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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as M
import math
from torch import Tensor
from torch.nn import Parameter
'''https://github.com/orashi/AlacGAN/blob/master/models/standard.py'''
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
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 = torch.dot(u.data, 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 = Parameter(w.data.new(height).normal_(0, 1), requires_grad=False)
v = Parameter(w.data.new(width).normal_(0, 1), requires_grad=False)
u.data = l2normalize(u.data)
v.data = l2normalize(v.data)
w_bar = 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)
class Selayer(nn.Module):
def __init__(self, inplanes):
super(Selayer, self).__init__()
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(2, inplanes // 16, kernel_size=1, stride=1)
self.conv2 = nn.Conv2d(inplanes // 16, 2, kernel_size=1, stride=1)
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.global_avgpool(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.sigmoid(out)
return x * out
class SelayerSpectr(nn.Module):
def __init__(self, inplanes):
super(SelayerSpectr, self).__init__()
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.conv1 = SpectralNorm(nn.Conv2d(2, inplanes // 16, kernel_size=1, stride=1))
self.conv2 = SpectralNorm(nn.Conv2d(inplanes // 16, 2, kernel_size=1, stride=1))
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
out = self.global_avgpool(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.sigmoid(out)
return x * out
class ResNeXtBottleneck(nn.Module):
def __init__(self, in_channels=256, out_channels=256, stride=1, cardinality=32, dilate=1):
super(ResNeXtBottleneck, self).__init__()
D = out_channels // 2
self.out_channels = out_channels
self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)
self.conv_conv = nn.Conv2d(D, D, kernel_size=2 + stride, stride=stride, padding=dilate, dilation=dilate,
groups=cardinality,
bias=False)
self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.shortcut = nn.Sequential()
if stride != 1:
self.shortcut.add_module('shortcut',
nn.AvgPool2d(2, stride=2))
self.selayer = Selayer(out_channels)
def forward(self, x):
bottleneck = self.conv_reduce.forward(x)
bottleneck = F.leaky_relu(bottleneck, 0.2, True)
bottleneck = self.conv_conv.forward(bottleneck)
bottleneck = F.leaky_relu(bottleneck, 0.2, True)
bottleneck = self.conv_expand.forward(bottleneck)
bottleneck = self.selayer(bottleneck)
x = self.shortcut.forward(x)
return x + bottleneck
class SpectrResNeXtBottleneck(nn.Module):
def __init__(self, in_channels=256, out_channels=256, stride=1, cardinality=32, dilate=1):
super(SpectrResNeXtBottleneck, self).__init__()
D = out_channels // 2
self.out_channels = out_channels
self.conv_reduce = SpectralNorm(nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False))
self.conv_conv = SpectralNorm(nn.Conv2d(D, D, kernel_size=2 + stride, stride=stride, padding=dilate, dilation=dilate,
groups=cardinality,
bias=False))
self.conv_expand = SpectralNorm(nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False))
self.shortcut = nn.Sequential()
if stride != 1:
self.shortcut.add_module('shortcut',
nn.AvgPool2d(2, stride=2))
self.selayer = SelayerSpectr(out_channels)
def forward(self, x):
bottleneck = self.conv_reduce.forward(x)
bottleneck = F.leaky_relu(bottleneck, 0.2, True)
bottleneck = self.conv_conv.forward(bottleneck)
bottleneck = F.leaky_relu(bottleneck, 0.2, True)
bottleneck = self.conv_expand.forward(bottleneck)
bottleneck = self.selayer(bottleneck)
x = self.shortcut.forward(x)
return x + bottleneck
class FeatureConv(nn.Module):
def __init__(self, input_dim=512, output_dim=512):
super(FeatureConv, self).__init__()
no_bn = True
seq = []
seq.append(nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=1, padding=1, bias=False))
if not no_bn: seq.append(nn.BatchNorm2d(output_dim))
seq.append(nn.ReLU(inplace=True))
seq.append(nn.Conv2d(output_dim, output_dim, kernel_size=3, stride=2, padding=1, bias=False))
