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networks.py
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"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from torch import nn
from torch.autograd import Variable
import torch
import torch.nn.functional as F
try:
from itertools import izip as zip
except ImportError: # will be 3.x series
pass
##################################################################################
# Discriminator
##################################################################################
class MsImageDis(nn.Module):
# Multi-scale discriminator architecture
def __init__(self, input_dim, params, cuda_device='cuda:0'):
super(MsImageDis, self).__init__()
self.prev_real_input = None
self.n_layer = params['n_layer']
self.gan_type = params['gan_type']
self.dim = params['dim']
self.norm = params['norm']
self.activ = params['activ']
self.num_scales = params['num_scales']
self.pad_type = params['pad_type']
self.cuda_device = cuda_device
self.input_dim = input_dim
self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
self.cnns = nn.ModuleList()
for _ in range(self.num_scales):
self.cnns.append(self._make_net().cuda(self.cuda_device))
def _make_net(self):
dim = self.dim
cnn_x = []
cnn_x += [Conv2dBlock(self.input_dim, dim, 4, 2, 1, norm='none', activation=self.activ, pad_type=self.pad_type)]
for i in range(self.n_layer - 1):
cnn_x += [Conv2dBlock(dim, dim * 2, 4, 2, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)]
dim *= 2
cnn_x += [nn.Conv2d(dim, 1, 1, 1, 0)]
cnn_x = nn.Sequential(*cnn_x)
return cnn_x
def forward(self, x):
outputs = []
x = x.cuda(self.cuda_device)
for model in self.cnns:
outputs.append(model(x))
x = self.downsample(x)
return outputs
def calc_dis_loss(self, input_fake, input_real):
# calculate the loss to train D
outs0 = self.forward(input_fake)
outs1 = self.forward(input_real)
loss = 0
for it, (out0, out1) in enumerate(zip(outs0, outs1)):
if self.gan_type == 'lsgan':
loss += torch.mean((out0 - 0)**2) + torch.mean((out1 - 1)**2)
elif self.gan_type == 'nsgan':
all0 = Variable(torch.zeros_like(out0.data).cuda(self.cuda_device), requires_grad=False)
all1 = Variable(torch.ones_like(out1.data).cuda(self.cuda_device), requires_grad=False)
loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all0) +
F.binary_cross_entropy(F.sigmoid(out1), all1))
elif self.gan_type == 'RelativisticAverageHingeGAN':
self.prev_real_input = input_real # save it for the gen train later
# difference between real and fake:
r_f_diff = out1 - torch.mean(out0, dim=0, keepdim=True).repeat(10, 1, 1, 1)
# difference between fake and real samples
f_r_diff = out0 - torch.mean(out1, dim=0, keepdim=True).repeat(10, 1, 1, 1)
# return the loss
loss += (torch.mean(torch.nn.ReLU()(1 - r_f_diff))
+ torch.mean(torch.nn.ReLU()(1 + f_r_diff)))
else:
assert 0, "Unsupported GAN type: {}".format(self.gan_type)
return loss
def calc_gen_loss(self, input_fake, input_real=None):
# calculate the loss to train G
outs0 = self.forward(input_fake)
loss = 0
for it, (out0) in enumerate(outs0):
if self.gan_type == 'lsgan':
loss += torch.mean((out0 - 1)**2) # LSGAN
elif self.gan_type == 'nsgan':
all1 = Variable(torch.ones_like(out0.data).cuda(self.cuda_device), requires_grad=False)
loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all1))
elif self.gan_type == 'RelativisticAverageHingeGAN':
if input_real is not None:
outs1 = self.forward(input_real)
elif self.prev_real_input is not None:
outs1 = self.forward(self.prev_real_input)
else:
assert 0, "try using cal_gan_loss with RelativisticAverageHingeGAN but did not provid input_real"
out1 = outs1[it]
# difference between real and fake:
r_f_diff = out1 - torch.mean(out0, dim=0, keepdim=True).repeat(10, 1, 1, 1)
# difference between fake and real samples
f_r_diff = out0 - torch.mean(out1, dim=0, keepdim=True).repeat(10, 1, 1, 1)
loss = torch.mean(torch.nn.ReLU()(1 + r_f_diff)) + torch.mean(torch.nn.ReLU()(1 - f_r_diff))
else:
assert 0, "Unsupported GAN type: {}".format(self.gan_type)
return loss
##################################################################################
