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ligang-cs / PseCo

An official implementation of the PseCo (ECCV2022)
Apache License 2.0
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FileNotFoundError: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json' #10

Open liuhaolinwen opened 2 years ago

liuhaolinwen commented 2 years ago

loading annotations into memory... Traceback (most recent call last): File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg return obj_cls(**args) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/custom.py", line 89, in init self.data_infos = self.load_annotations(self.ann_file) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/coco.py", line 49, in load_annotations self.coco = COCO(ann_file) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/api_wrappers/coco_api.py", line 28, in init super().init(annotation_file=annotation_file) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/pycocotools/coco.py", line 81, in init with open(annotation_file, 'r') as f: FileNotFoundError: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg return obj_cls(args) File "/home/liuhaolin/PseCo/ssod/datasets/dataset_wrappers.py", line 10, in init super().init([build_dataset(sup), build_dataset(unsup)], kwargs) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/builder.py", line 78, in build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') FileNotFoundError: CocoDataset: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "tools/train.py", line 198, in main() File "tools/train.py", line 172, in main datasets = [build_dataset(cfg.data.train)] File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/builder.py", line 78, in build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') FileNotFoundError: SemiDataset: CocoDataset: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json' loading annotations into memory... Traceback (most recent call last): File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg return obj_cls(**args) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/custom.py", line 89, in init self.data_infos = self.load_annotations(self.ann_file) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/coco.py", line 49, in load_annotations self.coco = COCO(ann_file) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/api_wrappers/coco_api.py", line 28, in init super().init(annotation_file=annotation_file) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/pycocotools/coco.py", line 81, in init with open(annotation_file, 'r') as f: FileNotFoundError: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg return obj_cls(args) File "/home/liuhaolin/PseCo/ssod/datasets/dataset_wrappers.py", line 10, in init super().init([build_dataset(sup), build_dataset(unsup)], kwargs) File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/builder.py", line 78, in build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') FileNotFoundError: CocoDataset: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "tools/train.py", line 198, in main() File "tools/train.py", line 172, in main datasets = [build_dataset(cfg.data.train)] File "/home/liuhaolin/PseCo/thirdparty/mmdetection/mmdet/datasets/builder.py", line 78, in build_dataset dataset = build_from_cfg(cfg, DATASETS, default_args) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg raise type(e)(f'{obj_cls.name}: {e}') FileNotFoundError: SemiDataset: CocoDataset: [Errno 2] No such file or directory: '../data/annotations/semi_supervised/instances_train2017.1@10.json' Killing subprocess 94576 Killing subprocess 94577 Traceback (most recent call last): File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/torch/distributed/launch.py", line 340, in main() File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/torch/distributed/launch.py", line 326, in main sigkill_handler(signal.SIGTERM, None) # not coming back File "/sdb/liuhaolin/anaconda3/envs/pseco/lib/python3.8/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd) subprocess.CalledProcessError: Command '['/sdb/liuhaolin/anaconda3/envs/pseco/bin/python', '-u', 'tools/train.py', '--local_rank=1', 'configs/PseCo/PseCo_faster_rcnn_r50_caffe_fpn_coco_180k.py', '--work-dir', '/home/liuhaolin/PseCo/', '--launcher=pytorch', '--cfg-options', 'fold=1', 'percent=10']' returned non-zero exit status 1.

Luojlong commented 2 years ago

the program can not find out the 10% labeling ratio in fold 1.you need to create the dataset by /tools/dataset/prepare_coco_data.sh.

liuhaolinwen commented 2 years ago

Great， solved it

Luojlong commented 2 years ago

Have you run into the problem for valueError in training which make me annoyed?

liuhaolinwen commented 2 years ago

i have solved the problem following your advice， we need generate the annotations of 10% labeling ratio in fold 1， besides we should modify the dataset directory in base.py and PseCo_faster_rcnn_celoss_r50_caffe_fpn_coco_180k.py

monsterlv-lhj commented 2 years ago 
interval=5000, by_epoch=False, max_keep_ckpts=10, create_symlink=False)

log_config = dict( interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) custom_hooks = [ dict(type='NumClassCheckHook'), dict(type='WeightSummary'), dict(type='MeanTeacher', momentum=0.999, warm_up=0), dict(type='GetCurrentIter') ] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] mmdet_base = '../../thirdparty/mmdetection/configs/base' strong_pipeline = [ dict( type='Sequential', transforms=[ dict( type='RandResize', img_scale=[(1333, 400), (1333, 1200)], multiscale_mode='range', keep_ratio=True), dict(type='RandFlip', flip_ratio=0.5), dict( type='ShuffledSequential', transforms=[ dict( type='OneOf', transforms=[ dict(type='Identity'), dict(type='AutoContrast'), dict(type='RandEqualize'), dict(type='RandSolarize'), dict(type='RandColor'), dict(type='RandContrast'), dict(type='RandBrightness'), dict(type='RandSharpness'), dict(type='RandPosterize') ]), dict( type='OneOf', transforms=[{ 'type': 'RandTranslate', 'x': (-0.1, 0.1) }, { 'type': 'RandTranslate', 'y': (-0.1, 0.1) }, { 'type': 'RandRotate', 'angle': (-30, 30) }, [{ 'type': 'RandShear', 'x': (-30, 30) }, { 'type': 'RandShear', 'y': (-30, 30) }]]) ]), dict( type='RandErase', n_iterations=(1, 5), size=[0, 0.2], squared=True) ], record=True), dict(type='Pad', size_divisor=32), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='ExtraAttrs', tag='unsup_student'), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag', 'transform_matrix', 'flip', 'flip_direction')) ] weak_pipeline = [ dict( type='Sequential', transforms=[ dict( type='RandResize', img_scale=[(1333, 400), (1333, 1200)], multiscale_mode='range', keep_ratio=True), dict(type='RandFlip', flip_ratio=0.5) ], record=True), dict(type='Pad', size_divisor=32), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='ExtraAttrs', tag='unsup_teacher'), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag', 'transform_matrix', 'flip', 'flip_direction')) ] unsup_pipeline = [ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='PseudoSamples', with_bbox=True), dict( type='MultiBranch', unsup_student=[ dict( type='Sequential', transforms=[ dict( type='RandResize', img_scale=[(1333, 400), (1333, 1200)], multiscale_mode='range', keep_ratio=True), dict(type='RandFlip', flip_ratio=0.5), dict( type='ShuffledSequential', transforms=[ dict( type='OneOf', transforms=[ dict(type='Identity'), dict(type='AutoContrast'), dict(type='RandEqualize'), dict(type='RandSolarize'), dict(type='RandColor'), dict(type='RandContrast'), dict(type='RandBrightness'), dict(type='RandSharpness'), dict(type='RandPosterize') ]), dict( type='OneOf', transforms=[{ 'type': 'RandTranslate', 'x': (-0.1, 0.1) }, { 'type': 'RandTranslate', 'y': (-0.1, 0.1) }, { 'type': 'RandRotate', 'angle': (-30, 30) }, [{ 'type': 'RandShear', 'x': (-30, 30) }, { 'type': 'RandShear', 'y': (-30, 30) }]]) ]), dict( type='RandErase', n_iterations=(1, 5), size=[0, 0.2], squared=True) ], record=True), dict(type='Pad', size_divisor=32), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='ExtraAttrs', tag='unsup_student'), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag', 'transform_matrix', 'flip', 'flip_direction')) ], unsup_teacher=[ dict( type='Sequential', transforms=[ dict( type='RandResize', img_scale=[(1333, 400), (1333, 1200)], multiscale_mode='range', keep_ratio=True), dict(type='RandFlip', flip_ratio=0.5) ], record=True), dict(type='Pad', size_divisor=32), dict( type='Normalize', mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False), dict(type='ExtraAttrs', tag='unsup_teacher'), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', 'pad_shape', 'scale_factor', 'tag', 'transform_matrix', 'flip', 'flip_direction')) ]) ] thres = 0.9 refresh = False fp16 = dict(loss_scale='dynamic') fold = 1 percent = 10 auto_resume = True find_unused_parameters = True backend = 'disk' work_dir = '/home/lihejun/hbzn_workspace_lee/semi-supervised-objection' cfg_name = 'PseCo_faster_rcnn_r50_caffe_fpn_coco_180k' gpu_ids = range(0, 4)