if not no_bn: seq.append(nn.BatchNorm2d(output_dim))
seq.append(nn.ReLU(inplace=True))
seq.append(nn.Conv2d(output_dim, output_dim, kernel_size=3, stride=1, padding=1, bias=False))
seq.append(nn.ReLU(inplace=True))
self.network = nn.Sequential(*seq)
def forward(self, x):
return self.network(x)
class Generator(nn.Module):
def __init__(self, ngf=64, input_channels=9): # Cambia el número de canales de entrada aquí
super(Generator, self).__init__()
self.feature_conv = FeatureConv()
self.to0 = self._make_encoder_block_first(input_channels, 32)
self.to1 = self._make_encoder_block(32, 64)
self.to2 = self._make_encoder_block(64, 128)
self.to3 = self._make_encoder_block(128, 256)
self.to4 = self._make_encoder_block(256, 512)
self.deconv_for_decoder = nn.Sequential(
nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1), # output is 64 * 64
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1), # output is 128 * 128
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(64, 32, 3, stride=2, padding=1, output_padding=1), # output is 256 * 256
nn.LeakyReLU(0.2),
nn.ConvTranspose2d(32, 3, 3, stride=1, padding=1, output_padding=0), # output is 256 * 256
nn.Tanh(),
)
tunnel4 = nn.Sequential(*[ResNeXtBottleneck(ngf * 8, ngf * 8, cardinality=32, dilate=1) for _ in range(20)])
self.tunnel4 = nn.Sequential(nn.Conv2d(ngf * 8 + 512, ngf * 8, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, True),
tunnel4,
nn.Conv2d(ngf * 8, ngf * 4 * 4, kernel_size=3, stride=1, padding=1),
nn.PixelShuffle(2),
nn.LeakyReLU(0.2, True)
) # 64
depth = 2
tunnel = [ResNeXtBottleneck(ngf * 4, ngf * 4, cardinality=32, dilate=1) for _ in range(depth)]
tunnel += [ResNeXtBottleneck(ngf * 4, ngf * 4, cardinality=32, dilate=2) for _ in range(depth)]
tunnel += [ResNeXtBottleneck(ngf * 4, ngf * 4, cardinality=32, dilate=4) for _ in range(depth)]
tunnel += [ResNeXtBottleneck(ngf * 4, ngf * 4, cardinality=32, dilate=2),
ResNeXtBottleneck(ngf * 4, ngf * 4, cardinality=32, dilate=1)]
tunnel3 = nn.Sequential(*tunnel)
self.tunnel3 = nn.Sequential(nn.Conv2d(ngf * 8, ngf * 4, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, True),
tunnel3,
nn.Conv2d(ngf * 4, ngf * 2 * 4, kernel_size=3, stride=1, padding=1),
nn.PixelShuffle(2),
nn.LeakyReLU(0.2, True)
) # 128
tunnel = [ResNeXtBottleneck(ngf * 2, ngf * 2, cardinality=32, dilate=1) for _ in range(depth)]
tunnel += [ResNeXtBottleneck(ngf * 2, ngf * 2, cardinality=32, dilate=2) for _ in range(depth)]
tunnel += [ResNeXtBottleneck(ngf * 2, ngf * 2, cardinality=32, dilate=4) for _ in range(depth)]
tunnel += [ResNeXtBottleneck(ngf * 2, ngf * 2, cardinality=32, dilate=2),
ResNeXtBottleneck(ngf * 2, ngf * 2, cardinality=32, dilate=1)]
tunnel2 = nn.Sequential(*tunnel)
self.tunnel2 = nn.Sequential(nn.Conv2d(ngf * 4, ngf * 2, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, True),
tunnel2,
nn.Conv2d(ngf * 2, ngf * 4, kernel_size=3, stride=1, padding=1),
nn.PixelShuffle(2),
nn.LeakyReLU(0.2, True)
)
tunnel = [ResNeXtBottleneck(ngf, ngf, cardinality=16, dilate=1)]
tunnel += [ResNeXtBottleneck(ngf, ngf, cardinality=16, dilate=2)]
tunnel += [ResNeXtBottleneck(ngf, ngf, cardinality=16, dilate=4)]
tunnel += [ResNeXtBottleneck(ngf, ngf, cardinality=16, dilate=2),
ResNeXtBottleneck(ngf, ngf, cardinality=16, dilate=1)]
tunnel1 = nn.Sequential(*tunnel)
self.tunnel1 = nn.Sequential(nn.Conv2d(ngf * 2, ngf, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, True),
tunnel1,
nn.Conv2d(ngf, ngf * 2, kernel_size=3, stride=1, padding=1),
nn.PixelShuffle(2),
nn.LeakyReLU(0.2, True)
)
self.exit = nn.Conv2d(ngf, 3, kernel_size=3, stride=1, padding=1)
def _make_encoder_block(self, inplanes, planes):
return nn.Sequential(
nn.Conv2d(inplanes, planes, 3, 2, 1),
nn.LeakyReLU(0.2),
nn.Conv2d(planes, planes, 3, 1, 1),
nn.LeakyReLU(0.2),
)
def _make_encoder_block_first(self, inplanes, planes):
return nn.Sequential(
nn.Conv2d(inplanes, planes, 3, 1, 1),
nn.LeakyReLU(0.2),
nn.Conv2d(planes, planes, 3, 1, 1),
nn.LeakyReLU(0.2),
)
def forward(self, sketch, sketch_feat):