# Council Discriminator
##################################################################################
class MsImageDisCouncil(nn.Module):
# Multi-scale discriminator architecture
def __init__(self, input_dim, params, cuda_device='cuda:0'):
super(MsImageDisCouncil, self).__init__()
self.n_layer = params['n_layer']
self.gan_type = params['gan_type']
self.dim = params['dim']
self.norm = params['norm']
self.activ = params['activ']
self.num_scales = params['num_scales']
self.pad_type = params['pad_type']
self.cuda_device = cuda_device
self.input_dim = input_dim
self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
self.cnns = nn.ModuleList()
for _ in range(self.num_scales):
self.cnns.append(self._make_net())
def _make_net(self):
dim = self.dim
cnn_x = []
# cnn_x += [Conv2dBlock(input_dim=2 * self.input_dim, output_dim=dim, kernel_size=4, stride=2, padding=1, norm='none', activation=self.activ, pad_type=self.pad_type)] # original
cnn_x += [Conv2dBlock(input_dim=2 * self.input_dim, output_dim=dim, kernel_size=3, stride=1, padding=1, norm='none', activation=self.activ, pad_type=self.pad_type)] # ON
for i in range(self.n_layer - 1):
cnn_x += [Conv2dBlock(dim, dim * 2, 4, 2, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)]
dim *= 2
cnn_x += [nn.Conv2d(dim, dim, 1, 1, 0)]
cnn_x += [nn.Conv2d(dim, 1, 1, 1, 0)]
cnn_x = nn.Sequential(*cnn_x).cuda(self.cuda_device)
return cnn_x
def forward(self, x, x_input):
x = x.cuda(self.cuda_device)
x_input = x_input.cuda(self.cuda_device)
outputs = []
for model in self.cnns:
model_input = torch.cat((x, x_input), 1)
outputs.append(model(model_input))
x = self.downsample(x)
x_input = self.downsample(x_input)
return outputs
def calc_dis_loss(self, input_fake, input_real, input):
# calculate the loss to train D
outs0 = self.forward(input_fake, input)
outs1 = self.forward(input_real, input)
loss = 0
for it, (out0, out1) in enumerate(zip(outs0, outs1)):
if self.gan_type == 'lsgan':
loss += torch.mean((out0 - 0)**2) + torch.mean((out1 - 1)**2)
elif self.gan_type == 'nsgan':
all0 = Variable(torch.zeros_like(out0.data).cuda(self.cuda_device), requires_grad=False)
all1 = Variable(torch.ones_like(out1.data).cuda(self.cuda_device), requires_grad=False)
loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all0) +
F.binary_cross_entropy(F.sigmoid(out1), all1))
elif self.gan_type == 'RelativisticAverageHingeGAN':
self.prev_real_input = input_real # save it for the gen train later
self.prev_input = input # save it for the gen train later
# difference between real and fake:
r_f_diff = out1 - torch.mean(out0, dim=0, keepdim=True).repeat(10, 1, 1, 1)
# difference between fake and real samples
f_r_diff = out0 - torch.mean(out1, dim=0, keepdim=True).repeat(10, 1, 1, 1)
loss = torch.mean(torch.nn.ReLU()(1 + r_f_diff)) + torch.mean(torch.nn.ReLU()(1 - f_r_diff))
# return the loss
loss += (torch.mean(torch.nn.ReLU()(1 - r_f_diff))
+ torch.mean(torch.nn.ReLU()(1 + f_r_diff)))
else:
assert 0, "Unsupported GAN type: {}".format(self.gan_type)
return loss
def calc_gen_loss(self, input_fake, input, input_real=None):
# calculate the loss to train G
outs0 = self.forward(input_fake, input)
loss = 0
for it, (out0) in enumerate(outs0):
if self.gan_type == 'lsgan':
loss += torch.mean((out0 - 1)**2) # LSGAN
elif self.gan_type == 'nsgan':
all1 = Variable(torch.ones_like(out0.data).cuda(self.cuda_device), requires_grad=False)
loss += torch.mean(F.binary_cross_entropy(F.sigmoid(out0), all1))
elif self.gan_type == 'RelativisticAverageHingeGAN':
if input_real is not None:
outs1 = self.forward(input_real)
elif self.prev_real_input is not None:
outs1 = self.forward(self.prev_real_input, self.prev_input)
else:
assert 0, "try using cal_gan_loss with RelativisticAverageHingeGAN but did not provid input_real"
out1 = outs1[it]
# difference between real and fake:
r_f_diff = out1 - torch.mean(out0, dim=0, keepdim=True).repeat(10, 1, 1, 1)
# difference between fake and real samples
f_r_diff = out0 - torch.mean(out1, dim=0, keepdim=True).repeat(10, 1, 1, 1)
