How to use config_train_segresnet to train Task01_BrainTumour in SDK4.0?

Hi, I am trying to train Task01_BrainTumour with config_train_segresnet.json, but I am a noob, I keep making mistakes during training.
By the way, the same method can train Task09_Spleen .
Hope someone can help me. Mant thank!
dataset.json:

{
    "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",
        ....
    ]
}

The error when train config_train_segresnet.json first:

Loading dataset:   0%|                                    | 0/4 [00:00<?, ?it/s]input data information of the runtime error transform:
2021-06-16 00:33:22,354 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-16 00:33:22,369 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
2021-06-16 00:33:22,374 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
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([  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_393.nii.gz'}
2021-06-16 00:33:22,376 - 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_393.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, 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_393.nii.gz'}
2021-06-16 00:33:22,378 - 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.],
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       [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_393.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155, 4), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
2021-06-16 00:33:22,379 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155, 4), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, 'orig_size': (240, 240, 155), 'extra_info': {'meta_data_key': 'label_meta_dict', 'old_affine': array([[1., 0., 0., 0.],
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       [0., 0., 1., 0.],
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2021-06-16 00:33:22,379 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-16 00:33:22,411 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-16 00:33:22,427 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
2021-06-16 00:33:22,431 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
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([  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.],
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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([  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.],
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2021-06-16 00:33:22,433 - 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.],
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       [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_033.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, 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., 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': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/labelsTr/BRATS_033.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, 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., 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': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/labelsTr/BRATS_033.nii.gz'}
2021-06-16 00:33:22,436 - 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., 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': 'no_channel', 'filename_or_obj': '/claraDevDay/Data/DecathlonDataset/Task01_BrainTumour/labelsTr/BRATS_033.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155, 4), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155, 4), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
2021-06-16 00:33:22,436 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155, 4), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
2021-06-16 00:33:22,436 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 140066138151664, '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}}, {'class': 'CropForegroundd', 'id': 140066138152192, 'orig_size': (240, 240, 155), 'extra_info': {'box_start': array([0, 0, 0, 0]), 'box_end': array([240, 240, 155,   4])}}]
Loading dataset:   0%|                                    | 0/4 [00:01<?, ?it/s]
Error processing config /claraDevDay/BrainTumorTest/commands/../config/config_train_segresnet.json: applying transform <monai.transforms.croppad.dictionary.CropForegroundd object at 0x7f63b068dd00>
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 622, in __call__
    d[key] = self.cropper.crop_pad(d[key], box_start, box_end)
  File "/opt/monai/monai/transforms/croppad/array.py", line 466, in crop_pad
    pad_to_end = np.maximum(box_end - np.asarray(img.shape[1:]), 0)
ValueError: operands could not be broadcast together with shapes (4,) (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 482, 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.CropForegroundd object at 0x7f63b068dd00>

Error after deleting CropForegroundd in config_train_segresnet.json:

