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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | # 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 Dict, Optional, Tuple, Type, Union
import torch
import torch.nn as nn
from monai.networks.blocks.convolutions import Convolution
from monai.networks.layers.factories import Act, Conv, Dropout, Norm, split_args
def get_acti_layer(act: Union[Tuple[str, Dict], str], nchan: int = 0):
if act == "prelu":
act = ("prelu", {"num_parameters": nchan})
act_name, act_args = split_args(act)
act_type = Act[act_name]
return act_type(**act_args)
class LUConv(nn.Module):
def __init__(self, spatial_dims: int, nchan: int, act: Union[Tuple[str, Dict], str]):
super(LUConv, self).__init__()
self.act_function = get_acti_layer(act, nchan)
self.conv_block = Convolution(
dimensions=spatial_dims,
in_channels=nchan,
out_channels=nchan,
kernel_size=5,
act=None,
norm=Norm.BATCH,
)
def forward(self, x):
out = self.conv_block(x)
out = self.act_function(out)
return out
def _make_nconv(spatial_dims: int, nchan: int, depth: int, act: Union[Tuple[str, Dict], str]):
layers = []
for _ in range(depth):
layers.append(LUConv(spatial_dims, nchan, act))
return nn.Sequential(*layers)
class InputTransition(nn.Module):
def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, act: Union[Tuple[str, Dict], str]):
super(InputTransition, self).__init__()
if 16 % in_channels != 0:
raise ValueError(f"16 should be divisible by in_channels, got in_channels={in_channels}.")
self.spatial_dims = spatial_dims
self.in_channels = in_channels
self.act_function = get_acti_layer(act, 16)
self.conv_block = Convolution(
dimensions=spatial_dims,
in_channels=in_channels,
out_channels=16,
kernel_size=5,
act=None,
norm=Norm.BATCH,
)
def forward(self, x):
out = self.conv_block(x)
repeat_num = 16 // self.in_channels
x16 = x.repeat([1, repeat_num, 1, 1, 1][: self.spatial_dims + 2])
out = self.act_function(torch.add(out, x16))
return out
class DownTransition(nn.Module):
def __init__(
self,
spatial_dims: int,
in_channels: int,
nconvs: int,
act: Union[Tuple[str, Dict], str],
dropout_prob: Optional[float] = None,
dropout_dim: int = 3,
):
super(DownTransition, self).__init__()
conv_type: Type[Union[nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]
norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]
dropout_type: Type[Union[nn.Dropout, nn.Dropout2d, nn.Dropout3d]] = Dropout[Dropout.DROPOUT, dropout_dim]
out_channels = 2 * in_channels
self.down_conv = conv_type(in_channels, out_channels, kernel_size=2, stride=2)
self.bn1 = norm_type(out_channels)
self.act_function1 = get_acti_layer(act, out_channels)
self.act_function2 = get_acti_layer(act, out_channels)
self.ops = _make_nconv(spatial_dims, out_channels, nconvs, act)
self.dropout = dropout_type(dropout_prob) if dropout_prob is not None else None
def forward(self, x):
down = self.act_function1(self.bn1(self.down_conv(x)))
if self.dropout is not None:
out = self.dropout(down)
else:
out = down
out = self.ops(out)
out = self.act_function2(torch.add(out, down))
return out
class UpTransition(nn.Module):
def __init__(
self,
spatial_dims: int,
in_channels: int,
out_channels: int,
nconvs: int,
act: Union[Tuple[str, Dict], str],
dropout_prob: Optional[float] = None,
dropout_dim: int = 3,
):
super(UpTransition, self).__init__()
conv_trans_type: Type[Union[nn.ConvTranspose2d, nn.ConvTranspose3d]] = Conv[Conv.CONVTRANS, spatial_dims]
norm_type: Type[Union[nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]
dropout_type: Type[Union[nn.Dropout, nn.Dropout2d, nn.Dropout3d]] = Dropout[Dropout.DROPOUT, dropout_dim]
self.up_conv = conv_trans_type(in_channels, out_channels // 2, kernel_size=2, stride=2)
self.bn1 = norm_type(out_channels // 2)
