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Repository: torch/torch7
Branch: master
Commit: 814ea4afd6be
Files: 166
Total size: 1.4 MB
Directory structure:
gitextract_kg0yjonf/
├── .gitignore
├── .travis.yml
├── CMakeLists.txt
├── CONTRIBUTING.md
├── COPYRIGHT.txt
├── CmdLine.lua
├── DiskFile.c
├── FFInterface.lua
├── File.c
├── File.lua
├── Generator.c
├── MemoryFile.c
├── PipeFile.c
├── README.md
├── ROADMAP.md
├── Storage.c
├── Tensor.c
├── Tensor.lua
├── TensorMath.lua
├── TensorOperator.c
├── TestSuite.lua
├── Tester.lua
├── Timer.c
├── cmake/
│ ├── TorchConfig.cmake.in
│ ├── TorchExports.cmake
│ ├── TorchPackage.cmake
│ ├── TorchPaths.cmake
│ ├── TorchPathsInit.cmake
│ ├── TorchWrap.cmake
│ └── TorchWrap.cmake.in
├── doc/
│ ├── cmdline.md
│ ├── diskfile.md
│ ├── file.md
│ ├── index.md
│ ├── maths.md
│ ├── memoryfile.md
│ ├── pipefile.md
│ ├── random.md
│ ├── serialization.md
│ ├── storage.md
│ ├── tensor.md
│ ├── tester.md
│ ├── timer.md
│ └── utility.md
├── general.h
├── generic/
│ ├── Storage.c
│ ├── Tensor.c
│ ├── TensorOperator.c
│ └── luaG.h
├── init.c
├── init.lua
├── lib/
│ ├── CMakeLists.txt
│ ├── TH/
│ │ ├── CMakeLists.txt
│ │ ├── README.md
│ │ ├── TH.h
│ │ ├── THAllocator.c
│ │ ├── THAllocator.h
│ │ ├── THAtomic.c
│ │ ├── THAtomic.h
│ │ ├── THBlas.c
│ │ ├── THBlas.h
│ │ ├── THConfig.cmake.in
│ │ ├── THDiskFile.c
│ │ ├── THDiskFile.h
│ │ ├── THFile.c
│ │ ├── THFile.h
│ │ ├── THFilePrivate.h
│ │ ├── THGeneral.c
│ │ ├── THGeneral.h.in
│ │ ├── THGenerateAllTypes.h
│ │ ├── THGenerateByteType.h
│ │ ├── THGenerateCharType.h
│ │ ├── THGenerateDoubleType.h
│ │ ├── THGenerateFloatType.h
│ │ ├── THGenerateFloatTypes.h
│ │ ├── THGenerateHalfType.h
│ │ ├── THGenerateIntType.h
│ │ ├── THGenerateIntTypes.h
│ │ ├── THGenerateLongType.h
│ │ ├── THGenerateShortType.h
│ │ ├── THHalf.c
│ │ ├── THHalf.h
│ │ ├── THLapack.c
│ │ ├── THLapack.h
│ │ ├── THLogAdd.c
│ │ ├── THLogAdd.h
│ │ ├── THMath.h
│ │ ├── THMemoryFile.c
│ │ ├── THMemoryFile.h
│ │ ├── THRandom.c
│ │ ├── THRandom.h
│ │ ├── THSize.c
│ │ ├── THSize.h
│ │ ├── THStorage.c
│ │ ├── THStorage.h
│ │ ├── THTensor.c
│ │ ├── THTensor.h
│ │ ├── THTensorApply.h
│ │ ├── THTensorDimApply.h
│ │ ├── THTensorMacros.h
│ │ ├── THVector.c
│ │ ├── THVector.h
│ │ ├── cmake/
│ │ │ ├── FindARM.cmake
│ │ │ ├── FindBLAS.cmake
│ │ │ ├── FindLAPACK.cmake
│ │ │ ├── FindMKL.cmake
│ │ │ └── FindSSE.cmake
│ │ ├── generic/
│ │ │ ├── THBlas.c
│ │ │ ├── THBlas.h
│ │ │ ├── THLapack.c
│ │ │ ├── THLapack.h
│ │ │ ├── THStorage.c
│ │ │ ├── THStorage.h
│ │ │ ├── THStorageCopy.c
│ │ │ ├── THStorageCopy.h
│ │ │ ├── THTensor.c
│ │ │ ├── THTensor.h
│ │ │ ├── THTensorConv.c
│ │ │ ├── THTensorConv.h
│ │ │ ├── THTensorCopy.c
│ │ │ ├── THTensorCopy.h
│ │ │ ├── THTensorLapack.c
│ │ │ ├── THTensorLapack.h
│ │ │ ├── THTensorMath.c
│ │ │ ├── THTensorMath.h
│ │ │ ├── THTensorRandom.c
│ │ │ ├── THTensorRandom.h
│ │ │ ├── THVector.h
│ │ │ ├── THVectorDefault.c
│ │ │ ├── THVectorDispatch.c
│ │ │ └── simd/
│ │ │ ├── common_simd.h
│ │ │ ├── convolve.c
│ │ │ ├── convolve.h
│ │ │ ├── convolve5x5_avx.c
│ │ │ ├── convolve5x5_sse.c
│ │ │ └── simd.h
│ │ └── vector/
│ │ ├── AVX.c
│ │ ├── AVX.h
│ │ ├── AVX2.c
│ │ ├── AVX2.h
│ │ ├── NEON.c
│ │ ├── SSE.c
│ │ └── VSX.c
│ └── luaT/
│ ├── CMakeLists.txt
│ ├── README.md
│ ├── luaT.c
│ ├── luaT.h
│ └── luaTConfig.cmake.in
├── mkdocs.yml
├── paths.lua.in
├── random.lua
├── rocks/
│ └── torch-scm-1.rockspec
├── test/
│ ├── longSize.lua
│ ├── test.lua
│ ├── test_Multinomial.lua
│ ├── test_Tester.lua
│ ├── test_aliasMultinomial.lua
│ ├── test_half.lua
│ ├── test_qr.lua
│ ├── test_sharedmem.lua
│ ├── test_timer.lua
│ ├── test_writeObject.lua
│ └── timeSort.lua
├── torchcwrap.lua
├── utils.c
└── utils.h
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
build/
================================================
FILE: .travis.yml
================================================
language: c
compiler:
- gcc
- clang
cache:
directories:
- $HOME/OpenBlasInstall
sudo: false
env:
- TORCH_LUA_VERSION=LUAJIT21
- TORCH_LUA_VERSION=LUA51
- TORCH_LUA_VERSION=LUA52
os:
- linux
addons:
apt:
packages:
- cmake
- gfortran
- gcc-multilib
- gfortran-multilib
- liblapack-dev
- build-essential
- gcc
- g++
- curl
- cmake
- libreadline-dev
- git-core
- libqt4-core
- libqt4-gui
- libqt4-dev
- libjpeg-dev
- libpng-dev
- ncurses-dev
- imagemagick
- libzmq3-dev
- gfortran
- unzip
- gnuplot
- gnuplot-x11
before_script:
- export ROOT_TRAVIS_DIR=$(pwd)
- export INSTALL_PREFIX=~/torch/install
- ls $HOME/OpenBlasInstall/lib || (cd /tmp/ && git clone https://github.com/xianyi/OpenBLAS.git -b master && cd OpenBLAS && (make NO_AFFINITY=1 -j$(getconf _NPROCESSORS_ONLN) 2>/dev/null >/dev/null) && make PREFIX=$HOME/OpenBlasInstall install)
- git clone https://github.com/torch/distro.git ~/torch --recursive
- cd ~/torch && git submodule update --init --recursive
- mkdir build && cd build
- export CMAKE_LIBRARY_PATH=$HOME/OpenBlasInstall/include:$HOME/OpenBlasInstall/lib:$CMAKE_LIBRARY_PATH
- cmake .. -DCMAKE_INSTALL_PREFIX="${INSTALL_PREFIX}" -DCMAKE_BUILD_TYPE=Release -DWITH_${TORCH_LUA_VERSION}=ON
- make && make install
- cd $ROOT_TRAVIS_DIR
- export LD_LIBRARY_PATH=${INSTALL_PREFIX}/lib:$LD_LIBRARY_PATH
script:
- ${INSTALL_PREFIX}/bin/luarocks make rocks/torch-scm-1.rockspec
- ${INSTALL_PREFIX}/bin/luarocks install luaffi
- export PATH=${INSTALL_PREFIX}/bin:$PATH
- export TESTLUA=$(which luajit lua | head -n 1)
- ${TESTLUA} -ltorch -e "t=torch.test(); if t.errors[1] then os.exit(1) end"
- cd test
- ${TESTLUA} test_writeObject.lua
- ${TESTLUA} test_Tester.lua
================================================
FILE: CMakeLists.txt
================================================
IF(APPLE)
CMAKE_MINIMUM_REQUIRED(VERSION 2.8.12 FATAL_ERROR)
CMAKE_POLICY(VERSION 2.8.12)
ELSE()
CMAKE_MINIMUM_REQUIRED(VERSION 2.8 FATAL_ERROR)
CMAKE_POLICY(VERSION 2.8)
ENDIF()
SET(CMAKE_MODULE_PATH
"${CMAKE_CURRENT_SOURCE_DIR}/cmake"
"${CMAKE_MODULE_PATH}")
IF (NOT MSVC)
IF (MINGW)
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=format")
ELSE()
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=implicit-function-declaration -Werror=format")
ENDIF(MINGW)
ENDIF(NOT MSVC)
# Flags
# When using MSVC
IF(MSVC)
# we want to respect the standard, and we are bored of those **** .
ADD_DEFINITIONS(-D_CRT_SECURE_NO_DEPRECATE=1)
ENDIF(MSVC)
# OpenMP support?
SET(WITH_OPENMP ON CACHE BOOL "OpenMP support if available?")
IF (APPLE AND CMAKE_COMPILER_IS_GNUCC)
EXEC_PROGRAM (uname ARGS -v OUTPUT_VARIABLE DARWIN_VERSION)
STRING (REGEX MATCH "[0-9]+" DARWIN_VERSION ${DARWIN_VERSION})
MESSAGE (STATUS "MAC OS Darwin Version: ${DARWIN_VERSION}")
IF (DARWIN_VERSION GREATER 9)
SET(APPLE_OPENMP_SUCKS 1)
ENDIF (DARWIN_VERSION GREATER 9)
EXECUTE_PROCESS (COMMAND ${CMAKE_C_COMPILER} -dumpversion
OUTPUT_VARIABLE GCC_VERSION)
IF (APPLE_OPENMP_SUCKS AND GCC_VERSION VERSION_LESS 4.6.2)
MESSAGE(STATUS "Warning: Disabling OpenMP (unstable with this version of GCC)")
MESSAGE(STATUS " Install GCC >= 4.6.2 or change your OS to enable OpenMP")
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-unknown-pragmas")
SET(WITH_OPENMP OFF CACHE BOOL "OpenMP support if available?" FORCE)
ENDIF ()
ENDIF ()
IF (WITH_OPENMP)
FIND_PACKAGE(OpenMP)
IF(OPENMP_FOUND)
MESSAGE(STATUS "Compiling with OpenMP support")
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
SET(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
ENDIF(OPENMP_FOUND)
ENDIF (WITH_OPENMP)
# Includes
INCLUDE(TorchPaths)
INCLUDE(TorchPathsInit)
INCLUDE(TorchPackage)
INCLUDE(TorchWrap)
INCLUDE(TorchExports)
# Torch libraries
ADD_SUBDIRECTORY(lib)
CONFIGURE_FILE(paths.lua.in "${CMAKE_CURRENT_BINARY_DIR}/paths.lua")
INCLUDE_DIRECTORIES(BEFORE "${LUA_INCDIR}")
INCLUDE_DIRECTORIES(BEFORE "${CMAKE_CURRENT_SOURCE_DIR}/lib/TH")
INCLUDE_DIRECTORIES(BEFORE "${CMAKE_CURRENT_BINARY_DIR}/lib/TH")
INCLUDE_DIRECTORIES(BEFORE "${CMAKE_CURRENT_SOURCE_DIR}/lib/luaT")
LINK_DIRECTORIES("${LUA_LIBDIR}")
SET(src DiskFile.c File.c MemoryFile.c PipeFile.c Storage.c Tensor.c Timer.c utils.c init.c TensorOperator.c TensorMath.c random.c Generator.c)
SET(luasrc init.lua File.lua Tensor.lua CmdLine.lua FFInterface.lua Tester.lua TestSuite.lua ${CMAKE_CURRENT_BINARY_DIR}/paths.lua test/test.lua)
# Necessary do generate wrapper
ADD_TORCH_WRAP(tensormathwrap TensorMath.lua)
ADD_TORCH_WRAP(randomwrap random.lua)
ADD_TORCH_PACKAGE(torch "${src}" "${luasrc}")
TARGET_LINK_LIBRARIES(torch luaT TH)
IF(LUALIB)
TARGET_LINK_LIBRARIES(torch ${LUALIB})
ENDIF()
INSTALL(FILES "README.md" DESTINATION "${Torch_INSTALL_LUA_PATH_SUBDIR}/torch")
INSTALL(DIRECTORY "doc" DESTINATION "${Torch_INSTALL_LUA_PATH_SUBDIR}/torch")
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to Torch7 Core (torch7, nn, cutorch, cunn)
Thanks a lot! There are plenty of ways you can help!
Please take a moment to review this document in order to make the contribution
process easy and effective for everyone involved.
Following these guidelines helps to communicate that you respect the time of
the developers managing and developing this open source project. In return,
they should reciprocate that respect in addressing your issue or assessing
patches and features.
## Using the issue tracker
The [issue tracker](https://github.com/torch/torch7/issues) is
the preferred channel for [bug reports](#bugs), [features requests](#features)
and [submitting pull requests](#pull-requests), but please respect the following
restrictions:
* Please **do not** use the issue tracker for personal support requests (use
[mailing-list](https://groups.google.com/forum/#!forum/torch7)).
* Please **do not** open issues regarding the code in a torch package
outside the core. For example don't open issues about the
REPL in the torch7 issue tracker, use the trepl issue tracker for that.
<a name="bugs"></a>
## Bug reports
A bug is a _demonstrable problem_ that is caused by the code in the repository.
Good bug reports are extremely helpful - thank you!
Guidelines for bug reports:
1. **Use the GitHub issue search** — check if the issue has already been
reported.
2. **Check if the issue has been fixed** — try to reproduce it using the
latest `master` or development branch in the repository.
3. **Isolate the problem** — ideally create test case that is within reason,
preferably within 100 lines of code.
A good bug report shouldn't leave others needing to chase you up for more
information. Please try to be as detailed as possible in your report. What is
your environment? What steps will reproduce the issue? What OS do you
experience the problem? What would you expect to be the outcome? All these
details will help people to fix any potential bugs.
<a name="features"></a>
## Feature requests
Feature requests are welcome to be filed. Torch is community-developed,
the maintainers are not exclusive torch developers, so keep that in mind.
The purpose of feature requests is for others who are looking to implement
a feature are aware of the interest in the feature.
<a name="pull-requests"></a>
## Pull requests
Good pull requests - patches, improvements, new features - are a fantastic
help. They should remain focused in scope **and avoid containing unrelated
commits.**
**Please ask first** before embarking on any significant pull request (e.g.
implementing features, refactoring code, porting to a different language),
otherwise you risk spending a lot of time working on something that the
project's developers might not want to merge into the project.
Please adhere to the coding conventions used throughout a project (indentation,
accurate comments, etc.) and any other requirements (such as test coverage).
Adhering to the following this process is the best way to get your work
included in the project:
1. [Fork](https://help.github.com/articles/fork-a-repo) the project, clone your
fork, and configure the remotes:
```bash
# Clone your fork of the repo into the current directory
git clone https://github.com/<your-username>/torch7.git
# Navigate to the newly cloned directory
cd torch7
# Assign the original repo to a remote called "upstream"
git remote add upstream https://github.com/torch/torch7.git
```
2. If you cloned a while ago, get the latest changes from upstream:
```bash
git checkout master
git pull upstream master
```
3. Create a new topic branch (off the main project development branch) to
contain your feature, change, or fix:
```bash
git checkout -b <topic-branch-name>
```
4. Commit your changes in logical chunks. Please try to adhere to these [git commit
message guidelines](http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html)
. Use Git's [interactive rebase](https://help.github.com/articles/about-git-rebase)
feature to tidy up your commits before making them public. This helps us keep the
commit history in logical blocks and clean, as torch grows.
For example:
- If you are adding a new function or a module, keep the module + tests + doc
to a single commit unless logically warranted.
- If you are fixing a bug, keep the bugfix to a single commit unless logically warranted.
5. Locally merge (or rebase) the upstream development branch into your topic branch:
```bash
git pull [--rebase] upstream master
```
6. Push your topic branch up to your fork:
```bash
git push origin <topic-branch-name>
```
7. [Open a Pull Request](https://help.github.com/articles/using-pull-requests/)
with a clear title and description.
**IMPORTANT**: By submitting a patch, you agree to allow the project owners to
license your work under the terms of the BSD License.
================================================
FILE: COPYRIGHT.txt
================================================
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the names of Deepmind Technologies, NYU, NEC Laboratories America
and IDIAP Research Institute nor the names of its contributors may be
used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
================================================
FILE: CmdLine.lua
================================================
local CmdLine = torch.class('torch.CmdLine')
local function strip(str)
return string.match(str, '%-*(.*)')
end
local function pad(str, sz)
return str .. string.rep(' ', sz-#str)
end
function CmdLine:error(msg)
print('')
io.stderr:write(msg)
print('')
self:help()
os.exit(1)
end
function CmdLine:__readArgument__(params, arg, i, nArgument)
local argument = self.arguments[nArgument]
local value = arg[i]
if nArgument > #self.arguments then
self:error('invalid argument: ' .. value)
end
if argument.type and type(value) ~= argument.type then
self:error('invalid argument type for argument ' .. argument.key .. ' (should be ' .. argument.type .. ')')
end
params[strip(argument.key)] = value
return 1
end
function CmdLine:__readOption__(params, arg, i)
local key = arg[i]
local option = self.options[key]
if not option then
self:error('unknown option ' .. key)
end
if option.type and option.type == 'boolean' then
params[strip(key)] = not option.default
return 1
else
local value = arg[i+1]
if not value then
self:error('missing argument for option ' .. key)
end
if not option.type or option.type == 'string' then
elseif option.type == 'number' then
value = tonumber(value)
else
self:error('unknown required option type ' .. option.type)
end
if not value then
self:error('invalid type for option ' .. key .. ' (should be ' .. option.type .. ')')
end
params[strip(key)] = value
return 2
end
end
function CmdLine:__init(argseparator_,keyseparator_)
self.argseparator = argseparator_ or ','
self.keyseparator = keyseparator_ or '='
self.options = {}
self.arguments = {}
self.helplines = {}
self.dateformat = nil
self.silentio = false
end
function CmdLine:silent()
self.silentio = true
end
function CmdLine:addTime(name, format)
format = format or '%Y-%m-%d %H:%M:%S'
if type(format) ~= 'string' then
error('Argument has to be string')
end
if name ~= nil then
name = '[' .. name .. ']: '
else
name = ''
end
self.dateformat = format .. name
end
function CmdLine:argument(key, help, _type_)
table.insert(self.arguments, {key=key, help=help, type=_type_})
table.insert(self.helplines, self.arguments[#self.arguments])
end
function CmdLine:option(key, default, help, _type_)
if default == nil then
error('option ' .. key .. ' has no default value')
end
_type_ = _type_ or type(default)
if type(default) ~= _type_ then
error('option ' .. key .. ' has wrong default type value')
end
self.options[key] = {key=key, default=default, help=help, type=_type_}
table.insert(self.helplines, self.options[key])
end
function CmdLine:default()
local params = {}
for option,v in pairs(self.options) do
params[strip(option)] = v.default
end
return params
end
function CmdLine:parse(arg)
local i = 1
local params = self:default()
local nArgument = 0
while i <= #arg do
if arg[i] == '-help' or arg[i] == '-h' or arg[i] == '--help' then
self:help(arg)
os.exit(0)
end
if self.options[arg[i]] then
i = i + self:__readOption__(params, arg, i)
else
nArgument = nArgument + 1
i = i + self:__readArgument__(params, arg, i, nArgument)
end
end
if nArgument ~= #self.arguments then
self:error('not enough arguments')
end
return params
end
function CmdLine:string(prefix, params, ignore)
local arguments = {}
local options = {}
prefix = prefix or ''
for k,v in pairs(params) do
if ignore[k] then
print('-- ignore option ' .. k)
elseif self.options['-' .. k] then
if v ~= self.options['-' .. k].default or ignore[k] == false then
if type(v) == 'boolean' then
if v then
v = 't'
else
v = 'f'
end
end
table.insert(options, k .. self.keyseparator .. v)
print(k,v,self.options['-' .. k].default)
end
else
local narg
for i=1,#self.arguments do
if strip(self.arguments[i].key) == k then
narg = i
end
end
if narg then
arguments[narg] = k .. self.keyseparator .. v
else
print('WARNING: unknown option/argument: ' .. k .. ' IGNORING for DIRECTORY NAME')
end
end
end
table.sort(options)
local str = table.concat(arguments, self.argseparator)
if str == '' then
str = table.concat(options, self.argseparator)
else
str = str .. self.argseparator .. table.concat(options, self.argseparator)
end
if str == '' then
return prefix
else
return prefix .. self.argseparator .. str
end
end
local oprint = nil
function CmdLine:log(file, params)
local f = (io.type(file) == 'file' and file) or io.open(file, 'w')
oprint = oprint or print -- get the current print function lazily
function print(...)
local n = select("#", ...)
local arg = {...}
if not self.silentio then
oprint(...)
end
local str = {}
if self.dateformat then
table.insert(str, os.date(self.dateformat))
end
for i=1,n do
table.insert(str,tostring(arg[i]))
end
table.insert(str,'\n')
f:write(table.concat(str,' '))
f:flush()
end
print('[program started on ' .. os.date() .. ']')
print('[command line arguments]')
if params then
for k,v in pairs(params) do
print(k,v)
end
end
print('[----------------------]')
end
function CmdLine:text(txt)
txt = txt or ''
assert(type(txt) == 'string')
table.insert(self.helplines, txt)
end
function CmdLine:help(arg)
io.write('Usage: ')
if arg then io.write(arg[0] .. ' ') end
io.write('[options]')
for i=1,#self.arguments do
io.write(' <' .. strip(self.arguments[i].key) .. '>')
end
io.write('\n')
-- first pass to compute max length
local optsz = 0
for _,option in ipairs(self.helplines) do
if type(option) == 'table' then
if option.default ~= nil then -- it is an option
if #option.key > optsz then
optsz = #option.key
end
else -- it is an argument
if #strip(option.key)+2 > optsz then
optsz = #strip(option.key)+2
end
end
end
end
-- second pass to print
for _,option in ipairs(self.helplines) do
if type(option) == 'table' then
io.write(' ')
if option.default ~= nil then -- it is an option
io.write(pad(option.key, optsz))
if option.help then io.write(' ' .. option.help) end
io.write(' [' .. tostring(option.default) .. ']')
else -- it is an argument
io.write(pad('<' .. strip(option.key) .. '>', optsz))
if option.help then io.write(' ' .. option.help) end
end
else
io.write(option) -- just some additional help
end
io.write('\n')
end
end
================================================
FILE: DiskFile.c
================================================
#include "general.h"
static int torch_DiskFile_new(lua_State *L)
{
const char *name = luaL_checkstring(L, 1);
const char *mode = luaL_optstring(L, 2, "r");
int isQuiet = luaT_optboolean(L, 3, 0);
THFile *self = THDiskFile_new(name, mode, isQuiet);
luaT_pushudata(L, self, "torch.DiskFile");
return 1;
}
static int torch_DiskFile_free(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
THFile_free(self);
return 0;
}
static int torch_DiskFile_isLittleEndianCPU(lua_State *L)
{
lua_pushboolean(L, THDiskFile_isLittleEndianCPU());
return 1;
}
static int torch_DiskFile_isBigEndianCPU(lua_State *L)
{
lua_pushboolean(L, !THDiskFile_isLittleEndianCPU());
return 1;
}
static int torch_DiskFile_nativeEndianEncoding(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
THDiskFile_nativeEndianEncoding(self);
lua_settop(L, 1);
return 1;
}
static int torch_DiskFile_littleEndianEncoding(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
THDiskFile_littleEndianEncoding(self);
lua_settop(L, 1);
return 1;
}
static int torch_DiskFile_bigEndianEncoding(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
THDiskFile_bigEndianEncoding(self);
lua_settop(L, 1);
return 1;
}
static int torch_DiskFile_longSize(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
THDiskFile_longSize(self, lua_tointeger(L, 2));
lua_settop(L, 1);
return 1;
}
static int torch_DiskFile_noBuffer(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
THDiskFile_noBuffer(self);
lua_settop(L, 1);
return 1;
}
static int torch_DiskFile___tostring__(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.DiskFile");
lua_pushfstring(L, "torch.DiskFile on <%s> [status: %s -- mode %c%c]",
THDiskFile_name(self),
(THFile_isOpened(self) ? "open" : "closed"),
(THFile_isReadable(self) ? 'r' : ' '),
(THFile_isWritable(self) ? 'w' : ' '));
return 1;
}
static const struct luaL_Reg torch_DiskFile__ [] = {
{"isLittleEndianCPU", torch_DiskFile_isLittleEndianCPU},
{"isBigEndianCPU", torch_DiskFile_isBigEndianCPU},
{"nativeEndianEncoding", torch_DiskFile_nativeEndianEncoding},
{"littleEndianEncoding", torch_DiskFile_littleEndianEncoding},
{"bigEndianEncoding", torch_DiskFile_bigEndianEncoding},
{"longSize", torch_DiskFile_longSize},
{"noBuffer", torch_DiskFile_noBuffer},
{"__tostring__", torch_DiskFile___tostring__},
{NULL, NULL}
};
void torch_DiskFile_init(lua_State *L)
{
luaT_newmetatable(L, "torch.DiskFile", "torch.File",
torch_DiskFile_new, torch_DiskFile_free, NULL);
luaT_setfuncs(L, torch_DiskFile__, 0);
lua_pop(L, 1);
}
================================================
FILE: FFInterface.lua
================================================
-- if this causes issues, you may need to:
-- luarocks remove --force ffi
-- and follow instructions to install
-- https://github.com/facebook/luaffifb
local ok, ffi = pcall(require, 'ffi')
local function checkArgument(condition, fn, ud, msg, level)
local level = level or 3
if not condition then
error("bad argument #" .. ud .. " to '" .. fn .. "' (" .. msg .. ")", level)
end
end
local function checkArgumentType(expected, actual, fn, ud, level)
local level = level or 3
if expected ~= actual then
checkArgument(false, fn, ud, expected .. " expected, got " .. actual, level + 1)
end
end
if ok then
local Real2real = {
Byte='unsigned char',
Char='char',
Short='short',
Int='int',
Long='long',
Float='float',
Double='double',
Half='THHalf'
}
-- Allocator
ffi.cdef[[
typedef struct THAllocator {
void* (*malloc)(void*, ptrdiff_t);
void* (*realloc)(void*, void*, ptrdiff_t);
void (*free)(void*, void*);
} THAllocator;
]]
-- Half
ffi.cdef[[
typedef struct {
unsigned short x;
} __THHalf;
typedef __THHalf THHalf;
]]
-- Storage
for Real, real in pairs(Real2real) do
local cdefs = [[
typedef struct THRealStorage
{
real *data;
ptrdiff_t size;
int refcount;
char flag;
THAllocator *allocator;
void *allocatorContext;
} THRealStorage;
]]
cdefs = cdefs:gsub('Real', Real):gsub('real', real)
ffi.cdef(cdefs)
local Storage = torch.getmetatable(string.format('torch.%sStorage', Real))
local Storage_tt = ffi.typeof('TH' .. Real .. 'Storage**')
rawset(Storage,
"cdata",
function(self)
return Storage_tt(self)[0]
end)
rawset(Storage,
"data",
function(self)
return Storage_tt(self)[0].data
end)
end
-- Tensor
for Real, real in pairs(Real2real) do
local cdefs = [[
typedef struct THRealTensor
{
long *size;
long *stride;
int nDimension;
THRealStorage *storage;
ptrdiff_t storageOffset;
int refcount;
char flag;
} THRealTensor;
]]
cdefs = cdefs:gsub('Real', Real):gsub('real', real)
ffi.cdef(cdefs)
local Tensor_type = string.format('torch.%sTensor', Real)
local Tensor = torch.getmetatable(Tensor_type)
local Tensor_tt = ffi.typeof('TH' .. Real .. 'Tensor**')
rawset(Tensor,
"cdata",
function(self)
if not self then return nil; end
return Tensor_tt(self)[0]
end)
rawset(Tensor,
"data",
function(self)
if not self then return nil; end
self = Tensor_tt(self)[0]
return self.storage ~= nil and self.storage.data + self.storageOffset or nil
end)
-- faster apply (contiguous case)
if Tensor_type ~= 'torch.HalfTensor' then
local apply = Tensor.apply
rawset(Tensor,
"apply",
function(self, func)
if self:isContiguous() and self.data then
local self_d = self:data()
for i=0,self:nElement()-1 do
local res = func(tonumber(self_d[i])) -- tonumber() required for long...
if res then
self_d[i] = res
end
end
return self
else
return apply(self, func)
end
end)
-- faster map (contiguous case)
local map = Tensor.map
rawset(Tensor,
"map",
function(self, src, func)
checkArgument(torch.isTensor(src), "map", 1, "tensor expected")
checkArgumentType(self:type(), src:type(), "map", 1)
if self:isContiguous() and src:isContiguous() and self.data and src.data then
local self_d = self:data()
local src_d = src:data()
assert(src:nElement() == self:nElement(), 'size mismatch')
for i=0,self:nElement()-1 do
local res = func(tonumber(self_d[i]), tonumber(src_d[i])) -- tonumber() required for long...
if res then
self_d[i] = res
end
end
return self
else
return map(self, src, func)
end
end)
-- faster map2 (contiguous case)
local map2 = Tensor.map2
rawset(Tensor,
"map2",
function(self, src1, src2, func)
checkArgument(torch.isTensor(src1), "map", 1, "tensor expected")
checkArgument(torch.isTensor(src2), "map", 2, "tensor expected")
checkArgumentType(self:type(), src1:type(), "map", 1)
checkArgumentType(self:type(), src2:type(), "map", 2)
if self:isContiguous() and src1:isContiguous() and src2:isContiguous() and self.data and src1.data and src2.data then
local self_d = self:data()
local src1_d = src1:data()
local src2_d = src2:data()
assert(src1:nElement() == self:nElement(), 'size mismatch')
assert(src2:nElement() == self:nElement(), 'size mismatch')
for i=0,self:nElement()-1 do
local res = func(tonumber(self_d[i]), tonumber(src1_d[i]), tonumber(src2_d[i])) -- tonumber() required for long...
