791 lines
35 KiB
Python
791 lines
35 KiB
Python
from model import Generator, AvgBlurGenerator
|
|
from model import Discriminator
|
|
from torch.autograd import Variable
|
|
from torchvision.utils import save_image
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import numpy as np
|
|
import os
|
|
import time
|
|
import datetime
|
|
import attacks
|
|
|
|
from PIL import ImageFilter
|
|
from PIL import Image
|
|
from torchvision import transforms
|
|
|
|
|
|
class Solver(object):
|
|
"""Solver for training and testing StarGAN."""
|
|
|
|
def __init__(self, celeba_loader, rafd_loader, config):
|
|
"""Initialize configurations."""
|
|
|
|
# Data loader.
|
|
self.celeba_loader = celeba_loader
|
|
self.rafd_loader = rafd_loader
|
|
|
|
# Model configurations.
|
|
self.c_dim = config.c_dim
|
|
self.c2_dim = config.c2_dim
|
|
self.image_size = config.image_size
|
|
self.g_conv_dim = config.g_conv_dim
|
|
self.d_conv_dim = config.d_conv_dim
|
|
self.g_repeat_num = config.g_repeat_num
|
|
self.d_repeat_num = config.d_repeat_num
|
|
self.lambda_cls = config.lambda_cls
|
|
self.lambda_rec = config.lambda_rec
|
|
self.lambda_gp = config.lambda_gp
|
|
|
|
# Training configurations.
|
|
self.dataset = config.dataset
|
|
self.batch_size = config.batch_size
|
|
self.num_iters = config.num_iters
|
|
self.num_iters_decay = config.num_iters_decay
|
|
self.g_lr = config.g_lr
|
|
self.d_lr = config.d_lr
|
|
self.n_critic = config.n_critic
|
|
self.beta1 = config.beta1
|
|
self.beta2 = config.beta2
|
|
self.resume_iters = config.resume_iters
|
|
self.selected_attrs = config.selected_attrs
|
|
|
|
# Test configurations.
|
|
self.test_iters = config.test_iters
|
|
|
|
# Miscellaneous.
|
|
self.use_tensorboard = config.use_tensorboard
|
|
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
|
|
|
# Directories.
|
|
self.log_dir = config.log_dir
|
|
self.sample_dir = config.sample_dir
|
|
self.model_save_dir = config.model_save_dir
|
|
self.result_dir = config.result_dir
|
|
|
|
# Step size.
|
|
self.log_step = config.log_step
|
|
self.sample_step = config.sample_step
|
|
self.model_save_step = config.model_save_step
|
|
self.lr_update_step = config.lr_update_step
|
|
|
|
# Build the model and tensorboard.
|
|
self.build_model()
|
|
if self.use_tensorboard:
|
|
self.build_tensorboard()
|
|
|
|
def build_model(self):
|
|
"""Create a generator and a discriminator."""
|
|
if self.dataset in ['CelebA', 'RaFD']:
|
|
# self.G = Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num)
|
|
# self.G = AvgBlurGenerator(self.g_conv_dim, self.c_dim, self.g_repeat_num)
|
|
self.G = Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num)
|
|
self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim, self.d_repeat_num)
|
|
elif self.dataset in ['Both']:
|
|
self.G = Generator(self.g_conv_dim, self.c_dim+self.c2_dim+2, self.g_repeat_num) # 2 for mask vector.
|
|
self.D = Discriminator(self.image_size, self.d_conv_dim, self.c_dim+self.c2_dim, self.d_repeat_num)
|
|
|
|
self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])
|
|
self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr, [self.beta1, self.beta2])
|
|
self.print_network(self.G, 'G')
|
|
self.print_network(self.D, 'D')
|
|
|
|
self.G.to(self.device)
|
|
self.D.to(self.device)
|
|
|
|
def print_network(self, model, name):
|
|
"""Print out the network information."""
|
|
num_params = 0
|
|
for p in model.parameters():
|
|
num_params += p.numel()
|
|
print(model)
|
|
print(name)
|
|
print("The number of parameters: {}".format(num_params))
|
|
|
|
def restore_model(self, resume_iters):
|
|
"""Restore the trained generator and discriminator."""
