folder_name="AE_samples_train"), # DiscriminatorOverfitMonitor(dataset, valid_dataset, 100, args), ModelSaver(output_path, n=1, overwrite=True, print_output=True), ] reconstruction_loss_mode = "pixelwise" if not args.use_dis_l_reconstruction_loss else "dis_l" if frs_model is not None: reconstruction_loss_mode = "frs" train_loop = ALITrainLoop(listeners=listeners, Gz=Gz, Gx=Gx, D=D, optim_G=G_optimizer, optim_D=D_optimizer, dataloader=dataloader, cuda=args.cuda, epochs=args.epochs, morgan_alpha=args.morgan_alpha, d_real_label=args.d_real_label, d_img_noise_std=args.instance_noise_std, decrease_noise=True, use_sigmoid=True, reconstruction_loss_mode=reconstruction_loss_mode, frs_model=frs_model, r1_reg_gamma=args.r1_gamma, non_saturating_G_loss=args.ns_gan, disable_D_limiting=args.no_D_limit) train_loop.train()
Gx = Gx.cuda() D = D.cuda() Gz.init_weights() Gx.init_weights() D.init_weights() listeners = [ LossReporter(), AEImageSampleLogger(output_path, valid_dataset, args, folder_name="AE_samples_valid"), AEImageSampleLogger(output_path, dataset, args, folder_name="AE_samples_train"), ModelSaver(output_path, n=1, overwrite=True, print_output=True), KillSwitchListener(output_path) ] train_loop = ALITrainLoop( listeners=listeners, Gz=Gz, Gx=Gx, D=D, optim_G=G_optimizer, optim_D=D_optimizer, dataloader=dataloader, cuda=args.cuda, epochs=args.epochs, d_img_noise_std=0.1, decrease_noise=True, use_sigmoid=True ) train_loop.train()
valid, output_reproductions=True, discriminator_output=True, cuda=args.cuda, sample_reconstructions=True, every_n_epochs=10, output_latent=True, output_grad_norm=True, ns_gan=args.ns_gan, ) ] trainloop = ALITrainLoop( listeners, Gz, Gx, D, G_optimizer, D_optimizer, dataloader, cuda=args.cuda, epochs=args.epochs, morgan_alpha=args.morgan_alpha, d_img_noise_std=args.instance_noise_std, decrease_noise=True, r1_reg_gamma=args.r1_gamma, non_saturating_G_loss=args.ns_gan, disable_D_limiting=args.no_D_limit ) trainloop.train()
D = D.cuda() Gz.init_weights() Gx.init_weights() D.init_weights() listeners = [ LossReporter(), AEImageSampleLogger(output_path, valid_dataset, args, folder_name="AE_samples_valid", print_stats=True), AEImageSampleLogger(output_path, dataset, args, folder_name="AE_samples_train"), ModelSaver(output_path, n=1, overwrite=True, print_output=True) ] train_loop = ALITrainLoop( listeners=listeners, Gz=Gz, Gx=Gx, D=D, optim_G=G_optimizer, optim_D=D_optimizer, dataloader=dataloader, cuda=args.cuda, epochs=args.epochs, morgan_alpha=args.morgan_alpha, d_img_noise_std=0.0, use_sigmoid=True, reconstruction_loss_mode="pixelwise" if not args.use_dis_l_reconstruction_loss else "dis_l", r1_reg_gamma=args.r1_gamma, ) train_loop.train()
listeners = [ LossReporter(), AEImageSampleLogger(output_path, valid_dataset, args, folder_name="AE_samples_valid", print_stats=True), AEImageSampleLogger(output_path, dataset, args, folder_name="AE_samples_train"), # DiscriminatorOverfitMonitor(dataset, valid_dataset, 100, args), ModelSaver(output_path, n=1, overwrite=True, print_output=True), ] train_loop = ALITrainLoop(listeners=listeners, Gz=Gz, Gx=Gx, D=D, optim_G=G_optimizer, optim_D=D_optimizer, dataloader=dataloader, cuda=args.cuda, epochs=args.epochs, morgan_alpha=args.morgan_alpha, d_real_label=args.d_real_label, d_img_noise_std=args.instance_noise_std, decrease_noise=True, use_sigmoid=True) train_loop.train()