深度学习框架 Torch 7 问题笔记

深度学习框架 Torch 7 问题笔记

　　1. 尝试第一个 CNN 的 torch版本, 代码如下:

　　

--    We now have 5 steps left to do in training our first torch neural network
--    1. Load and normalize data
--    2. Define Neural Network
--    3. Define Loss function
--    4. Train network on training data
--    5. Test network on test data.

--    1. Load and normalize data
require 'paths'
require 'image';
if (not paths.filep("cifar10torchsmall.zip")) then
os.execute('wget -c https://s3.amazonaws.com/torch7/data/cifar10torchsmall.zip') os.execute('unzip cifar10torchsmall.zip')
end
trainset = torch.load('cifar10-train.t7')
testset = torch.load('cifar10-test.t7')
classes = {'airplane', 'automobile', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck'}

print(trainset)
print(#trainset.data)

itorch.image(trainset.data[100]) -- display the 100-th image in dataset
print(classes[trainset.label[100]])

-- ignore setmetatable for now, it is a feature beyond the scope of this tutorial.
-- It sets the index operator
setmetatable(trainset,
{__index = function(t, i)
return {t.data[i], t.label[i]}
end}
);
trainset.data = trainset.data:double()  -- convert the data from a ByteTensor to a DoubleTensor.

function trainset:size()
return self.data:size(1)
end

print(trainset:size())
print(trainset[33])
itorch.image(trainset[33][11])

redChannel = trainset.data[{ {}, {1}, {}, {} }] -- this pick {all images, 1st channel, all vertical pixels, all horizontal pixels}
print(#redChannel)

-- TODO:fill
mean = {}
stdv = {}
for i = 1,3 do
mean[i] = trainset.data[{ {}, {i}, {}, {} }]:mean()  -- mean estimation
print('Channel ' .. i .. ' , Mean: ' .. mean[i])
trainset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction

stdv[i] = trainset.data[ { {}, {i}, {}, {} }]:std()  -- std estimation
print('Channel ' .. i .. ' , Standard Deviation: ' .. stdv[i])
trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i])  -- std scaling
end

--    2. Define Neural Network
net = nn.Sequential()
net:add(nn.SpatialConvolution(3, 6, 5, 5)) -- 3 input image channels, 6 output channels, 5x5 convolution kernel
net:add(nn.ReLU())                       -- non-linearity
net:add(nn.SpatialMaxPooling(2,2,2,2))     -- A max-pooling operation that looks at 2x2 windows and finds the max.
net:add(nn.SpatialConvolution(6, 16, 5, 5))
net:add(nn.ReLU())                       -- non-linearity
net:add(nn.SpatialMaxPooling(2,2,2,2))
net:add(nn.View(16*5*5))                    -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 16*5*5
net:add(nn.Linear(16*5*5, 120))             -- fully connected layer (matrix multiplication between input and weights)
net:add(nn.ReLU())                       -- non-linearity
net:add(nn.Linear(120, 84))
net:add(nn.ReLU())                       -- non-linearity
net:add(nn.Linear(84, 10))                   -- 10 is the number of outputs of the network (in this case, 10 digits)
net:add(nn.LogSoftMax())                     -- converts the output to a log-probability. Useful for classification problems

-- 3. Let us difine the Loss function
criterion = nn.ClassNLLCriterion()

-- 4. Train the neural network
trainer = nn.StochasticGradient(net, criterion)
trainer.learningRate = 0.001
trainer.maxIteration = 5 -- just do 5 epochs of training.
trainer:train(trainset)

-- 5. Test the network, print accuracy
print(classes[testset.label[100]])
itorch.image(testset.data[100])

testset.data = testset.data:double()   -- convert from Byte tensor to Double tensor
for i=1,3 do -- over each image channel
testset.data[{ {}, {i}, {}, {}  }]:add(-mean[i]) -- mean subtraction
testset.data[{ {}, {i}, {}, {}  }]:div(stdv[i]) -- std scaling
end