if res then
self_d[i] = res
end
end
return self
else
return map2(self, src1, src2, func)
end
end)
end
end
-- torch.data
-- will fail if :data() is not defined
function torch.data(self, asnumber)
if not self then return nil; end
local data = self:data()
if asnumber then
return ffi.cast('intptr_t', data)
else
return data
end
end
-- torch.cdata
-- will fail if :cdata() is not defined
function torch.cdata(self, asnumber)
if not self then return nil; end
local cdata = self:cdata()
if asnumber then
return ffi.cast('intptr_t', cdata)
else
return cdata
end
end
end
================================================
FILE: File.c
================================================
#include "general.h"
#include "THFile.h"
#include "luaT.h"
#define IMPLEMENT_TORCH_FILE_FLAG(NAME) \
static int torch_File_##NAME(lua_State *L) \
{ \
THFile *self = luaT_checkudata(L, 1, "torch.File"); \
lua_pushboolean(L, THFile_##NAME(self)); \
return 1; \
}
IMPLEMENT_TORCH_FILE_FLAG(isQuiet)
IMPLEMENT_TORCH_FILE_FLAG(isReadable)
IMPLEMENT_TORCH_FILE_FLAG(isWritable)
IMPLEMENT_TORCH_FILE_FLAG(isBinary)
IMPLEMENT_TORCH_FILE_FLAG(isAutoSpacing)
IMPLEMENT_TORCH_FILE_FLAG(hasError)
#define IMPLEMENT_TORCH_FILE_FUNC(NAME) \
static int torch_File_##NAME(lua_State *L) \
{ \
THFile *self = luaT_checkudata(L, 1, "torch.File"); \
THFile_##NAME(self); \
lua_settop(L, 1); \
return 1; \
}
IMPLEMENT_TORCH_FILE_FUNC(binary)
IMPLEMENT_TORCH_FILE_FUNC(ascii)
IMPLEMENT_TORCH_FILE_FUNC(autoSpacing)
IMPLEMENT_TORCH_FILE_FUNC(noAutoSpacing)
IMPLEMENT_TORCH_FILE_FUNC(quiet)
IMPLEMENT_TORCH_FILE_FUNC(pedantic)
IMPLEMENT_TORCH_FILE_FUNC(clearError)
IMPLEMENT_TORCH_FILE_FUNC(synchronize)
static int torch_File_seek(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.File");
ptrdiff_t position = luaL_checkinteger(L, 2)-1;
// >= 0 because it has 1 already subtracted
THArgCheck(position >= 0, 2, "position has to be greater than 0!");
THFile_seek(self, (size_t)position);
lua_settop(L, 1);
return 1;
}
IMPLEMENT_TORCH_FILE_FUNC(seekEnd)
static int torch_File_position(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.File");
lua_pushnumber(L, THFile_position(self)+1);
return 1;
}
IMPLEMENT_TORCH_FILE_FUNC(close)
#define IMPLEMENT_TORCH_FILE_RW(TYPEC, TYPE) \
static int torch_File_read##TYPEC(lua_State *L) \
{ \
THFile *self = luaT_checkudata(L, 1, "torch.File"); \
int narg = lua_gettop(L); \
\
if(narg == 1) \
{ \
lua_pushnumber(L, THFile_read##TYPEC##Scalar(self)); \
return 1; \
} \
else if(narg == 2) \
{ \
if(lua_isnumber(L, 2)) \
{ \
ptrdiff_t size = lua_tonumber(L, 2); \
ptrdiff_t nread; \
\
TH##TYPEC##Storage *storage = TH##TYPEC##Storage_newWithSize(size); \
luaT_pushudata(L, storage, "torch." #TYPEC "Storage"); \
nread = THFile_read##TYPEC(self, storage); \
if(nread != size) \
TH##TYPEC##Storage_resize(storage, nread); \
return 1; \
} \
else if(luaT_toudata(L, 2, "torch." #TYPEC "Storage")) \
{ \
TH##TYPEC##Storage *storage = luaT_toudata(L, 2, "torch." #TYPEC "Storage"); \
lua_pushnumber(L, THFile_read##TYPEC(self, storage)); \
return 1; \
} \
} \
\
luaL_error(L, "nothing, number, or " #TYPEC "Storage expected"); \
return 0; \
} \
\
static int torch_File_write##TYPEC(lua_State *L) \
{ \
THFile *self = luaT_checkudata(L, 1, "torch.File"); \
int narg = lua_gettop(L); \
\
if(narg == 2) \
{ \
if(lua_isnumber(L, 2)) \
{ \
TYPE value = lua_tonumber(L, 2); \
THFile_write##TYPEC##Scalar(self, (TYPE)value); \
return 0; \
} \
else if(luaT_toudata(L, 2, "torch." #TYPEC "Storage")) \
{ \
TH##TYPEC##Storage *storage = luaT_toudata(L, 2, "torch." #TYPEC "Storage"); \
lua_pushnumber(L, THFile_write##TYPEC(self, storage)); \
return 1; \
} \
} \
\
luaL_error(L, "number, or " #TYPEC "Storage expected"); \
return 0; \
}
IMPLEMENT_TORCH_FILE_RW(Byte, unsigned char)
IMPLEMENT_TORCH_FILE_RW(Char, char)
IMPLEMENT_TORCH_FILE_RW(Short, short)
IMPLEMENT_TORCH_FILE_RW(Int, int)
IMPLEMENT_TORCH_FILE_RW(Long, long)
IMPLEMENT_TORCH_FILE_RW(Float, float)
IMPLEMENT_TORCH_FILE_RW(Double, double)
static int torch_File_readString(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.File");
const char *format = luaL_checkstring(L, 2);
char *str;
ptrdiff_t size;
size = THFile_readStringRaw(self, format, &str);
lua_pushlstring(L, str, size);
THFree(str);
return 1;
}
static int torch_File_writeString(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.File");
const char *str = NULL;
size_t size;
luaL_checktype(L, 2, LUA_TSTRING);
str = lua_tolstring(L, 2, &size);
lua_pushnumber(L, THFile_writeStringRaw(self, str, size));
return 1;
}
static const struct luaL_Reg torch_File__ [] = {
{"isQuiet", torch_File_isQuiet},
{"isReadable", torch_File_isReadable},
{"isWritable", torch_File_isWritable},
{"isBinary", torch_File_isBinary},
{"isAutoSpacing", torch_File_isAutoSpacing},
{"hasError", torch_File_hasError},
{"binary", torch_File_binary},
{"ascii", torch_File_ascii},
{"autoSpacing", torch_File_autoSpacing},
{"noAutoSpacing", torch_File_noAutoSpacing},
{"quiet", torch_File_quiet},
{"pedantic", torch_File_pedantic},
{"clearError", torch_File_clearError},
/* DEBUG: CHECK DISK FREE & READ/WRITE STRING*/
{"readByte", torch_File_readByte},
{"readChar", torch_File_readChar},
{"readShort", torch_File_readShort},
{"readInt", torch_File_readInt},
{"readLong", torch_File_readLong},
{"readFloat", torch_File_readFloat},
{"readDouble", torch_File_readDouble},
{"readString", torch_File_readString},
{"writeByte", torch_File_writeByte},
{"writeChar", torch_File_writeChar},
{"writeShort", torch_File_writeShort},
{"writeInt", torch_File_writeInt},
{"writeLong", torch_File_writeLong},
{"writeFloat", torch_File_writeFloat},
{"writeDouble", torch_File_writeDouble},
{"writeString", torch_File_writeString},
{"synchronize", torch_File_synchronize},
{"seek", torch_File_seek},
{"seekEnd", torch_File_seekEnd},
{"position", torch_File_position},
{"close", torch_File_close},
{NULL, NULL}
};
void torch_File_init(lua_State *L)
{
luaT_newmetatable(L, "torch.File", NULL, NULL, NULL, NULL);
luaT_setfuncs(L, torch_File__, 0);
lua_pop(L, 1);
}
================================================
FILE: File.lua
================================================
local File = torch.getmetatable('torch.File')
function File:writeBool(value)
if value then
self:writeInt(1)
else
self:writeInt(0)
end
end
function File:readBool()
return (self:readInt() == 1)
end
local TYPE_NIL = 0
local TYPE_NUMBER = 1
local TYPE_STRING = 2
local TYPE_TABLE = 3
local TYPE_TORCH = 4
local TYPE_BOOLEAN = 5
local TYPE_FUNCTION = 6
local TYPE_RECUR_FUNCTION = 8
local LEGACY_TYPE_RECUR_FUNCTION = 7
-- Lua 5.2 compatibility
local loadstring = loadstring or load
function File:isWritableObject(object)
local typename = type(object)
local typeidx
if type(object) ~= 'boolean' and not object then
typeidx = TYPE_NIL
elseif torch.typename(object) and torch.factory(torch.typename(object)) then
typeidx = TYPE_TORCH
elseif typename == 'table' then
typeidx = TYPE_TABLE
elseif typename == 'number' then
typeidx = TYPE_NUMBER
elseif typename == 'string' then
typeidx = TYPE_STRING
elseif typename == 'boolean' then
typeidx = TYPE_BOOLEAN
elseif typename == 'function' and pcall(string.dump, object) then
typeidx = TYPE_RECUR_FUNCTION
end
return typeidx
end
function File:referenced(ref)
-- we use an environment to keep a record of written objects
if not torch.getenv(self).writeObjects then
torch.setenv(self, {
writeObjects={}, writeObjectsRef={},
readObjects={},
objectNameStack={},
upvalueRefToId={}, upvalueIdToClosure={},
})
end
local env = torch.getenv(self)
env.force = not ref
torch.setenv(self,env)
return self
end
function File:isReferenced()
-- if no environment, then no forcing setup yet
if not torch.getenv(self).writeObjects then
return true
end
local env = torch.getenv(self)
return not env.force
end
local function getmetamethod(obj, name)
local func
local status
-- check getmetatable(obj).__name or
-- check getmetatable(obj).name
status, func = pcall(
function()
-- note that sometimes the metatable is hidden
-- we get it for sure through the torch type system
local mt = torch.getmetatable(torch.typename(obj))
if mt then
return mt['__' .. name] or mt[name]
end
end
)
if status and type(func) == 'function' then
return func
end
end
local UPVALUES_TOKEN = {} -- unique object
local function formatStack(objectNameStack)
-- Format object name stack skipping UPVALUES_TOKEN and upvalue index
local parts = {}
for i, v in ipairs(objectNameStack) do
if v ~= UPVALUES_TOKEN and objectNameStack[i-1] ~= UPVALUES_TOKEN then
table.insert(parts, v)
end
end
return table.concat(parts, '.')
end
function File:writeObject(object, debugname, hook)
-- define a default hook function if not provided
hook = hook or function(object) return object end
-- we use an environment to keep a record of written objects
if not torch.getenv(self).writeObjects then
torch.setenv(self, {
writeObjects={}, writeObjectsRef={},
readObjects={},
objectNameStack={},
upvalueRefToId={}, upvalueIdToClosure={},
})
end
-- That guy is used for references' book-keeping
local sobject = object
-- That guy is the object that is actually persisted
-- hook(object) can be used to modify the object before writing it to the file.
-- Useful for serializing objects under a config
-- that we want to deserialize safely under another config.
-- (e.g. Cuda to Float tensors, cudnn to nn, ...)
object = hook(object)
local force = torch.getenv(self).force
-- if nil object, only write the type and return
if type(object) ~= 'boolean' and not object then
self:writeInt(TYPE_NIL)
return
end
local objectNameStack = torch.getenv(self).objectNameStack
table.insert(objectNameStack, debugname or '<?>')
-- check the type we are dealing with
local typeidx = self:isWritableObject(object)
if not typeidx then
error(string.format('Unwritable object <%s> at %s', type(object), formatStack(objectNameStack)))
end
self:writeInt(typeidx)
if typeidx == TYPE_NUMBER then
self:writeDouble(object)
elseif typeidx == TYPE_BOOLEAN then
self:writeBool(object)
elseif typeidx == TYPE_STRING then
local stringStorage = torch.CharStorage():string(object)
self:writeInt(#stringStorage)
self:writeChar(stringStorage)
elseif typeidx == TYPE_TORCH or typeidx == TYPE_TABLE or typeidx == TYPE_RECUR_FUNCTION then
-- check it exists already (we look at the pointer!)
local objects = torch.getenv(self).writeObjects
local objectsRef = torch.getenv(self).writeObjectsRef
local index = objects[torch.pointer(sobject)]
if index and (not force) then
-- if already exists, write only its index
self:writeInt(index)
else
-- else write the object itself
index = objects.nWriteObject or 0
index = index + 1
if not force then
objects[torch.pointer(sobject)] = index
objectsRef[object] = index -- we make sure the object is not going to disappear
end
self:writeInt(index)
objects.nWriteObject = index
if typeidx == TYPE_RECUR_FUNCTION then
local upvalueRefToId = torch.getenv(self).upvalueRefToId
-- Unique ID for each ref since lightuserdata are not serializable
local nextId = 1
for _ in pairs(upvalueRefToId) do nextId=nextId+1 end
local upvalues = {}
local counter = 0
while true do
counter = counter + 1
local name,value = debug.getupvalue(object, counter)
if not name then break end
if name == '_ENV' then value = nil end
local id=nil
-- debug.upvalueid exists only for lua>=5.2 and luajit
if debug.upvalueid then
local upvalueRef = debug.upvalueid(object, counter)
if not upvalueRefToId[upvalueRef] then
upvalueRefToId[upvalueRef] = nextId
nextId = nextId + 1
end
id = upvalueRefToId[upvalueRef]
end
table.insert(upvalues, {name=name, id=id, value=value})
end
local dumped = string.dump(object)
local stringStorage = torch.CharStorage():string(dumped)
self:writeInt(#stringStorage)
self:writeChar(stringStorage)
self:writeObject(upvalues, UPVALUES_TOKEN, hook)
elseif typeidx == TYPE_TORCH then
local version = torch.CharStorage():string('V ' .. torch.version(object))
local className = torch.CharStorage():string(torch.typename(object))
self:writeInt(#version)
self:writeChar(version)
self:writeInt(#className)
self:writeChar(className)
local write = getmetamethod(object, 'write')
if write then
write(object, self)
elseif type(object) == 'table' then
local var = {}
for k,v in pairs(object) do
if self:isWritableObject(v) then
var[k] = v
else
print(string.format('$ Warning: cannot write object field <%s> of <%s> %s', k, torch.typename(object), formatStack(objectNameStack)))
end
end
self:writeObject(var, torch.typename(object), hook)
else
error(string.format('<%s> is a non-serializable Torch object %s', torch.typename(object), formatStack(objectNameStack)))
end
else -- it is a table
local size = 0; for k,v in pairs(object) do size = size + 1 end
self:writeInt(size)
for k,v in pairs(object) do
self:writeObject(k, nil, hook)
local name = (type(k) == 'string' or type(k) == 'number') and tostring(k) or nil
-- special case name for upvalues
if objectNameStack[#objectNameStack-1] == UPVALUES_TOKEN and
name == 'value' and type(object.name) == 'string' then
name = object.name
end
self:writeObject(v, name, hook)
end
end
end
else
error('Unwritable object')
end
table.remove(objectNameStack)
end
function File:readObject()
-- we use an environment to keep a record of read objects
if not torch.getenv(self).writeObjects then
torch.setenv(self, {
writeObjects={}, writeObjectsRef={},
readObjects={},
objectNameStack={},
upvalueRefToId={}, upvalueIdToClosure={},
})
end
local force = torch.getenv(self).force
-- read the typeidx
local typeidx = self:readInt()
-- is it nil?
if typeidx == TYPE_NIL then
return nil
end
if typeidx == TYPE_NUMBER then
return self:readDouble()
elseif typeidx == TYPE_BOOLEAN then
return self:readBool()
elseif typeidx == TYPE_STRING then
local size = self:readInt()
return self:readChar(size):string()
elseif typeidx == TYPE_FUNCTION then
local size = self:readInt()
local dumped = self:readChar(size):string()
local func, err = loadstring(dumped)
if not func then
io.stderr:write(string.format('Warning: Failed to load function from bytecode: %s', err))
end
local upvalues = self:readObject()
for index,upvalue in ipairs(upvalues) do
debug.setupvalue(func, index, upvalue)
end
return func
elseif typeidx == TYPE_TABLE or typeidx == TYPE_TORCH or typeidx == TYPE_RECUR_FUNCTION or typeidx == LEGACY_TYPE_RECUR_FUNCTION then
-- read the index
local index = self:readInt()
-- check it is loaded already
local objects = torch.getenv(self).readObjects
if objects[index] and not force then
return objects[index]
end
-- otherwise read it
if typeidx == TYPE_RECUR_FUNCTION or typeidx == LEGACY_TYPE_RECUR_FUNCTION then
local size = self:readInt()
local dumped = self:readChar(size):string()
local func, err = loadstring(dumped)
if not func then
io.stderr:write(string.format('Warning: Failed to load function from bytecode: %s', err))
end
if not force then
objects[index] = func
end
local upvalueIdToClosure = torch.getenv(self).upvalueIdToClosure
local upvalues = self:readObject()
for index,upvalue in ipairs(upvalues) do
if typeidx == LEGACY_TYPE_RECUR_FUNCTION then
debug.setupvalue(func, index, upvalue)
elseif upvalue.name == '_ENV' then
debug.setupvalue(func, index, _ENV)
else
debug.setupvalue(func, index, upvalue.value)
-- debug.upvaluejoin exists only for lua>=5.2 and luajit
if debug.upvaluejoin and upvalue.id then
if upvalueIdToClosure[upvalue.id] then
-- This upvalue is linked to another one
local otherClosure = upvalueIdToClosure[upvalue.id]
debug.upvaluejoin(func, index, otherClosure.func, otherClosure.index)
else
-- Save this closure for next time
upvalueIdToClosure[upvalue.id] = {
func = func,
index = index,
}
end
end
end
end
return func
elseif typeidx == TYPE_TORCH then
local version, className, versionNumber
version = self:readChar(self:readInt()):string()
versionNumber = tonumber(string.match(version, '^V (.*)$'))
if not versionNumber then
className = version
versionNumber = 0 -- file created before existence of versioning system
else
className = self:readChar(self:readInt()):string()
end
if not torch.factory(className) then
error(string.format('unknown Torch class <%s>', tostring(className)))
end
local object = torch.factory(className)(self)
if not force then
objects[index] = object
end
local read = getmetamethod(object, 'read')
if read then
read(object, self, versionNumber)
elseif type(object) == 'table' then
local var = self:readObject()
for k,v in pairs(var) do
object[k] = v
end
else
error(string.format('Cannot load object class <%s>', tostring(className)))
end
return object
else -- it is a table
local size = self:readInt()
local object = {}
if not force then
objects[index] = object
end
for i = 1,size do
local k = self:readObject()
local v = self:readObject()
object[k] = v
end
return object
end
else
error('unknown object')
end
end
-- simple helpers to save/load arbitrary objects/tables
function torch.save(filename, object, mode, referenced)
assert(mode == nil or mode == 'binary' or mode == 'ascii', '"binary" or "ascii" (or nil) expected for mode')
assert(referenced == nil or referenced == true or referenced == false, 'true or false (or nil) expected for referenced')
mode = mode or 'binary'
referenced = referenced == nil and true or referenced
local file = torch.DiskFile(filename, 'w')
file[mode](file)
file:referenced(referenced)
file:writeObject(object)
file:close()
end
function torch.load(filename, mode, referenced)
assert(mode == 'binary' or mode == 'b32' or mode == 'b64' or
mode == nil or mode == 'ascii',
'"binary", "b32", "b64" or "ascii" (or nil) expected for mode')
assert(referenced == nil or referenced == true or referenced == false,
'true or false (or nil) expected for referenced')
local longSize
if mode == 'b32' or mode == 'b64' then
longSize = tonumber(mode:match('%d+')) / 8
mode = 'binary'
end
mode = mode or 'binary'
referenced = referenced == nil and true or referenced
local file = torch.DiskFile(filename, 'r')
file[mode](file)
file:referenced(referenced)
if longSize then file:longSize(longSize) end
local object = file:readObject()
file:close()
return object
end
-- simple helpers to serialize/deserialize arbitrary objects/tables
function torch.serialize(object, mode)
local storage = torch.serializeToStorage(object, mode)
return storage:string()
end
-- Serialize to a CharStorage, not a lua string. This avoids
function torch.serializeToStorage(object, mode)
mode = mode or 'binary'
local f = torch.MemoryFile()
f = f[mode](f)
f:writeObject(object)
local storage = f:storage()
-- the storage includes an extra NULL character: get rid of it
storage:resize(storage:size()-1)
f:close()
return storage
end
function torch.deserializeFromStorage(storage, mode)
mode = mode or 'binary'
local tx = torch.CharTensor(storage)
local xp = torch.CharStorage(tx:size(1)+1)
local txp = torch.CharTensor(xp)
txp:narrow(1,1,tx:size(1)):copy(tx)
txp[tx:size(1)+1] = 0
local f = torch.MemoryFile(xp)
f = f[mode](f)
local object = f:readObject()
f:close()
return object
end
function torch.deserialize(str, mode)
local storage = torch.CharStorage():string(str)
return torch.deserializeFromStorage(storage, mode)
end
-- public API (saveobj/loadobj are safe for global import)
torch.saveobj = torch.save
torch.loadobj = torch.load
================================================
FILE: Generator.c
================================================
#include <general.h>
int torch_Generator_new(lua_State *L)
{
THGenerator *gen = THGenerator_new();
luaT_pushudata(L, gen, torch_Generator);
return 1;
}
int torch_Generator_free(lua_State *L)
{
THGenerator *gen= luaT_checkudata(L, 1, torch_Generator);
THGenerator_free(gen);
return 0;
}
static int torch_Generator_write(lua_State *L)
{
THGenerator *gen = luaT_checkudata(L, 1, torch_Generator);
THFile *file = luaT_checkudata(L, 2, "torch.File");
THFile_writeByteRaw(file, (unsigned char *)gen, sizeof(THGenerator));
return 0;
}
static int torch_Generator_read(lua_State *L)
{
THGenerator *gen = luaT_checkudata(L, 1, torch_Generator);
THFile *file = luaT_checkudata(L, 2, "torch.File");
THFile_readByteRaw(file, (unsigned char *)gen, sizeof(THGenerator));
return 0;
}
static const struct luaL_Reg torch_Generator_table_ [] = {
{"write", torch_Generator_write},
{"read", torch_Generator_read},
{NULL, NULL}
};
#define torch_Generator_factory torch_Generator_new
void torch_Generator_init(lua_State *L)
{
luaT_newmetatable(L, torch_Generator, NULL,
torch_Generator_new, torch_Generator_free, torch_Generator_factory);
luaT_setfuncs(L, torch_Generator_table_, 0);
lua_pop(L, 1);
}
================================================
FILE: MemoryFile.c
================================================
#include "general.h"
static int torch_MemoryFile_new(lua_State *L)
{
const char *mode;
THCharStorage *storage = luaT_toudata(L, 1, "torch.CharStorage");
THFile *self;
if(storage)
{
mode = luaL_optstring(L, 2, "rw");
self = THMemoryFile_newWithStorage(storage, mode);
}
else
{
mode = luaL_optstring(L, 1, "rw");
self = THMemoryFile_new(mode);
}
luaT_pushudata(L, self, "torch.MemoryFile");
return 1;
}
static int torch_MemoryFile_storage(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.MemoryFile");
THCharStorage_retain(THMemoryFile_storage(self));
luaT_pushudata(L, THMemoryFile_storage(self), "torch.CharStorage");
return 1;
}
static int torch_longSize(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.MemoryFile");
THMemoryFile_longSize(self, lua_tointeger(L, 2));
lua_settop(L, 1);
return 1;
}
static int torch_MemoryFile_free(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.MemoryFile");
THFile_free(self);
return 0;
}
static int torch_MemoryFile___tostring__(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.MemoryFile");
lua_pushfstring(L, "torch.MemoryFile [status: %s -- mode: %c%c]",
(THFile_isOpened(self) ? "open" : "closed"),
(THFile_isReadable(self) ? 'r' : ' '),
(THFile_isWritable(self) ? 'w' : ' '));
return 1;
}
static const struct luaL_Reg torch_MemoryFile__ [] = {
{"storage", torch_MemoryFile_storage},
{"longSize", torch_longSize},
{"__tostring__", torch_MemoryFile___tostring__},
{NULL, NULL}
};
void torch_MemoryFile_init(lua_State *L)
{
luaT_newmetatable(L, "torch.MemoryFile", "torch.File",
torch_MemoryFile_new, torch_MemoryFile_free, NULL);
luaT_setfuncs(L, torch_MemoryFile__, 0);
lua_pop(L, 1);
}
================================================
FILE: PipeFile.c
================================================
#include "general.h"
static int torch_PipeFile_new(lua_State *L)
{
const char *name = luaL_checkstring(L, 1);
const char *mode = luaL_optstring(L, 2, "r");
int isQuiet = luaT_optboolean(L, 3, 0);
THFile *self = THPipeFile_new(name, mode, isQuiet);
luaT_pushudata(L, self, "torch.PipeFile");
return 1;
}
static int torch_PipeFile_free(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.PipeFile");
THFile_free(self);
return 0;
}
static int torch_PipeFile___tostring__(lua_State *L)
{
THFile *self = luaT_checkudata(L, 1, "torch.PipeFile");
lua_pushfstring(L, "torch.PipeFile on <%s> [status: %s -- mode: %c%c]",
THDiskFile_name(self),
(THFile_isOpened(self) ? "open" : "closed"),
(THFile_isReadable(self) ? 'r' : ' '),
(THFile_isWritable(self) ? 'w' : ' '));
return 1;
}
static const struct luaL_Reg torch_PipeFile__ [] = {
{"__tostring__", torch_PipeFile___tostring__},
{NULL, NULL}
};
void torch_PipeFile_init(lua_State *L)
{
luaT_newmetatable(L, "torch.PipeFile", "torch.DiskFile",
torch_PipeFile_new, torch_PipeFile_free, NULL);
luaT_setfuncs(L, torch_PipeFile__, 0);
lua_pop(L, 1);
}
================================================
FILE: README.md
================================================
[](https://gitter.im/torch/torch7?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://travis-ci.org/torch/torch7)
## Development Status
Torch is not in active development. The functionality provided by the C backend of Torch, which are the TH, THNN, THC, THCUNN libraries is actively extended and re-written in the ATen C++11 library ([source](https://github.com/pytorch/pytorch/tree/master/aten), [mirror](https://github.com/zdevito/ATen/)).
ATen exposes all operators you would expect from torch7, nn, cutorch, and cunn directly in C++11 and includes additional support for sparse tensors and distributed operations. It is to note however that the API and semantics of the backend libraries in Torch-7 are different from the semantice provided by ATen. For example ATen provides numpy-style broadcasting while TH* dont. For information on building the forked Torch-7 libraries in C, refer to ["The C interface" in pytorch/aten/src/README.md](https://github.com/pytorch/pytorch/tree/master/aten/src#the-c-interface).
## Need help? ##
Torch7 community support can be found at the following locations. As of 2019, the Torch-7 community is close to non-existent.
* Questions, Support, Install issues: [Google groups](https://groups.google.com/forum/#!forum/torch7)
* Reporting bugs: [torch7](https://github.com/torch/torch7/issues) [nn](https://github.com/torch/nn/issues) [cutorch](https://github.com/torch/cutorch/issues) [cunn](https://github.com/torch/cutorch/issues) [optim](https://github.com/torch/optim/issues) [threads](https://github.com/torch/threads/issues)
* Hanging out with other developers and users (strictly no install issues, no large blobs of text): [Gitter Chat](https://gitter.im/torch/torch7)
<a name="torch.reference.dok"></a>
# Torch Package Reference Manual #
__Torch__ is the main package in [Torch7](http://torch.ch) where data
structures for multi-dimensional tensors and mathematical operations
over these are defined. Additionally, it provides many utilities for
accessing files, serializing objects of arbitrary types and other
useful utilities.
<a name="torch.overview.dok"></a>
## Torch Packages ##
* Tensor Library
* [Tensor](doc/tensor.md) defines the _all powerful_ tensor object that provides multi-dimensional numerical arrays with type templating.
* [Mathematical operations](doc/maths.md) that are defined for the tensor object types.
* [Storage](doc/storage.md) defines a simple storage interface that controls the underlying storage for any tensor object.
* File I/O Interface Library
* [File](doc/file.md) is an abstract interface for common file operations.
* [Disk File](doc/diskfile.md) defines operations on files stored on disk.
* [Memory File](doc/memoryfile.md) defines operations on stored in RAM.
* [Pipe File](doc/pipefile.md) defines operations for using piped commands.
* [High-Level File operations](doc/serialization.md) defines higher-level serialization functions.
* Useful Utilities
* [Timer](doc/timer.md) provides functionality for _measuring time_.
* [Tester](doc/tester.md) is a generic tester framework.
* [CmdLine](doc/cmdline.md) is a command line argument parsing utility.
* [Random](doc/random.md) defines a random number generator package with various distributions.
* Finally useful [utility](doc/utility.md) functions are provided for easy handling of torch tensor types and class inheritance.
<a name="torch.links.dok"></a>
## Useful Links ##
* [Community packages](https://github.com/torch/torch7/wiki/Cheatsheet)
* [Torch Blog](http://torch.ch/blog/)
* [Torch Slides](https://github.com/soumith/cvpr2015/blob/master/cvpr-torch.pdf)
================================================
FILE: ROADMAP.md
================================================
# Torch Roadmap (August 2015 - March 2016)
This roadmap document is intended to serve as a loose plan of our vision for Torch in the short term.
It is open to community feedback and contribution and only intends to serve as an initial draft.
After community feedback, we shall freeze it and work on it.
The roadmap focuses on five separate things
- Core development: improving the core technically. Design changes, code refactors, performance, they go here.
- Documentation and Accessibility: Outlining the changes in documentation, and improving general user and developer documentation in various ways.
- Versioning and Packaging: Planned and much needed changes to the packaging of Torch are discussed here.
- Continuous Build Infrastructure: Making our continuous builds more robust, introducing CUDA and OpenCL contbuilds etc.
- Other improvements
## Torch Core Project Development
- New class system:
- **[definite]** with no global side-effects (i.e. the class constructor should be scoped into its parent package)
Get rid of every statement/system that has a global effect on the environment (torch.setdefaultensortype => dangerous and not clean)
- **[needs discussion]** fully serializable (i.e. when deserializing/reloading a model, there shouldn't be a need to load libraries that defined the class originally, like nn; the class definition should be serialized as well: this would remove a lot of backward compatibility hacks that we have to add to class definitions currently
- **koray**: I like this, but wouldn't it break backward compatibility?
Currently, whatever we serialize, it is just the data and implementation is defined
at load time, so if a bug is fixed (or introduced) you use that.
And it starts being ambiguous, what if I load a layer from file and
create a new one and their implementation is inconsistent...)
- **[definite]** Get rid of non-tensor-related stuff (like serialization) in TH, and move it to lua side
- **[needs discussion]** OpenMP: Should it stay or go? Is Threads sufficient?
- **Ronan**: I really wonder about this guy, especially now that I have been using threads intensively. I am not sure that fine-grine threading is necessary.
- **koray**: I guess you mean with threading, there is no need for OpenMP, but I disagree.
Our convolution layer will use multiple threads and then if we run a ReLu over a huge state space, it would become embarrassingly slow.
We shouldn't expect everyone to run their experiments in a threading framework. It is more work than necessary sometimes.)
- **[needs discussion]** Templated C++ in TH Core?
- **Ronan**: Should I cleanup TH core? In the end, I am scared to move to C++, but some iterators based taking a closure could be nice (I have some of those that I could add easily).
I could move to C++ if it was only template + keeping pointers (and not C++11/14/17, because that would limit the number of users that it can reach because of the latest compilers needed etc.).
- **[definite]** Migrate to a single, better/modern testing support
- **koray**: like some aspects of Totem, but should be in core Tester
- **[definite]** Benchmarking support in Tester
- **[definite]** Consistent testing scripts across all core projects
- **[definite]** 'nn' container unified interface between containers and graph
- **[mostly definite]** Switch to batch only assumption in 'nn'. Right now, the code is unnecessarily complicated for stochastic/batch confusion, we needed extra functions like nInputDims and such.
- **[needs discussion]** Support named arguments in the constructor for all 'nn' layers.
- **[definite]** 'rnn' package.
- **Soumith**: Nicholas Leonard's seems to be a good one.
- **[mostly definite]** argcheck for all core functions in torch. Get rid of cwrap's ugliness.
- **[definite]** improve paths to support more file system operations
- **Clement**: could lfs and penlight be made more standard? penlight is a heavy package but provides so much utility
- **Soumith**: I think penlight is lightweight and provides strong utility, definitely consider dependence.
- **[definite]** JIT/Lua/FFI/GC:
- **koray**: I think Torch should be agnostic to whatever is the backend;
- **clement**: yes!
- at this point, we need to have all core packages use the regular Lua api (almost the case)
- **Ronan**: agreed.
- **[definite]** plan to have standalone FFI?
- Facebook releases their puc LUA based FFI package mostly improved by Sam Gross
- [needs discussion] **Ronan** improves it a bit more to use Leon's C99 parser
- **Koray**: I am not opposed to Leon's C99 parser, but we should not have the QT like situation where
it relies mostly on Leon to maintain it.
And, still we need to have FFI since there are people and packages that rely on it now.
- **[definite]** Lua 5.2 migration (I think it's already finished ;) ).
- **[mostly definite]** Lua 5.3 migration
- **[mostly definite]** Optionally replace GC by Ref-counting (existing version in luajit-rocks; but completely broken but will need to be fixed)
- **[needs discussion]** Make OpenCL support more visible under torch/opencl (**Soumith**: Hugh Perkins will maintain it of course ;) ).
- **[definite]** Split nn into THNN and nn. THNN would be NN package using TH as backend and nn would be the lua layer. THNN can be used as a standalone C library. Same for cunn
- **[Definite]** CUDA typed tensor support - CudaHalfTensor CudaDoubleTensor etc.
- **[Definite]** better plotting support
- **[needs discussion]** UI package that doesn't suck?
- **Ronan**: something based on cairo?
- **clement**: not sure if this would have much adoption
- **Ronan**: yes, it is a worry. I started to do some fancy stuff there, it is not that hard.
However, I would need quite some time to polish it.
I think having something fully customizable from lua really
makes a difference (rather than something like Qt, for example).
- something based on a web client?
- **clement**: i like the idea of itorch but could never easily build it, build process is too big.
- **Ronan**: I cannot use something which forces me to use global variables.
- **koray**: I think at the end of the day, we need to have both a GUI client and a web based client.
My main problem with web based clients is that I can't easily create
custom displays to play an animation or such.
It is an offline process that I need to generate a movie and then load it in.
This and similar things make it hard to use for me.
Also, I agree, I actually could not install iTorch on my laptop
before cvpr tutorial somehow, it did not want to work :).
- **soumith**: I think we should propose a common display API that any interface can implement,
that way the users don't need to change scripts across different UI backends.
Also, szym/display is a good candidate for the Web UI, ITorch is indeed a bit of a pain to install.
- Should we endorse iTorch for everyone to use?
- **Ronan**: I know **Soumith** likes it, but I am not a big fan.
- Heavy+encourages the use of global variables. Excellent for tutorials, though.
