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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torchvision.models as models
class ResidualBlock(nn.Module):
"""Residual Block with instance normalization."""
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, track_running_stats=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, track_running_stats=True))
def forward(self, x):
return x + self.main(x)
class Generator(nn.Module):
"""Generator network."""
def __init__(self, conv_dim=64, c_dim=5, repeat_num=6):
super(Generator, self).__init__()
layers = []
layers.append(nn.Conv2d(3 + c_dim, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
layers.append(nn.InstanceNorm2d(conv_dim, affine=True, track_running_stats=True))
layers.append(nn.ReLU(inplace=True))
# Down-sampling layers.
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, track_running_stats=True))
layers.append(nn.ReLU(inplace=True))
curr_dim = curr_dim * 2
# Bottleneck layers.
for i in range(repeat_num):
layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))
# Up-sampling layers.
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, track_running_stats=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, c):
# Replicate spatially and concatenate domain information.
# Note that this type of label conditioning does not work at all if we use reflection padding in Conv2d.
# This is because instance normalization ignores the shifting (or bias) effect.
c = c.view(c.size(0), c.size(1), 1, 1)
c = c.repeat(1, 1, x.size(2), x.size(3))
x = torch.cat([x, c], dim=1)
return self.main(x)
# class Discriminator(nn.Module):
# """Discriminator network with PatchGAN."""
#
# def __init__(self, image_size=128, conv_dim=64, c_dim=5, repeat_num=6):
# super(Discriminator, self).__init__()
# layers = []
# layers.append(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
# layers.append(nn.LeakyReLU(0.01))
#
# curr_dim = conv_dim
# for i in range(1, repeat_num):
# layers.append(nn.Conv2d(curr_dim, curr_dim * 2, kernel_size=4, stride=2, padding=1))
# layers.append(nn.LeakyReLU(0.01))
# curr_dim = curr_dim * 2
#
# kernel_size = int(image_size / np.power(2, repeat_num))
# self.main = nn.Sequential(*layers)
# self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=3, stride=1, padding=1, bias=False)
# self.conv2 = nn.Conv2d(curr_dim, c_dim, kernel_size=kernel_size, bias=False)
#
# def forward(self, x):
# h = self.main(x)
# out_src = self.conv1(h)
# out_cls = self.conv2(h)
# return out_src, out_cls.view(out_cls.size(0), out_cls.size(1))
class Discriminator(nn.Module):
def __init__(self, conv_dim=64, c_dim=5, repeat_num=6):
super(Discriminator, self).__init__()
layers = []
layers.append(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
layers.append(nn.LeakyReLU(0.01))
curr_dim = conv_dim
for i in range(1, repeat_num):
layers.append(nn.Conv2d(curr_dim, curr_dim * 2, kernel_size=4, stride=2, padding=1))
# layers.append(nn.BatchNorm2d(curr_dim * 2, momentum=0.9, eps=1e-5))
layers.append(nn.LeakyReLU(0.01))
curr_dim = curr_dim * 2
self.main = nn.Sequential(*layers)
self.pool = nn.AdaptiveAvgPool2d((1,1))
# self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=3, stride=1, padding=1, bias=False)
# self.conv2 = nn.Conv2d(curr_dim, c_dim, kernel_size=kernel_size, bias=False)
self.fc = nn.Conv2d(curr_dim, c_dim, kernel_size=1, bias=False)
def forward(self, x):
h = self.main(x)
h = self.pool(h)
# out_src = self.conv1(h)
out_cls = self.fc(h)
out_cls = out_cls.view(out_cls.size(0), out_cls.size(1))
# out_cls = F.softmax(out_cls,dim=1)
return out_cls
class DomianClassifier(nn.Module):
def __init__(self, conv_dim=64, domain=3, repeat_num=6):
super(DomianClassifier, self).__init__()
layers = []
layers.append(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
layers.append(nn.LeakyReLU(0.01))
curr_dim = conv_dim
for i in range(1, repeat_num):
layers.append(nn.Conv2d(curr_dim, curr_dim * 2, kernel_size=4, stride=2, padding=1))
# layers.append(nn.BatchNorm2d(curr_dim * 2, momentum=0.9, eps=1e-5))
layers.append(nn.LeakyReLU(0.01))
curr_dim = curr_dim * 2
self.main = nn.Sequential(*layers)
self.pool = nn.AdaptiveAvgPool2d((1,1))
# self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=3, stride=1, padding=1, bias=False)
# self.conv2 = nn.Conv2d(curr_dim, c_dim, kernel_size=kernel_size, bias=False)
self.fc = nn.Conv2d(curr_dim, domain, kernel_size=1, bias=False)
def forward(self, x):
h = self.main(x)
h = self.pool(h)
# out_src = self.conv1(h)
out_cls = self.fc(h)
out_cls = out_cls.view(out_cls.size(0), out_cls.size(1))
# out_cls = F.softmax(out_cls,dim=1)
return out_cls