|
|
print('Loading the trained models from step {}...'.format(resume_iters))
|
|
G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(resume_iters))
|
|
D_path = os.path.join(self.model_save_dir, '{}-D.ckpt'.format(resume_iters))
|
|
|
|
# self.G.load_state_dict(torch.load(G_path, map_location=lambda storage, loc: storage))
|
|
self.load_model_weights(self.G, G_path)
|
|
self.D.load_state_dict(torch.load(D_path, map_location=lambda storage, loc: storage))
|
|
|
|
def load_model_weights(self, model, path):
|
|
pretrained_dict = torch.load(path, map_location=lambda storage, loc: storage)
|
|
model_dict = model.state_dict()
|
|
|
|
# 1. filter out unnecessary keys
|
|
pretrained_dict = {k: v for k, v in pretrained_dict.items() if 'preprocessing' not in k}
|
|
# 2. overwrite entries in the existing state dict
|
|
model_dict.update(pretrained_dict)
|
|
# 3. load the new state dict
|
|
model.load_state_dict(pretrained_dict, strict=False)
|
|
|
|
def build_tensorboard(self):
|
|
"""Build a tensorboard logger."""
|
|
from logger import Logger
|
|
self.logger = Logger(self.log_dir)
|
|
|
|
def update_lr(self, g_lr, d_lr):
|
|
"""Decay learning rates of the generator and discriminator."""
|
|
for param_group in self.g_optimizer.param_groups:
|
|
param_group['lr'] = g_lr
|
|
for param_group in self.d_optimizer.param_groups:
|
|
param_group['lr'] = d_lr
|
|
|
|
def reset_grad(self):
|
|
"""Reset the gradient buffers."""
|
|
self.g_optimizer.zero_grad()
|
|
self.d_optimizer.zero_grad()
|
|
|
|
def denorm(self, x):
|
|
"""Convert the range from [-1, 1] to [0, 1]."""
|
|
out = (x + 1) / 2
|
|
return out.clamp_(0, 1)
|
|
|
|
def gradient_penalty(self, y, x):
|
|
"""Compute gradient penalty: (L2_norm(dy/dx) - 1)**2."""
|
|
weight = torch.ones(y.size()).to(self.device)
|
|
dydx = torch.autograd.grad(outputs=y,
|
|
inputs=x,
|
|
grad_outputs=weight,
|
|
retain_graph=True,
|
|
create_graph=True,
|
|
only_inputs=True)[0]
|
|
|
|
dydx = dydx.view(dydx.size(0), -1)
|
|
dydx_l2norm = torch.sqrt(torch.sum(dydx**2, dim=1))
|
|
return torch.mean((dydx_l2norm-1)**2)
|
|
|
|
def label2onehot(self, labels, dim):
|
|
"""Convert label indices to one-hot vectors."""
|
|
batch_size = labels.size(0)
|
|
out = torch.zeros(batch_size, dim)
|
|
out[np.arange(batch_size), labels.long()] = 1
|
|
return out
|
|
|
|
def create_labels(self, c_org, c_dim=5, dataset='CelebA', selected_attrs=None):
|
|
"""Generate target domain labels for debugging and testing."""
|
|
# Get hair color indices.
|
|
if dataset == 'CelebA':
|
|
hair_color_indices = []
|
|
for i, attr_name in enumerate(selected_attrs):
|
|
if attr_name in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair']:
|
|
hair_color_indices.append(i)
|
|
|
|
c_trg_list = []
|
|
for i in range(c_dim):
|
|
if dataset == 'CelebA':
|
|
c_trg = c_org.clone()
|
|
if i in hair_color_indices: # Set one hair color to 1 and the rest to 0.
|
|
c_trg[:, i] = 1
|
|
for j in hair_color_indices:
|
|
if j != i:
|
|
c_trg[:, j] = 0
|
|
else:
|
|
c_trg[:, i] = (c_trg[:, i] == 0) # Reverse attribute value.
|
|
elif dataset == 'RaFD':
|
|
c_trg = self.label2onehot(torch.ones(c_org.size(0))*i, c_dim)
|
|
|
|
c_trg_list.append(c_trg.to(self.device))
|
|
return c_trg_list
|
|
|
|
def classification_loss(self, logit, target, dataset='CelebA'):
|
|
"""Compute binary or softmax cross entropy loss."""
|
|
if dataset == 'CelebA':
|
|
return F.binary_cross_entropy_with_logits(logit, target, size_average=False) / logit.size(0)
|
|
elif dataset == 'RaFD':
|
|
return F.cross_entropy(logit, target)
|
|
|
|
def train(self):
|
|
"""Train StarGAN within a single dataset."""
