From 51a38c1209560f18259aa8ef348a6a06c6479f60 Mon Sep 17 00:00:00 2001
From: Eugenio <culurciello@gmail.com>
Date: Mon, 9 Jun 2014 15:37:44 -0400
Subject: [PATCH] colorized face-detect

---
 face-detector-elab/run.lua         |  2 --
 profiling/conv-cpu.lua             | 11 ++++++-----
 train-face-detector-elab/run.lua   | 14 +++++++++-----
 train-face-detector-elab/test.lua  |  2 +-
 train-face-detector-elab/train.lua |  2 +-
 5 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/face-detector-elab/run.lua b/face-detector-elab/run.lua
index 3f6b543..12fb9ef 100755
--- a/face-detector-elab/run.lua
+++ b/face-detector-elab/run.lua
@@ -16,8 +16,6 @@ require 'qtuiloader'
 require 'camera'
 require 'image'
 require 'nnx'
-require 'torchffi'
-local ffi = require('ffi')
 
 print '==> processing options'
 
diff --git a/profiling/conv-cpu.lua b/profiling/conv-cpu.lua
index 5cd21e7..7f4ad18 100644
--- a/profiling/conv-cpu.lua
+++ b/profiling/conv-cpu.lua
@@ -1,11 +1,12 @@
 
 -- libs:
 require 'sys'
-require 'cunn'
+require 'nnx'
+--require 'cunn'
 
 -- dev:
-cutorch.setDevice(arg[1] or 1)
-print('DEVID = ' .. cutorch.getDevice())
+--utorch.setDevice(arg[1] or 1)
+--print('DEVID = ' .. cutorch.getDevice())
 
 -- params:
 batchSize = 32
@@ -25,7 +26,7 @@ n:add( nn.SpatialConvolutionMM(ninputs, nhiddens, fsize, fsize, stride, stride)
 -- pre-alloc states:
 n:forward(i)
 n:backward(i, n.output)
-cutorch.synchronize()
+--cutorch.synchronize()
 
 -- nb of operations:
 opsPerMAC = 2
@@ -41,7 +42,7 @@ for t = 1,nbOfAverages do
    n:forward(i)
    n:backward(i, n.output)
 end
-cutorch.synchronize()
+--cutorch.synchronize()
 t = sys.toc()/nbOfAverages
 
 -- result:
diff --git a/train-face-detector-elab/run.lua b/train-face-detector-elab/run.lua
index 7c95f63..f101363 100644
--- a/train-face-detector-elab/run.lua
+++ b/train-face-detector-elab/run.lua
@@ -7,9 +7,13 @@
 ----------------------------------------------------------------------
 
 require 'pl'
+require 'trepl'
+require 'torch'   -- torch
+require 'image'   -- to visualize the dataset
+require 'nn'      -- provides all sorts of trainable modules/layers
 
 ----------------------------------------------------------------------
-print '==> processing options'
+print(sys.COLORS.red ..  '==> processing options')
 
 opt = lapp[[
    -r,--learningRate       (default 1e-3)         learning rate
@@ -33,21 +37,21 @@ torch.setdefaulttensortype('torch.FloatTensor')
 
 -- type:
 if opt.type == 'cuda' then
-   print('==> switching to CUDA')
+   print(sys.COLORS.red ..  '==> switching to CUDA')
    require 'cunn'
    cutorch.setDevice(opt.devid)
-   print('==> using GPU #' .. cutorch.getDevice())
+   print(sys.COLORS.red ..  '==> using GPU #' .. cutorch.getDevice())
 end
 
 ----------------------------------------------------------------------
-print '==> load modules'
+print(sys.COLORS.red ..  '==> load modules')
 
 local data  = require 'data'
 local train = require 'train'
 local test  = require 'test'
 
 ----------------------------------------------------------------------
-print '==> training!'
+print(sys.COLORS.red .. '==> training!')
 
 while true do
    train(data.trainData)
diff --git a/train-face-detector-elab/test.lua b/train-face-detector-elab/test.lua
index 33804ff..4b30017 100644
--- a/train-face-detector-elab/test.lua
+++ b/train-face-detector-elab/test.lua
@@ -41,7 +41,7 @@ function test(testData)
    local time = sys.clock()
 
    -- test over test data
-   print('==> testing on test set:')
+   print(sys.COLORS.red .. '==> testing on test set:')
    for t = 1,testData:size(),opt.batchSize do
       -- disp progress
       xlua.progress(t, testData:size())
diff --git a/train-face-detector-elab/train.lua b/train-face-detector-elab/train.lua
index 3edde27..3fedc97 100644
--- a/train-face-detector-elab/train.lua
+++ b/train-face-detector-elab/train.lua
@@ -99,7 +99,7 @@ local function train(trainData)
    local shuffle = torch.randperm(trainData:size())
 
    -- do one epoch
-   print('==> doing epoch on training data:')
+   print(sys.COLORS.magenta .. '==> doing epoch on training data:') 
    print("==> online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ']')
    for t = 1,trainData:size(),opt.batchSize do
       -- disp progress