- This ties to the first question in **Other Questions** section.
- Can we/community do pull requests on iTorch? ( **Soumith**: Yes )
- First step would be to leanify dependencies and/or install procedure (**Soumith**: agreed)
- **[needs discussion]** How about Penlight? It has many crucial things that people use.
Should we endorse it, use some things from it? Replicate some things in penlight in torch?
- **clement**: upvoting this! we use it extensively.
- **Ronan**: I live better with less abstractions, but I can be convinced there.
However, I find penlight quite big.
There are things like the classes that I do not like as well (because of the way they chose for creating classes).
- **[needs discussion]** how about Moses? New lean functional package that's pretty useful
- **[definite]** A style guide
- Guidelines are super important:
- for Lua: at least impose strict camel case + 3 spaces (no tab)
- for C: camel case + use of underscore to represent namespace scoping + 2 spaces
## Documentation + Accessibility
- Tutorials: provide guidelines and basic framework/standard to write and publish tutorials?
- Universal dataset API
- Dataset classes for several popular datasets
- high performance, thread support etc.
- support CPU and GPU
- Model Zoo + Training scripts, with training scripts we can highlight Torch's strengths
- How do we build a super friendly model zoo? git repo of pre-trained models?
- Better documentation support, have a doc server
- Documentation for TH/THC interface and design
- Inline documentation parser
- doc/shell integration (maybe this is still working but needs redoing?)
## Versioning + Packaging
- Package owners need to start releasing frequent versions (i.e. torch v7.0.1, 7.0.2, ...)
- scm packages should become deprecated
- Packages need to avoid global side effects, and return themselves as simple tables (Lua 5.2 started enforcing this on the C side)
- Provide standard AMI instances that people can launch (already loosely done by the community). We can load it with many standard+optional packages and/or provide one line option to update to latest.
## Build Infrastructure Requirements
- Prepare core distro release
- Professional Continuous build for distro and individual core projects
- Continuous build for GPU
- continuous build should include testing
- The distro should be build and tested at every pull into any of the member projects
- CI for Linux and OSX
## Other Questions?
- If there is a project that seems good from outside or consortium, how do we endorse/improve/modify that?
- do we put some technical criteria to do that?
- being able to do pull requests?
- Licensing?
- or maybe maintain a list of suggested packages?
- when does existence of a package stop us from developing the same in core torch?
- **Soumith**: I think this should largely be community driven and by popularity. Top starred or watched repos in the ecosystem would be a good start.
================================================
FILE: Storage.c
================================================
#include "general.h"
#define torch_Storage_(NAME) TH_CONCAT_4(torch_,Real,Storage_,NAME)
#define THFile_readRealRaw TH_CONCAT_3(THFile_read, Real, Raw)
#define THFile_writeRealRaw TH_CONCAT_3(THFile_write, Real, Raw)
#define torch_Storage TH_CONCAT_STRING_3(torch.,Real,Storage)
#include "generic/Storage.c"
#include "THGenerateAllTypes.h"
#include "generic/Storage.c"
#include "THGenerateHalfType.h"
================================================
FILE: Tensor.c
================================================
#include "general.h"
#define torch_Storage_(NAME) TH_CONCAT_4(torch_,Real,Storage_,NAME)
#define torch_Storage TH_CONCAT_STRING_3(torch.,Real,Storage)
#define torch_Tensor_(NAME) TH_CONCAT_4(torch_,Real,Tensor_,NAME)
#define torch_Tensor TH_CONCAT_STRING_3(torch.,Real,Tensor)
#include "generic/Tensor.c"
#include "THGenerateAllTypes.h"
#include "generic/Tensor.c"
#include "THGenerateHalfType.h"
================================================
FILE: Tensor.lua
================================================
-- additional methods for Storage
local Storage = {}
-- additional methods for Tensor
local Tensor = {}
-- types
local types = {'Byte', 'Char', 'Short', 'Int', 'Long', 'Float', 'Half', 'Double'}
-- Lua 5.2 compatibility
local log10 = math.log10 or function(x) return math.log(x, 10) end
-- tostring() functions for Tensor and Storage
local function Storage__printformat(self)
if self:size() == 0 then
return "", nil, 0
end
local intMode = true
local type = torch.typename(self)
-- if type == 'torch.FloatStorage' or type == 'torch.DoubleStorage' then
for i=1,self:size() do
if self[i] ~= math.ceil(self[i]) then
intMode = false
break
end
end
-- end
local tensor = torch.DoubleTensor(torch.DoubleStorage(self:size()):copy(self), 1, self:size()):abs()
local expMin = tensor:min()
if expMin ~= 0 then
expMin = math.floor(log10(expMin)) + 1
else
expMin = 1
end
local expMax = tensor:max()
if expMax ~= 0 then
expMax = math.floor(log10(expMax)) + 1
else
expMax = 1
end
local format
local scale
local sz
if intMode then
if expMax > 9 then
format = "%11.4e"
sz = 11
else
format = "%SZd"
sz = expMax + 1
end
else
if expMax-expMin > 4 then
format = "%SZ.4e"
sz = 11
if math.abs(expMax) > 99 or math.abs(expMin) > 99 then
sz = sz + 1
end
else
if expMax > 5 or expMax < 0 then
format = "%SZ.4f"
sz = 7
scale = math.pow(10, expMax-1)
else
format = "%SZ.4f"
if expMax == 0 then
sz = 7
else
sz = expMax+6
end
end
end
end
format = string.gsub(format, 'SZ', sz)
if scale == 1 then
scale = nil
end
return format, scale, sz
end
function Storage.__tostring__(self)
local strt = {}
local format,scale = Storage__printformat(self)
if format:sub(2,4) == 'nan' then format = '%f' end
if scale then
table.insert(strt, string.format('%g', scale) .. ' *\n')
for i = 1,self:size() do
table.insert(strt, string.format(format, self[i]/scale) .. '\n')
end
else
for i = 1,self:size() do
table.insert(strt, string.format(format, self[i]) .. '\n')
end
end
table.insert(strt, '[' .. torch.typename(self) .. ' of size ' .. self:size() .. ']\n')
local str = table.concat(strt)
return str
end
for _,type in ipairs(types) do
local metatable = torch.getmetatable('torch.' .. type .. 'Storage')
for funcname, func in pairs(Storage) do
rawset(metatable, funcname, func)
end
end
local function Tensor__printMatrix(self, indent)
local format,scale,sz = Storage__printformat(self:storage())
if format:sub(2,4) == 'nan' then format = '%f' end
-- print('format = ' .. format)
scale = scale or 1
indent = indent or ''
local strt = {indent}
local nColumnPerLine = math.floor((80-#indent)/(sz+1))
-- print('sz = ' .. sz .. ' and nColumnPerLine = ' .. nColumnPerLine)
local firstColumn = 1
local lastColumn = -1
while firstColumn <= self:size(2) do
if firstColumn + nColumnPerLine - 1 <= self:size(2) then
lastColumn = firstColumn + nColumnPerLine - 1
else
lastColumn = self:size(2)
end
if nColumnPerLine < self:size(2) then
if firstColumn ~= 1 then
table.insert(strt, '\n')
end
table.insert(strt, 'Columns ' .. firstColumn .. ' to ' .. lastColumn .. '\n' .. indent)
end
if scale ~= 1 then
table.insert(strt, string.format('%g', scale) .. ' *\n ' .. indent)
end
for l=1,self:size(1) do
local row = self:select(1, l)
for c=firstColumn,lastColumn do
table.insert(strt, string.format(format, row[c]/scale))
if c == lastColumn then
table.insert(strt, '\n')
if l~=self:size(1) then
if scale ~= 1 then
table.insert(strt, indent .. ' ')
else
table.insert(strt, indent)
end
end
else
table.insert(strt, ' ')
end
end
end
firstColumn = lastColumn + 1
end
local str = table.concat(strt)
return str
end
local function Tensor__printTensor(self)
local counter = torch.LongStorage(self:nDimension()-2)
local strt = {''}
local finished
counter:fill(1)
counter[1] = 0
while true do
for i=1,self:nDimension()-2 do
counter[i] = counter[i] + 1
if counter[i] > self:size(i) then
if i == self:nDimension()-2 then
finished = true
break
end
counter[i] = 1
else
break
end
end
if finished then
break
end
-- print(counter)
if #strt > 1 then
table.insert(strt, '\n')
end
table.insert(strt, '(')
local tensor = self
for i=1,self:nDimension()-2 do
tensor = tensor:select(1, counter[i])
table.insert(strt, counter[i] .. ',')
end
table.insert(strt, '.,.) = \n')
table.insert(strt, Tensor__printMatrix(tensor, ' '))
end
return table.concat(strt)
end
function Tensor.__tostring__(self)
local strt = {''}
if self:nDimension() == 0 then
table.insert(strt, '[' .. torch.typename(self) .. ' with no dimension]\n')
else
local tensor = torch.DoubleTensor():resize(self:size()):copy(self)
if tensor:nDimension() == 1 then
local format,scale,sz = Storage__printformat(tensor:storage())
if format:sub(2,4) == 'nan' then format = '%f' end
if scale then
table.insert(strt, string.format('%g', scale) .. ' *\n')
for i = 1,tensor:size(1) do
table.insert(strt, string.format(format, tensor[i]/scale) .. '\n')
end
else
for i = 1,tensor:size(1) do
table.insert(strt, string.format(format, tensor[i]) .. '\n')
end
end
table.insert(strt, '[' .. torch.typename(self) .. ' of size ' .. tensor:size(1) .. ']\n')
elseif tensor:nDimension() == 2 then
table.insert(strt, Tensor__printMatrix(tensor))
table.insert(strt, '[' .. torch.typename(self) .. ' of size ' .. tensor:size(1) .. 'x' .. tensor:size(2) .. ']\n')
else
table.insert(strt, Tensor__printTensor(tensor))
table.insert(strt, '[' .. torch.typename(self) .. ' of size ')
for i=1,tensor:nDimension() do
table.insert(strt, tensor:size(i))
if i ~= tensor:nDimension() then
table.insert(strt, 'x')
end
end
table.insert(strt, ']\n')
end
end
return table.concat(strt)
end
function Tensor.type(self,type)
local current = torch.typename(self)
if not type then return current end
if type ~= current then
local new = torch.getmetatable(type).new()
if self:nElement() > 0 then
new:resize(self:size()):copy(self)
end
return new
else
return self
end
end
function Tensor.typeAs(self,tensor)
return self:type(tensor:type())
end
function Tensor.byte(self)
return self:type('torch.ByteTensor')
end
function Tensor.char(self)
return self:type('torch.CharTensor')
end
function Tensor.short(self)
return self:type('torch.ShortTensor')
end
function Tensor.int(self)
return self:type('torch.IntTensor')
end
function Tensor.long(self)
return self:type('torch.LongTensor')
end
function Tensor.float(self)
return self:type('torch.FloatTensor')
end
function Tensor.double(self)
return self:type('torch.DoubleTensor')
end
function Tensor.half(self)
return self:type('torch.HalfTensor')
end
function Tensor.real(self)
return self:type(torch.getdefaulttensortype())
end
function Tensor.expand(result,tensor,...)
-- get sizes
local sizes = {...}
local t = torch.type(tensor)
if (t == 'number' or t == 'torch.LongStorage') then
table.insert(sizes,1,tensor)
tensor = result
result = tensor.new()
end
-- check type
local size
if torch.type(sizes[1])=='torch.LongStorage' then
size = sizes[1]
else
size = torch.LongStorage(#sizes)
for i,s in ipairs(sizes) do
size[i] = s
end
end
-- get dimensions
local tensor_dim = tensor:dim()
local tensor_stride = tensor:stride()
local tensor_size = tensor:size()
-- check nb of dimensions
if #size ~= tensor:dim() then
error('the number of dimensions provided must equal tensor:dim()')
end
-- create a new geometry for tensor:
for i = 1,tensor_dim do
if tensor_size[i] == 1 then
tensor_size[i] = size[i]
tensor_stride[i] = 0
elseif tensor_size[i] ~= size[i] then
error('incorrect size: only supporting singleton expansion (size=1)')
end
end
-- create new view, with singleton expansion:
result:set(tensor:storage(), tensor:storageOffset(),
tensor_size, tensor_stride)
return result
end
torch.expand = Tensor.expand
function Tensor.expandAs(result,tensor,template)
if template then
return result:expand(tensor,template:size())
end
return result:expand(tensor:size())
end
torch.expandAs = Tensor.expandAs
function Tensor.repeatTensor(result,tensor,...)
-- get sizes
local sizes = {...}
local t = torch.type(tensor)
if (t == 'number' or t == 'torch.LongStorage') then
table.insert(sizes,1,tensor)
tensor = result
result = tensor.new()
end
-- if not contiguous, then force the tensor to be contiguous
if not tensor:isContiguous() then tensor = tensor:clone() end
-- check type
local size
if torch.type(sizes[1])=='torch.LongStorage' then
size = sizes[1]
else
size = torch.LongStorage(#sizes)
for i,s in ipairs(sizes) do
size[i] = s
end
end
if size:size() < tensor:dim() then
error('Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor')
end
local xtensor = tensor.new():set(tensor)
local xsize = xtensor:size():totable()
for i=1,size:size()-tensor:dim() do
table.insert(xsize,1,1)
end
size = torch.DoubleTensor(xsize):cmul(torch.DoubleTensor(size:totable())):long():storage()
xtensor:resize(torch.LongStorage(xsize))
result:resize(size)
local urtensor = result.new(result)
for i=1,xtensor:dim() do
urtensor = urtensor:unfold(i,xtensor:size(i),xtensor:size(i))
end
for i=1,urtensor:dim()-xtensor:dim() do
table.insert(xsize,1,1)
end
xtensor:resize(torch.LongStorage(xsize))
local xxtensor = xtensor:expandAs(urtensor)
urtensor:copy(xxtensor)
return result
end
torch.repeatTensor = Tensor.repeatTensor
--- One of the size elements can be -1,
--- a new LongStorage is then returned.
--- The length of the unspecified dimension
--- is inferred from the number of remaining elements.
local function specifyFully(size, nElements)
local nCoveredElements = 1
local remainingDim = nil
local sizes = size:totable()
for i = 1, #sizes do
local wantedDimSize = sizes[i]
if wantedDimSize == -1 then
if remainingDim then
error("Only one of torch.view dimensions can be -1.")
end
remainingDim = i
else
nCoveredElements = nCoveredElements * wantedDimSize
end
end
if not remainingDim then
return size
end
assert(nElements % nCoveredElements == 0, "The number of covered elements is not a multiple of all elements.")
local copy = torch.LongStorage(sizes)
copy[remainingDim] = nElements / nCoveredElements
return copy
end
-- TODO : This should be implemented in TH and and wrapped.
function Tensor.view(result, src, ...)
local size = ...
local view, tensor
local function istensor(tensor)
return torch.typename(tensor) and torch.typename(tensor):find('torch.*Tensor')
end
local function isstorage(storage)
return torch.typename(storage) and torch.typename(storage) == 'torch.LongStorage'
end
if istensor(result) and istensor(src) and type(size) == 'number' then
size = torch.LongStorage{...}
view = result
tensor = src
elseif istensor(result) and istensor(src) and isstorage(size) then
size = size
view = result
tensor = src
elseif istensor(result) and isstorage(src) and size == nil then
size = src
tensor = result
view = tensor.new()
elseif istensor(result) and type(src) == 'number' then
size = {...}
table.insert(size,1,src)
size = torch.LongStorage(size)
tensor = result
view = tensor.new()
else
local t1 = 'torch.Tensor, torch.Tensor, number [, number ]*'
local t2 = 'torch.Tensor, torch.Tensor, torch.LongStorage'
local t3 = 'torch.Tensor, torch.LongStorage'
local t4 = 'torch.Tensor, number [, number ]*'
error(string.format('torch.view, expected (%s) or\n (%s) or\n (%s)\n or (%s)', t1, t2, t3, t4))
end
local origNElement = tensor:nElement()
size = specifyFully(size, origNElement)
assert(tensor:isContiguous(), "expecting a contiguous tensor")
view:set(tensor:storage(), tensor:storageOffset(), size)
if view:nElement() ~= origNElement then
local inputSize = table.concat(tensor:size():totable(), "x")
local outputSize = table.concat(size:totable(), "x")
error(string.format("Wrong size for view. Input size: %s. Output size: %s",
inputSize, outputSize))
end
return view
end
torch.view = Tensor.view
function Tensor.viewAs(result, src, template)
if template and torch.typename(template) then
return result:view(src, template:size())
elseif template == nil then
template = src
src = result
result = src.new()
return result:view(src, template:size())
else
local t1 = 'torch.Tensor, torch.Tensor, torch.LongStorage'
local t2 = 'torch.Tensor, torch.LongStorage'
error(string.format('expecting (%s) or (%s)', t1, t2))
end
end
torch.viewAs = Tensor.viewAs
function Tensor.split(result, tensor, splitSize, dim)
if torch.type(result) ~= 'table' then
dim = splitSize
splitSize = tensor
tensor = result
result = {}
else
-- empty existing result table before using it
for k,v in pairs(result) do
result[k] = nil
end
end
dim = dim or 1
local start = 1
while start <= tensor:size(dim) do
local size = math.min(splitSize, tensor:size(dim) - start + 1)
local split = tensor:narrow(dim, start, size)
table.insert(result, split)
start = start + size
end
return result
end
torch.split = Tensor.split
function Tensor.chunk(result, tensor, nChunk, dim)
if torch.type(result) ~= 'table' then
dim = nChunk
nChunk = tensor
tensor = result
result = {}
end
dim = dim or 1
local splitSize = math.ceil(tensor:size(dim)/nChunk)
return torch.split(result, tensor, splitSize, dim)
end
torch.chunk = Tensor.chunk
function Tensor.totable(tensor)
local result = {}
local dim = tensor:dim()
if dim == 1 then
tensor:apply(function(i) table.insert(result, i) end)
elseif dim > 0 then
for i = 1, tensor:size(1) do
table.insert(result, tensor[i]:totable())
end
end
return result
end
torch.totable = Tensor.totable
function Tensor.permute(tensor, ...)
local perm = {...}
local nDims = tensor:dim()
assert(#perm == nDims, 'Invalid permutation')
local j
for i, p in ipairs(perm) do
if p ~= i and p ~= 0 then
j = i
repeat
assert(0 < perm[j] and perm[j] <= nDims, 'Invalid permutation')
tensor = tensor:transpose(j, perm[j])
j, perm[j] = perm[j], 0
until perm[j] == i
perm[j] = j
end
end
return tensor
end
torch.permute = Tensor.permute
for _,type in ipairs(types) do
local metatable = torch.getmetatable('torch.' .. type .. 'Tensor')
for funcname, func in pairs(Tensor) do
if funcname ~= 'totable' or type ~='Half' then
rawset(metatable, funcname, func)
else
local function Tensor__totable(self)
local host_tensor = self:float()
return self:float():totable()
end
rawset(torch.getmetatable('torch.HalfTensor'), 'totable', Tensor__totable)
end
end
end
================================================
FILE: TensorMath.lua
================================================
local wrap = require 'cwrap'
require 'torchcwrap'
local interface = wrap.CInterface.new()
local method = wrap.CInterface.new()
local argtypes = wrap.CInterface.argtypes
argtypes['ptrdiff_t'] = wrap.types.ptrdiff_t
interface:print([[
#include "TH.h"
#include "THMath.h"
#include "luaT.h"
#include "utils.h"
]])
-- specific to torch: we generate a 'dispatch' function
-- first we create a helper function
-- note that it let the "torch" table on the stack
interface:print([[
static const void* torch_istensortype(lua_State *L, const char *tname)
{
if(!tname)
return NULL;
if(!luaT_pushmetatable(L, tname))
return NULL;
lua_pushstring(L, "torch");
lua_rawget(L, -2);
if(lua_istable(L, -1))
return tname;
else
{
lua_pop(L, 2);
return NULL;
}
return NULL;
}
]])
interface:print([[
static int torch_isnonemptytable(lua_State *L, int idx)
{
int empty;
if (!lua_istable(L, idx)) return 0;
lua_rawgeti(L, idx, 1);
empty = lua_isnil(L, -1);
lua_pop(L, 1);
return !empty;
}
]])
interface:print([[
static const void* torch_istensorarray(lua_State *L, int idx)
{
const char* tname;
int tensor_idx;
if (!torch_isnonemptytable(L, idx)) return 0;
lua_checkstack(L, 3);
lua_rawgeti(L, idx, 1);
tensor_idx = lua_gettop(L);
tname = (torch_istensortype(L, luaT_typename(L, -1)));
lua_remove(L, tensor_idx);
return tname;
}
]])
interface.dispatchregistry = {}
function interface:wrap(name, ...)
-- usual stuff
wrap.CInterface.wrap(self, name, ...)
-- dispatch function
if not interface.dispatchregistry[name] then
interface.dispatchregistry[name] = true
table.insert(interface.dispatchregistry, {name=name, wrapname=string.format("torch_%s", name)})
interface:print(string.gsub([[
static int torch_NAME(lua_State *L)
{
int narg = lua_gettop(L);
const void *tname;
if(narg >= 1 && (tname = torch_istensortype(L, luaT_typename(L, 1)))) /* first argument is tensor? */
{
}
else if(narg >= 2 && (tname = torch_istensortype(L, luaT_typename(L, 2)))) /* second? */
{
}
else if(narg >= 1 && (tname = torch_istensorarray(L, 1))) /* torch table argument? */
{
}
else if(narg >= 1 && lua_type(L, narg) == LUA_TSTRING
&& (tname = torch_istensortype(L, lua_tostring(L, narg)))) /* do we have a valid tensor type string then? */
{
lua_remove(L, -2);
}
else if(!(tname = torch_istensortype(L, torch_getdefaulttensortype(L))))
luaL_error(L, "internal error: the default tensor type does not seem to be an actual tensor");
lua_pushstring(L, "NAME");
lua_rawget(L, -2);
if(lua_isfunction(L, -1))
{
lua_insert(L, 1);
lua_pop(L, 2); /* the two tables we put on the stack above */
lua_call(L, lua_gettop(L)-1, LUA_MULTRET);
}
else
return luaL_error(L, "%s does not implement the torch.NAME() function", tname);
return lua_gettop(L);
}
]], 'NAME', name))
end
end
function interface:dispatchregister(name)
local txt = self.txt
table.insert(txt, string.format('static const struct luaL_Reg %s [] = {', name))
for _,reg in ipairs(self.dispatchregistry) do
table.insert(txt, string.format('{"%s", %s},', reg.name, reg.wrapname))
end
table.insert(txt, '{NULL, NULL}')
table.insert(txt, '};')
table.insert(txt, '')
self.dispatchregistry = {}
end
interface:print('/* WARNING: autogenerated file */')
interface:print('')
local function wrap(...)
local args = {...}
-- interface
interface:wrap(...)
-- method: we override things possibly in method table field
for _,x in ipairs(args) do
if type(x) == 'table' then -- ok, now we have a list of args
for _, arg in ipairs(x) do
if arg.method then
for k,v in pairs(arg.method) do
if v == 'nil' then -- special case, we erase the field
arg[k] = nil
else
arg[k] = v
end
end
end
end
end
end
local unpack = unpack or table.unpack
method:wrap(unpack(args))
end
local reals = {ByteTensor='unsigned char',
CharTensor='char',
ShortTensor='short',
IntTensor='int',
LongTensor='long',
FloatTensor='float',
HalfTensor='half',
DoubleTensor='double'}
local accreals = {ByteTensor='long',
CharTensor='long',
ShortTensor='long',
IntTensor='long',
LongTensor='long',
FloatTensor='double',
HalfTensor='float',
DoubleTensor='double'}
for _,Tensor in ipairs({"ByteTensor", "CharTensor",
"ShortTensor", "IntTensor", "LongTensor",
"FloatTensor", "HalfTensor", "DoubleTensor"}) do
local real = reals[Tensor]
local accreal = accreals[Tensor]
function interface.luaname2wrapname(self, name)
return string.format('torch_%s_%s', Tensor, name)
end
function method.luaname2wrapname(self, name)
return string.format('m_torch_%s_%s', Tensor, name)
end
local function cname(name)
return string.format('TH%s_%s', Tensor, name)
end
local function lastdim(argn)
return function(arg)
return string.format("TH%s_nDimension(%s)", Tensor, arg.args[argn]:carg())
end
end
local function lastdimarray(argn)
return function(arg)
return string.format("TH%s_nDimension(arg%d_data[0])", Tensor, arg.args[argn].i)
end
end
if Tensor ~= 'HalfTensor' then
wrap("zero",
cname("zero"),
{{name=Tensor, returned=true}})
wrap("fill",
cname("fill"),
{{name=Tensor, returned=true},
{name=real}})
wrap("zeros",
cname("zeros"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name="LongArg"}})
wrap("ones",
cname("ones"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name="LongArg"}})
wrap("reshape",
cname("reshape"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="LongArg"}})
wrap("gather",
cname("gather"),
{{name=Tensor, default=true, returned=true,
init=function(arg)
return table.concat(
{
arg.__metatable.init(arg),
string.format("THLongStorage* %s_size = THLongTensor_newSizeOf(%s);", arg:carg(), arg.args[4]:carg()),
string.format("TH%s_resize(%s, %s_size, NULL);", Tensor, arg:carg(), arg:carg()),
string.format("THLongStorage_free(%s_size);", arg:carg())
}, '\n')
end
},
{name=Tensor},
{name="index"},
{name="IndexTensor", noreadadd=true}})
wrap("scatter",
cname("scatter"),
{{name=Tensor, returned=true},
{name="index"},
{name="IndexTensor", noreadadd=true},
{name=Tensor}},
cname("scatterFill"),
{{name=Tensor, returned=true},
{name="index"},
{name="IndexTensor", noreadadd=true},
{name=real}})
wrap("dot",
cname("dot"),
{{name=Tensor},
{name=Tensor},
{name=accreal, creturned=true}})
wrap("equal",
cname("equal"),
{{name=Tensor},
{name=Tensor},
{name="boolean", creturned=true}})
wrap("add",
cname("add"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}},
cname("cadd"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real, default=1},
{name=Tensor}})
wrap("csub",
cname("sub"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}},
cname("csub"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real, default=1},
{name=Tensor}})
wrap("mul",
cname("mul"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("div",
cname("div"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("lshift",
cname("lshift"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("rshift",
cname("rshift"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("fmod",
cname("fmod"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("remainder",
cname("remainder"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("bitand",
cname("bitand"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("bitor",
cname("bitor"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("bitxor",
cname("bitxor"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
-- mod alias
wrap("mod",
cname("fmod"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}})
wrap("clamp",
cname("clamp"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real},
{name=real}})
wrap("match",
cname("match"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor},
{name=Tensor},
{name=real, default=1}
})
wrap("cmul",
cname("cmul"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cpow",
cname("cpow"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cdiv",
cname("cdiv"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("clshift",
cname("clshift"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("crshift",
cname("crshift"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cfmod",
cname("cfmod"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cremainder",
cname("cremainder"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cbitand",
cname("cbitand"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cbitor",
cname("cbitor"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("cbitxor",
cname("cbitxor"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
-- cmod alias
wrap("cmod",
cname("cfmod"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}})
wrap("addcmul",
cname("addcmul"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real, default=1},
{name=Tensor},
{name=Tensor}})
wrap("addcdiv",
cname("addcdiv"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real, default=1},
{name=Tensor},
{name=Tensor}})
wrap("mv",
cname("addmv"),
{{name=Tensor, default=true, returned=true, method={default='nil'},
init=function(arg)
return table.concat(
{
arg.__metatable.init(arg),
string.format("TH%s_resize1d(%s, %s->size[0]);", Tensor, arg:carg(), arg.args[5]:carg())
}, '\n')
end,
precall=function(arg)
return table.concat(
{
string.format("TH%s_zero(%s);", Tensor, arg:carg()),
arg.__metatable.precall(arg)
}, '\n')
end,
},
{name=real, default=0, invisible=true},
{name=Tensor, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=2},
{name=Tensor, dim=1}}
)
wrap("mm",
cname("addmm"),
{{name=Tensor, default=true, returned=true, method={default='nil'},
init=function(arg)
return table.concat(
{
arg.__metatable.init(arg),
string.format("TH%s_resize2d(%s, %s->size[0], %s->size[1]);", Tensor, arg:carg(), arg.args[5]:carg(), arg.args[6]:carg())
}, '\n')
end,
precall=function(arg)
return table.concat(
{
string.format("TH%s_zero(%s);", Tensor, arg:carg()),
arg.__metatable.precall(arg)
}, '\n')
end,
},
{name=real, default=0, invisible=true},
{name=Tensor, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=2},
{name=Tensor, dim=2}}
)
wrap("bmm",
cname("baddbmm"),
{{name=Tensor, default=true, returned=true, method={default='nil'},
init=function(arg)
return table.concat(
{
arg.__metatable.init(arg),
string.format("TH%s_resize3d(%s, %s->size[0], %s->size[1], %s->size[2]);",
Tensor, arg:carg(), arg.args[5]:carg(), arg.args[5]:carg(), arg.args[6]:carg())
}, '\n')
end,
precall=function(arg)
return table.concat(
{
string.format("TH%s_zero(%s);", Tensor, arg:carg()),
arg.__metatable.precall(arg)
}, '\n')
end,
},
{name=real, default=0, invisible=true},
{name=Tensor, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=3}}
)
wrap("ger",
cname("addr"),
{{name=Tensor, default=true, returned=true, method={default='nil'},
init=function(arg)
return table.concat(
{
arg.__metatable.init(arg),
string.format("TH%s_resize2d(%s, %s->size[0], %s->size[0]);", Tensor, arg:carg(), arg.args[5]:carg(), arg.args[6]:carg())
}, '\n')
end,
precall=function(arg)
return table.concat(
{
string.format("TH%s_zero(%s);", Tensor, arg:carg()),
arg.__metatable.precall(arg)
}, '\n')
end
},
{name=real, default=1, invisible=true},
{name=Tensor, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=1},
{name=Tensor, dim=1}}
)
for _,f in ipairs({
{name="addmv", dim1=1, dim2=2, dim3=1},
{name="addmm", dim1=2, dim2=2, dim3=2},
{name="addr", dim1=2, dim2=1, dim3=1},
{name="addbmm", dim1=2, dim2=3, dim3=3},
{name="baddbmm", dim1=3, dim2=3, dim3=3},
}
) do
interface:wrap(f.name,
cname(f.name),
{{name=Tensor, default=true, returned=true},
{name=real, default=1},
{name=Tensor, dim=f.dim1},
{name=real, default=1},
{name=Tensor, dim=f.dim2},
{name=Tensor, dim=f.dim3}})
-- there is an ambiguity here, hence the more complicated setup
method:wrap(f.name,
cname(f.name),
{{name=Tensor, returned=true, dim=f.dim1},
{name=real, default=1, invisible=true},
{name=Tensor, default=1, dim=f.dim1},
{name=real, default=1},
{name=Tensor, dim=f.dim2},
{name=Tensor, dim=f.dim3}},
cname(f.name),
{{name=Tensor, returned=true, dim=f.dim1},
{name=real},
{name=Tensor, default=1, dim=f.dim1},
{name=real},
{name=Tensor, dim=f.dim2},
{name=Tensor, dim=f.dim3}})
end
wrap("numel",
cname("numel"),
{{name=Tensor},
{name="ptrdiff_t", creturned=true}})
for _,name in ipairs({"cumsum", "cumprod"}) do
wrap(name,
cname(name),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="index", default=1}})
end
wrap("sum",
cname("sumall"),
{{name=Tensor},
{name=accreal, creturned=true}},
cname("sum"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="index"},
{name="boolean", default=true, invisible=true}})
wrap("prod",
cname("prodall"),
{{name=Tensor},
{name=accreal, creturned=true}},
cname("prod"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="index"},
{name="boolean", default=true, invisible=true}})
for _,name in ipairs({"min", "max"}) do
wrap(name,
cname(name .. "all"),
{{name=Tensor},
{name=real, creturned=true}},
cname(name),
{{name=Tensor, default=true, returned=true},
{name="IndexTensor", default=true, returned=true, noreadadd=true},
{name=Tensor},
{name="index"},
{name="boolean", default=true, invisible=true}})
end
for _,name in ipairs({"cmin", "cmax"}) do
wrap(name,
cname(name),
{{name=Tensor, default=true, returned=true},
{name=Tensor, method={default=1}},
{name=Tensor}},
cname(name .. "Value"),
{{name=Tensor, default=true, returned=true},
{name=Tensor, method={default=1}},
{name=real}})
end
wrap("trace",
cname("trace"),
{{name=Tensor},
{name=accreal, creturned=true}})
wrap("cross",
cname("cross"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name=Tensor},
{name="index", default=0}})
wrap("diag",
cname("diag"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="long", default=0}})
wrap("eye",
cname("eye"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name="long"},
{name="long", default=0}})
wrap("range",
cname("range"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=accreal},
{name=accreal},
{name=accreal, default=1}})
wrap("randperm",
cname("randperm"),
{{name=Tensor, default=true, returned=true, method={default='nil'},
postcall=function(arg)
return table.concat(
{
arg.__metatable.postcall(arg),
string.format("TH%s_add(%s, %s, 1);", Tensor, arg:carg(), arg:carg())
}, '\n')
end},
{name="Generator", default=true},
{name="long"}})
wrap("sort",
cname("sort"),
{{name=Tensor, default=true, returned=true},
{name="IndexTensor", default=true, returned=true, noreadadd=true},
{name=Tensor},
{name="index", default=lastdim(3)},
{name="boolean", default=0}})
wrap("topk",
cname("topk"),
{{name=Tensor, default=true, returned=true},
{name="IndexTensor", default=true, returned=true, noreadadd=true},
{name=Tensor},
{name="long", default=1},
{name="index", default=lastdim(3)},
{name="boolean", default=0},
{name="boolean", default=0}})
wrap("kthvalue",
cname("kthvalue"),
{{name=Tensor, default=true, returned=true},
{name="IndexTensor", default=true, returned=true, noreadadd=true},
{name=Tensor},
{name="long"},
{name="index", default=lastdim(3)},
{name="boolean", default=true, invisible=true}})
wrap("mode",
cname("mode"),
{{name=Tensor, default=true, returned=true},
{name="IndexTensor", default=true, returned=true, noreadadd=true},
{name=Tensor},
{name="index", default=lastdim(3)},
{name="boolean", default=true, invisible=true}})
wrap("median",
cname("median"),
{{name=Tensor, default=true, returned=true},
{name="IndexTensor", default=true, returned=true, noreadadd=true},
{name=Tensor},
{name="index", default=lastdim(3)},
{name="boolean", default=true, invisible=true}})
wrap("tril",
cname("tril"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="int", default=0}})
wrap("triu",
cname("triu"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="int", default=0}})