|
|
# Set data loader.
|
|
if self.dataset == 'CelebA':
|
|
data_loader = self.celeba_loader
|
|
elif self.dataset == 'RaFD':
|
|
data_loader = self.rafd_loader
|
|
|
|
# Fetch fixed inputs for debugging.
|
|
data_iter = iter(data_loader)
|
|
x_fixed, c_org = next(data_iter)
|
|
x_fixed = x_fixed.to(self.device)
|
|
c_fixed_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
|
|
|
|
# Learning rate cache for decaying.
|
|
g_lr = self.g_lr
|
|
d_lr = self.d_lr
|
|
|
|
# Start training from scratch or resume training.
|
|
start_iters = 0
|
|
if self.resume_iters:
|
|
start_iters = self.resume_iters
|
|
self.restore_model(self.resume_iters)
|
|
|
|
# Start training.
|
|
print('Start training...')
|
|
start_time = time.time()
|
|
for i in range(start_iters, self.num_iters):
|
|
|
|
# =================================================================================== #
|
|
# 1. Preprocess input data #
|
|
# =================================================================================== #
|
|
|
|
# Fetch real images and labels.
|
|
try:
|
|
x_real, label_org = next(data_iter)
|
|
except:
|
|
data_iter = iter(data_loader)
|
|
x_real, label_org = next(data_iter)
|
|
|
|
# Generate target domain labels randomly.
|
|
rand_idx = torch.randperm(label_org.size(0))
|
|
label_trg = label_org[rand_idx]
|
|
|
|
if self.dataset == 'CelebA':
|
|
c_org = label_org.clone()
|
|
c_trg = label_trg.clone()
|
|
elif self.dataset == 'RaFD':
|
|
c_org = self.label2onehot(label_org, self.c_dim)
|
|
c_trg = self.label2onehot(label_trg, self.c_dim)
|
|
|
|
x_real = x_real.to(self.device) # Input images.
|
|
c_org = c_org.to(self.device) # Original domain labels.
|
|
c_trg = c_trg.to(self.device) # Target domain labels.
|
|
label_org = label_org.to(self.device) # Labels for computing classification loss.
|
|
label_trg = label_trg.to(self.device) # Labels for computing classification loss.
|
|
|
|
# =================================================================================== #
|
|
# 2. Train the discriminator #
|
|
# =================================================================================== #
|
|
|
|
# Compute loss with real images.
|
|
out_src, out_cls = self.D(x_real)
|
|
d_loss_real = - torch.mean(out_src)
|
|
d_loss_cls = self.classification_loss(out_cls, label_org, self.dataset)
|
|
|
|
# Compute loss with fake images.
|
|
x_fake = self.G(x_real, c_trg)
|
|
out_src, out_cls = self.D(x_fake.detach())
|
|
d_loss_fake = torch.mean(out_src)
|
|
|
|
# Compute loss for gradient penalty.
|
|
alpha = torch.rand(x_real.size(0), 1, 1, 1).to(self.device)
|
|
x_hat = (alpha * x_real.data + (1 - alpha) * x_fake.data).requires_grad_(True)
|
|
out_src, _ = self.D(x_hat)
|
|
d_loss_gp = self.gradient_penalty(out_src, x_hat)
|
|
|
|
# Backward and optimize.
|
|
d_loss = d_loss_real + d_loss_fake + self.lambda_cls * d_loss_cls + self.lambda_gp * d_loss_gp
|
|
self.reset_grad()
|
|
d_loss.backward()
|
|
self.d_optimizer.step()
|
|
|
|
# Logging.
|
|
loss = {}
|
|
loss['D/loss_real'] = d_loss_real.item()
|
|
loss['D/loss_fake'] = d_loss_fake.item()
|
|
loss['D/loss_cls'] = d_loss_cls.item()
|
|
loss['D/loss_gp'] = d_loss_gp.item()
|
|
|
|
# =================================================================================== #
|
|
# 3. Train the generator #
|
|
# =================================================================================== #
|
|
|
|
if (i+1) % self.n_critic == 0:
|
|
# Original-to-target domain.
|
|
x_fake = self.G(x_real, c_trg)
|
|
out_src, out_cls = self.D(x_fake)
|
|
g_loss_fake = - torch.mean(out_src)
|
|
g_loss_cls = self.classification_loss(out_cls, label_trg, self.dataset)
|
|
|
|
# Target-to-original domain.
|
|
x_reconst = self.G(x_fake, c_org)
|
|
g_loss_rec = torch.mean(torch.abs(x_real - x_reconst))
