Wrappers to use torch and lua from python
- create torch tensors, call operations on them
- instantiate
nnnetwork modules, train them, make predictions - create your own lua class, call methods on that
import PyTorch
a = PyTorch.FloatTensor(2,3).uniform()
a += 3
print('a', a)
print('a.sum()', a.sum())
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)
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)
self.net = nn.Sequential()
local net = self.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.net:float()
self.crit:float()
end
function TorchModel:trainBatch(learningRate, input, labels)
self.net:zeroGradParameters()
local output = self.net:forward(input)
local loss = self.crit:forward(output, labels)
local gradOutput = self.crit:backward(output, labels)
self.net:backward(input, gradOutput)
self.net:updateParameters(learningRate)
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 = self.net:forward(input)
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.load_lua_class('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 https://github.com/torch/distro
- 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-devRun:
pip install -r requirements.txt
- To be able to run tests, also do:
pip install -r test/requirements.txt
Run:
git clone https://github.com/hughperkins/pytorch.git
cd pytorch
source ~/torch/install/bin/torch-activate
./build.sh
Run:
source ~/torch/install/bin/torch-activate
cd pytorch
./run_tests.sh
- 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
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)
Please note that currently, right now, I'm focused 100.000% on cuda-on-cl, so please be patient during this period
12 September:
- Yannick Hold-Geoffroy added conversion of lists and tuples to Lua tables
8 September:
- added
PyTorchAug.save(filename, object)andPyTorchAug.load(filename), to save/load Torch.t7files
26 August:
- if not deploying to a virtual environment, will install with
--user, 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
nnnow, without needing to explicitly register insidepytorch- you need to upgrade to
v3.0.0to enable this, which is a breaking change, since thennclasses are now inPyTorchAug.nn, instead of directly inPyTorchAug
- you need to upgrade to
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 https://travis-ci.org/hughperkins/pytorch/builds/112292866
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
1.0.0to2.0.0-SNAPSHOT... - added dependency on
numpy - added
.asNumpyTensor()to convert a torch tensor to a numpy tensor
24th February:
- added support for passing strings to methods
- added
require - created prototype for importing your own classes, and calling methods on those
- works with Python 3 now :-)
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