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import sys
import math
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Function
from torch.nn.utils.spectral_norm import spectral_norm as SpectralNorm
class LayerNorm2d(nn.Module):
def __init__(self, n_out, affine=True):
super(LayerNorm2d, self).__init__()
self.n_out = n_out
self.affine = affine
if self.affine:
self.weight = nn.Parameter(torch.ones(n_out, 1, 1))
self.bias = nn.Parameter(torch.zeros(n_out, 1, 1))
def forward(self, x):
normalized_shape = x.size()[1:]
if self.affine:
return F.layer_norm(x, normalized_shape, \
self.weight.expand(normalized_shape),
self.bias.expand(normalized_shape))
else:
return F.layer_norm(x, normalized_shape)
class ADAINHourglass(nn.Module):
def __init__(self, image_nc, pose_nc, ngf, img_f, encoder_layers, decoder_layers, nonlinearity, use_spect):
super(ADAINHourglass, self).__init__()
self.encoder = ADAINEncoder(image_nc, pose_nc, ngf, img_f, encoder_layers, nonlinearity, use_spect)
self.decoder = ADAINDecoder(pose_nc, ngf, img_f, encoder_layers, decoder_layers, True, nonlinearity, use_spect)
self.output_nc = self.decoder.output_nc
def forward(self, x, z):
return self.decoder(self.encoder(x, z), z)
class ADAINEncoder(nn.Module):
def __init__(self, image_nc, pose_nc, ngf, img_f, layers, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(ADAINEncoder, self).__init__()
self.layers = layers
self.input_layer = nn.Conv2d(image_nc, ngf, kernel_size=7, stride=1, padding=3)
for i in range(layers):
in_channels = min(ngf * (2**i), img_f)
out_channels = min(ngf *(2**(i+1)), img_f)
model = ADAINEncoderBlock(in_channels, out_channels, pose_nc, nonlinearity, use_spect)
setattr(self, 'encoder' + str(i), model)
self.output_nc = out_channels
def forward(self, x, z):
out = self.input_layer(x)
out_list = [out]
for i in range(self.layers):
model = getattr(self, 'encoder' + str(i))
out = model(out, z)
out_list.append(out)
return out_list
class ADAINDecoder(nn.Module):
"""docstring for ADAINDecoder"""
def __init__(self, pose_nc, ngf, img_f, encoder_layers, decoder_layers, skip_connect=True,
nonlinearity=nn.LeakyReLU(), use_spect=False):
super(ADAINDecoder, self).__init__()
self.encoder_layers = encoder_layers
self.decoder_layers = decoder_layers
self.skip_connect = skip_connect
use_transpose = True
for i in range(encoder_layers-decoder_layers, encoder_layers)[::-1]:
in_channels = min(ngf * (2**(i+1)), img_f)
in_channels = in_channels*2 if i != (encoder_layers-1) and self.skip_connect else in_channels
out_channels = min(ngf * (2**i), img_f)
model = ADAINDecoderBlock(in_channels, out_channels, out_channels, pose_nc, use_transpose, nonlinearity, use_spect)
setattr(self, 'decoder' + str(i), model)
self.output_nc = out_channels*2 if self.skip_connect else out_channels
def forward(self, x, z):
out = x.pop() if self.skip_connect else x
for i in range(self.encoder_layers-self.decoder_layers, self.encoder_layers)[::-1]:
model = getattr(self, 'decoder' + str(i))
out = model(out, z)
out = torch.cat([out, x.pop()], 1) if self.skip_connect else out
return out
class ADAINEncoderBlock(nn.Module):
def __init__(self, input_nc, output_nc, feature_nc, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(ADAINEncoderBlock, self).__init__()
kwargs_down = {'kernel_size': 4, 'stride': 2, 'padding': 1}
kwargs_fine = {'kernel_size': 3, 'stride': 1, 'padding': 1}
self.conv_0 = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_down), use_spect)
self.conv_1 = spectral_norm(nn.Conv2d(output_nc, output_nc, **kwargs_fine), use_spect)
self.norm_0 = ADAIN(input_nc, feature_nc)
self.norm_1 = ADAIN(output_nc, feature_nc)
self.actvn = nonlinearity
def forward(self, x, z):
x = self.conv_0(self.actvn(self.norm_0(x, z)))
x = self.conv_1(self.actvn(self.norm_1(x, z)))
return x
class ADAINDecoderBlock(nn.Module):
def __init__(self, input_nc, output_nc, hidden_nc, feature_nc, use_transpose=True, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(ADAINDecoderBlock, self).__init__()