loss = torch.mean(torch.nn.ReLU()(1 + r_f_diff)) + torch.mean(torch.nn.ReLU()(1 - f_r_diff))
loss = torch.mean(torch.nn.ReLU()(1 + r_f_diff)) + torch.mean(torch.nn.ReLU()(1 - f_r_diff))
else:
assert 0, "Unsupported GAN type: {}".format(self.gan_type)
return loss
##################################################################################
# Generator
##################################################################################
class AdaINGen(nn.Module):
# AdaIN auto-encoder architecture
def __init__(self, input_dim, params, cuda_device='cuda:0'):
super(AdaINGen, self).__init__()
dim = params['dim']
style_dim = params['style_dim']
self.n_downsample = params['n_downsample']
n_res = params['n_res']
self.activ = params['activ']
pad_type = params['pad_type']
mlp_dim = params['mlp_dim']
self.do_my_style = params['do_my_style']
self.cuda_device = cuda_device
# style encoder
self.enc_style = StyleEncoder(4, input_dim, dim, style_dim, norm='none', activ=self.activ, pad_type=pad_type).cuda(self.cuda_device)
# content encoder
self.enc_content = ContentEncoder(self.n_downsample, n_res, input_dim, dim, 'in', self.activ, pad_type=pad_type).cuda(self.cuda_device)
if self.do_my_style:
self.dec = Decoder_V2_atten(n_upsample=self.n_downsample, n_res=n_res, dim=self.enc_content.output_dim + style_dim, output_dim=input_dim, res_norm='in', activ=self.activ, pad_type=pad_type, num_of_mask_dim_to_add=params['num_of_mask_dim_to_add']).cuda(self.cuda_device)
else:
self.dec = Decoder_V2_atten(self.n_downsample, n_res, self.enc_content.output_dim, input_dim, res_norm='adain', activ=self.activ, pad_type=pad_type, num_of_mask_dim_to_add=params['num_of_mask_dim_to_add']).cuda(self.cuda_device)
# MLP to generate AdaIN parameters or adding sytle my way
if self.do_my_style:
self.mlp = MLP(input_dim=style_dim, output_dim=style_dim, dim=mlp_dim, n_blk=3, norm='none',
activ=self.activ).cuda(self.cuda_device)
else:
self.mlp = MLP(input_dim=style_dim, output_dim=self.get_num_adain_params(self.dec), dim=mlp_dim, n_blk=3,
norm='none', activ=self.activ).cuda(self.cuda_device)
def forward(self, images, return_mask=False):
# reconstruct an image
images = images.cuda(self.cuda_device)
content, style_fake = self.encode(images)
if return_mask:
images_recon, mask = self.decode(content=content, style=style_fake, images=images, return_mask=return_mask)
return images_recon, mask
else:
images_recon = self.decode(content=content, style=style_fake, images=images, return_mask=return_mask)
return images_recon
def forward(self, images, style, return_mask=False):
# reconstruct an image
content, _ = self.encode(images)
if return_mask:
images_recon, mask = self.decode(content=content, style=style, images=images, return_mask=return_mask)
return images_recon, mask
else:
images_recon = self.decode(content=content, style=style, images=images, return_mask=return_mask)
return images_recon
def encode(self, images):
images = images.cuda(self.cuda_device)
# encode an image to its content and style codes
style_fake = self.enc_style(images)
content = self.enc_content(images)
return content, style_fake
def decode(self, content, style, images, return_mask=False):
content, style, images = content.cuda(self.cuda_device), style.cuda(self.cuda_device), images.cuda(self.cuda_device)
# decode content and style codes to an image
if self.do_my_style:
style_to_add = self.mlp(style)
style_to_add = style_to_add.repeat(content.shape[2], content.shape[3], 1, 1)
style_to_add = style_to_add.transpose(0, 2).transpose(3, 1)
content = torch.cat((content, style_to_add), 1)
else:
adain_params = self.mlp(style)
self.assign_adain_params(adain_params, self.dec)
if return_mask:
images, mask = self.dec(content, images, return_mask)
return images, mask
else:
images = self.dec(content, images)
return images
def assign_adain_params(self, adain_params, model):
# assign the adain_params to the AdaIN layers in model
start_ind = 0
for m in model.modules():
if m.__class__.__name__ == "AdaptiveInstanceNorm2d":
mean = adain_params[:, start_ind: start_ind + m.num_features]
std = adain_params[:, start_ind + m.num_features: start_ind + 2*m.num_features]
