How to use MONAI and JSON file in clara train v4.0 ?

[Minxiangliu]

Hi, I am using nvidia clara train v4.0 to train Task01_BrainTumour, I get some problem when I use config_train_segresnet.json.

I have deleted CropForegroundd because it will make me wrong, but it still have some problem:

please give me some advice, thank!

By the way, the same method can train Task09_Spleen.

========== End of Train Config Result ===========
2021-06-15 09:10:03,943 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 2 epochs
input data information of the runtime error transform:
2021-06-15 09:10:05,399 - DataStats - INFO - input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-15 09:10:05,411 - DataStats - INFO - input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-15 09:10:05,416 - DataStats - INFO - input data information of the runtime error transform:
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2021-06-15 09:10:05,447 - DataStats - INFO - label statistics:
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2021-06-15 09:10:05,448 - DataStats - INFO - image statistics:
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2021-06-15 09:10:05,459 - DataStats - INFO - input data information of the runtime error transform:
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image_meta_dict statistics:
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Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  4, 240, 240, 155,   4,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 1., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(0., dtype=float32), 'qoffset_y': array(0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([1., 0., 0., 0.], dtype=float32), 'srow_y': array([0., 1., 0., 0.], dtype=float32), 'srow_z': array([0., 0., 1., 0.], dtype=float32), 'affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'as_closest_canonical': False, 'spatial_shape': array([240, 240, 155], dtype=int16), 'original_channel_dim': -1, 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/imagesTr/BRATS_362.nii.gz'}
2021-06-15 09:10:05,463 - DataStats - INFO - image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  4, 240, 240, 155,   4,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 1., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(0., dtype=float32), 'qoffset_y': array(0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([1., 0., 0., 0.], dtype=float32), 'srow_y': array([0., 1., 0., 0.], dtype=float32), 'srow_z': array([0., 0., 1., 0.], dtype=float32), 'affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'as_closest_canonical': False, 'spatial_shape': array([240, 240, 155], dtype=int16), 'original_channel_dim': -1, 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/imagesTr/BRATS_362.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155, 4), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-15 09:10:05,463 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155, 4), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-15 09:10:05,463 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 240, 240, 155,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 0., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(0., dtype=float32), 'qoffset_y': array(0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([1., 0., 0., 0.], dtype=float32), 'srow_y': array([0., 1., 0., 0.], dtype=float32), 'srow_z': array([0., 0., 1., 0.], dtype=float32), 'affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'as_closest_canonical': False, 'spatial_shape': array([240, 240, 155], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/labelsTr/BRATS_362.nii.gz'}
2021-06-15 09:10:05,465 - DataStats - INFO - label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 240, 240, 155,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1., 1., 1., 1., 0., 0., 0., 0.], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(0., dtype=float32), 'qoffset_y': array(0., dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([1., 0., 0., 0.], dtype=float32), 'srow_y': array([0., 1., 0., 0.], dtype=float32), 'srow_z': array([0., 0., 1., 0.], dtype=float32), 'affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'original_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'as_closest_canonical': False, 'spatial_shape': array([240, 240, 155], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/labelsTr/BRATS_362.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155, 4), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-15 09:10:05,466 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155, 4), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-15 09:10:05,466 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140286812654320, 'orig_size': (240, 240, 155), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
       [0., 1., 0., 0.],
       [0., 0., 1., 0.],
       [0., 0., 0., 1.]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-15 09:10:05,467 - ignite.engine.engine.SupervisedTrainer - ERROR - Current run is terminating due to exception: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 818, in __call__
    self.randomize(label, fg_indices, bg_indices, image)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 803, in randomize
    fg_indices_, bg_indices_ = map_binary_to_indices(label, image, self.image_threshold)
  File "/opt/monai/monai/transforms/utils.py", line 255, in map_binary_to_indices
    bg_indices = np.nonzero(np.logical_and(img_flat, ~label_flat))[0]
ValueError: operands could not be broadcast together with shapes (35712000,) (8928000,) 

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

Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/compose.py", line 144, in __call__
    input_ = apply_transform(_transform, input_)
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.croppad.dictionary.RandCropByPosNegLabeld object at 0x7f9711a28d00>

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
    data = fetcher.fetch(index)
  File "/opt/conda/lib/python3.8/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/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/monai/monai/data/dataset.py", line 93, in __getitem__
    return self._transform(index)
  File "/opt/monai/monai/data/dataset.py", line 568, in _transform
    return super()._transform(index)
  File "/opt/monai/monai/data/dataset.py", line 79, in _transform
    return apply_transform(self.transform, data_i) if self.transform is not None else data_i
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.compose.Compose object at 0x7f9712be20d0>

2021-06-15 09:10:05,478 - ignite.engine.engine.SupervisedTrainer - INFO - Exception_raised, saved exception checkpoint: checkpoint_final_iteration=0.pt
2021-06-15 09:10:05,478 - ignite.engine.engine.SupervisedTrainer - ERROR - Engine run is terminating due to exception: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 818, in __call__
    self.randomize(label, fg_indices, bg_indices, image)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 803, in randomize
    fg_indices_, bg_indices_ = map_binary_to_indices(label, image, self.image_threshold)
  File "/opt/monai/monai/transforms/utils.py", line 255, in map_binary_to_indices
    bg_indices = np.nonzero(np.logical_and(img_flat, ~label_flat))[0]
ValueError: operands could not be broadcast together with shapes (35712000,) (8928000,) 

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

Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/compose.py", line 144, in __call__
    input_ = apply_transform(_transform, input_)
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.croppad.dictionary.RandCropByPosNegLabeld object at 0x7f9711a28d00>

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
    data = fetcher.fetch(index)
  File "/opt/conda/lib/python3.8/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/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/monai/monai/data/dataset.py", line 93, in __getitem__
    return self._transform(index)
  File "/opt/monai/monai/data/dataset.py", line 568, in _transform
    return super()._transform(index)
  File "/opt/monai/monai/data/dataset.py", line 79, in _transform
    return apply_transform(self.transform, data_i) if self.transform is not None else data_i
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.compose.Compose object at 0x7f9712be20d0>

