Project-MONAI / Project-MONAI/MONAI

RandSpatialCropSamplesd

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主要言語
Python
スター
8.7k
フォーク
1.6k
平均マージ
5日 1時間
マージ済み PR（30日）
20

説明

Hi，I want to use batch_size = 2 for training. When using CenterSpatialCropd(keys=["image", "label"],roi_size=(224, 224, 224)), I can train the network, but when I use RandSpatialCropSamplesd(keys=["image", "label"],roi_size=(224, 224, 224), random_size=False, num_samples=2),ResizeWithPadOrCropd( keys=["image", "label"], spatial_size=224, mode='constant', ), the following error occurred.

collate/stack a list of tensors
E: stack expects each tensor to be equal size, but got [1, 275, 386, 386] at entry 0 and [1, 275, 380, 380] at entry 2, shape [torch.Size([1, 275, 386, 386]), torch.Size([1, 275, 386, 386]), torch.Size([1, 275, 380, 380]), torch.Size([1, 275, 380, 380])] in collate([tensor([[[[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
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      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
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      [0., 0., 0.,  ..., 0., 0., 0.]],

     ...,

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
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     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]]]]), tensor([[[[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     ...,

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]]]]), tensor([[[[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
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     [[0., 0., 0.,  ..., 0., 0., 0.],
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      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     ...,

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]]]]), tensor([[[[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
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      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
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      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     ...,

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]],

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      ...,
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.]]]])])

collate dict key "image_meta_dict" out of 4 keys

collate dict key "sizeof_hdr" out of 43 keys

collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "extents" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "session_error" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "dim_info" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "dim" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "intent_p1" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "intent_p2" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "intent_p3" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "intent_code" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "datatype" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "bitpix" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "slice_start" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "pixdim" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "vox_offset" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "scl_slope" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "scl_inter" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "slice_end" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "slice_code" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "xyzt_units" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "cal_max" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "cal_min" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "slice_duration" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "toffset" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "glmax" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "glmin" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "qform_code" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "sform_code" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "quatern_b" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "quatern_c" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "quatern_d" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "qoffset_x" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "qoffset_y" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "qoffset_z" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "srow_x" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "srow_y" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "srow_z" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "affine" out of 43 keys
collate/stack a list of tensors
collate dict key "original_affine" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "as_closest_canonical" out of 43 keys
collate dict key "spatial_shape" out of 43 keys
collate/stack a list of numpy arrays
collate/stack a list of tensors
collate dict key "space" out of 43 keys
collate dict key "original_channel_dim" out of 43 keys
collate dict key "filename_or_obj" out of 43 keys
Traceback (most recent call last):
File "/mnt/store/zhengsch/Ostia_landmark/tools/train.py", line 572, in
Trainer().run()
File "/mnt/store/zhengsch/Ostia_landmark/tools/train.py", line 332, in run
self.train_step(epoch)
File "/mnt/store/zhengsch/Ostia_landmark/tools/train.py", line 392, in train_step
for i, batch_data in enumerate(self.trn_dl):
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 681, in next
data = self._next_data()
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1376, in _next_data
return self._process_data(data)
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1402, in _process_data
data.reraise()
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/_utils.py", line 461, in reraise
raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/data/utils.py", line 516, in list_data_collate
ret = collate_fn(data)
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/data/utils.py", line 478, in collate_meta_tensor
return {k: collate_meta_tensor([d[k] for d in batch]) for k in elem_0}
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/data/utils.py", line 478, in
return {k: collate_meta_tensor([d[k] for d in batch]) for k in elem_0}
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/data/utils.py", line 483, in collate_meta_tensor
return default_collate(batch)
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/utils/data/utils/collate.py", line 140, in default_collate
out = elem.new(storage).resize(len(batch), *list(elem.size()))
RuntimeError: Trying to resize storage that is not resizable

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/opt/miniforge3/envs/monai/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
return self.collate_fn(data)
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/data/utils.py", line 696, in pad_list_data_collate
return PadListDataCollate(method=method, mode=mode, **kwargs)(batch)
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/transforms/croppad/batch.py", line 114, in call
return list_data_collate(batch)
File "/home/zhengsch/.local/lib/python3.9/site-packages/monai/data/utils.py", line 529, in list_data_collate
raise RuntimeError(re_str) from re
RuntimeError: Trying to resize storage that is not resizable

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調査の方向性

RandSpatialCropSamplesd、ResizeWithPadOrCropd、batch_size=2 を使用して報告されたパイプラインを再現し、その後、collate エラーと異なるテンソル形状を調査します。サンプリングされた出力に期待される動作を特定し、報告されたバッチ構成で一貫した形状のテンソルが生成されること、またはサポート対象外であることが明確に文書化されていることを検証します。

索引モデルが issue の本文から書いたものです。

評価

技術スタック
python, pytorch
領域
data, machine-learning
issue の種類
バグ
難易度
4/5
見積もり時間
3〜5日
活発さ
停滞
明瞭さ
説明が足りない
初心者へのやさしさ
25/100

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