Closed vansin closed 1 year ago
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
master branch https://github.com/open-mmlab/mmrotate
(mmroate) ➜ mmrotate git:(qd) ✗ python mmrotate/utils/collect_env.py sys.platform: linux Python: 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0: Tesla V100-SXM2-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.4, V11.4.152 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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-sign-compare -Wno-unused-parameter -Wno-unused-variable -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.11.0+cu111 OpenCV: 4.6.0 MMEngine: 0.4.0 MMRotate: 1.0.0rc0+3db9cc0
python tools/train.py configs/rotated_rtmdet/rotated_rtmdet_l-3x-dota.py
(mmroate) ➜ mmrotate git:(qd) ✗ python tools/train.py configs/rotated_rtmdet/rotated_rtmdet_l-3x-dota.py 01/02 00:48:06 - mmengine - WARNING - The "log_processor" registry in mmrotate did not set import location. Fallback to call `mmrotate.utils.register_all_modules` instead. 01/02 00:48:06 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.15 (default, Nov 24 2022, 15:19:38) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1418043970 GPU 0: Tesla V100-SXM2-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.4, V11.4.152 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.10.0+cu111 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - CUDA Runtime 11.1 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.0.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -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-sign-compare -Wno-unused-parameter -Wno-unused-variable -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.11.0+cu111 OpenCV: 4.6.0 MMEngine: 0.4.0 Runtime environment: cudnn_benchmark: False mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: none Distributed training: False GPU number: 1 ------------------------------------------------------------ 01/02 00:48:07 - mmengine - INFO - Config: default_scope = 'mmrotate' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=1), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', interval=12, max_keep_ckpts=3), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='mmdet.DetVisualizationHook')) env_cfg = dict( cudnn_benchmark=False, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='RotLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = None resume = False custom_hooks = [ dict(type='mmdet.NumClassCheckHook'), dict( type='EMAHook', ema_type='mmdet.ExpMomentumEMA', momentum=0.0002, update_buffers=True, priority=49) ] max_epochs = 36 base_lr = 0.00025 interval = 12 train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=12) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') param_scheduler = [ dict( type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0, end=1000), dict( type='CosineAnnealingLR', eta_min=1.25e-05, begin=18, end=36, T_max=18, by_epoch=True, convert_to_iter_based=True) ] optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.00025, weight_decay=0.05), paramwise_cfg=dict( norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) dataset_type = 'DOTADataset' data_root = 'data/icdar2019_tracka_modern_qbox/' file_client_args = dict(backend='disk') METAINFO = dict(classes=('table', )) train_pipeline = [ dict( type='mmdet.LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), dict( type='mmdet.RandomFlip', prob=0.75, direction=['horizontal', 'vertical', 'diagonal']), dict( type='RandomRotate', prob=0.5, angle_range=180, rect_obj_labels=[9, 11]), dict( type='mmdet.Pad', size=(1024, 1024), pad_val=dict(img=(114, 114, 114))), dict(type='mmdet.PackDetInputs') ] val_pipeline = [ dict( type='mmdet.LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), dict( type='mmdet.Pad', size=(1024, 1024), pad_val=dict(img=(114, 114, 114))), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] test_pipeline = [ dict( type='mmdet.LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), dict( type='mmdet.Pad', size=(1024, 1024), pad_val=dict(img=(114, 114, 114))), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')) ] train_dataloader = dict( batch_size=4, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=True), batch_sampler=None, pin_memory=False, dataset=dict( type='DOTADataset', metainfo=dict(classes=('table', )), data_root='data/icdar2019_tracka_modern_qbox/', ann_file='train_rotate_qbox/', data_prefix=dict(img_path='train_rotate_img/'), img_shape=(1024, 1024), filter_cfg=dict(filter_empty_gt=True), pipeline=[ dict( type='mmdet.LoadImageFromFile', file_client_args=dict(backend='disk')), dict( type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), dict( type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), dict( type='mmdet.RandomFlip', prob=0.75, direction=['horizontal', 'vertical', 'diagonal']), dict( type='RandomRotate', prob=0.5, angle_range=180, rect_obj_labels=[9, 11]), dict( type='mmdet.Pad', size=(1024, 1024), pad_val=dict(img=(114, 114, 114))), dict(type='mmdet.PackDetInputs') ])) val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='DOTADataset', data_root='data/icdar2019_tracka_modern_qbox/', metainfo=dict(classes=('table', )), ann_file='test_rotate_qbox/', data_prefix=dict(img_path='test_rotate_img/'), img_shape=(1024, 1024), test_mode=True, pipeline=[ dict( type='mmdet.LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), dict( type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), dict( type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), dict( type='mmdet.Pad', size=(1024, 1024), pad_val=dict(img=(114, 114, 114))), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='DOTADataset', data_root='data/icdar2019_tracka_modern_qbox/', metainfo=dict(classes=('table', )), ann_file='test_rotate_qbox/', data_prefix=dict(img_path='test_rotate_img/'), img_shape=(1024, 1024), test_mode=True, pipeline=[ dict( type='mmdet.LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True), dict( type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'), dict( type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')), dict( type='mmdet.Pad', size=(1024, 1024), pad_val=dict(img=(114, 114, 114))), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')) ])) val_evaluator = dict(type='DOTAMetric', metric='mAP') test_evaluator = dict(type='DOTAMetric', metric='mAP') checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth' angle_version = 'le90' model = dict( type='mmdet.RTMDet', data_preprocessor=dict( type='mmdet.DetDataPreprocessor', mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], bgr_to_rgb=False, boxtype2tensor=False, batch_augments=None), backbone=dict( type='mmdet.CSPNeXt', arch='P5', expand_ratio=0.5, deepen_factor=1, widen_factor=1, channel_attention=True, norm_cfg=dict(type='SyncBN'), act_cfg=dict(type='SiLU'), init_cfg=dict( type='Pretrained', prefix='backbone.', checkpoint= 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth' )), neck=dict( type='mmdet.CSPNeXtPAFPN', in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3, expand_ratio=0.5, norm_cfg=dict(type='SyncBN'), act_cfg=dict(type='SiLU')), bbox_head=dict( type='RotatedRTMDetSepBNHead', num_classes=1, in_channels=256, stacked_convs=2, feat_channels=256, angle_version='le90', anchor_generator=dict( type='mmdet.MlvlPointGenerator', offset=0, strides=[8, 16, 32]), bbox_coder=dict(type='DistanceAnglePointCoder', angle_version='le90'), loss_cls=dict( type='mmdet.QualityFocalLoss', use_sigmoid=True, beta=2.0, loss_weight=1.0), loss_bbox=dict(type='RotatedIoULoss', mode='linear', loss_weight=2.0), with_objectness=False, exp_on_reg=True, share_conv=True, pred_kernel_size=1, use_hbbox_loss=False, scale_angle=False, loss_angle=None, norm_cfg=dict(type='SyncBN'), act_cfg=dict(type='SiLU')), train_cfg=dict( assigner=dict( type='mmdet.DynamicSoftLabelAssigner', iou_calculator=dict(type='RBboxOverlaps2D'), topk=13), allowed_border=-1, pos_weight=-1, debug=False), test_cfg=dict( nms_pre=2000, min_bbox_size=0, score_thr=0.05, nms=dict(type='nms_rotated', iou_threshold=0.1), max_per_img=2000)) launcher = 'none' work_dir = './work_dirs/rotated_rtmdet_l-3x-dota' 01/02 00:48:14 - mmengine - WARNING - The "optimizer" registry in mmrotate did not set import location. Fallback to call `mmrotate.utils.register_all_modules` instead. 01/02 00:48:14 - mmengine - WARNING - Failed to search registry with scope "mmrotate" in the "optim_wrapper" registry tree. As a workaround, the current "optim_wrapper" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmrotate" is a correct scope, or whether the registry is initialized. 