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# 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 __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
from monai.networks.blocks.convolutions import Convolution
from monai.networks.blocks.upsample import UpSample
from monai.networks.layers.factories import Act, Conv, Norm
from monai.utils import optional_import
models, _ = optional_import("torchvision", name="models")
class GCN(nn.Module):
"""
The Global Convolutional Network module using large 1D
Kx1 and 1xK kernels to represent 2D kernels.
"""
def __init__(self, inplanes: int, planes: int, ks: int = 7):
"""
Args:
inplanes: number of input channels.
planes: number of output channels.
ks: kernel size for one dimension. Defaults to 7.
"""
super().__init__()
conv2d_type: type[nn.Conv2d] = Conv[Conv.CONV, 2]
self.conv_l1 = conv2d_type(in_channels=inplanes, out_channels=planes, kernel_size=(ks, 1), padding=(ks // 2, 0))
self.conv_l2 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=(1, ks), padding=(0, ks // 2))
self.conv_r1 = conv2d_type(in_channels=inplanes, out_channels=planes, kernel_size=(1, ks), padding=(0, ks // 2))
self.conv_r2 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=(ks, 1), padding=(ks // 2, 0))
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: in shape (batch, inplanes, spatial_1, spatial_2).
"""
x_l = self.conv_l1(x)
x_l = self.conv_l2(x_l)
x_r = self.conv_r1(x)
x_r = self.conv_r2(x_r)
x = x_l + x_r
return x
class Refine(nn.Module):
"""
Simple residual block to refine the details of the activation maps.
"""
def __init__(self, planes: int):
"""
Args:
planes: number of input channels.
"""
super().__init__()
relu_type: type[nn.ReLU] = Act[Act.RELU]
conv2d_type: type[nn.Conv2d] = Conv[Conv.CONV, 2]
norm2d_type: type[nn.BatchNorm2d] = Norm[Norm.BATCH, 2]
self.bn = norm2d_type(num_features=planes)
self.relu = relu_type(inplace=True)
self.conv1 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=3, padding=1)
self.conv2 = conv2d_type(in_channels=planes, out_channels=planes, kernel_size=3, padding=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: in shape (batch, planes, spatial_1, spatial_2).
"""
residual = x
x = self.bn(x)
x = self.relu(x)
x = self.conv1(x)
x = self.bn(x)
x = self.relu(x)
x = self.conv2(x)
return residual + x
class FCN(nn.Module):
"""
2D FCN network with 3 input channels. The small decoder is built
with the GCN and Refine modules.
The code is adapted from `lsqshr's official 2D code <https://github.com/lsqshr/AH-Net/blob/master/net2d.py>`_.
Args:
out_channels: number of output channels. Defaults to 1.
upsample_mode: [``"transpose"``, ``"bilinear"``]
The mode of upsampling manipulations.
Using the second mode cannot guarantee the model's reproducibility. Defaults to ``bilinear``.
- ``transpose``, uses transposed convolution layers.
- ``bilinear``, uses bilinear interpolation.
pretrained: If True, returns a model pre-trained on ImageNet
progress: If True, displays a progress bar of the download to stderr.
