def test(config, **kwargs): evaluator = Evaluator(config, **kwargs) evaluator.run()
dir_prediction = 'predictions/2d/' create_dir(dir_checkpoint) create_dir(dir_prediction) dataset_props = load_pickle('dataset_props.pkl') pool_layer_kernel_sizes = dataset_props['plan_2d'][ 'pool_layer_kernel_sizes'] args = get_args() model = ResUNet(in_ch=1, base_num_features=30, num_classes=3, norm_type='batch', nonlin_type='relu', pool_type='max', pool_layer_kernel_sizes=pool_layer_kernel_sizes, deep_supervision=True, mode='2D') trainer = Trainer(model, dir_h5_train, dir_checkpoint, args) trainer.run() model = ResUNet(in_ch=1, base_num_features=30, num_classes=3, norm_type='batch', nonlin_type='relu', pool_type='max', pool_layer_kernel_sizes=pool_layer_kernel_sizes, deep_supervision=False, mode='2D') evaluator = Evaluator(model, dir_h5_train, dir_checkpoint, dir_prediction, args) evaluator.run()