Spaces:
Running
Running
File size: 2,641 Bytes
f2fa83b |
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 64 65 66 |
import torch.nn as nn
import torch.nn.functional as F
from models.conv_blocks import InvertedResBlock
from models.conv_blocks import ConvBlock
from models.conv_blocks import UpConvLNormLReLU
from utils.common import initialize_weights
class GeneratorV2(nn.Module):
def __init__(self, dataset=''):
super(GeneratorV2, self).__init__()
self.name = f'{self.__class__.__name__}_{dataset}'
self.conv_block1 = nn.Sequential(
ConvBlock(3, 32, kernel_size=7, stride=1, padding=3, norm_type="layer"),
ConvBlock(32, 64, kernel_size=3, stride=2, padding=(0, 1, 0, 1), norm_type="layer"),
ConvBlock(64, 64, kernel_size=3, stride=1, norm_type="layer"),
)
self.conv_block2 = nn.Sequential(
ConvBlock(64, 128, kernel_size=3, stride=2, padding=(0, 1, 0, 1), norm_type="layer"),
ConvBlock(128, 128, kernel_size=3, stride=1, norm_type="layer"),
)
self.res_blocks = nn.Sequential(
ConvBlock(128, 128, kernel_size=3, stride=1, norm_type="layer"),
InvertedResBlock(128, 256, expand_ratio=2, norm_type="layer"),
InvertedResBlock(256, 256, expand_ratio=2, norm_type="layer"),
InvertedResBlock(256, 256, expand_ratio=2, norm_type="layer"),
InvertedResBlock(256, 256, expand_ratio=2, norm_type="layer"),
ConvBlock(256, 128, kernel_size=3, stride=1, norm_type="layer"),
)
self.conv_block3 = nn.Sequential(
# UpConvLNormLReLU(128, 128, norm_type="layer"),
ConvBlock(128, 128, kernel_size=3, stride=1, norm_type="layer"),
ConvBlock(128, 128, kernel_size=3, stride=1, norm_type="layer"),
)
self.conv_block4 = nn.Sequential(
# UpConvLNormLReLU(128, 64, norm_type="layer"),
ConvBlock(128, 64, kernel_size=3, stride=1, norm_type="layer"),
ConvBlock(64, 64, kernel_size=3, stride=1, norm_type="layer"),
ConvBlock(64, 32, kernel_size=7, padding=3, stride=1, norm_type="layer"),
)
self.decode_blocks = nn.Sequential(
nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
nn.Tanh(),
)
initialize_weights(self)
def forward(self, x):
out = self.conv_block1(x)
out = self.conv_block2(out)
out = self.res_blocks(out)
out = F.interpolate(out, scale_factor=2, mode="bilinear")
out = self.conv_block3(out)
out = F.interpolate(out, scale_factor=2, mode="bilinear")
out = self.conv_block4(out)
img = self.decode_blocks(out)
return img
|