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-- Use three (consecutive) points to fit a 1D parabola
-- and return its maximum value. To achieve sub-pixel
-- precision for a 2D maxima (x,y), just fit the parabola over the
-- x and y coordinates separately with two neighboring points.
local function fitParabola(x1,x2,x3,y1,y2,y3)
  local x1_sqr = x1*x1
  local x2_sqr = x2*x2
  local x3_sqr = x3*x3
  
  local div = (x1_sqr-x1*(x2+x3)+x2*x3)*(x2-x3)

farrajota

local function dimnarrow(x,sz,pad,dim)
    local xn = x
    for i=1,x:dim() do
        if i > dim then
            xn = xn:narrow(i,pad[i]+1,sz[i])
        end
    end
    return xn
end

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function svhn_convert_json(path)
% convert .mat to json files

%% Create .json file

if isempty(path)
    error('Must specify a path to the dataset')
end

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local function ConvertBNcudnn2nn(net)
  local function ConvertModule(net)
    return net:replace(function(x)
        if torch.type(x) == 'cudnn.BatchNormalization' then
          return cudnn.convert(x, nn)
        else
          return x
        end
    end)
  end

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model.modules[1].parameters = function() return nil end -- freezes the layer when using optim 
model.modules[1].accGradParameters = function() end -- overwrite this to reduce computations

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function mpii_convert_json(  )
% convert mpii annotations .mat file to .json


%% load annotation file
fprintf('Load annotations... ')
data = load('/media/HDD2/Datasets/Human_Pose/mpii/mpii_human_pose_v1_u12_2/mpii_human_pose_v1_u12_1.mat');
fprintf('Done.\n')
%% open file
fprintf('Open file mpii_human_pose_annotations.json\n')

farrajota

require 'image'

local M = {}

function M.Compose(transforms)
   return function(input)
      for _, transform in ipairs(transforms) do
         input = transform(input)
      end
      return input

farrajota

--[[
    Create a binary tree using two ways: NN containers and nn.gModule containers. 
    This example is fairly simple, and the default fully-connected layers are all
    of size 100. However, this should also be simple to modify to allow different 
    fc layers with varying inputs/outputs if desired (for example: input a table
    storing input+output configuration values for each of the sub-branch's level).
]]

require 'nn'
require 'nngraph'

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-- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters.

require 'torch'
require 'nn'
require 'optim'
torch.setdefaulttensortype('torch.FloatTensor')

-- (1) Define a model for this example.
local model = nn.Sequential()
model:add(nn.Linear(10,20))

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function save_configs(filename, opt)
-- prints the contents of a table into a file

    local function table_print (tt, indent, done)
                    done = done or {}
                    indent = indent or 0
                    if type(tt) == "table" then
                      local sb = {}
                      for key, value in pairs (tt) do
                        table.insert(sb, string.rep (" ", indent)) -- indent it