wrap("cat",
cname("cat"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name=Tensor},
{name="index", default=-1}},
cname("catArray"),
{{name=Tensor, default=true, returned=true},
{name=Tensor .. "Array"},
{name="index", default=-1}})
if Tensor == 'ByteTensor' then -- we declare this only once
interface:print(
[[
static long THRandom_random2__(THGenerator *gen, long a, long b)
{
THArgCheck(b >= a, 2, "upper bound must be larger than lower bound");
return((THRandom_random(gen) % (b+1-a)) + a);
}
static long THRandom_random1__(THGenerator *gen, long b)
{
THArgCheck(b > 0, 1, "upper bound must be strictly positive");
return(THRandom_random(gen) % b + 1);
}
]])
end
interface:print(string.gsub(
[[
static void THTensor_random2__(THTensor *self, THGenerator *gen, long a, long b)
{
THArgCheck(b >= a, 2, "upper bound must be larger than lower bound");
TH_TENSOR_APPLY(real, self, *self_data = ((THRandom_random(gen) % (b+1-a)) + a);)
}
static void THTensor_random1__(THTensor *self, THGenerator *gen, long b)
{
THArgCheck(b > 0, 1, "upper bound must be strictly positive");
TH_TENSOR_APPLY(real, self, *self_data = (THRandom_random(gen) % b + 1);)
}
]], 'Tensor', Tensor):gsub('real', real))
wrap('random',
'THRandom_random2__',
{{name='Generator', default=true},
{name='long'},
{name='long'},
{name='long', creturned=true}},
'THRandom_random1__',
{{name='Generator', default=true},
{name='long'},
{name='long', creturned=true}},
'THRandom_random',
{{name='Generator', default=true},
{name='long', creturned=true}},
cname("random2__"),
{{name=Tensor, returned=true},
{name='Generator', default=true},
{name='long'},
{name='long'}},
cname("random1__"),
{{name=Tensor, returned=true},
{name='Generator', default=true},
{name='long'}},
cname("random"),
{{name=Tensor, returned=true},
{name='Generator', default=true}})
wrap("geometric",
"THRandom_geometric",
{{name="Generator", default=true},
{name="double"},
{name="double", creturned=true}},
cname("geometric"),
{{name=Tensor, returned=true},
{name="Generator", default=true},
{name="double"}})
wrap("bernoulli",
"THRandom_bernoulli",
{{name="Generator", default=true},
{name="double", default=0.5},
{name="double", creturned=true}},
cname("bernoulli"),
{{name=Tensor, returned=true},
{name="Generator", default=true},
{name="double", default=0.5}},
cname("bernoulli_FloatTensor"),
{{name=Tensor, returned=true},
{name="Generator", default=true},
{name="FloatTensor"}},
cname("bernoulli_DoubleTensor"),
{{name=Tensor, returned=true},
{name="Generator", default=true},
{name="DoubleTensor"}})
wrap("squeeze",
cname("squeeze"),
{{name=Tensor, default=true, returned=true, postcall=function(arg)
local txt = {}
if arg.returned then
table.insert(txt, string.format('if(arg%d->nDimension == 1 && arg%d->size[0] == 1)', arg.i, arg.i)) -- number
table.insert(txt, string.format('lua_pushnumber(L, (lua_Number)(*TH%s_data(arg%d)));', Tensor, arg.i))
end
return table.concat(txt, '\n')
end},
{name=Tensor}},
cname("squeeze1d"),
{{name=Tensor, default=true, returned=true,
postcall=
function(arg)
local txt = {}
if arg.returned then
table.insert(txt, string.format('if(!hasdims && arg%d->nDimension == 1 && arg%d->size[0] == 1)', arg.i, arg.i)) -- number
table.insert(txt, string.format('lua_pushnumber(L, (lua_Number)(*TH%s_data(arg%d)));}', Tensor, arg.i))
end
return table.concat(txt, '\n')
end},
{name=Tensor,
precall=
function(arg)
return string.format('{int hasdims = arg%d->nDimension > 1;', arg.i)
end},
{name="index"}})
wrap("sign",
cname("sign"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}})
wrap("conv2",
cname("conv2Dmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=2},
{name=Tensor, dim=2},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="C", invisible=true}},
cname("conv2Dcmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=3},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="C", invisible=true}},
cname("conv2Dmv"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=4},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="C", invisible=true}}
)
wrap("xcorr2",
cname("conv2Dmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=2},
{name=Tensor, dim=2},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="X", invisible=true}},
cname("conv2Dcmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=3},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="X", invisible=true}},
cname("conv2Dmv"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=4},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="X", invisible=true}}
)
wrap("conv3",
cname("conv3Dmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=3},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="C", invisible=true}},
cname("conv3Dcmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=4},
{name=Tensor, dim=4},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="C", invisible=true}},
cname("conv3Dmv"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=4},
{name=Tensor, dim=5},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="C", invisible=true}}
)
wrap("xcorr3",
cname("conv3Dmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=3},
{name=Tensor, dim=3},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="X", invisible=true}},
cname("conv3Dcmul"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=4},
{name=Tensor, dim=4},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="X", invisible=true}},
cname("conv3Dmv"),
{{name=Tensor, default=true, returned=true},
{name=real, default=0, invisible=true},
{name=real, default=1, invisible=true},
{name=Tensor, dim=4},
{name=Tensor, dim=5},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name=real, default=1, invisible=true},
{name='charoption', values={'V', 'F'}, default='V'},
{name='charoption', default="X", invisible=true}}
)
for _,name in pairs({'lt','gt','le','ge','eq','ne'}) do
wrap(name,
cname(name .. 'Value'),
{{name='ByteTensor',default=true, returned=true},
{name=Tensor},
{name=real}},
cname(name .. 'ValueT'),
{{name=Tensor, returned=true},
{name=Tensor},
{name=real}},
cname(name .. 'Tensor'),
{{name='ByteTensor',default=true, returned=true},
{name=Tensor},
{name=Tensor}},
cname(name .. 'TensorT'),
{{name=Tensor, returned=true},
{name=Tensor},
{name=Tensor}})
end
wrap("nonzero",
cname("nonzero"),
{{name="IndexTensor", default=true, returned=true},
{name=Tensor}})
end -- ~= HalfTensor
if Tensor == 'ByteTensor' then
-- Logical accumulators only apply to ByteTensor
for _,name in ipairs({'all', 'any'}) do
wrap(name,
cname('logical' .. name),
{{name=Tensor},
{name="boolean", creturned=true}})
end
end
if Tensor == 'IntTensor' then
wrap("abs",
cname("abs"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
"abs",
{{name=real},
{name=real, creturned=true}})
elseif Tensor == 'LongTensor' then
wrap("abs",
cname("abs"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
"labs",
{{name=real},
{name=real, creturned=true}})
end
if Tensor == 'FloatTensor' or Tensor == 'DoubleTensor' then
wrap("mean",
cname("meanall"),
{{name=Tensor},
{name=accreal, creturned=true}},
cname("mean"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="index"},
{name="boolean", default=true, invisible=true}})
for _,name in ipairs({"var", "std"}) do
wrap(name,
cname(name .. "all"),
{{name=Tensor},
{name="boolean", default=false},
{name=accreal, creturned=true}
},
cname(name),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="index"},
{name="boolean", default=false},
{name="boolean", default=true, invisible=true}})
end
wrap("histc",
cname("histc"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="long",default=100},
{name="double",default=0},
{name="double",default=0}})
wrap("bhistc",
cname("bhistc"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name="long",default=100},
{name="double",default=0},
{name="double",default=0}})
wrap("norm",
cname("normall"),
{{name=Tensor},
{name=real, default=2},
{name=accreal, creturned=true}},
cname("norm"),
{{name=Tensor, default=true, returned=true},
{name=Tensor},
{name=real},
{name="index"},
{name="boolean", default=true, invisible=true}})
wrap("renorm",
cname("renorm"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real},
{name="index"},
{name=real}})
wrap("dist",
cname("dist"),
{{name=Tensor},
{name=Tensor},
{name=real, default=2},
{name=accreal, creturned=true}})
wrap("linspace",
cname("linspace"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=real},
{name=real},
{name="long", default=100}})
wrap("logspace",
cname("logspace"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=real},
{name=real},
{name="long", default=100}})
for _,name in ipairs({"log", "log1p", "exp",
"cos", "acos", "cosh",
"sin", "asin", "sinh",
"tan", "atan", "tanh",
"sqrt", "round", "ceil",
"floor", "trunc", }) do
wrap(name,
cname(name),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
name,
{{name=real},
{name=real, creturned=true}})
end
wrap("abs",
cname("abs"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
"fabs",
{{name=real},
{name=real, creturned=true}})
wrap("frac",
cname("frac"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
"TH_frac",
{{name=real},
{name=real, creturned=true}})
wrap("rsqrt",
cname("rsqrt"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
"TH_rsqrt",
{{name=real},
{name=real, creturned=true}})
wrap("sigmoid",
cname("sigmoid"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}},
"TH_sigmoid",
{{name=real},
{name=real, creturned=true}})
wrap("neg",
cname("neg"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}})
wrap("cinv",
cname("cinv"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}}})
wrap("lerp",
cname("lerp"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor},
{name=real}},
"TH_lerp",
{{name=real},
{name=real},
{name=real},
{name=real, creturned=true}})
wrap("atan2",
cname("atan2"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=Tensor}},
"atan2",
{{name=real},
{name=real},
{name=real, creturned=true}})
wrap("pow",
cname("pow"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=Tensor, method={default=1}},
{name=real}},
cname("tpow"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name=real},
{name=Tensor, method={default=1}}},
"pow",
{{name=real},
{name=real},
{name=real, creturned=true}})
wrap("rand",
cname("rand"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name='Generator', default=true},
{name="LongArg"}})
wrap("randn",
cname("randn"),
{{name=Tensor, default=true, returned=true, method={default='nil'}},
{name='Generator', default=true},
{name="LongArg"}})
wrap("multinomial",
cname("multinomial"),
{{name="IndexTensor", default=true, returned=true, method={default='nil'}},
{name='Generator', default=true},
{name=Tensor},
{name="int"},
{name="boolean", default=false}})
wrap("multinomialAliasSetup_",
cname("multinomialAliasSetup"),
{{name=Tensor},
{name="IndexTensor", default=true, returned=true, method={default='nil'}},
{name=Tensor, default=true, returned=true, method={default='nil'}}})
wrap("multinomialAlias_",
cname("multinomialAliasDraw"),
{{name="IndexTensor", default=true, returned=true, method={default='nil'}},
{name='Generator', default=true},
{name="IndexTensor"},
{name=Tensor}
})
for _,f in ipairs({{name='uniform', a=0, b=1},
{name='normal', a=0, b=1},
{name='cauchy', a=0, b=1},
{name='logNormal', a=1, b=2}}) do
wrap(f.name,
string.format("THRandom_%s", f.name),
{{name='Generator', default=true},
{name="double", default=f.a},
{name="double", default=f.b},
{name="double", creturned=true}},
cname(f.name),
{{name=Tensor, returned=true},
{name='Generator', default=true},
{name=real, default=f.a},
{name=real, default=f.b}})
end
for _,f in ipairs({{name='exponential'}}) do
wrap(f.name,
string.format("THRandom_%s", f.name),
{{name='Generator', default=true},
{name="double", default=f.a},
{name="double", creturned=true}},
cname(f.name),
{{name=Tensor, returned=true},
{name='Generator', default=true},
{name=real, default=f.a}})
end
for _,name in ipairs({"gesv","gels"}) do
interface:wrap(name,
cname(name),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor},
{name=Tensor}},
cname(name),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name=Tensor}}
)
end
interface:wrap("trtrs",
cname("trtrs"),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}, -- uplo
{name='charoption', values={'N', 'T'}, default='N'}, -- trans
{name='charoption', values={'N', 'U'}, default='N'}}, -- diag
cname("trtrs"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}, -- uplo
{name='charoption', values={'N', 'T'}, default='N'}, -- trans
{name='charoption', values={'N', 'U'}, default='N'}} -- diag
)
interface:wrap("symeig",
cname("syev"),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor},
{name='charoption', values={'N', 'V'}, default='N'},
{name='charoption', values={'U', 'L'}, default='U'}},
cname("syev"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name='charoption', values={'N', 'V'}, default='N'},
{name='charoption', values={'U', 'L'}, default='U'}}
)
interface:wrap("eig",
cname("geev"),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor},
{name='charoption', values={'N', 'V'}, default='N'}},
cname("geev"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name='charoption', values={'N', 'V'}, default='N'}}
)
interface:wrap("svd",
cname("gesvd"),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor},
{name='charoption', values={'A', 'S'}, default='S'}},
cname("gesvd"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name='charoption', values={'A', 'S'}, default='S'}}
)
interface:wrap("inverse",
cname("getri"),
{{name=Tensor, returned=true},
{name=Tensor}},
cname("getri"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor}}
)
interface:wrap("potrf",
cname("potrf"),
{{name=Tensor, returned=true},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}}, -- uplo
cname("potrf"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}}
)
interface:wrap("potrs",
cname("potrs"),
{{name=Tensor, returned=true},
{name=Tensor},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}}, -- uplo
cname("potrs"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}}
)
interface:wrap("potri",
cname("potri"),
{{name=Tensor, returned=true},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}}, -- uplo
cname("potri"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}} -- uplo
)
interface:wrap("pstrf",
cname("pstrf"),
{{name=Tensor, returned=true},
{name='IntTensor', returned=true},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}, -- uplo
{name=real, default=-1}},
cname("pstrf"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name='IntTensor', default=true, returned=true, invisible=true},
{name=Tensor},
{name='charoption', values={'U', 'L'}, default='U'}, -- uplo
{name=real, default=-1}}
)
interface:wrap("qr",
cname("qr"),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor}},
cname("qr"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor}}
)
interface:wrap("geqrf",
cname("geqrf"),
{{name=Tensor, returned=true},
{name=Tensor, returned=true},
{name=Tensor}},
cname("geqrf"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor}}
)
interface:wrap("orgqr",
cname("orgqr"),
{{name=Tensor, returned=true},
{name=Tensor},
{name=Tensor}},
cname("orgqr"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name=Tensor}}
)
interface:wrap("ormqr",
cname("ormqr"),
{{name=Tensor, returned=true},
{name=Tensor},
{name=Tensor},
{name=Tensor},
{name='charoption', values={'L', 'R'}, default='L'},
{name='charoption', values={'N', 'T'}, default='N'}},
cname("ormqr"),
{{name=Tensor, default=true, returned=true, invisible=true},
{name=Tensor},
{name=Tensor},
{name=Tensor},
{name='charoption', values={'L', 'R'}, default='L'},
{name='charoption', values={'N', 'T'}, default='N'}}
)
end
method:register(string.format("m_torch_%sMath__", Tensor))
interface:print(method:tostring())
method:clearhistory()
interface:register(string.format("torch_%sMath__", Tensor))
interface:print(string.gsub([[
static void torch_TensorMath_init(lua_State *L)
{
luaT_pushmetatable(L, "torch.Tensor");
/* register methods */
luaT_setfuncs(L, m_torch_TensorMath__, 0);
/* register functions into the "torch" field of the tensor metaclass */
lua_pushstring(L, "torch");
lua_newtable(L);
luaT_setfuncs(L, torch_TensorMath__, 0);
lua_rawset(L, -3);
lua_pop(L, 1);
}
]], 'Tensor', Tensor))
end
interface:dispatchregister("torch_TensorMath__")
interface:print([[
void torch_TensorMath_init(lua_State *L)
{
torch_ByteTensorMath_init(L);
torch_CharTensorMath_init(L);
torch_ShortTensorMath_init(L);
torch_IntTensorMath_init(L);
torch_LongTensorMath_init(L);
torch_FloatTensorMath_init(L);
torch_DoubleTensorMath_init(L);
luaT_setfuncs(L, torch_TensorMath__, 0);
}
]])
if arg[1] then
interface:tofile(arg[1])
else
print(interface:tostring())
end
================================================
FILE: TensorOperator.c
================================================
#include "general.h"
#define torch_TensorOperator_(NAME) TH_CONCAT_4(torch_,Real,TensorOperator_,NAME)
#define torch_Tensor_id TH_CONCAT_3(torch_,Real,Tensor_id)
#define torch_Tensor TH_CONCAT_STRING_3(torch.,Real,Tensor)
#include "generic/TensorOperator.c"
#include "THGenerateAllTypes.h"
================================================
FILE: TestSuite.lua
================================================
function torch.TestSuite()
local obj = {
__tests = {},
__isTestSuite = true
}
local metatable = {}
function metatable:__index(key)
return self.__tests[key]
end
function metatable:__newindex(key, value)
if self.__tests[key] ~= nil then
error("Test " .. tostring(key) .. " is already defined.")
end
if type(value) ~= "function" then
if type(value) == "table" then
error("Nested tables of tests are not supported")
else
error("Only functions are supported as members of a TestSuite")
end
end
self.__tests[key] = value
end
setmetatable(obj, metatable)
return obj
end
================================================
FILE: Tester.lua
================================================
-- Lua 5.2 compatibility
local unpack = unpack or table.unpack
local check = {} -- helper functions, defined at the bottom of the file
local Tester = torch.class('torch.Tester')
function Tester:__init()
self.errors = {}
self.tests = {}
self.warnings = {}
self._warningCount = {}
self.disabledTests = {}
self._currentTestName = ''
-- To maintain backwards compatibility (at least for a short while),
-- disable exact dimension checking of tensors when :assertTensorEq is
-- called. Thus {{1}} == {1} when this flag is true.
--
-- Note that other methods that suppose tensor checking (such as
-- :assertGeneralEq) ignore this flag, since previously they didn't
-- exist or support tensor equality checks at all, so there is no
-- old code that uses these functions and relies on the behaviour.
--
-- Note also that if the dimension check fails with this flag is true, then
-- will show a warning.
self._assertTensorEqIgnoresDims = true
end
function Tester:setEarlyAbort(earlyAbort)
self.earlyAbort = earlyAbort
end
function Tester:setRethrowErrors(rethrow)
self.rethrow = rethrow
end
function Tester:setSummaryOnly(summaryOnly)
self.summaryOnly = summaryOnly
end
-- Add a success to the test.
function Tester:_success()
local name = self._currentTestName
self.assertionPass[name] = self.assertionPass[name] + 1
return true
end
function Tester:_addDebugInfo(message)
local ss = debug.traceback('tester', 3) or ''
ss = ss:match('.-\n([^\n]+\n[^\n]+)\n[^\n]+xpcall') or ''
local name = self._currentTestName
return (name ~= '' and name .. '\n' or '') .. message .. '\n' .. ss
end
-- Add a failure to the test.
function Tester:_failure(message)
if self.rethrow then error(message, 2) end
local name = self._currentTestName
self.assertionFail[name] = self.assertionFail[name] + 1
self.errors[#self.errors + 1] = self:_addDebugInfo(message)
return false
end
-- Add a warning to the test
function Tester:_warning(message)
local name = self._currentTestName
self._warningCount[name] = (self._warningCount[name] or 0) + 1
self.warnings[#self.warnings + 1] = self:_addDebugInfo(message)
end
-- Call this during a test run with `condition = true` to log a success, or with
-- `condition = false` to log a failure (using `message`).
function Tester:_assert_sub(condition, message)
if condition then
return self:_success()
else
return self:_failure(message)
end
end
local function getMessage(message, ...)
assert(next{...} == nil, "Unexpected arguments passed to test function")
if message then
assert(type(message) == 'string', 'message parameter must be a string')
if message ~= '' then
return message .. '\n'
end
end
return ''
end
--[[ Historically, some test functions have accepted both a message and a
tolerance, and some just a message (e.g., assertTableEq). Now assertTableEq
accepts both a tolerance and a message, so allow the two arguments to be passed
in either order to maintain backwards compatibility (and more generally,
for convenience). (We still document the ordering as "tolerance, message" for
clarity.) This function also sanitizes them (ensures they are non-nil, etc).
]]
local function getToleranceAndMessage(defaultTolerance, ...)
local args = {...}
local message = nil
local tolerance = nil
for _, a in ipairs(args) do
if type(a) == 'string' then
if message then
error("Unexpected string argument; already have message", a)
end
message = a .. '\n'
elseif type(a) == 'number' then
if tolerance then
error("Unexpected number argument; already have tolerance", a)
end
tolerance = a
assert(tolerance >= 0, "tolerance cannot be negative")
else
error("Unrecognized argument; should be a tolerance or message", a)
end
end
message = message or ''
tolerance = tolerance or defaultTolerance
return tolerance, message
end
function Tester:assert(condition, ...)
local message = getMessage(...)
if type(condition) ~= 'boolean' then
self:_warning(" :assert should only be used for boolean conditions. "
.. "To check for non-nil variables, do this explicitly: "
.. "Tester:assert(var ~= nil).")
end
return self:_assert_sub(condition,
string.format('%sBOOL violation condition=%s',
message, tostring(condition)))
end
function Tester:assertGeneralEq(got, expected, ...)
return self:_eqOrNeq(got, expected, false, ...)
end
function Tester:eq(got, expected, ...)
return self:assertGeneralEq(got, expected, ...)
end
function Tester:assertGeneralNe(got, unexpected, ...)
return self:_eqOrNeq(got, unexpected, true, ...)
end
function Tester:ne(got, unexpected, ...)
return self:assertGeneralNe(got, unexpected, ...)
end
function Tester:_eqOrNeq(got, expected, negate, ...)
local tolerance, message = getToleranceAndMessage(0, ...)
local success, subMessage = check.areEq(got, expected, tolerance, negate)
subMessage = subMessage or ''
return self:_assert_sub(success, message .. subMessage)
end
function Tester:assertlt(a, b, ...)
local message = getMessage(...)
return self:_assert_sub(a < b,
string.format('%sLT failed: %s >= %s',
message, tostring(a), tostring(b)))
end
function Tester:assertgt(a, b, ...)
local message = getMessage(...)
return self:_assert_sub(a > b,
string.format('%sGT failed: %s <= %s',
message, tostring(a), tostring(b)))
end
function Tester:assertle(a, b, ...)
local message = getMessage(...)
return self:_assert_sub(a <= b,
string.format('%sLE failed: %s > %s',
message, tostring(a), tostring(b)))
end
function Tester:assertge(a, b, ...)
local message = getMessage(...)
return self:_assert_sub(a >= b,
string.format('%sGE failed: %s < %s',
message, tostring(a), tostring(b)))
end
function Tester:assertalmosteq(a, b, ...)
local tolerance, message = getToleranceAndMessage(1e-16, ...)
local diff = math.abs(a - b)
return self:_assert_sub(
diff <= tolerance,
string.format(
'%sALMOST_EQ failed: %s ~= %s with tolerance=%s',
message, tostring(a), tostring(b), tostring(tolerance)))
end
function Tester:asserteq(a, b, ...)
local message = getMessage(...)
return self:_assert_sub(a == b,
string.format('%sEQ failed: %s ~= %s',
message, tostring(a), tostring(b)))
end
function Tester:assertne(a, b, ...)
local message = getMessage(...)
if type(a) == type(b) and type(a) == 'table' or type(a) == 'userdata' then
self:_warning(" :assertne should only be used to compare basic lua "
.. "objects (numbers, booleans, etc). Consider using "
.. "either :assertGeneralNe or :assert(a ~= b).")
end
return self:_assert_sub(a ~= b,
string.format('%sNE failed: %s == %s',
message, tostring(a), tostring(b)))
end
function Tester:assertTensorEq(ta, tb, ...)
return self:_assertTensorEqOrNeq(ta, tb, false, ...)
end
function Tester:assertTensorNe(ta, tb, ...)
return self:_assertTensorEqOrNeq(ta, tb, true, ...)
end
function Tester:_assertTensorEqOrNeq(ta, tb, negate, ...)
assert(torch.isTensor(ta), "First argument should be a Tensor")
assert(torch.isTensor(tb), "Second argument should be a Tensor")
local tolerance, message = getToleranceAndMessage(0, ...)
local success, subMessage =
check.areTensorsEq(ta, tb, tolerance, negate,
self._assertTensorEqIgnoresDims)
subMessage = subMessage or ''
if self._assertTensorEqIgnoresDims and (not negate) and success
and not ta:isSameSizeAs(tb) then
self:_warning("Tensors have the same content but different dimensions. "
.. "For backwards compatibility, they are considered equal, "
.. "but this may change in the future. Consider using :eq "
.. "to check for equality instead.")
end
return self:_assert_sub(success, message .. subMessage)
end
function Tester:assertTableEq(ta, tb, ...)
return self:_assertTableEqOrNeq(ta, tb, false, ...)
end
function Tester:assertTableNe(ta, tb, ...)
return self:_assertTableEqOrNeq(ta, tb, true, ...)
end
function Tester:_assertTableEqOrNeq(ta, tb, negate, ...)
assert(type(ta) == 'table', "First argument should be a Table")
assert(type(tb) == 'table', "Second argument should be a Table")
return self:_eqOrNeq(ta, tb, negate, ...)
end
function Tester:assertError(f, ...)
return self:assertErrorObj(f, function() return true end, ...)
end
function Tester:assertNoError(f, ...)
local message = getMessage(...)
local status, err = pcall(f)
return self:_assert_sub(status,
string.format('%sERROR violation: err=%s', message,
tostring(err)))
end
function Tester:assertErrorMsg(f, errmsg, ...)
return self:assertErrorObj(f, function(err) return err == errmsg end, ...)
end
function Tester:assertErrorPattern(f, errPattern, ...)
local function errcomp(err)
return string.find(err, errPattern) ~= nil
end
return self:assertErrorObj(f, errcomp, ...)
end
function Tester:assertErrorObj(f, errcomp, ...)
local message = getMessage(...)
local status, err = pcall(f)
return self:_assert_sub((not status) and errcomp(err),
string.format('%sERROR violation: err=%s', message,
tostring(err)))
end
function Tester:add(f, name)
if type(f) == "table" then
assert(name == nil, "Name parameter is forbidden for a table of tests, "
.. "since its use is ambiguous")
if f.__isTestSuite then
f = f.__tests
else
self:_warning("Should use TestSuite rather than plain lua table")
end
for i, v in pairs(f) do
-- We forbid nested tests because the "expected" behaviour when a named
-- test is run in the case that the named test is in fact a table of
-- tests is not supported. Similar issue with _setUp and _tearDown
-- functions inside nested tests.
assert(type(v) ~= 'table', "Nested sets of tests are not supported")
self:add(v, i)
end
return self
end
assert(type(f) == 'function',
"Only tables of functions and functions supported")
if name == '_setUp' then
assert(not self._setUp, "Only one set-up function allowed")
self._setUp = f
elseif name == '_tearDown' then
assert(not self._tearDown, "Only one tear-down function allowed")
self._tearDown = f
else
name = name or 'unknown'
if self.tests[name] ~= nil then
error('Test with name ' .. name .. ' already exists!')
end
self.tests[name] = f
end
return self
end
function Tester:disable(testNames)
if type(testNames) == 'string' then
testNames = {testNames}
end
assert(type(testNames) == 'table', "Expecting name or list for disable")
for _, name in ipairs(testNames) do
assert(self.tests[name], "Unrecognized test '" .. name .. "'")
self.disabledTests[name] = true
end
return self
end
function Tester:run(testNames)
local tests = self:_getTests(testNames)
self.assertionPass = {}
self.assertionFail = {}
self.haveWarning = {}
self.testError = {}
for name in pairs(tests) do
self.assertionPass[name] = 0
self.assertionFail[name] = 0
self.testError[name] = 0
self._warningCount[name] = 0
end
self:_run(tests)
self:_report(tests)
-- Throws an error on test failure/error, so that test script returns
-- with nonzero return value.
for name in pairs(tests) do
assert(self.assertionFail[name] == 0,
'An error was found while running tests!')
assert(self.testError[name] == 0,
'An error was found while running tests!')
end
return 0
end
local function pluralize(num, str)
local stem = num .. ' ' .. str
if num == 1 then
return stem
else
return stem .. 's'
end
end
local NCOLS = 80
local coloured
local enable_colors, c = pcall(require, 'sys.colors')
if arg and enable_colors then -- have we been invoked from the commandline?
coloured = function(str, colour)
return colour .. str .. c.none
end
else
c = {}
coloured = function(str)
return str
end
end
function Tester:_run(tests)
local ntests = 0
for _ in pairs(tests) do
ntests = ntests + 1
end
local ntestsAsString = string.format('%u', ntests)
local cfmt = string.format('%%%uu/%u ', ntestsAsString:len(), ntestsAsString)
local cfmtlen = ntestsAsString:len() * 2 + 2
local function bracket(str)
return '[' .. str .. ']'
end
io.write('Running ' .. pluralize(ntests, 'test') .. '\n')
local i = 1
for name, fn in pairs(tests) do
self._currentTestName = name
-- TODO: compute max length of name and cut it down to size if needed
local strinit = coloured(string.format(cfmt, i), c.cyan)
.. self._currentTestName .. ' '
.. string.rep('.',
NCOLS - 6 - 2 -
cfmtlen - self._currentTestName:len())
.. ' '
io.write(strinit .. bracket(coloured('WAIT', c.cyan)))
io.flush()
local status, message, pass, skip
if self.disabledTests[name] then
skip = true
else
skip = false
if self._setUp then
self._setUp(name)
end
if self.rethrow then
status = true
local nerr = #self.errors
message = fn()
pass = nerr == #self.errors
else
status, message, pass = self:_pcall(fn)
end
if self._tearDown then
self._tearDown(name)
end
end
io.write('\r')
io.write(strinit)
if skip then
io.write(bracket(coloured('SKIP', c.yellow)))
elseif not status then
self.testError[name] = 1
io.write(bracket(coloured('ERROR', c.magenta)))
elseif not pass then
io.write(bracket(coloured('FAIL', c.red)))
else
io.write(bracket(coloured('PASS', c.green)))
if self._warningCount[name] > 0 then
io.write('\n' .. string.rep(' ', NCOLS - 10))
io.write(bracket(coloured('+warning', c.yellow)))
end
end
io.write('\n')
io.flush()
if self.earlyAbort and (i < ntests) and (not status or not pass)
and (not skip) then
io.write('Aborting on first error, not all tests have been executed\n')
break
end
i = i + 1
collectgarbage()
end
end
function Tester:_pcall(f)
local nerr = #self.errors
local stat, result = xpcall(f, debug.traceback)
if not stat then
self.errors[#self.errors + 1] =
self._currentTestName .. '\n Function call failed\n' .. result .. '\n'
end
return stat, result, stat and (nerr == #self.errors)
end
function Tester:_getTests(testNames)
if testNames == nil then
return self.tests
end
if type(testNames) == 'string' then
testNames = {testNames}
end
assert(type(testNames) == 'table',
"Only accept a name or table of test names (or nil for all tests)")
local function getMatchingNames(pattern)
local matchingNames = {}
for name in pairs(self.tests) do
if string.match(name, pattern) then
table.insert(matchingNames, name)
end
end
return matchingNames
end
local tests = {}
for _, pattern in ipairs(testNames) do
local matchingNames = getMatchingNames(pattern)
assert(#matchingNames > 0, "Couldn't find test '" .. pattern .. "'")
for _, name in ipairs(matchingNames) do
tests[name] = self.tests[name]
end
end
return tests
end
function Tester:_report(tests)
local ntests = 0
local nfailures = 0
local nerrors = 0
local nskipped = 0
local nwarnings = 0
self.countasserts = 0
for name in pairs(tests) do
ntests = ntests + 1
self.countasserts = self.countasserts + self.assertionFail[name]
+ self.assertionPass[name]
if self.assertionFail[name] > 0 then
nfailures = nfailures + 1
end
if self.testError[name] > 0 then
nerrors = nerrors + 1
end
if self._warningCount[name] > 0 then
nwarnings = nwarnings + 1
end
if self.disabledTests[name] then
nskipped = nskipped + 1
end
end
if self._warningCount[''] then
nwarnings = nwarnings + self._warningCount['']
end
io.write('Completed ' .. pluralize(self.countasserts, 'assert'))
io.write(' in ' .. pluralize(ntests, 'test') .. ' with ')
io.write(coloured(pluralize(nfailures, 'failure'),
nfailures == 0 and c.green or c.red))
io.write(' and ')
io.write(coloured(pluralize(nerrors, 'error'),
nerrors == 0 and c.green or c.magenta))
if nwarnings > 0 then
io.write(' and ')
io.write(coloured(pluralize(nwarnings, 'warning'), c.yellow))
end
if nskipped > 0 then
io.write(' and ')
io.write(coloured(nskipped .. ' disabled', c.yellow))
end
io.write('\n')
-- Prints off a message separated by -----
local haveSection = false
local function addSection(text)
local function printDashes()
io.write(string.rep('-', NCOLS) .. '\n')
end
if not haveSection then
printDashes()
haveSection = true
end
io.write(text .. '\n')
printDashes()
end
if not self.summaryOnly then
for _, v in ipairs(self.errors) do
addSection(v)
end
for _, v in ipairs(self.warnings) do
addSection(v)
end
end
end
--[[ Tests for tensor equality between two tensors of matching sizes and types.
