如何快速将返回的Python-in-Lua numpy数组转换为Lua Torch Tensor？

Question

我有一个Python函数,返回一个多维的numpy数组.我想从Lua调用这个Python函数,并尽快将数据导入Lua Torch Tensor.我有一个非常缓慢的解决方案,我正在寻找一种明显更快的方式(10fps或更高的顺序).我不确定这是否可行.

考虑到Facebook支持的Torch日益普及以及Lua缺乏的Python中广泛易用的图像处理工具,我相信这对其他人会有用.

我正在使用lunatic-python的Bastibe fork来从Lua调用Python函数.在上一个问题和本文档的帮助下,我提出了一些有效的代码,但速度太慢了.我正在使用Lua 5.1和Python 2.7.6,如果需要可以更新它们.

Lua代码:"test_lua.lua"

require 'torch'

print(package.loadlib("libpython2.7.so", "*"))
require("lua-python")

getImage = python.import "test_python".getImage

pb = python.builtins()

function getImageTensor(pythonImageHandle,width,height)
    imageTensor = torch.Tensor(3,height,width)
    image_0 = python.asindx(pythonImageHandle(height,width))
    for i=0,height-1 do
        image_1 = python.asindx(image_0[i])
        for j=0,width-1 do
            image_2 = python.asindx(image_1[j])
            for k=0,2 do
                -- Tensor indices begin at 1
                -- User python inbuilt to-int function to return integer
                imageTensor[k+1][i+1][j+1] = pb.int(image_2[k])/255
            end
        end
    end
    return imageTensor
end


a = getImageTensor(getImage,600,400)

Python代码:"test_python.py"

import numpy
import os,sys
import Image

def getImage(width, height):
    return numpy.asarray(Image.open("image.jpg"))

Answer 1

尝试lutorpy，它有一个 python 中的 lua 引擎，并且能够与 torch 共享 numpy 内存，所以它非常快，这里是适合您的情况的代码：

import numpy
import Image
import lutorpy as lua

getImage = numpy.asarray(Image.open("image.jpg"))
a = torch.fromNumpyArray(getImage)

# now you can use your image as torch Tensor
# for example: use SpatialConvolution from nn to process the image
require("nn")
n = nn.SpatialConvolution(1,16,12,12)
res = n._forward(a)
print(res._size())

# convert back to numpy array
output = res.asNumpyArray()