ubuntu14.04 cpu-ssd

1. ssd-caffe部署

五年半前老笔记本，没有GPU（其实有，AMD的，不能装CUDA），之前装过CPU版的Caffe

新建一个目录，然后参考网上步骤

sudo git clone https://github.com/weiliu89/caffe.git
cd caffe
sudo git checkout ssd 　　--如果输出分支说明正确
sudo cp ./.(反正就是之前部署好的caffe目录)./../Makefile.config Makefile.config --这里第一次是尝试着手动修改Makefile.config编译失败了。
sudo make all -j4
sudo make runtest -j4 (5、6步骤之间应该make test的，眼花看错行，居然make和test一起做了）
sudo make pycaffe -j4
cd python
python
import caffe

表示CPU版的ssd在Caffe已经部署好的机器上很容易配置，但windows下不成，win10+cuda8.0+vs2013+python2.7+matlab2014版的微软caffe也部署成功了，但是编译ssd-caffe时各种失败未解决。

2. ssd_detect.py试验

在caffe/examples/ssd目录下，进入该目录

python ssd_detect.py  -- import caffe错误

修改ssd_detect.py内指定路径：

sys.path.insert(0, './../../python')

报错变为：cannot use GPU in CPU only caffe

修改其中gpu相关代码：

#caffe.set_device(gpu_id)
caffe.set_mode_cpu()

报错变为/modules/VGGNet/...

参考http://blog.csdn.net/u013738531/article/details/56678247

在提供的网盘下载模型和数据

sudo gedit ./models/VGGNet/VOC0712Plus/SSD_300x300/deploy.prototxt

按作者所示修改，会报错说找不到test_name_size.txt

因为没有做过训练，没生成数据和文件列表。

-----------------2018.02.02 新服务器编译照1执行---------------------------------

下载好作者提供的模型放在/models/下以后直接在caffe目录下python examples/ssd/ssd_detect.py即可，什么都不用改

 3. 生成数据

sudo ./data/VOC0712/create_list.sh

各种报错，主要是因为路径不对

把下载到的数据（VOC2007train、test、VOC2012那三个）解压到ssd这一版本的caffe/data目录下，解压后自动生成的文件名是VOCdevkit

sudo gedit ./data/VOC0712/create_list.sh

修改root_dir=./data/VOCdevkit

ssd这一版本的caffe目录下sudo ./data/VOC0712/create_list.sh

可以看到成功生成了test_name_size.txt

4. ssd_detect.bin测试单张（或多张图片）

仍在ssd这一版本的caffe目录下：
song@song-Lenovo-G470:/home/ssd/caffe$ sudo ./build/examples/ssd/ssd_detect.bin ./models/VGGNet/VOC0712Plus/SSD_300x300/deploy.prototxt ./models/VGGNet/VOC0712Plus/SSD_300x300/VGG_VOC0712Plus_SSD_300x300_iter_240000.caffemodel ./examples/images/test.txt
会报错说num_test_image不一致，再次

