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