megenginelite.tensor

class LiteTensor(layout=None, device_type=LiteDeviceType.LITE_CPU, device_id=0, is_pinned_host=False, shapes=None, dtype=None)

the tensor to hold a block of data

copy_from(src_tensor)

copy memory form the src_tensor

property device_id

get device id of the tensor

property device_type

get device of the tensor

fill_zero()

fill the buffer memory with zero

get_ctypes_memory()

get the memory of the tensor, return c_void_p of the tensor memory

get_data_by_share()

get the data in the tensor, add share the data with a new numpy, and return the numpy arrray, be careful, the data in numpy is valid before the tensor memory is write again, such as LiteNetwok forward next time.

property is_continue

whether the tensor memory is continue

property is_pinned_host

whether the tensor is pinned tensor

property layout

property nbytes

get the length of the meomry in byte

reshape(shape)

reshape the tensor with data not change, only change the shape :param shape: int arrary of dst_shape

set_data_by_copy(data, data_length=0, layout=None)

copy the data to the tensor param data: the data to copy to tensor, it should be list, numpy.ndarraya or ctypes with length

set_data_by_share(data, length=0, layout=None)

share the data to the tensor param data: the data will shared to the tensor, it should be a numpy.ndarray or ctypes data

share_memory_with(src_tensor)

share the same memory with the src_tensor, the self memory will be freed

slice(start, end, step=None)

slice the tensor with gaven start, end, step :param start: silce begin index of each dim :param end: silce end index of each dim :param step: silce step of each dim

to_numpy()

get the buffer of the tensor

update()

update the member from C, this will auto used after slice, share

class LiteLayout(shape=None, dtype=None)

the simple layout description

data_type

Structure/Union member

property dtype

ndim

Structure/Union member

property shapes

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