sudo gedit ./models/VGGNet/VOC0712Plus/SSD_300x300/deploy.prototxt

修改一下就行了，./examples/images/test.txt是之前手工建好的，内容为

examples/images/cat.jpg

examples/images/fish-bike.jpg

破笔记本风扇又开始呼呼的响了有一会儿(大概一分钟？），就可以了

运行结果：

examples/images/cat.jpg 8 0.999512 166 17 350 342
examples/images/cat.jpg 17 0.0125665 297 301 333 346
examples/images/cat.jpg 17 0.010942 11 304 91 356
examples/images/fish-bike.jpg 1 0.10214 1 5 49 23
examples/images/fish-bike.jpg 1 0.0192221 13 8 36 23
examples/images/fish-bike.jpg 1 0.0136562 453 43 477 58
examples/images/fish-bike.jpg 1 0.0128765 22 133 464 315
examples/images/fish-bike.jpg 1 0.0121259 16 4 42 18
examples/images/fish-bike.jpg 1 0.0109429 237 110 315 160
examples/images/fish-bike.jpg 1 0.0106769 0 -10 65 22
examples/images/fish-bike.jpg 1 0.0101953 447 43 466 56
examples/images/fish-bike.jpg 2 0.17723 34 136 474 313
examples/images/fish-bike.jpg 2 0.0162963 92 260 235 305
examples/images/fish-bike.jpg 2 0.010479 48 243 175 293
examples/images/fish-bike.jpg 4 0.0434637 22 133 464 315
examples/images/fish-bike.jpg 7 0.0490763 454 46 480 60
examples/images/fish-bike.jpg 7 0.0285298 434 42 448 55
examples/images/fish-bike.jpg 7 0.0273768 426 43 440 55
examples/images/fish-bike.jpg 7 0.0238768 219 21 235 34
examples/images/fish-bike.jpg 7 0.0224352 197 18 209 34
examples/images/fish-bike.jpg 7 0.0222405 203 19 215 33
examples/images/fish-bike.jpg 7 0.0203195 434 46 463 59
examples/images/fish-bike.jpg 7 0.0202004 427 46 446 58
examples/images/fish-bike.jpg 7 0.0190711 221 25 233 35
examples/images/fish-bike.jpg 7 0.0188878 206 24 219 34
examples/images/fish-bike.jpg 7 0.0186781 233 22 254 35
examples/images/fish-bike.jpg 7 0.0181446 222 28 239 38
examples/images/fish-bike.jpg 7 0.0176258 413 42 423 53
examples/images/fish-bike.jpg 7 0.0167608 442 43 474 60
examples/images/fish-bike.jpg 7 0.0162482 429 50 439 57
examples/images/fish-bike.jpg 7 0.0162143 209 28 227 38
examples/images/fish-bike.jpg 7 0.0160853 438 50 452 58
examples/images/fish-bike.jpg 7 0.0156584 403 41 414 52
examples/images/fish-bike.jpg 7 0.0150175 212 18 224 31
examples/images/fish-bike.jpg 7 0.0148612 419 46 433 56
examples/images/fish-bike.jpg 7 0.0145784 450 49 471 59
examples/images/fish-bike.jpg 7 0.0138213 427 47 435 55
examples/images/fish-bike.jpg 7 0.0134302 229 18 242 30
examples/images/fish-bike.jpg 7 0.0133286 438 46 448 56
examples/images/fish-bike.jpg 7 0.0128786 441 54 461 62
examples/images/fish-bike.jpg 7 0.0128691 393 41 403 51
examples/images/fish-bike.jpg 7 0.0126478 416 50 425 57
examples/images/fish-bike.jpg 7 0.0118132 199 28 216 38
examples/images/fish-bike.jpg 7 0.0118091 407 46 419 55
examples/images/fish-bike.jpg 7 0.0116668 236 29 255 40
examples/images/fish-bike.jpg 7 0.011606 404 50 412 57
examples/images/fish-bike.jpg 7 0.0115945 431 54 445 62
examples/images/fish-bike.jpg 7 0.0114097 188 24 198 33
examples/images/fish-bike.jpg 7 0.0113932 402 47 408 55
examples/images/fish-bike.jpg 7 0.0113243 416 41 429 54
examples/images/fish-bike.jpg 7 0.0113237 413 47 420 55
examples/images/fish-bike.jpg 7 0.0105125 402 38 411 50
examples/images/fish-bike.jpg 7 0.0104816 447 43 466 56
examples/images/fish-bike.jpg 9 0.0131654 293 255 373 312
examples/images/fish-bike.jpg 12 0.017695 259 110 334 161
examples/images/fish-bike.jpg 12 0.014104 291 126 335 158
examples/images/fish-bike.jpg 14 0.0288007 27 130 459 314
examples/images/fish-bike.jpg 15 0.999933 200 3 336 162
examples/images/fish-bike.jpg 15 0.0887778 267 5 330 70
examples/images/fish-bike.jpg 15 0.079791 182 40 347 271
examples/images/fish-bike.jpg 15 0.073601 238 119 315 158
examples/images/fish-bike.jpg 15 0.0449383 232 131 288 161
examples/images/fish-bike.jpg 15 0.0401796 196 17 207 32
examples/images/fish-bike.jpg 15 0.0395075 177 16 188 31
examples/images/fish-bike.jpg 15 0.0321272 144 13 153 30
examples/images/fish-bike.jpg 15 0.0313811 187 18 195 30
examples/images/fish-bike.jpg 15 0.0285742 211 103 286 161
examples/images/fish-bike.jpg 15 0.027668 201 17 213 31
examples/images/fish-bike.jpg 15 0.0267013 400 40 412 53
examples/images/fish-bike.jpg 15 0.0264357 390 40 400 52
examples/images/fish-bike.jpg 15 0.0260199 262 98 336 159
examples/images/fish-bike.jpg 15 0.0260001 156 16 165 31
examples/images/fish-bike.jpg 15 0.0259439 278 9 313 60
examples/images/fish-bike.jpg 15 0.0258899 387 39 396 48
examples/images/fish-bike.jpg 15 0.0257002 166 16 177 32
examples/images/fish-bike.jpg 15 0.0232971 162 15 172 30
examples/images/fish-bike.jpg 15 0.0230115 400 39 409 48
examples/images/fish-bike.jpg 15 0.0229916 133 12 143 30
examples/images/fish-bike.jpg 15 0.0229652 298 17 322 58
examples/images/fish-bike.jpg 15 0.0227231 189 16 198 27
examples/images/fish-bike.jpg 15 0.0222746 148 13 157 31
examples/images/fish-bike.jpg 15 0.021702 336 39 345 49
examples/images/fish-bike.jpg 15 0.0216578 373 38 381 46
examples/images/fish-bike.jpg 15 0.0212417 407 41 419 54
examples/images/fish-bike.jpg 15 0.0212224 357 38 364 45
examples/images/fish-bike.jpg 15 0.0212188 191 17 202 32
examples/images/fish-bike.jpg 15 0.0208593 378 39 386 51
examples/images/fish-bike.jpg 15 0.0207649 151 15 163 30
examples/images/fish-bike.jpg 15 0.0207481 412 39 420 51
examples/images/fish-bike.jpg 15 0.0203485 381 39 389 50
examples/images/fish-bike.jpg 15 0.0201077 333 36 342 46
examples/images/fish-bike.jpg 15 0.0199127 255 113 294 155
examples/images/fish-bike.jpg 15 0.0196864 394 39 405 53
examples/images/fish-bike.jpg 15 0.019666 336 36 344 44
examples/images/fish-bike.jpg 15 0.0193763 326 31 336 43
examples/images/fish-bike.jpg 15 0.0192419 273 7 329 39
examples/images/fish-bike.jpg 15 0.01892 351 38 357 47
examples/images/fish-bike.jpg 15 0.0185818 330 35 338 47
examples/images/fish-bike.jpg 15 0.0183361 349 39 355 49
examples/images/fish-bike.jpg 15 0.0181116 204 15 213 22
examples/images/fish-bike.jpg 15 0.0178212 255 20 320 91
examples/images/fish-bike.jpg 15 0.0174843 375 40 383 50
examples/images/fish-bike.jpg 15 0.0172716 254 23 273 38
examples/images/fish-bike.jpg 15 0.0172101 193 14 203 23
examples/images/fish-bike.jpg 15 0.0171742 221 21 231 31
examples/images/fish-bike.jpg 15 0.0169892 174 13 185 23
examples/images/fish-bike.jpg 15 0.0165922 343 37 350 45
examples/images/fish-bike.jpg 15 0.0162931 242 20 259 36
examples/images/fish-bike.jpg 15 0.0161449 197 15 205 24
examples/images/fish-bike.jpg 15 0.0161009 274 27 304 82
examples/images/fish-bike.jpg 15 0.0159696 368 38 374 48
examples/images/fish-bike.jpg 15 0.0158297 312 30 331 47
examples/images/fish-bike.jpg 15 0.0156176 185 14 192 22
examples/images/fish-bike.jpg 15 0.01529 119 11 132 29
examples/images/fish-bike.jpg 15 0.0151928 212 18 224 31
examples/images/fish-bike.jpg 15 0.0150705 416 41 429 54
examples/images/fish-bike.jpg 15 0.014883 342 31 351 39
examples/images/fish-bike.jpg 15 0.014865 207 17 217 26
examples/images/fish-bike.jpg 15 0.0147874 215 130 265 159
examples/images/fish-bike.jpg 15 0.0146961 419 40 428 49
examples/images/fish-bike.jpg 15 0.0146003 168 13 177 22
examples/images/fish-bike.jpg 15 0.0143927 364 40 371 48
examples/images/fish-bike.jpg 15 0.0142413 156 13 164 22
examples/images/fish-bike.jpg 15 0.0139997 358 32 366 39
examples/images/fish-bike.jpg 15 0.0137995 284 27 317 86
examples/images/fish-bike.jpg 15 0.0137608 168 22 176 31
examples/images/fish-bike.jpg 15 0.0137498 232 22 245 32
examples/images/fish-bike.jpg 15 0.0137089 262 19 289 42
examples/images/fish-bike.jpg 15 0.0136703 222 15 230 23
examples/images/fish-bike.jpg 15 0.0136547 242 26 265 42
examples/images/fish-bike.jpg 15 0.0134454 217 15 226 22
examples/images/fish-bike.jpg 15 0.0130665 236 122 267 155
examples/images/fish-bike.jpg 15 0.0128749 402 47 408 55
examples/images/fish-bike.jpg 15 0.0128543 142 11 152 23
examples/images/fish-bike.jpg 15 0.012737 255 26 299 50
examples/images/fish-bike.jpg 15 0.0127311 248 25 279 49
examples/images/fish-bike.jpg 15 0.0126138 301 20 328 42
examples/images/fish-bike.jpg 15 0.0126017 324 34 334 49
examples/images/fish-bike.jpg 15 0.0125893 285 2 349 51
examples/images/fish-bike.jpg 15 0.0124293 355 39 363 48
examples/images/fish-bike.jpg 15 0.0123481 291 126 335 158
examples/images/fish-bike.jpg 15 0.0121919 311 36 333 54
examples/images/fish-bike.jpg 15 0.0121331 225 18 234 26
examples/images/fish-bike.jpg 15 0.0121244 185 21 193 31
examples/images/fish-bike.jpg 15 0.0120606 423 43 436 55
examples/images/fish-bike.jpg 15 0.0120263 313 18 333 35
examples/images/fish-bike.jpg 15 0.0118891 2 5 16 22
examples/images/fish-bike.jpg 15 0.0118119 121 17 130 28
examples/images/fish-bike.jpg 15 0.0118004 257 26 269 37
examples/images/fish-bike.jpg 15 0.0117509 50 102 431 313
examples/images/fish-bike.jpg 15 0.011688 291 3 312 22
examples/images/fish-bike.jpg 15 0.0113686 332 32 344 42
examples/images/fish-bike.jpg 15 0.0113332 380 38 386 44
examples/images/fish-bike.jpg 15 0.0112034 386 34 393 39
examples/images/fish-bike.jpg 15 0.0112005 413 47 420 55
examples/images/fish-bike.jpg 15 0.0111634 305 30 326 55
examples/images/fish-bike.jpg 15 0.0111193 367 37 373 43
examples/images/fish-bike.jpg 15 0.0110268 373 33 380 39
examples/images/fish-bike.jpg 15 0.0109819 393 39 400 45
examples/images/fish-bike.jpg 15 0.0109263 271 32 315 58
examples/images/fish-bike.jpg 15 0.0109096 230 22 297 87
examples/images/fish-bike.jpg 15 0.0108865 399 35 406 40
examples/images/fish-bike.jpg 15 0.0108715 146 22 152 31
examples/images/fish-bike.jpg 15 0.0105892 329 39 342 51
examples/images/fish-bike.jpg 15 0.010463 251 34 302 60
examples/images/fish-bike.jpg 15 0.0103912 229 18 242 30
examples/images/fish-bike.jpg 15 0.0102471 273 16 296 32
examples/images/fish-bike.jpg 15 0.010216 250 105 280 148
examples/images/fish-bike.jpg 15 0.0100929 233 16 242 23
examples/images/fish-bike.jpg 15 0.010052 242 24 253 33
examples/images/fish-bike.jpg 16 0.0153302 297 254 386 312
examples/images/fish-bike.jpg 19 0.0166149 27 130 459 314

 选概率大的自己手动画了一下：

再次sudo python ssd_detect.py也可以了，输出比.bin那个好看一点点：

/usr/local/lib/python2.7/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.
  warn("The default mode, 'constant', will be changed to 'reflect' in "
[[0.4162176, 0.010038316, 0.70032334, 0.50228781, 15, 0.99993026, u'person']]
481 323
[0.4162176, 0.010038316, 0.70032334, 0.50228781, 15, 0.99993026, u'person']
[200, 3, 337, 162]
[200, 3] person

 也不用傻不拉几的自己画图了，在caffe目录下保存了detect_result.jpg

5. 用VOC2007数据训练

create_data.sh中将data_root_dir改成自己数据的绝对路径，运行错误参考http://blog.csdn.net/lanyuelvyun/article/details/73628152解决。

（就是import caffe的问题：添加python路径，export PYTHONPATH=/home/ssd/caffe/python:$PYTHONPATH，是一次性修改，每次重启都要）

可以使用echo $PYTHONPATH查看Python环境变量

在ssd/caffe目录下python ./examples/ssd/ssd_pascal.py运行

出现了各种错误，需要修改几个.cpp之后重新编译一下caffe

依稀记得的有：

math_functions.cpp:250] Check failed: a <= b <0 vs -1.19209e-007>

修改方案按这个http://www.mamicode.com/info-detail-1869191.html，注释掉mat_functions.cpp里的CHACK_LE(a,b)，重新编译，OK

