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Python wrappers for torch and lua

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Wrappers to use torch and lua from python

What is pytorch?

  • create torch tensors, call operations on them
  • instantiate
    network modules, train them, make predictions
  • create your own lua class, call methods on that

Create torch tensors

import PyTorch
a = PyTorch.FloatTensor(2,3).uniform()
a += 3
print('a', a)
print('a.sum()', a.sum())

Instantiate nn network modules

import PyTorch
from PyTorchAug import nn

net = nn.Sequential() net.add(nn.SpatialConvolutionMM(1, 16, 5, 5, 1, 1, 2, 2)) net.add(nn.ReLU()) net.add(nn.SpatialMaxPooling(3, 3, 3, 3))

net.add(nn.SpatialConvolutionMM(16, 32, 3, 3, 1, 1, 1, 1)) net.add(nn.ReLU()) net.add(nn.SpatialMaxPooling(2, 2, 2, 2))

net.add(nn.Reshape(32 * 4 * 4)) net.add(nn.Linear(32 * 4 * 4, 150)) net.add(nn.Tanh()) net.add(nn.Linear(150, 10)) net.add(nn.LogSoftMax()) net.float()

crit = nn.ClassNLLCriterion() crit.float()

net.zeroGradParameters() input = PyTorch.FloatTensor(5, 1, 28, 28).uniform() labels = PyTorch.ByteTensor(5).geometric(0.9).icmin(10) output = net.forward(input) loss = crit.forward(output, labels) gradOutput = crit.backward(output, labels) gradInput = net.backward(input, gradOutput) net.updateParameters(0.02)

Write your own lua class, call methods on it

Example lua class: ``` require 'torch' require 'nn'

local TorchModel = torch.class('TorchModel')

function TorchModel:__init(backend, imageSize, numClasses) self:buildModel(backend, imageSize, numClasses) self.imageSize = imageSize self.numClasses = numClasses self.backend = backend end

function TorchModel:buildModel(backend, imageSize, numClasses) = nn.Sequential() local net =

net:add(nn.SpatialConvolutionMM(1, 16, 5, 5, 1, 1, 2, 2)) net:add(nn.ReLU()) net:add(nn.SpatialMaxPooling(3, 3, 3, 3)) net:add(nn.SpatialConvolutionMM(16, 32, 3, 3, 1, 1, 1, 1)) net:add(nn.ReLU()) net:add(nn.SpatialMaxPooling(2, 2, 2, 2)) net:add(nn.Reshape(32 * 4 * 4)) net:add(nn.Linear(32 * 4 * 4, 150)) net:add(nn.Tanh()) net:add(nn.Linear(150, numClasses)) net:add(nn.LogSoftMax())

self.crit = nn.ClassNLLCriterion() self.crit:float() end

function TorchModel:trainBatch(learningRate, input, labels)

local output = local loss = self.crit:forward(output, labels) local gradOutput = self.crit:backward(output, labels), gradOutput)

local _, prediction = output:max(2) local numRight = labels:int():eq(prediction:int()):sum() return {loss=loss, numRight=numRight} -- you can return a table, it will become a python dictionary end

function TorchModel:predict(input) local output = local _, prediction = output:max(2) return prediction:byte() end ```

Python script that calls this. Assume the lua class is stored in file "torch_model.lua" ``` import PyTorch import PyTorchHelpers import numpy as np from mnist import MNIST

batchSize = 32 numEpochs = 2 learningRate = 0.02

TorchModel = PyTorchHelpers.loadluaclass('torch_model.lua', 'TorchModel') torchModel = TorchModel(backend, 28, 10)

mndata = MNIST('../../data/mnist') imagesList, labelsList = mndata.load_training() labels = np.array(labelsList, dtype=np.uint8) images = np.array(imagesList, dtype=np.float32) labels += 1 # since torch/lua labels are 1-based N = labels.shape[0]

numBatches = N // batchSize for epoch in range(numEpochs): epochLoss = 0 epochNumRight = 0 for b in range(numBatches): res = torchModel.trainBatch( learningRate, images[b * batchSize:(b+1) * batchSize], labels[b * batchSize:(b+1) * batchSize]) numRight = res['numRight'] epochNumRight += numRight print('epoch ' + str(epoch) + ' accuracy: ' + str(epochNumRight * 100.0 / N) + '%') ```

It's easy to modify the lua script to use CUDA, or OpenCL.



  • Have installed torch, following instructions at
  • Have installed 'nn' torch module:
    luarocks install nn
  • Have installed python (tested with 2.7 and 3.4)
  • lua51 headers should be installed, ie something like
    sudo apt-get install lua5.1 liblua5.1-dev
    pip install -r requirements.txt
  • To be able to run tests, also do:
    pip install -r test/requirements.txt



git clone
cd pytorch
source ~/torch/install/bin/torch-activate



source ~/torch/install/bin/torch-activate
cd pytorch

Python 2 vs Python 3?

  • pytorch is developed and maintained on python 3
  • you should be able to use it with python 2, but there might be the occasional oversight. Please log an issue for any python 2 incompatibilities you notice

Maintainer guidelines

Maintainer guidelines


semantic versioning

Related projects

Examples of training models/networks using pytorch: * pytorch-residual-networks port of Michael Wilber's torch-residual-networks, to handle data loading and preprocessing from Python, via pytorch * cifar.pytorch pytorch implementation of Sergey's cifar.torch

Addons, for using cuda tensors and opencl tensors directly from python (no need for this to train networks. could be useful if you want to manipulate cuda tensor directly from python) * pycltorch python wrappers for cltorch and clnn * pycudatorch python wrappers for cutorch and cunn


Please note that currently, right now, I'm focused 100.000% on cuda-on-cl, so please be patient during this period

Recent news

12 September: * Yannick Hold-Geoffroy added conversion of lists and tuples to Lua tables

8 September: * added, object)
, to save/load Torch

26 August: * if not deploying to a virtual environment, will install with

, into home directory

14 April: * stack trace should be a bit more useful now :-)

17 March: * ctrl-c works now (tested on linux)

16 March: * uses luajit on linux now (mac os x continues to use lua)

6 March: * all classes should be usable from

now, without needing to explicitly register inside
* you need to upgrade to
to enable this, which is a breaking change, since the
classes are now in
, instead of directly in

5 March: * added

PyTorchHelpers.load_lua_class(lua_filename, lua_classname)
to easily import a lua class from a lua file * can pass parameters to lua class constructors, from python * can pass tables to lua functions, from python (pass in as python dictionaries, become lua tables) * can return tables from lua functions, to python (returned as python dictionaries)

2 March: * removed requirements on Cython, Jinja2 for installation

28th Februrary: * builds ok on Mac OS X now :-) See

26th February: * modified

to be the div operation for float and double tensors, and
for int-type tensors, such as byte, long, int * since the div change is incompatible with 1.0.0 div operators, jumping radically from
... * added dependency on
* added
to convert a torch tensor to a numpy tensor

24th February: * added support for passing strings to methods * added

* created prototype for importing your own classes, and calling methods on those * works with Python 3 now :-)

Older changes

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