2021-06-15 09:10:05,479 - ignite.engine.engine.SupervisedTrainer - INFO - Deleted previous saved final checkpoint: checkpoint_final_iteration=0.pt
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-15 09:10:05,485 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-15 09:10:05,488 - ignite.engine.engine.SupervisedTrainer - INFO - Exception_raised, saved exception checkpoint: checkpoint_final_iteration=0.pt
Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "apps/train.py", line 35, in <module>
  File "apps/train.py", line 27, in main
  File "apps/mmar_conf.py", line 31, in train_mmar
  File "/opt/monai/monai/engines/trainer.py", line 49, in run
    super().run()
  File "/opt/monai/monai/engines/workflow.py", line 206, in run
    super().run(data=self.data_loader, max_epochs=self.state.max_epochs)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 702, in run
    return self._internal_run()
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 775, in _internal_run
    self._handle_exception(e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 467, in _handle_exception
    self._fire_event(Events.EXCEPTION_RAISED, e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 424, in _fire_event
    func(*first, *(event_args + others), **kwargs)
  File "/opt/monai/monai/handlers/checkpoint_saver.py", line 275, in exception_raised
    raise e
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 745, in _internal_run
    time_taken = self._run_once_on_dataset()
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 850, in _run_once_on_dataset
    self._handle_exception(e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 467, in _handle_exception
    self._fire_event(Events.EXCEPTION_RAISED, e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 424, in _fire_event
    func(*first, *(event_args + others), **kwargs)
  File "/opt/monai/monai/handlers/checkpoint_saver.py", line 275, in exception_raised
    raise e
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 801, in _run_once_on_dataset
    self.state.batch = next(self._dataloader_iter)
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
    data = self._next_data()
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1199, in _next_data
    return self._process_data(data)
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data
    data.reraise()
  File "/opt/conda/lib/python3.8/site-packages/torch/_utils.py", line 429, in reraise
    raise self.exc_type(msg)
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 818, in __call__
    self.randomize(label, fg_indices, bg_indices, image)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 803, in randomize
    fg_indices_, bg_indices_ = map_binary_to_indices(label, image, self.image_threshold)
  File "/opt/monai/monai/transforms/utils.py", line 255, in map_binary_to_indices
    bg_indices = np.nonzero(np.logical_and(img_flat, ~label_flat))[0]
ValueError: operands could not be broadcast together with shapes (35712000,) (8928000,) 

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

Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/compose.py", line 144, in __call__
    input_ = apply_transform(_transform, input_)
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.croppad.dictionary.RandCropByPosNegLabeld object at 0x7f9711a28d00>

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop
    data = fetcher.fetch(index)
  File "/opt/conda/lib/python3.8/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/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/opt/monai/monai/data/dataset.py", line 93, in __getitem__
    return self._transform(index)
  File "/opt/monai/monai/data/dataset.py", line 568, in _transform
    return super()._transform(index)
  File "/opt/monai/monai/data/dataset.py", line 79, in _transform
    return apply_transform(self.transform, data_i) if self.transform is not None else data_i
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.compose.Compose object at 0x7f9712be20d0>

label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 2.0)

[Nic-Ma]

Hi @Minxiangliu ,

Thanks for your interest in MONAI and NVIDIA Clara-Train SDK.
Seems your image and label have different data shape.
Could you please help share your JSON config?

Thanks.

[Minxiangliu]

Hi @Nic-Ma ,
Glad your reply.
I'm sorry I don't have dataset JOSN of Task01_BrainTumour at the moment.
I will make up as soon as possible.
config_train_segresnet.json:

{
  "epochs": 2,
  "use_gpu": true,
  "multi_gpu": false,
  "amp": true,
  "num_interval_per_valid": 20,
  "learning_rate": 2e-4,
  "determinism": {
    "random_seed": 0
  },
  "train": {
    "loss": {
      "name": "DiceLoss",
      "args":{
        "to_onehot_y": true,
        "softmax": true
      }
    },
    "optimizer": {
      "name": "Adam",
      "args": {
        "lr": "{learning_rate}"
      }
    },
    "lr_scheduler": {
      "name": "StepLR",
      "args": {
        "step_size": 5000,
        "gamma": 0.1
      }
    },
    "model": {
      "name": "SegResNet",
      "args": {
        "blocks_down": [1,2,2,2],
        "blocks_up": [1,1,1],
        "init_filters": 8,
        "in_channels": 1,
        "out_channels": 2,
        "dropout_prob": 0.2
      }
    },
    "pre_transforms": [
      {
        "name": "LoadImaged",
        "args": {
          "keys": ["image", "label"]
        }
      },
      {
        "name": "AddChanneld",
        "args": {
          "keys": ["image", "label"]
        }
      },
      {
        "name": "Spacingd",
        "args": {
          "keys": ["image", "label"],
            "pixdim": [1.0, 1.0, 1.0],
            "mode":["bilinear", "nearest"]
        }
      },
      {
        "name": "ScaleIntensityRanged",
        "args": {
          "keys": "image",
          "a_min": -57,
          "a_max": 164,
          "b_min": 0.0,
          "b_max": 1.0,
          "clip": true
        }
      },
      {
        "name": "RandCropByPosNegLabeld",
        "args": {
          "keys": ["image", "label"],
          "label_key": "label",
          "spatial_size": [96, 96, 96],
          "pos": 1,
          "neg": 1,
          "num_samples": 4,
          "image_key": "image",
          "image_threshold": 0
        }
      },
      {
        "name": "RandShiftIntensityd",
        "args": {
          "keys": "image",
          "offsets": 0.1,
          "prob": 0.5
        }
      },
      {
        "name": "ToTensord",
        "args": {
          "keys": ["image", "label"]
        }
      }
    ],
    "dataset": {
      "name": "CacheDataset",
      "data_list_file_path": "{DATASET_JSON}",
      "data_file_base_dir": "{DATA_ROOT}",
      "data_list_key": "training",
      "args": {
        "cache_num": 4,
        "cache_rate": 1.0,
        "num_workers": 2
      }
    },
    "dataloader": {
      "name": "DataLoader",
      "args": {
        "batch_size": 2,
        "shuffle": true,
        "num_workers": 4
      }
    },
    "inferer": {
      "name": "SimpleInferer"
    },
    "handlers": [
      {
        "name": "LrScheduleHandler",
        "args": {
          "print_lr": true
        }
      },
      {
        "name": "ValidationHandler",
        "args": {
          "interval": "{num_interval_per_valid}",
          "epoch_level": true
        }
      },
      {
        "name": "CheckpointSaver",
        "rank": 0,
        "args": {
          "save_dir": "{MMAR_CKPT_DIR}",
          "save_dict": ["model", "optimizer", "lr_scheduler"],
          "save_final": true,
          "save_interval": 5
        }
      },
      {
        "name": "StatsHandler",
        "rank": 0,
        "args": {
          "tag_name": "train_loss",
          "output_transform": "lambda x: x['loss']"
        }
      },
      {
        "name": "TensorBoardStatsHandler",
        "rank": 0,
        "args": {
          "log_dir": "{MMAR_CKPT_DIR}",
          "tag_name": "train_loss",
          "output_transform": "lambda x: x['loss']"
        }
      }
    ],
    "post_transforms": [
      {
        "name": "Activationsd",
        "args": {
          "keys": "pred",
          "softmax": true
        }
      },
      {
        "name": "AsDiscreted",
        "args": {
          "keys": ["pred", "label"],
          "argmax": [true, false],
          "to_onehot": true,
          "n_classes": 2
        }
      }
    ],
    "metrics": [
      {
      "name": "Accuracy",
        "log_label": "train_acc",
      "is_key_metric": true,
      "args": {
        "output_transform": "lambda x: (x['pred'], x['label'])"
        }
      }
    ],
    "trainer": {
      "name": "SupervisedTrainer"
    }
  },
  "validate": {
    "pre_transforms": [
      {
        "ref": "LoadImaged"
      },
      {
        "ref": "Spacingd"
      },
      {
        "ref": "AddChanneld"
      },
      {
        "ref": "ScaleIntensityRanged"
      },
      {
        "ref": "ToTensord"
      }
    ],
    "dataset": {
      "name": "CacheDataset",
      "data_list_file_path": "{DATASET_JSON}",
      "data_file_base_dir": "{DATA_ROOT}",
      "data_list_key": "validation",
      "args": {
        "cache_num": 4,
        "cache_rate": 1.0,
        "num_workers": 2
      }
    },
    "dataloader": {
      "name": "DataLoader",
      "args": {
        "batch_size": 1,
        "shuffle": false,
        "num_workers": 4
      }
    },
    "inferer": {
      "name": "SlidingWindowInferer",
      "args": {
        "roi_size": [160, 160, 160],
        "sw_batch_size": 4,
        "overlap": 0.5
      }
    },
    "handlers": [
      {
        "name": "StatsHandler",
        "rank": 0,
        "args": {
          "output_transform": "lambda x: None"
        }
      },
      {
        "name": "TensorBoardStatsHandler",
        "rank": 0,
        "args": {
          "log_dir": "{MMAR_CKPT_DIR}",
          "output_transform": "lambda x: None"
        }
      },
      {
        "name": "CheckpointSaver",
        "rank": 0,
        "args": {
          "save_dir": "{MMAR_CKPT_DIR}",
          "save_dict": ["model"],
          "save_key_metric": true
        }
      }
    ],
    "post_transforms": [
      {
        "ref": "Activationsd"
      },
      {
        "ref": "AsDiscreted"
      }
    ],
    "metrics": [
      {
        "name": "MeanDice",
        "log_label": "val_mean_dice",
        "is_key_metric": true,
        "args": {
          "include_background": false,
          "output_transform": "lambda x: (x['pred'], x['label'])"
        }
      },
      {
        "name": "Accuracy",
        "log_label": "val_acc",
        "args": {
          "output_transform": "lambda x: (x['pred'], x['label'])"
        }
      }
    ],
    "evaluator": {
      "name": "SupervisedEvaluator"
    }
  }
}

