Summary on deep learning framework --- Torch7

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发布时间：2019-06-23

本文共 24389 字，大约阅读时间需要 81 分钟。

Summary on deep learning framework --- Torch7

2018-07-22 21:30:28

1. 尝试第一个 CNN 的 torch版本, 代码如下:

1 --    We now have 5 steps left to do in training our first torch neural network  2 --    1. Load and normalize data  3 --    2. Define Neural Network  4 --    3. Define Loss function  5 --    4. Train network on training data  6 --    5. Test network on test data.  7   8   9  10  11 --    1. Load and normalize data 12 require 'paths' 13 require 'image'; 14 if (not paths.filep("cifar10torchsmall.zip")) then 15     os.execute('wget -c https://s3.amazonaws.com/torch7/data/cifar10torchsmall.zip') 16     os.execute('unzip cifar10torchsmall.zip') 17 end 18 trainset = torch.load('cifar10-train.t7') 19 testset = torch.load('cifar10-test.t7') 20 classes = {
     'airplane', 'automobile', 'bird', 'cat', 21            'deer', 'dog', 'frog', 'horse', 'ship', 'truck'} 22  23 print(trainset) 24 print(#trainset.data) 25  26 itorch.image(trainset.data[100]) -- display the 100-th image in dataset 27 print(classes[trainset.label[100]]) 28  29 -- ignore setmetatable for now, it is a feature beyond the scope of this tutorial. 30 -- It sets the index operator  31 setmetatable(trainset, 32     {__index = function(t, i) 33                     return {t.data[i], t.label[i]} 34                 end} 35 ); 36 trainset.data = trainset.data:double()  -- convert the data from a ByteTensor to a DoubleTensor. 37  38 function trainset:size() 39     return self.data:size(1) 40 end 41  42 print(trainset:size()) 43 print(trainset[33]) 44 itorch.image(trainset[33][11]) 45  46 redChannel = trainset.data[{ {}, {
     1}, {}, {} }] -- this pick {all images, 1st channel, all vertical pixels, all horizontal pixels} 47 print(#redChannel) 48  49 -- TODO:fill 50 mean = {} 51 stdv = {} 52 for i = 1,3 do  53     mean[i] = trainset.data[{ {}, {i}, {}, {} }]:mean()  -- mean estimation  54     print('Channel ' .. i .. ' , Mean: ' .. mean[i]) 55     trainset.data[{ {}, {i}, {}, {} }]:add(-mean[i]) -- mean subtraction  56  57     stdv[i] = trainset.data[ { {}, {i}, {}, {} }]:std()  -- std estimation  58     print('Channel ' .. i .. ' , Standard Deviation: ' .. stdv[i]) 59     trainset.data[{ {}, {i}, {}, {} }]:div(stdv[i])  -- std scaling  60 end  61  62  63  64 --    2. Define Neural Network 65 net = nn.Sequential() 66 net:add(nn.SpatialConvolution(3, 6, 5, 5)) -- 3 input image channels, 6 output channels, 5x5 convolution kernel 67 net:add(nn.ReLU())                       -- non-linearity  68 net:add(nn.SpatialMaxPooling(2,2,2,2))     -- A max-pooling operation that looks at 2x2 windows and finds the max. 69 net:add(nn.SpatialConvolution(6, 16, 5, 5)) 70 net:add(nn.ReLU())                       -- non-linearity  71 net:add(nn.SpatialMaxPooling(2,2,2,2)) 72 net:add(nn.View(16*5*5))                    -- reshapes from a 3D tensor of 16x5x5 into 1D tensor of 16*5*5 73 net:add(nn.Linear(16*5*5, 