-- for fun, print the mean and standard-deviation of example-100
horse = testset.data[100]
print(horse:mean(), horse:std())

print(classes[testset.label[100]])
itorch.image(testset.data[100])
predicted = net:forward(testset.data[100])

-- the output of the network is Log-Probabilities. To convert them to probabilities, you have to take e^x
print(predicted:exp())

for i=1,predicted:size(1) do
print(classes[i], predicted[i])
end

-- test the accuracy
correct = 0
for i=1,10000 do
local groundtruth = testset.label[i]
local prediction = net:forward(testset.data[i])
local confidences, indices = torch.sort(prediction, true)  -- true means sort in descending order
if groundtruth == indices[1] then
correct = correct + 1
end
end

print(correct, 100*correct/10000 .. ' % ')

class_performance = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
for i=1,10000 do
local groundtruth = testset.label[i]
local prediction = net:forward(testset.data[i])
local confidences, indices = torch.sort(prediction, true)  -- true means sort in descending order
if groundtruth == indices[1] then
class_performance[groundtruth] = class_performance[groundtruth] + 1
end
end

for i=1,#classes do
print(classes[i], 100*class_performance[i]/1000 .. ' %')
end

require 'cunn';
net = net:cuda()
criterion = criterion:cuda()
trainset.data = trainset.data:cuda()
trainset.label = trainset.label:cuda()

trainer = nn.StochasticGradient(net, criterion)
trainer.learningRate = 0.001
trainer.maxIteration = 5 -- just do 5 epochs of training.

trainer:train(trainset)

View Code
　　

　　 那么,运行起来 却出现如下的问题:

　　(1).

/home/wangxiao/torch/install/bin/luajit: ./train_network.lua:26: attempt to index global 'itorch' (a nil value)
stack traceback:
./train_network.lua:26: in main chunk
[C]: in function 'dofile'
...xiao/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: at 0x00406670
wangxiao@AHU:~/Documents/Lua test examples$



　　 主要是 itorch 的问题, 另外就是 要引用 require 'nn' 来解决 无法辨别 nn 的问题.

　　我是把 带有 itorch 的那些行都暂时注释了.

[b]2. 'libcudnn (R5) not found in library path.[/b]

wangxiao@AHU:~/Downloads/wide-residual-networks-master$ th ./train_Single_Multilabel_Image_Classification.lua
nil
/home/wangxiao/torch/install/bin/luajit: /home/wangxiao/torch/install/share/lua/5.1/trepl/init.lua:384: /home/wangxiao/torch/install/share/lua/5.1/trepl/init.lua:384: /home/wangxiao/torch/install/share/lua/5.1/cudnn/ffi.lua:1600: 'libcudnn (R5) not found in library path.
Please install CuDNN from https://developer.nvidia.com/cuDNN Then make sure files named as libcudnn.so.5 or libcudnn.5.dylib are placed in your library load path (for example /usr/local/lib , or manually add a path to LD_LIBRARY_PATH)

stack traceback:
[C]: in function 'error'
/home/wangxiao/torch/install/share/lua/5.1/trepl/init.lua:384: in function 'require'
./train_Single_Multilabel_Image_Classification.lua:8: in main chunk
[C]: in function 'dofile'
...xiao/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: at 0x00406670
wangxiao@AHU:~/Downloads/wide-residual-networks-master$

================================================================>>

答案是:

　　重新下载了 cudnn-7.5-linux-x64-v5.0-ga.tgz

　　并且重新配置了,但是依然提醒这个问题,那么,问题何在呢?查看了博客:http://blog.csdn.net/hungryof/article/details/51557666 中的内容:

坑4 可能出现’libcudnn not found in library path’的情况

截取其中一段错误信息：

Please install CuDNN from https://developer.nvidia.com/cuDNN Then make sure files named as libcudnn.so.5 or libcudnn.5.dylib are placed in your library load path (for example /usr/local/lib , or manually add a path to LD_LIBRARY_PATH)