About Task01_BrainTumour JSON file, can refer to this.

1.3 Exploring the Dataset
Our dataset is stored in the NifTI-1 format and we will be using the NiBabel library to interact with the files. Each training sample is composed of two separate files:

The first file is an image file containing a 4D array of MR image in the shape of (240, 240, 155, 4).

The first 3 dimensions are the X, Y, and Z values for each point in the 3D volume, which is commonly called a voxel.
The 4th dimension is the values for 4 different sequences
    0: FLAIR: “Fluid Attenuated Inversion Recovery” (FLAIR)
    1: T1w: “T1-weighted”
    2: t1gd: “T1-weighted with gadolinium contrast enhancement” (T1-Gd)
    3: T2w: “T2-weighted”
The second file in each training example is a label file containing a 3D array with the shape of (240, 240, 155).

The integer values in this array indicate the “label” for each voxel in the corresponding image files:
    0: background
    1: edema
    2: non-enhancing tumor
    3: enhancing tumor

[Minxiangliu]

Hi @Nic-Ma,
This is my dataset JSON file, thank your help:

{
    "name": "BRATS",
    "description": "Gliomas segmentation tumour and oedema in on brain images",
    "reference": "https://www.med.upenn.edu/sbia/brats2017.html",
    "licence": "CC-BY-SA 4.0",
    "release": "2.0 04/05/2018",
    "tensorImageSize": "4D",
    "modality": {
        "0": "FLAIR",
        "1": "T1w",
        "2": "t1gd",
        "3": "T2w"
    },
    "labels": {
        "0": "background",
        "1": "edema",
        "2": "non-enhancing tumor",
        "3": "enhancing tumour"
    },
    "numTraining": 435,
    "numValidation": 49,
    "numTest": 266,
    "training": [
        {
            "image": "./imagesTr/BRATS_033.nii.gz",
            "label": "./labelsTr/BRATS_033.nii.gz"
        },
        ...
    ],
    "validation": [
        {
            "image": "./imagesTr/BRATS_045.nii.gz",
            "label": "./labelsTr/BRATS_045.nii.gz"
        },
        ....
    ],
    "test": [
        "./imagesTs/BRATS_557.nii.gz",
        "./imagesTs/BRATS_549.nii.gz",
        ....
    ]
}

[Nic-Ma]

Hi @myron ,

Could you please help share some comments about this question?
I think you developed the Brats MMAR of Clara based on MONAI APIs.

Thanks in advance.

[Nic-Ma]

Hi @Minxiangliu ,

Actually, the Brats MMAR of Clara 4.0 is based on the original Brats challenge dataset, not the Task01 of Decathlon.
So in order to update your MMAR config, I suggest you to check our tutorial for Decathlon Task01:
https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/brats_segmentation_3d.ipynb

Thanks.

[Minxiangliu]

Hi, @Nic-Ma ,
Thank your suggestion. I try to rewrite the MMAR config and create my own function according to the example here, but I get some problem when I run train.sh scrip.

My folder structure:
Test
   |---config
   |---command
   |---models
   |---custom
   |---eval

In command folder have set_env.sh:

#!/usr/bin/env bash

# SPDX-License-Identifier: Apache-2.0

export PYTHONPATH="$PYTHONPATH:/opt/nvidia"
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
export MMAR_ROOT=${DIR}/..
export PYTHONPATH="$PYTHONPATH:/opt/nvidia:$MMAR_ROOT/custom"
echo PYTHONPATH is $PYTHONPATH

In custom folder have myTransformation.py:

import numpy as np
from monai.transforms import MapTransform

class ConvertToMultiChannelBasedOnBratsClassesd(MapTransform):
    """
    Convert labels to multi channels based on brats classes:
    label 1 is the peritumoral edema
    label 2 is the GD-enhancing tumor
    label 3 is the necrotic and non-enhancing tumor core
    The possible classes are TC (Tumor core), WT (Whole tumor)
    and ET (Enhancing tumor).

    """

    def __call__(self, data):
        d = dict(data)
        for key in self.keys:
            result = []
            # merge label 2 and label 3 to construct TC
            result.append(np.logical_or(d[key] == 2, d[key] == 3))
            # merge labels 1, 2 and 3 to construct WT
            result.append(
                np.logical_or(
                    np.logical_or(d[key] == 2, d[key] == 3), d[key] == 1
                )
            )
            # label 2 is ET
            result.append(d[key] == 2)
            d[key] = np.stack(result, axis=0).astype(np.float32)
        return d

In config folder have config_train.json:

......
"pre_transforms": [
.......
      {
        "path": "myTransformation.ConvertToMultiChannelBasedOnBratsClassesd",
        "args": {
          "keys": "label"
        }
      },
.......

An error message appears when I train:
ConvertToMultiChannelBasedOnBratsClassesd is not define
What am I missing? Thank.

[Nic-Ma]

Hi @Minxiangliu ,

Thanks for your update.
Could you please help share your latest MMAR? Then @madil90 and I can investigate more details.

Thanks in advance.

[madil90]

@Minxiangliu During your run, can you share what the PYTHONPATH variable's value is. You are displaying it in set_env.sh. As Nic said, if you can share your MMAR, that will be very helpful. Thanks.

