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net.py
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net.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from ops.spectral_norm import spectral_norm as SpectralNorm
# Defines the GAN loss which uses either LSGAN or the regular GAN.
# When LSGAN is used, it is basically same as MSELoss,
# but it abstracts away the need to create the target label tensor
# that has the same size as the input
class ResidualBlock(nn.Module):
"""Residual Block."""
def __init__(self, dim_in, dim_out):
super(ResidualBlock, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(dim_in, dim_out, kernel_size=3, stride=1, padding=1, bias=False),
nn.InstanceNorm2d(dim_out, affine=True),
nn.ReLU(inplace=True),
nn.Conv2d(dim_out, dim_out, kernel_size=3, stride=1, padding=1, bias=False),
nn.InstanceNorm2d(dim_out, affine=True))
def forward(self, x):
return x + self.main(x)
class Generator(nn.Module):
"""Generator. Encoder-Decoder Architecture."""
def __init__(self, conv_dim=64, repeat_num=6):
super(Generator, self).__init__()
layers = []
layers.append(nn.Conv2d(3, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
layers.append(nn.InstanceNorm2d(conv_dim, affine=True))
layers.append(nn.ReLU(inplace=True))
# Down-Sampling
curr_dim = conv_dim
for i in range(2):
layers.append(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=2, padding=1, bias=False))
layers.append(nn.InstanceNorm2d(curr_dim*2, affine=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim * 2
# Bottleneck
for i in range(repeat_num):
layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))
# Up-Sampling
for i in range(2):
layers.append(nn.ConvTranspose2d(curr_dim, curr_dim//2, kernel_size=4, stride=2, padding=1, bias=False))
layers.append(nn.InstanceNorm2d(curr_dim//2, affine=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim // 2
layers.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
layers.append(nn.Tanh())
self.main = nn.Sequential(*layers)
def forward(self, x):
out = self.main(x)
return out
class Generator_makeup(nn.Module):
"""Generator. Encoder-Decoder Architecture."""
# input 2 images and output 2 images as well
def __init__(self, conv_dim=64, repeat_num=6, input_nc=6):
super(Generator_makeup, self).__init__()
layers = []
layers.append(nn.Conv2d(input_nc, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
layers.append(nn.InstanceNorm2d(conv_dim, affine=True))
layers.append(nn.ReLU(inplace=True))
# Down-Sampling
curr_dim = conv_dim
for i in range(2):
layers.append(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=2, padding=1, bias=False))
layers.append(nn.InstanceNorm2d(curr_dim*2, affine=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim * 2
# Bottleneck
for i in range(repeat_num):
layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))
# Up-Sampling
for i in range(2):
layers.append(nn.ConvTranspose2d(curr_dim, curr_dim//2, kernel_size=4, stride=2, padding=1, bias=False))
layers.append(nn.InstanceNorm2d(curr_dim//2, affine=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim // 2
self.main = nn.Sequential(*layers)
layers_1 = []
layers_1.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
layers_1.append(nn.Tanh())
self.branch_1 = nn.Sequential(*layers_1)
layers_2 = []
layers_2.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
layers_2.append(nn.Tanh())
self.branch_2 = nn.Sequential(*layers_2)
def forward(self, x, y):
input_x = torch.cat((x, y), dim=1)
out = self.main(input_x)
out_A = self.branch_1(out)
out_B = self.branch_2(out)
return out_A, out_B
class Generator_branch(nn.Module):
"""Generator. Encoder-Decoder Architecture."""
# input 2 images and output 2 images as well
def __init__(self, conv_dim=64, repeat_num=6, input_nc=3):
super(Generator_branch, self).__init__()
# Branch input
layers_branch = []
layers_branch.append(nn.Conv2d(input_nc, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
layers_branch.append(nn.InstanceNorm2d(conv_dim, affine=True))
layers_branch.append(nn.ReLU(inplace=True))
layers_branch.append(nn.Conv2d(conv_dim, conv_dim*2, kernel_size=4, stride=2, padding=1, bias=False))
layers_branch.append(nn.InstanceNorm2d(conv_dim*2, affine=True))
layers_branch.append(nn.ReLU(inplace=True))
self.Branch_0 = nn.Sequential(*layers_branch)
# Branch input
layers_branch = []
layers_branch.append(nn.Conv2d(input_nc, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
layers_branch.append(nn.InstanceNorm2d(conv_dim, affine=True))
layers_branch.append(nn.ReLU(inplace=True))
layers_branch.append(nn.Conv2d(conv_dim, conv_dim*2, kernel_size=4, stride=2, padding=1, bias=False))
layers_branch.append(nn.InstanceNorm2d(conv_dim*2, affine=True))
layers_branch.append(nn.ReLU(inplace=True))
self.Branch_1 = nn.Sequential(*layers_branch)
# Down-Sampling, branch merge
layers = []
curr_dim = conv_dim*2
layers.append(nn.Conv2d(curr_dim*2, curr_dim*2, kernel_size=4, stride=2, padding=1, bias=False))
layers.append(nn.InstanceNorm2d(curr_dim*2, affine=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim * 2
