Spaces:
Runtime error
Runtime error
File size: 2,757 Bytes
1cae80b |
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 |
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())
|