一个特麻烦的，大概是数据的错误，大概长这样(好像最开始是2 vs. 0，改了哪里变成12了）：

Check failed: mdb_status == 0 (12 vs. 0) Cannot allocate memory

按搜到的方案，有说是lmdb数据错误，修改create_data.sh生成leveldb格式数据训练，报错

删除VOC2012所有内容，重新create_data.sh生成只有VOC2007的训练验证集

按http://blog.csdn.net/apsvvfb/article/details/50885335各种修改/src/caffe/util里的db_lmdb.cpp源码

MDB_CHECK(mdb_env_set_mapsize(mdb_env, 1024>>20); //等等各种，这里试了1GB,2GB，都不行

最后又改回原来的new_size（不懂什么意思，是说根据机器当前状态分配吗？）重新编译

总之各种试，依稀记得当晚python ./examples/ssd/ssd_pascal.py最后一次报错是一个关于snapshot的，好像是说上次训练中断时候记录的数据不存在，太晚了，睡了，没管它

隔了一天，今天下午重新运行，居然似乎是在训练了，小电脑风扇又开始呼呼呼的响，控制台状态：

I1023 13:20:48.597777  2746 net.cpp:150] Setting up conv7_2_mbox_conf
I1023 13:20:48.597807  2746 net.cpp:157] Top shape: 8 126 5 5 (25200)
I1023 13:20:48.597818  2746 net.cpp:165] Memory required for data: 1997809664
I1023 13:20:48.597837  2746 layer_factory.hpp:77] Creating layer conv7_2_mbox_conf_perm
I1023 13:20:48.597854  2746 net.cpp:100] Creating Layer conv7_2_mbox_conf_perm
I1023 13:20:48.597867  2746 net.cpp:434] conv7_2_mbox_conf_perm <- conv7_2_mbox_conf
I1023 13:20:48.597882  2746 net.cpp:408] conv7_2_mbox_conf_perm -> conv7_2_mbox_conf_perm
I1023 13:20:48.597908  2746 net.cpp:150] Setting up conv7_2_mbox_conf_perm
I1023 13:20:48.597926  2746 net.cpp:157] Top shape: 8 5 5 126 (25200)
I1023 13:20:48.597936  2746 net.cpp:165] Memory required for data: 1997910464
I1023 13:20:48.597947  2746 layer_factory.hpp:77] Creating layer conv7_2_mbox_conf_flat
I1023 13:20:48.597970  2746 net.cpp:100] Creating Layer conv7_2_mbox_conf_flat
I1023 13:20:48.597985  2746 net.cpp:434] conv7_2_mbox_conf_flat <- conv7_2_mbox_conf_perm
I1023 13:20:48.598001  2746 net.cpp:408] conv7_2_mbox_conf_flat -> conv7_2_mbox_conf_flat
I1023 13:20:48.598022  2746 net.cpp:150] Setting up conv7_2_mbox_conf_flat
I1023 13:20:48.598038  2746 net.cpp:157] Top shape: 8 3150 (25200)
I1023 13:20:48.598048  2746 net.cpp:165] Memory required for data: 1998011264
I1023 13:20:48.598059  2746 layer_factory.hpp:77] Creating layer conv7_2_mbox_priorbox
I1023 13:20:48.598074  2746 net.cpp:100] Creating Layer conv7_2_mbox_priorbox
I1023 13:20:48.598088  2746 net.cpp:434] conv7_2_mbox_priorbox <- conv7_2_conv7_2_relu_0_split_3
I1023 13:20:48.598101  2746 net.cpp:434] conv7_2_mbox_priorbox <- data_data_0_split_4
I1023 13:20:48.598117  2746 net.cpp:408] conv7_2_mbox_priorbox -> conv7_2_mbox_priorbox
I1023 13:20:48.598140  2746 net.cpp:150] Setting up conv7_2_mbox_priorbox
I1023 13:20:48.598156  2746 net.cpp:157] Top shape: 1 2 600 (1200)
I1023 13:20:48.598168  2746 net.cpp:165] Memory required for data: 1998016064
I1023 13:20:48.598181  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_loc
I1023 13:20:48.598199  2746 net.cpp:100] Creating Layer conv8_2_mbox_loc
I1023 13:20:48.598213  2746 net.cpp:434] conv8_2_mbox_loc <- conv8_2_conv8_2_relu_0_split_1
I1023 13:20:48.598233  2746 net.cpp:408] conv8_2_mbox_loc -> conv8_2_mbox_loc
I1023 13:20:48.599309  2746 net.cpp:150] Setting up conv8_2_mbox_loc
I1023 13:20:48.599329  2746 net.cpp:157] Top shape: 8 16 3 3 (1152)
I1023 13:20:48.599342  2746 net.cpp:165] Memory required for data: 1998020672
I1023 13:20:48.599380  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_loc_perm
I1023 13:20:48.599397  2746 net.cpp:100] Creating Layer conv8_2_mbox_loc_perm
I1023 13:20:48.599411  2746 net.cpp:434] conv8_2_mbox_loc_perm <- conv8_2_mbox_loc
I1023 13:20:48.599428  2746 net.cpp:408] conv8_2_mbox_loc_perm -> conv8_2_mbox_loc_perm
I1023 13:20:48.599457  2746 net.cpp:150] Setting up conv8_2_mbox_loc_perm
I1023 13:20:48.599473  2746 net.cpp:157] Top shape: 8 3 3 16 (1152)
I1023 13:20:48.599485  2746 net.cpp:165] Memory required for data: 1998025280
I1023 13:20:48.599496  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_loc_flat
I1023 13:20:48.599529  2746 net.cpp:100] Creating Layer conv8_2_mbox_loc_flat
I1023 13:20:48.599541  2746 net.cpp:434] conv8_2_mbox_loc_flat <- conv8_2_mbox_loc_perm
I1023 13:20:48.599556  2746 net.cpp:408] conv8_2_mbox_loc_flat -> conv8_2_mbox_loc_flat
I1023 13:20:48.599575  2746 net.cpp:150] Setting up conv8_2_mbox_loc_flat
I1023 13:20:48.599591  2746 net.cpp:157] Top shape: 8 144 (1152)
I1023 13:20:48.599603  2746 net.cpp:165] Memory required for data: 1998029888
I1023 13:20:48.599614  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_conf
I1023 13:20:48.599638  2746 net.cpp:100] Creating Layer conv8_2_mbox_conf
I1023 13:20:48.599653  2746 net.cpp:434] conv8_2_mbox_conf <- conv8_2_conv8_2_relu_0_split_2
I1023 13:20:48.599671  2746 net.cpp:408] conv8_2_mbox_conf -> conv8_2_mbox_conf
I1023 13:20:48.605247  2746 net.cpp:150] Setting up conv8_2_mbox_conf
I1023 13:20:48.605273  2746 net.cpp:157] Top shape: 8 84 3 3 (6048)
I1023 13:20:48.605283  2746 net.cpp:165] Memory required for data: 1998054080
I1023 13:20:48.605300  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_conf_perm
I1023 13:20:48.605319  2746 net.cpp:100] Creating Layer conv8_2_mbox_conf_perm
I1023 13:20:48.605334  2746 net.cpp:434] conv8_2_mbox_conf_perm <- conv8_2_mbox_conf
I1023 13:20:48.605348  2746 net.cpp:408] conv8_2_mbox_conf_perm -> conv8_2_mbox_conf_perm
I1023 13:20:48.605379  2746 net.cpp:150] Setting up conv8_2_mbox_conf_perm
I1023 13:20:48.605396  2746 net.cpp:157] Top shape: 8 3 3 84 (6048)
I1023 13:20:48.605408  2746 net.cpp:165] Memory required for data: 1998078272
I1023 13:20:48.605419  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_conf_flat
I1023 13:20:48.605433  2746 net.cpp:100] Creating Layer conv8_2_mbox_conf_flat
I1023 13:20:48.605446  2746 net.cpp:434] conv8_2_mbox_conf_flat <- conv8_2_mbox_conf_perm
I1023 13:20:48.605461  2746 net.cpp:408] conv8_2_mbox_conf_flat -> conv8_2_mbox_conf_flat
I1023 13:20:48.605480  2746 net.cpp:150] Setting up conv8_2_mbox_conf_flat
I1023 13:20:48.605496  2746 net.cpp:157] Top shape: 8 756 (6048)
I1023 13:20:48.605509  2746 net.cpp:165] Memory required for data: 1998102464
I1023 13:20:48.605520  2746 layer_factory.hpp:77] Creating layer conv8_2_mbox_priorbox
I1023 13:20:48.605535  2746 net.cpp:100] Creating Layer conv8_2_mbox_priorbox
I1023 13:20:48.605548  2746 net.cpp:434] conv8_2_mbox_priorbox <- conv8_2_conv8_2_relu_0_split_3
I1023 13:20:48.605561  2746 net.cpp:434] conv8_2_mbox_priorbox <- data_data_0_split_5
I1023 13:20:48.605579  2746 net.cpp:408] conv8_2_mbox_priorbox -> conv8_2_mbox_priorbox
I1023 13:20:48.605602  2746 net.cpp:150] Setting up conv8_2_mbox_priorbox
I1023 13:20:48.605618  2746 net.cpp:157] Top shape: 1 2 144 (288)
I1023 13:20:48.605630  2746 net.cpp:165] Memory required for data: 1998103616
I1023 13:20:48.605641  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_loc
I1023 13:20:48.605664  2746 net.cpp:100] Creating Layer conv9_2_mbox_loc
I1023 13:20:48.605677  2746 net.cpp:434] conv9_2_mbox_loc <- conv9_2_conv9_2_relu_0_split_0
I1023 13:20:48.605693  2746 net.cpp:408] conv9_2_mbox_loc -> conv9_2_mbox_loc
I1023 13:20:48.606783  2746 net.cpp:150] Setting up conv9_2_mbox_loc
I1023 13:20:48.606804  2746 net.cpp:157] Top shape: 8 16 1 1 (128)
I1023 13:20:48.606817  2746 net.cpp:165] Memory required for data: 1998104128
