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

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

#    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
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.


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