m.bias = mean.contiguous().view(-1)
m.weight = std.contiguous().view(-1)
start_ind += 2*m.num_features
def get_num_adain_params(self, model):
# return the number of AdaIN parameters needed by the model
num_adain_params = 0
for m in model.modules():
if m.__class__.__name__ == "AdaptiveInstanceNorm2d":
num_adain_params += 2*m.num_features
return num_adain_params
def get_num_adain_params(self, model):
# return the number of AdaIN parameters needed by the model
num_adain_params = 0
for m in model.modules():
if m.__class__.__name__ == "AdaptiveInstanceNorm2d":
num_adain_params += 2*m.num_features
return num_adain_params
##################################################################################
# Encoder and Decoders
##################################################################################
class StyleEncoder(nn.Module):
def __init__(self, n_downsample, input_dim, dim, style_dim, norm, activ, pad_type):
super(StyleEncoder, self).__init__()
self.model = []
self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type=pad_type)]
for i in range(2):
self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]
dim *= 2
for i in range(n_downsample - 2):
self.model += [Conv2dBlock(dim, dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]
self.model += [nn.AdaptiveAvgPool2d(1)] # global average pooling
self.model += [nn.Conv2d(dim, style_dim, 1, 1, 0)]
self.model = nn.Sequential(*self.model)
self.output_dim = dim
def forward(self, x):
return self.model(x)
class ContentEncoder(nn.Module):
def __init__(self, n_downsample, n_res, input_dim, dim, norm, activ, pad_type):
super(ContentEncoder, self).__init__()
self.model = []
self.model += [Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, activation=activ, pad_type=pad_type)] # ORIGINAL
for i in range(n_downsample):
self.model += [Conv2dBlock(dim, 2 * dim, 4, 2, 1, norm=norm, activation=activ, pad_type=pad_type)]
dim *= 2
# residual blocks
self.model += [ResBlocks(n_res, dim, norm=norm, activation=activ, pad_type=pad_type)]
self.model = nn.Sequential(*self.model)
self.output_dim = dim
def forward(self, x):
return self.model(x)
class Decoder_V2_atten(nn.Module):
def __init__(self, n_upsample, n_res, dim, output_dim, res_norm='adain', activ='relu', pad_type='zero', num_of_mask_dim_to_add=1):
super(Decoder_V2_atten, self).__init__()
self.num_of_mask_dim_to_add = num_of_mask_dim_to_add # 3 # 2
self.model = []
self.output_dim = output_dim
self.mask_s = []
# AdaIN residual blocks
self.model += [ResBlocks(n_res, dim, res_norm, activ, pad_type=pad_type)]
# upsampling blocks
for i in range(n_upsample):
self.model += [nn.Upsample(scale_factor=2)]
self.model += [Conv2dBlock(input_dim=dim, output_dim=dim // 2, kernel_size=3, stride=1, padding=1, norm='adain',
activation=activ, pad_type=pad_type)]
dim //= 2
self.model += [Conv2dBlock(input_dim=dim, output_dim=dim, kernel_size=3, stride=1, padding=1, norm='adain',
activation=activ, pad_type=pad_type)]
self.model += [Conv2dBlock(input_dim=dim, output_dim=dim, kernel_size=1, stride=1, padding=0, norm='none', activation=activ, pad_type=pad_type)]
self.model += [Conv2dBlock(input_dim=dim, output_dim=dim, kernel_size=1, stride=1, padding=0, norm='none', activation=activ, pad_type=pad_type)]
self.model += [Conv2dBlock(input_dim=dim, output_dim=(output_dim*self.num_of_mask_dim_to_add+self.num_of_mask_dim_to_add), kernel_size=1, stride=1, padding=0, norm='none', activation='tanh', pad_type=pad_type)]
self.model = nn.Sequential(*self.model)
def forward(self, x, im_in, return_mask=False):
new_x = self.model(x)
self.mask_s = ((torch.tanh(10 * new_x[:, (-1 * self.num_of_mask_dim_to_add):, :, :]) + 1) / 2)
new_im = im_in
curr_ind = 0
for k in range(self.num_of_mask_dim_to_add):
new_im_o = new_x[:, curr_ind:self.output_dim * (k + 1), :, :] ##
curr_ind = self.output_dim * (k + 1) ##
mask = self.mask_s[:, k, :, :].unsqueeze(1).repeat(1, new_im.shape[1], 1, 1)
new_im = (1 - mask) * new_im + mask * new_im_o
if return_mask:
if self.mask_s.shape[1] != 3:
mask_s_prePixTot = torch.sum(self.mask_s, 1).unsqueeze(1).repeat(1, 3, 1, 1) / self.mask_s.shape[1]