120))             -- fully connected layer (matrix multiplication between input and weights) 74 net:add(nn.ReLU())                       -- non-linearity  75 net:add(nn.Linear(120, 84)) 76 net:add(nn.ReLU())                       -- non-linearity  77 net:add(nn.Linear(84, 10))                   -- 10 is the number of outputs of the network (in this case, 10 digits) 78 net:add(nn.LogSoftMax())                     -- converts the output to a log-probability. Useful for classification problems 79  80  81 -- 3. Let us difine the Loss function  82 criterion = nn.ClassNLLCriterion() 83  84  85  86 -- 4. Train the neural network  87 trainer = nn.StochasticGradient(net, criterion) 88 trainer.learningRate = 0.001 89 trainer.maxIteration = 5 -- just do 5 epochs of training.  90 trainer:train(trainset) 91  92  93  94 -- 5. Test the network, print accuracy 95 print(classes[testset.label[100]]) 96 itorch.image(testset.data[100]) 97  98 testset.data = testset.data:double()   -- convert from Byte tensor to Double tensor 99 for i=1,3 do -- over each image channel100     testset.data[{ {}, {i}, {}, {}  }]:add(-mean[i]) -- mean subtraction    101     testset.data[{ {}, {i}, {}, {}  }]:div(stdv[i]) -- std scaling102 end103 104 -- for fun, print the mean and standard-deviation of example-100105 horse = testset.data[100]106 print(horse:mean(), horse:std())107 108 print(classes[testset.label[100]])109 itorch.image(testset.data[100])110 predicted = net:forward(testset.data[100])111  112 -- the output of the network is Log-Probabilities. To convert them to probabilities, you have to take e^x 113 print(predicted:exp())114 115 116 for i=1,predicted:size(1) do117     print(classes[i], predicted[i])118 end119 120 121 -- test the accuracy 122 correct = 0123 for i=1,10000 do124     local groundtruth = testset.label[i]125     local prediction = net:forward(testset.data[i])126     local confidences, indices = torch.sort(prediction, true)  -- true means sort in descending order127     if groundtruth == indices[1] then128         correct = correct + 1129     end130 end131 132 133 print(correct, 100*correct/10000 .. ' % ')134 135 class_performance = {
     0, 0, 0, 0, 0, 0, 0, 0, 0, 0}136 for i=1,10000 do137     local groundtruth = testset.label[i]138     local prediction = net:forward(testset.data[i])139     local confidences, indices = torch.sort(prediction, true)  -- true means sort in descending order140     if groundtruth == indices[1] then141         class_performance[groundtruth] = class_performance[groundtruth] + 1142     end143 end144 145 146 for i=1,#classes do147     print(classes[i], 100*class_performance[i]/1000 .. ' %')148 end149 150 require 'cunn';151 net = net:cuda()152 criterion = criterion:cuda()153 trainset.data = trainset.data:cuda()154 trainset.label = trainset.label:cuda()155 156 trainer = nn.StochasticGradient(net, criterion)157 trainer.learningRate = 0.001158 trainer.maxIteration = 5 -- just do 5 epochs of training.159 160 161 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.