|
|
|
|
# Backward and optimize.
|
|
g_loss = g_loss_fake + self.lambda_rec * g_loss_rec + self.lambda_cls * g_loss_cls
|
|
self.reset_grad()
|
|
g_loss.backward()
|
|
self.g_optimizer.step()
|
|
|
|
# Logging.
|
|
loss['G/loss_fake'] = g_loss_fake.item()
|
|
loss['G/loss_rec'] = g_loss_rec.item()
|
|
loss['G/loss_cls'] = g_loss_cls.item()
|
|
|
|
# =================================================================================== #
|
|
# 4. Miscellaneous #
|
|
# =================================================================================== #
|
|
|
|
# Print out training information.
|
|
if (i+1) % self.log_step == 0:
|
|
et = time.time() - start_time
|
|
et = str(datetime.timedelta(seconds=et))[:-7]
|
|
log = "Elapsed [{}], Iteration [{}/{}]".format(et, i+1, self.num_iters)
|
|
for tag, value in loss.items():
|
|
log += ", {}: {:.4f}".format(tag, value)
|
|
print(log)
|
|
|
|
if self.use_tensorboard:
|
|
for tag, value in loss.items():
|
|
self.logger.scalar_summary(tag, value, i+1)
|
|
|
|
# Translate fixed images for debugging.
|
|
if (i+1) % self.sample_step == 0:
|
|
with torch.no_grad():
|
|
x_fake_list = [x_fixed]
|
|
for c_fixed in c_fixed_list:
|
|
x_fake_list.append(self.G(x_fixed, c_fixed))
|
|
x_concat = torch.cat(x_fake_list, dim=3)
|
|
sample_path = os.path.join(self.sample_dir, '{}-images.jpg'.format(i+1))
|
|
save_image(self.denorm(x_concat.data.cpu()), sample_path, nrow=1, padding=0)
|
|
print('Saved real and fake images into {}...'.format(sample_path))
|
|
|
|
# Save model checkpoints.
|
|
if (i+1) % self.model_save_step == 0:
|
|
G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(i+1))
|
|
D_path = os.path.join(self.model_save_dir, '{}-D.ckpt'.format(i+1))
|
|
torch.save(self.G.state_dict(), G_path)
|
|
torch.save(self.D.state_dict(), D_path)
|
|
print('Saved model checkpoints into {}...'.format(self.model_save_dir))
|
|
|
|
# Decay learning rates.
|
|
if (i+1) % self.lr_update_step == 0 and (i+1) > (self.num_iters - self.num_iters_decay):
|
|
g_lr -= (self.g_lr / float(self.num_iters_decay))
|
|
d_lr -= (self.d_lr / float(self.num_iters_decay))
|
|
self.update_lr(g_lr, d_lr)
|
|
print ('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(g_lr, d_lr))
|
|
|
|
def train_multi(self):
|
|
"""Train StarGAN with multiple datasets."""
|
|
# Data iterators.
|
|
celeba_iter = iter(self.celeba_loader)
|
|
rafd_iter = iter(self.rafd_loader)
|
|
|
|
# Fetch fixed inputs for debugging.
|
|
x_fixed, c_org = next(celeba_iter)
|
|
x_fixed = x_fixed.to(self.device)
|
|
c_celeba_list = self.create_labels(c_org, self.c_dim, 'CelebA', self.selected_attrs)
|
|
c_rafd_list = self.create_labels(c_org, self.c2_dim, 'RaFD')
|
|
zero_celeba = torch.zeros(x_fixed.size(0), self.c_dim).to(self.device) # Zero vector for CelebA.
|
|
zero_rafd = torch.zeros(x_fixed.size(0), self.c2_dim).to(self.device) # Zero vector for RaFD.
|
|
mask_celeba = self.label2onehot(torch.zeros(x_fixed.size(0)), 2).to(self.device) # Mask vector: [1, 0].
|
|
mask_rafd = self.label2onehot(torch.ones(x_fixed.size(0)), 2).to(self.device) # Mask vector: [0, 1].
|
|
|
|
# Learning rate cache for decaying.
|
|
g_lr = self.g_lr
|
|
d_lr = self.d_lr
|
|
|
|
# Start training from scratch or resume training.
|
|
start_iters = 0
|
|
if self.resume_iters:
|
|
start_iters = self.resume_iters
|
|
self.restore_model(self.resume_iters)
|
|
|
|
# Start training.
|
|
print('Start training...')
