def test_shape_dataset_creation(shapes_image_size, shapes_nb_labels):
"""Create a Shapes dataset
"""
d = ShapeDataset(shapes_image_size)
assert d.image_size == shapes_image_size
assert d.get_nb_labels() == shapes_nb_labels
assert d.get_nb_images() == 0
def test_shape_dataset_population(shapes_image_size, shapes_nb_images,
shapes_nb_labels, shapes_config,
shapes_temp_dir):
"""Populate a Shapes dataset
"""
d = ShapeDataset(shapes_image_size)
d.populate(str(shapes_temp_dir), nb_images=shapes_nb_images)
d.save(str(shapes_config))
assert d.get_nb_labels() == shapes_nb_labels
assert d.get_nb_images() == shapes_nb_images
assert os.path.isfile(str(shapes_config))
assert all(
len(os.listdir(os.path.join(str(shapes_temp_dir), tmp_dir))) ==
shapes_nb_images for tmp_dir in ["images", "labels"])
def test_shape_dataset_loading(shapes_image_size, shapes_nb_images,
shapes_nb_labels, shapes_sample_config):
"""Load images into a Shapes dataset
"""
d = ShapeDataset(shapes_image_size)
d.load(shapes_sample_config)
assert d.get_nb_labels() == shapes_nb_labels
assert d.get_nb_images() == shapes_nb_images
aggregate_value = "full" if not args.aggregate_label else "aggregated"
input_folder = utils.prepare_input_folder(args.datapath, args.dataset)
prepro_folder = utils.prepare_preprocessed_folder(args.datapath,
args.dataset,
args.image_size,
aggregate_value)
# Dataset creation
if args.dataset == "mapillary":
config_name = "config.json" if not args.aggregate_label else "config_aggregate.json"
config_path = os.path.join(input_folder, config_name)
train_dataset = MapillaryDataset(args.image_size, config_path)
validation_dataset = MapillaryDataset(args.image_size, config_path)
test_dataset = MapillaryDataset(args.image_size, config_path)
elif args.dataset == "shapes":
train_dataset = ShapeDataset(args.image_size)
validation_dataset = ShapeDataset(args.image_size)
test_dataset = ShapeDataset(args.image_size)
else:
utils.logger.error(
"Unsupported dataset type. Please choose 'mapillary' or 'shapes'")
sys.exit(1)
# Dataset populating/loading (depends on the existence of a specification file)
if os.path.isfile(prepro_folder["training_config"]):
train_dataset.load(prepro_folder["training_config"],
args.nb_training_image)
else:
utils.logger.info(
("No existing configuration file for this dataset. Create {}"
"").format(prepro_folder["training_config"]))