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

# 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/batchgenerators (commit id: 01f225d)
#
# Copyright 2021 Division of Medical Image Computing, German Cancer Research Center (DKFZ)
# and Applied Computer Vision Lab, Helmholtz Imaging Platform
#
# 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.

from builtins import range

import numpy as np

from cerebras.modelzoo.data.internal.vision.segmentation.transforms.augmentation_utils import (
    uniform,
)
from cerebras.modelzoo.data.vision.segmentation.transforms.utils import (  # original nnUnet used skimage.resize
    nd_resize,
)


[docs]def augment_linear_downsampling_scipy( data_sample, zoom_range=(0.5, 1), per_channel=True, p_per_channel=1, channels=None, order_downsample=1, order_upsample=0, ignore_axes=None, ): ''' Downsamples each sample (linearly) by a random factor and upsamples to original resolution again (nearest neighbor) Info: * Uses scipy zoom for resampling. A bit faster than nilearn. * Resamples all dimensions (channels, x, y, z) with same downsampling factor (like isotropic=True from linear_downsampling_generator_nilearn) Args: zoom_range: can be either tuple/list/np.ndarray or tuple of tuple. If tuple/list/np.ndarray, then the zoom factor will be sampled from zoom_range[0], zoom_range[1] (zoom < 0 = downsampling!). If tuple of tuple then each inner tuple will give a sampling interval for each axis (allows for different range of zoom values for each axis p_per_channel: probability for downsampling/upsampling a channel per_channel (bool): whether to draw a new zoom_factor for each channel or keep one for all channels channels (list, tuple): if None then all channels can be augmented. If list then only the channel indices can be augmented (but may not always be depending on p_per_channel) order_downsample: order_upsample: ignore_axes: tuple/list ''' if not isinstance(zoom_range, (list, tuple, np.ndarray)): zoom_range = [zoom_range] shp = np.array(data_sample.shape[1:]) dim = len(shp) if not per_channel: if isinstance(zoom_range[0], (tuple, list, np.ndarray)): assert len(zoom_range) == dim zoom = np.array([uniform(i[0], i[1]) for i in zoom_range]) else: zoom = uniform(zoom_range[0], zoom_range[1]) target_shape = np.round(shp * zoom).astype(int) if ignore_axes is not None: for i in ignore_axes: target_shape[i] = shp[i] if channels is None: channels = list(range(data_sample.shape[0])) for c in channels: if np.random.uniform() < p_per_channel: if per_channel: if isinstance(zoom_range[0], (tuple, list, np.ndarray)): assert len(zoom_range) == dim zoom = np.array([uniform(i[0], i[1]) for i in zoom_range]) else: zoom = uniform(zoom_range[0], zoom_range[1]) target_shape = np.round(shp * zoom).astype(int) if ignore_axes is not None: for i in ignore_axes: target_shape[i] = shp[i] downsampled = nd_resize( data_sample[c].astype(float), target_shape, order=order_downsample, mode='edge', anti_aliasing=False, ) data_sample[c] = nd_resize( downsampled, shp, order=order_upsample, mode='edge', anti_aliasing=False, ) return data_sample