|
|
start_time = time.time()
|
|
for i in range(start_iters, self.num_iters):
|
|
for dataset in ['CelebA', 'RaFD']:
|
|
|
|
# =================================================================================== #
|
|
# 1. Preprocess input data #
|
|
# =================================================================================== #
|
|
|
|
# Fetch real images and labels.
|
|
data_iter = celeba_iter if dataset == 'CelebA' else rafd_iter
|
|
|
|
try:
|
|
x_real, label_org = next(data_iter)
|
|
except:
|
|
if dataset == 'CelebA':
|
|
celeba_iter = iter(self.celeba_loader)
|
|
x_real, label_org = next(celeba_iter)
|
|
elif dataset == 'RaFD':
|
|
rafd_iter = iter(self.rafd_loader)
|
|
x_real, label_org = next(rafd_iter)
|
|
|
|
# Generate target domain labels randomly.
|
|
rand_idx = torch.randperm(label_org.size(0))
|
|
label_trg = label_org[rand_idx]
|
|
|
|
if dataset == 'CelebA':
|
|
c_org = label_org.clone()
|
|
c_trg = label_trg.clone()
|
|
zero = torch.zeros(x_real.size(0), self.c2_dim)
|
|
mask = self.label2onehot(torch.zeros(x_real.size(0)), 2)
|
|
c_org = torch.cat([c_org, zero, mask], dim=1)
|
|
c_trg = torch.cat([c_trg, zero, mask], dim=1)
|
|
elif dataset == 'RaFD':
|
|
c_org = self.label2onehot(label_org, self.c2_dim)
|
|
c_trg = self.label2onehot(label_trg, self.c2_dim)
|
|
zero = torch.zeros(x_real.size(0), self.c_dim)
|
|
mask = self.label2onehot(torch.ones(x_real.size(0)), 2)
|
|
c_org = torch.cat([zero, c_org, mask], dim=1)
|
|
c_trg = torch.cat([zero, c_trg, mask], dim=1)
|
|
|
|
x_real = x_real.to(self.device) # Input images.
|
|
c_org = c_org.to(self.device) # Original domain labels.
|
|
c_trg = c_trg.to(self.device) # Target domain labels.
|
|
label_org = label_org.to(self.device) # Labels for computing classification loss.
|
|
label_trg = label_trg.to(self.device) # Labels for computing classification loss.
|
|
|
|
# =================================================================================== #
|
|
# 2. Train the discriminator #
|
|
# =================================================================================== #
|
|
|
|
# Compute loss with real images.
|
|
out_src, out_cls = self.D(x_real)
|
|
out_cls = out_cls[:, :self.c_dim] if dataset == 'CelebA' else out_cls[:, self.c_dim:]
|
|
d_loss_real = - torch.mean(out_src)
|
|
d_loss_cls = self.classification_loss(out_cls, label_org, dataset)
|
|
|
|
# Compute loss with fake images.
|
|
x_fake = self.G(x_real, c_trg)
|
|
out_src, _ = self.D(x_fake.detach())
|
|
d_loss_fake = torch.mean(out_src)
|
|
|
|
# Compute loss for gradient penalty.
|
|
alpha = torch.rand(x_real.size(0), 1, 1, 1).to(self.device)
|
|
x_hat = (alpha * x_real.data + (1 - alpha) * x_fake.data).requires_grad_(True)
|
|
out_src, _ = self.D(x_hat)
|
|
d_loss_gp = self.gradient_penalty(out_src, x_hat)
|
|
|
|
# Backward and optimize.
|
|
d_loss = d_loss_real + d_loss_fake + self.lambda_cls * d_loss_cls + self.lambda_gp * d_loss_gp
|
|
self.reset_grad()
|
|
d_loss.backward()
|
|
self.d_optimizer.step()
|
|
|
|
# Logging.
|
|
loss = {}
|
|
loss['D/loss_real'] = d_loss_real.item()
|
|
loss['D/loss_fake'] = d_loss_fake.item()
|
|
loss['D/loss_cls'] = d_loss_cls.item()
|
|
loss['D/loss_gp'] = d_loss_gp.item()
|
|
|
|
# =================================================================================== #
|
|
# 3. Train the generator #
|
|
# =================================================================================== #
|
|
|
|
if (i+1) % self.n_critic == 0:
|
|
# Original-to-target domain.
|
|
x_fake = self.G(x_real, c_trg)
|
|
out_src, out_cls = self.D(x_fake)
|
|
out_cls = out_cls[:, :self.c_dim] if dataset == 'CelebA' else out_cls[:, self.c_dim:]
|
|
g_loss_fake = - torch.mean(out_src)
|
|
g_loss_cls = self.classification_loss(out_cls, label_trg, dataset)
