batch_size=2, shuffle=True, pin_memory=True) valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=2, shuffle=True, pin_memory=True) data_root = r'D:\liver2\liver2' lr = 3e-5 x_train_dir = os.path.join(data_root, 'train-150') y_train_dir = os.path.join(data_root, 'train/masks') x_test_dir = os.path.join(data_root, 'test/imgs') y_test_dir = os.path.join(data_root, 'test/masks') loss_unet = loss.DiceLoss(weight=0.2, activation='softmax2d', ignore_channels=[0]) + loss.FocalLoss() optimizer_unet = torch.optim.Adam(unet.parameters(), lr=lr) metrics = [ metrics.SMPIoU(threshold=0.5, ignore_channels=[0], activation='softmax2d'), metrics.Fscore(threshold=0.5, ignore_channels=[0], activation='softmax2d'), ] train_epoch = run.TrainEpoch( model=unet, loss=loss_unet, metrics=metrics, optimizer=optimizer_unet, device=DEVICE, verbose=True, )
r"D:\liver2\liver2\test\imgs", r"D:\liver2\liver2\test\masks", augmentation=pet_augmentation_valid(), preprocessing=get_preprocessing(preproc_fn), classes=['tissue', 'pancreas'], maxsize=99999 ) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=2, shuffle=True) valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=2, shuffle=True) data_root = r'D:\liver2\liver2' lr = 3e-5 x_train_dir = os.path.join(data_root, 'train-150') y_train_dir = os.path.join(data_root, 'train/masks') x_test_dir = os.path.join(data_root, 'test/imgs') y_test_dir = os.path.join(data_root, 'test/masks') loss_unet = loss.DiceLoss(weight=0.2, activation='softmax2d', ignore_channels=[0]) + loss.FocalLoss() optimizer_unet = torch.optim.Adam(unet.parameters(), lr=lr) metrics = [ metrics.SMPIoU(threshold=0.5, ignore_channels=[0], activation='softmax2d'), metrics.Fscore(threshold=0.5, ignore_channels=[0], activation='softmax2d'), ] train_epoch = run.TrainEpoch( model=unet, loss=loss_unet, metrics=metrics, optimizer=optimizer_unet, device=DEVICE, verbose=True, )