szagoruyko

OpenCV Bindings for Torch

The OpenCV library implements tons of useful image processing and computer vision algorithms, as well as the high-level GUI API. Written in C++, it has bindings in Python, Java, MATLAB/Octave, C#, Perl and Ruby. We present the Lua bindings that are based on Torch, made by VisionLabs with support from Facebook and Google Deepmind.

By combining OpenCV with scientific computation abilities of Torch, one gets an even more powerful framework capable of handling computer vision routines (e.g. face detection), interfacing video streams (including cameras), easier data visualization, GUI interaction and many more. In addition, most of the computationally intensive algorithms are available on GPU via Cutorch. All these features may be essentially useful for those dealing with deep learning applied to images.

Usage Examples

szagoruyko

OpenCV Bindings for Torch

The OpenCV library implements tons of useful image processing and computer vision algorithms, as well as the high-level GUI API. Written in C++, it has bindings in Python, Java, MATLAB/Octave, C#, Perl and Ruby. We present the Lua bindings that are based on Torch, made by VisionLabs with support from Facebook and Google Deepmind.

By combining OpenCV with scientific computation abilities of Torch, one gets an even more powerful framework capable of handling computer vision routines (e.g. face detection), interfacing video streams (including cameras), easier data visualization, GUI interaction and many more. In addition, most of the computationally intensive algorithms are available on GPU via Cutorch. All these features may be essentially useful for those dealing with deep learning applied to images.

Usage Examples

szagoruyko

require 'xlua'
local grad = require 'autograd'
local tablex = require 'pl.tablex'
grad.optimize(true)

local function cast(x)
   if type(x) == 'table' then
      for k,v in pairs(x) do x[k] = cast(v) end
      return x
   else

szagoruyko

require 'nn'

-- you just need to provide the linear module you want to convert,
-- and the dimensions of the field of view of the linear layer
function convertLinear2Conv1x1(linmodule,in_size)
   local s_in = linmodule.weight:size(2)/(in_size[1]*in_size[2])
   local s_out = linmodule.weight:size(1)
   local convmodule = nn.SpatialConvolutionMM(s_in,s_out,in_size[1],in_size[2],1,1)
   convmodule.weight:copy(linmodule.weight)
   convmodule.bias:copy(linmodule.bias)

szagoruyko

local Linear, parent = torch.class('nn.NoBiasLinear', 'nn.Linear')

function Linear:__init(inputSize, outputSize)
   parent.__init(self, inputSize, outputSize)
   self.bias:fill(0)
end

function Linear:accGradParameters(input, gradOutput, scale)
   scale = scale or 1
   if input:dim() == 1 then