|
|
|
|
# Target-to-original domain.
|
|
x_reconst = self.G(x_fake, c_org)
|
|
g_loss_rec = torch.mean(torch.abs(x_real - x_reconst))
|
|
|
|
# Backward and optimize.
|
|
g_loss = g_loss_fake + self.lambda_rec * g_loss_rec + self.lambda_cls * g_loss_cls
|
|
self.reset_grad()
|
|
g_loss.backward()
|
|
self.g_optimizer.step()
|
|
|
|
# Logging.
|
|
loss['G/loss_fake'] = g_loss_fake.item()
|
|
loss['G/loss_rec'] = g_loss_rec.item()
|
|
loss['G/loss_cls'] = g_loss_cls.item()
|
|
|
|
# =================================================================================== #
|
|
# 4. Miscellaneous #
|
|
# =================================================================================== #
|
|
|
|
# Print out training info.
|
|
if (i+1) % self.log_step == 0:
|
|
et = time.time() - start_time
|
|
et = str(datetime.timedelta(seconds=et))[:-7]
|
|
log = "Elapsed [{}], Iteration [{}/{}], Dataset [{}]".format(et, i+1, self.num_iters, dataset)
|
|
for tag, value in loss.items():
|
|
log += ", {}: {:.4f}".format(tag, value)
|
|
print(log)
|
|
|
|
if self.use_tensorboard:
|
|
for tag, value in loss.items():
|
|
self.logger.scalar_summary(tag, value, i+1)
|
|
|
|
# Translate fixed images for debugging.
|
|
if (i+1) % self.sample_step == 0:
|
|
with torch.no_grad():
|
|
x_fake_list = [x_fixed]
|
|
for c_fixed in c_celeba_list:
|
|
c_trg = torch.cat([c_fixed, zero_rafd, mask_celeba], dim=1)
|
|
x_fake_list.append(self.G(x_fixed, c_trg))
|
|
for c_fixed in c_rafd_list:
|
|
c_trg = torch.cat([zero_celeba, c_fixed, mask_rafd], dim=1)
|
|
x_fake_list.append(self.G(x_fixed, c_trg))
|
|
x_concat = torch.cat(x_fake_list, dim=3)
|
|
sample_path = os.path.join(self.sample_dir, '{}-images.jpg'.format(i+1))
|
|
save_image(self.denorm(x_concat.data.cpu()), sample_path, nrow=1, padding=0)
|
|
print('Saved real and fake images into {}...'.format(sample_path))
|
|
|
|
# Save model checkpoints.
|
|
if (i+1) % self.model_save_step == 0:
|
|
G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(i+1))
|
|
D_path = os.path.join(self.model_save_dir, '{}-D.ckpt'.format(i+1))
|
|
torch.save(self.G.state_dict(), G_path)
|
|
torch.save(self.D.state_dict(), D_path)
|
|
print('Saved model checkpoints into {}...'.format(self.model_save_dir))
|
|
|
|
# Decay learning rates.
|
|
if (i+1) % self.lr_update_step == 0 and (i+1) > (self.num_iters - self.num_iters_decay):
|
|
g_lr -= (self.g_lr / float(self.num_iters_decay))
|
|
d_lr -= (self.d_lr / float(self.num_iters_decay))
|
|
self.update_lr(g_lr, d_lr)
|
|
print ('Decayed learning rates, g_lr: {}, d_lr: {}.'.format(g_lr, d_lr))
|
|
|
|
def test(self):
|
|
"""Translate images using StarGAN trained on a single dataset."""
|
|
# Load the trained generator.
|
|
self.restore_model(self.test_iters)
|
|
|
|
# Set data loader.
|
|
if self.dataset == 'CelebA':
|
|
data_loader = self.celeba_loader
|
|
elif self.dataset == 'RaFD':
|
|
data_loader = self.rafd_loader
|
|
|
|
with torch.no_grad():
|
|
for i, (x_real, c_org) in enumerate(data_loader):
|
|
|
|
# Prepare input images and target domain labels.
|
|
x_real = x_real.to(self.device)
|
|
c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
|
|
|
|
# Translate images.
|
|
x_fake_list = [x_real]
|
|
for c_trg in c_trg_list:
|
|
x_fake_list.append(self.G(x_real, c_trg))
|
|
|
|
# Save the translated images.
|
|
x_concat = torch.cat(x_fake_list, dim=3)
|
|
result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
|
|
save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
|
|
print('Saved real and fake images into {}...'.format(result_path))
|
|
|
|
def test_attack(self):
|
|
"""Translate images using StarGAN trained on a single dataset."""
