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model.py
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model.py
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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision.models import vgg19_bn
import scipy.ndimage as ndimage
from torchvision import transforms
class ConvBlock(nn.Module):
def __init__(self):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(64, 64, 3, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
self.bn = nn.BatchNorm2d(64)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.bn(x)
x = F.relu(self.conv2(x))
x = self.bn(x)
return x
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 9, padding=4)
self.blocks = nn.Sequential(
ConvBlock(),
ConvBlock(),
ConvBlock(),
ConvBlock(),
)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
self.conv4 = nn.Conv2d(64, 3, 3, padding=1)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.blocks(x)
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
return x
class Discriminator(nn.Module):
def __init__(self, input_ch):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(input_ch, 48, 11, stride=4, padding=5)
self.conv2 = nn.Conv2d(48, 128, 5, stride=2, padding=2)
self.bn1 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 192, 3, padding=1)
self.bn2 = nn.BatchNorm2d(192)
self.conv4 = nn.Conv2d(192, 192, 3, padding=1)
self.conv5 = nn.Conv2d(192, 128, 3, stride=2, padding=1)
self.fc = nn.Linear(128*7*7, 1024)
self.out = nn.Linear(1024, 2)
def forward(self, x):
batch_size = x.size()[0]
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = self.bn1(x)
x = F.relu(self.conv3(x))
x = self.bn2(x)
x = F.relu(self.conv4(x))
x = self.bn2(x)
x = F.relu(self.conv5(x))
x = self.bn1(x)
x = x.view(batch_size, 128*7*7)
x = x.view(batch_size, 128*7*7)
x = F.sigmoid(self.fc(x))
x = F.softmax(self.out(x))
return x
class VGG(nn.Module):
def __init__(self):
super(VGG, self).__init__()
self.model = vgg19_bn(True).features
def forward(self, x):
x = self.model(x)
return x
class TVLoss(nn.Module):
def __init__(self, tv_weight):
super(TVLoss, self).__init__()
self.tv_weight = tv_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self._tensor_size(x[:, :, 1:, :])
count_w = self._tensor_size(x[:, :, :, 1:])
h_tv = torch.pow((x[:, :, 1:, :]-x[:, :, :h_x-1, :]), 2).sum()
w_tv = torch.pow((x[:, :, :, 1:]-x[:, :, :, :w_x-1]),2).sum()
return self.tv_weight*2*(h_tv/count_h+w_tv/count_w)/batch_size
def _tensor_size(self, t):
return t.size()[1]*t.size()[2]*t.size()[3]
class GANLoss(nn.Module):
def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0,
tensor=torch.FloatTensor):
super(GANLoss, self).__init__()
self.real_label = target_real_label
self.fake_label = target_fake_label
self.real_label_var = None
self.fake_label_var = None
self.Tensor = tensor
if use_lsgan:
self.loss = nn.MSELoss()
else:
self.loss = nn.BCELoss()
def get_target_tensor(self, input, target_is_real):
target_tensor = None
if target_is_real:
create_label = ((self.real_label_var is None) or
(self.real_label_var.numel() != input.numel()))
if create_label:
real_tensor = self.Tensor(input.size()).fill_(self.real_label)
self.real_label_var = Variable(real_tensor, requires_grad=False)
target_tensor = self.real_label_var
else:
create_label = ((self.fake_label_var is None) or
(self.fake_label_var.numel() != input.numel()))
if create_label:
fake_tensor = self.Tensor(input.size()).fill_(self.fake_label)
self.fake_label_var = Variable(fake_tensor, requires_grad=False)
target_tensor = self.fake_label_var
return target_tensor
def __call__(self, input, target_is_real):
target_tensor = self.get_target_tensor(input, target_is_real)
return self.loss(input, target_tensor)