Project-MONAI / Project-MONAI/MONAI

RandSpatialCropSamplesd

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Descrizione

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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Direzione di ricerca

Riprodurre la pipeline segnalata utilizzando RandSpatialCropSamplesd, ResizeWithPadOrCropd e batch_size=2, quindi esaminare l’errore di collate e le diverse forme dei tensori. Determinare il comportamento previsto per gli output campionati e verificare che la configurazione del batch segnalata produca tensori con forme coerenti oppure sia chiaramente documentata come non supportata.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
python, pytorch
Ambito
data, machine-learning
Tipo di issue
Bug
Difficoltà
4/5
Tempo stimato
3-5 giorni
Stato di attività
Ferma
Chiarezza
Da chiarire
Idoneità per principianti
25/100

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