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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import models.networks as networks
import util.util as util
class Pix2PixModel(torch.nn.Module):
@staticmethod
def modify_commandline_options(parser, is_train):
networks.modify_commandline_options(parser, is_train)
return parser
def __init__(self, opt):
super().__init__()
self.opt = opt
self.FloatTensor = torch.cuda.FloatTensor if self.use_gpu() else torch.FloatTensor
self.ByteTensor = torch.cuda.ByteTensor if self.use_gpu() else torch.ByteTensor
self.netG, self.netD, self.netE = self.initialize_networks(opt)
# set loss functions
if opt.isTrain:
self.criterionGAN = networks.GANLoss(opt.gan_mode, tensor=self.FloatTensor, opt=self.opt)
self.criterionFeat = torch.nn.L1Loss()
if not opt.no_vgg_loss:
self.criterionVGG = networks.VGGLoss(self.opt.gpu_ids)
if opt.use_vae:
self.KLDLoss = networks.KLDLoss()
# Entry point for all calls involving forward pass
# of deep networks. We used this approach since DataParallel module
# can't parallelize custom functions, we branch to different
# routines based on |mode|.
def forward(self, data, mode):
input_semantics, real_image, degraded_image = self.preprocess_input(data)
if mode == "generator":
g_loss, generated = self.compute_generator_loss(input_semantics, degraded_image, real_image)
return g_loss, generated
elif mode == "discriminator":
d_loss = self.compute_discriminator_loss(input_semantics, degraded_image, real_image)
return d_loss
elif mode == "encode_only":
z, mu, logvar = self.encode_z(real_image)
return mu, logvar
elif mode == "inference":
with torch.no_grad():
fake_image, _ = self.generate_fake(input_semantics, degraded_image, real_image)
return fake_image
else:
raise ValueError("|mode| is invalid")
def create_optimizers(self, opt):
G_params = list(self.netG.parameters())
if opt.use_vae:
G_params += list(self.netE.parameters())
if opt.isTrain:
D_params = list(self.netD.parameters())
beta1, beta2 = opt.beta1, opt.beta2
if opt.no_TTUR:
G_lr, D_lr = opt.lr, opt.lr
else:
G_lr, D_lr = opt.lr / 2, opt.lr * 2
optimizer_G = torch.optim.Adam(G_params, lr=G_lr, betas=(beta1, beta2))
optimizer_D = torch.optim.Adam(D_params, lr=D_lr, betas=(beta1, beta2))
return optimizer_G, optimizer_D
def save(self, epoch):
util.save_network(self.netG, "G", epoch, self.opt)
util.save_network(self.netD, "D", epoch, self.opt)
if self.opt.use_vae:
util.save_network(self.netE, "E", epoch, self.opt)
############################################################################
# Private helper methods
############################################################################
def initialize_networks(self, opt):
netG = networks.define_G(opt)
netD = networks.define_D(opt) if opt.isTrain else None
netE = networks.define_E(opt) if opt.use_vae else None
if not opt.isTrain or opt.continue_train:
netG = util.load_network(netG, "G", opt.which_epoch, opt)
if opt.isTrain:
netD = util.load_network(netD, "D", opt.which_epoch, opt)
if opt.use_vae:
netE = util.load_network(netE, "E", opt.which_epoch, opt)
return netG, netD, netE
# preprocess the input, such as moving the tensors to GPUs and
# transforming the label map to one-hot encoding
# |data|: dictionary of the input data
def preprocess_input(self, data):
# move to GPU and change data types
# data['label'] = data['label'].long()
if not self.opt.isTrain:
if self.use_gpu():
data["label"] = data["label"].cuda()
data["image"] = data["image"].cuda()
return data["label"], data["image"], data["image"]
## While testing, the input image is the degraded face
if self.use_gpu():
data["label"] = data["label"].cuda()
data["degraded_image"] = data["degraded_image"].cuda()
data["image"] = data["image"].cuda()
# # create one-hot label map
# label_map = data['label']
# bs, _, h, w = label_map.size()
# nc = self.opt.label_nc + 1 if self.opt.contain_dontcare_label \
# else self.opt.label_nc
# input_label = self.FloatTensor(bs, nc, h, w).zero_()
# input_semantics = input_label.scatter_(1, label_map, 1.0)
return data["label"], data["image"], data["degraded_image"]