1

2

LD_LIBRARY_PATH是该环境变量，主要用于指定查找共享库（动态链接库）时除了默认路径之外的其他路径。由于刚才已经将
“libcudnn*”复制到了/usr/local/cuda-7.5/lib64/下面，因此需要

sudo gedit /etc/ld.so.conf.d/cudnn.conf 就是新建一个conf文件。名字随便

加入刚才的路径/usr/local/cuda-7.5/lib64/

反正我还添加了/usr/local/cuda-7.5/include/,这个估计不要也行。

保存后，再sudo ldconfig来更新缓存。（可能会出现libcudnn.so.5不是符号连接的问题，不过无所谓了！！）

此时运行

th neural_style.lua -gpu 0 -backend cudnn

1

成功了！！！！

============================================================>>>>

评价:　　按照这种做法试了,确实成功了! 赞一个 !!!

　　3. 利用 gm 加载图像时,提示错误,但是装上那个包仍然提示错误:

　　


　　

Load library:

gm = require 'graphicsmagick'

First, we provide two high-level functions to load/save directly into/form tensors:

img = gm.load('/path/to/image.png' [, type])    -- type = 'float' (default) | 'double' | 'byte'
gm.save('/path/to/image.jpg' [,quality])        -- quality = 0 to 100 (for jpegs only)

The following provide a more controlled flow for loading/saving jpegs.

Create an image, from a file:

image = gm.Image('/path/to/image.png')
-- or
image = gm.Image()
image:load('/path/to/image.png')

　　但是悲剧的仍然有错, 只好换了用  image.load() 的方式加载图像:
　　

--To load as byte tensor for rgb imagefile
local img = image.load(imagefile,3,'byte')

　　4. Torch 保存 txt 文件：
　　-- save opt
　　file = torch.DiskFile(paths.concat(opt.checkpoints_dir, opt.name, 'opt.txt'), 'w')
　　file:writeObject(opt)
　　file:close()
　　
　　5. Torch 创建新的文件夹
　　opts.modelPath = opt.modelDir .. opt.modelName
　　if not paths.dirp(opt.modelPath) then
　　　　paths.mkdir(opts.modelPath)
　　end

　　6. Torch Lua 保存 图像到文件夹
　　借助 image package，首先安装： luarocks install image
　　然后 require 'image'
　　就可以使用了： local img = image.save('./saved_pos_neg_image/candidate_' .. tostring(i) .. tostring(j) .. '.png', pos_patch, 1, 32, 32)

　　7. module 'bit' not found:No LuaRocks module found for bit

wangxiao@AHU:/media/wangxiao/724eaeef-e688-4b09-9cc9-dfaca44079b2/fast-neural-style-master$ th ./train.lua
/home/wangxiao/torch/install/bin/lua: /home/wangxiao/torch/install/share/lua/5.2/trepl/init.lua:389: /home/wangxiao/torch/install/share/lua/5.2/trepl/init.lua:389: /home/wangxiao/torch/install/share/lua/5.2/trepl/init.lua:389: module 'bit' not found:No LuaRocks module found for bit
no field package.preload['bit']
no file '/home/wangxiao/.luarocks/share/lua/5.2/bit.lua'
no file '/home/wangxiao/.luarocks/share/lua/5.2/bit/init.lua'
no file '/home/wangxiao/torch/install/share/lua/5.2/bit.lua'
no file '/home/wangxiao/torch/install/share/lua/5.2/bit/init.lua'
no file '/home/wangxiao/.luarocks/share/lua/5.1/bit.lua'
no file '/home/wangxiao/.luarocks/share/lua/5.1/bit/init.lua'
no file '/home/wangxiao/torch/install/share/lua/5.1/bit.lua'
no file '/home/wangxiao/torch/install/share/lua/5.1/bit/init.lua'
no file './bit.lua'
no file '/home/wangxiao/torch/install/share/luajit-2.1.0-beta1/bit.lua'
no file '/usr/local/share/lua/5.1/bit.lua'
no file '/usr/local/share/lua/5.1/bit/init.lua'
no file '/home/wangxiao/.luarocks/lib/lua/5.2/bit.so'
no file '/home/wangxiao/torch/install/lib/lua/5.2/bit.so'
no file '/home/wangxiao/torch/install/lib/bit.so'
no file '/home/wangxiao/.luarocks/lib/lua/5.1/bit.so'
no file '/home/wangxiao/torch/install/lib/lua/5.1/bit.so'
no file './bit.so'
no file '/usr/local/lib/lua/5.1/bit.so'
no file '/usr/local/lib/lua/5.1/loadall.so'
stack traceback:
[C]: in function 'error'
/home/wangxiao/torch/install/share/lua/5.2/trepl/init.lua:389: in function 'require'
./train.lua:5: in main chunk
[C]: in function 'dofile'
...xiao/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: in ?
wangxiao@AHU:/media/wangxiao/724eaeef-e688-4b09-9cc9-dfaca44079b2/fast-neural-style-master$

