def fetch_datasets(data_config_file, opt): all_datasets = load_datasets(data_config_file) dataset = all_datasets[opt.dataset.upper()] if opt.test: test_data = all_datasets[opt.test.upper()] else: test_data = dataset if opt.infer: infer_dir = Path(opt.infer) if infer_dir.exists(): # infer files in this directory if infer_dir.is_file(): images = [str(infer_dir)] else: images = list(infer_dir.glob('*')) if not images: images = infer_dir.iterdir() infer_data = Dataset(infer=images, mode='pil-image1', modcrop=False) else: infer_data = all_datasets[opt.infer.upper()] else: infer_data = test_data # TODO temp use, delete if not need if opt.cifar: cifar_data, cifar_test = tf.keras.datasets.cifar10.load_data() dataset = Dataset(**dataset) dataset.mode = 'numpy' dataset.train = [cifar_data[0]] dataset.val = [cifar_test[0]] return dataset, test_data, infer_data
def fetch_datasets(data_config_file, opt): all_datasets = load_datasets(data_config_file) dataset = all_datasets[opt.dataset.upper()] if opt.test: test_data = all_datasets[opt.test.upper()] else: test_data = dataset if opt.infer: infer_dir = Path(opt.infer) if infer_dir.exists(): # infer files in this directory if infer_dir.is_file(): images = [str(infer_dir)] else: images = list(infer_dir.glob('*')) if not images: images = infer_dir.iterdir() infer_data = Dataset(infer=images, mode='pil-image1', modcrop=False) else: infer_data = all_datasets[opt.infer.upper()] else: infer_data = test_data if opt.mode: dataset.mode = opt.mode test_data.mode = opt.mode infer_data.mode = opt.mode return dataset, test_data, infer_data