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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.
from copy import deepcopy
from nnunet.network_architecture.custom_modules.helperModules import Identity
from torch import nn
class ConvDropoutNormReLU(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, network_props):
"""
if network_props['dropout_op'] is None then no dropout
if network_props['norm_op'] is None then no norm
:param input_channels:
:param output_channels:
:param kernel_size:
:param network_props:
"""
super(ConvDropoutNormReLU, self).__init__()
network_props = deepcopy(network_props) # network_props is a dict and mutable, so we deepcopy to be safe.
self.conv = network_props['conv_op'](input_channels, output_channels, kernel_size,
padding=[(i - 1) // 2 for i in kernel_size],
**network_props['conv_op_kwargs'])
# maybe dropout
if network_props['dropout_op'] is not None:
self.do = network_props['dropout_op'](**network_props['dropout_op_kwargs'])
else:
self.do = Identity()
if network_props['norm_op'] is not None:
self.norm = network_props['norm_op'](output_channels, **network_props['norm_op_kwargs'])
else:
self.norm = Identity()
self.nonlin = network_props['nonlin'](**network_props['nonlin_kwargs'])
self.all = nn.Sequential(self.conv, self.do, self.norm, self.nonlin)
def forward(self, x):
return self.all(x)
class StackedConvLayers(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, network_props, num_convs, first_stride=None):
"""
if network_props['dropout_op'] is None then no dropout
if network_props['norm_op'] is None then no norm
:param input_channels:
:param output_channels:
:param kernel_size:
:param network_props:
"""
super(StackedConvLayers, self).__init__()
network_props = deepcopy(network_props) # network_props is a dict and mutable, so we deepcopy to be safe.
network_props_first = deepcopy(network_props)
if first_stride is not None:
network_props_first['conv_op_kwargs']['stride'] = first_stride
self.convs = nn.Sequential(
ConvDropoutNormReLU(input_channels, output_channels, kernel_size, network_props_first),
*[ConvDropoutNormReLU(output_channels, output_channels, kernel_size, network_props) for _ in
range(num_convs - 1)]
)
def forward(self, x):
return self.convs(x)
class BasicResidualBlock(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, props, stride=None):
"""
This is the conv bn nonlin conv bn nonlin kind of block
:param in_planes:
:param out_planes:
:param props:
:param override_stride:
"""
super().__init__()
self.kernel_size = kernel_size
props['conv_op_kwargs']['stride'] = 1
self.stride = stride
self.props = props
self.out_planes = out_planes
self.in_planes = in_planes
if stride is not None:
kwargs_conv1 = deepcopy(props['conv_op_kwargs'])
kwargs_conv1['stride'] = stride
else:
kwargs_conv1 = props['conv_op_kwargs']
self.conv1 = props['conv_op'](in_planes, out_planes, kernel_size, padding=[(i - 1) // 2 for i in kernel_size],
**kwargs_conv1)
self.norm1 = props['norm_op'](out_planes, **props['norm_op_kwargs'])
self.nonlin1 = props['nonlin'](**props['nonlin_kwargs'])
if props['dropout_op_kwargs']['p'] != 0:
self.dropout = props['dropout_op'](**props['dropout_op_kwargs'])
else:
self.dropout = Identity()
self.conv2 = props['conv_op'](out_planes, out_planes, kernel_size, padding=[(i - 1) // 2 for i in kernel_size],
**props['conv_op_kwargs'])
self.norm2 = props['norm_op'](out_planes, **props['norm_op_kwargs'])
self.nonlin2 = props['nonlin'](**props['nonlin_kwargs'])
if (self.stride is not None and any((i != 1 for i in self.stride))) or (in_planes != out_planes):
stride_here = stride if stride is not None else 1
self.downsample_skip = nn.Sequential(props['conv_op'](in_planes, out_planes, 1, stride_here, bias=False),
props['norm_op'](out_planes, **props['norm_op_kwargs']))
else:
self.downsample_skip = lambda x: x
def forward(self, x):
residual = x
out = self.dropout(self.conv1(x))
out = self.nonlin1(self.norm1(out))
out = self.norm2(self.conv2(out))
residual = self.downsample_skip(residual)
out += residual
return self.nonlin2(out)
class ResidualBottleneckBlock(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, props, stride=None):
"""
This is the conv bn nonlin conv bn nonlin kind of block
:param in_planes:
:param out_planes:
:param props:
:param override_stride:
"""
super().__init__()
if props['dropout_op_kwargs'] is None and props['dropout_op_kwargs'] > 0:
raise NotImplementedError("ResidualBottleneckBlock does not yet support dropout!")
self.kernel_size = kernel_size
props['conv_op_kwargs']['stride'] = 1
self.stride = stride
self.props = props
self.out_planes = out_planes
self.in_planes = in_planes
self.bottleneck_planes = out_planes // 4
if stride is not None:
kwargs_conv1 = deepcopy(props['conv_op_kwargs'])
kwargs_conv1['stride'] = stride
else:
kwargs_conv1 = props['conv_op_kwargs']
self.conv1 = props['conv_op'](in_planes, self.bottleneck_planes, [1 for _ in kernel_size], padding=[0 for i in kernel_size],
**kwargs_conv1)
self.norm1 = props['norm_op'](self.bottleneck_planes, **props['norm_op_kwargs'])
self.nonlin1 = props['nonlin'](**props['nonlin_kwargs'])
self.conv2 = props['conv_op'](self.bottleneck_planes, self.bottleneck_planes, kernel_size, padding=[(i - 1) // 2 for i in kernel_size],
**props['conv_op_kwargs'])
self.norm2 = props['norm_op'](self.bottleneck_planes, **props['norm_op_kwargs'])
self.nonlin2 = props['nonlin'](**props['nonlin_kwargs'])
self.conv3 = props['conv_op'](self.bottleneck_planes, out_planes, [1 for _ in kernel_size], padding=[0 for i in kernel_size],
**props['conv_op_kwargs'])
self.norm3 = props['norm_op'](out_planes, **props['norm_op_kwargs'])
self.nonlin3 = props['nonlin'](**props['nonlin_kwargs'])
if (self.stride is not None and any((i != 1 for i in self.stride))) or (in_planes != out_planes):
stride_here = stride if stride is not None else 1
self.downsample_skip = nn.Sequential(props['conv_op'](in_planes, out_planes, 1, stride_here, bias=False),
props['norm_op'](out_planes, **props['norm_op_kwargs']))
else:
self.downsample_skip = lambda x: x
def forward(self, x):
residual = x
out = self.nonlin1(self.norm1(self.conv1(x)))
out = self.nonlin2(self.norm2(self.conv2(out)))
out = self.norm3(self.conv3(out))
residual = self.downsample_skip(residual)
out += residual
return self.nonlin3(out)
class ResidualLayer(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, network_props, num_blocks, first_stride=None, block=BasicResidualBlock):
super().__init__()
network_props = deepcopy(network_props) # network_props is a dict and mutable, so we deepcopy to be safe.
self.convs = nn.Sequential(
block(input_channels, output_channels, kernel_size, network_props, first_stride),
*[block(output_channels, output_channels, kernel_size, network_props) for _ in
range(num_blocks - 1)]
)
def forward(self, x):
return self.convs(x)
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