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models.py
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models.py
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import torch.nn as nn
import torch
import torchvision.models as models
class TotalGenLoss(nn.Module):
def __init__(self, is_cuda):
super(TotalGenLoss, self).__init__()
self.vgg = VGGContent()
if is_cuda:
self.vgg = self.vgg.cuda()
def forward(self, org_image, gen_image):
vgg_org_image = self.vgg(org_image)
vgg_gen_image = self.vgg(gen_image)
bs = org_image.size(0)
content_loss = ((vgg_org_image - vgg_gen_image) ** 2).mean(1)
mae_gen_loss = (torch.abs(org_image - gen_image)).view(bs, -1).mean(1)
return (0.7 * mae_gen_loss + 0.3 * content_loss).mean()
class VGGContent(nn.Module):
def __init__(self):
super(VGGContent, self).__init__()
self.vgg = models.vgg19_bn(pretrained=True).features
def forward(self, x):
bs = x.size(0)
return self.vgg(x).view(bs, -1)
def build_conv_block(in_chans, out_chans, kernel_size=3, stride=2, padding=1, use_bn=True, bn_momentum=0.8, use_leaky=False):
layers = []
layers.append(nn.Conv2d(in_chans, out_chans, kernel_size, stride, padding))
if use_leaky:
layers.append(nn.LeakyReLU(negative_slope=0.2, inplace=True))
else:
layers.append(nn.ReLU(inplace=True))
if use_bn:
layers.append(nn.BatchNorm2d(out_chans, momentum=bn_momentum))
return nn.Sequential(*layers)
def build_deconv_block(in_chans, out_chans, use_bn=True):
layers = []
layers.append(nn.Upsample(scale_factor=2,
mode="bilinear", align_corners=True))
layers.append(nn.Conv2d(in_chans, out_chans, 3, 1, 1))
layers.append(nn.ReLU(inplace=True))
if use_bn:
layers.append(nn.BatchNorm2d(out_chans, momentum=0.8))
return nn.Sequential(*layers)
class FUnIEGeneratorV1(nn.Module):
def __init__(self, n_feats=32):
super(FUnIEGeneratorV1, self).__init__()
self.conv1 = build_conv_block(
3, n_feats, 5, padding=2, use_bn=False)
self.conv2 = build_conv_block(n_feats, n_feats*4, 4)
self.conv3 = build_conv_block(n_feats*4, n_feats*8, 4)
self.conv4 = build_conv_block(n_feats*8, n_feats*8)
self.conv5 = build_conv_block(n_feats*8, n_feats*8)
self.deconv1 = build_deconv_block(n_feats*8, n_feats*8)
self.deconv2 = build_deconv_block(n_feats*16, n_feats*8)
self.deconv3 = build_deconv_block(n_feats*16, n_feats*4)
self.deconv4 = build_deconv_block(n_feats*8, n_feats*1)
self.deconv5 = nn.Upsample(
scale_factor=2, mode="bilinear", align_corners=True)
# In this work, kernel size is 3 instead of 4
self.final = nn.Conv2d(n_feats*2, 3, 3, 1, 1)
self.act = nn.Tanh()
def forward(self, x):
# Downsample
d1 = self.conv1(x) # (B, 32, 128, 128)
d2 = self.conv2(d1) # (B, 128, 64, 64)
d3 = self.conv3(d2) # (B, 256, 32, 32)
d4 = self.conv4(d3) # (B, 256, 16, 16)
d5 = self.conv5(d4) # (B, 256, 8, 8)
# Upsample
u1 = torch.cat([self.deconv1(d5), d4], dim=1) # (B, 512, 16, 16)
u2 = torch.cat([self.deconv2(u1), d3], dim=1) # (B, 512, 32, 32)
u3 = torch.cat([self.deconv3(u2), d2], dim=1) # (B, 256, 64, 64)
u4 = torch.cat([self.deconv4(u3), d1], dim=1) # (B, 64, 128, 128)
u5 = self.deconv5(u4) # (B, 64, 256, 256)
return self.act(self.final(u5))
class FUnIEGeneratorV2(nn.Module):
def __init__(self, n_feats=32):
super(FUnIEGeneratorV2, self).__init__()
self.conv1 = build_conv_block(
3, n_feats, 5, stride=1, padding=2, use_bn=False)
# In this work, kernel size is 3 instead of 4
self.conv2 = build_conv_block(
n_feats, n_feats*2, stride=1, bn_momentum=0.75)
# In this work, kernel size is 3 instead of 4
self.conv3 = build_conv_block(