|
|
|
|
layer_dict = {0: 2, 1: 5, 2: 8, 3: 9, 4: 10, 5: 11, 6: 12, 7: 13, 8: 14, 9: 17, 10: 20, 11: None}
|
|
torch.manual_seed(0)
|
|
|
|
# for layer_num_orig in range(12):
|
|
# Load the trained generator.
|
|
self.restore_model(self.test_iters)
|
|
|
|
# Set data loader.
|
|
if self.dataset == 'CelebA':
|
|
data_loader = self.celeba_loader
|
|
elif self.dataset == 'RaFD':
|
|
data_loader = self.rafd_loader
|
|
|
|
# Initialize Metrics
|
|
l1_error = 0.0
|
|
l2_error = 0.0
|
|
min_dist = 0.0
|
|
l0_error = 0.0
|
|
perceptual_error = 0.0
|
|
n_samples = 0
|
|
|
|
for i, (x_real, c_org) in enumerate(data_loader):
|
|
# Black image
|
|
black = np.zeros((1,3,256,256))
|
|
black = torch.FloatTensor(black).to(self.device)
|
|
|
|
# black = torch.FloatTensor(torch.rand((1,3,256,256))).to(self.device)
|
|
|
|
# Prepare input images and target domain labels.
|
|
x_real = x_real.to(self.device)
|
|
c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
|
|
|
|
pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=None)
|
|
|
|
# Translate images.
|
|
x_fake_list = [x_real]
|
|
|
|
# x_advs = []
|
|
if i == 0:
|
|
x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg_list[0])
|
|
# for idx, c_trg in enumerate(c_trg_list):
|
|
# x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
|
|
# x_advs.append((x_adv, perturb))
|
|
# break
|
|
|
|
for idx, c_trg in enumerate(c_trg_list):
|
|
with torch.no_grad():
|
|
gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
|
|
# Attack
|
|
# x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
|
|
# _, perturb = x_advs[idx]
|
|
# x_adv = x_real + perturb
|
|
# x_adv = self.blur_tensor(x_adv)
|
|
|
|
# Metrics
|
|
with torch.no_grad():
|
|
# gen, preproc_x = self.G(x_adv, c_trg)
|
|
gen, gen_feats = self.G(x_adv, c_trg)
|
|
|
|
# Add to lists
|
|
# x_fake_list.append(preproc_x)
|
|
x_fake_list.append(x_adv)
|
|
x_fake_list.append(gen)
|
|
|
|
# No Attack
|
|
# gen_noattack, _ = self.G(x_real, c_trg)
|
|
# gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
|
|
|
|
l1_error += F.l1_loss(gen, gen_noattack)
|
|
l2_error += F.mse_loss(gen, gen_noattack)
|
|
l0_error += (gen - gen_noattack).norm(0)
|
|
min_dist += (gen - gen_noattack).norm(float('-inf'))
|
|
n_samples += 1
|
|
break
|
|
|
|
# Save the translated images.
|
|
x_concat = torch.cat(x_fake_list, dim=3)
|
|
result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
|
|
save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
|
|
# print('Saved real and fake images into {}...'.format(result_path))
|
|
# if i == 3:
|
|
# break
|
|
if i == 199:
|
|
break
|
|
|
|
# Print metrics
|
|
print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples,
|
|
l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
|
|
|
|
def test_attack_feats(self):
|
|
"""Translate images using StarGAN trained on a single dataset."""
|
|
|
|
layer_dict = {0: 2, 1: 5, 2: 8, 3: 9, 4: 10, 5: 11, 6: 12, 7: 13, 8: 14, 9: 17, 10: 20, 11: None}
|
|
|
|
for layer_num_orig in range(12):
|
|
# Load the trained generator.
|
|
self.restore_model(self.test_iters)
|
|
|
|
# Set data loader.
|
|
if self.dataset == 'CelebA':
|
|
data_loader = self.celeba_loader
|
|
elif self.dataset == 'RaFD':
|
|
data_loader = self.rafd_loader
|
|
|
|
# Initialize Metrics
|
|
l1_error = 0.0
|
|
l2_error = 0.0
|
|
min_dist = 0.0
|
|
l0_error = 0.0
|
|
perceptual_error = 0.0
|
|
n_samples = 0
|
|
|
|
# 11 layers + output
|
|
# layer_num_orig = 11
|
|
|
|
print('Layer', layer_num_orig)
|
|
for i, (x_real, c_org) in enumerate(data_loader):
|
|
# Black image
|
|
black = np.zeros((1,3,256,256))
|
|
black = torch.FloatTensor(black).to(self.device)