I1023 13:20:48.606833  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_loc_perm
I1023 13:20:48.606850  2746 net.cpp:100] Creating Layer conv9_2_mbox_loc_perm
I1023 13:20:48.606863  2746 net.cpp:434] conv9_2_mbox_loc_perm <- conv9_2_mbox_loc
I1023 13:20:48.606881  2746 net.cpp:408] conv9_2_mbox_loc_perm -> conv9_2_mbox_loc_perm
I1023 13:20:48.606907  2746 net.cpp:150] Setting up conv9_2_mbox_loc_perm
I1023 13:20:48.606925  2746 net.cpp:157] Top shape: 8 1 1 16 (128)
I1023 13:20:48.606936  2746 net.cpp:165] Memory required for data: 1998104640
I1023 13:20:48.606947  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_loc_flat
I1023 13:20:48.606961  2746 net.cpp:100] Creating Layer conv9_2_mbox_loc_flat
I1023 13:20:48.606973  2746 net.cpp:434] conv9_2_mbox_loc_flat <- conv9_2_mbox_loc_perm
I1023 13:20:48.607007  2746 net.cpp:408] conv9_2_mbox_loc_flat -> conv9_2_mbox_loc_flat
I1023 13:20:48.607028  2746 net.cpp:150] Setting up conv9_2_mbox_loc_flat
I1023 13:20:48.607044  2746 net.cpp:157] Top shape: 8 16 (128)
I1023 13:20:48.607056  2746 net.cpp:165] Memory required for data: 1998105152
I1023 13:20:48.607067  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_conf
I1023 13:20:48.607089  2746 net.cpp:100] Creating Layer conv9_2_mbox_conf
I1023 13:20:48.607102  2746 net.cpp:434] conv9_2_mbox_conf <- conv9_2_conv9_2_relu_0_split_1
I1023 13:20:48.607123  2746 net.cpp:408] conv9_2_mbox_conf -> conv9_2_mbox_conf
I1023 13:20:48.613224  2746 net.cpp:150] Setting up conv9_2_mbox_conf
I1023 13:20:48.613458  2746 net.cpp:157] Top shape: 8 84 1 1 (672)
I1023 13:20:48.613528  2746 net.cpp:165] Memory required for data: 1998107840
I1023 13:20:48.613605  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_conf_perm
I1023 13:20:48.613675  2746 net.cpp:100] Creating Layer conv9_2_mbox_conf_perm
I1023 13:20:48.613711  2746 net.cpp:434] conv9_2_mbox_conf_perm <- conv9_2_mbox_conf
I1023 13:20:48.613756  2746 net.cpp:408] conv9_2_mbox_conf_perm -> conv9_2_mbox_conf_perm
I1023 13:20:48.613818  2746 net.cpp:150] Setting up conv9_2_mbox_conf_perm
I1023 13:20:48.613857  2746 net.cpp:157] Top shape: 8 1 1 84 (672)
I1023 13:20:48.613888  2746 net.cpp:165] Memory required for data: 1998110528
I1023 13:20:48.613919  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_conf_flat
I1023 13:20:48.613955  2746 net.cpp:100] Creating Layer conv9_2_mbox_conf_flat
I1023 13:20:48.613986  2746 net.cpp:434] conv9_2_mbox_conf_flat <- conv9_2_mbox_conf_perm
I1023 13:20:48.614022  2746 net.cpp:408] conv9_2_mbox_conf_flat -> conv9_2_mbox_conf_flat
I1023 13:20:48.614068  2746 net.cpp:150] Setting up conv9_2_mbox_conf_flat
I1023 13:20:48.614104  2746 net.cpp:157] Top shape: 8 84 (672)
I1023 13:20:48.614135  2746 net.cpp:165] Memory required for data: 1998113216
I1023 13:20:48.614168  2746 layer_factory.hpp:77] Creating layer conv9_2_mbox_priorbox
I1023 13:20:48.614207  2746 net.cpp:100] Creating Layer conv9_2_mbox_priorbox
I1023 13:20:48.614243  2746 net.cpp:434] conv9_2_mbox_priorbox <- conv9_2_conv9_2_relu_0_split_2
I1023 13:20:48.614276  2746 net.cpp:434] conv9_2_mbox_priorbox <- data_data_0_split_6
I1023 13:20:48.614320  2746 net.cpp:408] conv9_2_mbox_priorbox -> conv9_2_mbox_priorbox
I1023 13:20:48.614364  2746 net.cpp:150] Setting up conv9_2_mbox_priorbox
I1023 13:20:48.614401  2746 net.cpp:157] Top shape: 1 2 16 (32)
I1023 13:20:48.614431  2746 net.cpp:165] Memory required for data: 1998113344
I1023 13:20:48.614462  2746 layer_factory.hpp:77] Creating layer mbox_loc
I1023 13:20:48.614503  2746 net.cpp:100] Creating Layer mbox_loc
I1023 13:20:48.614537  2746 net.cpp:434] mbox_loc <- conv4_3_norm_mbox_loc_flat
I1023 13:20:48.614570  2746 net.cpp:434] mbox_loc <- fc7_mbox_loc_flat
I1023 13:20:48.614605  2746 net.cpp:434] mbox_loc <- conv6_2_mbox_loc_flat
I1023 13:20:48.614640  2746 net.cpp:434] mbox_loc <- conv7_2_mbox_loc_flat
I1023 13:20:48.614672  2746 net.cpp:434] mbox_loc <- conv8_2_mbox_loc_flat
I1023 13:20:48.614711  2746 net.cpp:434] mbox_loc <- conv9_2_mbox_loc_flat
I1023 13:20:48.614761  2746 net.cpp:408] mbox_loc -> mbox_loc
I1023 13:20:48.614820  2746 net.cpp:150] Setting up mbox_loc
I1023 13:20:48.614881  2746 net.cpp:157] Top shape: 8 34928 (279424)
I1023 13:20:48.614918  2746 net.cpp:165] Memory required for data: 1999231040
I1023 13:20:48.614958  2746 layer_factory.hpp:77] Creating layer mbox_conf
I1023 13:20:48.615005  2746 net.cpp:100] Creating Layer mbox_conf
I1023 13:20:48.615046  2746 net.cpp:434] mbox_conf <- conv4_3_norm_mbox_conf_flat
I1023 13:20:48.615084  2746 net.cpp:434] mbox_conf <- fc7_mbox_conf_flat
I1023 13:20:48.615118  2746 net.cpp:434] mbox_conf <- conv6_2_mbox_conf_flat
I1023 13:20:48.615152  2746 net.cpp:434] mbox_conf <- conv7_2_mbox_conf_flat
I1023 13:20:48.615185  2746 net.cpp:434] mbox_conf <- conv8_2_mbox_conf_flat
I1023 13:20:48.615231  2746 net.cpp:434] mbox_conf <- conv9_2_mbox_conf_flat
I1023 13:20:48.615284  2746 net.cpp:408] mbox_conf -> mbox_conf
I1023 13:20:48.615327  2746 net.cpp:150] Setting up mbox_conf
I1023 13:20:48.615363  2746 net.cpp:157] Top shape: 8 183372 (1466976)
I1023 13:20:48.615393  2746 net.cpp:165] Memory required for data: 2005098944
I1023 13:20:48.615424  2746 layer_factory.hpp:77] Creating layer mbox_priorbox
I1023 13:20:48.615460  2746 net.cpp:100] Creating Layer mbox_priorbox
I1023 13:20:48.615491  2746 net.cpp:434] mbox_priorbox <- conv4_3_norm_mbox_priorbox
I1023 13:20:48.615525  2746 net.cpp:434] mbox_priorbox <- fc7_mbox_priorbox
I1023 13:20:48.615557  2746 net.cpp:434] mbox_priorbox <- conv6_2_mbox_priorbox
I1023 13:20:48.615592  2746 net.cpp:434] mbox_priorbox <- conv7_2_mbox_priorbox
I1023 13:20:48.615623  2746 net.cpp:434] mbox_priorbox <- conv8_2_mbox_priorbox
I1023 13:20:48.615656  2746 net.cpp:434] mbox_priorbox <- conv9_2_mbox_priorbox
I1023 13:20:48.615690  2746 net.cpp:408] mbox_priorbox -> mbox_priorbox
I1023 13:20:48.615731  2746 net.cpp:150] Setting up mbox_priorbox
I1023 13:20:48.615766  2746 net.cpp:157] Top shape: 1 2 34928 (69856)
I1023 13:20:48.615795  2746 net.cpp:165] Memory required for data: 2005378368
I1023 13:20:48.615828  2746 layer_factory.hpp:77] Creating layer mbox_conf_reshape
I1023 13:20:48.615869  2746 net.cpp:100] Creating Layer mbox_conf_reshape
I1023 13:20:48.615902  2746 net.cpp:434] mbox_conf_reshape <- mbox_conf
I1023 13:20:48.615942  2746 net.cpp:408] mbox_conf_reshape -> mbox_conf_reshape
I1023 13:20:48.616000  2746 net.cpp:150] Setting up mbox_conf_reshape
I1023 13:20:48.616037  2746 net.cpp:157] Top shape: 8 8732 21 (1466976)
I1023 13:20:48.616067  2746 net.cpp:165] Memory required for data: 2011246272
I1023 13:20:48.616097  2746 layer_factory.hpp:77] Creating layer mbox_conf_softmax
I1023 13:20:48.616132  2746 net.cpp:100] Creating Layer mbox_conf_softmax
I1023 13:20:48.616163  2746 net.cpp:434] mbox_conf_softmax <- mbox_conf_reshape
I1023 13:20:48.616201  2746 net.cpp:408] mbox_conf_softmax -> mbox_conf_softmax
I1023 13:20:48.616256  2746 net.cpp:150] Setting up mbox_conf_softmax
I1023 13:20:48.616297  2746 net.cpp:157] Top shape: 8 8732 21 (1466976)
