Source code for cerebras.modelzoo.data.vision.segmentation.transforms.resample_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/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.

import numpy as np

from cerebras.modelzoo.data.vision.segmentation.transforms.resample_augmentations import (
    augment_linear_downsampling_scipy,
)


[docs]class SimulateLowResolutionTransform: """Downsamples each sample (linearly) by a random factor and upsamples to original resolution again (nearest neighbor) Info: * Uses scipy zoom for resampling. * 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: 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: """ def __init__( self, zoom_range=(0.5, 1), per_channel=False, p_per_channel=1, channels=None, order_downsample=1, order_upsample=0, data_key="data", p_per_sample=1, ignore_axes=None, ): self.order_upsample = order_upsample self.order_downsample = order_downsample self.channels = channels self.per_channel = per_channel self.p_per_channel = p_per_channel self.p_per_sample = p_per_sample self.data_key = data_key self.zoom_range = zoom_range self.ignore_axes = ignore_axes def __call__(self, **data_dict): for b in range(len(data_dict[self.data_key])): if np.random.uniform() < self.p_per_sample: data_dict[self.data_key][b] = augment_linear_downsampling_scipy( data_dict[self.data_key][b], zoom_range=self.zoom_range, per_channel=self.per_channel, p_per_channel=self.p_per_channel, channels=self.channels, order_downsample=self.order_downsample, order_upsample=self.order_upsample, ignore_axes=self.ignore_axes, ) return data_dict