def _to_ndarray(java_tensor: JavaObject) -> Any:
if (java_tensor.getRank() == 0):
return np.array(java_tensor.scalar())
else:
return Tensor.__get_ndarray_from_tensor(java_tensor).reshape(java_tensor.getShape())
def j_tensor_wrapper_to_np_array(j_obj: JavaObject):
buffer, dtype_str, shape = j_obj.getBytes(), j_obj.getDtypeStr(
), j_obj.getShape()
arr = np.frombuffer(buffer, dtype=dtype_str).reshape(shape)
return arr
def _to_ndarray(java_tensor: JavaObject) -> numpy_types:
if java_tensor.getRank() == 0:
return np.array(java_tensor.scalar())
else:
return np.array(list(java_tensor.asFlatArray())).reshape(
java_tensor.getShape())
def from_java(cls, java_tensor_info: JavaObject) -> "TensorInfo":
shape = list(java_tensor_info.getShape())
type = java_gateway.get_field(java_tensor_info, "type").toString()
return cls(shape, type)