Tests whether the maximum element-wise difference between `ta` and `tb` is less
than or equal to `tolerance`.
Arguments:
* `ta` (tensor)
* `tb` (tensor)
* `tolerance` (number) maximum elementwise difference between `ta` and `tb`.
* `negate` (boolean) if true, we invert success and failure.
* `storage` (boolean) if true, we print an error message referring to Storages
rather than Tensors.
Returns:
1. success, boolean that indicates success
2. failure_message, string or nil
]]
function check.areSameFormatTensorsEq(ta, tb, tolerance, negate, storage)
local function ensureHasAbs(t)
-- Byte, Char and Short Tensors don't have abs
return t.abs and t or t:double()
end
ta = ensureHasAbs(ta)
tb = ensureHasAbs(tb)
local diff = ta:clone():add(-1, tb):abs()
local err = diff:max()
local success = err <= tolerance
if negate then
success = not success
end
local errMessage
if not success then
local prefix = storage and 'Storage' or 'Tensor'
local violation = negate and 'NE(==)' or 'EQ(==)'
errMessage = string.format('%s%s violation: max diff=%s, tolerance=%s',
prefix,
violation,
tostring(err),
tostring(tolerance))
end
return success, errMessage
end
--[[ Tests for tensor equality.
Tests whether the maximum element-wise difference between `ta` and `tb` is less
than or equal to `tolerance`.
Arguments:
* `ta` (tensor)
* `tb` (tensor)
* `tolerance` (number) maximum elementwise difference between `ta` and `tb`.
* `negate` (boolean) if negate is true, we invert success and failure.
* `ignoreTensorDims` (boolean, default false) if true, then tensors of the same
size but different dimensions can still be considered equal, e.g.,
{{1}} == {1}. For backwards compatibility.
Returns:
1. success, boolean that indicates success
2. failure_message, string or nil
]]
function check.areTensorsEq(ta, tb, tolerance, negate, ignoreTensorDims)
ignoreTensorDims = ignoreTensorDims or false
if not ignoreTensorDims and ta:dim() ~= tb:dim() then
return negate, 'The tensors have different dimensions'
end
if ta:type() ~= tb:type() then
return negate, 'The tensors have different types'
end
-- If we are comparing two empty tensors, return true.
-- This is needed because some functions below cannot be applied to tensors
-- of dimension 0.
if ta:dim() == 0 and tb:dim() == 0 then
return not negate, 'Both tensors are empty'
end
local sameSize
if ignoreTensorDims then
sameSize = ta:nElement() == tb:nElement()
else
sameSize = ta:isSameSizeAs(tb)
end
if not sameSize then
return negate, 'The tensors have different sizes'
end
return check.areSameFormatTensorsEq(ta, tb, tolerance, negate, false)
end
local typesMatching = {
['torch.ByteStorage'] = torch.ByteTensor,
['torch.CharStorage'] = torch.CharTensor,
['torch.ShortStorage'] = torch.ShortTensor,
['torch.IntStorage'] = torch.IntTensor,
['torch.LongStorage'] = torch.LongTensor,
['torch.FloatStorage'] = torch.FloatTensor,
['torch.DoubleStorage'] = torch.DoubleTensor,
['torch.HalfStorage'] = torch.HalfTensor,
}
--[[ Tests for storage equality.
Tests whether the maximum element-wise difference between `sa` and `sb` is less
than or equal to `tolerance`.
Arguments:
* `sa` (storage)
* `sb` (storage)
* `tolerance` (number) maximum elementwise difference between `a` and `b`.
* `negate` (boolean) if negate is true, we invert success and failure.
Returns:
1. success, boolean that indicates success
2. failure_message, string or nil
]]
function check.areStoragesEq(sa, sb, tolerance, negate)
if sa:size() ~= sb:size() then
return negate, 'The storages have different sizes'
end
local typeOfsa = torch.type(sa)
local typeOfsb = torch.type(sb)
if typeOfsa ~= typeOfsb then
return negate, 'The storages have different types'
end
local ta = typesMatching[typeOfsa](sa)
local tb = typesMatching[typeOfsb](sb)
return check.areSameFormatTensorsEq(ta, tb, tolerance, negate, true)
end
--[[ Tests for general (deep) equality.
The types of `got` and `expected` must match.
Tables are compared recursively. Keys and types of the associated values must
match, recursively. Numbers are compared with the given tolerance.
Torch tensors and storages are compared with the given tolerance on their
elementwise difference. Other types are compared for strict equality with the
regular Lua == operator.
Arguments:
* `got`
* `expected`
* `tolerance` (number) maximum elementwise difference between `a` and `b`.
* `negate` (boolean) if negate is true, we invert success and failure.
Returns:
1. success, boolean that indicates success
2. failure_message, string or nil
]]
function check.areEq(got, expected, tolerance, negate)
local errMessage
if type(got) ~= type(expected) then
if not negate then
errMessage = 'EQ failed: values have different types (first: '
.. type(got) .. ', second: ' .. type(expected) .. ')'
end
return negate, errMessage
elseif type(got) == 'number' then
local diff = math.abs(got - expected)
local ok = (diff <= tolerance)
if negate then
ok = not ok
end
if not ok then
if negate then
errMessage = string.format("NE failed: %s == %s",
tostring(got), tostring(expected))
else
errMessage = string.format("EQ failed: %s ~= %s",
tostring(got), tostring(expected))
end
if tolerance > 0 then
errMessage = errMessage .. " with tolerance=" .. tostring(tolerance)
end
end
return ok, errMessage
elseif type(expected) == "table" then
return check.areTablesEq(got, expected, tolerance, negate)
elseif torch.isTensor(got) then
return check.areTensorsEq(got, expected, tolerance, negate)
elseif torch.isStorage(got) then
return check.areStoragesEq(got, expected, tolerance, negate)
else
-- Below: we have the same type which is either userdata or a lua type
-- which is not a number.
local ok = (got == expected)
if negate then
ok = not ok
end
if not ok then
if negate then
errMessage = string.format("NE failed: %s (%s) == %s (%s)",
tostring(got), type(got),
tostring(expected), type(expected))
else
errMessage = string.format("EQ failed: %s (%s) ~= %s (%s)",
tostring(got), type(got),
tostring(expected), type(expected))
end
end
return ok, errMessage
end
end
--[[ Tests for (deep) table equality.
Tables are compared recursively. Keys and types of the associated values must
match, recursively. Numbers are compared with the given tolerance.
Torch tensors and storages are compared with the given tolerance on their
elementwise difference. Other types are compared for strict equality with the
regular Lua == operator.
Arguments:
* `t1` (table)
* `t2` (table)
* `tolerance` (number) maximum elementwise difference between `a` and `b`.
* `negate` (boolean) if negate is true, we invert success and failure.
Returns:
1. success, boolean that indicates success
2. failure_message, string or nil
]]
function check.areTablesEq(t1, t2, tolerance, negate)
-- Implementation detail: Instead of doing a depth-first table comparison
-- check (for example, using recursion), let's do a breadth-first search
-- using a queue. Why? Because if we have two tables that are quite deep
-- (e.g., a gModule from nngraph), then if they are different then it's
-- more useful to the user to show how they differ at as-shallow-a-depth
-- as possible.
local queue = {}
queue._head = 1
queue._tail = 1
function queue.isEmpty()
return queue._tail == queue._head
end
function queue.pop()
queue._head = queue._head + 1
return queue[queue._head - 1]
end
function queue.push(value)
queue[queue._tail] = value
queue._tail = queue._tail + 1
end
queue.push({t1, t2})
while not queue.isEmpty() do
local location
t1, t2, location = unpack(queue.pop())
local function toSublocation(key)
local keyAsString = tostring(key)
return (location and location .. "." .. keyAsString) or keyAsString
end
for key, value1 in pairs(t1) do
local sublocation = toSublocation(key)
if t2[key] == nil then
return negate, string.format(
"Entry %s missing in second table (is %s in first)",
sublocation, tostring(value1))
end
local value2 = t2[key]
if type(value1) == 'table' and type(value2) == 'table' then
queue.push({value1, value2, sublocation})
else
local ok, message = check.areEq(value1, value2, tolerance, false)
if not ok then
message = 'At table location ' .. sublocation .. ': ' .. message
return negate, message
end
end
end
for key, value2 in pairs(t2) do
local sublocation = toSublocation(key)
if t1[key] == nil then
return negate, string.format(
"Entry %s missing in first table (is %s in second)",
sublocation, tostring(value2))
end
end
end
return not negate, 'The tables are equal'
end
================================================
FILE: Timer.c
================================================
#include "general.h"
#ifdef _WIN32
#include <windows.h>
#include <assert.h>
#define TimeType __int64
static __declspec( thread ) TimeType ticksPerSecond = 0;
/*
* There is an example of getrusage for windows in following link:
* https://github.com/openvswitch/ovs/blob/master/lib/getrusage-windows.c
*/
#else
#include <sys/time.h>
#include <sys/resource.h>
#define TimeType double
#endif
typedef struct _Timer
{
int isRunning;
TimeType totalrealtime;
TimeType totalusertime;
TimeType totalsystime;
TimeType startrealtime;
TimeType startusertime;
TimeType startsystime;
} Timer;
static TimeType torch_Timer_realtime()
{
#ifdef _WIN32
TimeType current;
QueryPerformanceCounter(¤t);
return current;
#else
struct timeval current;
gettimeofday(¤t, NULL);
return (current.tv_sec + current.tv_usec/1000000.0);
#endif
}
static TimeType torch_Timer_usertime()
{
#ifdef _WIN32
return torch_Timer_realtime();
#else
struct rusage current;
getrusage(RUSAGE_SELF, ¤t);
return (current.ru_utime.tv_sec + current.ru_utime.tv_usec/1000000.0);
#endif
}
static TimeType torch_Timer_systime()
{
#ifdef _WIN32
return 0;
#else
struct rusage current;
getrusage(RUSAGE_SELF, ¤t);
return (current.ru_stime.tv_sec + current.ru_stime.tv_usec/1000000.0);
#endif
}
static int torch_Timer_new(lua_State *L)
{
#ifdef _WIN32
if (ticksPerSecond == 0)
{
assert(sizeof(LARGE_INTEGER) == sizeof(__int64));
QueryPerformanceFrequency(&ticksPerSecond);
}
#endif
Timer *timer = luaT_alloc(L, sizeof(Timer));
timer->isRunning = 1;
timer->totalrealtime = 0;
timer->totalusertime = 0;
timer->totalsystime = 0;
timer->startrealtime = torch_Timer_realtime();
timer->startusertime = torch_Timer_usertime();
timer->startsystime = torch_Timer_systime();
luaT_pushudata(L, timer, "torch.Timer");
return 1;
}
static int torch_Timer_reset(lua_State *L)
{
Timer *timer = luaT_checkudata(L, 1, "torch.Timer");
timer->totalrealtime = 0;
timer->totalusertime = 0;
timer->totalsystime = 0;
timer->startrealtime = torch_Timer_realtime();
timer->startusertime = torch_Timer_usertime();
timer->startsystime = torch_Timer_systime();
lua_settop(L, 1);
return 1;
}
static int torch_Timer_free(lua_State *L)
{
Timer *timer = luaT_checkudata(L, 1, "torch.Timer");
luaT_free(L, timer);
return 0;
}
static int torch_Timer_stop(lua_State *L)
{
Timer *timer = luaT_checkudata(L, 1, "torch.Timer");
if(timer->isRunning)
{
TimeType realtime = torch_Timer_realtime() - timer->startrealtime;
TimeType usertime = torch_Timer_usertime() - timer->startusertime;
TimeType systime = torch_Timer_systime() - timer->startsystime;
timer->totalrealtime += realtime;
timer->totalusertime += usertime;
timer->totalsystime += systime;
timer->isRunning = 0;
}
lua_settop(L, 1);
return 1;
}
static int torch_Timer_resume(lua_State *L)
{
Timer *timer = luaT_checkudata(L, 1, "torch.Timer");
if(!timer->isRunning)
{
timer->isRunning = 1;
timer->startrealtime = torch_Timer_realtime();
timer->startusertime = torch_Timer_usertime();
timer->startsystime = torch_Timer_systime();
}
lua_settop(L, 1);
return 1;
}
static int torch_Timer_time(lua_State *L)
{
Timer *timer = luaT_checkudata(L, 1, "torch.Timer");
double realtime = (timer->isRunning ? (timer->totalrealtime + torch_Timer_realtime() - timer->startrealtime) : timer->totalrealtime);
double usertime = (timer->isRunning ? (timer->totalusertime + torch_Timer_usertime() - timer->startusertime) : timer->totalusertime);
double systime = (timer->isRunning ? (timer->totalsystime + torch_Timer_systime() - timer->startsystime) : timer->totalsystime);
#ifdef _WIN32
realtime /= ticksPerSecond;
usertime /= ticksPerSecond;
systime /= ticksPerSecond;
#endif
lua_createtable(L, 0, 3);
lua_pushnumber(L, realtime);
lua_setfield(L, -2, "real");
lua_pushnumber(L, usertime);
lua_setfield(L, -2, "user");
lua_pushnumber(L, systime);
lua_setfield(L, -2, "sys");
return 1;
}
static int torch_Timer___tostring__(lua_State *L)
{
Timer *timer = luaT_checkudata(L, 1, "torch.Timer");
lua_pushfstring(L, "torch.Timer [status: %s]", (timer->isRunning ? "running" : "stopped"));
return 1;
}
static const struct luaL_Reg torch_Timer__ [] = {
{"reset", torch_Timer_reset},
{"stop", torch_Timer_stop},
{"resume", torch_Timer_resume},
{"time", torch_Timer_time},
{"__tostring__", torch_Timer___tostring__},
{NULL, NULL}
};
void torch_Timer_init(lua_State *L)
{
luaT_newmetatable(L, "torch.Timer", NULL, torch_Timer_new, torch_Timer_free, NULL);
luaT_setfuncs(L, torch_Timer__, 0);
lua_pop(L, 1);
}
================================================
FILE: cmake/TorchConfig.cmake.in
================================================
# This (ugly) setup assumes:
# CMAKE_PREFIX_PATH = LUA_BINDIR
# CMAKE_INSTALL_PREFIX = PREFIX
# Define Torch basic subpaths
SET(Torch_INSTALL_PREFIX "@Torch_INSTALL_PREFIX@")
SET(Torch_INSTALL_BIN_SUBDIR "@Torch_INSTALL_BIN_SUBDIR@")
SET(Torch_INSTALL_MAN_SUBDIR "@Torch_INSTALL_MAN_SUBDIR@")
SET(Torch_INSTALL_LIB_SUBDIR "@Torch_INSTALL_LIB_SUBDIR@")
SET(Torch_INSTALL_SHARE_SUBDIR "@Torch_INSTALL_SHARE_SUBDIR@")
SET(Torch_INSTALL_INCLUDE_SUBDIR "@Torch_INSTALL_INCLUDE_SUBDIR@")
SET(Torch_INSTALL_CMAKE_SUBDIR "@Torch_INSTALL_CMAKE_SUBDIR@")
SET(Torch_INSTALL_LUA_PATH_SUBDIR "@Torch_INSTALL_LUA_PATH_SUBDIR@")
SET(Torch_INSTALL_LUA_CPATH_SUBDIR "@Torch_INSTALL_LUA_CPATH_SUBDIR@")
SET(Torch_INSTALL_CMAKE_RIDBUS "@Torch_INSTALL_CMAKE_RIDBUS@")
FILE(RELATIVE_PATH Torch_INSTALL_LUA_PATH_SUBDIR "${Torch_INSTALL_PREFIX}" "${CMAKE_INSTALL_PREFIX}/lua")
FILE(RELATIVE_PATH Torch_INSTALL_LUA_CPATH_SUBDIR "${Torch_INSTALL_PREFIX}" "${CMAKE_INSTALL_PREFIX}/lib")
SET(CMAKE_MODULE_PATH "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_CMAKE_SUBDIR}" "${CMAKE_MODULE_PATH}")
SET(CMAKE_INSTALL_PREFIX "${Torch_INSTALL_PREFIX}") # override
INCLUDE(TorchPathsInit)
INCLUDE(TorchPackage)
INCLUDE(TorchWrap)
# Define Torch basic targets
INCLUDE(TorchExports)
INCLUDE_DIRECTORIES("${Torch_INSTALL_INCLUDE}")
INCLUDE_DIRECTORIES("${Torch_INSTALL_INCLUDE}/TH")
LINK_DIRECTORIES("${Torch_INSTALL_LIB}")
MESSAGE(STATUS "Found Torch7 in ${Torch_INSTALL_PREFIX}")
================================================
FILE: cmake/TorchExports.cmake
================================================
INSTALL(EXPORT TH-exports
DESTINATION "${Torch_INSTALL_CMAKE_SUBDIR}"
FILE "TorchExports.cmake")
CONFIGURE_FILE("cmake/TorchConfig.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/cmake-exports/TorchConfig.cmake" @ONLY)
CONFIGURE_FILE("cmake/TorchWrap.cmake.in" "${CMAKE_CURRENT_BINARY_DIR}/cmake-exports/TorchWrap.cmake" @ONLY)
INSTALL(
FILES
"${CMAKE_CURRENT_BINARY_DIR}/cmake-exports/TorchConfig.cmake"
"${CMAKE_CURRENT_BINARY_DIR}/cmake-exports/TorchWrap.cmake"
"cmake/TorchPathsInit.cmake"
"cmake/TorchPackage.cmake"
DESTINATION "${Torch_INSTALL_CMAKE_SUBDIR}")
================================================
FILE: cmake/TorchPackage.cmake
================================================
# -*- cmake -*-
MACRO(ADD_TORCH_LIBRARY package type src)
IF ("${type}" STREQUAL "STATIC")
if ("${src}" MATCHES "cu$" OR "${src}" MATCHES "cu;")
CUDA_ADD_LIBRARY(${package} STATIC ${src})
else()
ADD_LIBRARY(${package} STATIC ${src})
endif()
ELSE()
if ("${src}" MATCHES "cu$" OR "${src}" MATCHES "cu;")
CUDA_ADD_LIBRARY(${package} ${type} ${src})
else()
ADD_LIBRARY(${package} ${type} ${src})
endif()
ENDIF()
ENDMACRO()
MACRO(ADD_TORCH_PACKAGE package src luasrc)
INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
INCLUDE_DIRECTORIES(${Torch_LUA_INCLUDE_DIR})
### C/C++ sources
# As per CMake doc, macro arguments are not variables, so simple test syntax not working
IF(NOT "${src}" STREQUAL "")
ADD_TORCH_LIBRARY(${package} MODULE "${src}")
### Torch packages supposes libraries prefix is "lib"
SET_TARGET_PROPERTIES(${package} PROPERTIES
PREFIX "lib"
IMPORT_PREFIX "lib"
INSTALL_NAME_DIR "@executable_path/${Torch_INSTALL_BIN2CPATH}")
IF(APPLE)
SET_TARGET_PROPERTIES(${package} PROPERTIES
LINK_FLAGS "-undefined dynamic_lookup")
ENDIF()
IF (BUILD_STATIC OR "$ENV{STATIC_TH}" STREQUAL "YES")
ADD_TORCH_LIBRARY(${package}_static STATIC "${src}")
SET_TARGET_PROPERTIES(${package}_static PROPERTIES
COMPILE_FLAGS "-fPIC")
SET_TARGET_PROPERTIES(${package}_static PROPERTIES
PREFIX "lib" IMPORT_PREFIX "lib" OUTPUT_NAME "${package}")
ENDIF()
INSTALL(TARGETS ${package}
RUNTIME DESTINATION ${Torch_INSTALL_LUA_CPATH_SUBDIR}
LIBRARY DESTINATION ${Torch_INSTALL_LUA_CPATH_SUBDIR})
ENDIF(NOT "${src}" STREQUAL "")
### lua sources
IF(NOT "${luasrc}" STREQUAL "")
INSTALL(FILES ${luasrc}
DESTINATION ${Torch_INSTALL_LUA_PATH_SUBDIR}/${package})
ENDIF(NOT "${luasrc}" STREQUAL "")
ENDMACRO(ADD_TORCH_PACKAGE)
================================================
FILE: cmake/TorchPaths.cmake
================================================
# workaround another annoying cmake bug
# http://public.kitware.com/Bug/view.php?id=14462
# https://awesome.naquadah.org/bugs/index.php?do=details&task_id=869
MACRO(NORMALIZE_PATH _path_)
get_filename_component(${_path_}_abs "${${_path_}}" ABSOLUTE)
SET(${_path_} "${${_path_}_abs}")
ENDMACRO()
NORMALIZE_PATH(LUA_BINDIR)
NORMALIZE_PATH(LUA_LIBDIR)
NORMALIZE_PATH(LUA_INCDIR)
NORMALIZE_PATH(LUADIR)
NORMALIZE_PATH(LIBDIR)
GET_FILENAME_COMPONENT(CMAKE_INSTALL_PREFIX "${LUA_BINDIR}" PATH)
SET(Torch_INSTALL_PREFIX ${CMAKE_INSTALL_PREFIX})
FILE(RELATIVE_PATH Torch_INSTALL_BIN_SUBDIR "${CMAKE_INSTALL_PREFIX}" "${LUA_BINDIR}")
FILE(RELATIVE_PATH Torch_INSTALL_LIB_SUBDIR "${CMAKE_INSTALL_PREFIX}" "${LUA_LIBDIR}")
FILE(RELATIVE_PATH Torch_INSTALL_INCLUDE_SUBDIR "${CMAKE_INSTALL_PREFIX}" "${LUA_INCDIR}")
SET(Torch_INSTALL_MAN_SUBDIR "share/man" CACHE PATH
"Install dir for man pages (relative to Torch_INSTALL_PREFIX)")
SET(Torch_INSTALL_SHARE_SUBDIR "share" CACHE PATH
"Install dir for data (relative to Torch_INSTALL_PREFIX)")
SET(Torch_INSTALL_CMAKE_SUBDIR "share/cmake/torch" CACHE PATH
"Install dir for .cmake files (relative to Torch_INSTALL_PREFIX)")
FILE(RELATIVE_PATH Torch_INSTALL_LUA_PATH_SUBDIR "${CMAKE_INSTALL_PREFIX}" "${LUADIR}")
FILE(RELATIVE_PATH Torch_INSTALL_LUA_CPATH_SUBDIR "${CMAKE_INSTALL_PREFIX}" "${LIBDIR}")
================================================
FILE: cmake/TorchPathsInit.cmake
================================================
SET(Torch_INSTALL_BIN "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_BIN_SUBDIR}")
SET(Torch_INSTALL_MAN "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_MAN_SUBDIR}")
SET(Torch_INSTALL_LIB "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_LIB_SUBDIR}")
SET(Torch_INSTALL_SHARE "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_SHARE_SUBDIR}")
SET(Torch_INSTALL_INCLUDE "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_INCLUDE_SUBDIR}")
#SET(Torch_INSTALL_DOK "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_DOK_SUBDIR}")
#SET(Torch_INSTALL_HTML "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_HTML_SUBDIR}")
SET(Torch_INSTALL_CMAKE "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_CMAKE_SUBDIR}")
SET(Torch_INSTALL_LUA_PATH "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_LUA_PATH_SUBDIR}")
#SET(Torch_INSTALL_LUA_PKG_PATH "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_LUA_PKG_PATH_SUBDIR}")
SET(Torch_INSTALL_LUA_CPATH "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_LUA_CPATH_SUBDIR}")
#SET(Torch_INSTALL_LUAROCKS_SYSCONF "${Torch_INSTALL_PREFIX}/${Torch_INSTALL_LUAROCKS_SYSCONF_SUBDIR}")
# reverse relative path to prefix (ridbus is the palindrom of subdir)
FILE(RELATIVE_PATH Torch_INSTALL_BIN_RIDBUS "${Torch_INSTALL_BIN}" "${Torch_INSTALL_PREFIX}/.")
FILE(RELATIVE_PATH Torch_INSTALL_CMAKE_RIDBUS "${Torch_INSTALL_CMAKE}" "${Torch_INSTALL_PREFIX}/.")
GET_FILENAME_COMPONENT(Torch_INSTALL_BIN_RIDBUS "${Torch_INSTALL_BIN_RIDBUS}" PATH)
GET_FILENAME_COMPONENT(Torch_INSTALL_CMAKE_RIDBUS "${Torch_INSTALL_CMAKE_RIDBUS}" PATH)
IF(UNIX)
OPTION(WITH_RPATH "Build libraries with executable rpaths" ON)
IF(WITH_RPATH)
SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
FILE(RELATIVE_PATH Torch_INSTALL_BIN2LIB
"${Torch_INSTALL_BIN}" "${Torch_INSTALL_LIB}")
IF(APPLE)
SET(CMAKE_MACOSX_RPATH TRUE) # @rpath in libs
SET(CMAKE_INSTALL_RPATH "@executable_path/${Torch_INSTALL_BIN2LIB}") # exec
ELSE()
SET(CMAKE_INSTALL_RPATH "\$ORIGIN/${Torch_INSTALL_BIN2LIB}")
ENDIF()
ELSE()
SET(CMAKE_MACOSX_RPATH FALSE) # no @rpath in libs
ENDIF()
ENDIF(UNIX)
IF (WIN32)
SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
SET(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}")
ENDIF (WIN32)
================================================
FILE: cmake/TorchWrap.cmake
================================================
MACRO(ADD_TORCH_WRAP target luafile)
INCLUDE_DIRECTORIES("${CMAKE_CURRENT_BINARY_DIR}")
GET_FILENAME_COMPONENT(_file_ "${luafile}" NAME_WE)
SET(cfile "${_file_}.c")
IF (DEFINED CWRAP_CUSTOM_LUA)
ADD_CUSTOM_COMMAND(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
COMMAND ${CWRAP_CUSTOM_LUA} ARGS "${CMAKE_CURRENT_SOURCE_DIR}/${luafile}" "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
DEPENDS "${luafile}")
ELSE (DEFINED CWRAP_CUSTOM_LUA)
ADD_CUSTOM_COMMAND(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
COMMAND ${LUA} ARGS "${CMAKE_CURRENT_SOURCE_DIR}/${luafile}" "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
DEPENDS "${luafile}")
ENDIF (DEFINED CWRAP_CUSTOM_LUA)
ENDMACRO(ADD_TORCH_WRAP)
================================================
FILE: cmake/TorchWrap.cmake.in
================================================
MACRO(ADD_TORCH_WRAP target luafile)
INCLUDE_DIRECTORIES("${CMAKE_CURRENT_BINARY_DIR}")
GET_FILENAME_COMPONENT(_file_ "${luafile}" NAME_WE)
SET(cfile "${_file_}.c")
IF (DEFINED CWRAP_CUSTOM_LUA)
ADD_CUSTOM_COMMAND(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
COMMAND ${CWRAP_CUSTOM_LUA} ARGS "${CMAKE_CURRENT_SOURCE_DIR}/${luafile}" "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
DEPENDS "${luafile}")
ELSE (DEFINED CWRAP_CUSTOM_LUA)
ADD_CUSTOM_COMMAND(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
COMMAND @LUA@ ARGS "${CMAKE_CURRENT_SOURCE_DIR}/${luafile}" "${CMAKE_CURRENT_BINARY_DIR}/${cfile}"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
DEPENDS "${luafile}")
ENDIF (DEFINED CWRAP_CUSTOM_LUA)
ADD_CUSTOM_TARGET(${target} DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/${cfile}")
ENDMACRO(ADD_TORCH_WRAP)
================================================
FILE: doc/cmdline.md
================================================
<a name="torch.CmdLine.dok"></a>
# CmdLine #
This class provides a parameter parsing framework which is very
useful when one needs to run several experiments that rely on
different parameter settings that are passed in the command line.
This class will also override the default print function to direct
all the output to a log file as well as screen at the same time.
A sample `lua` file is given below that makes use of `CmdLine`
class.
```lua
cmd = torch.CmdLine()
cmd:text()
cmd:text()
cmd:text('Training a simple network')
cmd:text()
cmd:text('Options')
cmd:option('-seed',123,'initial random seed')
cmd:option('-booloption',false,'boolean option')
cmd:option('-stroption','mystring','string option')
cmd:text()
-- parse input params
params = cmd:parse(arg)
params.rundir = cmd:string('experiment', params, {dir=true})
paths.mkdir(params.rundir)
-- create log file
cmd:log(params.rundir .. '/log', params)
```
When this file is run on the th command line as follows
```shell
# th myscript.lua
```
It will produce the following output:
```
[program started on Tue Jan 10 15:33:49 2012]
[command line arguments]
booloption false
seed 123
rundir experiment
stroption mystring
[----------------------]
booloption false
seed 123
rundir experiment
stroption mystring
```
The same output will also be written to file
`experiment/log`. Whenever one of the options are passed on the
command line and is different than the default value, the `rundir`
is name is produced to reflect the parameter setting.
```shell
# th myscript.lua -seed 456 -stroption mycustomstring
```
This will produce the following output:
```
[program started on Tue Jan 10 15:36:55 2012]
[command line arguments]
booloption false
seed 456
rundir experiment,seed=456,stroption=mycustomstring
stroption mycustomstring
[----------------------]
booloption false
seed 456
rundir experiment,seed=456,stroption=mycustomstring
stroption mycustomstring
```
and the output will be logged in
`experiment,seed=456,stroption=mycustomstring/log`
<a name="torch.CmdLine.addtime"></a>
### addTime([name] [,format]) ###
Adds a prefix to every line in the log file with the date/time in the
given format with an optional name argument. The date/time format is
the same as `os.date()`. Note that the prefix is only added to the
log file, not the screen output. The default value for name is empty
and the default format is '%F %T'.
The final produced output for the following command is:
```lua
> cmd:addTime('your project name','%F %T')
> print('Your log message')
```
```
2012-02-07 08:21:56[your project name]: Your log message
```
<a name="torch.CmdLine.log"></a>
### log(filename, parameter_table) ###
It sets the log filename to `filename` and prints the values of
parameters in the `parameter_table`. If filename is an open file
descriptor, it will write to the file instead of creating a new one.
<a name="torch.CmdLine.option"></a>
### option(name, default, help) ###
Stores an option argument. The name should always start with '-'.
<a name="torch.CmdLine.parse"></a>
### [table] parse(arg) ###
Parses a given table, `arg` is by default the argument table that
is created by `lua` using the command line arguments passed to the
executable. Returns a table of option values.
<a name="torch.CmdLine.silent"></a>
### silent() ###
Silences the output to standard output. The only output is written to
the log file.
<a name="torch.CmdLine.string"></a>
### [string] string(prefix, params, ignore) ###
Returns a string representation of the options by concatenating the
non-default options. `ignore` is a table `{dir=true}`, which will
ensure that option named `dir` will be ignored while creating the
string representation.
This function is useful for creating unique experiment directories that
depend on the parameter settings.
<a name="torch.CmdLine.text"></a>
### text(string) ###
Logs a custom text message.
================================================
FILE: doc/diskfile.md
================================================
<a name="torch.DiskFile.dok"></a>
# DiskFile #
Parent classes: [File](file.md)
A `DiskFile` is a particular `File` which is able to perform basic read/write operations
on a file stored on disk. It implements all methods described in [File](file.md), and
some additional methods relative to _endian_ encoding.
By default, a `DiskFile` is in [ASCII](file.md#torch.File.ascii) mode. If changed to
the [binary](file.md#torch.File.binary) mode, the default endian encoding is the native
computer one.
The file might be open in read, write, or read-write mode, depending on the parameter
`mode` (which can take the value `"r"`, `"w"` or `"rw"` respectively)
given to the [torch.DiskFile(fileName, mode)](#torch.DiskFile).
