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