I1023 13:20:48.616333  2746 net.cpp:165] Memory required for data: 2017114176
I1023 13:20:48.616394  2746 layer_factory.hpp:77] Creating layer mbox_conf_flatten
I1023 13:20:48.616442  2746 net.cpp:100] Creating Layer mbox_conf_flatten
I1023 13:20:48.616477  2746 net.cpp:434] mbox_conf_flatten <- mbox_conf_softmax
I1023 13:20:48.616516  2746 net.cpp:408] mbox_conf_flatten -> mbox_conf_flatten
I1023 13:20:48.616564  2746 net.cpp:150] Setting up mbox_conf_flatten
I1023 13:20:48.616603  2746 net.cpp:157] Top shape: 8 183372 (1466976)
I1023 13:20:48.616634  2746 net.cpp:165] Memory required for data: 2022982080
I1023 13:20:48.616668  2746 layer_factory.hpp:77] Creating layer detection_out
I1023 13:20:48.616753  2746 net.cpp:100] Creating Layer detection_out
I1023 13:20:48.616796  2746 net.cpp:434] detection_out <- mbox_loc
I1023 13:20:48.616833  2746 net.cpp:434] detection_out <- mbox_conf_flatten
I1023 13:20:48.616868  2746 net.cpp:434] detection_out <- mbox_priorbox
I1023 13:20:48.616914  2746 net.cpp:408] detection_out -> detection_out
I1023 13:20:48.679901  2746 net.cpp:150] Setting up detection_out
I1023 13:20:48.679949  2746 net.cpp:157] Top shape: 1 1 1 7 (7)
I1023 13:20:48.680019  2746 net.cpp:165] Memory required for data: 2022982108
I1023 13:20:48.680063  2746 layer_factory.hpp:77] Creating layer detection_eval
I1023 13:20:48.680110  2746 net.cpp:100] Creating Layer detection_eval
I1023 13:20:48.680150  2746 net.cpp:434] detection_eval <- detection_out
I1023 13:20:48.680187  2746 net.cpp:434] detection_eval <- label
I1023 13:20:48.680227  2746 net.cpp:408] detection_eval -> detection_eval
I1023 13:20:48.683207  2746 net.cpp:150] Setting up detection_eval
I1023 13:20:48.683255  2746 net.cpp:157] Top shape: 1 1 21 5 (105)
I1023 13:20:48.683290  2746 net.cpp:165] Memory required for data: 2022982528
I1023 13:20:48.683334  2746 net.cpp:228] detection_eval does not need backward computation.
I1023 13:20:48.683385  2746 net.cpp:228] detection_out does not need backward computation.
I1023 13:20:48.683421  2746 net.cpp:228] mbox_conf_flatten does not need backward computation.
I1023 13:20:48.683455  2746 net.cpp:228] mbox_conf_softmax does not need backward computation.
I1023 13:20:48.683488  2746 net.cpp:228] mbox_conf_reshape does not need backward computation.
I1023 13:20:48.683521  2746 net.cpp:228] mbox_priorbox does not need backward computation.
I1023 13:20:48.683557  2746 net.cpp:228] mbox_conf does not need backward computation.
I1023 13:20:48.683594  2746 net.cpp:228] mbox_loc does not need backward computation.
I1023 13:20:48.683630  2746 net.cpp:228] conv9_2_mbox_priorbox does not need backward computation.
I1023 13:20:48.683665  2746 net.cpp:228] conv9_2_mbox_conf_flat does not need backward computation.
I1023 13:20:48.683698  2746 net.cpp:228] conv9_2_mbox_conf_perm does not need backward computation.
I1023 13:20:48.683732  2746 net.cpp:228] conv9_2_mbox_conf does not need backward computation.
I1023 13:20:48.683765  2746 net.cpp:228] conv9_2_mbox_loc_flat does not need backward computation.
I1023 13:20:48.683799  2746 net.cpp:228] conv9_2_mbox_loc_perm does not need backward computation.
I1023 13:20:48.683832  2746 net.cpp:228] conv9_2_mbox_loc does not need backward computation.
I1023 13:20:48.683866  2746 net.cpp:228] conv8_2_mbox_priorbox does not need backward computation.
I1023 13:20:48.683900  2746 net.cpp:228] conv8_2_mbox_conf_flat does not need backward computation.
I1023 13:20:48.683934  2746 net.cpp:228] conv8_2_mbox_conf_perm does not need backward computation.
I1023 13:20:48.683969  2746 net.cpp:228] conv8_2_mbox_conf does not need backward computation.
I1023 13:20:48.684001  2746 net.cpp:228] conv8_2_mbox_loc_flat does not need backward computation.
I1023 13:20:48.684034  2746 net.cpp:228] conv8_2_mbox_loc_perm does not need backward computation.
I1023 13:20:48.684068  2746 net.cpp:228] conv8_2_mbox_loc does not need backward computation.
I1023 13:20:48.684103  2746 net.cpp:228] conv7_2_mbox_priorbox does not need backward computation.
I1023 13:20:48.684136  2746 net.cpp:228] conv7_2_mbox_conf_flat does not need backward computation.
I1023 13:20:48.684170  2746 net.cpp:228] conv7_2_mbox_conf_perm does not need backward computation.
I1023 13:20:48.684203  2746 net.cpp:228] conv7_2_mbox_conf does not need backward computation.
I1023 13:20:48.684237  2746 net.cpp:228] conv7_2_mbox_loc_flat does not need backward computation.
I1023 13:20:48.684269  2746 net.cpp:228] conv7_2_mbox_loc_perm does not need backward computation.
I1023 13:20:48.684303  2746 net.cpp:228] conv7_2_mbox_loc does not need backward computation.
I1023 13:20:48.684336  2746 net.cpp:228] conv6_2_mbox_priorbox does not need backward computation.
I1023 13:20:48.684391  2746 net.cpp:228] conv6_2_mbox_conf_flat does not need backward computation.
I1023 13:20:48.684427  2746 net.cpp:228] conv6_2_mbox_conf_perm does not need backward computation.
I1023 13:20:48.684463  2746 net.cpp:228] conv6_2_mbox_conf does not need backward computation.
I1023 13:20:48.684496  2746 net.cpp:228] conv6_2_mbox_loc_flat does not need backward computation.
I1023 13:20:48.684530  2746 net.cpp:228] conv6_2_mbox_loc_perm does not need backward computation.
I1023 13:20:48.684562  2746 net.cpp:228] conv6_2_mbox_loc does not need backward computation.
I1023 13:20:48.684594  2746 net.cpp:228] fc7_mbox_priorbox does not need backward computation.
I1023 13:20:48.684628  2746 net.cpp:228] fc7_mbox_conf_flat does not need backward computation.
I1023 13:20:48.684659  2746 net.cpp:228] fc7_mbox_conf_perm does not need backward computation.
I1023 13:20:48.684690  2746 net.cpp:228] fc7_mbox_conf does not need backward computation.
I1023 13:20:48.684723  2746 net.cpp:228] fc7_mbox_loc_flat does not need backward computation.
I1023 13:20:48.684754  2746 net.cpp:228] fc7_mbox_loc_perm does not need backward computation.
I1023 13:20:48.684787  2746 net.cpp:228] fc7_mbox_loc does not need backward computation.
I1023 13:20:48.684836  2746 net.cpp:228] conv4_3_norm_mbox_priorbox does not need backward computation.
I1023 13:20:48.684871  2746 net.cpp:228] conv4_3_norm_mbox_conf_flat does not need backward computation.
I1023 13:20:48.684903  2746 net.cpp:228] conv4_3_norm_mbox_conf_perm does not need backward computation.
I1023 13:20:48.684936  2746 net.cpp:228] conv4_3_norm_mbox_conf does not need backward computation.
I1023 13:20:48.684967  2746 net.cpp:228] conv4_3_norm_mbox_loc_flat does not need backward computation.
I1023 13:20:48.684998  2746 net.cpp:228] conv4_3_norm_mbox_loc_perm does not need backward computation.
I1023 13:20:48.685030  2746 net.cpp:228] conv4_3_norm_mbox_loc does not need backward computation.
I1023 13:20:48.685063  2746 net.cpp:228] conv4_3_norm_conv4_3_norm_0_split does not need backward computation.
I1023 13:20:48.685096  2746 net.cpp:228] conv4_3_norm does not need backward computation.
I1023 13:20:48.685128  2746 net.cpp:228] conv9_2_conv9_2_relu_0_split does not need backward computation.
I1023 13:20:48.685161  2746 net.cpp:228] conv9_2_relu does not need backward computation.
