def predict_from_dirctory(self, dirname, target_shape=(224, 224, 3), **kwargs): img_data_manager = DataManager() img_data_manager.test_dir = dirname img_data_manager.target_shape = target_shape super().predict(img_data_manager, **kwargs) return self
def fit_from_directory(self, dirname, target_shape=(224, 224, 3), valid_split=0.1, **kwargs): img_data_manager = DataManager() if isinstance(dirname, (list, tuple)): if len(dirname) != 2: raise ValueError( "Expected one directory or a list or tuple of two directories for training and validation!" ) if dirname[1] is None: img_data_manager.train_valid_dir = dirname[0] else: img_data_manager.train_dir = dirname[0] img_data_manager.valid_dir = dirname[1] else: img_data_manager.train_valid_dir = dirname img_data_manager.target_shape = target_shape img_data_manager.split_size = valid_split kwargs['task_type'] = 'img_multilabel-indicator' kwargs['metric'] = kwargs.get('metric', 'acc') super().fit(img_data_manager, **kwargs) return self