在终端中执行：luarocks install luabitop
就可以了。

8.　　HDF5Group:read() - no such child 'media' for [HDF5Group 33554432 /]

/home/wangxiao/torch/install/bin/lua: /home/wangxiao/torch/install/share/lua/5.2/hdf5/group.lua:312: HDF5Group:read() - no such child 'media' for [HDF5Group 33554432 /]
stack traceback:
[C]: in function 'error'
/home/wangxiao/torch/install/share/lua/5.2/hdf5/group.lua:312: in function </home/wangxiao/torch/install/share/lua/5.2/hdf5/group.lua:302>
(...tail calls...)
./fast_neural_style/DataLoader.lua:44: in function '__init'
/home/wangxiao/torch/install/share/lua/5.2/torch/init.lua:91: in function </home/wangxiao/torch/install/share/lua/5.2/torch/init.lua:87>
[C]: in function 'DataLoader'
./train.lua:138: in function 'main'
./train.lua:327: in main chunk
[C]: in function 'dofile'
...xiao/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: in ?

最近在训练 类型迁移的代码，发现这个蛋疼的问题。哎。。纠结好几天了。。这个 hdf5 到底怎么回事 ？ 求解释 ！！！

------------------------------------------------------------------------------------------------

　　后来发现, 是我自己的数据集路径设置的有问题, 如: 应该是 CoCo/train/image/

　　但是,我只是给定了 CoCo/train/ ...

　　9. 怎么设置 torch代码在哪块 GPU 上运行 ? 或者 怎么设置在两块卡上同时运行 ?

　　


　 　


　　如图所示:

export CUDA_VISIBLE_DEVICES=0 即可指定代码在 GPU-0 上运行.

　　

　　10. When load the pre-trained VGG model, got the following errors:

　　　　MODULE data UNDEFINED
　　　　warning: module 'data [type 5]' not found
　　　　nn supports no groups!
　　　　warning: module 'conv2 [type 4]' not found
　　　　nn supports no groups!
　　　　warning: module 'conv4 [type 4]' not found
　　　　nn supports no groups!
　　　　warning: module 'conv5 [type 4]' not found

　　

using cudnn
Successfully loaded ./feature_transfer/AlexNet_files/bvlc_alexnet.caffemodel
MODULE data UNDEFINED
warning: module 'data [type 5]' not found
nn supports no groups!
warning: module 'conv2 [type 4]' not found
nn supports no groups!
warning: module 'conv4 [type 4]' not found
nn supports no groups!
warning: module 'conv5 [type 4]' not found