Loading dataset: 100%|████████████████████████████| 4/4 [00:02<00:00,  1.95it/s]
Loading dataset: 100%|████████████████████████████| 4/4 [00:02<00:00,  1.95it/s]
no max_epochs specified for SupervisedTrainer args, use global var 'epochs: 2'.
========== Train Config Result ===========
Num Epochs:  2
Use GPU:  True
Multi GPU:  False
Automatic Mixed Precision:  Enabled
Determinism Training:  Enabled
cuDNN BenchMark:  False
CUDA Matmul Allow TF32:  True
cuDNN Allow TF32:  True
Model:  <class 'monai.networks.nets.segresnet.SegResNet'>
Loss:  <class 'monai.losses.dice.DiceLoss'>
Optimizer:  <class 'torch.optim.adam.Adam'>
LR Scheduler:  <class 'torch.optim.lr_scheduler.StepLR'>
Train Dataset:  <class 'monai.data.dataset.CacheDataset'>
Train DataLoader:  <class 'monai.data.dataloader.DataLoader'>
Train Transform #1: <class 'monai.transforms.io.dictionary.LoadImaged'>
Train Transform #2: <class 'monai.transforms.utility.dictionary.AddChanneld'>
Train Transform #3: <class 'monai.transforms.spatial.dictionary.Spacingd'>
Train Transform #4: <class 'monai.transforms.intensity.dictionary.ScaleIntensityRanged'>
Train Transform #5: <class 'monai.transforms.croppad.dictionary.RandCropByPosNegLabeld'>
Train Transform #6: <class 'monai.transforms.intensity.dictionary.RandShiftIntensityd'>
Train Transform #7: <class 'monai.transforms.utility.dictionary.ToTensord'>
Validate Dataset:  <class 'monai.data.dataset.CacheDataset'>
Validate DataLoader:  <class 'monai.data.dataloader.DataLoader'>
Validate Transform #1: <class 'monai.transforms.io.dictionary.LoadImaged'>
Validate Transform #2: <class 'monai.transforms.spatial.dictionary.Spacingd'>
Validate Transform #3: <class 'monai.transforms.utility.dictionary.AddChanneld'>
Validate Transform #4: <class 'monai.transforms.intensity.dictionary.ScaleIntensityRanged'>
Validate Transform #5: <class 'monai.transforms.utility.dictionary.ToTensord'>
Train Handler #1: <class 'monai.handlers.lr_schedule_handler.LrScheduleHandler'>
Train Handler #2: <class 'monai.handlers.validation_handler.ValidationHandler'>
Train Handler #3: <class 'monai.handlers.checkpoint_saver.CheckpointSaver'>
Train Handler #4: <class 'monai.handlers.stats_handler.StatsHandler'>
Train Handler #5: <class 'monai.handlers.tensorboard_handlers.TensorBoardStatsHandler'>
Validate Handler #1: <class 'monai.handlers.stats_handler.StatsHandler'>
Validate Handler #2: <class 'monai.handlers.tensorboard_handlers.TensorBoardStatsHandler'>
Validate Handler #3: <class 'monai.handlers.checkpoint_saver.CheckpointSaver'>
Train Post Transforms #1: <class 'monai.transforms.post.dictionary.Activationsd'>
Train Post Transforms #2: <class 'monai.transforms.post.dictionary.AsDiscreted'>
Validate Post Transforms #1: <class 'monai.transforms.post.dictionary.Activationsd'>
Validate Post Transforms #2: <class 'monai.transforms.post.dictionary.AsDiscreted'>
Validate Inferer:  <class 'monai.inferers.inferer.SlidingWindowInferer'>
Validate Key Metric:  <class 'monai.handlers.mean_dice.MeanDice'>
Validate Additional Metric #val_acc: <class 'ignite.metrics.accuracy.Accuracy'>
Train Inferer:  <class 'monai.inferers.inferer.SimpleInferer'>
Train Key Metric:  <class 'ignite.metrics.accuracy.Accuracy'>
========== End of Train Config Result ===========
2021-06-16 00:52:10,653 - ignite.engine.engine.SupervisedTrainer - INFO - Engine run resuming from iteration 0, epoch 0 until 2 epochs
input data information of the runtime error transform:
2021-06-16 00:52:12,107 - DataStats - INFO - input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-16 00:52:12,108 - DataStats - INFO - input data information of the runtime error transform:
input data information of the runtime error transform:
2021-06-16 00:52:12,116 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-16 00:52:12,145 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-16 00:52:12,146 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
input data information of the runtime error transform:
2021-06-16 00:52:12,146 - DataStats - INFO - input data information of the runtime error transform:
image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
2021-06-16 00:52:12,152 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
2021-06-16 00:52:12,154 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
2021-06-16 00:52:12,155 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
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([  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_401.nii.gz'}
2021-06-16 00:52:12,157 - 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_401.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([  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.],
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2021-06-16 00:52:12,157 - 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.],
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label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
2021-06-16 00:52:12,159 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 3.0)
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.],
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2021-06-16 00:52:12,159 - 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.],
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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, 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.],
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2021-06-16 00:52:12,159 - 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.],
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       [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_328.nii.gz'}
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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}}]
2021-06-16 00:52:12,159 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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}}]
image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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}}]
2021-06-16 00:52:12,159 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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': 139705188264688, '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}}]
2021-06-16 00:52:12,160 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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}}]
2021-06-16 00:52:12,160 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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}}]
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'}
2021-06-16 00:52:12,161 - 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'}
2021-06-16 00:52:12,162 - 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 0x7f0fa61ead00>

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 0x7f0fa737e0d0>

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'}
2021-06-16 00:52:12,163 - 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': 139705188264688, '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}}]
2021-06-16 00:52:12,164 - DataStats - INFO - image_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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': 139705188264688, '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}}]
2021-06-16 00:52:12,164 - DataStats - INFO - label_transforms statistics:
Type: <class 'list'>
Value: [{'class': 'Spacingd', 'id': 139705188264688, '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}}]
2021-06-16 00:52:12,173 - ignite.engine.engine.SupervisedTrainer - INFO - Exception_raised, saved exception checkpoint: checkpoint_final_iteration=0.pt
2021-06-16 00:52:12,173 - 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 0x7f0fa61ead00>

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 0x7f0fa737e0d0>

2021-06-16 00:52:12,174 - 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)
2021-06-16 00:52:12,174 - DataStats - INFO - image statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155, 4)
Value range: (0.2579185664653778, 1.0)
label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 2.0)
2021-06-16 00:52:12,181 - DataStats - INFO - label statistics:
Type: <class 'numpy.ndarray'>
Shape: (1, 240, 240, 155)
Value range: (0.0, 2.0)
2021-06-16 00:52:12,181 - 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 0x7f0fa61ead00>

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 0x7f0fa737e0d0>

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_023.nii.gz'}
2021-06-16 00:52:12,184 - 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_023.nii.gz'}

Hi
Thanks for your interest in Clara Train SDK. Please note we have recently release clara train V4.0 based on MONAI which uses PyTorch.

Please check out the notebooks to get you started clara-train-examples/PyTorch/NoteBooks at master · NVIDIA/clara-train-examples · GitHub
It provides a step by step to get you to understand all of clara train concepts. Also please check out GTC 2021 free talks covering Clara Train SDK

Hope this helps