使用py-faster-rcnn训练VOC2007数据集时遇到问题

2022-03-19 04:36:09

使用py-faster-rcnn训练VOC2007数据集时遇到如下问题：

1. KeyError: 'chair'

File "/home/sai/py-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 217, in _load_pascal_annotation
cls = self._class_to_ind[obj.find('name').text.lower().strip()]
KeyError: 'chair'

解决：

You probably need to write some line of codes to ignore any objects with classes except the classes you are looking for when you are loading the annotation _load_pascal_annotation.
Something like
cls_objs = [
obj for obj, clas in objs, self._classes if obj.find('name').text== clas]

when you are loading the annotation in _load_pascal_annotation method, look for something like
objs = diff_objs (or non_diff_objs)
After that line insert something similar to below code

cls_objs = [obj for obj in objs if obj.find('name').text in self._classes]

objs = cls_objs

https://github.com/rbgirshick/py-faster-rcnn/issues/316

2. Check failed: error == cudaSuccess (30 vs. 0) unknown error

1. I'd just like to point out that CUDA runtime error (30) might show if your program is unable to create or open the /dev/nvidia-uvm device file. This is usually fixed by installing package nvidia-modprobe:
sudo apt-get install nvidia-modprobe

Note also that since your GPU has compute capability 2.1 as per this page you will not be able to use CuDNN and will need to disable support for CuDNN in the Caffe makefile.

 2. The problem happened because modprobe could not insert nvidia_340_uvm.

Thus, I had to install nvidia_340_uvm via: sudo apt-get install nvidia-340-uvm.
对于第二点，由于我不是340的显卡驱动，我运行后系统崩了。据说将其改为自己的驱动版本即可，我是378版本的驱动，将340换成378后显示无法定位软件包。

3. 我发现自己没有装cuda samples，参考别人的教程进行安装。（系统和应用版本不一致，需注意）
　　

编译CUDA Samples

命令：

  1. cd /usr/local/cuda-6.5/samples
  2. sudo make

编译完成后，进入路径：/samples/bin/x86_64/linux/release

运行命令：

  1. ./deviceQuery

输出：

  1. ./deviceQuery Starting...
  2. CUDA Device Query (Runtime API) version (CUDART static linking)
  3. Detected 1 CUDA Capable device(s)
  4. Device 0: "Tesla K40c"
  5. CUDA Driver Version / Runtime Version          6.5 / 6.5
  6. CUDA Capability Major/Minor version number:    3.5
  7. Total amount of global memory:                 11520 MBytes (12079136768 bytes)
  8. (15) Multiprocessors, (192) CUDA Cores/MP:     2880 CUDA Cores
  9. GPU Clock rate:                                745 MHz (0.75 GHz)
  10. Memory Clock rate:                             3004 Mhz
  11. Memory Bus Width:                              384-bit
  12. L2 Cache Size:                                 1572864 bytes
  13. Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  14. Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  15. Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  16. Total amount of constant memory:               65536 bytes
  17. Total amount of shared memory per block:       49152 bytes
  18. Total number of registers available per block: 65536
  19. Warp size:                                     32
  20. Maximum number of threads per multiprocessor:  2048
  21. Maximum number of threads per block:           1024
  22. Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  23. Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  24. Maximum memory pitch:                          2147483647 bytes
  25. Texture alignment:                             512 bytes
  26. Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
  27. Run time limit on kernels:                     No
  28. Integrated GPU sharing Host Memory:            No
  29. Support host page-locked memory mapping:       Yes
  30. Alignment requirement for Surfaces:            Yes
  31. Device has ECC support:                        Enabled
  32. Device supports Unified Addressing (UVA):      Yes
  33. Device PCI Bus ID / PCI location ID:           1 / 0
  34. Compute Mode:
  35. < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
  36. deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs = 1, Device0 = Tesla K40c
  37. Result = PASS
如果输出上述信息，恭喜你，NVIDIA和CUDA安装成功，则可以继续进行下一步安装Caffe环境。

（2.2.4.9）验证NVIDIA 驱动和CUDA是否安装成功

查看安装NVIDIA驱动版本 命令：

cat /proc/driver/nvidia/version

输出

  1. NVRM version: NVIDIA UNIX x86_64 Kernel Module  340.96  Sun Nov  8 22:33:28 PST 2015
  2. GCC version:  gcc version 4.7.3 (Ubuntu/Linaro 4.7.3-12ubuntu1)

3. 发现一个遇到了一样问题的人，注册nvidia帐号后向其请教。

https://devtalk.nvidia.com/default/topic/987119/problem-with-run-cuda-on-geforce-gt-755m/#reply

https://github.com/NVIDIA/DIGITS/issues/1663

Hello,

I have a problem with using GeForce GTX 1080Ti for machine learning (CAFFA framework)
My platform:
ubuntu 16.04, Cuda V8.0.61, CuDNN8.0

I suggest my version is too new and I have to downgrade.
Could you advise the best way for solve my problem?

