Wrappers to use torch and lua from python

• create torch tensors, call operations on those, add them together, multiply them, and so on
• create nn network modules, pass tensors into those, get the output, and so on
• create your own lua class, call methods on that, pass in tensors
• wrap numpy tensors in torch tensors

More info: Implemented.md

Run example script by doing:

source ~/torch/install/bin/torch-activate
cd pytorch
./run.sh

• Script is here: test_pytorch.py
• Output: test_pytorch_output.txt

Run example script by doing:

source ~/torch/install/bin/torch-activate
cd pytorch
./nn_run.sh

• Script is here: test_pynn.py
• Script output: test_pynn_output.txt

• Create a lua class, like say luabit.lua
  • it contains some methods, that we will call from Python
• Create a python script, like say pybit.py
  • it requires foo, then
  • creates a Foo object, then
  • calls methods on that object

Run like:

source ~/torch/install/bin/torch-activate
cd simpleexample
python pybit.py

When we run it we get:

foo.lua
Foo:__init()
Foo:teststuff()
Foo:teststuff2(	hello	)
setColor:	green
color:	green

• 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)
• Have installed the following python libraries:

pip install numpy
pip install pytest
pip install python-mnist  # used for unit tests

• lua51 headers should be installed, ie something like sudo apt-get install lua5.1 liblua5.1-dev

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

• The test scripts: test
• The test output: tests_output.txt

• pytorch is developed and maintained on python 3
• you should be able to use it with python 2, as long as you include the following at the top of your scripts:

from __future__ import print_function, division

Maintainer guidelines

semantic versioning

• pycltorch python wrappers for cltorch and clnn
• pycudatorch python wrappers for cutorch and cunn

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 nn now, without needing to explicitly register inside pytorch
  • you need to upgrade to v3.0.0 to enable this, which is a breaking change, since the nn classes are now in PyTorchAug.nn, instead of directly in PyTorchAug

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 1.0.0 to 2.0.0-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 :-)

12th December:

• created Implemented.md doc
• Added several network layers:
  • Reshape
  • SpatialConvolutionMM
  • SpatialMaxPooling
  • Tanh
  • ReLU

5th September:

• added DoubleTensor
• added ByteTensor
• moved test scripts and output out of readme, provide links instead
• test output linked from readme updated automatically
• added + - * / for tensor/scalar pairs, and + - for tensor pairs

4th September:

• added LongTensor
• size() now returns a LongTensor, rather than a FloatTensor
• under the covers:
  • started to use Jinja2 as a templating language, means easy to support other types

3rd September:

• modified Lua wrapper approach, so directly uses dynamic Python to wrap the Lua classes

2nd September:

• monkey-patchable, so can start work on PyClTorch

29th August:

• first created :-)