[W ProcessGroupNCCL.cpp:1569] Rank 2 using best-guess GPU 2 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device. 2022-10-27 14:41:05,808 - mmdet.ssod - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'} [W ProcessGroupNCCL.cpp:1569] Rank 1 using best-guess GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device. 2022-10-27 14:41:05,808 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe 2022-10-27 14:41:05,808 - mmcv - INFO - load checkpoint from openmmlab path: open-mmlab://detectron2/resnet50_caffe [W ProcessGroupNCCL.cpp:1569] Rank 0 using best-guess GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device. [W ProcessGroupNCCL.cpp:1569] Rank 3 using best-guess GPU 3 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device. 2022-10-27 14:41:09,259 - mmcv - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: conv1.bias

2022-10-27 14:41:09,282 - mmdet.ssod - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2022-10-27 14:41:09,342 - mmdet.ssod - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} 2022-10-27 14:41:09,347 - mmdet.ssod - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}] 2022-10-27 14:41:09,480 - mmdet.ssod - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'} 2022-10-27 14:41:09,481 - mmcv - INFO - load model from: open-mmlab://detectron2/resnet50_caffe 2022-10-27 14:41:09,481 - mmcv - INFO - load checkpoint from openmmlab path: open-mmlab://detectron2/resnet50_caffe 2022-10-27 14:41:09,602 - mmcv - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: conv1.bias

2022-10-27 14:41:09,629 - mmdet.ssod - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'} 2022-10-27 14:41:09,687 - mmdet.ssod - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01} 2022-10-27 14:41:09,692 - mmdet.ssod - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}] loading annotations into memory... loading annotations into memory... loading annotations into memory... loading annotations into memory... Done (t=0.09s) creating index... index created! loading annotations into memory... Done (t=0.09s) creating index... index created! Done (t=0.09s) creating index... loading annotations into memory... index created! Done (t=0.09s) creating index... index created! loading annotations into memory... loading annotations into memory... Done (t=14.84s) creating index... index created! Done (t=15.75s) creating index... Done (t=15.75s) creating index... Done (t=15.83s) creating index... index created! index created! index created! loading annotations into memory... loading annotations into memory... loading annotations into memory... loading annotations into memory... Done (t=0.47s) creating index... index created! Done (t=0.50s) creating index... Done (t=0.50s) creating index... Done (t=0.50s) creating index... index created! index created! index created! 2022-10-27 14:41:29,593 - mmdet.ssod - INFO - Start running, host: lihejun@vip14, work_dir: /home/lihejun/hbzn_workspace_lee/semi-supervised-objection 2022-10-27 14:41:29,594 - mmdet.ssod - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook
(ABOVE_NORMAL) Fp16OptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) WeightSummary
(NORMAL ) MeanTeacher
(80 ) SubModulesDistEvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(80 ) SubModulesDistEvalHook
(VERY_LOW ) TextLoggerHook

before_train_iter: (VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) MeanTeacher
(NORMAL ) GetCurrentIter
(LOW ) IterTimerHook
(80 ) SubModulesDistEvalHook

after_train_iter: (ABOVE_NORMAL) Fp16OptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) MeanTeacher
(LOW ) IterTimerHook
(80 ) SubModulesDistEvalHook
(VERY_LOW ) TextLoggerHook

after_train_epoch: (NORMAL ) CheckpointHook
(80 ) SubModulesDistEvalHook
(VERY_LOW ) TextLoggerHook

before_val_epoch: (NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_val_iter: (LOW ) IterTimerHook