"""
def __init__(
self, out_channels: int = 1, upsample_mode: str = "bilinear", pretrained: bool = True, progress: bool = True
):
super().__init__()
conv2d_type: type[nn.Conv2d] = Conv[Conv.CONV, 2]
self.upsample_mode = upsample_mode
self.conv2d_type = conv2d_type
self.out_channels = out_channels
resnet = models.resnet50(pretrained=pretrained, progress=progress)
self.conv1 = resnet.conv1
self.bn0 = resnet.bn1
self.relu = resnet.relu
self.maxpool = resnet.maxpool
self.layer1 = resnet.layer1
self.layer2 = resnet.layer2
self.layer3 = resnet.layer3
self.layer4 = resnet.layer4
self.gcn1 = GCN(2048, self.out_channels)
self.gcn2 = GCN(1024, self.out_channels)
self.gcn3 = GCN(512, self.out_channels)
self.gcn4 = GCN(64, self.out_channels)
self.gcn5 = GCN(64, self.out_channels)
self.refine1 = Refine(self.out_channels)
self.refine2 = Refine(self.out_channels)
self.refine3 = Refine(self.out_channels)
self.refine4 = Refine(self.out_channels)
self.refine5 = Refine(self.out_channels)
self.refine6 = Refine(self.out_channels)
self.refine7 = Refine(self.out_channels)
self.refine8 = Refine(self.out_channels)
self.refine9 = Refine(self.out_channels)
self.refine10 = Refine(self.out_channels)
self.transformer = self.conv2d_type(in_channels=256, out_channels=64, kernel_size=1)
if self.upsample_mode == "transpose":
self.up_conv = UpSample(spatial_dims=2, in_channels=self.out_channels, scale_factor=2, mode="deconv")
def forward(self, x: torch.Tensor):
"""
Args:
x: in shape (batch, 3, spatial_1, spatial_2).
"""
org_input = x
x = self.conv1(x)
x = self.bn0(x)
x = self.relu(x)
conv_x = x
x = self.maxpool(x)
pool_x = x
fm1 = self.layer1(x)
fm2 = self.layer2(fm1)
fm3 = self.layer3(fm2)
fm4 = self.layer4(fm3)
gcfm1 = self.refine1(self.gcn1(fm4))
gcfm2 = self.refine2(self.gcn2(fm3))
gcfm3 = self.refine3(self.gcn3(fm2))
gcfm4 = self.refine4(self.gcn4(pool_x))
gcfm5 = self.refine5(self.gcn5(conv_x))
if self.upsample_mode == "transpose":
fs1 = self.refine6(self.up_conv(gcfm1) + gcfm2)
fs2 = self.refine7(self.up_conv(fs1) + gcfm3)
fs3 = self.refine8(self.up_conv(fs2) + gcfm4)
fs4 = self.refine9(self.up_conv(fs3) + gcfm5)
return self.refine10(self.up_conv(fs4))
fs1 = self.refine6(F.interpolate(gcfm1, fm3.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm2)
fs2 = self.refine7(F.interpolate(fs1, fm2.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm3)
fs3 = self.refine8(F.interpolate(fs2, pool_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm4)
fs4 = self.refine9(F.interpolate(fs3, conv_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm5)
return self.refine10(F.interpolate(fs4, org_input.size()[2:], mode=self.upsample_mode, align_corners=True))
class MCFCN(FCN):
"""
The multi-channel version of the 2D FCN module.
Adds a projection layer to take arbitrary number of inputs.
Args:
in_channels: number of input channels. Defaults to 3.
out_channels: number of output channels. Defaults to 1.
upsample_mode: [``"transpose"``, ``"bilinear"``]
The mode of upsampling manipulations.
Using the second mode cannot guarantee the model's reproducibility. Defaults to ``bilinear``.
- ``transpose``, uses transposed convolution layers.
- ``bilinear``, uses bilinear interpolate.
pretrained: If True, returns a model pre-trained on ImageNet
progress: If True, displays a progress bar of the download to stderr.
"""
def __init__(
self,
in_channels: int = 3,
out_channels: int = 1,
upsample_mode: str = "bilinear",
pretrained: bool = True,
progress: bool = True,
):
super().__init__(
out_channels=out_channels, upsample_mode=upsample_mode, pretrained=pretrained, progress=progress
)
self.init_proj = Convolution(
spatial_dims=2,
in_channels=in_channels,
out_channels=3,
kernel_size=1,
act=("relu", {"inplace": True}),
norm=Norm.BATCH,
bias=False,
)
def forward(self, x: torch.Tensor):
"""
Args:
x: in shape (batch, in_channels, spatial_1, spatial_2).
"""
x = self.init_proj(x)
return super().forward(x)
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