如何快速转换返回的Python在Lua的numpy的阵列到一个Lua火炬张量？ [英] How to quickly convert a returned Python-in-Lua numpy array into a Lua Torch Tensor?

问题描述

我有一个返回多维数组numpy的Python函数。我想打电话从Lua这个Python的功能和数据进入一个Lua火炬张量尽可能快的。我有一个很慢有效的解决方案，并正在寻找一种方式，是显著快（10FPS以上顺序）。我不知道这是可能的。

我相信这将是利用他人考虑了Facebook的日益普及，支持火炬，并在Python提供广泛的易于使用的图像处理工具，其中Lua的缺乏。

我为了从Lua调用一个Python函数现在用疯子-蟒Bastibe叉。从这个previous <一个援助href=\"http://stackoverflow.com/questions/20400390/how-to-call-a-python-function-from-lua\">question这<一个href=\"https://labix.org/lunatic-python#head-d0597456187b5ea4006f9427346633f765432975\">documentation,我想出了一些code，它的工作原理，但过于缓慢。我使用的Lua 5.1和Python 2.7.6，如果需要可以更新这些。

Lua的code：test_lua.lua

 要求'火炬'打印（package.loadlib（libpython2.7.so，*））
要求（LUA-蟒蛇）的getImage = python.importtest_python.getImagePB = python.builtins（）功能getImageTensor（pythonImageHandle，宽度，高度）
    imageTensor = torch.Tensor（3，高度，宽度）
    image_0 = python.asindx（pythonImageHandle（高度，宽度））
    对于i = 0，身高1做
        IMAGE_1 = python.asindx（image_0 [I]）
        对于j = 0，宽度1做
            IMAGE_2 = python.asindx（IMAGE_1 [J]。）
            对于k = 0,2 DO
                 - 张量指数从1开始
                 - 用户蟒蛇内置到INT函数返回整数
                imageTensor [K + 1]第[i + 1] [J + 1] = pb.int（IMAGE_2 [K]）/ 255
            结束
        结束
    结束
    返回imageTensor
结束
A = getImageTensor（的getImage，600,400）
 

Python的code：test_python.py

 进口numpy的
进口操作系统，SYS
进口图片清晰度的getImage（宽度，高度）：
    返回numpy.asarray（Image.open（image.jpg的））
 

解决方案

尝试 lutorpy ，它有一个在python LUA引擎，并能够分享火炬numpy的内存，因此它的速度非常快，这里是code为您的情况：

 进口numpy的
进口图片
进口lutorpy作为LUA的getImage = numpy.asarray（Image.open（image.jpg文件））
A = torch.fromNumpyArray（的getImage）＃现在你可以使用你作为火炬手张量图像
＃例如：使用SpatialConvolution从神经网络来处理图像
要求（NN）
N = nn.SpatialConvolution（1,16,12,12）
RES = n._forward（一）
打印（res._size（））＃转换回numpy的数组
输出= res.asNumpyArray（）
 

I have a Python function that returns a multi-dimensional numpy array. I want to call this Python function from Lua and get the data into a Lua Torch Tensor as quickly as possible. I have a solution that works quite slowly and am looking for a way that is significantly faster (order of 10fps or more). I'm not sure if this is possible.

I believe this will be of use to others considering the growing popularity of Facebook backed Torch and the extensive easy-to-use image-processing tools available in Python of which Lua lacks.

I am using the Bastibe fork of lunatic-python in order to call a Python function from Lua. With aid from this previous question and this documentation, I have come up with some code that works, but is far too slow. I am using Lua 5.1 and Python 2.7.6 and can update these if necessary.

Lua Code: "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 Code: "test_python.py"

import numpy
import os,sys
import Image

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

解决方案

Try lutorpy, it has a lua engine in python and be able to share numpy memory with torch, so it's very fast, here is the code for your case:

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()

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