I1023 13:20:48.685192  2746 net.cpp:228] conv9_2 does not need backward computation.
I1023 13:20:48.685223  2746 net.cpp:228] conv9_1_relu does not need backward computation.
I1023 13:20:48.685253  2746 net.cpp:228] conv9_1 does not need backward computation.
I1023 13:20:48.685284  2746 net.cpp:228] conv8_2_conv8_2_relu_0_split does not need backward computation.
I1023 13:20:48.685317  2746 net.cpp:228] conv8_2_relu does not need backward computation.
I1023 13:20:48.685348  2746 net.cpp:228] conv8_2 does not need backward computation.
I1023 13:20:48.685379  2746 net.cpp:228] conv8_1_relu does not need backward computation.
I1023 13:20:48.685410  2746 net.cpp:228] conv8_1 does not need backward computation.
I1023 13:20:48.685441  2746 net.cpp:228] conv7_2_conv7_2_relu_0_split does not need backward computation.
I1023 13:20:48.685472  2746 net.cpp:228] conv7_2_relu does not need backward computation.
I1023 13:20:48.685503  2746 net.cpp:228] conv7_2 does not need backward computation.
I1023 13:20:48.685534  2746 net.cpp:228] conv7_1_relu does not need backward computation.
I1023 13:20:48.685565  2746 net.cpp:228] conv7_1 does not need backward computation.
I1023 13:20:48.685598  2746 net.cpp:228] conv6_2_conv6_2_relu_0_split does not need backward computation.
I1023 13:20:48.685631  2746 net.cpp:228] conv6_2_relu does not need backward computation.
I1023 13:20:48.685660  2746 net.cpp:228] conv6_2 does not need backward computation.
I1023 13:20:48.685691  2746 net.cpp:228] conv6_1_relu does not need backward computation.
I1023 13:20:48.685722  2746 net.cpp:228] conv6_1 does not need backward computation.
I1023 13:20:48.685756  2746 net.cpp:228] fc7_relu7_0_split does not need backward computation.
I1023 13:20:48.685787  2746 net.cpp:228] relu7 does not need backward computation.
I1023 13:20:48.685818  2746 net.cpp:228] fc7 does not need backward computation.
I1023 13:20:48.685849  2746 net.cpp:228] relu6 does not need backward computation.
I1023 13:20:48.685883  2746 net.cpp:228] fc6 does not need backward computation.
I1023 13:20:48.685914  2746 net.cpp:228] pool5 does not need backward computation.
I1023 13:20:48.685945  2746 net.cpp:228] relu5_3 does not need backward computation.
I1023 13:20:48.685976  2746 net.cpp:228] conv5_3 does not need backward computation.
I1023 13:20:48.686007  2746 net.cpp:228] relu5_2 does not need backward computation.
I1023 13:20:48.686038  2746 net.cpp:228] conv5_2 does not need backward computation.
I1023 13:20:48.686069  2746 net.cpp:228] relu5_1 does not need backward computation.
I1023 13:20:48.686100  2746 net.cpp:228] conv5_1 does not need backward computation.
I1023 13:20:48.686131  2746 net.cpp:228] pool4 does not need backward computation.
I1023 13:20:48.686162  2746 net.cpp:228] conv4_3_relu4_3_0_split does not need backward computation.
I1023 13:20:48.686194  2746 net.cpp:228] relu4_3 does not need backward computation.
I1023 13:20:48.686233  2746 net.cpp:228] conv4_3 does not need backward computation.
I1023 13:20:48.686275  2746 net.cpp:228] relu4_2 does not need backward computation.
I1023 13:20:48.686306  2746 net.cpp:228] conv4_2 does not need backward computation.
I1023 13:20:48.686337  2746 net.cpp:228] relu4_1 does not need backward computation.
I1023 13:20:48.686367  2746 net.cpp:228] conv4_1 does not need backward computation.
I1023 13:20:48.686398  2746 net.cpp:228] pool3 does not need backward computation.
I1023 13:20:48.686429  2746 net.cpp:228] relu3_3 does not need backward computation.
I1023 13:20:48.686460  2746 net.cpp:228] conv3_3 does not need backward computation.
I1023 13:20:48.686491  2746 net.cpp:228] relu3_2 does not need backward computation.
I1023 13:20:48.686522  2746 net.cpp:228] conv3_2 does not need backward computation.
I1023 13:20:48.686553  2746 net.cpp:228] relu3_1 does not need backward computation.
I1023 13:20:48.686583  2746 net.cpp:228] conv3_1 does not need backward computation.
I1023 13:20:48.686614  2746 net.cpp:228] pool2 does not need backward computation.
I1023 13:20:48.686645  2746 net.cpp:228] relu2_2 does not need backward computation.
I1023 13:20:48.686676  2746 net.cpp:228] conv2_2 does not need backward computation.
I1023 13:20:48.686705  2746 net.cpp:228] relu2_1 does not need backward computation.
I1023 13:20:48.686736  2746 net.cpp:228] conv2_1 does not need backward computation.
I1023 13:20:48.686767  2746 net.cpp:228] pool1 does not need backward computation.
I1023 13:20:48.686799  2746 net.cpp:228] relu1_2 does not need backward computation.
I1023 13:20:48.686828  2746 net.cpp:228] conv1_2 does not need backward computation.
I1023 13:20:48.686859  2746 net.cpp:228] relu1_1 does not need backward computation.
I1023 13:20:48.686889  2746 net.cpp:228] conv1_1 does not need backward computation.
I1023 13:20:48.686923  2746 net.cpp:228] data_data_0_split does not need backward computation.
I1023 13:20:48.686954  2746 net.cpp:228] data does not need backward computation.
I1023 13:20:48.686983  2746 net.cpp:270] This network produces output detection_eval
I1023 13:20:48.687135  2746 net.cpp:283] Network initialization done.
I1023 13:20:48.687716  2746 solver.cpp:75] Solver scaffolding done.
I1023 13:20:48.688022  2746 caffe.cpp:155] Finetuning from models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel
I1023 13:20:49.964323  2746 upgrade_proto.cpp:67] Attempting to upgrade input file specified using deprecated input fields: models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel
I1023 13:20:49.964531  2746 upgrade_proto.cpp:70] Successfully upgraded file specified using deprecated input fields.
W1023 13:20:49.964645  2746 upgrade_proto.cpp:72] Note that future Caffe releases will only support input layers and not input fields.
I1023 13:20:50.016149  2746 net.cpp:761] Ignoring source layer drop6
I1023 13:20:50.017864  2746 net.cpp:761] Ignoring source layer drop7
I1023 13:20:50.017911  2746 net.cpp:761] Ignoring source layer fc8
I1023 13:20:50.017922  2746 net.cpp:761] Ignoring source layer prob
I1023 13:20:50.366048  2746 upgrade_proto.cpp:67] Attempting to upgrade input file specified using deprecated input fields: models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel
I1023 13:20:50.366091  2746 upgrade_proto.cpp:70] Successfully upgraded file specified using deprecated input fields.
W1023 13:20:50.366102  2746 upgrade_proto.cpp:72] Note that future Caffe releases will only support input layers and not input fields.
I1023 13:20:50.399529  2746 net.cpp:761] Ignoring source layer drop6
I1023 13:20:50.402957  2746 net.cpp:761] Ignoring source layer drop7
I1023 13:20:50.403225  2746 net.cpp:761] Ignoring source layer fc8
I1023 13:20:50.403296  2746 net.cpp:761] Ignoring source layer prob
I1023 13:20:50.404901  2746 caffe.cpp:251] Starting Optimization
I1023 13:20:50.404959  2746 solver.cpp:294] Solving VGG_VOC0712_SSD_300x300_train
I1023 13:20:50.404994  2746 solver.cpp:295] Learning Rate Policy: multistep
I1023 13:20:50.461501  2746 blocking_queue.cpp:50] Data layer prefetch queue empty
   