2. 'libcudnn (R5) not found in library path.

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/cuDNNThen 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)

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

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 imagefilelocal 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

1 using cudnn 2 Successfully loaded ./feature_transfer/AlexNet_files/bvlc_alexnet.caffemodel 3 MODULE data UNDEFINED 4 warning: module 'data [type 5]' not found 5 nn supports no groups! 6 warning: module 'conv2 [type 4]' not found 7 nn supports no groups! 8 warning: module 'conv4 [type 4]' not found 9 nn supports no groups!10 warning: module 'conv5 [type 4]' not found

1 wangxiao@AHU:~/Downloads/multi-modal-visual-tracking$ qlua ./train_match_function_alexNet_version_2017_02_28.lua   2 using cudnn  3 Successfully loaded ./feature_transfer/AlexNet_files/bvlc_alexnet.caffemodel  4 MODULE data UNDEFINED  5 warning: module 'data [type 5]' not found  6 nn supports no groups!  7 warning: module 'conv2 [type 4]' not found  8 nn supports no groups!  9 warning: module 'conv4 [type 4]' not found 10 nn supports no groups! 11 warning: module 'conv5 [type 4]' not found 12 conv1: 96 3 11 11 13 conv3: 384 256 3 3 14 fc6: 1 1 9216 4096 15 fc7: 1 1 4096 4096 16 fc8: 1 1 4096 1000 17 nn.Sequential { 18 [input -> (1) -> (2) -> (3) -> output] 19 (1): nn.SplitTable 20 (2): nn.ParallelTable { 21 input 22 |`-> (1): nn.Sequential { 23 | [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> output] 24 | (1): nn.SpatialConvolution(3 -> 96, 11x11, 4,4) 25 | (2): nn.ReLU 26 | (3): nn.SpatialCrossMapLRN 27 | (4): nn.SpatialMaxPooling(3x3, 2,2) 28 | (5): nn.ReLU 29 | (6): nn.SpatialCrossMapLRN 30 | (7): nn.SpatialMaxPooling(3x3, 2,2) 31 | (8): nn.SpatialConvolution(256 -> 384, 3x3, 1,1, 1,1) 32 | (9): nn.ReLU 33 | (10): nn.ReLU 34 | (11): nn.ReLU 35 | (12): nn.SpatialMaxPooling(3x3, 2,2) 36 | (13): nn.View(-1) 37 | (14): nn.Linear(9216 -> 4096) 38 | (15): nn.ReLU 39 | (16): nn.Dropout(0.500000) 40 | (17): nn.Linear(4096 -> 4096) 41 | (18): nn.ReLU 42 | } 43 `-> (2): nn.Sequential { 44 [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> output] 45 (1): nn.SpatialConvolution(3 -> 96, 11x11, 4,4) 46 (2): nn.ReLU 47 (3): nn.SpatialCrossMapLRN 48 (4): nn.SpatialMaxPooling(3x3, 2,2) 49 (5): nn.ReLU 50 (6): nn.SpatialCrossMapLRN 51 (7): nn.SpatialMaxPooling(3x3, 2,2) 52 (8): nn.SpatialConvolution(256 -> 384, 3x3, 1,1, 1,1) 53 (9): nn.ReLU 54 (10): nn.ReLU 55 (11): nn.ReLU 56 (12): nn.SpatialMaxPooling(3x3, 2,2) 57 (13): nn.View(-1) 58 (14): nn.Linear(9216 -> 4096) 59 (15): nn.ReLU 60 (16): nn.Dropout(0.500000) 61 (17): nn.Linear(4096 -> 4096) 62 (18): nn.ReLU 63 } 64 ... -> output 65 } 66 (3): nn.PairwiseDistance 67 } 68 ================================================================================================================= 69 ================= AlextNet based Siamese Search for Visual Tracking ======================== 70 ================================================================================================================= 71 ==>> The Benchmark Contain: 36 videos ...  72 deal with video 1/36 video name: BlurFace ... please waiting ...  73 the num of gt bbox: 493 74 the num of video frames: 493 75 ========>>>> Begin to track 2 video name: nil-th frame, please waiting ...  76 ========>>>> Begin to track 3 video name: nil-th frame, please waiting ... ............] ETA: 0ms | Step: 0ms  77 ========>>>> Begin to track 4 video name: nil-th frame, please waiting ... ............] ETA: 39s424ms | Step: 80ms  78 ========>>>> Begin to track 5 video name: nil-th frame, please waiting ... ............] ETA: 33s746ms | Step: 69ms  79 ========>>>> Begin to track 6 video name: nil-th frame, please waiting ... ............] ETA: 31s817ms | Step: 65ms  80 ========>>>> Begin to track 7 video name: nil-th frame, please waiting ... ............] ETA: 32s575ms | Step: 66ms  81 ========>>>> Begin to track 8 video name: nil-th frame, please waiting ... ............] ETA: 34s376ms | Step: 70ms  82 ========>>>> Begin to track 9 video name: nil-th frame, please waiting ... ............] ETA: 40s240ms | Step: 82ms  83 ========>>>> Begin to track 10 video name: nil-th frame, please waiting ... ...........] ETA: 44s211ms | Step: 91ms  84 ========>>>> Begin to track 11 video name: nil-th frame, please waiting ... ...........] ETA: 45s993ms | Step: 95ms  85 ========>>>> Begin to track 12 video name: nil-th frame, please waiting ... ...........] ETA: 47s754ms | Step: 99ms  86 ========>>>> Begin to track 13 video name: nil-th frame, please waiting ... ...........] ETA: 50s392ms | Step: 104ms  87 ========>>>> Begin to track 14 video name: nil-th frame, please waiting ... ...........] ETA: 53s138ms | Step: 110ms  88 ========>>>> Begin to track 15 video name: nil-th frame, please waiting ... ...........] ETA: 55s793ms | Step: 116ms  89 ========>>>> Begin to track 16 video name: nil-th frame, please waiting ... ...........] ETA: 59s253ms | Step: 123ms  90 ========>>>> Begin to track 17 video name: nil-th frame, please waiting ... ...........] ETA: 1m2s | Step: 130ms  91 ========>>>> Begin to track 18 video name: nil-th frame, please waiting ... ...........] ETA: 1m5s | Step: 137ms  92 ========>>>> Begin to track 19 video name: nil-th frame, please waiting ... ...........] ETA: 1m8s | Step: 143ms  93 ========>>>> Begin to track 20 video name: nil-th frame, please waiting ... ...........] ETA: 1m11s | Step: 149ms  94 //..............] ETA: 1m14s | Step: 157ms  95 ==>> pos_proposal_list: 19 96 ==>> neg_proposal_list: 19 97 qlua: /home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:67:  98 In 2 module of nn.Sequential: 99 In 1 module of nn.ParallelTable:100 In 8 module of nn.Sequential:101 /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:49102 stack traceback:103 [C]: in function 'v'104 /home/wangxiao/torch/install/share/lua/5.1/nn/THNN.lua:117: in function 'SpatialConvolutionMM_updateOutput'105 ...ao/torch/install/share/lua/5.1/nn/SpatialConvolution.lua:79: in function <...ao/torch/install/share/lua/5.1/nn/SpatialConvolution.lua:76>106 [C]: in function 'xpcall'107 /home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'108 ...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>109 [C]: in function 'xpcall'110 /home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'111 ...angxiao/torch/install/share/lua/5.1/nn/ParallelTable.lua:12: in function <...angxiao/torch/install/share/lua/5.1/nn/ParallelTable.lua:10>112 [C]: in function 'xpcall'113 /home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'114 ...e/wangxiao/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'115 ./train_match_function_alexNet_version_2017_02_28.lua:525: in function 'opfunc'116 /home/wangxiao/torch/install/share/lua/5.1/optim/adam.lua:37: in function 'optim'117 ./train_match_function_alexNet_version_2017_02_28.lua:550: in main chunk118 119 120 121 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.122 stack traceback:123 [C]: at 0x7f86014df9c0124 [C]: in function 'error'125 /home/wangxiao/torch/install/share/lua/5.1/nn/Container.lua:67: in function 'rethrowErrors'126 ...e/wangxiao/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'127 ./train_match_function_alexNet_version_2017_02_28.lua:525: in function 'opfunc'128 /home/wangxiao/torch/install/share/lua/5.1/optim/adam.lua:37: in function 'optim'129 ./train_match_function_alexNet_version_2017_02_28.lua:550: in main chunk130 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. luarocks install class does not have any effect, it still shown me the error: No Module named "class" in Torch.

　　==>> in terminal, install this package in sudo.

　　==>> then, it will be OK.

14. How to install opencv 3.1 on Ubuntu 14.04 ???

　　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。

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

将下载好的文件  放入 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。

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:

(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. 安装torch-hdf5

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/”

17. iTorch安装

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