RandCropByPosNegLabel - Proposed random crop ROI is larger than the image size

[paulojamorim]

Hi

I am using the RandCropByPosNegLabel, the patch extrapolate the size of the image in some point and the error below occurs.

Is there any parameter to complete that missing part (increase the patch size)? or is it necessary to implement in MONAI?

Any help/suggestion appreciated.

Traceback (most recent call last):
  File "/code/monai_test.py", line 272, in <module>
    for batch_data in train_loader:
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
    data = self._next_data()
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1179, in _next_data
    return self._process_data(data)
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data
    data.reraise()
  File "/opt/conda/lib/python3.7/site-packages/torch/_utils.py", line 429, in reraise
    raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 14.
Original Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/transform.py", line 91, in apply_transform
    return _apply_transform(transform, data, unpack_items)
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/transform.py", line 55, in _apply_transform
    return transform(parameters)
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/croppad/dictionary.py", line 861, in __call__
    self.randomize(label, fg_indices, bg_indices, image)
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/croppad/dictionary.py", line 852, in randomize
    self.cropper.randomize(label=label, fg_indices=fg_indices, bg_indices=bg_indices, image=image)
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/croppad/array.py", line 1068, in randomize
    self.allow_smaller,
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/utils.py", line 520, in generate_pos_neg_label_crop_centers
    centers.append(correct_crop_centers(center, spatial_size, label_spatial_shape, allow_smaller))
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/utils.py", line 447, in correct_crop_centers
    raise ValueError("The size of the proposed random crop ROI is larger than the image size.")
ValueError: The size of the proposed random crop ROI is larger than the image size.

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
    data = fetcher.fetch(index)
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/conda/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/conda/lib/python3.7/site-packages/monai/data/dataset.py", line 105, in __getitem__
    return self._transform(index)
  File "/opt/conda/lib/python3.7/site-packages/monai/data/dataset.py", line 878, in _transform
    data = apply_transform(_transform, data)
  File "/opt/conda/lib/python3.7/site-packages/monai/transforms/transform.py", line 118, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.croppad.dictionary.RandCropByPosNegLabeld object at 0x7fe9134df150>```

[wyli]

Yes, I think you can set allow_smaller=True

MONAI/monai/transforms/croppad/array.py

Lines 1004 to 1006 in e4a0de9

	allow_smaller: if `False`, an exception will be raised if the image is smaller than
	the requested ROI in any dimension. If `True`, any smaller dimensions will be set to
	match the cropped size (i.e., no cropping in that dimension).

or use SpatialPad accordingly to make the input large enough

MONAI/monai/transforms/croppad/array.py

Lines 217 to 223 in e4a0de9

	class SpatialPad(Pad):
	"""
	Performs padding to the data, symmetric for all sides or all on one side for each dimension.
	Args:
	spatial_size: the spatial size of output data after padding, if a dimension of the input
	data size is larger than the pad size, will not pad that dimension.

[paulojamorim]

Thank you @wyli !