after_val_iter: (LOW ) IterTimerHook

after_val_epoch: (VERY_LOW ) TextLoggerHook

after_run: (VERY_LOW ) TextLoggerHook

2022-10-27 14:41:29,595 - mmdet.ssod - INFO - workflow: [('train', 1)], max: 180000 iters 2022-10-27 14:41:29,597 - mmdet.ssod - INFO - Checkpoints will be saved to /home/lihejun/hbzn_workspace_lee/semi-supervised-objection by HardDiskBackend. 2022-10-27 14:41:29,747 - mmdet.ssod - INFO - +--------------------------------------------------------------------------------------------------------------------+ | Model Information | +------------------------------------------------+-----------+---------------+-----------------------+------+--------+ | Name | Optimized | Shape | Value Scale [Min,Max] | Lr | Wd | +------------------------------------------------+-----------+---------------+-----------------------+------+--------+ | teacher.backbone.conv1.weight | N | 64X3X7X7 | Min:-0.671 Max:0.704 | 0.01 | 0.0001 | | teacher.backbone.bn1.weight | N | 64 | Min:0.513 Max:2.669 | 0.01 | 0.0001 | | teacher.backbone.bn1.bias | N | 64 | Min:-2.654 Max:6.354 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.conv1.weight | N | 64X64X1X1 | Min:-0.717 Max:0.392 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.bn1.weight | N | 64 | Min:0.509 Max:2.066 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.bn1.bias | N | 64 | Min:-2.411 Max:3.608 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.conv2.weight | N | 64X64X3X3 | Min:-0.390 Max:0.364 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.bn2.weight | N | 64 | Min:0.420 Max:2.530 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.bn2.bias | N | 64 | Min:-2.286 Max:5.913 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.conv3.weight | N | 256X64X1X1 | Min:-0.397 Max:0.348 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.bn3.weight | N | 256 | Min:0.011 Max:2.820 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.bn3.bias | N | 256 | Min:-1.126 Max:1.522 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.downsample.0.weight | N | 256X64X1X1 | Min:-0.772 Max:0.900 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.downsample.1.weight | N | 256 | Min:0.004 Max:3.064 | 0.01 | 0.0001 | | teacher.backbone.layer1.0.downsample.1.bias | N | 256 | Min:-1.126 Max:1.522 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.conv1.weight | N | 64X256X1X1 | Min:-0.297 Max:0.220 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.bn1.weight | N | 64 | Min:0.746 Max:1.949 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.bn1.bias | N | 64 | Min:-1.688 Max:1.578 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.conv2.weight | N | 64X64X3X3 | Min:-0.240 Max:0.318 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.bn2.weight | N | 64 | Min:0.621 Max:1.618 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.bn2.bias | N | 64 | Min:-2.003 Max:2.398 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.conv3.weight | N | 256X64X1X1 | Min:-0.240 Max:0.280 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.bn3.weight | N | 256 | Min:-0.017 Max:2.130 | 0.01 | 0.0001 | | teacher.backbone.layer1.1.bn3.bias | N | 256 | Min:-1.711 Max:1.291 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.conv1.weight | N | 64X256X1X1 | Min:-0.210 Max:0.264 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.bn1.weight | N | 64 | Min:0.574 Max:1.688 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.bn1.bias | N | 64 | Min:-1.876 Max:1.090 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.conv2.weight | N | 64X64X3X3 | Min:-0.218 Max:0.201 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.bn2.weight | N | 64 | Min:0.757 Max:1.649 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.bn2.bias | N | 64 | Min:-2.221 Max:1.878 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.conv3.weight | N | 256X64X1X1 | Min:-0.275 Max:0.350 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.bn3.weight | N | 256 | Min:-0.058 Max:2.154 | 0.01 | 0.0001 | | teacher.backbone.layer1.2.bn3.bias | N | 256 | Min:-1.570 Max:1.535 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.conv1.weight | N | 128X256X1X1 | Min:-0.334 Max:0.300 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.bn1.weight | N | 128 | Min:0.610 Max:1.642 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.bn1.bias | N | 128 | Min:-1.579 Max:1.449 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.conv2.weight | N | 128X128X3X3 | Min:-0.384 Max:0.377 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.bn2.weight | N | 128 | Min:0.605 Max:1.622 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.bn2.bias | N | 128 | Min:-2.768 Max:1.747 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.conv3.weight | N | 512X128X1X1 | Min:-0.374 Max:0.434 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.bn3.weight | N | 512 | Min:-0.007 Max:2.730 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.bn3.bias | N | 512 | Min:-1.545 Max:1.256 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.downsample.0.weight | N | 512X256X1X1 | Min:-0.466 Max:0.642 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.downsample.1.weight | N | 512 | Min:0.006 Max:2.552 | 0.01 | 0.0001 | | teacher.backbone.layer2.0.downsample.1.bias | N | 512 | Min:-1.545 Max:1.256 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.conv1.weight | N | 128X512X1X1 | Min:-0.162 Max:0.195 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.bn1.weight | N | 128 | Min:0.578 Max:1.429 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.bn1.bias | N | 128 | Min:-4.348 Max:0.588 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.conv2.weight | N | 128X128X3X3 | Min:-0.176 Max:0.177 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.bn2.weight | N | 128 | Min:0.511 Max:1.794 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.bn2.bias | N | 128 | Min:-3.825 Max:1.343 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.conv3.weight | N | 512X128X1X1 | Min:-0.344 Max:0.336 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.bn3.weight | N | 512 | Min:-0.072 Max:2.122 | 0.01 | 0.0001 | | teacher.backbone.layer2.1.bn3.bias | N | 512 | Min:-1.502 Max:1.166 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.conv1.weight | N | 128X512X1X1 | Min:-0.330 Max:0.369 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.bn1.weight | N | 128 | Min:0.406 Max:1.696 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.bn1.bias | N | 128 | Min:-2.696 Max:1.944 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.conv2.weight | N | 128X128X3X3 | Min:-0.326 Max:0.374 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.bn2.weight | N | 128 | Min:0.460 Max:2.179 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.bn2.bias | N | 128 | Min:-1.587 Max:0.589 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.conv3.weight | N | 512X128X1X1 | Min:-0.288 Max:0.232 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.bn3.weight | N | 512 | Min:-0.006 Max:3.043 | 0.01 | 0.0001 | | teacher.backbone.layer2.2.bn3.bias | N | 512 | Min:-2.369 Max:0.440 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.conv1.weight | N | 128X512X1X1 | Min:-0.298 Max:0.346 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.bn1.weight | N | 128 | Min:0.736 Max:2.394 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.bn1.bias | N | 128 | Min:-2.643 Max:0.756 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.conv2.weight | N | 128X128X3X3 | Min:-0.272 Max:0.208 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.bn2.weight | N | 128 | Min:0.682 Max:1.694 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.bn2.bias | N | 128 | Min:-1.365 Max:1.599 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.conv3.weight | N | 512X128X1X1 | Min:-0.279 Max:0.281 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.bn3.weight | N | 512 | Min:-0.009 Max:1.721 | 0.01 | 0.0001 | | teacher.backbone.layer2.3.bn3.bias | N | 512 | Min:-1.897 Max:1.182 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.conv1.weight | N | 256X512X1X1 | Min:-0.230 Max:0.341 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.bn1.weight | N | 256 | Min:0.621 Max:1.636 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.bn1.bias | N | 256 | Min:-1.420 Max:0.917 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.conv2.weight | N | 256X256X3X3 | Min:-0.267 Max:0.179 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.bn2.weight | N | 256 | Min:0.585 Max:1.749 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.bn2.bias | N | 256 | Min:-1.837 Max:1.398 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.conv3.weight | N | 1024X256X1X1 | Min:-0.333 Max:0.384 