n_feats*2, n_feats*2, stride=1, bn_momentum=0.75)
self.conv4 = build_conv_block(
n_feats*2, n_feats*4, stride=1, bn_momentum=0.75)
self.conv5 = build_conv_block(
n_feats*4, n_feats*4, stride=1, bn_momentum=0.75)
self.conv6 = build_conv_block(
n_feats*4, n_feats*8, stride=1, bn_momentum=0.75)
self.pool = nn.MaxPool2d(2, 2)
self.deconv1 = build_deconv_block(n_feats*8, n_feats*8)
self.deconv2 = build_deconv_block(n_feats*12, n_feats*8)
self.deconv3 = build_deconv_block(n_feats*10, n_feats*4)
self.out1 = build_conv_block(
n_feats*5, n_feats*4, stride=1, bn_momentum=0.75)
self.out2 = build_conv_block(
n_feats*4, n_feats*8, stride=1, bn_momentum=0.75)
# In this work, kernel size is 3 instead of 4
self.final = nn.Conv2d(n_feats*8, 3, 3, 1, 1)
self.act = nn.Tanh()
def forward(self, x):
# Downsample
d1 = self.conv1(x)
d1a = self.pool(d1) # (B, 32, 128, 128)
d2 = self.conv2(d1a)
d3 = self.conv3(d2)
d3a = self.pool(d3) # (B, 64, 64, 64)
d4 = self.conv4(d3a)
d5 = self.conv5(d4)
d5a = self.pool(d5) # (B, 128, 32, 32)
d6 = self.conv6(d5a) # (B, 256, 32, 32)
# Upsample
u1 = torch.cat([self.deconv1(d6), d5], dim=1) # (B, 384, 64, 64)
u2 = torch.cat([self.deconv2(u1), d3], dim=1) # (B, 320, 128, 128)
u3 = torch.cat([self.deconv3(u2), d1], dim=1) # (B, 160, 256, 256)
return self.act(self.final(self.out2(self.out1(u3))))
class FUnIEDiscriminator(nn.Module):
def __init__(self, n_feats=32):
super(FUnIEDiscriminator, self).__init__()
# Build discriminator blocks
self.block1 = self._block(3*2, n_feats, False)
self.block2 = self._block(n_feats, n_feats*2)
self.block3 = self._block(n_feats*2, n_feats*4)
self.block4 = self._block(n_feats*4, n_feats*8)
# Validility block
# In this work, kernel size is 3 instead of 4
self.validility = nn.Conv2d(n_feats*8, 1, 3, 1, 1)
def _block(self, in_chans, out_chans, use_bn=True):
layers = []
layers.append(nn.Conv2d(in_chans, out_chans, 3, 2, 1))
layers.append(nn.ReLU(inplace=True))
if use_bn:
layers.append(nn.BatchNorm2d(out_chans, momentum=0.8))
return nn.Sequential(*layers)
def forward(self, x1, x2):
x = torch.cat([x1, x2], dim=1) # (B, 6, 256, 256)
x = self.block1(x) # (B, 32, 128, 128)
x = self.block2(x) # (B, 64, 64, 64)
x = self.block3(x) # (B, 128, 32, 32)
x = self.block4(x) # (B, 256, 16, 16)
valid = self.validility(x) # (B, 1, 16, 16)
return valid.squeeze(1)
class ResidualBlock(nn.Module):
def __init__(self, n_feats=64):
super(ResidualBlock, self).__init__()
layers = []
layers.append(nn.Conv2d(n_feats, n_feats, 3, stride=1, padding=1))
layers.append(nn.BatchNorm2d(n_feats, momentum=0.8))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(n_feats, n_feats, 3, stride=1, padding=1))
layers.append(nn.BatchNorm2d(n_feats, momentum=0.8))
self.block = nn.Sequential(*layers)
def forward(self, x):
identity = x
x = self.block(x)
return x + identity
class FUnIEUpGenerator(nn.Module):
def __init__(self, n_feats=32):
super(FUnIEUpGenerator, self).__init__()
# Conv blocks
self.conv1 = build_conv_block(
3, n_feats, 5, padding=2, use_bn=False, use_leaky=True)
self.conv2 = build_conv_block(n_feats, n_feats*4, 4, use_leaky=True)
self.conv3 = build_conv_block(n_feats*4, n_feats*8, 4, use_leaky=True)
self.conv4 = build_conv_block(n_feats*8, n_feats*8, use_leaky=True)
self.conv5 = build_conv_block(n_feats*8, n_feats*8, use_leaky=True)
# Three additional conv layers
self.add_conv1 = nn.Conv2d(n_feats*8, 64, 3, stride=1, padding=1)