[Minxiangliu]

Hi @Nic-Ma , @madil90,
Thank your reply.
The above error is that I forgot to add path to pre_transforms of validate.
But another error occurred in pre_transforms(CenterSpatialCropd) of validate
My MMAR json:

{
    "epochs": 100,
    "num_interval_per_valid": 1,
    "multi_gpu": false,
    "amp": true,
    "learning_rate": 1e-4,
    "tf32": true,
    "cudnn_benchmark": false,
    "dont_load_ckpt_model": true,
    "determinism": {
      "random_seed": 0
    },
    "train": {
      "loss": {
        "name": "DiceLoss",
        "args":{
          "to_onehot_y": false,
          "sigmoid": true,
          "squared_pred": true
        }
      },
      "optimizer": {
        "name": "Adam",
        "args": {
          "lr": "{learning_rate}",
          "weight_decay":1e-5, 
          "amsgrad": true
        }
      },
      "lr_scheduler": {
        "name": "StepLR",
        "args": {
          "step_size": 20,
          "gamma": 0.1
        }
      },
      "model": {
        "name": "UNet",
        "args": {
          "dimensions": 3,
          "in_channels": 4,
          "out_channels": 3,
          "channels": [16, 32, 64, 128, 256],
          "strides": [2, 2, 2, 2],
          "num_res_units": 2
        }
      },
      "pre_transforms": [
        {
          "name": "LoadImaged",
          "args": {
            "keys": ["image", "label"]
          }
        },
        {
          "name": "AsChannelFirstd",
          "args":{
            "keys": "image"
          }
        },
        {
          "path": "myTransformation.ConvertToMultiChannelBasedOnBratsClassesd",
          "args":{
            "keys": "label"
          }
        },
        {
          "name": "Spacingd",
          "args": {
              "keys": ["image", "label"],
              "pixdim": [1.5, 1.5, 2.0],
              "mode":["bilinear", "nearest"]
          }
        },
        {
          "name": "Orientationd",
          "args": {
              "keys": ["image", "label"],
              "axcodes":"RAS"
          }
        },
        {
          "name": "RandSpatialCropd",
          "args": {
              "keys": ["image", "label"],
              "roi_size":[128, 128, 64],
              "random_size":false
          }
        },
        {
          "name": "RandFlipd",
          "args": {
              "keys": ["image", "label"],
              "prob": 0.5,
              "spatial_axis":0
          }
        },
        {
          "name": "NormalizeIntensityd",
          "args": {
              "keys": "image",
              "nonzero": true,
              "channel_wise":true
          }
        },
        {
          "name": "RandScaleIntensityd",
          "args": {
              "keys": "image",
              "factors": 0.1,
              "prob":0.5
          }
        },
        {
          "name": "RandShiftIntensityd",
          "args": {
              "keys": "image",
              "offsets": 0.1,
              "prob":0.5
          }
        },
        {
          "name": "ToTensord",
          "args": {
            "keys": ["image", "label"]
          }
        }
      ],
      "dataset": {
        "name": "CacheDataset",
        "data_list_file_path": "{DATASET_JSON}",
        "data_file_base_dir": "{DATA_ROOT}",
        "data_list_key": "training",
        "args": {
          "cache_num": 4,
          "cache_rate": 1.0,
          "num_workers": 2
        }
      },
      "dataloader": {
        "name": "DataLoader",
        "args": {
          "batch_size": 2,
          "shuffle": true,
          "num_workers": 4
        }
      },
      "inferer": {
        "name": "SimpleInferer"
      },
      "handlers": [
        {
          "name": "CheckpointLoader",
          "disabled": "{dont_load_ckpt_model}",
          "args": {
            "load_path": "{MMAR_CKPT}",
            "load_dict": ["model"]
          }
        },
        {
          "name": "LrScheduleHandler",
          "args": {
            "print_lr": true
          }
        },
        {
          "name": "ValidationHandler",
          "args": {
            "interval": "{num_interval_per_valid}",
            "epoch_level": true
          }
        },
        {
          "name": "CheckpointSaver",
          "rank": 0,
          "args": {
            "save_dir": "{MMAR_CKPT_DIR}",
            "save_dict": ["model", "optimizer", "lr_scheduler"],
            "save_final": true,
            "save_interval": 5
          }
        },
        {
          "name": "StatsHandler",
          "rank": 0,
          "args": {
            "tag_name": "train_loss",
            "output_transform": "lambda x: x['loss']"
          }
        },
        {
          "name": "TensorBoardStatsHandler",
          "rank": 0,
          "args": {
            "log_dir": "{MMAR_CKPT_DIR}",
            "tag_name": "train_loss",
            "output_transform": "lambda x: x['loss']"
          }
        }
      ],
      "post_transforms": [
        {
          "name": "Activationsd",
          "args": {
            "keys": "pred",
            "softmax": true
          }
        },
        {
          "name": "AsDiscreted",
          "args": {
            "keys": ["pred", "label"],
            "argmax": [true, false],
            "to_onehot": true,
            "n_classes": 2
          }
        }
      ],
      "metrics": [
        {
          "name": "Accuracy",
          "log_label": "train_acc",
          "is_key_metric": true,
          "args": {
            "output_transform": "lambda x: (x['pred'], x['label'])"
          }
        }
      ],
      "trainer": {
        "name": "SupervisedTrainer",
        "args": {
          "max_epochs": "{epochs}"
        }
      }
    },
    "validate": {
      "pre_transforms": [
        {
          "ref": "LoadImaged"
        },
        {
          "ref": "AsChannelFirstd"
        },
        {
          "ref": "myTransformation.ConvertToMultiChannelBasedOnBratsClassesd"
        },
        {
          "ref": "Spacingd"
        },
        {
          "name": "CenterSpatialCropd",
          "args": {
              "keys": ["image","label"],
              "roi_size": [128, 128, 64]
          }
        },
        {
          "ref": "NormalizeIntensityd"
        },
        {
          "ref": "ToTensord"
        }
      ],
      "dataset": {
        "name": "CacheDataset",
        "data_list_file_path": "{DATASET_JSON}",
        "data_file_base_dir": "{DATA_ROOT}",
        "data_list_key": "validation",
        "args": {
          "cache_num": 4,
          "cache_rate": 1.0,
          "num_workers": 2
        }
      },
      "dataloader": {
        "name": "DataLoader",
        "args": {
          "batch_size": 1,
          "shuffle": false,
          "num_workers": 4
        }
      },
      "inferer": {
        "name": "SlidingWindowInferer",
        "args": {
          "roi_size": [160, 160, 160],
          "sw_batch_size": 4,
          "overlap": 0.5
        }
      },
      "handlers": [
        {
          "name": "StatsHandler",
          "rank": 0,
          "args": {
            "output_transform": "lambda x: None"
          }
        },
        {
          "name": "TensorBoardStatsHandler",
          "rank": 0,
          "args": {
            "log_dir": "{MMAR_CKPT_DIR}",
            "output_transform": "lambda x: None"
          }
        },
        {
          "name": "CheckpointSaver",
          "rank": 0,
          "args": {
            "save_dir": "{MMAR_CKPT_DIR}",
            "save_dict": ["model","train_conf"],
            "save_key_metric": true
          }
        }
      ],
      "post_transforms": [
        {
          "ref": "Activationsd"
        },
        {
          "ref": "AsDiscreted"
        }
      ],
      "metrics": [
        {
          "name": "MeanDice",
          "log_label": "val_mean_dice",
          "is_key_metric": true,
          "args": {
            "include_background": false,
            "output_transform": "lambda x: (x['pred'], x['label'])"
          }
        },
        {
          "name": "Accuracy",
          "log_label": "val_acc",
          "args": {
            "output_transform": "lambda x: (x['pred'], x['label'])"
          }
        }
      ],
      "evaluator": {
        "name": "SupervisedEvaluator"
      }
    }
  }

