TypeError: impad() takes 1 positional argument but 2 positional arguments (and 1 keyword-only argument) were given

[Chandhana99]

Getting this issue, please help me solve this.

Traceback (most recent call last): File "mmdetection/tools/train.py", line 151, in main() File "mmdetection/tools/train.py", line 147, in main meta=meta) File "/content/mmdetection/mmdet/apis/train.py", line 165, in train_detector runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 122, in run epoch_runner(data_loaders[i], **kwargs) File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 27, in train for i, data_batch in enumerate(data_loader): File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 345, in next data = self._next_data() File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 856, in _next_data return self._process_data(data) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 881, in _process_data data.reraise() File "/usr/local/lib/python3.7/dist-packages/torch/_utils.py", line 394, in reraise raise self.exc_type(msg) TypeError: Caught TypeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/content/mmdetection/mmdet/datasets/custom.py", line 132, in getitem data = self.prepare_train_img(idx) File "/content/mmdetection/mmdet/datasets/custom.py", line 145, in prepare_train_img return self.pipeline(results) File "/content/mmdetection/mmdet/datasets/pipelines/compose.py", line 24, in call data = t(data) File "/content/mmdetection/mmdet/datasets/pipelines/transforms.py", line 323, in call self._pad_masks(results) File "/content/mmdetection/mmdet/datasets/pipelines/transforms.py", line 310, in _pad_masks for mask in results[key] File "/content/mmdetection/mmdet/datasets/pipelines/transforms.py", line 310, in for mask in results[key] TypeError: impad() takes 1 positional argument but 2 positional arguments (and 1 keyword-only argument) were given

[AronLin]

1. It seems that the version of mmdet is too old, I can not find this line.
2. Please use the issue template to show more information. We cannot locate the problem with the little information.

[Chandhana99]

I have changed the MMDET version to newer version, now i am not facing that problem. But i am facing the new issue.

2021-10-20 07:34:34,613 - mmdet - INFO - Environment info:

sys.platform: linux Python: 3.7.12 (default, Sep 10 2021, 00:21:48) [GCC 7.5.0] CUDA available: True GPU 0: Tesla K80 CUDA_HOME: /usr/local/cuda NVCC: Build cuda_11.1.TC455_06.29190527_0 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 1.4.0+cu100 PyTorch compiling details: PyTorch built with:

• GCC 7.3
• Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v0.21.1 (Git Hash 7d2fd500bc78936d1d648ca713b901012f470dbc)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• NNPACK is enabled
• CUDA Runtime 10.0
• 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
• CuDNN 7.6.3
• Magma 2.5.1
• Build settings: BLAS=MKL, BUILD_NAMEDTENSOR=OFF, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -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-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -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 -Wno-stringop-overflow, DISABLE_NUMA=1, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.5.0+cu100 OpenCV: 4.5.3 MMCV: 1.1.5 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 11.1 MMDetection: 2.5.0+0f33c08

2021-10-20 07:34:34,614 - mmdet - INFO - Distributed training: False 2021-10-20 07:34:34,614 - mmdet - INFO - Config: /content/mmdetection/CascadeTabNet/Config/cascade_mask_rcnn_hrnetv2p_w32_20e_v2.py

  # model settings
  model = dict(
      type='CascadeRCNN',
      # num_stages=3,
      pretrained='https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth',
      backbone=dict(
          type='HRNet',
          extra=dict(
              stage1=dict(
                  num_modules=1,
                  num_branches=1,
                  block='BOTTLENECK',
                  num_blocks=(4, ),
                  num_channels=(64, )),
              stage2=dict(
                  num_modules=1,
                  num_branches=2,
                  block='BASIC',
                  num_blocks=(4, 4),
                  num_channels=(32, 64)),
              stage3=dict(
                  num_modules=4,
                  num_branches=3,
                  block='BASIC',
                  num_blocks=(4, 4, 4),
                  num_channels=(32, 64, 128)),
              stage4=dict(
                  num_modules=3,
                  num_branches=4,
                  block='BASIC',
                  num_blocks=(4, 4, 4, 4),
                  num_channels=(32, 64, 128, 256)))),
      neck=dict(type='HRFPN', in_channels=[32, 64, 128, 256], out_channels=256),
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              scales=[8],
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[.0, .0, .0, .0],
              target_stds=[1.0, 1.0, 1.0, 1.0]),
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
      roi_head=dict(
          type='CascadeRoIHead',
          num_stages=3,
          stage_loss_weights=[1, 0.5, 0.25],
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),  # may conflict
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          bbox_head=[
              dict(
                  type='Shared2FCBBoxHead',
                  # num_fcs=2,
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=1,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.1, 0.1, 0.2, 0.2]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
              dict(
                  type='Shared2FCBBoxHead',
                  # num_fcs=2,
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=1,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.05, 0.05, 0.1, 0.1]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
              dict(
                  type='Shared2FCBBoxHead',
                  # num_fcs=2,
                  in_channels=256,
                  fc_out_channels=1024,
                  roi_feat_size=7,
                  num_classes=1,
                  bbox_coder=dict(
                      type='DeltaXYWHBBoxCoder',
                      target_means=[0., 0., 0., 0.],
                      target_stds=[0.033, 0.033, 0.067, 0.067]),
                  reg_class_agnostic=True,
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
          ],
          mask_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          mask_head=dict(
              type='FCNMaskHead',
              num_convs=4,
              in_channels=256,
              conv_out_channels=256,
              num_classes=1,
              loss_mask=dict(
                  type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
  )

