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model.py
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import torch
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=2, padding=1, activation='relu', batch_norm=True):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size, stride, padding)
self.batch_norm = batch_norm
self.bn = torch.nn.InstanceNorm2d(output_size)
self.activation = activation
self.relu = torch.nn.ReLU(True)
self.lrelu = torch.nn.LeakyReLU(0.2, True)
self.tanh = torch.nn.Tanh()
def forward(self, x):
if self.batch_norm:
out = self.bn(self.conv(x))
else:
out = self.conv(x)
if self.activation == 'relu':
return self.relu(out)
elif self.activation == 'lrelu':
return self.lrelu(out)
elif self.activation == 'tanh':
return self.tanh(out)
elif self.activation == 'no_act':
return out
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=2, padding=1, output_padding=1, activation='relu', batch_norm=True):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_size, kernel_size, stride, padding, output_padding)
self.batch_norm = batch_norm
self.bn = torch.nn.InstanceNorm2d(output_size)
self.activation = activation
self.relu = torch.nn.ReLU(True)
def forward(self, x):
if self.batch_norm:
out = self.bn(self.deconv(x))
else:
out = self.deconv(x)
if self.activation == 'relu':
return self.relu(out)
elif self.activation == 'lrelu':
return self.lrelu(out)
elif self.activation == 'tanh':
return self.tanh(out)
elif self.activation == 'no_act':
return out
class ResnetBlock(torch.nn.Module):
def __init__(self, num_filter, kernel_size=3, stride=1, padding=0):
super(ResnetBlock, self).__init__()
conv1 = torch.nn.Conv2d(num_filter, num_filter, kernel_size, stride, padding)
conv2 = torch.nn.Conv2d(num_filter, num_filter, kernel_size, stride, padding)
bn = torch.nn.InstanceNorm2d(num_filter)
relu = torch.nn.ReLU(True)
pad = torch.nn.ReflectionPad2d(1)
self.resnet_block = torch.nn.Sequential(
pad,
conv1,
bn,
relu,
pad,
conv2,
bn
)
def forward(self, x):
out = self.resnet_block(x)
return out
class Generator(torch.nn.Module):
def __init__(self, input_dim, num_filter, output_dim, num_resnet):
super(Generator, self).__init__()
# Reflection padding
self.pad = torch.nn.ReflectionPad2d(3)
# Encoder
self.conv1 = ConvBlock(input_dim, num_filter, kernel_size=7, stride=1, padding=0)
self.conv2 = ConvBlock(num_filter, num_filter * 2)
self.conv3 = ConvBlock(num_filter * 2, num_filter * 4)
# Resnet blocks
self.resnet_blocks = []
for i in range(num_resnet):
self.resnet_blocks.append(ResnetBlock(num_filter * 4))
self.resnet_blocks = torch.nn.Sequential(*self.resnet_blocks)
# Decoder
self.deconv1 = DeconvBlock(num_filter * 4, num_filter * 2)
self.deconv2 = DeconvBlock(num_filter * 2, num_filter)
self.deconv3 = ConvBlock(num_filter, output_dim,
kernel_size=7, stride=1, padding=0, activation='tanh', batch_norm=False)
def forward(self, x):
# Encoder
enc1 = self.conv1(self.pad(x))
enc2 = self.conv2(enc1)
enc3 = self.conv3(enc2)
# Resnet blocks
res = self.resnet_blocks(enc3)
# Decoder
dec1 = self.deconv1(res)
dec2 = self.deconv2(dec1)
out = self.deconv3(self.pad(dec2))
return out
def normal_weight_init(self, mean=0.0, std=0.02):
for m in self.children():
if isinstance(m, ConvBlock):
torch.nn.init.normal(m.conv.weight, mean, std)
if isinstance(m, DeconvBlock):
torch.nn.init.normal(m.deconv.weight, mean, std)
if isinstance(m, ResnetBlock):
torch.nn.init.normal(m.conv.weight, mean, std)
torch.nn.init.constant(m.conv.bias, 0)
class Discriminator(torch.nn.Module):
def __init__(self, input_dim, num_filter, output_dim):
super(Discriminator, self).__init__()
conv1 = ConvBlock(input_dim, num_filter, kernel_size=4, stride=2, padding=1, activation='lrelu', batch_norm=False)
conv2 = ConvBlock(num_filter, num_filter * 2, kernel_size=4, stride=2, padding=1, activation='lrelu')
conv3 = ConvBlock(num_filter * 2, num_filter * 4, kernel_size=4, stride=2, padding=1, activation='lrelu')
conv4 = ConvBlock(num_filter * 4, num_filter * 8, kernel_size=4, stride=1, padding=1, activation='lrelu')
conv5 = ConvBlock(num_filter * 8, output_dim, kernel_size=4, stride=1, padding=1, activation='no_act', batch_norm=False)
self.conv_blocks = torch.nn.Sequential(
conv1,
conv2,
conv3,
conv4,
conv5
)
def forward(self, x):
out = self.conv_blocks(x)
return out
def normal_weight_init(self, mean=0.0, std=0.02):
for m in self.children():
if isinstance(m, ConvBlock):
torch.nn.init.normal(m.conv.weight, mean, std)