x0 = self.to0(sketch)
x1 = self.to1(x0)
x2 = self.to2(x1)
x3 = self.to3(x2)
x4 = self.to4(x3)
sketch_feat = self.feature_conv(sketch_feat)
out = self.tunnel4(torch.cat([x4, sketch_feat], 1))
x = self.tunnel3(torch.cat([out, x3], 1))
x = self.tunnel2(torch.cat([x, x2], 1))
x = self.tunnel1(torch.cat([x, x1], 1))
x = torch.tanh(self.exit(torch.cat([x, x0], 1)))
decoder_output = self.deconv_for_decoder(out)
return x, decoder_output
'''
class Colorizer(nn.Module):
def __init__(self, extractor_path = 'model/model.pth'):
super(Colorizer, self).__init__()
self.generator = Generator()
self.extractor = se_resnext_half(dump_path=extractor_path, num_classes=370, input_channels=1)
def extractor_eval(self):
for param in self.extractor.parameters():
param.requires_grad = False
def extractor_train(self):
for param in extractor.parameters():
param.requires_grad = True
def forward(self, x, extractor_grad = False):
if extractor_grad:
features = self.extractor(x[:, 0:1])
else:
with torch.no_grad():
features = self.extractor(x[:, 0:1]).detach()
fake, guide = self.generator(x, features)
return fake, guide
'''
class Colorizer(nn.Module):
def __init__(self, generator_model, extractor_model):
super(Colorizer, self).__init__()
self.generator = generator_model
self.extractor = extractor_model
def load_generator_weights(self, gen_weights):
self.generator.load_state_dict(gen_weights)
def load_extractor_weights(self, ext_weights):
self.extractor.load_state_dict(ext_weights)
def extractor_eval(self):
for param in self.extractor.parameters():
param.requires_grad = False
self.extractor.eval()
def extractor_train(self):
for param in extractor.parameters():
param.requires_grad = True
self.extractor.train()
def forward(self, x, extractor_grad = False):
if extractor_grad:
features = self.extractor(x[:, 0:1])
else:
with torch.no_grad():
features = self.extractor(x[:, 0:1]).detach()
fake, guide = self.generator(x, features)
return fake, guide
class Discriminator(nn.Module):
def __init__(self, ndf=64):
super(Discriminator, self).__init__()
self.feed = nn.Sequential(SpectralNorm(nn.Conv2d(3, 64, 3, 1, 1)),
nn.LeakyReLU(0.2, True),
SpectralNorm(nn.Conv2d(64, 64, 3, 2, 0)),
nn.LeakyReLU(0.2, True),
SpectrResNeXtBottleneck(ndf, ndf, cardinality=8, dilate=1),
SpectrResNeXtBottleneck(ndf, ndf, cardinality=8, dilate=1, stride=2), # 128
SpectralNorm(nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=False)),
nn.LeakyReLU(0.2, True),
SpectrResNeXtBottleneck(ndf * 2, ndf * 2, cardinality=8, dilate=1),
SpectrResNeXtBottleneck(ndf * 2, ndf * 2, cardinality=8, dilate=1, stride=2), # 64
SpectralNorm(nn.Conv2d(ndf * 2, ndf * 4, kernel_size=1, stride=1, padding=0, bias=False)),
nn.LeakyReLU(0.2, True),
SpectrResNeXtBottleneck(ndf * 4, ndf * 4, cardinality=8, dilate=1),
SpectrResNeXtBottleneck(ndf * 4, ndf * 4, cardinality=8, dilate=1, stride=2), # 32,
SpectralNorm(nn.Conv2d(ndf * 4, ndf * 8, kernel_size=1, stride=1, padding=1, bias=False)),
nn.LeakyReLU(0.2, True),
SpectrResNeXtBottleneck(ndf * 8, ndf * 8, cardinality=8, dilate=1),
SpectrResNeXtBottleneck(ndf * 8, ndf * 8, cardinality=8, dilate=1, stride=2), # 16
SpectrResNeXtBottleneck(ndf * 8, ndf * 8, cardinality=8, dilate=1),
SpectrResNeXtBottleneck(ndf * 8, ndf * 8, cardinality=8, dilate=1),
nn.AdaptiveAvgPool2d((1, 1))
)
self.out = nn.Linear(512, 1)
def forward(self, color):
x = self.feed(color)
out = self.out(x.view(color.size(0), -1))
return out
class Content(nn.Module):
def __init__(self, path):
super(Content, self).__init__()
vgg16 = M.vgg16()
vgg16.load_state_dict(torch.load(path))
vgg16.features = nn.Sequential(
*list(vgg16.features.children())[:9]
)
self.model = vgg16.features
self.register_buffer('mean', torch.FloatTensor([0.485 - 0.5, 0.456 - 0.5, 0.406 - 0.5]).view(1, 3, 1, 1))
self.register_buffer('std', torch.FloatTensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
def forward(self, images):
return self.model((images.mul(0.5) - self.mean) / self.std)
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