ERROR:

Loading dataset: 100%|████████████████████████████| 4/4 [00:05<00:00,  1.40s/it]
Loading dataset:   0%|                                    | 0/4 [00:00<?, ?it/s]input data information of the runtime error transform:
2021-06-18 04:40:04,925 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
2021-06-18 04:40:04,929 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
2021-06-18 04:40:04,954 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-345.3241, dtype=float32), 'qoffset_y': array(-345.3241, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.675781,    0.      ,    0.      , -345.3241  ], dtype=float32), 'srow_y': array([   0.      ,    0.675781,    0.      , -345.3241  ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -345.32409668],
       [   0.        ,    1.5       ,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  80], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_62.nii.gz'}
2021-06-18 04:40:04,958 - DataStats - INFO - image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-345.3241, dtype=float32), 'qoffset_y': array(-345.3241, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.675781,    0.      ,    0.      , -345.3241  ], dtype=float32), 'srow_y': array([   0.      ,    0.675781,    0.      , -345.3241  ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -345.32409668],
       [   0.        ,    1.5       ,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  80], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_62.nii.gz'}
label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-345.3241, dtype=float32), 'qoffset_y': array(-345.3241, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.675781,    0.      ,    0.      , -345.3241  ], dtype=float32), 'srow_y': array([   0.      ,    0.675781,    0.      , -345.3241  ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -345.32409668],
       [   0.        ,    1.5       ,    0.        , -345.32409668],
       [   0.        ,    0.        ,    2.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  80], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_62.nii.gz'}
2021-06-18 04:40:04,960 - DataStats - INFO - label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-345.3241, dtype=float32), 'qoffset_y': array(-345.3241, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.675781,    0.      ,    0.      , -345.3241  ], dtype=float32), 'srow_y': array([   0.      ,    0.675781,    0.      , -345.3241  ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -345.32409668],
       [   0.        ,    1.5       ,    0.        , -345.32409668],
       [   0.        ,    0.        ,    2.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  80], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_62.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-18 04:40:04,960 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 80), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-18 04:40:04,961 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 80), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
       [   0.        ,    0.67578101,    0.        , -345.32409668],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-18 04:40:06,212 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
2021-06-18 04:40:06,214 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
2021-06-18 04:40:06,230 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  41,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.744141, 0.744141, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-380.25604, dtype=float32), 'qoffset_y': array(-380.25604, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.744141,    0.      ,    0.      , -380.25604 ], dtype=float32), 'srow_y': array([   0.      ,    0.744141,    0.      , -380.25604 ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -380.25604248],
       [   0.        ,    1.5       ,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  41], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_9.nii.gz'}
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  41,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.744141, 0.744141, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-380.25604, dtype=float32), 'qoffset_y': array(-380.25604, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.744141,    0.      ,    0.      , -380.25604 ], dtype=float32), 'srow_y': array([   0.      ,    0.744141,    0.      , -380.25604 ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -380.25604248],
       [   0.        ,    1.5       ,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  41], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_9.nii.gz'}
2021-06-18 04:40:06,233 - DataStats - INFO - image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  41,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.744141, 0.744141, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-380.25604, dtype=float32), 'qoffset_y': array(-380.25604, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.744141,    0.      ,    0.      , -380.25604 ], dtype=float32), 'srow_y': array([   0.      ,    0.744141,    0.      , -380.25604 ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -380.25604248],
       [   0.        ,    1.5       ,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  41], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_9.nii.gz'}
label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  41,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.744141, 0.744141, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-380.25604, dtype=float32), 'qoffset_y': array(-380.25604, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.744141,    0.      ,    0.      , -380.25604 ], dtype=float32), 'srow_y': array([   0.      ,    0.744141,    0.      , -380.25604 ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -380.25604248],
       [   0.        ,    1.5       ,    0.        , -380.25604248],
       [   0.        ,    0.        ,    2.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  41], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_9.nii.gz'}
label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  41,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.744141, 0.744141, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-380.25604, dtype=float32), 'qoffset_y': array(-380.25604, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.744141,    0.      ,    0.      , -380.25604 ], dtype=float32), 'srow_y': array([   0.      ,    0.744141,    0.      , -380.25604 ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -380.25604248],
       [   0.        ,    1.5       ,    0.        , -380.25604248],
       [   0.        ,    0.        ,    2.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  41], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_9.nii.gz'}
2021-06-18 04:40:06,235 - DataStats - INFO - label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  41,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.744141, 0.744141, 5.      , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-380.25604, dtype=float32), 'qoffset_y': array(-380.25604, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.744141,    0.      ,    0.      , -380.25604 ], dtype=float32), 'srow_y': array([   0.      ,    0.744141,    0.      , -380.25604 ], dtype=float32), 'srow_z': array([0., 0., 5., 0.], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -380.25604248],
       [   0.        ,    1.5       ,    0.        , -380.25604248],
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       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512,  41], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_9.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-18 04:40:06,235 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-18 04:40:06,236 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
       [   0.        ,    0.74414098,    0.        , -380.25604248],
       [   0.        ,    0.        ,    5.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
input data information of the runtime error transform:
input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-18 04:40:07,243 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
2021-06-18 04:40:07,256 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
2021-06-18 04:40:07,278 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512, 168], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_20.nii.gz'}
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-477.06653, dtype=float32), 'qoffset_y': array(-477.06653, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.933594,    0.      ,    0.      , -477.06653 ], dtype=float32), 'srow_y': array([   0.      ,    0.933594,    0.      , -477.06653 ], dtype=float32), 'srow_z': array([0. , 0. , 1.5, 0. ], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -477.06652832],
       [   0.        ,    1.5       ,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512, 168], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_20.nii.gz'}
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-477.06653, dtype=float32), 'qoffset_y': array(-477.06653, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.933594,    0.      ,    0.      , -477.06653 ], dtype=float32), 'srow_y': array([   0.      ,    0.933594,    0.      , -477.06653 ], dtype=float32), 'srow_z': array([0. , 0. , 1.5, 0. ], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -477.06652832],
       [   0.        ,    1.5       ,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512, 168], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/imagesTr/spleen_20.nii.gz'}
2021-06-18 04:40:07,280 - DataStats - INFO - image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512, 168], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_20.nii.gz'}
label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-477.06653, dtype=float32), 'qoffset_y': array(-477.06653, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.933594,    0.      ,    0.      , -477.06653 ], dtype=float32), 'srow_y': array([   0.      ,    0.933594,    0.      , -477.06653 ], dtype=float32), 'srow_z': array([0. , 0. , 1.5, 0. ], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -477.06652832],
       [   0.        ,    1.5       ,    0.        , -477.06652832],
       [   0.        ,    0.        ,    2.        ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'original_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512, 168], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_20.nii.gz'}
2021-06-18 04:40:07,282 - DataStats - INFO - label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
       0.      , 0.      ], dtype=float32), 'vox_offset': array(0., dtype=float32), 'scl_slope': array(nan, dtype=float32), 'scl_inter': array(nan, dtype=float32), 'slice_end': array(0, dtype=int16), 'slice_code': array(0, dtype=uint8), 'xyzt_units': array(10, dtype=uint8), 'cal_max': array(0., dtype=float32), 'cal_min': array(0., dtype=float32), 'slice_duration': array(0., dtype=float32), 'toffset': array(0., dtype=float32), 'glmax': array(0, dtype=int32), 'glmin': array(0, dtype=int32), 'qform_code': array(1, dtype=int16), 'sform_code': array(1, dtype=int16), 'quatern_b': array(0., dtype=float32), 'quatern_c': array(0., dtype=float32), 'quatern_d': array(0., dtype=float32), 'qoffset_x': array(-477.06653, dtype=float32), 'qoffset_y': array(-477.06653, dtype=float32), 'qoffset_z': array(0., dtype=float32), 'srow_x': array([   0.933594,    0.      ,    0.      , -477.06653 ], dtype=float32), 'srow_y': array([   0.      ,    0.933594,    0.      , -477.06653 ], dtype=float32), 'srow_z': array([0. , 0. , 1.5, 0. ], dtype=float32), 'affine': array([[   1.5       ,    0.        ,    0.        , -477.06652832],
       [   0.        ,    1.5       ,    0.        , -477.06652832],
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       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'as_closest_canonical': False, 'spatial_shape': array([512, 512, 168], dtype=int16), 'original_channel_dim': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task09_Spleen/labelsTr/spleen_20.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-18 04:40:07,282 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
2021-06-18 04:40:07,283 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140291668820224, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
Loading dataset:   0%|                                    | 0/4 [00:04<?, ?it/s]
Error processing config /claraDevDay/BrainTumorTest/commands/../config/config_train_UnetV2.json: applying transform <monai.transforms.croppad.dictionary.CenterSpatialCropd object at 0x7f983315c190>
Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 369, in __call__
    d[key] = self.cropper(d[key])
  File "/opt/monai/monai/transforms/croppad/array.py", line 285, in __call__
    self.roi_size = fall_back_tuple(self.roi_size, img.shape[1:])
  File "/opt/monai/monai/utils/misc.py", line 174, in fall_back_tuple
    user = ensure_tuple_rep(user_provided, ndim)
  File "/opt/monai/monai/utils/misc.py", line 131, in ensure_tuple_rep
    raise ValueError(f"Sequence must have length {dim}, got {len(tup)}.")
ValueError: Sequence must have length 2, got 3.