wangxiao@AHU:~/Downloads/multi-modal-visual-tracking$ qlua ./train_match_function_alexNet_version_2017_02_28.lua
using cudnn
Successfully loaded ./feature_transfer/AlexNet_files/bvlc_alexnet.caffemodel
MODULE data UNDEFINED
warning: module 'data [type 5]' not found
nn supports no groups!
warning: module 'conv2 [type 4]' not found
nn supports no groups!
warning: module 'conv4 [type 4]' not found
nn supports no groups!
warning: module 'conv5 [type 4]' not found
conv1: 96 3 11 11
conv3: 384 256 3 3
fc6: 1 1 9216 4096
fc7: 1 1 4096 4096
fc8: 1 1 4096 1000
nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.SplitTable
(2): nn.ParallelTable {
input
|`-> (1): nn.Sequential {
| [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> output]
| (1): nn.SpatialConvolution(3 -> 96, 11x11, 4,4)
| (2): nn.ReLU
| (3): nn.SpatialCrossMapLRN
| (4): nn.SpatialMaxPooling(3x3, 2,2)
| (5): nn.ReLU
| (6): nn.SpatialCrossMapLRN
| (7): nn.SpatialMaxPooling(3x3, 2,2)
| (8): nn.SpatialConvolution(256 -> 384, 3x3, 1,1, 1,1)
| (9): nn.ReLU
| (10): nn.ReLU
| (11): nn.ReLU
| (12): nn.SpatialMaxPooling(3x3, 2,2)
| (13): nn.View(-1)
| (14): nn.Linear(9216 -> 4096)
| (15): nn.ReLU
| (16): nn.Dropout(0.500000)
| (17): nn.Linear(4096 -> 4096)
| (18): nn.ReLU
| }
`-> (2): nn.Sequential {
[input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> output]
(1): nn.SpatialConvolution(3 -> 96, 11x11, 4,4)
(2): nn.ReLU
(3): nn.SpatialCrossMapLRN
(4): nn.SpatialMaxPooling(3x3, 2,2)
(5): nn.ReLU
(6): nn.SpatialCrossMapLRN
(7): nn.SpatialMaxPooling(3x3, 2,2)
(8): nn.SpatialConvolution(256 -> 384, 3x3, 1,1, 1,1)
(9): nn.ReLU
(10): nn.ReLU
(11): nn.ReLU
(12): nn.SpatialMaxPooling(3x3, 2,2)
(13): nn.View(-1)
(14): nn.Linear(9216 -> 4096)
(15): nn.ReLU
(16): nn.Dropout(0.500000)
(17): nn.Linear(4096 -> 4096)
(18): nn.ReLU
}
... -> output
}
(3): nn.PairwiseDistance
}
=================================================================================================================
================= AlextNet based Siamese Search for Visual Tracking ========================
=================================================================================================================
==>> The Benchmark Contain: 36 videos ...
deal with video 1/36 video name: BlurFace ... please waiting ...
the num of gt bbox: 493
the num of video frames: 493
========>>>> Begin to track 2 video name: nil-th frame, please waiting ...
========>>>> Begin to track 3 video name: nil-th frame, please waiting ... ............] ETA: 0ms | Step: 0ms
========>>>> Begin to track 4 video name: nil-th frame, please waiting ... ............] ETA: 39s424ms | Step: 80ms
========>>>> Begin to track 5 video name: nil-th frame, please waiting ... ............] ETA: 33s746ms | Step: 69ms
========>>>> Begin to track 6 video name: nil-th frame, please waiting ... ............] ETA: 31s817ms | Step: 65ms
========>>>> Begin to track 7 video name: nil-th frame, please waiting ... ............] ETA: 32s575ms | Step: 66ms
========>>>> Begin to track 8 video name: nil-th frame, please waiting ... ............] ETA: 34s376ms | Step: 70ms
