Chris Xiao

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#    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.


import torch
from batchgenerators.augmentations.utils import convert_seg_image_to_one_hot_encoding_batched, resize_segmentation
from batchgenerators.transforms.abstract_transforms import AbstractTransform
from torch.nn.functional import avg_pool2d, avg_pool3d
import numpy as np


class DownsampleSegForDSTransform3(AbstractTransform):
    '''
    returns one hot encodings of the segmentation maps if downsampling has occured (no one hot for highest resolution)
    downsampled segmentations are smooth, not 0/1
    returns torch tensors, not numpy arrays!
    always uses seg channel 0!!
    you should always give classes! Otherwise weird stuff may happen
    '''
    def __init__(self, ds_scales=(1, 0.5, 0.25), input_key="seg", output_key="seg", classes=None):
        self.classes = classes
        self.output_key = output_key
        self.input_key = input_key
        self.ds_scales = ds_scales

    def __call__(self, **data_dict):
        data_dict[self.output_key] = downsample_seg_for_ds_transform3(data_dict[self.input_key][:, 0], self.ds_scales, self.classes)
        return data_dict


def downsample_seg_for_ds_transform3(seg, ds_scales=((1, 1, 1), (0.5, 0.5, 0.5), (0.25, 0.25, 0.25)), classes=None):
    output = []
    one_hot = torch.from_numpy(convert_seg_image_to_one_hot_encoding_batched(seg, classes)) # b, c,

    for s in ds_scales:
        if all([i == 1 for i in s]):
            output.append(torch.from_numpy(seg))
        else:
            kernel_size = tuple(int(1 / i) for i in s)
            stride = kernel_size
            pad = tuple((i-1) // 2 for i in kernel_size)

            if len(s) == 2:
                pool_op = avg_pool2d
            elif len(s) == 3:
                pool_op = avg_pool3d
            else:
                raise RuntimeError()

            pooled = pool_op(one_hot, kernel_size, stride, pad, count_include_pad=False, ceil_mode=False)

            output.append(pooled)
    return output


class DownsampleSegForDSTransform2(AbstractTransform):
    '''
    data_dict['output_key'] will be a list of segmentations scaled according to ds_scales
    '''
    def __init__(self, ds_scales=(1, 0.5, 0.25), order=0, input_key="seg", output_key="seg", axes=None):
        self.axes = axes
        self.output_key = output_key
        self.input_key = input_key
        self.order = order
        self.ds_scales = ds_scales

    def __call__(self, **data_dict):
        data_dict[self.output_key] = downsample_seg_for_ds_transform2(data_dict[self.input_key], self.ds_scales,
                                                                      self.order, self.axes)
        return data_dict


def downsample_seg_for_ds_transform2(seg, ds_scales=((1, 1, 1), (0.5, 0.5, 0.5), (0.25, 0.25, 0.25)), order=0, axes=None):
    if axes is None:
        axes = list(range(2, len(seg.shape)))
    output = []
    for s in ds_scales:
        if all([i == 1 for i in s]):
            output.append(seg)
        else:
            new_shape = np.array(seg.shape).astype(float)
            for i, a in enumerate(axes):
                new_shape[a] *= s[i]
            new_shape = np.round(new_shape).astype(int)
            out_seg = np.zeros(new_shape, dtype=seg.dtype)
            for b in range(seg.shape[0]):
                for c in range(seg.shape[1]):
                    out_seg[b, c] = resize_segmentation(seg[b, c], new_shape[2:], order)
            output.append(out_seg)
    return output

	# 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.


	import torch
	from batchgenerators.augmentations.utils import convert_seg_image_to_one_hot_encoding_batched, resize_segmentation
	from batchgenerators.transforms.abstract_transforms import AbstractTransform
	from torch.nn.functional import avg_pool2d, avg_pool3d
	import numpy as np


	class DownsampleSegForDSTransform3(AbstractTransform):
	'''
	returns one hot encodings of the segmentation maps if downsampling has occured (no one hot for highest resolution)
	downsampled segmentations are smooth, not 0/1

	returns torch tensors, not numpy arrays!

	always uses seg channel 0!!

	you should always give classes! Otherwise weird stuff may happen
	'''
	def __init__(self, ds_scales=(1, 0.5, 0.25), input_key="seg", output_key="seg", classes=None):
	self.classes = classes
	self.output_key = output_key
	self.input_key = input_key
	self.ds_scales = ds_scales

	def __call__(self, **data_dict):
	data_dict[self.output_key] = downsample_seg_for_ds_transform3(data_dict[self.input_key][:, 0], self.ds_scales, self.classes)
	return data_dict


	def downsample_seg_for_ds_transform3(seg, ds_scales=((1, 1, 1), (0.5, 0.5, 0.5), (0.25, 0.25, 0.25)), classes=None):
	output = []
	one_hot = torch.from_numpy(convert_seg_image_to_one_hot_encoding_batched(seg, classes)) # b, c,

	for s in ds_scales:
	if all([i == 1 for i in s]):
	output.append(torch.from_numpy(seg))
	else:
	kernel_size = tuple(int(1 / i) for i in s)
	stride = kernel_size
	pad = tuple((i-1) // 2 for i in kernel_size)

	if len(s) == 2:
	pool_op = avg_pool2d
	elif len(s) == 3:
	pool_op = avg_pool3d
	else:
	raise RuntimeError()

	pooled = pool_op(one_hot, kernel_size, stride, pad, count_include_pad=False, ceil_mode=False)

	output.append(pooled)
	return output


	class DownsampleSegForDSTransform2(AbstractTransform):
	'''
	data_dict['output_key'] will be a list of segmentations scaled according to ds_scales
	'''
	def __init__(self, ds_scales=(1, 0.5, 0.25), order=0, input_key="seg", output_key="seg", axes=None):
	self.axes = axes
	self.output_key = output_key
	self.input_key = input_key
	self.order = order
	self.ds_scales = ds_scales

	def __call__(self, **data_dict):
	data_dict[self.output_key] = downsample_seg_for_ds_transform2(data_dict[self.input_key], self.ds_scales,
	self.order, self.axes)
	return data_dict


	def downsample_seg_for_ds_transform2(seg, ds_scales=((1, 1, 1), (0.5, 0.5, 0.5), (0.25, 0.25, 0.25)), order=0, axes=None):
	if axes is None:
	axes = list(range(2, len(seg.shape)))
	output = []
	for s in ds_scales:
	if all([i == 1 for i in s]):
	output.append(seg)
	else:
	new_shape = np.array(seg.shape).astype(float)
	for i, a in enumerate(axes):
	new_shape[a] *= s[i]
	new_shape = np.round(new_shape).astype(int)
	out_seg = np.zeros(new_shape, dtype=seg.dtype)
	for b in range(seg.shape[0]):
	for c in range(seg.shape[1]):
	out_seg[b, c] = resize_segmentation(seg[b, c], new_shape[2:], order)
	output.append(out_seg)
	return output