  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=256,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          allowed_border=0,
          pos_weight=-1,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=2000,
          max_num=2000,
          nms_thr=0.7,
          nms=dict(type='nms', iou_threshold=0.7),
          max_per_img=2000,
          min_bbox_size=0),
      rcnn=[
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              mask_size=28,
              pos_weight=-1,
              debug=False),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.6,
                  neg_iou_thr=0.6,
                  min_pos_iou=0.6,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              mask_size=28,
              pos_weight=-1,
              debug=False),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.7,
                  min_pos_iou=0.7,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              mask_size=28,
              pos_weight=-1,
              debug=False)
      ],
      stage_loss_weights=[1, 0.5, 0.25])
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=1000,
          nms_post=1000,
          max_num=1000,
          nms_thr=0.7,
          nms=dict(type='nms', iou_threshold=0.7),
          max_per_img=1000,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=0.05,
          nms=dict(type='nms', iou_thr=0.5),
          max_per_img=100,
          mask_thr_binary=0.5))
  # dataset settings
  dataset_type = 'CocoDataset'
  data_root = 'data/coco/'
  classes=('table',)
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1333, 800),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      imgs_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/trainval.json',
          img_prefix= data_root + 'train/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file= data_root + 'annotations/test.json',
          img_prefix= data_root + 'test/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file= data_root + 'annotations/test.json',
          img_prefix= data_root + 'test/',
          pipeline=test_pipeline))
  # evaluation = dict(interval=1, metric=['bbox'])
  # optimizer
  optimizer = dict(type='SGD', lr=0.0012, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=1.0 / 3,
      step=[16, 19])
  checkpoint_config = dict(interval=1,create_symlink=False)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  # runtime settings
  total_epochs = 36
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = '/content/mmdetection/work_dirs'
  load_from = None
  # resume_from = '/content/mmdetection/epoch24.pth'
  resume_from = None
  workflow = [('train', 1)]

/usr/local/lib/python3.7/dist-packages/mmcv/utils/misc.py:304: UserWarning: "out_size" is deprecated in RoIAlign.__init__, please use "output_size" instead f'"{src_arg_name}" is deprecated in ' /usr/local/lib/python3.7/dist-packages/mmcv/utils/misc.py:304: UserWarning: "sample_num" is deprecated in RoIAlign.__init__, please use "sampling_ratio" instead f'"{src_arg_name}" is deprecated in ' 2021-10-20 07:34:35,575 - mmdet - INFO - load model from: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth 2021-10-20 07:34:36,224 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.num_batches_tracked, backbone.conv2.weight, backbone.bn2.weight, backbone.bn2.bias, backbone.bn2.running_mean, backbone.bn2.running_var, backbone.bn2.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer1.1.conv3.weight, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.num_batches_tracked, backbone.layer1.2.conv1.weight, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.num_batches_tracked, backbone.layer1.2.conv2.weight, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.num_batches_tracked, backbone.layer1.2.conv3.weight, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.num_batches_tracked, backbone.layer1.3.conv1.weight, backbone.layer1.3.bn1.weight, backbone.layer1.3.bn1.bias, backbone.layer1.3.bn1.running_mean, backbone.layer1.3.bn1.running_var, backbone.layer1.3.bn1.num_batches_tracked, backbone.layer1.3.conv2.weight, backbone.layer1.3.bn2.weight, backbone.layer1.3.bn2.bias, backbone.layer1.3.bn2.running_mean, backbone.layer1.3.bn2.running_var, backbone.layer1.3.bn2.num_batches_tracked, backbone.layer1.3.conv3.weight, backbone.layer1.3.bn3.weight, backbone.layer1.3.bn3.bias, backbone.layer1.3.bn3.running_mean, 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loading annotations into memory... Done (t=0.00s) creating index... index created! 2021-10-20 07:34:36,836 - mmdet - WARNING - "imgs_per_gpu" is deprecated in MMDet V2.0. Please use "samples_per_gpu" instead 2021-10-20 07:34:36,836 - mmdet - WARNING - Automatically set "samples_per_gpu"="imgs_per_gpu"=2 in this experiments 2021-10-20 07:34:38,535 - mmdet - INFO - Start running, host: root@a4f68a77cc8b, work_dir: /content/mmdetection/work_dirs 2021-10-20 07:34:38,535 - mmdet - INFO - workflow: [('train', 1)], max: 36 epochs Traceback (most recent call last): File "tools/train.py", line 151, in main() File "tools/train.py", line 147, in main meta=meta) File "/usr/local/lib/python3.7/dist-packages/mmdet/apis/train.py", line 143, in train_detector runner.run(data_loaders, cfg.workflow, cfg.total_epochs) File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 125, in run epoch_runner(data_loaders[i], **kwargs) File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 47, in train for i, data_batch in enumerate(self.data_loader): File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 279, in iter return _MultiProcessingDataLoaderIter(self) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 746, in init self._try_put_index() File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 861, in _try_put_index index = self._next_index() File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 339, in _next_index return next(self._sampler_iter) # may raise StopIteration File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/sampler.py", line 200, in iter for idx in self.sampler: File "/usr/local/lib/python3.7/dist-packages/mmdet/datasets/samplers/group_sampler.py", line 36, in iter indices = np.concatenate(indices) File "<__array_function__ internals>", line 6, in concatenate ValueError: need at least one array to concatenate

[AronLin]

Can you use the latest version and modify the config file to the latest format? I'm not sure whether it is a bug that has been fixed.

[Chandhana99]

Sure