def compute_generator_loss(self, input_semantics, degraded_image, real_image):
G_losses = {}
fake_image, KLD_loss = self.generate_fake(
input_semantics, degraded_image, real_image, compute_kld_loss=self.opt.use_vae
)
if self.opt.use_vae:
G_losses["KLD"] = KLD_loss
pred_fake, pred_real = self.discriminate(input_semantics, fake_image, real_image)
G_losses["GAN"] = self.criterionGAN(pred_fake, True, for_discriminator=False)
if not self.opt.no_ganFeat_loss:
num_D = len(pred_fake)
GAN_Feat_loss = self.FloatTensor(1).fill_(0)
for i in range(num_D): # for each discriminator
# last output is the final prediction, so we exclude it
num_intermediate_outputs = len(pred_fake[i]) - 1
for j in range(num_intermediate_outputs): # for each layer output
unweighted_loss = self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach())
GAN_Feat_loss += unweighted_loss * self.opt.lambda_feat / num_D
G_losses["GAN_Feat"] = GAN_Feat_loss
if not self.opt.no_vgg_loss:
G_losses["VGG"] = self.criterionVGG(fake_image, real_image) * self.opt.lambda_vgg
return G_losses, fake_image
def compute_discriminator_loss(self, input_semantics, degraded_image, real_image):
D_losses = {}
with torch.no_grad():
fake_image, _ = self.generate_fake(input_semantics, degraded_image, real_image)
fake_image = fake_image.detach()
fake_image.requires_grad_()
pred_fake, pred_real = self.discriminate(input_semantics, fake_image, real_image)
D_losses["D_Fake"] = self.criterionGAN(pred_fake, False, for_discriminator=True)
D_losses["D_real"] = self.criterionGAN(pred_real, True, for_discriminator=True)
return D_losses
def encode_z(self, real_image):
mu, logvar = self.netE(real_image)
z = self.reparameterize(mu, logvar)
return z, mu, logvar
def generate_fake(self, input_semantics, degraded_image, real_image, compute_kld_loss=False):
z = None
KLD_loss = None
if self.opt.use_vae:
z, mu, logvar = self.encode_z(real_image)
if compute_kld_loss:
KLD_loss = self.KLDLoss(mu, logvar) * self.opt.lambda_kld
fake_image = self.netG(input_semantics, degraded_image, z=z)
assert (
not compute_kld_loss
) or self.opt.use_vae, "You cannot compute KLD loss if opt.use_vae == False"
return fake_image, KLD_loss
# Given fake and real image, return the prediction of discriminator
# for each fake and real image.
def discriminate(self, input_semantics, fake_image, real_image):
if self.opt.no_parsing_map:
fake_concat = fake_image
real_concat = real_image
else:
fake_concat = torch.cat([input_semantics, fake_image], dim=1)
real_concat = torch.cat([input_semantics, real_image], dim=1)
# In Batch Normalization, the fake and real images are
# recommended to be in the same batch to avoid disparate
# statistics in fake and real images.
# So both fake and real images are fed to D all at once.
fake_and_real = torch.cat([fake_concat, real_concat], dim=0)
discriminator_out = self.netD(fake_and_real)
pred_fake, pred_real = self.divide_pred(discriminator_out)
return pred_fake, pred_real
# Take the prediction of fake and real images from the combined batch
def divide_pred(self, pred):
# the prediction contains the intermediate outputs of multiscale GAN,
# so it's usually a list
if type(pred) == list:
fake = []
real = []
for p in pred:
fake.append([tensor[: tensor.size(0) // 2] for tensor in p])
real.append([tensor[tensor.size(0) // 2 :] for tensor in p])
else:
fake = pred[: pred.size(0) // 2]
real = pred[pred.size(0) // 2 :]
return fake, real
def get_edges(self, t):
edge = self.ByteTensor(t.size()).zero_()
edge[:, :, :, 1:] = edge[:, :, :, 1:] | (t[:, :, :, 1:] != t[:, :, :, :-1])
edge[:, :, :, :-1] = edge[:, :, :, :-1] | (t[:, :, :, 1:] != t[:, :, :, :-1])
edge[:, :, 1:, :] = edge[:, :, 1:, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
edge[:, :, :-1, :] = edge[:, :, :-1, :] | (t[:, :, 1:, :] != t[:, :, :-1, :])
return edge.float()
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps.mul(std) + mu
def use_gpu(self):
return len(self.opt.gpu_ids) > 0
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