Project-MONAI / Project-MONAI/MONAI

RandSpatialCropSamplesd

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

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

     ...,

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

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

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

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

     ...,

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

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

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

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

     ...,

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

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

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

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

     ...,

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

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

     [[0., 0., 0.,  ..., 0., 0., 0.],
      [0., 0., 0.,  ..., 0., 0., 0.],
      [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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Hướng nghiên cứu

Tái hiện pipeline đã được báo cáo bằng cách sử dụng RandSpatialCropSamplesd, ResizeWithPadOrCropd và batch_size=2, sau đó kiểm tra lỗi collate và các hình dạng tensor khác nhau. Xác định hành vi mong đợi đối với các đầu ra được lấy mẫu và xác minh rằng cấu hình batch đã được báo cáo либо tạo ra các tensor có hình dạng nhất quán, либо được ghi rõ là không được hỗ trợ.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.

Đánh giá

Công nghệ
python, pytorch
Lĩnh vực
data, machine-learning
Loại issue
Lỗi
Độ khó
4/5
Thời gian dự kiến
3-5 ngày
Mức độ hoạt động
Đình trệ
Độ rõ ràng
Cần làm rõ
Mức phù hợp với người mới
25/100

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