# Bottleneck
for i in range(repeat_num):
layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))
# Up-Sampling
for i in range(2):
layers.append(nn.ConvTranspose2d(curr_dim, curr_dim//2, kernel_size=4, stride=2, padding=1, bias=False))
layers.append(nn.InstanceNorm2d(curr_dim//2, affine=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim // 2
self.main = nn.Sequential(*layers)
layers_1 = []
layers_1.append(nn.Conv2d(curr_dim, curr_dim, kernel_size=3, stride=1, padding=1, bias=False))
layers_1.append(nn.InstanceNorm2d(curr_dim, affine=True))
layers_1.append(nn.ReLU(inplace=True))
layers_1.append(nn.Conv2d(curr_dim, curr_dim, kernel_size=3, stride=1, padding=1, bias=False))
layers_1.append(nn.InstanceNorm2d(curr_dim, affine=True))
layers_1.append(nn.ReLU(inplace=True))
layers_1.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
layers_1.append(nn.Tanh())
self.branch_1 = nn.Sequential(*layers_1)
layers_2 = []
layers_2.append(nn.Conv2d(curr_dim, curr_dim, kernel_size=3, stride=1, padding=1, bias=False))
layers_2.append(nn.InstanceNorm2d(curr_dim, affine=True))
layers_2.append(nn.ReLU(inplace=True))
layers_2.append(nn.Conv2d(curr_dim, curr_dim, kernel_size=3, stride=1, padding=1, bias=False))
layers_2.append(nn.InstanceNorm2d(curr_dim, affine=True))
layers_2.append(nn.ReLU(inplace=True))
layers_2.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
layers_2.append(nn.Tanh())
self.branch_2 = nn.Sequential(*layers_2)
def forward(self, x, y):
input_x = self.Branch_0(x)
input_y = self.Branch_1(y)
input_fuse = torch.cat((input_x, input_y), dim=1)
out = self.main(input_fuse)
out_A = self.branch_1(out)
out_B = self.branch_2(out)
return out_A, out_B
class Discriminator(nn.Module):
"""Discriminator. PatchGAN."""
def __init__(self, image_size=128, conv_dim=64, repeat_num=3, norm='SN'):
super(Discriminator, self).__init__()
layers = []
if norm=='SN':
layers.append(SpectralNorm(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1)))
else:
layers.append(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
layers.append(nn.LeakyReLU(0.01, inplace=True))
curr_dim = conv_dim
for i in range(1, repeat_num):
if norm=='SN':
layers.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=2, padding=1)))
else:
layers.append(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=2, padding=1))
layers.append(nn.LeakyReLU(0.01, inplace=True))
curr_dim = curr_dim * 2
#k_size = int(image_size / np.power(2, repeat_num))
if norm=='SN':
layers.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=1, padding=1)))
else:
layers.append(nn.Conv2d(curr_dim, curr_dim*2, kernel_size=4, stride=1, padding=1))
layers.append(nn.LeakyReLU(0.01, inplace=True))
curr_dim = curr_dim *2
self.main = nn.Sequential(*layers)
if norm=='SN':
self.conv1 = SpectralNorm(nn.Conv2d(curr_dim, 1, kernel_size=4, stride=1, padding=1, bias=False))
else:
self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=4, stride=1, padding=1, bias=False)
# conv1 remain the last square size, 256*256-->30*30
#self.conv2 = SpectralNorm(nn.Conv2d(curr_dim, 1, kernel_size=k_size, bias=False))
#conv2 output a single number
def forward(self, x):
h = self.main(x)
#out_real = self.conv1(h)
out_makeup = self.conv1(h)
#return out_real.squeeze(), out_makeup.squeeze()
return out_makeup.squeeze()
class VGG(nn.Module):
def __init__(self, pool='max'):
super(VGG, self).__init__()
# vgg modules
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.conv3_4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv4_4 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv5_4 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
if pool == 'max':
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
elif pool == 'avg':
self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)
self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)
self.pool3 = nn.AvgPool2d(kernel_size=2, stride=2)
self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2)
self.pool5 = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x, out_keys):
out = {}
out['r11'] = F.relu(self.conv1_1(x))
out['r12'] = F.relu(self.conv1_2(out['r11']))
out['p1'] = self.pool1(out['r12'])
out['r21'] = F.relu(self.conv2_1(out['p1']))
out['r22'] = F.relu(self.conv2_2(out['r21']))
out['p2'] = self.pool2(out['r22'])
out['r31'] = F.relu(self.conv3_1(out['p2']))
out['r32'] = F.relu(self.conv3_2(out['r31']))
out['r33'] = F.relu(self.conv3_3(out['r32']))
out['r34'] = F.relu(self.conv3_4(out['r33']))
out['p3'] = self.pool3(out['r34'])
out['r41'] = F.relu(self.conv4_1(out['p3']))
out['r42'] = F.relu(self.conv4_2(out['r41']))
out['r43'] = F.relu(self.conv4_3(out['r42']))
out['r44'] = F.relu(self.conv4_4(out['r43']))
out['p4'] = self.pool4(out['r44'])
out['r51'] = F.relu(self.conv5_1(out['p4']))
out['r52'] = F.relu(self.conv5_2(out['r51']))
out['r53'] = F.relu(self.conv5_3(out['r52']))
out['r54'] = F.relu(self.conv5_4(out['r53']))
out['p5'] = self.pool5(out['r54'])
return [out[key] for key in out_keys]