<a name="torch.DiskFile"></a>
### torch.DiskFile(fileName, [mode], [quiet]) ###
_Constructor_ which opens `fileName` on disk, using the given `mode`. Valid `mode` are
`"r"` (read), `"w"` (write) or `"rw"` (read-write). Default is read mode.
If read-write mode, the file _will be created_ if it does not exists. If it
exists, it will be positioned at the beginning of the file after opening.
If (and only if) `quiet` is `true`, no error will be raised in case of
problem opening the file: instead `nil` will be returned.
The file is opened in [ASCII](file.md#torch.File.ascii) mode by default.
<a name="torch.DiskFile.bigEndianEncoding"></a>
### bigEndianEncoding() ###
In [binary](file.md#torch.File.binary) mode, force encoding in _big endian_.
(_big end first_: decreasing numeric significance with increasing memory
addresses)
<a name="torch.DiskFile.isBigEndianCPU"></a>
### [boolean] isBigEndianCPU() ###
Returns `true` if, and only if, the computer CPU operates in _big endian_.
_Big end first_: decreasing numeric significance with increasing
memory addresses.
<a name="torch.DiskFile.isLittleEndianCPU"></a>
### [boolean] isLittleEndianCPU() ###
Returns `true` if, and only if, the computer CPU operates in _little endian_.
_Little end first_: increasing numeric significance with increasing
memory addresses.
<a name="torch.DiskFile.littleEndianEncoding"></a>
### littleEndianEncoding() ###
In [binary](file.md#torch.File.binary) mode, force encoding in _little endian_.
(_little end first_: increasing numeric significance with increasing memory
addresses)
<a name="torch.DiskFile.nativeEndianEncoding"></a>
### nativeEndianEncoding() ###
In [binary](file.md#torch.File.binary) mode, force encoding in _native endian_.
<a name="torch.DiskFile.longSize"/></a>
### longSize([size]) ###
Longs will be written and read from the file as `size` bytes long, which
can be 0, 4 or 8. 0 means system default.
<a name="torch.DiskFile.noBuffer"/></a>
### noBuffer() ###
Disables read and write buffering on the `DiskFile`.
================================================
FILE: doc/file.md
================================================
<a name="torch.File.dok"></a>
# File #
This is an _abstract_ class. It defines most methods implemented by its
child classes, like [DiskFile](diskfile.md),
[MemoryFile](memoryfile.md) and [PipeFile](pipefile.md).
Methods defined here are intended for basic read/write functionalities.
Read/write methods might write in [ASCII](#torch.File.ascii) mode or
[binary](#torch.File.binary) mode.
In [ASCII](#torch.File.ascii) mode, numbers are converted in human readable
format (characters). Booleans are converted into `0` (false) or `1` (true).
In [binary](#torch.File.binary) mode, numbers and boolean are directly encoded
as represented in a register of the computer. While not being human
readable and less portable, the binary mode is obviously faster.
In [ASCII](#torch.File.ascii) mode, if the default option
[autoSpacing()](#torch.File.autoSpacing) is chosen, a space will be generated
after each written number or boolean. A carriage return will also be added
after each call to a write method. With this option, the spaces are
supposed to exist while reading. This option can be deactivated with
[noAutoSpacing()](#torch.File.noAutoSpacing).
A `Lua` error might or might not be generated in case of read/write error
or problem in the file. This depends on the choice made between
[quiet()](#torch.File.quiet) and [pedantic()](#torch.File.pedantic) options. It
is possible to query if an error occurred in the last operation by calling
[hasError()](#torch.File.hasError).
<a name="torch.File.read"></a>
## Read methods ##
<a name="torch.File.readByte"></a>
<a name="torch.File.readBool"></a>
<a name="torch.File.readShort"></a>
<a name="torch.File.readChar"></a>
<a name="torch.File.readLong"></a>
<a name="torch.File.readInt"></a>
<a name="torch.File.readDouble"></a>
<a name="torch.File.readFloat"></a>
They are three types of reading methods:
- `[number] readTYPE()`
- `[TYPEStorage] readTYPE(n)`
- `[number] readTYPE(TYPEStorage)`
where `TYPE` can be either `Byte`, `Char`, `Short`, `Int`, `Long`, `Float` or `Double`.
A convenience method also exist for boolean types: `[boolean] readBool()`. It reads
a value on the file with `readInt()` and returns `true` if and only if this value is `1`. It is not possible
to read storages of booleans.
All these methods depends on the encoding choice: [ASCII](#torch.File.ascii)
or [binary](#torch.File.binary) mode. I
gitextract_kg0yjonf/ ├── .gitignore ├── .travis.yml ├── CMakeLists.txt ├── CONTRIBUTING.md ├── COPYRIGHT.txt ├── CmdLine.lua ├── DiskFile.c ├── FFInterface.lua ├── File.c ├── File.lua ├── Generator.c ├── MemoryFile.c ├── PipeFile.c ├── README.md ├── ROADMAP.md ├── Storage.c ├── Tensor.c ├── Tensor.lua ├── TensorMath.lua ├── TensorOperator.c ├── TestSuite.lua ├── Tester.lua ├── Timer.c ├── cmake/ │ ├── TorchConfig.cmake.in │ ├── TorchExports.cmake │ ├── TorchPackage.cmake │ ├── TorchPaths.cmake │ ├── TorchPathsInit.cmake │ ├── TorchWrap.cmake │ └── TorchWrap.cmake.in ├── doc/ │ ├── cmdline.md │ ├── diskfile.md │ ├── file.md │ ├── index.md │ ├── maths.md │ ├── memoryfile.md │ ├── pipefile.md │ ├── random.md │ ├── serialization.md │ ├── storage.md │ ├── tensor.md │ ├── tester.md │ ├── timer.md │ └── utility.md ├── general.h ├── generic/ │ ├── Storage.c │ ├── Tensor.c │ ├── TensorOperator.c │ └── luaG.h ├── init.c ├── init.lua ├── lib/ │ ├── CMakeLists.txt │ ├── TH/ │ │ ├── CMakeLists.txt │ │ ├── README.md │ │ ├── TH.h │ │ ├── THAllocator.c │ │ ├── THAllocator.h │ │ ├── THAtomic.c │ │ ├── THAtomic.h │ │ ├── THBlas.c │ │ ├── THBlas.h │ │ ├── THConfig.cmake.in │ │ ├── THDiskFile.c │ │ ├── THDiskFile.h │ │ ├── THFile.c │ │ ├── THFile.h │ │ ├── THFilePrivate.h │ │ ├── THGeneral.c │ │ ├── THGeneral.h.in │ │ ├── THGenerateAllTypes.h │ │ ├── THGenerateByteType.h │ │ ├── THGenerateCharType.h │ │ ├── THGenerateDoubleType.h │ │ ├── THGenerateFloatType.h │ │ ├── THGenerateFloatTypes.h │ │ ├── THGenerateHalfType.h │ │ ├── THGenerateIntType.h │ │ ├── THGenerateIntTypes.h │ │ ├── THGenerateLongType.h │ │ ├── THGenerateShortType.h │ │ ├── THHalf.c │ │ ├── THHalf.h │ │ ├── THLapack.c │ │ ├── THLapack.h │ │ ├── THLogAdd.c │ │ ├── THLogAdd.h │ │ ├── THMath.h │ │ ├── THMemoryFile.c │ │ ├── THMemoryFile.h │ │ ├── THRandom.c │ │ ├── THRandom.h │ │ ├── THSize.c │ │ ├── THSize.h │ │ ├── THStorage.c │ │ ├── THStorage.h │ │ ├── THTensor.c │ │ ├── THTensor.h │ │ ├── THTensorApply.h │ │ ├── THTensorDimApply.h │ │ ├── THTensorMacros.h │ │ ├── THVector.c │ │ ├── THVector.h │ │ ├── cmake/ │ │ │ ├── FindARM.cmake │ │ │ ├── FindBLAS.cmake │ │ │ ├── FindLAPACK.cmake │ │ │ ├── FindMKL.cmake │ │ │ └── FindSSE.cmake │ │ ├── generic/ │ │ │ ├── THBlas.c │ │ │ ├── THBlas.h │ │ │ ├── THLapack.c │ │ │ ├── THLapack.h │ │ │ ├── THStorage.c │ │ │ ├── THStorage.h │ │ │ ├── THStorageCopy.c │ │ │ ├── THStorageCopy.h │ │ │ ├── THTensor.c │ │ │ ├── THTensor.h │ │ │ ├── THTensorConv.c │ │ │ ├── THTensorConv.h │ │ │ ├── THTensorCopy.c │ │ │ ├── THTensorCopy.h │ │ │ ├── THTensorLapack.c │ │ │ ├── THTensorLapack.h │ │ │ ├── THTensorMath.c │ │ │ ├── THTensorMath.h │ │ │ ├── THTensorRandom.c │ │ │ ├── THTensorRandom.h │ │ │ ├── THVector.h │ │ │ ├── THVectorDefault.c │ │ │ ├── THVectorDispatch.c │ │ │ └── simd/ │ │ │ ├── common_simd.h │ │ │ ├── convolve.c │ │ │ ├── convolve.h │ │ │ ├── convolve5x5_avx.c │ │ │ ├── convolve5x5_sse.c │ │ │ └── simd.h │ │ └── vector/ │ │ ├── AVX.c │ │ ├── AVX.h │ │ ├── AVX2.c │ │ ├── AVX2.h │ │ ├── NEON.c │ │ ├── SSE.c │ │ └── VSX.c │ └── luaT/ │ ├── CMakeLists.txt │ ├── README.md │ ├── luaT.c │ ├── luaT.h │ └── luaTConfig.cmake.in ├── mkdocs.yml ├── paths.lua.in ├── random.lua ├── rocks/ │ └── torch-scm-1.rockspec ├── test/ │ ├── longSize.lua │ ├── test.lua │ ├── test_Multinomial.lua │ ├── test_Tester.lua │ ├── test_aliasMultinomial.lua │ ├── test_half.lua │ ├── test_qr.lua │ ├── test_sharedmem.lua │ ├── test_timer.lua │ ├── test_writeObject.lua │ └── timeSort.lua ├── torchcwrap.lua ├── utils.c └── utils.h
SYMBOL INDEX (486 symbols across 50 files)
FILE: DiskFile.c
function torch_DiskFile_new (line 3) | static int torch_DiskFile_new(lua_State *L)
function torch_DiskFile_free (line 14) | static int torch_DiskFile_free(lua_State *L)
function torch_DiskFile_isLittleEndianCPU (line 21) | static int torch_DiskFile_isLittleEndianCPU(lua_State *L)
function torch_DiskFile_isBigEndianCPU (line 27) | static int torch_DiskFile_isBigEndianCPU(lua_State *L)
function torch_DiskFile_nativeEndianEncoding (line 33) | static int torch_DiskFile_nativeEndianEncoding(lua_State *L)
function torch_DiskFile_littleEndianEncoding (line 41) | static int torch_DiskFile_littleEndianEncoding(lua_State *L)
function torch_DiskFile_bigEndianEncoding (line 49) | static int torch_DiskFile_bigEndianEncoding(lua_State *L)
function torch_DiskFile_longSize (line 57) | static int torch_DiskFile_longSize(lua_State *L)
function torch_DiskFile_noBuffer (line 65) | static int torch_DiskFile_noBuffer(lua_State *L)
function torch_DiskFile___tostring__ (line 73) | static int torch_DiskFile___tostring__(lua_State *L)
type luaL_Reg (line 84) | struct luaL_Reg
function torch_DiskFile_init (line 96) | void torch_DiskFile_init(lua_State *L)
FILE: File.c
function IMPLEMENT_TORCH_FILE_FUNC (line 29) | IMPLEMENT_TORCH_FILE_FUNC(binary)
function torch_File_position (line 50) | IMPLEMENT_TORCH_FILE_FUNC(seekEnd)
function torch_File_readString (line 132) | static int torch_File_readString(lua_State *L)
function torch_File_writeString (line 146) | static int torch_File_writeString(lua_State *L)
type luaL_Reg (line 158) | struct luaL_Reg
function torch_File_init (line 202) | void torch_File_init(lua_State *L)
FILE: Generator.c
function torch_Generator_new (line 3) | int torch_Generator_new(lua_State *L)
function torch_Generator_free (line 10) | int torch_Generator_free(lua_State *L)
function torch_Generator_write (line 17) | static int torch_Generator_write(lua_State *L)
function torch_Generator_read (line 26) | static int torch_Generator_read(lua_State *L)
type luaL_Reg (line 36) | struct luaL_Reg
function torch_Generator_init (line 44) | void torch_Generator_init(lua_State *L)
FILE: MemoryFile.c
function torch_MemoryFile_new (line 3) | static int torch_MemoryFile_new(lua_State *L)
function torch_MemoryFile_storage (line 24) | static int torch_MemoryFile_storage(lua_State *L)
function torch_longSize (line 32) | static int torch_longSize(lua_State *L)
function torch_MemoryFile_free (line 40) | static int torch_MemoryFile_free(lua_State *L)
function torch_MemoryFile___tostring__ (line 47) | static int torch_MemoryFile___tostring__(lua_State *L)
type luaL_Reg (line 57) | struct luaL_Reg
function torch_MemoryFile_init (line 64) | void torch_MemoryFile_init(lua_State *L)
FILE: PipeFile.c
function torch_PipeFile_new (line 3) | static int torch_PipeFile_new(lua_State *L)
function torch_PipeFile_free (line 14) | static int torch_PipeFile_free(lua_State *L)
function torch_PipeFile___tostring__ (line 21) | static int torch_PipeFile___tostring__(lua_State *L)
type luaL_Reg (line 32) | struct luaL_Reg
function torch_PipeFile_init (line 37) | void torch_PipeFile_init(lua_State *L)
FILE: Timer.c
type Timer (line 23) | typedef struct _Timer
function TimeType (line 36) | static TimeType torch_Timer_realtime()
function TimeType (line 49) | static TimeType torch_Timer_usertime()
function TimeType (line 60) | static TimeType torch_Timer_systime()
function torch_Timer_new (line 71) | static int torch_Timer_new(lua_State *L)
function torch_Timer_reset (line 92) | static int torch_Timer_reset(lua_State *L)
function torch_Timer_free (line 105) | static int torch_Timer_free(lua_State *L)
function torch_Timer_stop (line 112) | static int torch_Timer_stop(lua_State *L)
function torch_Timer_resume (line 129) | static int torch_Timer_resume(lua_State *L)
function torch_Timer_time (line 143) | static int torch_Timer_time(lua_State *L)
function torch_Timer___tostring__ (line 164) | static int torch_Timer___tostring__(lua_State *L)
type luaL_Reg (line 171) | struct luaL_Reg
function torch_Timer_init (line 180) | void torch_Timer_init(lua_State *L)
FILE: generic/Storage.c
type luaL_Reg (line 263) | struct luaL_Reg
FILE: generic/Tensor.c
type luaL_Reg (line 1299) | struct luaL_Reg
FILE: generic/TensorOperator.c
type luaL_Reg (line 176) | struct luaL_Reg
FILE: generic/luaG.h
function real (line 33) | static real luaG_(checkreal)(lua_State *L, int idx) {
function real (line 54) | static real luaG_(optreal)(lua_State *L, int idx, real n) {
FILE: init.c
function luaopen_libtorch (line 44) | int luaopen_libtorch(lua_State *L)
FILE: lib/TH/THAllocator.c
function THDefaultAllocator_free (line 26) | static void THDefaultAllocator_free(void* ctx, void* ptr) {
type THMapAllocatorContext_ (line 38) | struct THMapAllocatorContext_ {
type THMapInfo (line 47) | typedef struct {
function THMapAllocatorContext (line 53) | THMapAllocatorContext *THMapAllocatorContext_new(const char *filename, i...
function THMapAllocatorContext (line 76) | THMapAllocatorContext *THMapAllocatorContext_newWithFd(const char *filen...
function THMapAllocatorContext_fd (line 89) | int THMapAllocatorContext_fd(THMapAllocatorContext *ctx)
function THMapAllocatorContext_size (line 94) | ptrdiff_t THMapAllocatorContext_size(THMapAllocatorContext *ctx)
function THMapAllocatorContext_free (line 99) | void THMapAllocatorContext_free(THMapAllocatorContext *ctx)
type stat (line 204) | struct stat
function THMapAllocator_free (line 334) | static void THMapAllocator_free(void* ctx_, void* data) {
function THMapAllocatorContext (line 368) | THMapAllocatorContext *THMapAllocatorContext_new(const char *filename, i...
function THMapAllocatorContext_free (line 373) | void THMapAllocatorContext_free(THMapAllocatorContext *ctx) {
function THMapAllocator_free (line 387) | static void THMapAllocator_free(void* ctx, void* data) {
function THRefcountedMapAllocator_free (line 425) | static void THRefcountedMapAllocator_free(void* ctx_, void* data) {
function THRefcountedMapAllocator_incref (line 449) | void THRefcountedMapAllocator_incref(THMapAllocatorContext *ctx, void *d...
function THRefcountedMapAllocator_decref (line 455) | int THRefcountedMapAllocator_decref(THMapAllocatorContext *ctx, void *data)
function THRefcountedMapAllocator_free (line 473) | static void THRefcountedMapAllocator_free(void* ctx_, void* data) {
function THRefcountedMapAllocator_incref (line 477) | void THRefcountedMapAllocator_incref(THMapAllocatorContext *ctx, void *d...
function THRefcountedMapAllocator_decref (line 482) | int THRefcountedMapAllocator_decref(THMapAllocatorContext *ctx, void *data)
FILE: lib/TH/THAllocator.h
type THAllocator (line 16) | typedef struct THAllocator {
type THMapAllocatorContext (line 29) | typedef struct THMapAllocatorContext_ THMapAllocatorContext;
FILE: lib/TH/THAtomic.c
function THAtomicSet (line 22) | void THAtomicSet(int volatile *a, int newvalue)
function THAtomicGet (line 39) | int THAtomicGet(int volatile *a)
function THAtomicAdd (line 52) | int THAtomicAdd(int volatile *a, int value)
function THAtomicIncrementRef (line 70) | void THAtomicIncrementRef(int volatile *a)
function THAtomicDecrementRef (line 75) | int THAtomicDecrementRef(int volatile *a)
function THAtomicCompareAndSwap (line 80) | int THAtomicCompareAndSwap(int volatile *a, int oldvalue, int newvalue)
function THAtomicSetLong (line 109) | void THAtomicSetLong(long volatile *a, long newvalue)
function THAtomicGetLong (line 125) | long THAtomicGetLong(long volatile *a)
function THAtomicAddLong (line 138) | long THAtomicAddLong(long volatile *a, long value)
function THAtomicCompareAndSwapLong (line 155) | long THAtomicCompareAndSwapLong(long volatile *a, long oldvalue, long ne...
function THAtomicSetPtrdiff (line 183) | void THAtomicSetPtrdiff(ptrdiff_t volatile *a, ptrdiff_t newvalue)
function THAtomicGetPtrdiff (line 203) | ptrdiff_t THAtomicGetPtrdiff(ptrdiff_t volatile *a)
function THAtomicAddPtrdiff (line 216) | ptrdiff_t THAtomicAddPtrdiff(ptrdiff_t volatile *a, ptrdiff_t value)
function THAtomicCompareAndSwapPtrdiff (line 237) | ptrdiff_t THAtomicCompareAndSwapPtrdiff(ptrdiff_t volatile *a, ptrdiff_t...
FILE: lib/TH/THDiskFile.c
type THDiskFile (line 10) | typedef struct THDiskFile__
function THDiskFile_isOpened (line 21) | static int THDiskFile_isOpened(THFile *self)
function fread__ (line 35) | size_t fread__(void *ptr, size_t size, size_t nitems, FILE *stream)
function THDiskFile_mode (line 136) | static int THDiskFile_mode(const char *mode, int *isReadable, int *isWri...
function THDiskFile_synchronize (line 165) | static void THDiskFile_synchronize(THFile *self)
function THDiskFile_seek (line 172) | static void THDiskFile_seek(THFile *self, size_t position)
function THDiskFile_seekEnd (line 195) | static void THDiskFile_seekEnd(THFile *self)
function THDiskFile_position (line 215) | static size_t THDiskFile_position(THFile *self)
function THDiskFile_close (line 235) | static void THDiskFile_close(THFile *self)
function THDiskFile_reverseMemory (line 245) | static void THDiskFile_reverseMemory(void *dst, const void *src, size_t ...
function THDiskFile_isLittleEndianCPU (line 267) | int THDiskFile_isLittleEndianCPU(void)
function THDiskFile_isBigEndianCPU (line 278) | int THDiskFile_isBigEndianCPU(void)
function THDiskFile_nativeEndianEncoding (line 283) | void THDiskFile_nativeEndianEncoding(THFile *self)
function THDiskFile_littleEndianEncoding (line 290) | void THDiskFile_littleEndianEncoding(THFile *self)
function THDiskFile_bigEndianEncoding (line 297) | void THDiskFile_bigEndianEncoding(THFile *self)
function THDiskFile_longSize (line 306) | void THDiskFile_longSize(THFile *self, int size)
function THDiskFile_noBuffer (line 314) | void THDiskFile_noBuffer(THFile *self)
function THDiskFile_free (line 323) | static void THDiskFile_free(THFile *self)
function THDiskFile_readLong (line 368) | static size_t THDiskFile_readLong(THFile *self, long *data, size_t n)
function THDiskFile_writeLong (line 430) | static size_t THDiskFile_writeLong(THFile *self, long *data, size_t n)
function THDiskFile_readString (line 503) | static size_t THDiskFile_readString(THFile *self, const char *format, ch...
function THDiskFile_writeString (line 593) | static size_t THDiskFile_writeString(THFile *self, const char *str, size...
function THFile (line 612) | THFile *THDiskFile_new(const char *name, const char *mode, int isQuiet)
function THPipeFile_mode (line 697) | static int THPipeFile_mode(const char *mode, int *isReadable, int *isWri...
function THPipeFile_free (line 717) | static void THPipeFile_free(THFile *self)
function THFile (line 726) | THFile *THPipeFile_new(const char *name, const char *mode, int isQuiet)
FILE: lib/TH/THFile.c
function THFile_readStringRaw (line 24) | size_t THFile_readStringRaw(THFile *self, const char *format, char **str_)
function THFile_writeStringRaw (line 29) | size_t THFile_writeStringRaw(THFile *self, const char *str, size_t size)
function THFile_synchronize (line 34) | void THFile_synchronize(THFile *self)
function THFile_seek (line 39) | void THFile_seek(THFile *self, size_t position)
function THFile_seekEnd (line 44) | void THFile_seekEnd(THFile *self)
function THFile_position (line 49) | size_t THFile_position(THFile *self)
function THFile_close (line 54) | void THFile_close(THFile *self)
function THFile_free (line 59) | void THFile_free(THFile *self)
function THFile_isOpened (line 64) | int THFile_isOpened(THFile *self)
function IMPLEMENT_THFILE_FLAGS (line 75) | IMPLEMENT_THFILE_FLAGS(isQuiet)
function THFile_ascii (line 87) | void THFile_ascii(THFile *self)
function THFile_autoSpacing (line 92) | void THFile_autoSpacing(THFile *self)
function THFile_noAutoSpacing (line 97) | void THFile_noAutoSpacing(THFile *self)
function THFile_quiet (line 102) | void THFile_quiet(THFile *self)
function THFile_pedantic (line 107) | void THFile_pedantic(THFile *self)
function THFile_clearError (line 112) | void THFile_clearError(THFile *self)
FILE: lib/TH/THFile.h
type THFile (line 6) | typedef struct THFile__ THFile;
FILE: lib/TH/THFilePrivate.h
type THFile__ (line 6) | struct THFile__
type THFileVTable (line 20) | struct THFileVTable
FILE: lib/TH/THGeneral.c
function defaultErrorHandlerFunction (line 25) | static void defaultErrorHandlerFunction(const char *msg, void *data)
function _THError (line 36) | void _THError(const char *file, const int line, const char *fmt, ...)
function _THAssertionFailed (line 57) | void _THAssertionFailed(const char *file, const int line, const char *ex...
function THSetErrorHandler (line 66) | void THSetErrorHandler(THErrorHandlerFunction new_handler, void *data)
function THSetDefaultErrorHandler (line 72) | void THSetDefaultErrorHandler(THErrorHandlerFunction new_handler, void *...
function defaultArgErrorHandlerFunction (line 82) | static void defaultArgErrorHandlerFunction(int argNumber, const char *ms...
function _THArgCheck (line 96) | void _THArgCheck(const char *file, int line, int condition, int argNumbe...
function THSetArgErrorHandler (line 119) | void THSetArgErrorHandler(THArgErrorHandlerFunction new_handler, void *d...
function THSetDefaultArgErrorHandler (line 125) | void THSetDefaultArgErrorHandler(THArgErrorHandlerFunction new_handler, ...
function THSetGCHandler (line 154) | void THSetGCHandler( void (*torchGCFunction_)(void *data), void *data )
function getAllocSize (line 161) | static ptrdiff_t getAllocSize(void *ptr) {
function applyHeapDelta (line 173) | static ptrdiff_t applyHeapDelta() {
function maybeTriggerGC (line 190) | static void maybeTriggerGC(ptrdiff_t curHeapSize) {
function THHeapUpdate (line 204) | void THHeapUpdate(ptrdiff_t size) {
function THFree (line 306) | void THFree(void *ptr)
function THLog1p (line 312) | double THLog1p(const double x)
function THSetNumThreads (line 322) | void THSetNumThreads(int num_threads)
function THGetNumThreads (line 329) | int THGetNumThreads(void)
function THGetNumCores (line 338) | int THGetNumCores(void)
function TH_API (line 351) | TH_API void THInferNumThreads(void)
function TH_API (line 362) | TH_API THDescBuff _THSizeDesc(const long *size, const long ndim) {
FILE: lib/TH/THHalf.c
function THHalf (line 5) | THHalf TH_float2half(float f)
function TH_API (line 12) | TH_API float TH_half2float(THHalf h)
function TH_halfbits2float (line 21) | void TH_halfbits2float(unsigned short* src, float* res)
function TH_float2halfbits (line 49) | void TH_float2halfbits(float* src, unsigned short* dest)
FILE: lib/TH/THHalf.h
type __thalign__ (line 16) | struct __thalign__
type __thalign__ (line 20) | struct __thalign__
type __THHalf (line 24) | typedef __THHalf THHalf;
type __THHalf2 (line 25) | typedef __THHalf2 THHalf2;
FILE: lib/TH/THLogAdd.c
function THLogAdd (line 15) | double THLogAdd(double log_a, double log_b)
function THLogSub (line 37) | double THLogSub(double log_a, double log_b)
function THExpMinusApprox (line 58) | double THExpMinusApprox(const double x)
FILE: lib/TH/THMath.h
function TH_sigmoid (line 4) | static inline double TH_sigmoid(double value) {
function TH_frac (line 8) | static inline double TH_frac(double x) {
function TH_rsqrt (line 12) | static inline double TH_rsqrt(double x) {
function TH_lerp (line 16) | static inline double TH_lerp(double a, double b, double weight) {
function TH_sigmoidf (line 20) | static inline float TH_sigmoidf(float value) {
function TH_fracf (line 24) | static inline float TH_fracf(float x) {
function TH_rsqrtf (line 28) | static inline float TH_rsqrtf(float x) {
function TH_lerpf (line 32) | static inline float TH_lerpf(float a, float b, float weight) {
FILE: lib/TH/THMemoryFile.c
type THMemoryFile (line 5) | typedef struct THMemoryFile__
function THMemoryFile_isOpened (line 15) | static int THMemoryFile_isOpened(THFile *self)
function THMemoryFile_grow (line 45) | static void THMemoryFile_grow(THMemoryFile *self, size_t size)
function THMemoryFile_mode (line 67) | static int THMemoryFile_mode(const char *mode, int *isReadable, int *isW...
function THMemoryFile_longSize (line 219) | void THMemoryFile_longSize(THFile *self, int size)
function THCharStorage (line 226) | THCharStorage *THMemoryFile_storage(THFile *self)
function THMemoryFile_synchronize (line 236) | static void THMemoryFile_synchronize(THFile *self)
function THMemoryFile_seek (line 242) | static void THMemoryFile_seek(THFile *self, size_t position)
function THMemoryFile_seekEnd (line 259) | static void THMemoryFile_seekEnd(THFile *self)
function THMemoryFile_position (line 267) | static size_t THMemoryFile_position(THFile *self)
function THMemoryFile_close (line 274) | static void THMemoryFile_close(THFile *self)
function THMemoryFile_free (line 282) | static void THMemoryFile_free(THFile *self)
function THMemoryFile_readLong (line 354) | static size_t THMemoryFile_readLong(THFile *self, long *data, size_t n)
function THMemoryFile_writeLong (line 434) | static size_t THMemoryFile_writeLong(THFile *self, long *data, size_t n)
function THMemoryFile_readString (line 531) | static size_t THMemoryFile_readString(THFile *self, const char *format, ...
function THMemoryFile_writeString (line 595) | static size_t THMemoryFile_writeString(THFile *self, const char *str, si...
function THFile (line 614) | THFile *THMemoryFile_newWithStorage(THCharStorage *storage, const char *...
function THFile (line 682) | THFile *THMemoryFile_new(const char *mode)
FILE: lib/TH/THRandom.c
function THGenerator (line 14) | static THGenerator* THGenerator_newUnseeded()
function THGenerator (line 25) | THGenerator* THGenerator_new()
function THGenerator (line 32) | THGenerator* THGenerator_copy(THGenerator *self, THGenerator *from)
function THGenerator_free (line 38) | void THGenerator_free(THGenerator *self)
function THGenerator_isValid (line 43) | int THGenerator_isValid(THGenerator *_generator)
function readURandomLong (line 53) | static unsigned long readURandomLong()
function THRandom_seed (line 69) | unsigned long THRandom_seed(THGenerator *_generator)
function THRandom_manualSeed (line 140) | void THRandom_manualSeed(THGenerator *_generator, unsigned long the_seed_)
function THRandom_initialSeed (line 164) | unsigned long THRandom_initialSeed(THGenerator *_generator)
function THRandom_nextState (line 169) | void THRandom_nextState(THGenerator *_generator)
function THRandom_random (line 186) | unsigned long THRandom_random(THGenerator *_generator)
function __uniform__ (line 204) | static double __uniform__(THGenerator *_generator)
function THRandom_uniform (line 218) | double THRandom_uniform(THGenerator *_generator, double a, double b)
function THRandom_normal (line 223) | double THRandom_normal(THGenerator *_generator, double mean, double stdv)
function THRandom_exponential (line 244) | double THRandom_exponential(THGenerator *_generator, double lambda)
function THRandom_cauchy (line 249) | double THRandom_cauchy(THGenerator *_generator, double median, double si...
function THRandom_logNormal (line 256) | double THRandom_logNormal(THGenerator *_generator, double mean, double s...
function THRandom_geometric (line 262) | int THRandom_geometric(THGenerator *_generator, double p)
function THRandom_bernoulli (line 268) | int THRandom_bernoulli(THGenerator *_generator, double p)
FILE: lib/TH/THRandom.h
type THGenerator (line 9) | typedef struct THGenerator {
FILE: lib/TH/THSize.c
function THSize_isSameSizeAs (line 3) | int THSize_isSameSizeAs(const long *sizeA, long dimsA, const long *sizeB...
function THSize_nElement (line 15) | ptrdiff_t THSize_nElement(long dims, long *size) {
FILE: lib/TH/THStorage.c
function THDescBuff (line 17) | THDescBuff THLongStorage_sizeDesc(const THLongStorage *size) {
function THLongStorage (line 21) | THLongStorage *THLongStorage_newInferSize(THLongStorage *size, ptrdiff_t...
function THLongStorage_inferSize2 (line 51) | int THLongStorage_inferSize2(THLongStorage *output, long *sizesA, long d...
function THLongStorage_inferSizeN (line 82) | int THLongStorage_inferSizeN(THLongStorage *output, int n, long **sizes,...
function THLongStorage_inferExpandGeometry (line 119) | int THLongStorage_inferExpandGeometry(long *tensorSizes, long *tensorStr...
