runner.run(data_loaders, cfg.workflow) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run | |
| iter_runner(iter_loaders[i], **kwargs) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train | |
| data_batch = next(data_loader) | |
| File "/p/project/training2411/strube1/HDCRS-school-2024/sc_venv_template/venv/lib/python3.10/site-packages/mmcv/runner/iter_based_runner.py", line 41, in __next__ | |
| data = next(self.iter_loader) | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 652, in __next__ | |
| data = self._next_data() | |
| File "/p/software/juwelsbooster/stages/2023/software/PyTorch/1.12.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1320, in _next_data | |
| raise StopIteration | |
| StopIteration | |
| /p/software/juwelsbooster/stages/2023/software/Python/3.10.4-GCCcore-11.3.0/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown | |
| warnings.warn('resource_tracker: There appear to be %d ' | |
| /p/software/juwelsbooster/stages/2023/software/Python/3.10.4-GCCcore-11.3.0/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown | |
| warnings.warn('resource_tracker: There appear to be %d ' | |
| /p/software/juwelsbooster/stages/2023/software/Python/3.10.4-GCCcore-11.3.0/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown | |
| warnings.warn('resource_tracker: There appear to be %d ' | |
| srun: error: jwb0096: tasks 4-6: Terminated | |
| srun: launch/slurm: _step_signal: Terminating StepId=9886567.0 | |
| srun: error: jwb0096: task 7: Exited with exit code 1 | |
| srun: error: jwb0088: tasks 0-2: Terminated | |
| srun: error: jwb0088: task 3: Exited with exit code 1 | |
| srun: Force Terminated StepId=9886567.0 |
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