self.add_conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.add_conv3 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
# Residual blocks
self.res_block1 = ResidualBlock()
self.res_block2 = ResidualBlock()
self.res_block3 = ResidualBlock()
self.res_block4 = ResidualBlock()
self.res_block5 = ResidualBlock()
# Deconv blocks
self.deconv1 = self._deconv_block(n_feats*2, n_feats*8)
self.deconv2 = self._deconv_block(n_feats*(8+8), n_feats*8)
self.deconv3 = self._deconv_block(n_feats*(8+8), n_feats*4)
self.deconv4 = self._deconv_block(n_feats*(4+4), n_feats*1)
self.up = nn.Upsample(
scale_factor=2, mode="bilinear", align_corners=True)
# In this work, kernel size is 3 instead of 4
self.final = nn.Conv2d(n_feats*2, 3, 3, stride=1, padding=1)
self.act = nn.Tanh()
def _deconv_block(self, in_chans, out_chans, use_bn=True):
layers = []
layers.append(nn.Upsample(scale_factor=2,
mode="bilinear", align_corners=True))
layers.append(nn.Conv2d(in_chans, out_chans, 3, stride=1, padding=1))
layers.append(nn.ReLU(inplace=True))
if use_bn:
layers.append(nn.BatchNorm2d(out_chans, momentum=0.8))
return nn.Sequential(*layers)
def forward(self, x):
# Downsample
d1 = self.conv1(x) # (B, 32, 128, 128)
d2 = self.conv2(d1) # (B, 128, 64, 64)
d3 = self.conv3(d2) # (B, 256, 32, 32)
d4 = self.conv4(d3) # (B, 256, 16, 16)
d5 = self.conv5(d4) # (B, 256, 8, 8)
# Additional conv layers
a1 = self.relu(self.add_conv1(d5)) # (B, 64, 8, 8)
a2 = self.relu(self.add_conv2(a1))
bridge = self.relu(self.add_conv3(a2))
# Residual blocks
bridge = self.res_block1(bridge)
bridge = self.res_block2(bridge)
bridge = self.res_block3(bridge)
bridge = self.res_block4(bridge)
bridge = self.res_block5(bridge)
bridge += a1
# Upsample
u1 = torch.cat([self.deconv1(bridge), d4], dim=1) # (B, 512, 16, 16)
u2 = torch.cat([self.deconv2(u1), d3], dim=1) # (B, 512, 32, 32)
u3 = torch.cat([self.deconv3(u2), d2], dim=1) # (B, 256, 64, 64)
u4 = torch.cat([self.deconv4(u3), d1], dim=1) # (B, 64, 128, 128)
u4 = self.up(u4) # (B, 64, 256, 256)
return self.act(self.final(u4))
class FUnIEUpDiscriminator(nn.Module):
def __init__(self, n_feats=32):
super(FUnIEUpDiscriminator, self).__init__()
# Build discriminator blocks
self.block1 = build_conv_block(
3, n_feats, use_bn=False, use_leaky=True)
self.block2 = build_conv_block(n_feats, n_feats*2, use_leaky=True)
self.block3 = build_conv_block(n_feats*2, n_feats*4, use_leaky=True)
self.block4 = build_conv_block(n_feats*4, n_feats*8, use_leaky=True)
self.block5 = build_conv_block(
n_feats*8, n_feats*8, stride=1, use_leaky=True)
# Validility block
# In this work, kernel size is 3 instead of 4
self.validility = nn.Conv2d(n_feats*8, 1, 3, stride=1, padding=1)
def forward(self, x):
x = self.block1(x) # (B, 32, 128, 128)
x = self.block2(x) # (B, 64, 64, 64)
x = self.block3(x) # (B, 128, 32, 32)
x = self.block4(x) # (B, 256, 16, 16)
x = self.block5(x) # (B, 256, 16, 16)
valid = self.validility(x) # (B, 1, 16, 16)
return valid.squeeze(1)
if __name__ == "__main__":
model = FUnIEGeneratorV1()
x = torch.rand(1, 3, 256, 256)
print(model(x).size())
model = FUnIEGeneratorV2()
x = torch.rand(1, 3, 256, 256)
print(model(x).size())
model = FUnIEDiscriminator()
x1 = torch.rand(1, 3, 256, 256)
x2 = torch.rand(1, 3, 256, 256)
print(model(x1, x2).size())
model = VGGContent()
x = torch.rand(1, 3, 256, 256)
print(model(x).size())
model = FUnIEUpGenerator()
x = torch.rand(1, 3, 256, 256)
print(model(x).size())
model = FUnIEUpDiscriminator()
x = torch.rand(1, 3, 256, 256)
print(model(x).size())