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.bn3.weight | N | 1024 | Min:0.071 Max:2.367 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.bn3.bias | N | 1024 | Min:-0.938 Max:0.887 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.downsample.0.weight | N | 1024X512X1X1 | Min:-0.333 Max:0.421 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.downsample.1.weight | N | 1024 | Min:0.034 Max:2.779 | 0.01 | 0.0001 | | teacher.backbone.layer3.0.downsample.1.bias | N | 1024 | Min:-0.938 Max:0.887 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.conv1.weight | N | 256X1024X1X1 | Min:-0.197 Max:0.236 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.bn1.weight | N | 256 | Min:0.566 Max:1.743 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.bn1.bias | N | 256 | Min:-2.703 Max:1.042 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.conv2.weight | N | 256X256X3X3 | Min:-0.436 Max:0.196 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.bn2.weight | N | 256 | Min:0.515 Max:2.301 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.bn2.bias | N | 256 | Min:-2.548 Max:1.856 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.conv3.weight | N | 1024X256X1X1 | Min:-0.438 Max:0.295 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.bn3.weight | N | 1024 | Min:0.055 Max:1.943 | 0.01 | 0.0001 | | teacher.backbone.layer3.1.bn3.bias | N | 1024 | Min:-1.647 Max:1.016 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.conv1.weight | N | 256X1024X1X1 | Min:-0.387 Max:0.337 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.bn1.weight | N | 256 | Min:0.463 Max:1.886 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.bn1.bias | N | 256 | Min:-2.399 Max:0.488 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.conv2.weight | N | 256X256X3X3 | Min:-0.165 Max:0.258 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.bn2.weight | N | 256 | Min:0.555 Max:1.901 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.bn2.bias | N | 256 | Min:-1.655 Max:0.704 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.conv3.weight | N | 1024X256X1X1 | Min:-0.290 Max:0.261 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.bn3.weight | N | 1024 | Min:0.049 Max:1.450 | 0.01 | 0.0001 | | teacher.backbone.layer3.2.bn3.bias | N | 1024 | Min:-1.201 Max:0.587 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.conv1.weight | N | 256X1024X1X1 | Min:-0.194 Max:0.295 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.bn1.weight | N | 256 | Min:0.442 Max:1.353 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.bn1.bias | N | 256 | Min:-2.322 Max:0.509 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.conv2.weight | N | 256X256X3X3 | Min:-0.201 Max:0.176 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.bn2.weight | N | 256 | Min:0.529 Max:1.939 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.bn2.bias | N | 256 | Min:-1.610 Max:0.776 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.conv3.weight | N | 1024X256X1X1 | Min:-0.205 Max:0.239 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.bn3.weight | N | 1024 | Min:-0.037 Max:1.646 | 0.01 | 0.0001 | | teacher.backbone.layer3.3.bn3.bias | N | 1024 | Min:-1.484 Max:0.344 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.conv1.weight | N | 256X1024X1X1 | Min:-0.226 Max:0.306 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.bn1.weight | N | 256 | Min:0.438 Max:1.446 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.bn1.bias | N | 256 | Min:-2.511 Max:0.557 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.conv2.weight | N | 256X256X3X3 | Min:-0.147 Max:0.223 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.bn2.weight | N | 256 | Min:0.651 Max:1.858 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.bn2.bias | N | 256 | Min:-1.588 Max:0.661 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.conv3.weight | N | 1024X256X1X1 | Min:-0.178 Max:0.265 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.bn3.weight | N | 1024 | Min:-0.001 Max:1.501 | 0.01 | 0.0001 | | teacher.backbone.layer3.4.bn3.bias | N | 1024 | Min:-1.108 Max:0.639 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.conv1.weight | N | 256X1024X1X1 | Min:-0.153 Max:0.330 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.bn1.weight | N | 256 | Min:0.425 Max:1.547 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.bn1.bias | N | 256 | Min:-1.972 Max:0.823 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.conv2.weight | N | 256X256X3X3 | Min:-0.293 Max:0.276 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.bn2.weight | N | 256 | Min:0.650 Max:2.942 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.bn2.bias | N | 256 | Min:-1.093 Max:0.771 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.conv3.weight | N | 1024X256X1X1 | Min:-0.232 Max:0.294 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.bn3.weight | N | 1024 | Min:0.004 Max:1.984 | 0.01 | 0.0001 | | teacher.backbone.layer3.5.bn3.bias | N | 1024 | Min:-1.636 Max:1.250 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.conv1.weight | N | 512X1024X1X1 | Min:-0.184 Max:0.331 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.bn1.weight | N | 512 | Min:0.535 Max:1.594 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.bn1.bias | N | 512 | Min:-1.756 Max:0.288 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.conv2.weight | N | 512X512X3X3 | Min:-0.175 Max:0.272 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.bn2.weight | N | 512 | Min:0.456 Max:1.542 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.bn2.bias | N | 512 | Min:-1.820 Max:0.839 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.conv3.weight | N | 2048X512X1X1 | Min:-0.332 Max:0.432 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.bn3.weight | N | 2048 | Min:0.888 Max:3.492 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.bn3.bias | N | 2048 | Min:-1.810 Max:0.980 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.downsample.0.weight | N | 2048X1024X1X1 | Min:-0.622 Max:0.465 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.downsample.1.weight | N | 2048 | Min:0.261 Max:4.575 | 0.01 | 0.0001 | | teacher.backbone.layer4.0.downsample.1.bias | N | 2048 | Min:-1.810 Max:0.980 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.conv1.weight | N | 512X2048X1X1 | Min:-0.316 Max:0.577 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.bn1.weight | N | 512 | Min:0.398 Max:1.429 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.bn1.bias | N | 512 | Min:-1.380 Max:0.428 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.conv2.weight | N | 512X512X3X3 | Min:-0.217 Max:0.284 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.bn2.weight | N | 512 | Min:0.349 Max:1.550 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.bn2.bias | N | 512 | Min:-1.867 Max:0.880 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.conv3.weight | N | 2048X512X1X1 | Min:-0.200 Max:0.277 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.bn3.weight | N | 2048 | Min:0.574 Max:2.847 | 0.01 | 0.0001 | | teacher.backbone.layer4.1.bn3.bias | N | 2048 | Min:-2.638 Max:0.544 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.conv1.weight | N | 512X2048X1X1 | Min:-0.289 Max:0.514 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.bn1.weight | N | 512 | Min:0.366 Max:1.249 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.bn1.bias | N | 512 | Min:-1.664 Max:0.753 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.conv2.weight | N | 512X512X3X3 | Min:-0.142 Max:0.144 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.bn2.weight | N | 512 | Min:0.516 Max:1.335 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.bn2.bias | N | 512 | Min:-1.871 Max:1.181 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.conv3.weight | N | 2048X512X1X1 | Min:-0.135 Max:0.300 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.bn3.weight | N | 2048 | Min:0.435 Max:3.073 | 0.01 | 0.0001 | | teacher.backbone.layer4.2.bn3.bias | N | 2048 | Min:-3.885 Max:-0.249 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.0.conv.weight | N | 256X256X1X1 | Min:-0.108 Max:0.108 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.0.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.1.conv.weight | N | 256X512X1X1 | Min:-0.088 Max:0.088 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.1.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.2.conv.weight | N | 256X1024X1X1 | Min:-0.068 Max:0.068 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.2.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.3.conv.weight | N | 256X2048X1X1 | Min:-0.051 Max:0.051 | 0.01 | 0.0001 | | teacher.neck.lateral_convs.3.