elif opts.trainer == 'UNIT': trainer = UNIT_Trainer(config) else: sys.exit("Only support MUNIT|UNIT") try: state_dict = torch.load(opts.checkpoint) trainer.gen_a.load_state_dict(state_dict['a']) trainer.gen_b.load_state_dict(state_dict['b']) except: state_dict = pytorch03_to_pytorch04(torch.load(opts.checkpoint), opts.trainer) trainer.gen_a.load_state_dict(state_dict['a']) trainer.gen_b.load_state_dict(state_dict['b']) trainer.cuda() trainer.eval() encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function style_encode = trainer.gen_b.encode if opts.a2b else trainer.gen_a.encode # encode function decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function if 'new_size' in config: new_size = config['new_size'] else: if opts.a2b == 1: new_size = config['new_size_a'] else: new_size = config['new_size_b'] with torch.no_grad(): transform = transforms.Compose([
data_loader = get_data_loader_folder(opts.input_folder, 1, False, new_size=config['new_size_a'], crop=False) config['vgg_model_path'] = opts.output_path if opts.trainer == 'MUNIT': style_dim = config['gen']['style_dim'] trainer = MUNIT_Trainer(config) elif opts.trainer == 'UNIT': trainer = UNIT_Trainer(config) else: sys.exit("Only support MUNIT|UNIT") state_dict = torch.load(opts.checkpoint) trainer.gen_a.load_state_dict(state_dict['a']) trainer.gen_b.load_state_dict(state_dict['b']) trainer.cuda() trainer.eval() encode = trainer.gen_a.encode if opts.a2b else trainer.gen_b.encode # encode function decode = trainer.gen_b.decode if opts.a2b else trainer.gen_a.decode # decode function if opts.trainer == 'MUNIT': # Start testing style_fixed = Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True) for i, (images, names) in enumerate(zip(data_loader,image_names)): print(names[1]) images = Variable(images.cuda(), volatile=True) content, _ = encode(images) style = style_fixed if opts.synchronized else Variable(torch.randn(opts.num_style, style_dim, 1, 1).cuda(), volatile=True) for j in range(opts.num_style): s = style[j].unsqueeze(0) outputs = decode(content, s)
def main(argv): (opts, args) = parser.parse_args(argv) cudnn.benchmark = True # Load experiment setting config = get_config(opts.config) max_iter = config['max_iter'] # Setup logger and output folders output_subfolders = prepare_logging_folders(config['output_root'], config['experiment_name']) logger = create_logger( os.path.join(output_subfolders['logs'], 'train_log.log')) shutil.copy(opts.config, os.path.join( output_subfolders['logs'], 'config.yaml')) # copy config file to output folder tb_logger = tensorboard_logger.Logger(output_subfolders['logs']) logger.info('============ Initialized logger ============') logger.info('Config File: {}'.format(opts.config)) # Setup model and data loader trainer = MUNIT_Trainer(config, opts) trainer.cuda() loaders = get_all_data_loaders(config) val_display_images = next(iter(loaders['val'])) logger.info('Test images: {}'.format(val_display_images['A_paths'])) # Start training iterations = trainer.resume(opts.model_path, hyperparameters=config) if opts.resume else 0 while True: for it, images in enumerate(loaders['train']): trainer.update_learning_rate() images_a = images['A'] images_b = images['B'] images_a, images_b = Variable(images_a.cuda()), Variable( images_b.cuda()) # Main training code trainer.dis_update(images_a, images_b, config) trainer.gen_update(images_a, images_b, config) # Dump training stats in log file if (iterations + 1) % config['log_iter'] == 0: for tag, value in trainer.loss.items(): tb_logger.scalar_summary(tag, value, iterations) val_output_imgs = trainer.sample( Variable(val_display_images['A'].cuda()), Variable(val_display_images['B'].cuda())) tb_imgs = [] for imgs in val_output_imgs.values(): tb_imgs.append(torch.cat(torch.unbind(imgs, 0), dim=2)) tb_logger.image_summary(list(val_output_imgs.keys()), tb_imgs, iterations) if (iterations + 1) % config['print_iter'] == 0: logger.info( "Iteration: {:08}/{:08} Discriminator Loss: {:.4f} Generator Loss: {:.4f}" .format(iterations + 1, max_iter, trainer.loss['D/total'], trainer.loss['G/total'])) # Write images # if (iterations + 1) % config['image_save_iter'] == 0: # val_output_imgs = trainer.sample( # Variable(val_display_images['A'].cuda()), # Variable(val_display_images['B'].cuda())) # # for key, imgs in val_output_imgs.items(): # key = key.replace('/', '_') # write_images(imgs, config['display_size'], '{}/{}_{:08}.jpg'.format(output_subfolders['images'], key, iterations+1)) # # logger.info('Saved images to: {}'.format(output_subfolders['images'])) # Save network weights if (iterations + 1) % config['snapshot_save_iter'] == 0: trainer.save(output_subfolders['models'], iterations) iterations += 1 if iterations >= max_iter: return