| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| __all__ = ['UNet', 'NestedUNet'] | |
| """Taken from https://github.com/4uiiurz1/pytorch-nested-unet""" | |
| class VGGBlock(nn.Module): | |
| def __init__(self, in_channels, middle_channels, out_channels): | |
| super().__init__() | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1) | |
| self.bn1 = nn.BatchNorm2d(middle_channels) | |
| self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1) | |
| self.bn2 = nn.BatchNorm2d(out_channels) | |
| def forward(self, x): | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| return out | |
| class UNet(nn.Module): | |
| def __init__(self, num_classes, input_channels=3, **kwargs): | |
| super().__init__() | |
| nb_filter = [32, 64, 128, 256, 512] | |
| self.pool = nn.MaxPool2d(2, 2) | |
| self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4]) | |
| self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3]) | |
| self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| x0_0 = self.conv0_0(input) | |
| x1_0 = self.conv1_0(self.pool(x0_0)) | |
| x2_0 = self.conv2_0(self.pool(x1_0)) | |
| x3_0 = self.conv3_0(self.pool(x2_0)) | |
| x4_0 = self.conv4_0(self.pool(x3_0)) | |
| x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1)) | |
| x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1)) | |
| x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1)) | |
| x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1)) | |
| output = self.final(x0_4) | |
| return output | |
| class NestedUNet(nn.Module): | |
| """ | |
| U-Net Plus plus architecture | |
| Reference: https://arxiv.org/abs/1807.10165 | |
| """ | |
| def __init__(self, num_classes=1, input_channels=3, deep_supervision=False, **kwargs): | |
| super().__init__() | |
| nb_filter = [32, 64, 128, 256, 512] | |
| self.deep_supervision = deep_supervision | |
| self.pool = nn.MaxPool2d(2, 2) | |
| self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4]) | |
| self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3]) | |
| self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| if self.deep_supervision: | |
| self.final1 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final2 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final3 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final4 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1) | |
| else: | |
| self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| x0_0 = self.conv0_0(input) | |
| x1_0 = self.conv1_0(self.pool(x0_0)) | |
| x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1)) | |
| x2_0 = self.conv2_0(self.pool(x1_0)) | |
| x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1)) | |
| x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1)) | |
| x3_0 = self.conv3_0(self.pool(x2_0)) | |
| x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1)) | |
| x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1)) | |
| x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1)) | |
| x4_0 = self.conv4_0(self.pool(x3_0)) | |
| x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1)) | |
| x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1)) | |
| x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1)) | |
| x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1)) | |
| if self.deep_supervision: | |
| output1 = self.final1(x0_1) | |
| output2 = self.final2(x0_2) | |
| output3 = self.final3(x0_3) | |
| output4 = self.final4(x0_4) | |
| return [output1, output2, output3, output4] | |
| else: | |
| output = self.final(x0_4) | |
| return output | |