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "apps/train.py", line 35, in <module>
  File "apps/train.py", line 27, in main
  File "apps/mmar_conf.py", line 21, in train_mmar
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 172, in configure
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 167, in configure
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 163, in _do_configure
  File "apps/train_configer.py", line 571, in finalize_config
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 200, in build_component
  File "<nvflare-0.1.4>/dlmed/utils/class_utils.py", line 36, in instantiate_class
  File "/opt/monai/monai/data/dataset.py", line 532, in __init__
    self._cache: List = self._fill_cache()
  File "/opt/monai/monai/data/dataset.py", line 541, in _fill_cache
    return list(
  File "/opt/conda/lib/python3.8/site-packages/tqdm/std.py", line 1193, in __iter__
    for obj in iterable:
  File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 868, in next
    raise value
  File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/opt/monai/monai/data/dataset.py", line 562, in _load_cache_item
    item = apply_transform(_transform, item)
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.croppad.dictionary.CenterSpatialCropd object at 0x7f983315c190>
terminate called without an active exception
/claraDevDay/BrainTumorTest//commands/train.sh: line 47:   153 Aborted                 (core dumped) python3 -u -m medl.apps.train -m $MMAR_ROOT -c $CONFIG_FILE -e $ENVIRONMENT_FILE --write_train_stats --set print_conf=True MMAR_CKPT_DIR=$MMAR_CKPT_DIR

[Nic-Ma]

It looks like the data shape is not correct, please print out or use DataStatsd transform to get the shape of image and label before CenterSpatialCropd to double confirm, expect to be [C, H, W, D] shape.

Thanks.

[Nic-Ma]

And BTW, you can submit a ticket to Clara forum if you are facing Clara config issue instead of MONAI issue: https://forums.developer.nvidia.com/c/healthcare/clara-train-transfer-learning-toolkit-for-medi/154

Thanks.

[Minxiangliu]

Hi @Nic-Ma ,
I'm not sure if I am doing this correctly, the error message looks a lot like.
MMAR json:

"validate": {
    "pre_transforms": [
      {
        "ref": "LoadImaged"
      },
      {
        "ref": "AsChannelFirstd"
      },
      {
        "ref": "myTransformation.ConvertToMultiChannelBasedOnBratsClassesd"
      },
      {
        "ref": "Spacingd"
      },
      {
        "name": "DataStatsd",
        "args": {
            "keys": ["image","label"],
            "data_value": false
        }
      },
      {
        "name": "CenterSpatialCropd",
        "args": {
            "keys": ["image","label"],
            "roi_size": [128, 128, 64]
        }
      },
      {
        "ref": "NormalizeIntensityd"
      },
      {
        "ref": "ToTensord"
      }
    ],

error message:

Loading dataset: 100%|████████████████████████████| 4/4 [00:05<00:00,  1.41s/it]
Loading dataset:   0%|                                    | 0/4 [00:00<?, ?it/s]Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
2021-06-18 06:14:52,549 - DataStats - INFO - Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
2021-06-18 06:14:52,575 - DataStats - INFO - Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-18 06:14:52,575 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
2021-06-18 06:14:52,579 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (80, 231, 231)
Value range: (-1024.0, 1514.890625)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
2021-06-18 06:14:52,606 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 231, 231, 198)
Value range: (0.0, 1.0)
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
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image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
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2021-06-18 06:14:52,608 - DataStats - INFO - image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
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label_meta_dict statistics:
Type: <class 'dict'>
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label_meta_dict statistics:
Type: <class 'dict'>
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2021-06-18 06:14:52,611 - DataStats - INFO - label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512,  80,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.675781, 0.675781, 5.      , 0.      , 0.      ,
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
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2021-06-18 06:14:52,611 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
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label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 80), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
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label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 80), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
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2021-06-18 06:14:52,611 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 80), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.67578101,    0.        ,    0.        , -345.32409668],
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Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
2021-06-18 06:14:53,889 - DataStats - INFO - Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
2021-06-18 06:14:53,905 - DataStats - INFO - Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
input data information of the runtime error transform:
input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-18 06:14:53,905 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (41, 255, 255)
Value range: (-1024.0, 1915.705078125)
2021-06-18 06:14:53,908 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
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label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 255, 255, 101)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
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2021-06-18 06:14:53,924 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
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image_meta_dict statistics:
Type: <class 'dict'>
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image_meta_dict statistics:
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image_meta_dict statistics:
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2021-06-18 06:14:53,927 - DataStats - INFO - image_meta_dict statistics:
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label_meta_dict statistics:
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label_meta_dict statistics:
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label_meta_dict statistics:
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2021-06-18 06:14:53,929 - DataStats - INFO - label_meta_dict statistics:
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image_transforms statistics:
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image_transforms statistics:
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
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2021-06-18 06:14:53,929 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
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       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
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       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
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       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
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2021-06-18 06:14:53,930 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 41), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.74414098,    0.        ,    0.        , -380.25604248],
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Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
2021-06-18 06:14:54,975 - DataStats - INFO - Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
2021-06-18 06:14:54,998 - DataStats - INFO - Data statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
input data information of the runtime error transform:
input data information of the runtime error transform:
input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-18 06:14:54,998 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
2021-06-18 06:14:55,008 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (168, 319, 319)
Value range: (-1024.0, 1273.060791015625)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
2021-06-18 06:14:55,029 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (3, 319, 319, 126)
Value range: (0.0, 1.0)
image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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2021-06-18 06:14:55,032 - DataStats - INFO - image_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(16, dtype=int16), 'bitpix': array(32, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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label_meta_dict statistics:
Type: <class 'dict'>
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label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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2021-06-18 06:14:55,034 - DataStats - INFO - label_meta_dict statistics:
Type: <class 'dict'>
Value: {'sizeof_hdr': array(348, dtype=int32), 'extents': array(0, dtype=int32), 'session_error': array(0, dtype=int16), 'dim_info': array(0, dtype=uint8), 'dim': array([  3, 512, 512, 168,   1,   1,   1,   1], dtype=int16), 'intent_p1': array(0., dtype=float32), 'intent_p2': array(0., dtype=float32), 'intent_p3': array(0., dtype=float32), 'intent_code': array(0, dtype=int16), 'datatype': array(2, dtype=int16), 'bitpix': array(8, dtype=int16), 'slice_start': array(0, dtype=int16), 'pixdim': array([1.      , 0.933594, 0.933594, 1.5     , 0.      , 0.      ,
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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2021-06-18 06:14:55,034 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512), 'extra_info': {'meta_data_key': 'image_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'bilinear', 'padding_mode': 'border', 'align_corners': False}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
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2021-06-18 06:14:55,035 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139932721416272, 'orig_size': (512, 512, 168), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[   0.93359399,    0.        ,    0.        , -477.06652832],
       [   0.        ,    0.93359399,    0.        , -477.06652832],
       [   0.        ,    0.        ,    1.5       ,    0.        ],
       [   0.        ,    0.        ,    0.        ,    1.        ]]), 'mode': 'nearest', 'padding_mode': 'border', 'align_corners': False}}]
Loading dataset:   0%|                                    | 0/4 [00:04<?, ?it/s]
Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/croppad/dictionary.py", line 369, in __call__
    d[key] = self.cropper(d[key])
  File "/opt/monai/monai/transforms/croppad/array.py", line 285, in __call__
    self.roi_size = fall_back_tuple(self.roi_size, img.shape[1:])
  File "/opt/monai/monai/utils/misc.py", line 174, in fall_back_tuple
    user = ensure_tuple_rep(user_provided, ndim)
  File "/opt/monai/monai/utils/misc.py", line 131, in ensure_tuple_rep
    raise ValueError(f"Sequence must have length {dim}, got {len(tup)}.")
ValueError: Sequence must have length 2, got 3.

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "apps/train.py", line 35, in <module>
  File "apps/train.py", line 27, in main
  File "apps/mmar_conf.py", line 21, in train_mmar
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 172, in configure
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 167, in configure
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 163, in _do_configure
  File "apps/train_configer.py", line 571, in finalize_config
  File "<nvflare-0.1.4>/dlmed/utils/wfconf.py", line 200, in build_component
  File "<nvflare-0.1.4>/dlmed/utils/class_utils.py", line 36, in instantiate_class
  File "/opt/monai/monai/data/dataset.py", line 532, in __init__
    self._cache: List = self._fill_cache()
  File "/opt/monai/monai/data/dataset.py", line 541, in _fill_cache
    return list(
  File "/opt/conda/lib/python3.8/site-packages/tqdm/std.py", line 1193, in __iter__
    for obj in iterable:
  File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 868, in next
    raise value
  File "/opt/conda/lib/python3.8/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/opt/monai/monai/data/dataset.py", line 562, in _load_cache_item
    item = apply_transform(_transform, item)
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.croppad.dictionary.CenterSpatialCropd object at 0x7f44a02709d0>
Error processing config /claraDevDay/BrainTumorTest/commands/../config/config_train_UnetV2.json: applying transform <monai.transforms.croppad.dictionary.CenterSpatialCropd object at 0x7f44a02709d0>
terminate called without an active exception
/claraDevDay/BrainTumorTest//commands/train.sh: line 47:   626 Aborted                 (core dumped) python3 -u -m medl.apps.train -m $MMAR_ROOT -c $CONFIG_FILE -e $ENVIRONMENT_FILE --write_train_stats --set print_conf=True MMAR_CKPT_DIR=$MMAR_CKPT_DIR

[Nic-Ma]

Seems you only use 1 image of the Brats dataset(it has 4 images for every label I remember), so it doesn't have channel dim.
Please change the AsChannelFirstd transform to EnsureChannelFirstd transform.

Thanks.

[Minxiangliu]

Hi @Nic-Ma ,
Very thank for your help. It can load image and label.
I hope I can learn more about the functions of MONAI.
But can I ask you about it last error message?
RuntimeError: Given groups=1, weight of size [16, 4, 3, 3, 3], expected input[2, 1, 128, 128, 64] to have 4 channels, but got 1 channels instead

In which direction can I adjust this error?

[Nic-Ma]

Hi @Minxiangliu ,

You are welcome.
As you only uses 1 image of the Brats dataset, the image data has 1 channel, so you need to change the network arg "in_channels" to use 1 channel instead of 4.

"model": {
  "name": "UNet",
  "args": {
    "dimensions": 3,
    "in_channels": 1,
    "out_channels": 3,
    "channels": [16, 32, 64, 128, 256],
    "strides": [2, 2, 2, 2],
    "num_res_units": 2
  }
},

Thanks.

[Minxiangliu]

Hi @Nic-Ma ,
Is this error about the label again? The number of my tags is not consistent with the actual number?

