Source code for cerebras.modelzoo.data.vision.segmentation.transforms.custom_transforms

# Copyright 2022 Cerebras Systems.
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#    Adapted from: https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunet/training/data_augmentation/
#    custom_transforms.py (commit id: f2282ed)

#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
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#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
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#        http://www.apache.org/licenses/LICENSE-2.0
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#    distributed under the License is distributed on an "AS IS" BASIS,
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#    See the License for the specific language governing permissions and
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[docs]class MaskTransform: def __init__( self, dct_for_where_it_was_used, mask_idx_in_seg=1, set_outside_to=0, data_key="data", seg_key="seg", ): """ data[mask < 0] = 0 Sets everything outside the mask to 0. CAREFUL! outside is defined as < 0, not =0 (in the Mask)!!! :param dct_for_where_it_was_used: :param mask_idx_in_seg: :param set_outside_to: :param data_key: :param seg_key: """ self.dct_for_where_it_was_used = dct_for_where_it_was_used self.seg_key = seg_key self.data_key = data_key self.set_outside_to = set_outside_to self.mask_idx_in_seg = mask_idx_in_seg def __call__(self, **data_dict): seg = data_dict.get(self.seg_key) if seg is None or seg.shape[1] < self.mask_idx_in_seg: raise Warning( "mask not found, seg may be missing or seg[:, mask_idx_in_seg] may not exist" ) data = data_dict.get(self.data_key) for b in range(data.shape[0]): mask = seg[b, self.mask_idx_in_seg] for c in range(data.shape[1]): if self.dct_for_where_it_was_used[c]: data[b, c][mask < 0] = self.set_outside_to data_dict[self.data_key] = data return data_dict