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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | # Copyright 2020 MONAI Consortium
# 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 typing import Sequence, Union
import torch
import torch.nn as nn
from monai.networks.blocks.convolutions import Convolution, ResidualUnit
from monai.networks.layers.factories import Act, Norm
from monai.networks.layers.simplelayers import SkipConnection
from monai.utils import alias, export
@export("monai.networks.nets")
@alias("Unet")
class UNet(nn.Module):
def __init__(
self,
dimensions: int,
in_channels: int,
out_channels: int,
channels: Sequence[int],
strides: Sequence[int],
kernel_size: Union[Sequence[int], int] = 3,
up_kernel_size: Union[Sequence[int], int] = 3,
num_res_units: int = 0,
act=Act.PRELU,
norm=Norm.INSTANCE,
dropout=0,
) -> None:
"""
Args:
dimensions: number of spatial dimensions.
in_channels: number of input channels.
out_channels: number of output channels.
channels: sequence of channels. Top block first.
strides: convolution stride.
kernel_size: convolution kernel size. Defaults to 3.
up_kernel_size: upsampling convolution kernel size. Defaults to 3.
num_res_units: number of residual units. Defaults to 0.
act: activation type and arguments. Defaults to PReLU.
norm: feature normalization type and arguments. Defaults to instance norm.
dropout: dropout ratio. Defaults to no dropout.
"""
super().__init__()
self.dimensions = dimensions
self.in_channels = in_channels
self.out_channels = out_channels
self.channels = channels
self.strides = strides
self.kernel_size = kernel_size
self.up_kernel_size = up_kernel_size
self.num_res_units = num_res_units
self.act = act
self.norm = norm
self.dropout = dropout
def _create_block(
inc: int, outc: int, channels: Sequence[int], strides: Sequence[int], is_top: bool
) -> nn.Sequential:
"""
Builds the UNet structure from the bottom up by recursing down to the bottom block, then creating sequential
blocks containing the downsample path, a skip connection around the previous block, and the upsample path.
Args:
inc: number of input channels.
outc: number of output channels.
channels: sequence of channels. Top block first.
strides: convolution stride.
is_top: True if this is the top block.
"""
c = channels[0]
s = strides[0]
subblock: Union[nn.Sequential, ResidualUnit, Convolution]
if len(channels) > 2:
subblock = _create_block(c, c, channels[1:], strides[1:], False) # continue recursion down
upc = c * 2
else:
# the next layer is the bottom so stop recursion, create the bottom layer as the sublock for this layer
subblock = self._get_bottom_layer(c, channels[1])
upc = c + channels[1]
down = self._get_down_layer(inc, c, s, is_top) # create layer in downsampling path
up = self._get_up_layer(upc, outc, s, is_top) # create layer in upsampling path
return nn.Sequential(down, SkipConnection(subblock), up)
self.model = _create_block(in_channels, out_channels, self.channels, self.strides, True)
def _get_down_layer(
self, in_channels: int, out_channels: int, strides: int, is_top: bool
) -> Union[ResidualUnit, Convolution]:
"""
Args:
in_channels: number of input channels.
out_channels: number of output channels.
strides: convolution stride.
is_top: True if this is the top block.
"""
if self.num_res_units > 0:
return ResidualUnit(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.kernel_size,
subunits=self.num_res_units,
act=self.act,
norm=self.norm,
dropout=self.dropout,
)
else:
return Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
)
def _get_bottom_layer(self, in_channels: int, out_channels: int) -> Union[ResidualUnit, Convolution]:
"""
Args:
in_channels: number of input channels.
out_channels: number of output channels.
"""
return self._get_down_layer(in_channels, out_channels, 1, False)
def _get_up_layer(
self, in_channels: int, out_channels: int, strides: int, is_top: bool
) -> Union[Convolution, nn.Sequential]:
"""
Args:
in_channels: number of input channels.
out_channels: number of output channels.
strides: convolution stride.
is_top: True if this is the top block.
"""
conv: Union[Convolution, nn.Sequential]
conv = Convolution(
self.dimensions,
in_channels,
out_channels,
strides=strides,
kernel_size=self.up_kernel_size,
act=self.act,
norm=self.norm,
dropout=self.dropout,
conv_only=is_top and self.num_res_units == 0,
is_transposed=True,
)
if self.num_res_units > 0:
ru = ResidualUnit(
self.dimensions,
out_channels,
out_channels,
strides=1,
kernel_size=self.kernel_size,
subunits=1,
act=self.act,
norm=self.norm,
dropout=self.dropout,
last_conv_only=is_top,
)
conv = nn.Sequential(conv, ru)
return conv
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.model(x)
return x
Unet = unet = UNet
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