self.mask_s = mask_s_prePixTot
return new_im, self.mask_s
return new_im
##################################################################################
# Sequential Models
##################################################################################
class ResBlocks(nn.Module):
def __init__(self, num_blocks, dim, norm='in', activation='relu', pad_type='zero'):
super(ResBlocks, self).__init__()
self.model = []
for i in range(num_blocks):
self.model += [ResBlock(dim, norm=norm, activation=activation, pad_type=pad_type)]
self.model = nn.Sequential(*self.model)
def forward(self, x):
return self.model(x)
class MLP(nn.Module):
def __init__(self, input_dim, output_dim, dim, n_blk, norm='none', activ='relu'):
super(MLP, self).__init__()
self.model = []
self.model += [LinearBlock(input_dim, dim, norm=norm, activation=activ)]
for i in range(n_blk - 2):
self.model += [LinearBlock(dim, dim, norm=norm, activation=activ)]
self.model += [LinearBlock(dim, output_dim, norm='none', activation='none')] # no output activations
self.model = nn.Sequential(*self.model)
def forward(self, x):
return self.model(x.view(x.size(0), -1))
##################################################################################
# Basic Blocks
##################################################################################
class ResBlock(nn.Module):
def __init__(self, dim, norm='in', activation='relu', pad_type='zero'):
super(ResBlock, self).__init__()
model = []
model += [Conv2dBlock(dim ,dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]
model += [Conv2dBlock(dim ,dim, 3, 1, 1, norm=norm, activation='none', pad_type=pad_type)]
self.model = nn.Sequential(*model)
def forward(self, x):
residual = x
out = self.model(x)
out += residual
return out
class Conv2dBlock(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride,
padding=0, norm='none', activation='relu', pad_type='zero'):
super(Conv2dBlock, self).__init__()
self.use_bias = True
# initialize padding
if pad_type == 'reflect':
self.pad = nn.ReflectionPad2d(padding)
elif pad_type == 'replicate':
self.pad = nn.ReplicationPad2d(padding)
elif pad_type == 'zero':
self.pad = nn.ZeroPad2d(padding)
else:
assert 0, "Unsupported padding type: {}".format(pad_type)
# initialize normalization
norm_dim = output_dim
if norm == 'bn':
self.norm = nn.BatchNorm2d(norm_dim)
elif norm == 'in':
self.norm = nn.InstanceNorm2d(norm_dim)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'adain':
self.norm = AdaptiveInstanceNorm2d(norm_dim)
elif norm == 'none' or norm == 'sn':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
# initialize convolution
if norm == 'sn':
self.conv = SpectralNorm(nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias))
else:
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias)
def forward(self, x):
x = self.conv(self.pad(x))
if self.norm:
x = self.norm(x)
if self.activation:
x = self.activation(x)
return x
class LinearBlock(nn.Module):
def __init__(self, input_dim, output_dim, norm='none', activation='relu'):
super(LinearBlock, self).__init__()
use_bias = True
# initialize fully connected layer
if norm == 'sn':
self.fc = SpectralNorm(nn.Linear(input_dim, output_dim, bias=use_bias))
else:
self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)
# initialize normalization
norm_dim = output_dim
if norm == 'bn':
self.norm = nn.BatchNorm1d(norm_dim)
elif norm == 'in':
self.norm = nn.InstanceNorm1d(norm_dim)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'none' or norm == 'sn':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
def forward(self, x):
out = self.fc(x)
if self.norm:
out = self.norm(out)
if self.activation:
out = self.activation(out)
return out
##################################################################################
# VGG network definition
##################################################################################
class Vgg16(nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
def forward(self, X):
h = F.relu(self.conv1_1(X), inplace=True)
h = F.relu(self.conv1_2(h), inplace=True)