top一下，觉得五年前写毕业论文时候买的学生笔记本真是良心制造，联想G470，4核core-i3，2G内存。

然后整个下午，一直到傍晚，都持续在这个状态，觉得不太正常，看ssd_pscal.py代码：

# Directory which stores the job script and log file.
job_dir = "jobs/VGGNet/VOC0712/{}".format(job_name)

发现.log也是停留在这里就没了

I1023 13:20:50.404901  2746 caffe.cpp:251] Starting Optimization
I1023 13:20:50.404959  2746 solver.cpp:294] Solving VGG_VOC0712_SSD_300x300_train
I1023 13:20:50.404994  2746 solver.cpp:295] Learning Rate Policy: multistep
I1023 13:20:50.461501  2746 blocking_queue.cpp:50] Data layer prefetch queue empty

之前曾经跑过一次ssd_pascal_ori.py，记录的log是：

I1021 22:33:31.553313  4519 solver.cpp:294] Solving VGG_VOC0712_SSD_300x300_orig_train
I1021 22:33:31.553562  4519 solver.cpp:295] Learning Rate Policy: multistep
F1021 22:36:47.937511  4519 syncedmem.hpp:25] Check failed: *ptr host allocation of size 40934400 failed
*** Check failure stack trace: ***
    @ 0xb723eefc  (unknown)
    @ 0xb723ee13  (unknown)
    @ 0xb723e85f  (unknown)
    @ 0xb72418b0  (unknown)
    @ 0xb7640e5d  caffe::SyncedMemory::mutable_cpu_data()
    @ 0xb746c763  caffe::Blob<>::mutable_cpu_diff()
    @ 0xb74df5d8  caffe::PoolingLayer<>::Backward_cpu()
    @ 0xb75c5a9c  caffe::Net<>::BackwardFromTo()
    @ 0xb75c5bc6  caffe::Net<>::Backward()
    @ 0xb745ebbc  caffe::Solver<>::Step()
    @ 0xb745f395  caffe::Solver<>::Solve()
    @  0x804fc5d  train()
    @  0x804cfd0  main
    @ 0xb6e70a83  (unknown)
    @  0x804d8e3  (unknown)

搜到的原因基本上是电脑配置不够，像这样：http://blog.csdn.net/genius_zz/article/details/54348232

看来硬件还是不行，CPU上跑caffe可能玩玩还行，真正做实验训练什么的还是得找个GPU。

综上，觉得真要用CNN做点东西，不只是学学源码什么的，带GPU的硬件平台+Linux OS+Caffe比较合适。

 转机：

在http://www.cnblogs.com/neopenx/p/5269852.html这里看到

灵机一动，既然用虚拟内存，是不是可以扩大交换分区呢？

照这里：http://www.cnblogs.com/ericsun/archive/2013/08/17/3263739.html

之前是2G物理内存+2G虚拟内存，扩大成2G+3G，果然开始训练了，不过好景不长~

I1025 12:11:56.203306  2755 caffe.cpp:251] Starting Optimization
I1025 12:11:56.203392  2755 solver.cpp:294] Solving VGG_VOC0712_SSD_300x300_train
I1025 12:11:56.203433  2755 solver.cpp:295] Learning Rate Policy: multistep
I1025 12:13:11.956351  2755 solver.cpp:243] Iteration 0, loss = 29.3416
I1025 12:13:12.201416  2755 solver.cpp:259]     Train net output #0: mbox_loss = 29.3416 (* 1 = 29.3416 loss)
I1025 12:13:12.268118  2755 sgd_solver.cpp:138] Iteration 0, lr = 0.001
I1025 12:13:14.233126  2755 blocking_queue.cpp:50] Data layer prefetch queue empty

 2G+6G，2G+8G，都是这样。

 后记：台式机上装了个ubuntu，版本和小笔记本一样，14.04，所以基本上依赖的包下载完以后把之前的目录考过来就可以直接编译了，试着跑了下cifar-10可以训练。

补一个ssd跑VOC2007：

也出现了data layer prefetch queue empty的问题，但是能训练，虽然很慢，CPU用了100%，memory持续上升ing，跑了一晚上，之前的小笔记本仍旧停留在老样子不动，新台式：

