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| import torch | |
| from torch import nn | |
| from modeling.base import BaseNetwork | |
| from modeling.ifrnet import Flatten | |
| from modules.blocks import DestyleResBlock, Destyler, ResBlock | |
| class UNet(BaseNetwork): | |
| def __init__(self, base_n_channels): | |
| super(UNet, self).__init__() | |
| self.ds_res1 = ResBlock(channels_in=3, channels_out=base_n_channels, kernel_size=5, stride=1, padding=2) | |
| self.ds_res2 = ResBlock(channels_in=base_n_channels, channels_out=base_n_channels * 2, kernel_size=3, stride=2, padding=1) | |
| self.ds_res3 = ResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels * 2, kernel_size=3, stride=1, padding=1) | |
| self.ds_res4 = ResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels * 4, kernel_size=3, stride=2, padding=1) | |
| self.ds_res5 = ResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 4, kernel_size=3, stride=1, padding=1) | |
| self.ds_res6 = ResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 8, kernel_size=3, stride=2, padding=1) | |
| self.upsample = nn.UpsamplingNearest2d(scale_factor=2.0) | |
| self.res1 = ResBlock(channels_in=base_n_channels * 8, channels_out=base_n_channels * 4, kernel_size=3, stride=1, padding=1) | |
| self.res2 = ResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 4, kernel_size=3, stride=1, padding=1) | |
| self.res3 = ResBlock(channels_in=base_n_channels * 4, channels_out=base_n_channels * 2, kernel_size=3, stride=1, padding=1) | |
| self.res4 = ResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels * 2, kernel_size=3, stride=1, padding=1) | |
| self.res5 = ResBlock(channels_in=base_n_channels * 2, channels_out=base_n_channels, kernel_size=3, stride=1, padding=1) | |
| self.conv1 = nn.Conv2d(base_n_channels, 3, kernel_size=3, stride=1, padding=1) | |
| self.init_weights(init_type="normal", gain=0.02) | |
| def forward(self, x): | |
| out = self.ds_res1(x) | |
| out = self.ds_res2(out) | |
| out = self.ds_res3(out) | |
| out = self.ds_res4(out) | |
| out = self.ds_res5(out) | |
| aux = self.ds_res6(out) | |
| out = self.upsample(aux) | |
| out = self.res1(out) | |
| out = self.res2(out) | |
| out = self.upsample(out) | |
| out = self.res3(out) | |
| out = self.res4(out) | |
| out = self.upsample(out) | |
| out = self.res5(out) | |
| out = self.conv1(out) | |
| return out, aux | |
| if __name__ == '__main__': | |
| import torchvision | |
| x = torch.rand((2, 3, 256, 256)).cuda() | |
| unet = UNet(32, 32).cuda() | |
| vgg16 = torchvision.models.vgg16(pretrained=True).features.eval().cuda() | |
| with torch.no_grad(): | |
| vgg_feat = vgg16(x) | |
| out = unet(x, vgg_feat) | |
| print(out.size()) | |