# Attributes
self.actvn = nonlinearity
hidden_nc = min(input_nc, output_nc) if hidden_nc is None else hidden_nc
kwargs_fine = {'kernel_size':3, 'stride':1, 'padding':1}
if use_transpose:
kwargs_up = {'kernel_size':3, 'stride':2, 'padding':1, 'output_padding':1}
else:
kwargs_up = {'kernel_size':3, 'stride':1, 'padding':1}
# create conv layers
self.conv_0 = spectral_norm(nn.Conv2d(input_nc, hidden_nc, **kwargs_fine), use_spect)
if use_transpose:
self.conv_1 = spectral_norm(nn.ConvTranspose2d(hidden_nc, output_nc, **kwargs_up), use_spect)
self.conv_s = spectral_norm(nn.ConvTranspose2d(input_nc, output_nc, **kwargs_up), use_spect)
else:
self.conv_1 = nn.Sequential(spectral_norm(nn.Conv2d(hidden_nc, output_nc, **kwargs_up), use_spect),
nn.Upsample(scale_factor=2))
self.conv_s = nn.Sequential(spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_up), use_spect),
nn.Upsample(scale_factor=2))
# define normalization layers
self.norm_0 = ADAIN(input_nc, feature_nc)
self.norm_1 = ADAIN(hidden_nc, feature_nc)
self.norm_s = ADAIN(input_nc, feature_nc)
def forward(self, x, z):
x_s = self.shortcut(x, z)
dx = self.conv_0(self.actvn(self.norm_0(x, z)))
dx = self.conv_1(self.actvn(self.norm_1(dx, z)))
out = x_s + dx
return out
def shortcut(self, x, z):
x_s = self.conv_s(self.actvn(self.norm_s(x, z)))
return x_s
def spectral_norm(module, use_spect=True):
"""use spectral normal layer to stable the training process"""
if use_spect:
return SpectralNorm(module)
else:
return module
class ADAIN(nn.Module):
def __init__(self, norm_nc, feature_nc):
super().__init__()
self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
nhidden = 128
use_bias=True
self.mlp_shared = nn.Sequential(
nn.Linear(feature_nc, nhidden, bias=use_bias),
nn.ReLU()
)
self.mlp_gamma = nn.Linear(nhidden, norm_nc, bias=use_bias)
self.mlp_beta = nn.Linear(nhidden, norm_nc, bias=use_bias)
def forward(self, x, feature):
# Part 1. generate parameter-free normalized activations
normalized = self.param_free_norm(x)
# Part 2. produce scaling and bias conditioned on feature
feature = feature.view(feature.size(0), -1)
actv = self.mlp_shared(feature)
gamma = self.mlp_gamma(actv)
beta = self.mlp_beta(actv)
# apply scale and bias
gamma = gamma.view(*gamma.size()[:2], 1,1)
beta = beta.view(*beta.size()[:2], 1,1)
out = normalized * (1 + gamma) + beta
return out
class FineEncoder(nn.Module):
"""docstring for Encoder"""
def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(FineEncoder, self).__init__()
self.layers = layers
self.first = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
for i in range(layers):
in_channels = min(ngf*(2**i), img_f)
out_channels = min(ngf*(2**(i+1)), img_f)
model = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
setattr(self, 'down' + str(i), model)
self.output_nc = out_channels
def forward(self, x):
x = self.first(x)
out=[x]
for i in range(self.layers):
model = getattr(self, 'down'+str(i))
x = model(x)
out.append(x)
return out
class FineDecoder(nn.Module):
"""docstring for FineDecoder"""
def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(FineDecoder, self).__init__()
self.layers = layers
for i in range(layers)[::-1]:
in_channels = min(ngf*(2**(i+1)), img_f)
out_channels = min(ngf*(2**i), img_f)
up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
res = FineADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
setattr(self, 'up' + str(i), up)
setattr(self, 'res' + str(i), res)
setattr(self, 'jump' + str(i), jump)
self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'tanh')
self.output_nc = out_channels
def forward(self, x, z):
out = x.pop()
for i in range(self.layers)[::-1]:
res_model = getattr(self, 'res' + str(i))
up_model = getattr(self, 'up' + str(i))
jump_model = getattr(self, 'jump' + str(i))
out = res_model(out, z)
out = up_model(out)
out = jump_model(x.pop()) + out
out_image = self.final(out)
return out_image
class FirstBlock2d(nn.Module):
"""
Downsampling block for use in encoder.