I1026 21:09:32.796375  3203 caffe.cpp:251] Starting Optimization
I1026 21:09:32.796382  3203 solver.cpp:294] Solving VGG_VOC0712_SSD_300x300_train
I1026 21:09:32.796386  3203 solver.cpp:295] Learning Rate Policy: fixed
I1026 21:09:32.813050  3203 blocking_queue.cpp:50] Data layer prefetch queue empty
I1026 21:13:19.556558  3203 solver.cpp:243] Iteration 0, loss = 22.509
I1026 21:13:19.556725  3203 solver.cpp:259]     Train net output #0: mbox_loss = 22.509 (* 1 = 22.509 loss)
I1026 21:13:19.556732  3203 sgd_solver.cpp:138] Iteration 0, lr = 0.001
I1026 21:17:03.379603  3203 solver.cpp:243] Iteration 1, loss = 21.7313
I1026 21:17:03.379711  3203 solver.cpp:259]     Train net output #0: mbox_loss = 20.9537 (* 1 = 20.9537 loss)
I1026 21:17:03.379731  3203 sgd_solver.cpp:138] Iteration 1, lr = 0.001
I1026 21:20:49.064519  3203 solver.cpp:243] Iteration 2, loss = 20.8767
I1026 21:20:49.064656  3203 solver.cpp:259]     Train net output #0: mbox_loss = 19.1674 (* 1 = 19.1674 loss)
I1026 21:20:49.064663  3203 sgd_solver.cpp:138] Iteration 2, lr = 0.001
I1026 21:24:33.324839  3203 solver.cpp:243] Iteration 3, loss = 20.0986
I1026 21:24:33.324995  3203 solver.cpp:259]     Train net output #0: mbox_loss = 17.7645 (* 1 = 17.7645 loss)
I1026 21:24:33.325002  3203 sgd_solver.cpp:138] Iteration 3, lr = 0.001
I1026 21:28:18.298053  3203 solver.cpp:243] Iteration 4, loss = 19.3101
I1026 21:28:18.298166  3203 solver.cpp:259]     Train net output #0: mbox_loss = 16.1561 (* 1 = 16.1561 loss)
I1026 21:28:18.298176  3203 sgd_solver.cpp:138] Iteration 4, lr = 0.001
I1026 21:32:03.106290  3203 solver.cpp:243] Iteration 5, loss = 18.8092
I1026 21:32:03.106421  3203 solver.cpp:259]     Train net output #0: mbox_loss = 16.3044 (* 1 = 16.3044 loss)
I1026 21:32:03.106428  3203 sgd_solver.cpp:138] Iteration 5, lr = 0.001
I1026 21:35:47.668949  3203 solver.cpp:243] Iteration 6, loss = 18.3751
I1026 21:35:47.669019  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.7708 (* 1 = 15.7708 loss)
I1026 21:35:47.669026  3203 sgd_solver.cpp:138] Iteration 6, lr = 0.001
I1026 21:39:33.073357  3203 solver.cpp:243] Iteration 7, loss = 18.1015
I1026 21:39:33.073503  3203 solver.cpp:259]     Train net output #0: mbox_loss = 16.1863 (* 1 = 16.1863 loss)
I1026 21:39:33.073511  3203 sgd_solver.cpp:138] Iteration 7, lr = 0.001
I1026 21:43:16.992905  3203 solver.cpp:243] Iteration 8, loss = 17.9009
I1026 21:43:16.992954  3203 solver.cpp:259]     Train net output #0: mbox_loss = 16.2956 (* 1 = 16.2956 loss)
I1026 21:43:16.992960  3203 sgd_solver.cpp:138] Iteration 8, lr = 0.001
I1026 21:47:02.906033  3203 solver.cpp:243] Iteration 9, loss = 17.7352
I1026 21:47:02.906191  3203 solver.cpp:259]     Train net output #0: mbox_loss = 16.2439 (* 1 = 16.2439 loss)
I1026 21:47:02.906199  3203 sgd_solver.cpp:138] Iteration 9, lr = 0.001
I1026 21:47:02.986310  3203 solver.cpp:433] Iteration 10, Testing net (#0)
I1026 21:47:02.986764  3203 net.cpp:693] Ignoring source layer mbox_loss
I1027 01:07:59.728220  3203 solver.cpp:546]     Test net output #0: detection_eval = 8.94167e-05
I1027 01:11:44.542400  3203 solver.cpp:243] Iteration 10, loss = 17.0653
I1027 01:11:44.542552  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.8105 (* 1 = 15.8105 loss)
I1027 01:11:44.542562  3203 sgd_solver.cpp:138] Iteration 10, lr = 0.001
I1027 01:15:28.281282  3203 solver.cpp:243] Iteration 11, loss = 16.5182
I1027 01:15:28.281368  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.4822 (* 1 = 15.4822 loss)
I1027 01:15:28.281375  3203 sgd_solver.cpp:138] Iteration 11, lr = 0.001
I1027 01:19:13.447082  3203 solver.cpp:243] Iteration 12, loss = 16.1136
I1027 01:19:13.447216  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.1213 (* 1 = 15.1213 loss)
I1027 01:19:13.447227  3203 sgd_solver.cpp:138] Iteration 12, lr = 0.001
I1027 01:22:56.734020  3203 solver.cpp:243] Iteration 13, loss = 15.9099
I1027 01:22:56.734146  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.7285 (* 1 = 15.7285 loss)
I1027 01:22:56.734169  3203 sgd_solver.cpp:138] Iteration 13, lr = 0.001
I1027 01:26:41.813959  3203 solver.cpp:243] Iteration 14, loss = 15.8432
I1027 01:26:41.814071  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.489 (* 1 = 15.489 loss)
I1027 01:26:41.814079  3203 sgd_solver.cpp:138] Iteration 14, lr = 0.001
I1027 01:30:25.233520  3203 solver.cpp:243] Iteration 15, loss = 15.7216
I1027 01:30:25.233619  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.0885 (* 1 = 15.0885 loss)
I1027 01:30:25.233626  3203 sgd_solver.cpp:138] Iteration 15, lr = 0.001
I1027 01:34:09.895038  3203 solver.cpp:243] Iteration 16, loss = 15.666
I1027 01:34:09.895109  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.2142 (* 1 = 15.2142 loss)
I1027 01:34:09.895117  3203 sgd_solver.cpp:138] Iteration 16, lr = 0.001
I1027 01:37:53.745384  3203 solver.cpp:243] Iteration 17, loss = 15.528
I1027 01:37:53.745455  3203 solver.cpp:259]     Train net output #0: mbox_loss = 14.8065 (* 1 = 14.8065 loss)
I1027 01:37:53.745461  3203 sgd_solver.cpp:138] Iteration 17, lr = 0.001
I1027 01:41:38.118726  3203 solver.cpp:243] Iteration 18, loss = 15.4515
I1027 01:41:38.118793  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.5301 (* 1 = 15.5301 loss)
I1027 01:41:38.118799  3203 sgd_solver.cpp:138] Iteration 18, lr = 0.001
I1027 01:45:22.254400  3203 solver.cpp:243] Iteration 19, loss = 15.3034
I1027 01:45:22.254480  3203 solver.cpp:259]     Train net output #0: mbox_loss = 14.7633 (* 1 = 14.7633 loss)
I1027 01:45:22.254487  3203 sgd_solver.cpp:138] Iteration 19, lr = 0.001
I1027 01:45:22.331688  3203 solver.cpp:433] Iteration 20, Testing net (#0)
I1027 01:45:22.331750  3203 net.cpp:693] Ignoring source layer mbox_loss
W1027 05:06:09.821015  3203 solver.cpp:524] Missing true_pos for label: 1
W1027 05:06:09.821113  3203 solver.cpp:524] Missing true_pos for label: 2
W1027 05:06:09.821122  3203 solver.cpp:524] Missing true_pos for label: 3
W1027 05:06:09.821125  3203 solver.cpp:524] Missing true_pos for label: 4
W1027 05:06:09.821130  3203 solver.cpp:524] Missing true_pos for label: 5
W1027 05:06:09.821131  3203 solver.cpp:524] Missing true_pos for label: 6
W1027 05:06:09.881377  3203 solver.cpp:524] Missing true_pos for label: 8
W1027 05:06:09.881528  3203 solver.cpp:524] Missing true_pos for label: 10
W1027 05:06:09.881533  3203 solver.cpp:524] Missing true_pos for label: 11
W1027 05:06:09.881536  3203 solver.cpp:524] Missing true_pos for label: 12
W1027 05:06:09.881539  3203 solver.cpp:524] Missing true_pos for label: 13
W1027 05:06:09.881542  3203 solver.cpp:524] Missing true_pos for label: 14
W1027 05:06:09.965651  3203 solver.cpp:524] Missing true_pos for label: 17
W1027 05:06:09.965673  3203 solver.cpp:524] Missing true_pos for label: 19
W1027 05:06:09.965677  3203 solver.cpp:524] Missing true_pos for label: 20
I1027 05:06:09.965680  3203 solver.cpp:546]     Test net output #0: detection_eval = 0.00013241
I1027 05:09:53.990563  3203 solver.cpp:243] Iteration 20, loss = 15.2165
I1027 05:09:53.990700  3203 solver.cpp:259]     Train net output #0: mbox_loss = 14.9418 (* 1 = 14.9418 loss)
I1027 05:09:53.990710  3203 sgd_solver.cpp:138] Iteration 20, lr = 0.001
I1027 05:13:38.261768  3203 solver.cpp:243] Iteration 21, loss = 15.1906
I1027 05:13:38.261819  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.2229 (* 1 = 15.2229 loss)
I1027 05:13:38.261828  3203 sgd_solver.cpp:138] Iteration 21, lr = 0.001
I1027 05:17:22.776798  3203 solver.cpp:243] Iteration 22, loss = 15.1411
I1027 05:17:22.776938  3203 solver.cpp:259]     Train net output #0: mbox_loss = 14.6261 (* 1 = 14.6261 loss)
I1027 05:17:22.776948  3203 sgd_solver.cpp:138] Iteration 22, lr = 0.001
I1027 05:21:06.807641  3203 solver.cpp:243] Iteration 23, loss = 15.092
I1027 05:21:06.807750  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.2372 (* 1 = 15.2372 loss)
I1027 05:21:06.807759  3203 sgd_solver.cpp:138] Iteration 23, lr = 0.001
I1027 05:24:51.765369  3203 solver.cpp:243] Iteration 24, loss = 15.0714
I1027 05:24:51.765485  3203 solver.cpp:259]     Train net output #0: mbox_loss = 15.2836 (* 1 = 15.2836 loss)
I1027 05:24:51.765493  3203 sgd_solver.cpp:138] Iteration 24, lr = 0.001
I1027 05:28:35.644573  3203 solver.cpp:243] Iteration 25, loss = 15.0133
I1027 05:28:35.644652  3203 solver.cpp:259]     Train net output #0: mbox_loss = 14.5068 (* 1 = 14.5068 loss)
I1027 05:28:35.644659  3203 sgd_solver.cpp:138] Iteration 25, lr = 0.001
I1027 05:32:20.744153  3203 solver.cpp:243] Iteration 26, loss = 14.9701
I1027 05:32:20.744256  3203 solver.cpp:259]     Train net output #0: mbox_loss = 14.7832 (* 1 = 14.7832 loss)
I1027 05:32:20.744263  3203 sgd_solver.cpp:138] Iteration 26, lr = 0.001
I1027 05:36:04.126538  3203 solver.cpp:243] Iteration 27, loss = 14.7926
I1027 05:36:04.126641  3203 solver.cpp:259]     Train net output #0: mbox_loss = 13.0314 (* 1 = 13.0314 loss)
I1027 05:36:04.126659  3203 sgd_solver.cpp:138] Iteration 27, lr = 0.001
I1027 05:39:49.326182  3203 solver.cpp:243] Iteration 28, loss = 14.6038
I1027 05:39:49.326277  3203 solver.cpp:259]     Train net output #0: mbox_loss = 13.6422 (* 1 = 13.6422 loss)
I1027 05:39:49.326295  3203 sgd_solver.cpp:138] Iteration 28, lr = 0.001
I1027 05:43:32.725708  3203 solver.cpp:243] Iteration 29, loss = 14.4194
I1027 05:43:32.725805  3203 solver.cpp:259]     Train net output #0: mbox_loss = 12.9187 (* 1 = 12.9187 loss)
I1027 05:43:32.725812  3203 sgd_solver.cpp:138] Iteration 29, lr = 0.001
I1027 05:43:32.803719  3203 solver.cpp:433] Iteration 30, Testing net (#0)
I1027 05:43:32.803767  3203 net.cpp:693] Ignoring source layer mbox_loss

 

 无意中看到这个，台式是4G显存的1050，这就尴尬了，虽然cuda还没有装，英伟达的网站从昨晚开始一直在维护......