# relu1_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv2_1(h), inplace=True)
h = F.relu(self.conv2_2(h), inplace=True)
# relu2_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv3_1(h), inplace=True)
h = F.relu(self.conv3_2(h), inplace=True)
h = F.relu(self.conv3_3(h), inplace=True)
# relu3_3 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv4_1(h), inplace=True)
h = F.relu(self.conv4_2(h), inplace=True)
h = F.relu(self.conv4_3(h), inplace=True)
# relu4_3 = h
h = F.relu(self.conv5_1(h), inplace=True)
h = F.relu(self.conv5_2(h), inplace=True)
h = F.relu(self.conv5_3(h), inplace=True)
relu5_3 = h
return relu5_3
# return [relu1_2, relu2_2, relu3_3, relu4_3]
##################################################################################
# Normalization layers
##################################################################################
class AdaptiveInstanceNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1):
super(AdaptiveInstanceNorm2d, self).__init__()
self.num_features = num_features
self.eps = eps
self.momentum = momentum
# weight and bias are dynamically assigned
self.weight = None
self.bias = None
# just dummy buffers, not used
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
def forward(self, x):
assert self.weight is not None and self.bias is not None, "Please assign weight and bias before calling AdaIN!"
b, c = x.size(0), x.size(1)
running_mean = self.running_mean.repeat(b)
running_var = self.running_var.repeat(b)
# Apply instance norm
x_reshaped = x.contiguous().view(1, b * c, *x.size()[2:])
out = F.batch_norm(
x_reshaped, running_mean, running_var, self.weight, self.bias,
True, self.momentum, self.eps)
return out.view(b, c, *x.size()[2:])
def __repr__(self):
return self.__class__.__name__ + '(' + str(self.num_features) + ')'
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
self.beta = nn.Parameter(torch.zeros(num_features))
def forward(self, x):
shape = [-1] + [1] * (x.dim() - 1)
# print(x.size())
if x.size(0) == 1:
# These two lines run much faster in pytorch 0.4 than the two lines listed below.
mean = x.view(-1).mean().view(*shape)
std = x.view(-1).std().view(*shape)
else:
mean = x.view(x.size(0), -1).mean(1).view(*shape)
std = x.view(x.size(0), -1).std(1).view(*shape)
x = (x - mean) / (std + self.eps)
if self.affine:
shape = [1, -1] + [1] * (x.dim() - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
"""
Based on the paper "Spectral Normalization for Generative Adversarial Networks" by Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
and the Pytorch implementation https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
"""
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__init__()
self.module = module
self.name = name
self.power_iterations = power_iterations
if not self._made_params():
self._make_params()
def _update_u_v(self):
u = getattr(self.module, self.name + "_u")
v = getattr(self.module, self.name + "_v")
w = getattr(self.module, self.name + "_bar")
height = w.data.shape[0]
for _ in range(self.power_iterations):
v.data = l2normalize(torch.mv(torch.t(w.view(height,-1).data), u.data))
u.data = l2normalize(torch.mv(w.view(height,-1).data, v.data))
sigma = u.dot(w.view(height, -1).mv(v))
setattr(self.module, self.name, w / sigma.expand_as(w))
def _made_params(self):
try:
u = getattr(self.module, self.name + "_u")
v = getattr(self.module, self.name + "_v")
w = getattr(self.module, self.name + "_bar")
return True
except AttributeError:
return False
def _make_params(self):
w = getattr(self.module, self.name)
height = w.data.shape[0]
width = w.view(height, -1).data.shape[1]
u = nn.Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
v = nn.Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
u.data = l2normalize(u.data)
v.data = l2normalize(v.data)
w_bar = nn.Parameter(w.data)
del self.module._parameters[self.name]
self.module.register_parameter(self.name + "_u", u)
self.module.register_parameter(self.name + "_v", v)
self.module.register_parameter(self.name + "_bar", w_bar)
def forward(self, *args):
self._update_u_v()
return self.module.forward(*args)