self.dropout = dropout_type(dropout_prob) if dropout_prob is not None else None
self.dropout2 = dropout_type(0.5)
self.act_function1 = get_acti_layer(act, out_channels // 2)
self.act_function2 = get_acti_layer(act, out_channels)
self.ops = _make_nconv(spatial_dims, out_channels, nconvs, act)
def forward(self, x, skipx):
if self.dropout is not None:
out = self.dropout(x)
else:
out = x
skipxdo = self.dropout2(skipx)
out = self.act_function1(self.bn1(self.up_conv(out)))
xcat = torch.cat((out, skipxdo), 1)
out = self.ops(xcat)
out = self.act_function2(torch.add(out, xcat))
return out
class OutputTransition(nn.Module):
def __init__(self, spatial_dims: int, in_channels: int, out_channels: int, act: Union[Tuple[str, Dict], str]):
super(OutputTransition, self).__init__()
conv_type: Type[Union[nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]
self.act_function1 = get_acti_layer(act, out_channels)
self.conv_block = Convolution(
dimensions=spatial_dims,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=5,
act=None,
norm=Norm.BATCH,
)
self.conv2 = conv_type(out_channels, out_channels, kernel_size=1)
def forward(self, x):
# convolve 32 down to 2 channels
out = self.conv_block(x)
out = self.act_function1(out)
out = self.conv2(out)
return out
class VNet(nn.Module):
"""
V-Net based on `Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
<https://arxiv.org/pdf/1606.04797.pdf>`_.
Adapted from `the official Caffe implementation
<https://github.com/faustomilletari/VNet>`_. and `another pytorch implementation
<https://github.com/mattmacy/vnet.pytorch/blob/master/vnet.py>`_.
The model supports 2D or 3D inputs.
Args:
spatial_dims: spatial dimension of the input data. Defaults to 3.
in_channels: number of input channels for the network. Defaults to 1.
The value should meet the condition that ``16 % in_channels == 0``.
out_channels: number of output channels for the network. Defaults to 1.
act: activation type in the network. Defaults to ``("elu", {"inplace": True})``.
dropout_prob: dropout ratio. Defaults to 0.5. Defaults to 3.
dropout_dim: determine the dimensions of dropout. Defaults to 3.
- ``dropout_dim = 1``, randomly zeroes some of the elements for each channel.
- ``dropout_dim = 2``, Randomly zeroes out entire channels (a channel is a 2D feature map).
- ``dropout_dim = 3``, Randomly zeroes out entire channels (a channel is a 3D feature map).
"""
def __init__(
self,
spatial_dims: int = 3,
in_channels: int = 1,
out_channels: int = 1,
act: Union[Tuple[str, Dict], str] = ("elu", {"inplace": True}),
dropout_prob: float = 0.5,
dropout_dim: int = 3,
):
super().__init__()
assert spatial_dims == 2 or spatial_dims == 3, "spatial_dims can only be 2 or 3."
self.in_tr = InputTransition(spatial_dims, in_channels, 16, act)
self.down_tr32 = DownTransition(spatial_dims, 16, 1, act)
self.down_tr64 = DownTransition(spatial_dims, 32, 2, act)
self.down_tr128 = DownTransition(spatial_dims, 64, 3, act, dropout_prob=dropout_prob)
self.down_tr256 = DownTransition(spatial_dims, 128, 2, act, dropout_prob=dropout_prob)
self.up_tr256 = UpTransition(spatial_dims, 256, 256, 2, act, dropout_prob=dropout_prob)
self.up_tr128 = UpTransition(spatial_dims, 256, 128, 2, act, dropout_prob=dropout_prob)
self.up_tr64 = UpTransition(spatial_dims, 128, 64, 1, act)
self.up_tr32 = UpTransition(spatial_dims, 64, 32, 1, act)
self.out_tr = OutputTransition(spatial_dims, 32, out_channels, act)
def forward(self, x):
out16 = self.in_tr(x)
out32 = self.down_tr32(out16)
out64 = self.down_tr64(out32)
out128 = self.down_tr128(out64)
out256 = self.down_tr256(out128)
x = self.up_tr256(out256, out128)
x = self.up_tr128(x, out64)
x = self.up_tr64(x, out32)
x = self.up_tr32(x, out16)
x = self.out_tr(x)
return x
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