FILE: lib/TH/generic/THBlas.c
function ffloat (line 25) | inline ffloat sdot_(const int *n, const float *x, const int *incx, const...
function real (line 167) | real THBlas_(dot)(long n, real *x, long incx, real *y, long incy)
FILE: lib/TH/generic/THStorage.c
function real (line 5) | real* THStorage_(data)(const THStorage *self)
function THStorage (line 20) | THStorage* THStorage_(new)(void)
function THStorage (line 25) | THStorage* THStorage_(newWithSize)(ptrdiff_t size)
function THStorage (line 30) | THStorage* THStorage_(newWithAllocator)(ptrdiff_t size,
function THStorage (line 44) | THStorage* THStorage_(newWithMapping)(const char *filename, ptrdiff_t si...
function THStorage (line 60) | THStorage* THStorage_(newWithSize1)(real data0)
function THStorage (line 67) | THStorage* THStorage_(newWithSize2)(real data0, real data1)
function THStorage (line 75) | THStorage* THStorage_(newWithSize3)(real data0, real data1, real data2)
function THStorage (line 84) | THStorage* THStorage_(newWithSize4)(real data0, real data1, real data2, ...
function THStorage (line 130) | THStorage* THStorage_(newWithData)(real *data, ptrdiff_t size)
function THStorage (line 136) | THStorage* THStorage_(newWithDataAndAllocator)(real* data, ptrdiff_t size,
function real (line 200) | real THStorage_(get)(const THStorage *self, ptrdiff_t idx)
type THStorage (line 214) | struct THStorage
FILE: lib/TH/generic/THStorage.h
type THStorage (line 24) | typedef struct THStorage
FILE: lib/TH/generic/THStorageCopy.h
type THByteStorage (line 9) | struct THByteStorage
type THCharStorage (line 10) | struct THCharStorage
type THShortStorage (line 11) | struct THShortStorage
type THIntStorage (line 12) | struct THIntStorage
type THLongStorage (line 13) | struct THLongStorage
type THFloatStorage (line 14) | struct THFloatStorage
type THDoubleStorage (line 15) | struct THDoubleStorage
type THHalfStorage (line 16) | struct THHalfStorage
FILE: lib/TH/generic/THTensor.c
function THStorage (line 6) | THStorage *THTensor_(storage)(const THTensor *self)
function THLongStorage (line 35) | THLongStorage *THTensor_(newSizeOf)(THTensor *self)
function THLongStorage (line 42) | THLongStorage *THTensor_(newStrideOf)(THTensor *self)
function real (line 49) | real *THTensor_(data)(const THTensor *self)
function THTensor (line 73) | THTensor *THTensor_(new)(void)
function THTensor (line 81) | THTensor *THTensor_(newWithTensor)(THTensor *tensor)
function THTensor (line 95) | THTensor *THTensor_(newWithStorage)(THStorage *storage, ptrdiff_t storag...
function THTensor (line 114) | THTensor *THTensor_(newWithStorage1d)(THStorage *storage, ptrdiff_t stor...
function THTensor (line 120) | THTensor *THTensor_(newWithStorage2d)(THStorage *storage, ptrdiff_t stor...
function THTensor (line 127) | THTensor *THTensor_(newWithStorage3d)(THStorage *storage, ptrdiff_t stor...
function THTensor (line 135) | THTensor *THTensor_(newWithStorage4d)(THStorage *storage, ptrdiff_t stor...
function THTensor (line 151) | THTensor *THTensor_(newWithSize)(THLongStorage *size, THLongStorage *str...
function THTensor (line 156) | THTensor *THTensor_(newWithSize1d)(long size0)
function THTensor (line 161) | THTensor *THTensor_(newWithSize2d)(long size0, long size1)
function THTensor (line 166) | THTensor *THTensor_(newWithSize3d)(long size0, long size1, long size2)
function THTensor (line 171) | THTensor *THTensor_(newWithSize4d)(long size0, long size1, long size2, l...
function THTensor (line 182) | THTensor *THTensor_(newClone)(THTensor *self)
function THTensor (line 190) | THTensor *THTensor_(newContiguous)(THTensor *self)
function THTensor (line 201) | THTensor *THTensor_(newSelect)(THTensor *tensor, int dimension_, long sl...
function THTensor (line 208) | THTensor *THTensor_(newNarrow)(THTensor *tensor, int dimension_, long fi...
function THTensor (line 215) | THTensor *THTensor_(newTranspose)(THTensor *tensor, int dimension1_, int...
function THTensor (line 222) | THTensor *THTensor_(newUnfold)(THTensor *tensor, int dimension_, long si...
function THTensor (line 229) | THTensor *THTensor_(newView)(THTensor *tensor, THLongStorage *size)
function THTensor (line 288) | THTensor* THTensor_(newExpand)(THTensor *tensor, THLongStorage *sizes) {
function real (line 865) | real THTensor_(get1d)(const THTensor *tensor, long x0)
function real (line 879) | real THTensor_(get2d)(const THTensor *tensor, long x0, long x1)
function real (line 893) | real THTensor_(get3d)(const THTensor *tensor, long x0, long x1, long x2)
function real (line 907) | real THTensor_(get4d)(const THTensor *tensor, long x0, long x1, long x2,...
function THDescBuff (line 914) | THDescBuff THTensor_(desc)(const THTensor *tensor) {
function THDescBuff (line 936) | THDescBuff THTensor_(sizeDesc)(const THTensor *tensor) {
FILE: lib/TH/generic/THTensor.h
type THTensor (line 9) | typedef struct THTensor
FILE: lib/TH/generic/THTensorCopy.h
type THByteTensor (line 8) | struct THByteTensor
type THCharTensor (line 9) | struct THCharTensor
type THShortTensor (line 10) | struct THShortTensor
type THIntTensor (line 11) | struct THIntTensor
type THLongTensor (line 12) | struct THLongTensor
type THFloatTensor (line 13) | struct THFloatTensor
type THDoubleTensor (line 14) | struct THDoubleTensor
type THHalfTensor (line 15) | struct THHalfTensor
FILE: lib/TH/generic/THTensorLapack.c
function THTensor (line 27) | static THTensor *THTensor_(newTransposedContiguous)(THTensor *self)
function THTensor (line 53) | static THTensor *THTensor_(checkLapackClone)(THTensor *result, THTensor ...
function THTensor (line 69) | static THTensor *THTensor_(cloneColumnMajorNrows)(THTensor *self, THTens...
function THTensor (line 99) | static THTensor *THTensor_(cloneColumnMajor)(THTensor *self, THTensor *src)
FILE: lib/TH/generic/THTensorMath.c
function accreal (line 521) | accreal THTensor_(dot)(THTensor *tensor, THTensor *src)
function real (line 591) | real THTensor_(medianall)(THTensor *tensor)
function accreal (line 616) | accreal THTensor_(sumall)(THTensor *tensor)
function accreal (line 623) | accreal THTensor_(prodall)(THTensor *tensor)
function else (line 1827) | else if (*t_data < 0) *r__data = -1;
function accreal (line 3096) | accreal THTensor_(normall)(THTensor *tensor, real value)
function accreal (line 3164) | accreal THTensor_(dist)(THTensor *tensor, THTensor *src, real value)
function accreal (line 3173) | accreal THTensor_(meanall)(THTensor *tensor)
function accreal (line 3179) | accreal THTensor_(varall)(THTensor *tensor, int biased)
function accreal (line 3188) | accreal THTensor_(stdall)(THTensor *tensor, int biased)
FILE: lib/TH/generic/THVectorDispatch.c
function TH_API (line 110) | TH_API void THVector_(adds)(real *r_, const real *t, const real value, c...
FILE: lib/TH/generic/simd/convolve.c
function __get_cpuid (line 6) | static __inline int __get_cpuid (unsigned int __level, unsigned int *__eax,
function xgetbv (line 15) | static void xgetbv(unsigned int op, unsigned int* eax, unsigned int* edx) {
function __get_cpuid (line 37) | static __inline int __get_cpuid (unsigned int __level, unsigned int *__eax,
function xgetbv (line 44) | static void xgetbv(unsigned int op, unsigned int* eax, unsigned int* edx) {
type ECPUFeature (line 51) | enum ECPUFeature
function checkCPUFeatures (line 62) | static unsigned int checkCPUFeatures() {
function haveCPUFeature (line 95) | static int haveCPUFeature(unsigned int feature) {
function convolve_5x5 (line 115) | void convolve_5x5(float* output, float* input, float* kernel, long outRo...
FILE: lib/TH/generic/simd/convolve5x5_avx.c
function convolve_5x5_1_avx (line 6) | void convolve_5x5_1_avx(float* output, float* image, float* weight, long...
function convolve_5x5_2_avx (line 15) | void convolve_5x5_2_avx(float* output, float* image, float* weight, long...
function convolve_5x5_4_avx (line 24) | void convolve_5x5_4_avx(float* output, float* image, float* weight, long...
function convolve_5x5_5_avx (line 33) | void convolve_5x5_5_avx(float* output, float* image, float* weight, long...
function convolve_5x5_6_avx (line 42) | void convolve_5x5_6_avx(float* output, float* image, float* weight, long...
function convolve_5x5_7_avx (line 51) | void convolve_5x5_7_avx(float* output, float* image, float* weight, long...
function convolve_5x5_8_avx (line 60) | void convolve_5x5_8_avx(float* output, float* image, float* weight, long...
function convolve_5x5_64x64_avx (line 69) | void convolve_5x5_64x64_avx(float* output, float* image, float* weight, ...
function convolve_5x5_32x32_avx (line 95) | void convolve_5x5_32x32_avx(float* output, float* image, float* weight, ...
function convolve_5x5_16x16_avx (line 113) | void convolve_5x5_16x16_avx(float* output, float* image, float* weight, ...
function convolve_5x5_8x8_avx (line 127) | void convolve_5x5_8x8_avx(float* output, float* image, float* weight, lo...
function convolve_5x5_avx (line 134) | void convolve_5x5_avx(float* output, float* input, float* kernel, long o...
FILE: lib/TH/generic/simd/convolve5x5_sse.c
function convolve_5x5_1_sse (line 6) | void convolve_5x5_1_sse(float* output, float* image, float* weight, long...
function convolve_5x5_2_sse (line 26) | void convolve_5x5_2_sse(float* output, float* image, float* weight, long...
function convolve_5x5_4_sse (line 49) | void convolve_5x5_4_sse(float* output, float* image, float* weight, long...
function convolve_5x5_6_sse (line 78) | void convolve_5x5_6_sse(float* output, float* image, float* weight, long...
function convolve_5x5_8_sse (line 113) | void convolve_5x5_8_sse(float* output, float* image, float* weight, long...
function convolve_5x5_64x64_sse (line 157) | void convolve_5x5_64x64_sse(float* output, float* image, float* weight, ...
function convolve_5x5_32x32_sse (line 199) | void convolve_5x5_32x32_sse(float* output, float* image, float* weight, ...
function convolve_5x5_16x16_sse (line 227) | void convolve_5x5_16x16_sse(float* output, float* image, float* weight, ...
function convolve_5x5_8x8_sse (line 245) | void convolve_5x5_8x8_sse(float* output, float* image, float* weight, lo...
function convolve_5x5_sse (line 253) | void convolve_5x5_sse(float* output, float* input, float* kernel, long o...
FILE: lib/TH/generic/simd/simd.h
type FunctionDescription (line 35) | typedef struct FunctionDescription
type SIMDExtensions (line 42) | enum SIMDExtensions
function detectHostSIMDExtensions (line 61) | static inline uint32_t detectHostSIMDExtensions()
function detectHostSIMDExtensions (line 68) | static inline uint32_t detectHostSIMDExtensions()
function detectHostSIMDExtensions (line 79) | static inline uint32_t detectHostSIMDExtensions()
function detectHostSIMDExtensions (line 92) | static inline uint32_t detectHostSIMDExtensions()
function cpuid (line 100) | static inline void cpuid(uint32_t *eax, uint32_t *ebx, uint32_t *ecx, ui...
function detectHostSIMDExtensions (line 123) | static inline uint32_t detectHostSIMDExtensions()
FILE: lib/TH/vector/AVX.c
function THDoubleVector_copy_AVX (line 10) | void THDoubleVector_copy_AVX(double *y, const double *x, const ptrdiff_t...
function THDoubleVector_fill_AVX (line 23) | void THDoubleVector_fill_AVX(double *x, const double c, const ptrdiff_t ...
function THDoubleVector_cdiv_AVX (line 39) | void THDoubleVector_cdiv_AVX(double *z, const double *x, const double *y...
function THDoubleVector_divs_AVX (line 57) | void THDoubleVector_divs_AVX(double *y, const double *x, const double c,...
function THDoubleVector_cmul_AVX (line 74) | void THDoubleVector_cmul_AVX(double *z, const double *x, const double *y...
function THDoubleVector_muls_AVX (line 92) | void THDoubleVector_muls_AVX(double *y, const double *x, const double c,...
function THDoubleVector_cadd_AVX (line 109) | void THDoubleVector_cadd_AVX(double *z, const double *x, const double *y...
function THDoubleVector_adds_AVX (line 125) | void THDoubleVector_adds_AVX(double *y, const double *x, const double c,...
function THFloatVector_copy_AVX (line 142) | void THFloatVector_copy_AVX(float *y, const float *x, const ptrdiff_t n) {
function THFloatVector_fill_AVX (line 155) | void THFloatVector_fill_AVX(float *x, const float c, const ptrdiff_t n) {
function THFloatVector_cdiv_AVX (line 171) | void THFloatVector_cdiv_AVX(float *z, const float *x, const float *y, co...
function THFloatVector_divs_AVX (line 189) | void THFloatVector_divs_AVX(float *y, const float *x, const float c, con...
function THFloatVector_cmul_AVX (line 206) | void THFloatVector_cmul_AVX(float *z, const float *x, const float *y, co...
function THFloatVector_muls_AVX (line 224) | void THFloatVector_muls_AVX(float *y, const float *x, const float c, con...
function THFloatVector_cadd_AVX (line 241) | void THFloatVector_cadd_AVX(float *z, const float *x, const float *y, co...
function THFloatVector_adds_AVX (line 257) | void THFloatVector_adds_AVX(float *y, const float *x, const float c, con...
FILE: lib/TH/vector/AVX2.c
function THDoubleVector_cadd_AVX2 (line 9) | void THDoubleVector_cadd_AVX2(double *z, const double *x, const double *...
function THFloatVector_cadd_AVX2 (line 28) | void THFloatVector_cadd_AVX2(float *z, const float *x, const float *y, c...
FILE: lib/TH/vector/NEON.c
function THFloatVector_fill_NEON (line 1) | static void THFloatVector_fill_NEON(float *x, const float c, const ptrdi...
function THFloatVector_cmul_NEON (line 17) | static void THFloatVector_cmul_NEON(float *z, const float *x, const floa...
function THFloatVector_muls_NEON (line 32) | static void THFloatVector_muls_NEON(float *y, const float *x, const floa...
function THFloatVector_cadd_NEON (line 47) | static void THFloatVector_cadd_NEON(float *z, const float *x, const floa...
function THFloatVector_adds_NEON (line 62) | static void THFloatVector_adds_NEON(float *y, const float *x, const floa...
function THFloatVector_cdiv_NEON (line 77) | static void THFloatVector_cdiv_NEON(float *z, const float *x, const floa...
function THFloatVector_divs_NEON (line 92) | static void THFloatVector_divs_NEON(float *y, const float *x, const floa...
FILE: lib/TH/vector/SSE.c
function THDoubleVector_fill_SSE (line 7) | static void THDoubleVector_fill_SSE(double *x, const double c, const ptr...
function THDoubleVector_cadd_SSE (line 23) | static void THDoubleVector_cadd_SSE(double *z, const double *x, const do...
function THDoubleVector_adds_SSE (line 39) | static void THDoubleVector_adds_SSE(double *y, const double *x, const do...
function THDoubleVector_cmul_SSE (line 56) | static void THDoubleVector_cmul_SSE(double *z, const double *x, const do...
function THDoubleVector_muls_SSE (line 81) | static void THDoubleVector_muls_SSE(double *y, const double *x, const do...
function THDoubleVector_cdiv_SSE (line 103) | static void THDoubleVector_cdiv_SSE(double *z, const double *x, const do...
function THDoubleVector_divs_SSE (line 121) | static void THDoubleVector_divs_SSE(double *y, const double *x, const do...
function THFloatVector_fill_SSE (line 138) | static void THFloatVector_fill_SSE(float *x, const float c, const ptrdif...
function THFloatVector_cadd_SSE (line 155) | static void THFloatVector_cadd_SSE(float *z, const float *x, const float...
function THFloatVector_adds_SSE (line 171) | static void THFloatVector_adds_SSE(float *y, const float *x, const float...
function THFloatVector_cmul_SSE (line 188) | static void THFloatVector_cmul_SSE(float *z, const float *x, const float...
function THFloatVector_muls_SSE (line 213) | static void THFloatVector_muls_SSE(float *y, const float *x, const float...
function THFloatVector_cdiv_SSE (line 235) | static void THFloatVector_cdiv_SSE(float *z, const float *x, const float...
function THFloatVector_divs_SSE (line 253) | static void THFloatVector_divs_SSE(float *y, const float *x, const float...
FILE: lib/TH/vector/VSX.c
function THDoubleVector_fill_VSX (line 9) | static void THDoubleVector_fill_VSX(double *x, const double c, const ptr...
function THDoubleVector_cadd_VSX (line 104) | static void THDoubleVector_cadd_VSX(double *z, const double *x, const do...
function THDoubleVector_adds_VSX (line 208) | static void THDoubleVector_adds_VSX(double *y, const double *x, const do...
function THDoubleVector_cmul_VSX (line 294) | static void THDoubleVector_cmul_VSX(double *z, const double *x, const do...
function THDoubleVector_muls_VSX (line 395) | static void THDoubleVector_muls_VSX(double *y, const double *x, const do...
function THDoubleVector_cdiv_VSX (line 481) | static void THDoubleVector_cdiv_VSX(double *z, const double *x, const do...
function THDoubleVector_divs_VSX (line 582) | static void THDoubleVector_divs_VSX(double *y, const double *x, const do...
function THFloatVector_fill_VSX (line 673) | static void THFloatVector_fill_VSX(float *x, const float c, const ptrdif...
function THFloatVector_cadd_VSX (line 768) | static void THFloatVector_cadd_VSX(float *z, const float *x, const float...
function THFloatVector_adds_VSX (line 872) | static void THFloatVector_adds_VSX(float *y, const float *x, const float...
function THFloatVector_cmul_VSX (line 956) | static void THFloatVector_cmul_VSX(float *z, const float *y, const float...
function THFloatVector_muls_VSX (line 1057) | static void THFloatVector_muls_VSX(float *y, const float *x, const float...
function THFloatVector_cdiv_VSX (line 1141) | static void THFloatVector_cdiv_VSX(float *z, const float *x, const float...
function THFloatVector_divs_VSX (line 1242) | static void THFloatVector_divs_VSX(float *y, const float*x, const float ...
function standardDouble_fill (line 1366) | static void standardDouble_fill(double *x, const double c, const ptrdiff...
function standardFloat_fill (line 1372) | static void standardFloat_fill(float *x, const float c, const ptrdiff_t n)
function standardDouble_cadd (line 1378) | static void standardDouble_cadd(double *z, const double *x, const doubl...
function standardFloat_cadd (line 1384) | static void standardFloat_cadd(float *z, const float *x, const float *y,...
function standardDouble_adds (line 1390) | static void standardDouble_adds(double *y, const double *x, const double...
function standardFloat_adds (line 1396) | static void standardFloat_adds(float *y, const float *x, const float c, ...
function standardDouble_cmul (line 1402) | static void standardDouble_cmul(double *z, const double *x, const doubl...
function standardFloat_cmul (line 1408) | static void standardFloat_cmul(float *z, const float *x, const float *y,...
function standardDouble_muls (line 1414) | static void standardDouble_muls(double *y, const double *x, const double...
function standardFloat_muls (line 1420) | static void standardFloat_muls(float *y, const float *x, const float c, ...
function standardDouble_cdiv (line 1426) | static void standardDouble_cdiv(double *z, const double *x, const doubl...
function standardFloat_cdiv (line 1432) | static void standardFloat_cdiv(float *z, const float *x, const float *y,...
function standardDouble_divs (line 1438) | static void standardDouble_divs(double *y, const double *x, const double...
function standardFloat_divs (line 1444) | static void standardFloat_divs(float *y, const float *x, const float c, ...
function randDouble (line 1450) | double randDouble()
function near (line 1455) | int near(double a, double b)
function test_THDoubleVector_fill_VSX (line 1475) | void test_THDoubleVector_fill_VSX()
function test_THFloatVector_fill_VSX (line 1546) | void test_THFloatVector_fill_VSX()
function test_THDoubleVector_cadd_VSX (line 1617) | void test_THDoubleVector_cadd_VSX()
function test_THFloatVector_cadd_VSX (line 1688) | void test_THFloatVector_cadd_VSX()
function test_THDoubleVector_adds_VSX (line 1759) | void test_THDoubleVector_adds_VSX()
function test_THFloatVector_adds_VSX (line 1826) | void test_THFloatVector_adds_VSX()
function test_THDoubleVector_cmul_VSX (line 1894) | void test_THDoubleVector_cmul_VSX()
function test_THFloatVector_cmul_VSX (line 1964) | void test_THFloatVector_cmul_VSX()
function test_THDoubleVector_muls_VSX (line 2034) | void test_THDoubleVector_muls_VSX()
function test_THFloatVector_muls_VSX (line 2104) | void test_THFloatVector_muls_VSX()
function test_THDoubleVector_cdiv_VSX (line 2175) | void test_THDoubleVector_cdiv_VSX()
function test_THFloatVector_cdiv_VSX (line 2245) | void test_THFloatVector_cdiv_VSX()
function test_THDoubleVector_divs_VSX (line 2315) | void test_THDoubleVector_divs_VSX()
function test_THFloatVector_divs_VSX (line 2385) | void test_THFloatVector_divs_VSX()
function main (line 2459) | int main()
FILE: lib/luaT/luaT.c
function luaT_free (line 44) | void luaT_free(lua_State *L, void *ptr)
function luaT_setfuncs (line 49) | void luaT_setfuncs(lua_State *L, const luaL_Reg *l, int nup)
function luaT_stackdump (line 67) | void luaT_stackdump(lua_State *L)
function luaT_pushmetatable (line 163) | int luaT_pushmetatable(lua_State *L, const char *tname)
function luaT_iscdata (line 252) | static int luaT_iscdata(lua_State *L, int ud)
function luaT_pushudata (line 297) | void luaT_pushudata(lua_State *L, void *udata, const char *tname)
function luaT_isudata (line 336) | int luaT_isudata(lua_State *L, int ud, const char *tname)
function luaT_pushlong (line 352) | void luaT_pushlong(lua_State *L, long n)
function luaT_checklong (line 367) | long luaT_checklong(lua_State *L, int idx)
function luaT_tolong (line 380) | long luaT_tolong(lua_State *L, int idx)
function luaT_pushinteger (line 393) | void luaT_pushinteger(lua_State *L, ptrdiff_t n)
function luaT_checkinteger (line 408) | ptrdiff_t luaT_checkinteger(lua_State *L, int idx)
function luaT_getfieldchecknumber (line 448) | double luaT_getfieldchecknumber(lua_State *L, int ud, const char *field)
function luaT_getfieldcheckint (line 458) | int luaT_getfieldcheckint(lua_State *L, int ud, const char *field)
function luaT_getfieldcheckboolean (line 478) | int luaT_getfieldcheckboolean(lua_State *L, int ud, const char *field)
function luaT_getfieldchecktable (line 488) | void luaT_getfieldchecktable(lua_State *L, int ud, const char *field)
function luaT_typerror (line 498) | int luaT_typerror(lua_State *L, int ud, const char *tname)
function luaT_checkboolean (line 513) | int luaT_checkboolean(lua_State *L, int ud)
function luaT_optboolean (line 520) | int luaT_optboolean(lua_State *L, int ud, int def)
function luaT_registeratname (line 528) | void luaT_registeratname(lua_State *L, const struct luaL_Reg *methods, c...
function luaT_fullparentname (line 571) | int luaT_fullparentname(const char *tname, char *parent_name)
function luaT_classmodulename (line 586) | int luaT_classmodulename(const char *tname, char *parent_name)
function luaT_outerparentname (line 596) | int luaT_outerparentname(const char *tname, char *parent_name)
function luaT_innerparentname (line 612) | int luaT_innerparentname(const char *tname, char *parent_name)
function luaT_getinnerparent (line 634) | void luaT_getinnerparent(lua_State *L, const char *tname)
function luaT_lua_newmetatable (line 675) | int luaT_lua_newmetatable(lua_State *L)
function luaT_lua_metatype (line 899) | int luaT_lua_metatype(lua_State *L)
function luaT_lua_pushudata (line 928) | int luaT_lua_pushudata(lua_State *L)
function luaT_lua_factory (line 947) | int luaT_lua_factory(lua_State *L)
function luaT_lua_getconstructortable (line 962) | int luaT_lua_getconstructortable(lua_State *L)
function luaT_lua_typename (line 975) | int luaT_lua_typename(lua_State *L)
function luaT_lua_isequal (line 987) | int luaT_lua_isequal(lua_State *L)
function luaT_pushpointer (line 1009) | static void luaT_pushpointer(lua_State *L, const void *ptr)
function luaT_lua_pointer (line 1024) | int luaT_lua_pointer(lua_State *L)
function luaT_lua_setenv (line 1066) | int luaT_lua_setenv(lua_State *L)
function luaT_lua_getenv (line 1075) | int luaT_lua_getenv(lua_State *L)
function luaT_lua_getmetatable (line 1085) | int luaT_lua_getmetatable(lua_State *L)
function luaT_lua_version (line 1093) | int luaT_lua_version(lua_State *L)
function luaT_lua_setmetatable (line 1118) | int luaT_lua_setmetatable(lua_State *L)
function luaT_mt__index (line 1131) | static int luaT_mt__index(lua_State *L)
function luaT_mt__newindex (line 1173) | static int luaT_mt__newindex(lua_State *L)
function luaT_cmt__call (line 1290) | int luaT_cmt__call(lua_State *L)
function luaT_cmt__newindex (line 1312) | int luaT_cmt__newindex(lua_State *L)
function luaT_pushmetaclass (line 1335) | int luaT_pushmetaclass(lua_State *L, const char *tname)
function luaT_getmetaclass (line 1355) | int luaT_getmetaclass(lua_State *L, int index)
function luaT_registeratid (line 1368) | void luaT_registeratid(lua_State *L, const struct luaL_Reg *methods, con...
FILE: lib/luaT/luaT.h
function luaL_typerror (line 41) | static int luaL_typerror(lua_State *L, int narg, const char *tname)
type luaL_Reg (line 92) | struct luaL_Reg
type luaL_Reg (line 133) | struct luaL_Reg
FILE: utils.c
function THLongStorage (line 10) | THLongStorage* torch_checklongargs(lua_State *L, int index)
function torch_islongargs (line 38) | int torch_islongargs(lua_State *L, int index)
function torch_isatty (line 66) | static int torch_isatty(lua_State *L)
function real_time (line 77) | static double real_time()
function torch_lua_tic (line 94) | static int torch_lua_tic(lua_State* L)
function torch_lua_toc (line 101) | static int torch_lua_toc(lua_State* L)
function torch_lua_getdefaulttensortype (line 109) | static int torch_lua_getdefaulttensortype(lua_State *L)
function torch_getnumthreads (line 168) | static int torch_getnumthreads(lua_State *L)
function torch_setnumthreads (line 174) | static int torch_setnumthreads(lua_State *L)
function torch_getnumcores (line 180) | static int torch_getnumcores(lua_State *L)
function luaTorchGCFunction (line 186) | static void luaTorchGCFunction(void *data)
function torch_setheaptracking (line 192) | static int torch_setheaptracking(lua_State *L)
function luaTorchErrorHandlerFunction (line 206) | static void luaTorchErrorHandlerFunction(const char *msg, void *data)
function luaTorchArgErrorHandlerFunction (line 212) | static void luaTorchArgErrorHandlerFunction(int argNumber, const char *m...
function torch_updateerrorhandlers (line 218) | static int torch_updateerrorhandlers(lua_State *L)
type luaL_Reg (line 225) | struct luaL_Reg
function torch_utils_init (line 251) | void torch_utils_init(lua_State *L)
Condensed preview — 166 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,513K chars).