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.0.conv.weight | N | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.0.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.1.conv.weight | N | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.1.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.2.conv.weight | N | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.2.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.3.conv.weight | N | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.3.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.4.conv.weight | N | 256X2048X3X3 | Min:-0.017 Max:0.017 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.4.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.5.conv.weight | N | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | teacher.neck.fpn_convs.5.conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.rpn_head.rpn_conv.weight | N | 256X256X3X3 | Min:-0.048 Max:0.046 | 0.01 | 0.0001 | | teacher.rpn_head.rpn_conv.bias | N | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.rpn_head.rpn_cls.weight | N | 3X256X1X1 | Min:-0.031 Max:0.031 | 0.01 | 0.0001 | | teacher.rpn_head.rpn_cls.bias | N | 3 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | teacher.rpn_head.rpn_reg.weight | N | 12X256X1X1 | 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student.backbone.bn1.weight | N | 64 | Min:0.513 Max:2.669 | 0.01 | 0.0001 | | student.backbone.bn1.bias | N | 64 | Min:-2.654 Max:6.354 | 0.01 | 0.0001 | | student.backbone.layer1.0.conv1.weight | N | 64X64X1X1 | Min:-0.717 Max:0.392 | 0.01 | 0.0001 | | student.backbone.layer1.0.bn1.weight | N | 64 | Min:0.509 Max:2.066 | 0.01 | 0.0001 | | student.backbone.layer1.0.bn1.bias | N | 64 | Min:-2.411 Max:3.608 | 0.01 | 0.0001 | | student.backbone.layer1.0.conv2.weight | N | 64X64X3X3 | Min:-0.390 Max:0.364 | 0.01 | 0.0001 | | student.backbone.layer1.0.bn2.weight | N | 64 | Min:0.420 Max:2.530 | 0.01 | 0.0001 | | student.backbone.layer1.0.bn2.bias | N | 64 | Min:-2.286 Max:5.913 | 0.01 | 0.0001 | | student.backbone.layer1.0.conv3.weight | N | 256X64X1X1 | Min:-0.397 Max:0.348 | 0.01 | 0.0001 | | student.backbone.layer1.0.bn3.weight | N | 256 | Min:0.011 Max:2.820 | 0.01 | 0.0001 | | student.backbone.layer1.0.bn3.bias | N | 256 | Min:-1.126 Max:1.522 | 0.01 | 0.0001 | | student.backbone.layer1.0.downsample.0.weight | N | 256X64X1X1 | Min:-0.772 Max:0.900 | 0.01 | 0.0001 | | student.backbone.layer1.0.downsample.1.weight | N | 256 | Min:0.004 Max:3.064 | 0.01 | 0.0001 | | student.backbone.layer1.0.downsample.1.bias | N | 256 | Min:-1.126 Max:1.522 | 0.01 | 0.0001 | | student.backbone.layer1.1.conv1.weight | N | 64X256X1X1 | Min:-0.297 Max:0.220 | 0.01 | 0.0001 | | student.backbone.layer1.1.bn1.weight | N | 64 | Min:0.746 Max:1.949 | 0.01 | 0.0001 | | student.backbone.layer1.1.bn1.bias | N | 64 | Min:-1.688 Max:1.578 | 0.01 | 0.0001 | | student.backbone.layer1.1.conv2.weight | N | 64X64X3X3 | Min:-0.240 Max:0.318 | 0.01 | 0.0001 | | student.backbone.layer1.1.bn2.weight | N | 64 | Min:0.621 Max:1.618 | 0.01 | 0.0001 | | student.backbone.layer1.1.bn2.bias | N | 64 | Min:-2.003 Max:2.398 | 0.01 | 0.0001 | | student.backbone.layer1.1.conv3.weight | N | 256X64X1X1 | Min:-0.240 Max:0.280 | 0.01 | 0.0001 | | student.backbone.layer1.1.bn3.weight | N | 256 | Min:-0.017 Max:2.130 | 0.01 | 0.0001 | | student.backbone.layer1.1.bn3.bias | N | 256 | Min:-1.711 Max:1.291 | 0.01 | 0.0001 | | student.backbone.layer1.2.conv1.weight | N | 64X256X1X1 | Min:-0.210 Max:0.264 | 0.01 | 0.0001 | | student.backbone.layer1.2.bn1.weight | N | 64 | Min:0.574 Max:1.688 | 0.01 | 0.0001 | | student.backbone.layer1.2.bn1.bias | N | 64 | Min:-1.876 Max:1.090 | 0.01 | 0.0001 | | student.backbone.layer1.2.conv2.weight | N | 64X64X3X3 | Min:-0.218 Max:0.201 | 0.01 | 0.0001 | | student.backbone.layer1.2.bn2.weight | N | 64 | Min:0.757 Max:1.649 | 0.01 | 0.0001 | | student.backbone.layer1.2.bn2.bias | N | 64 | Min:-2.221 Max:1.878 | 0.01 | 0.0001 | | student.backbone.layer1.2.conv3.weight | N | 256X64X1X1 | Min:-0.275 Max:0.350 | 0.01 | 0.0001 | | student.backbone.layer1.2.bn3.weight | N | 256 | Min:-0.058 Max:2.154 | 0.01 | 0.0001 | | student.backbone.layer1.2.bn3.bias | N | 256 | Min:-1.570 Max:1.535 | 0.01 | 0.0001 | | student.backbone.layer2.0.conv1.weight | Y | 128X256X1X1 | Min:-0.334 Max:0.300 | 0.01 | 0.0001 | | student.backbone.layer2.0.bn1.weight | N | 128 | Min:0.610 Max:1.642 | 0.01 | 0.0001 | | student.backbone.layer2.0.bn1.bias | N | 128 | Min:-1.579 Max:1.449 | 0.01 | 0.0001 | | student.backbone.layer2.0.conv2.weight | Y | 128X128X3X3 | Min:-0.384 Max:0.377 | 0.01 | 0.0001 | | student.backbone.layer2.0.bn2.weight | N | 128 | Min:0.605 Max:1.622 | 0.01 | 0.0001 | | student.backbone.layer2.0.bn2.bias | N | 128 | Min:-2.768 Max:1.747 | 0.01 | 0.0001 | | student.backbone.layer2.0.conv3.weight | Y | 512X128X1X1 | Min:-0.374 Max:0.434 | 0.01 | 0.0001 | | student.backbone.layer2.0.bn3.weight | N | 512 | Min:-0.007 Max:2.730 | 0.01 | 0.0001 | | student.backbone.layer2.0.bn3.bias | N | 512 | Min:-1.545 Max:1.256 | 0.01 | 0.0001 | | student.backbone.layer2.0.downsample.0.weight | Y | 512X256X1X1 | Min:-0.466 Max:0.642 | 0.01 | 0.0001 | | student.backbone.layer2.0.downsample.1.weight | N | 512 | Min:0.006 Max:2.552 | 0.01 | 0.0001 | | student.backbone.layer2.0.downsample.1.bias | N | 512 | Min:-1.545 Max:1.256 | 0.01 | 0.0001 | | student.backbone.layer2.1.conv1.weight | Y | 128X512X1X1 | Min:-0.162 Max:0.195 | 0.01 | 0.0001 | | student.backbone.layer2.1.bn1.weight | N | 128 | Min:0.578 Max:1.429 | 0.01 | 0.0001 | | student.backbone.layer2.1.bn1.bias | N | 128 | Min:-4.348 Max:0.588 | 0.01 | 0.0001 | | student.backbone.layer2.1.conv2.weight | Y | 128X128X3X3 | Min:-0.176 Max:0.177 | 0.01 | 0.0001 | | student.backbone.layer2.1.bn2.weight | N | 128 | Min:0.511 Max:1.794 | 0.01 | 0.0001 | | student.backbone.layer2.1.bn2.bias | N | 128 | Min:-3.825 Max:1.343 | 0.01 | 0.0001 | | student.backbone.layer2.1.conv3.weight | Y | 512X128X1X1 | Min:-0.344 Max:0.336 | 0.01 | 0.0001 | | student.backbone.layer2.1.bn3.weight | N | 512 | Min:-0.072 Max:2.122 | 0.01 | 0.0001 | | student.backbone.layer2.1.bn3.bias | N | 512 | Min:-1.502 Max:1.166 | 0.01 | 0.0001 | | student.backbone.layer2.2.conv1.weight | Y | 128X512X1X1 | Min:-0.330 Max:0.369 | 0.01 | 0.0001 | | student.backbone.layer2.2.bn1.weight | N | 128 | Min:0.406 Max:1.696 | 0.01 | 0.0001 | | student.backbone.layer2.2.bn1.bias | N | 128 | Min:-2.696 Max:1.944 | 0.01 | 0.0001 | | student.backbone.layer2.2.conv2.weight | Y | 128X128X3X3 | Min:-0.326 Max:0.374 | 0.01 | 0.0001 | | student.backbone.layer2.2.bn2.weight | N | 128 | Min:0.460 Max:2.179 | 0.01 | 0.0001 | | student.backbone.layer2.2.bn2.bias | N | 128 | Min:-1.587 Max:0.589 | 0.01 | 0.0001 | | student.backbone.layer2.2.conv3.weight | Y | 512X128X1X1 | Min:-0.288 Max:0.232 | 0.01 | 0.0001 | | student.backbone.layer2.2.bn3.weight | N | 512 | Min:-0.006 Max:3.043 | 0.01 | 0.0001 | | student.backbone.layer2.2.bn3.bias | N | 512 | Min:-2.369 Max:0.440 | 0.01 | 0.0001 | | student.backbone.layer2.3.conv1.weight | Y | 128X512X1X1 | Min:-0.298 Max:0.346 | 0.01 | 0.0001 | | student.backbone.layer2.3.bn1.weight | N | 128 | Min:0.736 Max:2.394 | 0.01 | 0.0001 | | student.backbone.layer2.3.bn1.bias | N | 128 | Min:-2.643 Max:0.756 | 0.01 | 0.0001 | | student.backbone.layer2.3.conv2.weight | Y | 128X128X3X3 | Min:-0.272 Max:0.208 | 0.01 | 0.0001 | | student.backbone.layer2.3.bn2.weight | N | 128 | Min:0.682 Max:1.694 | 0.01 | 0.0001 | | student.backbone.layer2.3.bn2.bias | N | 128 | Min:-1.365 Max:1.599 | 0.01 | 0.0001 | | student.backbone.layer2.3.conv3.weight | Y | 512X128X1X1 | Min:-0.279 Max:0.281 | 0.01 | 0.0001 | | student.backbone.layer2.3.bn3.weight | N | 512 | Min:-0.009 Max:1.721 | 0.01 | 0.0001 | | student.backbone.layer2.3.bn3.bias | N | 512 | Min:-1.897 Max:1.182 | 0.01 | 0.0001 | | student.backbone.layer3.0.conv1.weight | Y | 256X512X1X1 | Min:-0.230 Max:0.341 | 0.01 | 0.0001 | | student.backbone.layer3.0.bn1.weight | N | 256 | Min:0.621 Max:1.636 | 0.01 | 0.0001 | | student.backbone.layer3.0.bn1.bias | N | 256 | Min:-1.420 Max:0.917 | 0.01 | 0.0001 | | student.backbone.layer3.0.conv2.weight | Y | 256X256X3X3 | Min:-0.267 Max:0.179 | 0.01 | 0.0001 | | student.backbone.layer3.0.bn2.weight | N | 256 | Min:0.585 Max:1.749 | 0.01 | 0.0001 | | student.backbone.layer3.0.bn2.bias | N | 256 | Min:-1.837 Max:1.398 | 0.01 | 0.0001 | | student.backbone.layer3.0.conv3.weight | Y | 1024X256X1X1 | Min:-0.333 Max:0.384 | 0.01 | 0.0001 | | student.backbone.layer3.0.bn3.weight | N | 1024 | Min:0.071 Max:2.367 | 0.01 | 0.0001 | | student.backbone.layer3.0.bn3.bias | N | 1024 | Min:-0.938 Max:0.887 | 0.01 | 0.0001 | | student.backbone.layer3.0.downsample.0.weight | Y | 1024X512X1X1 | Min:-0.333 Max:0.421 | 0.01 | 0.0001 | | student.backbone.layer3.0.downsample.1.weight | N | 1024 | Min:0.034 Max:2.779 | 0.01 | 0.0001 | | student.backbone.layer3.0.downsample.1.bias | N | 1024 | Min:-0.938 Max:0.887 | 0.01 | 0.0001 | | student.backbone.layer3.1.conv1.weight | Y | 256X1024X1X1 | Min:-0.197 Max:0.236 | 0.01 | 0.0001 | | student.backbone.layer3.1.bn1.weight | N | 256 | Min:0.566 Max:1.743 | 0.01 | 0.0001 | | student.backbone.layer3.1.bn1.bias | N | 256 | Min:-2.703 Max:1.042 | 0.01 | 0.0001 | | student.backbone.layer3.1.conv2.weight | Y | 256X256X3X3 | Min:-0.436 Max:0.196 | 0.01 | 0.0001 | | student.backbone.layer3.1.bn2.weight | N | 256 | Min:0.515 Max:2.301 | 0.01 | 0.0001 | | student.backbone.layer3.1.bn2.bias | N | 256 | Min:-2.548 Max:1.856 | 0.01 | 0.0001 | | student.backbone.layer3.1.conv3.weight | Y | 1024X256X1X1 | Min:-0.438 Max:0.295 | 0.01 | 0.0001 | | student.backbone.layer3.1.bn3.weight | N | 1024 | Min:0.055 Max:1.943 | 0.01 | 0.0001 | | student.backbone.layer3.1.bn3.bias | N | 1024 | Min:-1.647 Max:1.016 | 0.01 | 0.0001 | | student.backbone.layer3.2.conv1.weight | Y | 256X1024X1X1 | Min:-0.387 Max:0.337 | 0.01 | 0.0001 | | student.backbone.layer3.2.bn1.weight | N | 256 | Min:0.463 Max:1.886 | 0.01 | 0.0001 | | student.backbone.layer3.2.bn1.bias | N | 256 | Min:-2.399 Max:0.488 | 0.01 | 0.0001 | | student.backbone.layer3.2.conv2.weight | Y | 256X256X3X3 | Min:-0.165 Max:0.258 | 0.01 | 0.0001 | | student.backbone.layer3.2.bn2.weight | N | 256 | Min:0.555 Max:1.901 | 0.01 | 0.0001 | | student.backbone.layer3.2.bn2.bias | N | 256 | Min:-1.655 Max:0.704 | 0.01 | 0.0001 | | student.backbone.layer3.2.conv3.weight | Y | 1024X256X1X1 | Min:-0.290 Max:0.261 | 0.01 | 0.0001 | | student.backbone.layer3.2.bn3.weight | N | 1024 | Min:0.049 Max:1.450 | 0.01 | 0.0001 | | student.backbone.layer3.2.bn3.bias | N | 1024 | Min:-1.201 Max:0.587 | 0.01 | 0.0001 | | student.backbone.layer3.3.conv1.weight | Y | 256X1024X1X1 | Min:-0.194 Max:0.295 | 0.01 | 0.0001 | | student.backbone.layer3.3.bn1.weight | N | 256 | Min:0.442 Max:1.353 | 0.01 | 0.0001 | | student.backbone.layer3.3.bn1.bias | N | 256 | Min:-2.322 Max:0.509 | 0.01 | 0.0001 | | student.backbone.layer3.3.conv2.weight | Y | 256X256X3X3 | Min:-0.201 Max:0.176 | 0.01 | 0.0001 | | student.backbone.layer3.3.bn2.weight | N | 256 | Min:0.529 Max:1.939 | 0.01 | 0.0001 | | student.backbone.layer3.3.bn2.bias | N | 256 | Min:-1.610 Max:0.776 | 0.01 | 0.0001 | | student.backbone.layer3.3.conv3.weight | Y | 1024X256X1X1 | Min:-0.205 Max:0.239 | 0.01 | 0.0001 | | student.backbone.layer3.3.bn3.weight | N | 1024 | Min:-0.037 Max:1.646 | 0.01 | 0.0001 | | student.backbone.layer3.3.bn3.bias | N | 1024 | Min:-1.484 Max:0.344 | 0.01 | 0.0001 | | student.backbone.layer3.4.conv1.weight | Y | 256X1024X1X1 | Min:-0.226 Max:0.306 | 0.01 | 0.0001 | | student.backbone.layer3.4.bn1.weight | N | 256 | Min:0.438 Max:1.446 | 0.01 | 0.0001 | | student.backbone.layer3.4.bn1.bias | N | 256 | Min:-2.511 Max:0.557 | 0.01 | 0.0001 | | student.backbone.layer3.4.conv2.weight | Y | 256X256X3X3 | Min:-0.147 Max:0.223 | 0.01 | 0.0001 | | student.backbone.layer3.4.bn2.weight | N | 256 | Min:0.651 Max:1.858 | 0.01 | 0.0001 | | student.backbone.layer3.4.bn2.bias | N | 256 | Min:-1.588 Max:0.661 | 0.01 | 0.0001 | | student.backbone.layer3.4.conv3.weight | Y | 1024X256X1X1 | Min:-0.178 Max:0.265 | 0.01 | 0.0001 | | student.backbone.layer3.4.bn3.weight | N | 1024 | Min:-0.001 Max:1.501 | 0.01 | 0.0001 | | student.backbone.layer3.4.bn3.bias | N | 1024 | Min:-1.108 Max:0.639 | 0.01 | 0.0001 | | student.backbone.layer3.5.conv1.weight | Y | 256X1024X1X1 | Min:-0.153 Max:0.330 | 0.01 | 0.0001 | | student.backbone.layer3.5.bn1.weight | N | 256 | Min:0.425 Max:1.547 | 0.01 | 0.0001 | | student.backbone.layer3.5.bn1.bias | N | 256 | Min:-1.972 Max:0.823 | 0.01 | 0.0001 | | student.backbone.layer3.5.conv2.weight | Y | 256X256X3X3 | Min:-0.293 Max:0.276 | 0.01 | 0.0001 | | student.backbone.layer3.5.bn2.weight | N | 256 | Min:0.650 Max:2.942 | 0.01 | 0.0001 | | student.backbone.layer3.5.bn2.bias | N | 256 | Min:-1.093 Max:0.771 | 0.01 | 0.0001 | | student.backbone.layer3.5.conv3.weight | Y | 1024X256X1X1 | Min:-0.232 Max:0.294 | 0.01 | 0.0001 | | student.backbone.layer3.5.bn3.weight | N | 1024 | Min:0.004 Max:1.984 | 0.01 | 0.0001 | | student.backbone.layer3.5.bn3.bias | N | 1024 | Min:-1.636 Max:1.250 | 0.01 | 0.0001 | | student.backbone.layer4.0.conv1.weight | Y | 512X1024X1X1 | Min:-0.184 Max:0.331 | 0.01 | 0.0001 | | student.backbone.layer4.0.bn1.weight | N | 512 | Min:0.535 Max:1.594 | 0.01 | 0.0001 | | student.backbone.layer4.0.bn1.bias | N | 512 | Min:-1.756 Max:0.288 | 0.01 | 0.0001 | | student.backbone.layer4.0.conv2.weight | Y | 512X512X3X3 | Min:-0.175 Max:0.272 | 0.01 | 0.0001 | | student.backbone.layer4.0.bn2.weight | N | 512 | Min:0.456 Max:1.542 | 0.01 | 0.0001 | | student.backbone.layer4.0.bn2.bias | N | 512 | Min:-1.820 Max:0.839 | 0.01 | 0.0001 | | student.backbone.layer4.0.conv3.weight | Y | 2048X512X1X1 | Min:-0.332 Max:0.432 | 0.01 | 0.0001 | | student.backbone.layer4.0.bn3.weight | N | 2048 | Min:0.888 Max:3.492 | 0.01 | 0.0001 | | student.backbone.layer4.0.bn3.bias | N | 2048 | Min:-1.810 Max:0.980 | 0.01 | 0.0001 | | student.backbone.layer4.0.downsample.0.weight | Y | 2048X1024X1X1 | Min:-0.622 Max:0.465 | 0.01 | 0.0001 | | student.backbone.layer4.0.downsample.1.weight | N | 2048 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student.backbone.layer4.2.conv1.weight | Y | 512X2048X1X1 | Min:-0.289 Max:0.514 | 0.01 | 0.0001 | | student.backbone.layer4.2.bn1.weight | N | 512 | Min:0.366 Max:1.249 | 0.01 | 0.0001 | | student.backbone.layer4.2.bn1.bias | N | 512 | Min:-1.664 Max:0.753 | 0.01 | 0.0001 | | student.backbone.layer4.2.conv2.weight | Y | 512X512X3X3 | Min:-0.142 Max:0.144 | 0.01 | 0.0001 | | student.backbone.layer4.2.bn2.weight | N | 512 | Min:0.516 Max:1.335 | 0.01 | 0.0001 | | student.backbone.layer4.2.bn2.bias | N | 512 | Min:-1.871 Max:1.181 | 0.01 | 0.0001 | | student.backbone.layer4.2.conv3.weight | Y | 2048X512X1X1 | Min:-0.135 Max:0.300 | 0.01 | 0.0001 | | student.backbone.layer4.2.bn3.weight | N | 2048 | Min:0.435 Max:3.073 | 0.01 | 0.0001 | | student.backbone.layer4.2.bn3.bias | N | 2048 | Min:-3.885 Max:-0.249 | 0.01 | 0.0001 | | student.neck.lateral_convs.0.conv.weight | Y | 256X256X1X1 | Min:-0.108 Max:0.108 | 0.01 | 0.0001 | | student.neck.lateral_convs.0.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.lateral_convs.1.conv.weight | Y | 256X512X1X1 | Min:-0.088 Max:0.088 | 0.01 | 0.0001 | | student.neck.lateral_convs.1.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.lateral_convs.2.conv.weight | Y | 256X1024X1X1 | Min:-0.068 Max:0.068 | 0.01 | 0.0001 | | student.neck.lateral_convs.2.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.lateral_convs.3.conv.weight | Y | 256X2048X1X1 | Min:-0.051 Max:0.051 | 0.01 | 0.0001 | | student.neck.lateral_convs.3.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.fpn_convs.0.conv.weight | Y | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | student.neck.fpn_convs.0.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.fpn_convs.1.conv.weight | Y | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | student.neck.fpn_convs.1.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.fpn_convs.2.conv.weight | Y | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | student.neck.fpn_convs.2.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.fpn_convs.3.conv.weight | Y | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | student.neck.fpn_convs.3.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.fpn_convs.4.conv.weight | Y | 256X2048X3X3 | Min:-0.017 Max:0.017 | 0.01 | 0.0001 | | student.neck.fpn_convs.4.