phase="val", valSize=4) print("Train Dataset Path :", trainData_path) print("Val Dataset Path :", valData_path) # make model pix2pixHD = Pix2PixHDModel(opt) # dataloader train_dataloader = DataLoader( dataset=train_dataset, batch_size=opt.batch_size, shuffle=True, num_workers=opt.nThreads, ) val_dataloader = DataLoader( dataset=val_dataset, batch_size=len(val_dataset), shuffle=False, num_workers=opt.nThreads, ) # updater updater = Updater(dataloader=train_dataloader, model=pix2pixHD) # trainer trainer = Trainer(updater, opt, val_dataloader=val_dataloader) # run log = trainer.run()
# The device (GPU/CPU) on which to execute the code device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # The model to train model = Glow(context_blocks=4, flow_steps=8, input_channels=3, hidden_channels=256, quantization=256, lu_decomposition=False) model.to(device) # Path to the directory where the results will be saved saving_directory = \ os.path.join(os.getcwd(), 'results', datetime.now().strftime("%Y-%m-%d_%H-%M-%S")) trainer = Trainer(model=model, data_path=data_dir, batch_size=4, learning_rate=0.0001, saving_directory=saving_directory, device=device, weight_norm=True, data_augmentation=0.1) # Start the learning trainer.run()