01/02 00:48:14 - mmengine - WARNING - The "parameter scheduler" registry in mmrotate did not set import location. Fallback to call `mmrotate.utils.register_all_modules` instead. 01/02 00:48:14 - mmengine - WARNING - The "metric" registry in mmrotate did not set import location. Fallback to call `mmrotate.utils.register_all_modules` instead. 01/02 00:48:15 - mmengine - WARNING - The "weight initializer" registry in mmrotate did not set import location. Fallback to call `mmrotate.utils.register_all_modules` instead. 01/02 00:48:15 - mmengine - INFO - load backbone. in model from: https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth Loads checkpoint by http backend from path: https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth 01/02 00:48:15 - mmengine - INFO - Checkpoints will be saved to /home/ubuntu/mmroate-1.x/mmrotate/work_dirs/rotated_rtmdet_l-3x-dota. /home/ubuntu/mmroate-1.x/mmrotate/mmrotate/structures/bbox/rotated_boxes.py:192: UserWarning: The `clip` function does nothing in `RotatedBoxes`. warnings.warn('The `clip` function does nothing in `RotatedBoxes`.') /home/ubuntu/mmroate-1.x/mmrotate/mmrotate/structures/bbox/rotated_boxes.py:192: UserWarning: The `clip` function does nothing in `RotatedBoxes`. warnings.warn('The `clip` function does nothing in `RotatedBoxes`.') /home/ubuntu/mmroate-1.x/mmrotate/mmrotate/structures/bbox/rotated_boxes.py:192: UserWarning: The `clip` function does nothing in `RotatedBoxes`. warnings.warn('The `clip` function does nothing in `RotatedBoxes`.') /home/ubuntu/mmroate-1.x/mmrotate/mmrotate/structures/bbox/rotated_boxes.py:192: UserWarning: The `clip` function does nothing in `RotatedBoxes`. warnings.warn('The `clip` function does nothing in `RotatedBoxes`.') /home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Traceback (most recent call last): File "tools/train.py", line 122, in <module> main() File "tools/train.py", line 118, in main runner.train() File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/runner/runner.py", line 1678, in train model = self.train_loop.run() # type: ignore File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/runner/loops.py", line 90, in run self.run_epoch() File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/runner/loops.py", line 106, in run_epoch self.run_iter(idx, data_batch) File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/runner/loops.py", line 122, in run_iter outputs = self.runner.model.train_step( File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/model/base_model/base_model.py", line 114, in train_step losses = self._run_forward(data, mode='loss') # type: ignore File "/home/ubuntu/mmroate-1.x/mmengine/mmengine/model/base_model/base_model.py", line 314, in _run_forward results = self(**data, mode=mode) File "/home/ubuntu/miniconda3/envs/mmroate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/detectors/base.py", line 92, in forward return self.loss(inputs, data_samples) File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/detectors/single_stage.py", line 78, in loss losses = self.bbox_head.loss(x, batch_data_samples) File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/dense_heads/base_dense_head.py", line 123, in loss losses = self.loss_by_feat(*loss_inputs) File "/home/ubuntu/mmroate-1.x/mmrotate/mmrotate/models/dense_heads/rotated_rtmdet_head.py", line 299, in loss_by_feat cls_reg_targets = self.get_targets( File "/home/ubuntu/mmroate-1.x/mmdetection/mmdet/models/dense_heads/rtmdet_head.py", line 355, in get_targets (all_anchors, all_labels, all_label_weights, all_bbox_targets, ValueError: not enough values to unpack (expected 6, got 5)
### Additional information ```shell python tools/train.py configs/rotated_rtmdet/rotated_rtmdet_l-3x-dota.py
We have fix it in https://github.com/open-mmlab/mmrotate/pull/676.
Prerequisite
Task
I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
Branch
master branch https://github.com/open-mmlab/mmrotate
Environment
Reproduces the problem - code sample
Reproduces the problem - command or script
Reproduces the problem - error message