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| import torch.nn as nn | |
| from torchinfo import summary | |
| import torchvision.models | |
| import torch | |
| def convrelu(in_channels, out_channels, kernel, padding): | |
| return nn.Sequential( | |
| nn.Conv2d(in_channels, out_channels, kernel, padding=padding), | |
| nn.ReLU(inplace=True), | |
| ) | |
| class ResNet18UNet(nn.Module): | |
| def __init__(self, max_depth, n_class=1): | |
| super().__init__() | |
| self.base_model = torchvision.models.resnet18(pretrained=True) | |
| self.base_layers = list(self.base_model.children()) | |
| self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2) | |
| self.layer0_1x1 = convrelu(64, 64, 1, 0) | |
| self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4) | |
| self.layer1_1x1 = convrelu(64, 64, 1, 0) | |
| self.layer2 = self.base_layers[5] # size=(N, 128, x.H/8, x.W/8) | |
| self.layer2_1x1 = convrelu(128, 128, 1, 0) | |
| self.layer3 = self.base_layers[6] # size=(N, 256, x.H/16, x.W/16) | |
| self.layer3_1x1 = convrelu(256, 256, 1, 0) | |
| self.layer4 = self.base_layers[7] # size=(N, 512, x.H/32, x.W/32) | |
| self.layer4_1x1 = convrelu(512, 512, 1, 0) | |
| self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.conv_up3 = convrelu(256 + 512, 512, 3, 1) | |
| self.conv_up2 = convrelu(128 + 512, 256, 3, 1) | |
| self.conv_up1 = convrelu(64 + 256, 256, 3, 1) | |
| self.conv_up0 = convrelu(64 + 256, 128, 3, 1) | |
| self.conv_original_size0 = convrelu(3, 64, 3, 1) | |
| self.conv_original_size1 = convrelu(64, 64, 3, 1) | |
| self.conv_original_size2 = convrelu(64 + 128, 64, 3, 1) | |
| self.conv_last = nn.Conv2d(64, n_class, 1) | |
| self.max_depth = max_depth | |
| def forward(self, input): | |
| x_original = self.conv_original_size0(input) | |
| x_original = self.conv_original_size1(x_original) | |
| layer0 = self.layer0(input) | |
| layer1 = self.layer1(layer0) | |
| layer2 = self.layer2(layer1) | |
| layer3 = self.layer3(layer2) | |
| layer4 = self.layer4(layer3) | |
| layer4 = self.layer4_1x1(layer4) | |
| x = self.upsample(layer4) | |
| layer3 = self.layer3_1x1(layer3) | |
| x = torch.cat([x, layer3], dim=1) | |
| x = self.conv_up3(x) | |
| x = self.upsample(x) | |
| layer2 = self.layer2_1x1(layer2) | |
| print(x.shape) | |
| print(layer2.shape) | |
| x = torch.cat([x, layer2], dim=1) | |
| x = self.conv_up2(x) | |
| x = self.upsample(x) | |
| layer1 = self.layer1_1x1(layer1) | |
| x = torch.cat([x, layer1], dim=1) | |
| x = self.conv_up1(x) | |
| x = self.upsample(x) | |
| layer0 = self.layer0_1x1(layer0) | |
| x = torch.cat([x, layer0], dim=1) | |
| x = self.conv_up0(x) | |
| x = self.upsample(x) | |
| x = torch.cat([x, x_original], dim=1) | |
| x = self.conv_original_size2(x) | |
| out = self.conv_last(x) | |
| out_depth = torch.sigmoid(out) * self.max_depth | |
| return {'pred_d': out_depth} | |
| if __name__ == "__main__": | |
| model = ResNet18UNet(max_depth=10).cuda() | |
| # print(model) | |
| summary(model, input_size=(1,3,256,256)) | |