[
{
"path": ".gitignore",
"chars": 7,
"preview": "build/\n"
},
{
"path": ".travis.yml",
"chars": 1797,
"preview": "language: c\ncompiler:\n - gcc\n - clang\ncache:\n directories:\n - $HOME/OpenBlasInstall\nsudo: false\nenv:\n - TORCH_LUA_V"
},
{
"path": "CMakeLists.txt",
"chars": 3126,
"preview": "IF(APPLE)\n CMAKE_MINIMUM_REQUIRED(VERSION 2.8.12 FATAL_ERROR)\n CMAKE_POLICY(VERSION 2.8.12)\nELSE()\n CMAKE_MINIMUM_REQ"
},
{
"path": "CONTRIBUTING.md",
"chars": 5005,
"preview": "# Contributing to Torch7 Core (torch7, nn, cutorch, cunn)\n\nThanks a lot! There are plenty of ways you can help!\n\nPlease "
},
{
"path": "COPYRIGHT.txt",
"chars": 2049,
"preview": "Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)\nCopyright (c) 2012-2014 Deepmind Technologies (Koray "
},
{
"path": "CmdLine.lua",
"chars": 7215,
"preview": "local CmdLine = torch.class('torch.CmdLine')\n\nlocal function strip(str)\n return string.match(str, '%-*(.*)')\nend\n\nloca"
},
{
"path": "DiskFile.c",
"chars": 2816,
"preview": "#include \"general.h\"\n\nstatic int torch_DiskFile_new(lua_State *L)\n{\n const char *name = luaL_checkstring(L, 1);\n const"
},
{
"path": "FFInterface.lua",
"chars": 6707,
"preview": "-- if this causes issues, you may need to:\n-- luarocks remove --force ffi\n-- and follow instructions to install\n-- https"
},
{
"path": "File.c",
"chars": 8863,
"preview": "#include \"general.h\"\n#include \"THFile.h\"\n#include \"luaT.h\"\n\n#define IMPLEMENT_TORCH_FILE_FLAG(NAME) \\\n"
},
{
"path": "File.lua",
"chars": 16037,
"preview": "local File = torch.getmetatable('torch.File')\n\nfunction File:writeBool(value)\n if value then\n self:writeInt(1)\n "
},
{
"path": "Generator.c",
"chars": 1248,
"preview": "#include <general.h>\n\nint torch_Generator_new(lua_State *L)\n{\n THGenerator *gen = THGenerator_new();\n luaT_pushudata(L"
},
{
"path": "MemoryFile.c",
"chars": 1836,
"preview": "#include \"general.h\"\n\nstatic int torch_MemoryFile_new(lua_State *L)\n{\n const char *mode;\n THCharStorage *storage = lua"
},
{
"path": "PipeFile.c",
"chars": 1229,
"preview": "#include \"general.h\"\n\nstatic int torch_PipeFile_new(lua_State *L)\n{\n const char *name = luaL_checkstring(L, 1);\n const"
},
{
"path": "README.md",
"chars": 3864,
"preview": "[](https://gitter.im/torch/t"
},
{
"path": "ROADMAP.md",
"chars": 10470,
"preview": "\n# Torch Roadmap (August 2015 - March 2016)\n\nThis roadmap document is intended to serve as a loose plan of our vision fo"
},
{
"path": "Storage.c",
"chars": 404,
"preview": "#include \"general.h\"\n\n#define torch_Storage_(NAME) TH_CONCAT_4(torch_,Real,Storage_,NAME)\n#define THFile_readRealRaw TH_"
},
{
"path": "Tensor.c",
"chars": 400,
"preview": "#include \"general.h\"\n\n#define torch_Storage_(NAME) TH_CONCAT_4(torch_,Real,Storage_,NAME)\n#define torch_Storage TH_CONCA"
},
{
"path": "Tensor.lua",
"chars": 16704,
"preview": "-- additional methods for Storage\nlocal Storage = {}\n\n-- additional methods for Tensor\nlocal Tensor = {}\n\n-- types\nlocal"
},
{
"path": "TensorMath.lua",
"chars": 53945,
"preview": "local wrap = require 'cwrap'\n\nrequire 'torchcwrap'\n\nlocal interface = wrap.CInterface.new()\nlocal method = wrap.CInterfa"
},
{
"path": "TensorOperator.c",
"chars": 292,
"preview": "#include \"general.h\"\n\n#define torch_TensorOperator_(NAME) TH_CONCAT_4(torch_,Real,TensorOperator_,NAME)\n#define torch_Te"
},
{
"path": "TestSuite.lua",
"chars": 703,
"preview": "function torch.TestSuite()\n local obj = {\n __tests = {},\n __isTestSuite = true\n }\n\n local metatable = {}"
},
{
"path": "Tester.lua",
"chars": 28355,
"preview": "\n-- Lua 5.2 compatibility\nlocal unpack = unpack or table.unpack\n\nlocal check = {} -- helper functions, defined at the bo"
},
{
"path": "Timer.c",
"chars": 4732,
"preview": "#include \"general.h\"\n\n#ifdef _WIN32\n\n#include <windows.h>\n#include <assert.h>\n#define TimeType __int64\nstatic __declspec"
},
{
"path": "cmake/TorchConfig.cmake.in",
"chars": 1450,
"preview": "# This (ugly) setup assumes:\n# CMAKE_PREFIX_PATH = LUA_BINDIR\n# CMAKE_INSTALL_PREFIX = PREFIX\n\n# Define Torch basic su"
},
{
"path": "cmake/TorchExports.cmake",
"chars": 575,
"preview": "INSTALL(EXPORT TH-exports\n DESTINATION \"${Torch_INSTALL_CMAKE_SUBDIR}\"\n FILE \"TorchExports.cmake\")\n\nCONFIGURE_FILE(\"cm"
},
{
"path": "cmake/TorchPackage.cmake",
"chars": 1897,
"preview": "# -*- cmake -*-\n\nMACRO(ADD_TORCH_LIBRARY package type src)\n IF (\"${type}\" STREQUAL \"STATIC\")\n if (\"${src}\" MATCHES \""
},
{
"path": "cmake/TorchPaths.cmake",
"chars": 1351,
"preview": "# workaround another annoying cmake bug\n# http://public.kitware.com/Bug/view.php?id=14462\n# https://awesome.naquadah.org"
},
{
"path": "cmake/TorchPathsInit.cmake",
"chars": 2160,
"preview": "SET(Torch_INSTALL_BIN \"${Torch_INSTALL_PREFIX}/${Torch_INSTALL_BIN_SUBDIR}\")\nSET(Torch_INSTALL_MAN \"${Torch_INSTALL_PREF"
},
{
"path": "cmake/TorchWrap.cmake",
"chars": 824,
"preview": "MACRO(ADD_TORCH_WRAP target luafile)\n INCLUDE_DIRECTORIES(\"${CMAKE_CURRENT_BINARY_DIR}\")\n GET_FILENAME_COMPONENT(_file"
},
{
"path": "cmake/TorchWrap.cmake.in",
"chars": 901,
"preview": "MACRO(ADD_TORCH_WRAP target luafile)\n INCLUDE_DIRECTORIES(\"${CMAKE_CURRENT_BINARY_DIR}\")\n GET_FILENAME_COMPONENT(_file"
},
{
"path": "doc/cmdline.md",
"chars": 3906,
"preview": "<a name=\"torch.CmdLine.dok\"></a>\n# CmdLine #\n\nThis class provides a parameter parsing framework which is very\nuseful whe"
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"path": "doc/diskfile.md",
"chars": 2760,
"preview": "<a name=\"torch.DiskFile.dok\"></a>\n# DiskFile #\n\nParent classes: [File](file.md)\n\nA `DiskFile` is a particular `File` whi"
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{
"path": "doc/file.md",
"chars": 14115,
"preview": "<a name=\"torch.File.dok\"></a>\n# File #\n\nThis is an _abstract_ class. It defines most methods implemented by its\nchild cl"
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{
"path": "doc/index.md",
"chars": 1958,
"preview": "<a name=\"torch.reference.dok\"></a>\n# Torch Package Reference Manual #\n\n[![Join the chat at https://gitter.im/torch/torch"
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{
"path": "doc/maths.md",
"chars": 95565,
"preview": "<a name=\"torch.maths.dok\"></a>\n# Math Functions #\n\nTorch provides MATLAB-like functions for manipulating [`Tensor`](tens"
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"path": "doc/memoryfile.md",
"chars": 1559,
"preview": "<a name=\"torch.MemoryFile.dok\"></a>\n# MemoryFile #\n\nParent classes: [File](file.md)\n\nA `MemoryFile` is a particular `Fil"
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{
"path": "doc/pipefile.md",
"chars": 866,
"preview": "<a name=\"torch.PipeFile.dok\"></a>\n# PipeFile #\n\nParent classes: [DiskFile](diskfile.md)\n\nA `PipeFile` is a particular `F"
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{
"path": "doc/random.md",
"chars": 5489,
"preview": "<a name=\"torch.random.dok\"></a>\n# Random Numbers #\n\nTorch provides accurate mathematical random generation, based on\n[Me"
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{
"path": "doc/serialization.md",
"chars": 3627,
"preview": "\n<a name=\"torch.serialization.dok\"></a>\n# Serialization #\n\nTorch provides 4 high-level methods to serialize/deserialize "
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"path": "doc/storage.md",
"chars": 9050,
"preview": "<a name=\"torch.Storage.dok\"></a>\n# Storage #\n<a name=\"torch.CharStorage.dok\"></a>\n<a name=\"torch.ByteStorage.dok\"></a>\n<"
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{
"path": "doc/tensor.md",
"chars": 58028,
"preview": "<a name=\"torch.Tensor.dok\"></a>\n# Tensor #\n\nThe `Tensor` class is probably the most important class in\n`Torch`. Almost e"
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{
"path": "doc/tester.md",
"chars": 12998,
"preview": "<a name=\"torch.Tester.dok\"></a>\n# Tester #\n\nThis class provides a generic unit testing framework. It is already\nbeing us"
},
{
"path": "doc/timer.md",
"chars": 1462,
"preview": "<a name=\"torch.Timer.dok\"></a>\n# Timer #\n\nThis class is able to measure time (in seconds) elapsed in a particular period"
},
{
"path": "doc/utility.md",
"chars": 10299,
"preview": "<a name=\"torch.utility.dok\"></a>\n# Torch utility functions #\n\nThese functions are used in all Torch package for creating"
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"path": "general.h",
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"preview": "#ifndef TORCH_GENERAL_INC\n#define TORCH_GENERAL_INC\n\n#include <stdlib.h>\n#include <string.h>\n#include <stddef.h>\n\n#inclu"
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"path": "generic/Storage.c",
"chars": 8237,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/Storage.c\"\n#else\n\n#include \"luaG.h\"\n\nstatic int torch_Storage_("
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"chars": 39805,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/Tensor.c\"\n#else\n\n#include \"luaG.h\"\n\nstatic void torch_Tensor_(c"
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{
"path": "generic/TensorOperator.c",
"chars": 4948,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/TensorOperator.c\"\n#else\n\n#include \"luaG.h\"\n\nstatic int torch_Te"
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{
"path": "generic/luaG.h",
"chars": 2265,
"preview": "#if !defined(real) || !defined(TH_GENERIC_FILE)\n#error \"luaG.h must not be included outside of a generic file.\"\n#endif\n\n"
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"path": "init.c",
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"preview": "#include \"general.h\"\n#include \"utils.h\"\n\nextern void torch_utils_init(lua_State *L);\nextern void torch_random_init(lua_S"
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{
"path": "init.lua",
"chars": 5688,
"preview": "-- We are using paths.require to appease mkl\n\n-- Make this work with LuaJIT in Lua 5.2 compatibility mode, which\n-- rena"
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{
"path": "lib/CMakeLists.txt",
"chars": 285,
"preview": "SET(TH_INSTALL_BIN_SUBDIR \"${Torch_INSTALL_BIN_SUBDIR}\")\nSET(TH_INSTALL_LIB_SUBDIR \"${Torch_INSTALL_LIB_SUBDIR}\")\nSET(TH"
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{
"path": "lib/TH/CMakeLists.txt",
"chars": 15624,
"preview": "cmake_minimum_required(VERSION 2.6)\n\n# avoid some cmake warnings\nIF(POLICY CMP0026)\n CMAKE_POLICY(SET CMP0026 OLD)\nENDIF"
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{
"path": "lib/TH/README.md",
"chars": 273,
"preview": "Environment variables control the disabling of certain explicit SIMD optimizations.\n\n```\nx64 options:\nTH_NO_AVX2=1 # dis"
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{
"path": "lib/TH/TH.h",
"chars": 412,
"preview": "#ifndef TH_INC\n#define TH_INC\n\n#include \"THGeneral.h\"\n\n#include \"THBlas.h\"\n#ifdef USE_LAPACK\n#include \"THLapack.h\"\n#endi"
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{
"path": "lib/TH/THAllocator.c",
"chars": 14607,
"preview": "#include \"THAllocator.h\"\n#include \"THAtomic.h\"\n\n/* stuff for mapped files */\n#ifdef _WIN32\n#include <windows.h>\n#endif\n\n"
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{
"path": "lib/TH/THAllocator.h",
"chars": 1505,
"preview": "#ifndef TH_ALLOCATOR_INC\n#define TH_ALLOCATOR_INC\n\n#include \"THGeneral.h\"\n\n#define TH_ALLOCATOR_MAPPED_SHARED 1\n#define "
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{
"path": "lib/TH/THAtomic.c",
"chars": 6132,
"preview": "#include \"THAtomic.h\"\n\n/*\n Note: I thank Leon Bottou for his useful comments.\n Ronan.\n*/\n\n#if defined(USE_C11_ATOMICS)"
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{
"path": "lib/TH/THAtomic.h",
"chars": 3022,
"preview": "#ifndef TH_ATOMIC_INC\n#define TH_ATOMIC_INC\n\n#include \"THGeneral.h\"\n\n/**************************************************"
},
{
"path": "lib/TH/THBlas.c",
"chars": 81,
"preview": "#include \"THBlas.h\"\n\n#include \"generic/THBlas.c\"\n#include \"THGenerateAllTypes.h\"\n"
},
{
"path": "lib/TH/THBlas.h",
"chars": 188,
"preview": "#ifndef TH_BLAS_INC\n#define TH_BLAS_INC\n\n#include \"THGeneral.h\"\n\n#define THBlas_(NAME) TH_CONCAT_4(TH,Real,Blas_,NAME)\n\n"
},
{
"path": "lib/TH/THConfig.cmake.in",
"chars": 259,
"preview": "# Find the TH includes and library\n#\n# TH_INCLUDE_DIR -- where to find the includes\n# TH_LIBRARIES -- list of libraries "
},
{
"path": "lib/TH/THDiskFile.c",
"chars": 25196,
"preview": "#include \"THGeneral.h\"\n#include \"THDiskFile.h\"\n#include \"THFilePrivate.h\"\n\n#include <stdint.h>\n#ifndef LLONG_MAX\n#define"
},
{
"path": "lib/TH/THDiskFile.h",
"chars": 661,
"preview": "#ifndef TH_DISK_FILE_INC\n#define TH_DISK_FILE_INC\n\n#include \"THFile.h\"\n\nTH_API THFile *THDiskFile_new(const char *name, "
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{
"path": "lib/TH/THFile.c",
"chars": 4557,
"preview": "#include \"THFile.h\"\n#include \"THFilePrivate.h\"\n\n#define IMPLEMENT_THFILE_RW(TYPEC, TYPE) \\\n si"
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{
"path": "lib/TH/THFile.h",
"chars": 4442,
"preview": "#ifndef TH_FILE_INC\n#define TH_FILE_INC\n\n#include \"THStorage.h\"\n\ntypedef struct THFile__ THFile;\n\nTH_API int THFile_isOp"
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{
"path": "lib/TH/THFilePrivate.h",
"chars": 1685,
"preview": "#include \"THGeneral.h\"\n\n#include \"THHalf.h\"\n\n\nstruct THFile__\n{\n struct THFileVTable *vtable;\n\n int isQuiet;\n i"
},
{
"path": "lib/TH/THGeneral.c",
"chars": 9871,
"preview": "#include \"THGeneral.h\"\n#include \"THAtomic.h\"\n\n#ifdef _OPENMP\n#include <omp.h>\n#endif\n\n#ifndef TH_HAVE_THREAD\n#define __t"
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{
"path": "lib/TH/THGeneral.h.in",
"chars": 4489,
"preview": "#ifndef TH_GENERAL_INC\n#define TH_GENERAL_INC\n\n#include <stdlib.h>\n#include <stdio.h>\n#include <stdarg.h>\n#include <math"
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{
"path": "lib/TH/THGenerateAllTypes.h",
"chars": 405,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateAllTypes.h\"\n#endif\n\n#ifndef T"
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{
"path": "lib/TH/THGenerateByteType.h",
"chars": 587,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateByteType.h\"\n#endif\n\n#define r"
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{
"path": "lib/TH/THGenerateCharType.h",
"chars": 577,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateCharType.h\"\n#endif\n\n#define r"
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{
"path": "lib/TH/THGenerateDoubleType.h",
"chars": 588,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateDoubleType.h\"\n#endif\n\n#define"
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{
"path": "lib/TH/THGenerateFloatType.h",
"chars": 583,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateFloatType.h\"\n#endif\n\n#define "
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{
"path": "lib/TH/THGenerateFloatTypes.h",
"chars": 414,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateFloatTypes.h\"\n#endif\n\n#ifndef"
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{
"path": "lib/TH/THGenerateHalfType.h",
"chars": 639,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateHalfType.h\"\n#endif\n\n#include "
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{
"path": "lib/TH/THGenerateIntType.h",
"chars": 571,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateIntType.h\"\n#endif\n\n#define re"
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{
"path": "lib/TH/THGenerateIntTypes.h",
"chars": 499,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateIntTypes.h\"\n#endif\n\n#ifndef T"
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{
"path": "lib/TH/THGenerateLongType.h",
"chars": 577,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateLongType.h\"\n#endif\n\n#define r"
},
{
"path": "lib/TH/THGenerateShortType.h",
"chars": 582,
"preview": "#ifndef TH_GENERIC_FILE\n#error \"You must define TH_GENERIC_FILE before including THGenerateShortType.h\"\n#endif\n\n#define "
},
{
"path": "lib/TH/THHalf.c",
"chars": 2379,
"preview": "#include \"THHalf.h\"\n\n/* Copyright 1993-2014 NVIDIA Corporation. All rights reserved. */\n\nTHHalf TH_float2half(float f)\n"
},
{
"path": "lib/TH/THHalf.h",
"chars": 875,
"preview": "#ifndef TH_HALF_H\n#define TH_HALF_H\n\n#include \"THGeneral.h\"\n#include <stdint.h>\n\n/* Neither built-in nor included from C"
},
{
"path": "lib/TH/THLapack.c",
"chars": 87,
"preview": "#include \"THLapack.h\"\n\n#include \"generic/THLapack.c\"\n#include \"THGenerateFloatTypes.h\"\n"
},
{
"path": "lib/TH/THLapack.h",
"chars": 951,
"preview": "#ifndef TH_LAPACK_INC\n#define TH_LAPACK_INC\n\n#include \"THGeneral.h\"\n\n#define THLapack_(NAME) TH_CONCAT_4(TH,Real,Lapack_"
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{
"path": "lib/TH/THLogAdd.c",
"chars": 1736,
"preview": "#include \"THLogAdd.h\"\n\n#include <float.h>\n\n#ifdef USE_DOUBLE\n#define MINUS_LOG_THRESHOLD -39.14\n#else\n#define MINUS_LOG_"
},
{
"path": "lib/TH/THLogAdd.h",
"chars": 323,
"preview": "#ifndef TH_LOG_ADD_INC\n#define TH_LOG_ADD_INC\n\n#include \"THGeneral.h\"\n\nTH_API const double THLog2Pi;\nTH_API const double"
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{
"path": "lib/TH/THMath.h",
"chars": 699,
"preview": "#ifndef _THMATH_H\n#define _THMATH_H\n\nstatic inline double TH_sigmoid(double value) {\n return 1.0 / (1.0 + exp(-value));"
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{
"path": "lib/TH/THMemoryFile.c",
"chars": 26258,
"preview": "#include \"THMemoryFile.h\"\n#include \"THFilePrivate.h\"\n#include \"stdint.h\"\n\ntypedef struct THMemoryFile__\n{\n THFile fil"
},
{
"path": "lib/TH/THMemoryFile.h",
"chars": 362,
"preview": "#ifndef TH_MEMORY_FILE_INC\n#define TH_MEMORY_FILE_INC\n\n#include \"THFile.h\"\n#include \"THStorage.h\"\n\nTH_API THFile *THMemo"
},
{
"path": "lib/TH/THRandom.c",
"chars": 8225,
"preview": "#include \"THGeneral.h\"\n#include \"THRandom.h\"\n\n#ifndef _WIN32\n#include <fcntl.h>\n#include <unistd.h>\n#endif\n\n/* Code for "
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{
"path": "lib/TH/THRandom.h",
"chars": 2998,
"preview": "#ifndef TH_RANDOM_INC\n#define TH_RANDOM_INC\n\n#include \"THGeneral.h\"\n\n#define _MERSENNE_STATE_N 624\n#define _MERSENNE_STA"
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{
"path": "lib/TH/THSize.c",
"chars": 466,
"preview": "#include \"THSize.h\"\n\nint THSize_isSameSizeAs(const long *sizeA, long dimsA, const long *sizeB, long dimsB) {\n int d;\n "
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{
"path": "lib/TH/THSize.h",
"chars": 412,
"preview": "#ifndef TH_SIZE_INC\n#define TH_SIZE_INC\n\n#include \"THGeneral.h\"\n#include <stddef.h>\n\n// THTensor functions that would wo"
},
{
"path": "lib/TH/THStorage.c",
"chars": 5842,
"preview": "#include \"THAtomic.h\"\n#include \"THStorage.h\"\n\n#include \"generic/THStorage.c\"\n#include \"THGenerateAllTypes.h\"\n\n#include \""
},
{
"path": "lib/TH/THStorage.h",
"chars": 1554,
"preview": "#ifndef TH_STORAGE_INC\n#define TH_STORAGE_INC\n\n#include \"THGeneral.h\"\n#include \"THAllocator.h\"\n\n#define THStorage "
},
{
"path": "lib/TH/THTensor.c",
"chars": 746,
"preview": "#include \"THAtomic.h\"\n#include \"THTensor.h\"\n#include \"THVector.h\"\n#include \"generic/simd/simd.h\"\n\n#include \"THBlas.h\"\n#i"
},
{
"path": "lib/TH/THTensor.h",
"chars": 889,
"preview": "#ifndef TH_TENSOR_INC\n#define TH_TENSOR_INC\n\n#include \"THStorage.h\"\n#include \"THTensorApply.h\"\n\n#define THTensor "
},
{
"path": "lib/TH/THTensorApply.h",
"chars": 10682,
"preview": "#ifndef TH_TENSOR_APPLY_INC\n#define TH_TENSOR_APPLY_INC\n\n/*\n * The basic strategy for apply is as follows:\n *\n * 1. Star"
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{
"path": "lib/TH/THTensorDimApply.h",
"chars": 13354,
"preview": "#ifndef TH_TENSOR_DIM_APPLY_INC\n#define TH_TENSOR_DIM_APPLY_INC\n\n#define TH_TENSOR_DIM_APPLY3(TYPE1, TENSOR1, TYPE2, TEN"
},
{
"path": "lib/TH/THTensorMacros.h",
"chars": 1610,
"preview": "#ifndef TH_TENSOR_MACROS_INC\n#define TH_TENSOR_MACROS_INC\n\n/* fast method to access to tensor data */\n\n#define THTensor_"
},
{
"path": "lib/TH/THVector.c",
"chars": 554,
"preview": "#include \"THVector.h\"\n\n#include \"generic/simd/simd.h\"\n\n#ifdef __NEON__\n#include \"vector/NEON.c\"\n#endif\n\n#ifdef __PPC64__"
},
{
"path": "lib/TH/THVector.h",
"chars": 325,
"preview": "#ifndef TH_VECTOR_INC\n#define TH_VECTOR_INC\n\n#include \"THGeneral.h\"\n\n#define THVector_(NAME) TH_CONCAT_4(TH,Real,Vector_"
},
{
"path": "lib/TH/cmake/FindARM.cmake",
"chars": 3264,
"preview": "# Check if the processor is an ARM and if Neon instruction are available on the machine where\n# the project is compiled."
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{
"path": "lib/TH/cmake/FindBLAS.cmake",
"chars": 8825,
"preview": "# - Find BLAS library\n# This module finds an installed fortran library that implements the BLAS \n# linear-algebra interf"
},
{
"path": "lib/TH/cmake/FindLAPACK.cmake",
"chars": 6756,
"preview": "# - Find LAPACK library\n# This module finds an installed fortran library that implements the LAPACK\n# linear-algebra int"
},
{
"path": "lib/TH/cmake/FindMKL.cmake",
"chars": 9574,
"preview": "# - Find INTEL MKL library\n#\n# This module finds the Intel Mkl libraries.\n#\n# This module sets the following variables:\n"
},
{
"path": "lib/TH/cmake/FindSSE.cmake",
"chars": 2813,
"preview": "INCLUDE(CheckCSourceRuns)\nINCLUDE(CheckCXXSourceRuns)\n\nSET(SSE1_CODE \"\n #include <xmmintrin.h>\n\n int main()\n {\n __"
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{
"path": "lib/TH/generic/THBlas.c",
"chars": 10058,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THBlas.c\"\n#else\n\n\n#ifdef BLAS_F2C\n# define ffloat double\n#else\n"
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{
"path": "lib/TH/generic/THBlas.h",
"chars": 903,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THBlas.h\"\n#else\n\n/* Level 1 */\nTH_API void THBlas_(swap)(long n"
},
{
"path": "lib/TH/generic/THLapack.c",
"chars": 11039,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THLapack.c\"\n#else\n\n\nTH_EXTERNC void dgesv_(int *n, int *nrhs, d"
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{
"path": "lib/TH/generic/THLapack.h",
"chars": 2482,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THLapack.h\"\n#else\n\n/* AX=B */\nTH_API void THLapack_(gesv)(int n"
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{
"path": "lib/TH/generic/THStorage.c",
"chars": 5867,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THStorage.c\"\n#else\n\nreal* THStorage_(data)(const THStorage *sel"
},
{
"path": "lib/TH/generic/THStorage.h",
"chars": 2555,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THStorage.h\"\n#else\n\n/* on pourrait avoir un liste chainee\n qu"
},
{
"path": "lib/TH/generic/THStorageCopy.c",
"chars": 2439,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THStorageCopy.c\"\n#else\n\nvoid THStorage_(rawCopy)(THStorage *sto"
},
{
"path": "lib/TH/generic/THStorageCopy.h",
"chars": 929,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THStorageCopy.h\"\n#else\n\n/* Support for copy between different S"
},
{
"path": "lib/TH/generic/THTensor.c",
"chars": 26989,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensor.c\"\n#else\n\n/**** access methods ****/\nTHStorage *THTens"
},
{
"path": "lib/TH/generic/THTensor.h",
"chars": 7555,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensor.h\"\n#else\n\n/* a la lua? dim, storageoffset, ... et les"
},
{
"path": "lib/TH/generic/THTensorConv.c",
"chars": 64115,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorConv.c\"\n#else\n\n/*\n 2D Input, 2D kernel : convolve giv"
},
{
"path": "lib/TH/generic/THTensorConv.h",
"chars": 4838,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorConv.h\"\n#else\n\nTH_API void THTensor_(validXCorr2Dptr)(r"
},
{
"path": "lib/TH/generic/THTensorCopy.c",
"chars": 4485,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorCopy.c\"\n#else\n\nint THTensor_(copyTransposeValid)(THTens"
},
{
"path": "lib/TH/generic/THTensorCopy.h",
"chars": 827,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorCopy.h\"\n#else\n\n/* Support for copy between different Te"
},
{
"path": "lib/TH/generic/THTensorLapack.c",
"chars": 35174,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorLapack.c\"\n#else\n\n/*\nCheck if self is transpose of a con"
},
{
"path": "lib/TH/generic/THTensorLapack.h",
"chars": 1799,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorLapack.h\"\n#else\n\nTH_API void THTensor_(gesv)(THTensor *"
},
{
"path": "lib/TH/generic/THTensorMath.c",
"chars": 105501,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorMath.c\"\n#else\n\n#ifndef NAN\n #define NAN (nan(NULL))\n#e"
},
{
"path": "lib/TH/generic/THTensorMath.h",
"chars": 12076,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorMath.h\"\n#else\n\nTH_API void THTensor_(fill)(THTensor *r_"
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{
"path": "lib/TH/generic/THTensorRandom.c",
"chars": 13023,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorRandom.c\"\n#else\n\nvoid THTensor_(random)(THTensor *self,"
},
{
"path": "lib/TH/generic/THTensorRandom.h",
"chars": 2153,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THTensorRandom.h\"\n#else\n\nTH_API void THTensor_(random)(THTensor"
},
{
"path": "lib/TH/generic/THVector.h",
"chars": 849,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THVector.h\"\n#else\n\nTH_API void THVector_(fill)(real *x, const r"
},
{
"path": "lib/TH/generic/THVectorDefault.c",
"chars": 2324,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THVectorDefault.c\"\n#else\n\nvoid THVector_(copy_DEFAULT)(real *x,"
},
{
"path": "lib/TH/generic/THVectorDispatch.c",
"chars": 9468,
"preview": "#ifndef TH_GENERIC_FILE\n#define TH_GENERIC_FILE \"generic/THVectorDispatch.c\"\n#else\n\n/* For now there are only SIMD imple"
},
{
"path": "lib/TH/generic/simd/common_simd.h",
"chars": 11953,
"preview": "#ifndef COMMON_SIMD_H\n#define COMMON_SIMD_H\n\n/* Weights */\n#define LOAD_WEIGHT(q, simd_type, inst_var) _m ## simd_type #"
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{
"path": "lib/TH/generic/simd/convolve.c",
"chars": 3498,
"preview": "#if defined(__AVX__)\n\n#ifdef _MSC_VER\n#include <intrin.h>\n\nstatic __inline int __get_cpuid (unsigned int __level, unsign"
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"path": "lib/TH/generic/simd/convolve.h",
"chars": 103,
"preview": "void convolve_5x5(float* output, float* input, float* kernel, long outRows, long outCols, long inCols);"
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{
"path": "lib/TH/generic/simd/convolve5x5_avx.c",
"chars": 6296,
"preview": "#include <immintrin.h>\n#include \"common_simd.h\"\n\n#define CLEAR_AVX() _mm256_zeroupper()\n\nvoid convolve_5x5_1_avx(float* "
},
{
"path": "lib/TH/generic/simd/convolve5x5_sse.c",
"chars": 10859,
"preview": "#include <smmintrin.h>\n#include \"common_simd.h\"\n\n\n/* SSE variants */\nvoid convolve_5x5_1_sse(float* output, float* image"
},
{
"path": "lib/TH/generic/simd/simd.h",
"chars": 4037,
"preview": "#ifndef TH_SIMD_INC\n#define TH_SIMD_INC\n\n#include <stdint.h>\n#include <stdlib.h>\n#if defined(_MSC_VER)\n#include <intrin."
},
{
"path": "lib/TH/vector/AVX.c",
"chars": 7539,
"preview": "#if defined(__AVX__)\n#ifndef _MSC_VER\n#include <x86intrin.h>\n#else\n#include <intrin.h>\n#endif\n\n#include \"AVX.h\"\n\nvoid TH"
},
{
"path": "lib/TH/vector/AVX.h",
"chars": 1493,
"preview": "#ifndef TH_AVX_H\n#define TH_AVX_H\n\n#include <stddef.h>\n\nvoid THDoubleVector_copy_AVX(double *y, const double *x, const p"
},
{
"path": "lib/TH/vector/AVX2.c",
"chars": 1345,
"preview": "#if defined(__AVX2__)\n#ifndef _MSC_VER\n#include <x86intrin.h>\n#else\n#include <intrin.h>\n#endif\n#include \"AVX2.h\"\n\nvoid T"
},
{
"path": "lib/TH/vector/AVX2.h",
"chars": 283,
"preview": "#ifndef TH_AVX2_H\n#define TH_AVX2_H\n\n#include <stddef.h>\n\nvoid THDoubleVector_cadd_AVX2(double *z, const double *x, cons"
},
{
"path": "lib/TH/vector/NEON.c",
"chars": 2049,
"preview": "static void THFloatVector_fill_NEON(float *x, const float c, const ptrdiff_t n) {\n long i = 0;\n\n for(; i < n-4; i += 4"
},
{
"path": "lib/TH/vector/SSE.c",
"chars": 7637,
"preview": "#ifndef _MSC_VER\n#include <x86intrin.h>\n#else\n#include <intrin.h>\n#endif\n\nstatic void THDoubleVector_fill_SSE(double *x,"
},
{
"path": "lib/TH/vector/VSX.c",
"chars": 103680,
"preview": "#ifdef __PPC64__\n#include <altivec.h>\n#include <stddef.h>\n\n\n//----------------------------------------------------------"
},
{
"path": "lib/luaT/CMakeLists.txt",
"chars": 1554,
"preview": "# avoid some cmake warnings\nIF(POLICY CMP0026)\n CMAKE_POLICY(SET CMP0026 OLD)\nENDIF()\n\nINCLUDE_DIRECTORIES(${LUA_INCDIR}"
},
{
"path": "lib/luaT/README.md",
"chars": 11618,
"preview": "<a name=\"luat.dok\"></a>\n# Lua Torch C API #\n\nluaT provides an API to interface Lua and C in Torch packages. It defines a"
},
{
"path": "lib/luaT/luaT.c",
"chars": 38451,
"preview": "#include <stdlib.h>\n#include <string.h>\n#include <stdint.h>\n\n#include \"luaT.h\"\n\nvoid* luaT_alloc(lua_State *L, ptrdiff_t"
},
{
"path": "lib/luaT/luaT.h",
"chars": 5432,
"preview": "#ifndef LUAT_UTILS_INC\n#define LUAT_UTILS_INC\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n#include <lua.h>\n#include <lauxlib"
},
{
"path": "lib/luaT/luaTConfig.cmake.in",
"chars": 277,
"preview": "# Find the luaT includes and library\n#\n# LUAT_INCLUDE_DIR -- where to find the includes\n# LUAT_LIBRARIES -- list of libr"
},
{
"path": "mkdocs.yml",
"chars": 733,
"preview": "site_name: torch7\ntheme : simplex\nrepo_url : https://github.com/torch/torch7\nuse_directory_urls : false\nmarkdown_extensi"
},
{
"path": "paths.lua.in",
"chars": 362,
"preview": "local paths = {}\n\npaths.install_prefix = [[@Torch_INSTALL_PREFIX@]]\npaths.install_bin = [[@Torch_INSTALL_BIN@]]\npaths.in"
},
{
"path": "random.lua",
"chars": 1364,
"preview": "local wrap = require 'cwrap'\n\nrequire 'torchcwrap'\n\nlocal interface = wrap.CInterface.new()\n\ninterface:print(\n [[\n#inc"
},
{
"path": "rocks/torch-scm-1.rockspec",
"chars": 1104,
"preview": "package = \"torch\"\nversion = \"scm-1\"\n\nsource = {\n url = \"git://github.com/torch/torch7.git\",\n}\n\ndescription = {\n summ"
},
{
"path": "test/longSize.lua",
"chars": 1233,
"preview": "require 'torch'\n\nlocal tester = torch.Tester()\nlocal tests = torch.TestSuite()\n\nlocal tensor = torch.rand(2,3)\n\nfunction"
},
{
"path": "test/test.lua",
"chars": 139386,
"preview": "--require 'torch'\n\nlocal mytester\nlocal torchtest = torch.TestSuite()\nlocal msize = 100\nlocal precision\n\n-- Lua 5.2 comp"
},
{
"path": "test/test_Multinomial.lua",
"chars": 764,
"preview": "-- Test multinomial for rare events (based on https://github.com/torch/torch7/issues/418)\n-- and for performance (cf. ht"
},
{
"path": "test/test_Tester.lua",
"chars": 20246,
"preview": "require 'torch'\n\nlocal tester = torch.Tester()\n\nlocal MESSAGE = \"a really useful informative error message\"\n\nlocal subte"
},
{
"path": "test/test_aliasMultinomial.lua",
"chars": 1819,
"preview": "local tester = torch.Tester()\n\n\nlocal function aliasMultinomial()\n local n_class = 10000\n local probs = torch.Tensor"
},
{
"path": "test/test_half.lua",
"chars": 1841,
"preview": "local mytester\nlocal torchtest = torch.TestSuite()\n\n-- Lua 5.2 compatibility\nlocal loadstring = loadstring or load\nlocal"
},
{
"path": "test/test_qr.lua",
"chars": 9997,
"preview": "-- This file contains tests for the QR decomposition functions in torch:\n-- torch.qr(), torch.geqrf() and torch.orgqr()."
},
{
"path": "test/test_sharedmem.lua",
"chars": 3030,
"preview": "require 'torch'\nlocal ffi = require 'ffi'\n\nlocal tester = torch.Tester()\nlocal tests = torch.TestSuite()\n\nlocal function"
},
{
"path": "test/test_timer.lua",
"chars": 1618,
"preview": "require 'torch'\nlocal ffi = require 'ffi'\n\nlocal tester = torch.Tester()\nlocal tests = torch.TestSuite()\n\nfunction tests"
},
{
"path": "test/test_writeObject.lua",
"chars": 6684,
"preview": "require 'torch'\n\nlocal myTester = torch.Tester()\n\nlocal tests = torch.TestSuite()\n\nfunction torch.HalfTensor:norm()\n r"
},
{
"path": "test/timeSort.lua",
"chars": 5173,
"preview": "-- gnuplot.figure(2)\n-- Test torch sort, show it suffers from the problems of quicksort\n-- i.e. complexity O(N^2) in wor"
},
{
"path": "torchcwrap.lua",
"chars": 19429,
"preview": "local wrap = require 'cwrap'\nlocal types = wrap.types\n\ntypes.Tensor = {\n\n helpname = function(arg)\n if"
},
{
"path": "utils.c",
"chars": 5420,
"preview": "#include \"general.h\"\n#include \"utils.h\"\n\n#ifdef WIN32\n# include <time.h>\n#else\n# include <sys/time.h>\n#endif\n\nTHLongStor"
},
{
"path": "utils.h",
"chars": 678,
"preview": "#ifndef TORCH_UTILS_INC\n#define TORCH_UTILS_INC\n\n#include \"luaT.h\"\n#include \"TH.h\"\n\n#include <lua.h>\n#include <lualib.h>"
}
]
About this extraction
This page contains the full source code of the torch/torch7 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 166 files (1.4 MB), approximately 449.1k tokens, and a symbol index with 486 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.