# transform = [transforms.ColorJitter(0.5, 0.5, 0.5, 0), # transforms.RandomAffine(180), # transforms.RandomErasing(p=1, value=1)] # train_dataset = ImagenetDataAugDataset(root_dir=args.train_dir, num_wt=3, mask_dim=args.mask_dim, wt=wt, # filters=filters_cpu, default_transform=default_transform, # transform=transform, p=0.1) # Create train dataset dataset = SampleDataset(file_path=args.sample_file) data_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) # Model and optimizer model = UNet_NTail_128_Mod(n_channels=12, n_classes=3, n_tails=12, bilinear=True).to(args.device) # Load weights if args.resume: print('Loading weights') model = load_weights(model, args.checkpoint_path, args) eval_biggan_unet128(model, data_loader, args)
transform=default_transform) valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) # Load 128 model print('Loading model 128 weights') model_128 = UNet_NTail_128_Mod(n_channels=12, n_classes=3, n_tails=12, bilinear=True).to(args.device) model_128 = load_weights(model_128, args.model_128_weights, args) # Model and optimizer model = UNet_NTail_128_Mod1(n_channels=48, n_classes=3, n_tails=48, bilinear=True).to(args.device) optimizer = optim.Adam(model.parameters(), lr=args.lr) state_dict = {'itr': 0} if args.resume: print('Loading weights & resuming from iteration {}'.format( args.checkpoint)) model, optimizer, logger = load_checkpoint(model, optimizer, '256', args)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) # Create validation dataset valid_dataset = dset.ImageFolder(root=args.valid_dir, transform=default_transform) valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) # Model and optimizer model = UNet_NTail_128_Mod(n_channels=48, n_classes=3, n_tails=48, bilinear=True).to(args.device) # Load weights print('Loading weights') model = load_weights(model, args.model_256_weights, args) eval_unet256(model, train_loader, 'train', args) eval_unet256(model, valid_loader, 'valid', args)
pin_memory=True, drop_last=True) # Create validation dataset valid_dataset = dset.ImageFolder(root=args.valid_dir, transform=default_transform) valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers, pin_memory=True, drop_last=True) # Model print('Loading UNet 128 and 256 weights') model_128 = UNet_NTail_128_Mod(n_channels=12, n_classes=3, n_tails=12, bilinear=True).to(args.device) model_128 = load_weights(model_128, args.model_128_weights, args) model_256 = UNet_NTail_128_Mod(n_channels=48, n_classes=3, n_tails=48, bilinear=True).to(args.device) model_256 = load_weights(model_256, args.model_256_weights, args) eval_unet_128_256(model_128, model_256, train_loader, 'train', args) eval_unet_128_256(model_128, model_256, valid_loader, 'valid', args)