|
|
|
|
# Prepare input images and target domain labels.
|
|
x_real = x_real.to(self.device)
|
|
c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
|
|
|
|
layer_num = layer_dict[layer_num_orig]
|
|
pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)
|
|
|
|
# Translate images.
|
|
x_fake_list = [x_real]
|
|
|
|
# if i == 0:
|
|
# x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
|
|
|
|
for c_trg in c_trg_list:
|
|
# Attack
|
|
x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
|
|
# x_adv = x_real + perturb
|
|
# x_adv = self.blur_tensor(x_adv)
|
|
|
|
# Metrics
|
|
with torch.no_grad():
|
|
# gen, preproc_x = self.G(x_adv, c_trg)
|
|
gen, gen_feats = self.G(x_adv, c_trg)
|
|
|
|
# Add to lists
|
|
# x_fake_list.append(preproc_x)
|
|
x_fake_list.append(x_adv)
|
|
x_fake_list.append(gen)
|
|
|
|
# No Attack
|
|
# gen_noattack, _ = self.G(x_real, c_trg)
|
|
gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
|
|
|
|
l1_error += F.l1_loss(gen, gen_noattack)
|
|
l2_error += F.mse_loss(gen, gen_noattack)
|
|
l0_error += (gen - gen_noattack).norm(0)
|
|
min_dist += (gen - gen_noattack).norm(float('-inf'))
|
|
n_samples += 1
|
|
|
|
# Save the translated images.
|
|
x_concat = torch.cat(x_fake_list, dim=3)
|
|
# result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
|
|
result_path = os.path.join(self.result_dir, '{}-{}-images.jpg'.format(layer_num_orig, i+1))
|
|
save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
|
|
# print('Saved real and fake images into {}...'.format(result_path))
|
|
if i == 3:
|
|
break
|
|
# if i == 199:
|
|
# break
|
|
|
|
# Print metrics
|
|
print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples,
|
|
l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
|
|
|
|
def test_multi(self):
|
|
"""Translate images using StarGAN trained on multiple datasets."""
|
|
# Load the trained generator.
|
|
self.restore_model(self.test_iters)
|
|
|
|
with torch.no_grad():
|
|
for i, (x_real, c_org) in enumerate(self.celeba_loader):
|
|
|
|
# Prepare input images and target domain labels.
|
|
x_real = x_real.to(self.device)
|
|
c_celeba_list = self.create_labels(c_org, self.c_dim, 'CelebA', self.selected_attrs)
|
|
c_rafd_list = self.create_labels(c_org, self.c2_dim, 'RaFD')
|
|
zero_celeba = torch.zeros(x_real.size(0), self.c_dim).to(self.device) # Zero vector for CelebA.
|
|
zero_rafd = torch.zeros(x_real.size(0), self.c2_dim).to(self.device) # Zero vector for RaFD.
|
|
mask_celeba = self.label2onehot(torch.zeros(x_real.size(0)), 2).to(self.device) # Mask vector: [1, 0].
|
|
mask_rafd = self.label2onehot(torch.ones(x_real.size(0)), 2).to(self.device) # Mask vector: [0, 1].
|
|
|
|
# Translate images.
|
|
x_fake_list = [x_real]
|
|
for c_celeba in c_celeba_list:
|
|
c_trg = torch.cat([c_celeba, zero_rafd, mask_celeba], dim=1)
|
|
x_fake_list.append(self.G(x_real, c_trg))
|
|
for c_rafd in c_rafd_list:
|
|
c_trg = torch.cat([zero_celeba, c_rafd, mask_rafd], dim=1)
|
|
x_fake_list.append(self.G(x_real, c_trg))
|
|
|
|
# Save the translated images.
|
|
x_concat = torch.cat(x_fake_list, dim=3)
|
|
result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
|
|
save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
|
|
print('Saved real and fake images into {}...'.format(result_path))
|
|
|
|
def blur_tensor(self, tensor):
|
|
# PIL to numpy
|
|
img = self.denorm(tensor[0].data.cpu())
|
|
img = transforms.ToPILImage()(img)
|
|
img = img.filter(ImageFilter.GaussianBlur(radius=1.3))
|
|
# img = img.filter(ImageFilter.BoxBlur(radius=1))
|
|
img = transforms.ToTensor()(img)
|
|
img = transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))(img)
|
|
img = torch.unsqueeze(img, 0).to(self.device)
|
|
return img |