"""
def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(FirstBlock2d, self).__init__()
kwargs = {'kernel_size': 7, 'stride': 1, 'padding': 3}
conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
if type(norm_layer) == type(None):
self.model = nn.Sequential(conv, nonlinearity)
else:
self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
def forward(self, x):
out = self.model(x)
return out
class DownBlock2d(nn.Module):
def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(DownBlock2d, self).__init__()
kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
pool = nn.AvgPool2d(kernel_size=(2, 2))
if type(norm_layer) == type(None):
self.model = nn.Sequential(conv, nonlinearity, pool)
else:
self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity, pool)
def forward(self, x):
out = self.model(x)
return out
class UpBlock2d(nn.Module):
def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(UpBlock2d, self).__init__()
kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
if type(norm_layer) == type(None):
self.model = nn.Sequential(conv, nonlinearity)
else:
self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
def forward(self, x):
out = self.model(F.interpolate(x, scale_factor=2))
return out
class FineADAINResBlocks(nn.Module):
def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(FineADAINResBlocks, self).__init__()
self.num_block = num_block
for i in range(num_block):
model = FineADAINResBlock2d(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
setattr(self, 'res'+str(i), model)
def forward(self, x, z):
for i in range(self.num_block):
model = getattr(self, 'res'+str(i))
x = model(x, z)
return x
class Jump(nn.Module):
def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(Jump, self).__init__()
kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
conv = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
if type(norm_layer) == type(None):
self.model = nn.Sequential(conv, nonlinearity)
else:
self.model = nn.Sequential(conv, norm_layer(input_nc), nonlinearity)
def forward(self, x):
out = self.model(x)
return out
class FineADAINResBlock2d(nn.Module):
"""
Define an Residual block for different types
"""
def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
super(FineADAINResBlock2d, self).__init__()
kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
self.conv1 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
self.conv2 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
self.norm1 = ADAIN(input_nc, feature_nc)
self.norm2 = ADAIN(input_nc, feature_nc)
self.actvn = nonlinearity
def forward(self, x, z):
dx = self.actvn(self.norm1(self.conv1(x), z))
dx = self.norm2(self.conv2(x), z)
out = dx + x
return out
class FinalBlock2d(nn.Module):
"""
Define the output layer
"""
def __init__(self, input_nc, output_nc, use_spect=False, tanh_or_sigmoid='tanh'):
super(FinalBlock2d, self).__init__()
kwargs = {'kernel_size': 7, 'stride': 1, 'padding':3}
conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
if tanh_or_sigmoid == 'sigmoid':
out_nonlinearity = nn.Sigmoid()
else:
out_nonlinearity = nn.Tanh()
self.model = nn.Sequential(conv, out_nonlinearity)
def forward(self, x):
out = self.model(x)
return out |