- paddle.fluid.lod_tensor. create_lod_tensor ( data, recursive_seq_lens, place )
Create a LoDTensor from a numpy array, list or existing LoDTensor.
The implementation is as follows:
Check whether the length-based LoD, i.e.,
recursive_seq_lensto a offset-based LoD.
place, copy the
datafrom a numpy array, list or existing LoDTensor to CPU or GPU device.
Set offset-based LoD to the output LoDTensor.
Suppose we want to create a LoDTensor to hold data for word sequences, where each word is represented by an integer. If we want to create a LoDTensor to represent two sentences, one of 2 words, and one of 3 words.
datawould be a numpy array of integers with shape (5, 1).
recursive_seq_lenswould be [[2, 3]], indicating the word number in each sentence. This length-based
recursive_seq_lens[[2, 3]] would be converted to offset-based LoD [[0, 2, 5]] inside the function call.
Please reference user_guide_lod_tensor for more details regarding LoD.
data (numpy.ndarray|list|LoDTensor) – a numpy array, a list or ad LoDTensor holding the data to be copied.
recursive_seq_lens (list[list[int]]) – a list of lists indicating the length-based LoD info.
place (CPUPlace|CUDAPlace) – CPU or GPU place indicating where the data in the created LoDTensor will be stored.
A LoDTensor with tensor data and recursive_seq_lens info.
import paddle.fluid as fluid import numpy as np t = fluid.create_lod_tensor(np.ndarray([5, 30]), [[2, 3]], fluid.CPUPlace())