File size: 9,418 Bytes
36fdbcf |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 |
# Copyright (c) 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 Optional, Sequence, Union
import torch
import torch.nn as nn
from monai.networks.blocks import Convolution, UpSample, ResidualUnit
from monai.networks.layers.factories import Conv, Pool
from monai.utils import deprecated_arg, ensure_tuple_rep
__all__ = ["BasicUnet", "Basicunet", "basicunet", "BasicUNet"]
class TwoConv(nn.Sequential):
"""two convolutions."""
def __init__(
self,
spatial_dims: int,
in_chns: int,
out_chns: int,
act: Union[str, tuple],
norm: Union[str, tuple],
bias: bool,
dropout: Union[float, tuple] = 0.0,
):
"""
Args:
spatial_dims: number of spatial dimensions.
in_chns: number of input channels.
out_chns: number of output channels.
act: activation type and arguments.
norm: feature normalization type and arguments.
bias: whether to have a bias term in convolution blocks.
dropout: dropout ratio. Defaults to no dropout.
"""
super().__init__()
conv_0 = Convolution(spatial_dims, in_chns, out_chns, act=act, norm=norm, dropout=dropout, bias=bias, padding=1)
conv_1 = Convolution(
spatial_dims, out_chns, out_chns, act=act, norm=norm, dropout=dropout, bias=bias, padding=1
)
self.add_module("conv_0", conv_0)
self.add_module("conv_1", conv_1)
class Down(nn.Sequential):
"""maxpooling downsampling and two convolutions."""
def __init__(
self,
spatial_dims: int,
in_chns: int,
out_chns: int,
act: Union[str, tuple],
norm: Union[str, tuple],
bias: bool,
dropout: Union[float, tuple] = 0.0,
):
"""
Args:
spatial_dims: number of spatial dimensions.
in_chns: number of input channels.
out_chns: number of output channels.
act: activation type and arguments.
norm: feature normalization type and arguments.
bias: whether to have a bias term in convolution blocks.
dropout: dropout ratio. Defaults to no dropout.
"""
super().__init__()
max_pooling = Pool["MAX", spatial_dims](kernel_size=2)
convs = TwoConv(spatial_dims, in_chns, out_chns, act, norm, bias, dropout)
self.add_module("max_pooling", max_pooling)
self.add_module("convs", convs)
class UpCat(nn.Module):
"""upsampling, concatenation with the encoder feature map, two convolutions"""
def __init__(
self,
spatial_dims: int,
in_chns: int,
cat_chns: int,
out_chns: int,
act: Union[str, tuple],
norm: Union[str, tuple],
bias: bool,
dropout: Union[float, tuple] = 0.0,
upsample: str = "deconv",
pre_conv: Optional[Union[nn.Module, str]] = "default",
interp_mode: str = "linear",
align_corners: Optional[bool] = True,
halves: bool = True,
is_pad: bool = True,
):
"""
Args:
spatial_dims: number of spatial dimensions.
in_chns: number of input channels to be upsampled.
cat_chns: number of channels from the encoder.
out_chns: number of output channels.
act: activation type and arguments.
norm: feature normalization type and arguments.
bias: whether to have a bias term in convolution blocks.
dropout: dropout ratio. Defaults to no dropout.
upsample: upsampling mode, available options are
``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
pre_conv: a conv block applied before upsampling.
Only used in the "nontrainable" or "pixelshuffle" mode.
interp_mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``}
Only used in the "nontrainable" mode.
align_corners: set the align_corners parameter for upsample. Defaults to True.
Only used in the "nontrainable" mode.
halves: whether to halve the number of channels during upsampling.
This parameter does not work on ``nontrainable`` mode if ``pre_conv`` is `None`.
is_pad: whether to pad upsampling features to fit features from encoder. Defaults to True.
"""
super().__init__()
if upsample == "nontrainable" and pre_conv is None:
up_chns = in_chns
else:
up_chns = in_chns // 2 if halves else in_chns
self.upsample = UpSample(
spatial_dims,
in_chns,
up_chns,
2,
mode=upsample,
pre_conv=pre_conv,
interp_mode=interp_mode,
align_corners=align_corners,
)
self.convs = TwoConv(spatial_dims, cat_chns + up_chns, out_chns, act, norm, bias, dropout)
self.is_pad = is_pad
def forward(self, x: torch.Tensor, x_e: Optional[torch.Tensor]):
"""
Args:
x: features to be upsampled.
x_e: features from the encoder.
"""
x_0 = self.upsample(x)
if x_e is not None:
if self.is_pad:
# handling spatial shapes due to the 2x maxpooling with odd edge lengths.
dimensions = len(x.shape) - 2
sp = [0] * (dimensions * 2)
for i in range(dimensions):
if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
sp[i * 2 + 1] = 1
x_0 = torch.nn.functional.pad(x_0, sp, "replicate")
x = self.convs(torch.cat([x_e, x_0], dim=1)) # input channels: (cat_chns + up_chns)
else:
x = self.convs(x_0)
return x
class Unet_decoder(nn.Module):
def __init__(
self,
spatial_dims: int = 3,
out_channels: int = 2,
features: Sequence[int] = (32, 32, 64, 128, 256, 32),
act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
norm: Union[str, tuple] = ("instance", {"affine": True}),
bias: bool = True,
dropout: Union[float, tuple] = 0.0,
upsample: str = "deconv",
dimensions: Optional[int] = None,
):
super().__init__()
if dimensions is not None:
spatial_dims = dimensions
fea = ensure_tuple_rep(features, 6)
print(f"Unet_decoder features: {fea}.")
self.upcat_4 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample)
self.upcat_3 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample)
self.upcat_2 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample)
self.upcat_1 = UpCat(spatial_dims, fea[1], fea[0], fea[5], act, norm, bias, dropout, upsample, halves=False)
self.final_conv = Conv["conv", spatial_dims](fea[5], out_channels, kernel_size=1)
def forward(self, image_embeddings, feature_list):
x4, x3, x2, x1, x0 = image_embeddings, feature_list[3], feature_list[2], feature_list[1], feature_list[0]
u4 = self.upcat_4(x4, x3)
u3 = self.upcat_3(u4, x2)
u2 = self.upcat_2(u3, x1)
u1 = self.upcat_1(u2, x0)
# logits = self.final_conv(u1)
return u1
class Unet_encoder(nn.Module):
def __init__(
self,
spatial_dims: int = 3,
in_channels: int = 1,
features: Sequence[int] = (32, 32, 64, 128, 256, 32),
act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
norm: Union[str, tuple] = ("instance", {"affine": True}),
bias: bool = True,
dropout: Union[float, tuple] = 0.0,
dimensions: Optional[int] = None,
):
super().__init__()
if dimensions is not None:
spatial_dims = dimensions
fea = ensure_tuple_rep(features, 6)
print(f"Unet_encoder features: {fea}.")
self.conv_0 = TwoConv(spatial_dims, in_channels, features[0], act, norm, bias, dropout)
self.down_1 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout)
self.down_2 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout)
self.down_3 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout)
self.down_4 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout)
def forward(self, x: torch.Tensor, deepest_only=False):
x0 = self.conv_0(x)
x1 = self.down_1(x0)
x2 = self.down_2(x1)
x3 = self.down_3(x2)
x4 = self.down_4(x3)
if deepest_only:
return x4
else:
return x4, x3, x2, x1, x0
# BasicUnet = Basicunet = basicunet = BasicUNet
|