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.neck.fpn_convs.5.conv.weight | Y | 256X256X3X3 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | student.neck.fpn_convs.5.conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.rpn_head.rpn_conv.weight | Y | 256X256X3X3 | Min:-0.050 Max:0.045 | 0.01 | 0.0001 | | student.rpn_head.rpn_conv.bias | Y | 256 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.rpn_head.rpn_cls.weight | Y | 3X256X1X1 | Min:-0.036 Max:0.036 | 0.01 | 0.0001 | | student.rpn_head.rpn_cls.bias | Y | 3 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.rpn_head.rpn_reg.weight | Y | 12X256X1X1 | Min:-0.038 Max:0.037 | 0.01 | 0.0001 | | student.rpn_head.rpn_reg.bias | Y | 12 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.roi_head.bbox_head.fc_cls.weight | Y | 81X1024 | Min:-0.046 Max:0.048 | 0.01 | 0.0001 | | student.roi_head.bbox_head.fc_cls.bias | Y | 81 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.roi_head.bbox_head.fc_reg.weight | Y | 320X1024 | Min:-0.004 Max:0.004 | 0.01 | 0.0001 | | student.roi_head.bbox_head.fc_reg.bias | Y | 320 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.roi_head.bbox_head.shared_fcs.0.weight | Y | 1024X12544 | Min:-0.021 Max:0.021 | 0.01 | 0.0001 | | student.roi_head.bbox_head.shared_fcs.0.bias | Y | 1024 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | | student.roi_head.bbox_head.shared_fcs.1.weight | Y | 1024X1024 | Min:-0.054 Max:0.054 | 0.01 | 0.0001 | | student.roi_head.bbox_head.shared_fcs.1.bias | Y | 1024 | Min:0.000 Max:0.000 | 0.01 | 0.0001 | +------------------------------------------------+-----------+---------------+-----------------------+------+--------+ 2022-10-27 14:41:29,752 - mmdet.ssod - INFO - Clone all parameters of student to teacher... /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /opt/conda/conda-bld/pytorch_1623448233824/work/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /opt/conda/conda-bld/pytorch_1623448233824/work/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /opt/conda/conda-bld/pytorch_1623448233824/work/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /opt/conda/conda-bld/pytorch_1623448233824/work/c10/core/TensorImpl.h:1156.) return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) Traceback (most recent call last): File "tools/train.py", line 199, in main() File "tools/train.py", line 194, in main meta=meta, File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/apis/train.py", line 207, in train_detector Traceback (most recent call last): File "tools/train.py", line 199, in main() File "tools/train.py", line 194, in main meta=meta, File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/apis/train.py", line 207, in train_detector Traceback (most recent call last): File "tools/train.py", line 199, in main() File "tools/train.py", line 194, in main meta=meta, File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/apis/train.py", line 207, in train_detector Traceback (most recent call last): File "tools/train.py", line 199, in main() File "tools/train.py", line 194, in main meta=meta, File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/apis/train.py", line 207, in train_detector runner.run(data_loaders, cfg.workflow) runner.run(data_loaders, cfg.workflow) runner.run(data_loaders, cfg.workflow) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run runner.run(data_loaders, cfg.workflow) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 134, in run iter_runner(iter_loaders[i], kwargs) iter_runner(iter_loaders[i], kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train iter_runner(iter_loaders[i], kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train outputs = self.model.train_step(data_batch, self.optimizer, kwargs) outputs = self.model.train_step(data_batch, self.optimizer, kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 52, in train_step File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 52, in train_step outputs = self.model.train_step(data_batch, self.optimizer, kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 52, in train_step output = self.module.train_step(*inputs[0], kwargs[0]) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 248, in train_step output = self.module.train_step(*inputs[0], *kwargs[0]) output = self.module.train_step(inputs[0], kwargs[0]) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 248, in train_step File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 248, in train_step iter_runner(iter_loaders[i], kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/iter_based_runner.py", line 61, in train outputs = self.model.train_step(data_batch, self.optimizer, kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/parallel/distributed.py", line 52, in train_step output = self.module.train_step(*inputs[0], kwargs[0]) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 248, in train_step losses = self(data) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl losses = self(data) losses = self(data) losses = self(data) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, *kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 128, in new_func return forward_call(input, kwargs) return forward_call(*input, kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 128, in new_func File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 128, in new_func return forward_call(*input, *kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 128, in new_func output = old_func(new_args, new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 172, in forward output = old_func(*new_args, new_kwargs) output = old_func(*new_args, *new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 172, in forward File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 172, in forward output = old_func(new_args, new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/detectors/base.py", line 172, in forward return self.forward_train(img, img_metas, kwargs) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 89, in forward_train return self.forward_train(img, img_metas, kwargs) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 89, in forward_train return self.forward_train(img, img_metas, kwargs) return self.forward_train(img, img_metas, kwargs) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 89, in forward_train File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 89, in forward_train data_groups["unsup_teacher"], data_groups["unsup_student"]) data_groups["unsup_teacher"], data_groups["unsup_student"]) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 166, in foward_unsup_train File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 166, in foward_unsup_train data_groups["unsup_teacher"], data_groups["unsup_student"]) data_groups["unsup_teacher"], data_groups["unsup_student"]) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 166, in foward_unsup_train File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 166, in foward_unsup_train teacher_img, img_metas_teacher) teacher_img, img_metas_teacher) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 388, in extract_teacher_info teacher_img, img_metas_teacher) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 388, in extract_teacher_info File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 388, in extract_teacher_info teacher_img, img_metas_teacher) File "/home/lihejun/hbzn_workspace_lee/semi-supervised-objection/PseCo/tools/ssod/models/PseCo_frcnn.py", line 388, in extract_teacher_info rpn_out, img_metas, cfg=proposal_cfg File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 214, in new_func rpn_out, img_metas, cfg=proposal_cfg rpn_out, img_metas, cfg=proposal_cfg File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 214, in new_func File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 214, in new_func rpn_out, img_metas, cfg=proposal_cfg File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmcv/runner/fp16_utils.py", line 214, in new_func output = old_func(*new_args, new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 81, in get_bboxes output = old_func(*new_args, *new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 81, in get_bboxes output = old_func(new_args, new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 81, in get_bboxes output = old_func(*new_args, **new_kwargs) File "/home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/models/dense_heads/base_dense_head.py", line 81, in get_bboxes assert len(cls_scores) == len(score_factors) assert len(cls_scores) == len(score_factors) assert len(cls_scores) == len(score_factors) AssertionError AssertionError assert len(cls_scores) == len(score_factors) AssertionError AssertionError ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 792712) of binary: /home/lihejun/anaconda3/envs/semi-det/bin/python ERROR:torch.distributed.elastic.agent.server.local_elastic_agent:[default] Worker group failed INFO:torch.distributed.elastic.agent.server.api:[default] Worker group FAILED. 3/3 attempts left; will restart worker group INFO:torch.distributed.elastic.agent.server.api:[default] Stopping worker group INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous'ing worker group INFO:torch.distributed.elastic.agent.server.api:[default] Rendezvous complete for workers. Result: restart_count=1 master_addr=127.0.0.1 master_port=29500 group_rank=0 group_world_size=1 local_ranks=[0, 1, 2, 3] role_ranks=[0, 1, 2, 3] global_ranks=[0, 1, 2, 3] role_world_sizes=[4, 4, 4, 4] global_world_sizes=[4, 4, 4, 4]