Find in following more details:

nvcc is warning about deprecation. But it not error and as I know it is about future.

nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release

CAFFE & py-faster-rcnn install with no error, but on training with py-faster-rcnn I recieve next massage:

I0601 15:30:44.833746 28338 layer_factory.hpp:77] Creating layer input-data

I0601 15:30:44.834151 28338 net.cpp:106] Creating Layer input-data

I0601 15:30:44.834161 28338 net.cpp:411] input-data -> data

I0601 15:30:44.834169 28338 net.cpp:411] input-data -> im_info

I0601 15:30:44.834178 28338 net.cpp:411] input-data -> gt_boxes

F0601 15:30:44.852488 28338 syncedmem.hpp:18] Check failed: error == cudaSuccess (30 vs. 0)  unknown error

*** Check failure stack trace: ***

More outputs:

~/caffe#nvcc -V gives

nvcc: NVIDIA (R) Cuda compiler driver

Copyright (c) 2005-2016 NVIDIA Corporation

Built on Tue_Jan_10_13:22:03_CST_2017

Cuda compilation tools, release 8.0, V8.0.61
~/caffe# nvidia-smi

+-----------------------------------------------------------------------------+

| NVIDIA-SMI 378.13                 Driver Version: 378.13                    |

|-------------------------------+----------------------+----------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|===============================+======================+======================|

|   0  Graphics Device     Off  | 0000:01:00.0      On |                  N/A |

| 23%   39C    P8    17W / 250W |    578MiB / 11171MiB |     15%      Default |

+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+

| Processes:                                                       GPU Memory |

|  GPU       PID  Type  Process name                               Usage      |

|=============================================================================|

|    0      1155    G   /usr/lib/xorg/Xorg                              20MiB |

|    0      1341    G   /usr/lib/xorg/Xorg                             262MiB |

|    0      1794    G   compiz                                          82MiB |

|    0      1939    G   fcitx-qimpanel                                   9MiB |

|    0      2015    G   ...el-token=3645A468136299F390B7B0886FE96671   173MiB |

+-----------------------------------------------------------------------------+

4. 仍然是2的错误，看见有人说 sudo ./experiments/scripts/faster_rcnn_alt_opt.sh 0 VGG16 pascal_voc    

可以解决该问题，但是试了之后出现如下问题：

ImportError: libcudart.so.8.0: cannot open shared object file: No such file or directory

全部显示结果如下：

+ set -e
+ export PYTHONUNBUFFERED=True
+ PYTHONUNBUFFERED=True
+ GPU_ID=0
+ NET=VGG16
+ NET_lc=vgg16
+ DATASET=pascal_voc
+ array=($@)
+ len=3
+ EXTRA_ARGS=
+ EXTRA_ARGS_SLUG=
+ case $DATASET in
+ TRAIN_IMDB=voc_2007_trainval
+ TEST_IMDB=voc_2007_test
+ PT_DIR=pascal_voc
+ ITERS=40000
++ date +%Y-%m-%d_%H-%M-%S
+ LOG=experiments/logs/faster_rcnn_alt_opt_VGG16_.txt.2017-06-01_14-56-06
+ exec
++ tee -a experiments/logs/faster_rcnn_alt_opt_VGG16_.txt.2017-06-01_14-56-06
+ echo Logging output to experiments/logs/faster_rcnn_alt_opt_VGG16_.txt.2017-06-01_14-56-06
Logging output to experiments/logs/faster_rcnn_alt_opt_VGG16_.txt.2017-06-01_14-56-06
+ ./tools/train_faster_rcnn_alt_opt.py --gpu 0 --net_name VGG16 --weights data/imagenet_models/VGG16.v2.caffemodel --imdb voc_2007_trainval --cfg experiments/cfgs/faster_rcnn_alt_opt.yml
Traceback (most recent call last):
File "./tools/train_faster_rcnn_alt_opt.py", line 17, in <module>
from fast_rcnn.train import get_training_roidb, train_net
File "/home/jz/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 10, in <module>
import caffe
File "/home/jz/py-faster-rcnn/tools/../caffe-fast-rcnn/python/caffe/__init__.py", line 1, in <module>
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
File "/home/jz/py-faster-rcnn/tools/../caffe-fast-rcnn/python/caffe/pycaffe.py", line 13, in <module>
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
ImportError: libcudart.so.8.0: cannot open shared object file: No such file or directory

5. 仍然是2的错误，有人说是显卡驱动的问题，于是我查询NVIDIA X server setting想看自己使用的是否是NVIDIA的驱动，发现ubuntu16.04没有prime profiles选项，无法一键切换驱动。

有人说要单独安装prime profiles，我sudo apt-get install nvidia-prime之后，在NVIDIA X server里仍然没有该选项。想起在安装驱动之后好像没有禁用ubuntu自带的显卡，于是在软件和更新里查看结果如下图，修改不使用设备后，修改nouveau后重启。使用py-faster-rcnn训练VOC2007数据集时遇到问题