========>>>> Begin to track 9 video name: nil-th frame, please waiting ... ............] ETA: 40s240ms | Step: 82ms
========>>>> Begin to track 10 video name: nil-th frame, please waiting ... ...........] ETA: 44s211ms | Step: 91ms
========>>>> Begin to track 11 video name: nil-th frame, please waiting ... ...........] ETA: 45s993ms | Step: 95ms
========>>>> Begin to track 12 video name: nil-th frame, please waiting ... ...........] ETA: 47s754ms | Step: 99ms
========>>>> Begin to track 13 video name: nil-th frame, please waiting ... ...........] ETA: 50s392ms | Step: 104ms
========>>>> Begin to track 14 video name: nil-th frame, please waiting ... ...........] ETA: 53s138ms | Step: 110ms
========>>>> Begin to track 15 video name: nil-th frame, please waiting ... ...........] ETA: 55s793ms | Step: 116ms
========>>>> Begin to track 16 video name: nil-th frame, please waiting ... ...........] ETA: 59s253ms | Step: 123ms
========>>>> Begin to track 17 video name: nil-th frame, please waiting ... ...........] ETA: 1m2s | Step: 130ms
========>>>> Begin to track 18 video name: nil-th frame, please waiting ... ...........] ETA: 1m5s | Step: 137ms
========>>>> Begin to track 19 video name: nil-th frame, please waiting ... ...........] ETA: 1m8s | Step: 143ms
========>>>> Begin to track 20 video name: nil-th frame, please waiting ... ...........] ETA: 1m11s | Step: 149ms
//////////////////////////////////////////////////////////////////////////..............] ETA: 1m14s | Step: 157ms
==>> pos_proposal_list: 19
==>> neg_proposal_list: 19
qlua: /home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:67:
In 2 module of nn.Sequential:
In 1 module of nn.ParallelTable:
In 8 module of nn.Sequential:
/home/wangxiao/torch/install/share/lua/5.1/nn/THNN.lua:117: Need input of dimension 3 and input.size[0] == 256 but got input to be of shape: [96 x 13 x 13] at /tmp/luarocks_cunn-scm-1-6210/cunn/lib/THCUNN/generic/SpatialConvolutionMM.cu:49
stack traceback:
[C]: in function 'v'
/home/wangxiao/torch/install/share/lua/5.1/nn/THNN.lua:117: in function 'SpatialConvolutionMM_updateOutput'
...ao/torch/install/share/lua/5.1/nn/SpatialConvolution.lua:79: in function <...ao/torch/install/share/lua/5.1/nn/SpatialConvolution.lua:76>
[C]: in function 'xpcall'
/home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
...e/wangxiao/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function <...e/wangxiao/torch/install/share/lua/5.1/nn/Sequential.lua:41>
[C]: in function 'xpcall'
/home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
...angxiao/torch/install/share/lua/5.1/nn/ParallelTable.lua:12: in function <...angxiao/torch/install/share/lua/5.1/nn/ParallelTable.lua:10>
[C]: in function 'xpcall'
/home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
...e/wangxiao/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
./train_match_function_alexNet_version_2017_02_28.lua:525: in function 'opfunc'
/home/wangxiao/torch/install/share/lua/5.1/optim/adam.lua:37: in function 'optim'
./train_match_function_alexNet_version_2017_02_28.lua:550: in main chunk

WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above.
stack traceback:
[C]: at 0x7f86014df9c0
[C]: in function 'error'
/home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:67: in function 'rethrowErrors'
...e/wangxiao/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
./train_match_function_alexNet_version_2017_02_28.lua:525: in function 'opfunc'
/home/wangxiao/torch/install/share/lua/5.1/optim/adam.lua:37: in function 'optim'
./train_match_function_alexNet_version_2017_02_28.lua:550: in main chunk
wangxiao@AHU:~/Downloads/multi-modal-visual-tracking$

　　Just like the screen shot above, change the 'nn' into 'cudnn' will be ok and passed.

　　11. both (null) and torch.FloatTensor have no less-than operator

　　　　qlua: ./test_MM_tracker_VGG_.lua:254: both (null) and torch.FloatTensor have no less-than operator
　　　　stack traceback:
　　　　[C]: at 0x7f628816e9c0
　　　　[C]: in function '__lt'
　　　　./test_MM_tracker_VGG_.lua:254: in main chunk

　　

　　Because it is floatTensor () style and you can change it like this if you want this value printed in a for loop:　predictValue -->> predictValue[i] .

　　

　　12.

========>>>> Begin to track the 6-th and the video name is ILSVRC2015_train_00109004 , please waiting ...
THCudaCheck FAIL file=/tmp/luarocks_cutorch-scm-1-707/cutorch/lib/THC/generic/THCStorage.cu line=66 error=2 : out of memory
qlua: cuda runtime error (2) : out of memory at /tmp/luarocks_cutorch-scm-1-707/cutorch/lib/THC/generic/THCStorage.cu:66
stack traceback:
[C]: at 0x7fa20a8f99c0
[C]: at 0x7fa1dddfbee0
[C]: in function 'Tensor'
./train_match_function_VGG_version_2017_03_02.lua:377: in main chunk
wangxiao@AHU:~/Downloads/multi-modal-visual-tracking$

Yes, it is just out of memory of GPU. Just turn the batchsize to a small value, it may work. It worked for me. Ha ha ...

13. [b]luarocks install class does not have any effect, it still shown me the error: No Module named "class" in Torch. [/b]

　　==>> in terminal, install this package in [b]sudo. [/b]

　　==>> then, it will be OK.

[b]14. How to install opencv 3.1 on Ubuntu 14.04 ??? [/b]

　　As we can found from: http://blog.csdn.net/a125930123/article/details/52091140

　　1. first, you should install torch successfully ;

　　2. then, just follow what the blog said here:

安装opencv3.1

1、安装必要的包

sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

2、下载opencv3.1

http://opencv.org/downloads.html
解压：unzip  opencv-3.1.0

3、安装
cd ~/opencv-3.1.0
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..

sudo make -j24
sudo make install -j24
sudo /bin/bash -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
sudo ldconfig

安装完成

4、问题

在安装过程中可能会出现无法下载

ippicv_linux_20151201.tgz

的问题。[/code]

解决方案：

手动下载

ippicv_linux_20151201.tgz

：https://raw.githubusercontent.com/Itseez/opencv_3rdparty/81a676001ca8075ada498583e4166079e5744668/ippicv/ippicv_linux_20151201.tgz[/code]

将下载好的文件  放入 [code]opencv-3.1.0/3rdparty/ippicv/downloads/linux-808b791a6eac9ed78d32a7666804320e

中，如果已经存在 ，则替换掉，这样就可以安装完成了。[/code]

5、最后执行命令

luarocks install cv

OpenCV bindings for Torch安装成功。

But, maybe you may found some errors, such as:

cudalegacy/src/graphcuts.cpp:120:54: error: ‘NppiGraphcutState’ has not been declared (solution draw from: http://blog.csdn.net/allyli0022/article/details/62859290)
At this moment, you need to change some files:

found graphcuts.cpp in opencv3.1, and do the following changes:

解决方案：需要修改一处源码：
在graphcuts.cpp中将

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)

改为

#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)|| (CUDART_VERSION >= 8000)

then, try again, it will be ok...this code just want to make opencv3.1 work under cuda 8.0, you know...skip that judge sentence...

15.  [b]安装torch-hdf5
[/b]

sudo apt-get install libhdf5-serial-dev hdf5-tools
git clone https://github.com/deepmind/torch-hdf5 cd torch-hdf5
sudo luarocks make hdf5-0-0.rockspec LIBHDF5_LIBDIR=”/usr/lib/x86_64-Linux-gnu/”

[b]17. iTorch安装 [/b]

git clone https://github.com/zeromq/zeromq4-1.git mkdir build-zeromq
cd build-zeromq
cmake ..
make && make install
安装完之后，luarocks install itorch
之后可以通过luarocks list查看是否安装成功