INFO:torch.distributed.elastic.agent.server.api:[default] Starting worker group INFO:torch.distributed.elastic.multiprocessing:Setting worker0 reply file to: /tmp/torchelastic_g7x53oc6/none9hjtg4n/attempt_1/0/error.json INFO:torch.distributed.elastic.multiprocessing:Setting worker1 reply file to: /tmp/torchelastic_g7x53oc6/none9hjtg4n/attempt_1/1/error.json INFO:torch.distributed.elastic.multiprocessing:Setting worker2 reply file to: /tmp/torchelastic_g7x53oc6/none9hjtg4n/attempt_1/2/error.json INFO:torch.distributed.elastic.multiprocessing:Setting worker3 reply file to: /tmp/torchelastic_g7x53oc6/none9hjtg4n/attempt_1/3/error.json /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be ' /home/lihejun/anaconda3/envs/semi-det/lib/python3.6/site-packages/mmdet/datasets/pipelines/formating.py:7: UserWarning: DeprecationWarning: mmdet.datasets.pipelines.formating will be deprecated, please replace it with mmdet.datasets.pipelines.formatting. warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be '

monsterlv-lhj commented 2 years ago

Hello author, the above error occurs when I execute the training script, what is the reason?

ligang-cs commented 1 year ago

Hello author, the above error occurs when I execute the training script, what is the reason?

Which mmdet do you use? mmdet in the folder thirdparty/mmdetection is prefered.