from torchvision.utils import save_image import torch import torch.nn.functional as F import numpy as np import os import time import datetime from .model import Generator from .model import Discriminator class Solver(object): """Solver for training and testing StarGAN.""" def __init__(self, celeba_loader, rafd_loader, config, should_script=False): """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(config.device) # 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(should_script) if self.use_tensorboard: self.build_tensorboard() def build_model(self, should_script): """Create a generator and a discriminator.""" if should_script: maybe_script = torch.jit.script else: maybe_script = lambda x: x if self.dataset in ['CelebA', 'RaFD']: self.G = maybe_script(Generator(self.g_conv_dim, self.c_dim, self.g_repeat_num)) self.D = maybe_script(Discriminator(self.image_size, self.d_conv_dim, self.c_dim, self.d_repeat_num)) elif self.dataset in ['Both']: self.G = maybe_script(Generator(self.g_conv_dim, self.c_dim+self.c2_dim+2, self.g_repeat_num)) # 2 for mask vector. self.D = maybe_script(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.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.D.load_state_dict(torch.load(D_path, map_location=lambda storage, loc: storage)) 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, debug=''): """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) # Save the last value to reference.out if i == self.num_iters - 1 and debug: to_be_saved = d_loss_cls - d_loss_fake torch.save(to_be_saved, debug) # 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 and debug: 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 and debug: 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 get_test_inputs(self): data_loader = self.dataset == 'CelebA' and self.celeba_loader or self.rafd_loader for x_real, c_org in data_loader: x_real = x_real.to(self.device) c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs) yield x_real, c_trg_list def test(self, restore=True, debug=''): """Translate images using StarGAN trained on a single dataset.""" # Load the trained generator. if restore: 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. if debug: 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_multi(self, restore=True, debug=''): """Translate images using StarGAN trained on multiple datasets.""" # Load the trained generator. if restore: 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. if debug: 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))