def __init__(self, image=None, label=None, path=None, bigdl_type="float"):
image_tensor = JTensor.from_ndarray(
image) if image is not None else None
label_tensor = JTensor.from_ndarray(
label) if label is not None else None
self.bigdl_type = bigdl_type
self.value = callBigDlFunc(bigdl_type,
JavaValue.jvm_class_constructor(self),
image_tensor, label_tensor, path)
def __init__(self, jvalue, bigdl_type, *args):
if (jvalue):
assert (type(jvalue) == JavaObject)
self.value = jvalue
else:
self.value = callBigDlFunc(bigdl_type,
JavaValue.jvm_class_constructor(self),
*args)
self.bigdl_type = bigdl_type
def __init__(self,
image_list=None,
label_list=None,
jvalue=None,
bigdl_type="float"):
assert jvalue or image_list, "jvalue and image_list cannot be None in the same time"
if jvalue:
self.value = jvalue
else:
# init from image ndarray list and label rdd(optional)
image_tensor_list = map(lambda image: JTensor.from_ndarray(image),
image_list)
label_tensor_list = map(lambda label: JTensor.from_ndarray(label),
label_list) if label_list else None
self.value = callBigDlFunc(bigdl_type,
JavaValue.jvm_class_constructor(self),
image_tensor_list, label_tensor_list)
self.bigdl_type = bigdl_type
def __init__(self, jvalue, bigdl_type, *args):
self.value = jvalue if jvalue else callBigDlFunc(
bigdl_type, JavaValue.jvm_class_constructor(self), *args)
self.bigdl_type = bigdl_type
def __init__(self, bigdl_type="float", *args):
self.value = callBigDlFunc(bigdl_type,
JavaValue.jvm_class_constructor(self),
*args)