2021-06-18 07:27:00,108 - ignite.engine.engine.SupervisedTrainer - ERROR - Current run is terminating due to exception: applying transform <monai.transforms.compose.Compose object at 0x7fa0fffa3b20>
2021-06-18 07:27:08,421 - ignite.engine.engine.SupervisedTrainer - ERROR - Engine run is terminating due to exception: CUDA error: device-side assert triggered
2021-06-18 07:27:08,422 - ignite.engine.engine.SupervisedTrainer - INFO - Deleted previous saved final checkpoint: checkpoint_final_iteration=1.pt
Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/post/dictionary.py", line 149, in __call__
    d[key] = self.converter(
  File "/opt/monai/monai/transforms/post/array.py", line 174, in __call__
    img = one_hot(img, _nclasses)
  File "/opt/monai/monai/networks/utils.py", line 57, in one_hot
    raise AssertionError("labels should have a channel with length equal to one.")
AssertionError: labels should have a channel with length equal to one.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/monai/monai/transforms/transform.py", line 48, in apply_transform
    return transform(data)
  File "/opt/monai/monai/transforms/compose.py", line 144, in __call__
    input_ = apply_transform(_transform, input_)
  File "/opt/monai/monai/transforms/transform.py", line 68, in apply_transform
    _log_stats(data=v, prefix=k)
  File "/opt/monai/monai/transforms/transform.py", line 61, in _log_stats
    datastats(img=data, data_shape=True, value_range=True, prefix=prefix)  # type: ignore
  File "/opt/monai/monai/transforms/utility/array.py", line 457, in __call__
    lines.append(f"Value range: ({torch.min(img)}, {torch.max(img)})")
  File "/opt/conda/lib/python3.8/site-packages/torch/tensor.py", line 524, in __format__
    return self.item().__format__(format_spec)
RuntimeError: CUDA error: device-side assert triggered

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

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 833, in _run_once_on_dataset
    self.state.output = self._process_function(self, self.state.batch)
  File "/opt/monai/monai/engines/trainer.py", line 182, in _iteration
    engine.fire_event(IterationEvents.MODEL_COMPLETED)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 448, in fire_event
    return self._fire_event(event_name)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 424, in _fire_event
    func(*first, *(event_args + others), **kwargs)
  File "/opt/monai/monai/engines/workflow.py", line 165, in run_post_transform
    engine.state.output = apply_transform(posttrans, engine.state.output)
  File "/opt/monai/monai/transforms/transform.py", line 71, in apply_transform
    raise RuntimeError(f"applying transform {transform}") from e
RuntimeError: applying transform <monai.transforms.compose.Compose object at 0x7fa0fffa3b20>

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 745, in _internal_run
    time_taken = self._run_once_on_dataset()
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 850, in _run_once_on_dataset
    self._handle_exception(e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 467, in _handle_exception
    self._fire_event(Events.EXCEPTION_RAISED, e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 424, in _fire_event
    func(*first, *(event_args + others), **kwargs)
  File "/opt/monai/monai/handlers/checkpoint_saver.py", line 269, in exception_raised
    self._final_checkpoint(engine)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 430, in __call__
    self.save_handler(checkpoint, filename, metadata)
  File "/opt/monai/monai/handlers/checkpoint_saver.py", line 126, in __call__
    super().__call__(checkpoint=checkpoint, filename=filename, metadata=metadata)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 683, in __call__
    self._save_native(checkpoint, path)
  File "/opt/conda/lib/python3.8/site-packages/ignite/distributed/utils.py", line 569, in wrapper
    ret = func(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 687, in _save_native
    self._save_func(checkpoint, path, torch.save)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 697, in _save_func
    func(checkpoint, path, **self.kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 372, in save
    _save(obj, opened_zipfile, pickle_module, pickle_protocol)
  File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 488, in _save
    storage = storage.cpu()
  File "/opt/conda/lib/python3.8/site-packages/torch/storage.py", line 72, in cpu
    return self.type(getattr(torch, self.__class__.__name__))
  File "/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py", line 492, in type
    return super(_CudaBase, self).type(*args, **kwargs)  # type: ignore[misc]
  File "/opt/conda/lib/python3.8/site-packages/torch/_utils.py", line 46, in _type
    return dtype(self.size()).copy_(self, non_blocking)
RuntimeError: CUDA error: device-side assert triggered

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "apps/train.py", line 35, in <module>
  File "apps/train.py", line 27, in main
  File "apps/mmar_conf.py", line 31, in train_mmar
  File "/opt/monai/monai/engines/trainer.py", line 49, in run
    super().run()
  File "/opt/monai/monai/engines/workflow.py", line 206, in run
    super().run(data=self.data_loader, max_epochs=self.state.max_epochs)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 702, in run
    return self._internal_run()
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 775, in _internal_run
    self._handle_exception(e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 467, in _handle_exception
    self._fire_event(Events.EXCEPTION_RAISED, e)
  File "/opt/conda/lib/python3.8/site-packages/ignite/engine/engine.py", line 424, in _fire_event
    func(*first, *(event_args + others), **kwargs)
  File "/opt/monai/monai/handlers/checkpoint_saver.py", line 269, in exception_raised
    self._final_checkpoint(engine)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 430, in __call__
    self.save_handler(checkpoint, filename, metadata)
  File "/opt/monai/monai/handlers/checkpoint_saver.py", line 126, in __call__
    super().__call__(checkpoint=checkpoint, filename=filename, metadata=metadata)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 683, in __call__
    self._save_native(checkpoint, path)
  File "/opt/conda/lib/python3.8/site-packages/ignite/distributed/utils.py", line 569, in wrapper
    ret = func(*args, **kwargs)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 687, in _save_native
    self._save_func(checkpoint, path, torch.save)
  File "/opt/conda/lib/python3.8/site-packages/ignite/handlers/checkpoint.py", line 697, in _save_func
    func(checkpoint, path, **self.kwargs)
  File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 372, in save
    _save(obj, opened_zipfile, pickle_module, pickle_protocol)
  File "/opt/conda/lib/python3.8/site-packages/torch/serialization.py", line 488, in _save
    storage = storage.cpu()
  File "/opt/conda/lib/python3.8/site-packages/torch/storage.py", line 72, in cpu
    return self.type(getattr(torch, self.__class__.__name__))
  File "/opt/conda/lib/python3.8/site-packages/torch/cuda/__init__.py", line 492, in type
    return super(_CudaBase, self).type(*args, **kwargs)  # type: ignore[misc]
  File "/opt/conda/lib/python3.8/site-packages/torch/_utils.py", line 46, in _type
    return dtype(self.size()).copy_(self, non_blocking)
RuntimeError: CUDA error: device-side assert triggered

[Nic-Ma]

Maybe you should use "to_onehot": [true, false] in AsDiscreted transform.
I see your custom transform already stacked label in One-Hot format.

Thanks.

[Minxiangliu]

OK, finally, I changed it to the following and it can work:
("n_classes": 2" to "n_classes": 3)

     {
        "name": "AsDiscreted",
        "args": {
          "keys": ["pred", "label"],
          "argmax": [true, false],
          "to_onehot": [true, false],
          "n_classes": 3
        }
      }

By the way, Is softmax in Activations related to loss function? My loss function set "sigmoid": true, I don’t know if this will affect.

"name": "Activationsd",
        "args": {
          "keys": "pred",
          "softmax": true
        }

"loss": {
      "name": "DiceLoss",
      "args":{
        "to_onehot_y": false,
        "squared_pred": true,
        "sigmoid": true
      }
    },

[Nic-Ma]

Hi @Minxiangliu ,

Glad to see your issue solved.
I think typically you should use the same activation layer for loss and post transform, @wyli please feel free to correct me if there are some different cases in the research area.

Thanks.

[Minxiangliu]

@Nic-Ma I need to apologize to you, I found that my training data is wrong, the real training data has 4 images.

[Nic-Ma]

No problem.
Please feel free to raise a ticket if you face any other issues.

Thanks.