def scripts_serving(file): target_path = Path(BASE_DIR.joinpath("scripts"), file) if target_path.exists() and not target_path.is_dir(): return send_file(target_path.open(mode="rb"), attachment_filename=target_path.name) abort(404)
# train train_data, train_label = preprocess(config.train_dataset, False, 2) print('train data: %d x (%d, %d)' % train_data.shape) print('train label: %d x (%d, %d)' % train_label.shape) model = FSRCNN(model_dir, scale) model.train(train_data, train_label, config.batch_size, train_step) # test performance = {} for test_dataset in config.test_dataset: test_data, test_label = load_dataset(test_dataset, scale) result_dir = Path(f'result-{scale}-{test_dataset}') if not result_dir.exists(): result_dir.mkdir() perf = [] for i, (lr_img, hr_img) in enumerate(zip(test_data, test_label)): for result in model.predict(lr_img, hr_img): save_result(result, result_dir, i) perf.append((i, result['cnn_psnr'], result['cnn_ssim'], result['bi_psnr'], result['bi_ssim'])) performance[test_dataset] = perf for test_dataset in config.test_dataset: print(f'Performance on {test_dataset}') print('no\tFSRCNN\tBicubic') for p in performance[test_dataset]: print(
def windows_path_arg(input: str): path = WindowsPath(input) if not path.exists(): raise argparse.ArgumentTypeError(f"'{input}' is not an existing file!") return path