  • 把 nouveau 驱动加入黑名单

      $sudo nano /etc/modprobe.d/blacklist-nouveau.conf

  在文件 blacklist-nouveau.conf 中加入如下内容：
    
  blacklist nouveau
    
  blacklist lbm-nouveau
    
  options nouveau modeset=0
    
  alias nouveau off
    
  alias lbm-nouveau off

  • 禁用 nouveau 内核模块

      $echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf

  $sudo update-initramfs -u

  • 重启

  • lsmod | grep nouveau

    如果什么都没有代表卸载成功，这时候重新启动你会发现屏幕分辨率明显变化了，如果没有变化，注意可能默认驱动没有被禁止，ubuntu16.04好像和14.04有区别，请查其他禁止驱动的方法

  • 禁用成功后仍然出现2的错误。

6. 在5的基础上，我试着跑caffe的mnist例程，未果。

输入：./examples/mnist/train_lenet.sh

输出：

F0601 20:57:37.822069 3866 db_lmdb.hpp:15] Check failed: mdb_status == 0 (13 vs. 0) Permission denied
*** Check failure stack trace: ***
@ 0x7f9308c9a95d google::LogMessage::Fail()
@ 0x7f9308c9c6e0 google::LogMessage::SendToLog()
@ 0x7f9308c9a543 google::LogMessage::Flush()
@ 0x7f9308c9d0ae google::LogMessageFatal::~LogMessageFatal()
@ 0x7f9309415428 caffe::db::LMDB::Open()
@ 0x7f93092b3b9f caffe::DataLayer<>::DataLayer()
@ 0x7f93092b3d32 caffe::Creator_DataLayer<>()
@ 0x7f9309468d90 caffe::Net<>::Init()
@ 0x7f930946b79e caffe::Net<>::Net()
@ 0x7f930944e865 caffe::Solver<>::InitTrainNet()
@ 0x7f930944fc55 caffe::Solver<>::Init()
@ 0x7f930944ff6f caffe::Solver<>::Solver()
@ 0x7f93094402b1 caffe::Creator_SGDSolver<>()
@ 0x40a9e8 train()
@ 0x4072e0 main
@ 0x7f9307c0b830 (unknown)
@ 0x407b09 _start

输入：sudo ./examples/mnist/train_lenet.sh

输出：

error while loading shared libraries: libcudart.so.8.0: cannot open shared object file: No such file or directory

解决：https://cgcvtutorials.wordpress.com/2016/10/14/error-while-loading-shared-libraries-libcudart-so-8-0-cannot-open-shared-object-file-no-such-file-or-directory/

sudo ldconfig /usr/local/cuda-8.0/lib64

再输入sudo ./examples/mnist/train_lenet.sh后成功训练mnist.

参考链接：https://devtalk.nvidia.com/default/topic/963814/cuda-setup-and-installation/cuda-8-libcudart-error/

https://askubuntu.com/questions/889015/cant-install-cuda-8-but-have-correct-nvidia-driver-ubuntu-16

https://github.com/tensorflow/tensorflow/issues/5343

http://blog.crboy.net/2012/05/solution-for-cannot-open-shared-object.html

7. 在6成功的基础上，已经可以开始训练py-faster-rcnn。 

sudo ./experiments/scripts/faster_rcnn_alt_opt.sh 0 VGG16 pascal_voc

 8. 成功训练完模型之后，测试时又出现如下问题：

输入：python ./tools/demo.py

输出：I0602 20:04:00.106807 10014 net.cpp:413] Input 0 -> data

F0602 20:04:00.110862 10014 syncedmem.hpp:18] Check failed: error == cudaSuccess (30 vs. 0) unknown error
*** Check failure stack trace: ***
已放弃 (核心已转储)

解决：先后使用了如下命令：

1. sudo ldconfig /usr/local/cuda-8.0/lib64（未果）

2. export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64（未果）

3. export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}（在faster-rcnn路径下，所以该句无效，修改根路径后依然未果）

4. export LD_LIBRARY_PATH=${CUDA_HOME}/lib64

export PATH=${CUDA_HOME}/bin:${PATH}（未果）

5. export CUDA_HOME=/usr/local/cuda | export LD_LIBRARY_PATH=${CUDA_HOME}/lib64（未果）

6. sudo ldconfig /usr/local/cuda/lib64

python ./tools/demo.py（未果）

7. sudo python ./tools/demo.py

报错：

Cannot copy param 0 weights from layer 'bbox_pred'; shape mismatch.  Source param shape is 8 4096 (32768); target param shape is 84 4096 (344064). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer.

解决：修改'/home/jz/py-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/faster_rcnn_test.pt'文件中的bbox_pred为out_num为8

再次输入7命令：sudo python ./tools/demo.py后成功测试结果。

8. 测试

sudo time ./tools/test_net.py --gpu 0 --def models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt --net data/faster_rcnn_models/VGG16_end2end_ignore-difficult1.caffemodel

最终： sudo ./experiments/scripts/faster_rcnn_end2end_test.sh 0 VGG16 pascal_voc

参考：sudo ./tools/test_net.py --gpu 0 --def models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt --net /home/jz/py-faster-rcnn/output/faster_rcnn_end2end/voc_2012_train/vgg16_faster_rcnn_iter_60000.caffemodel --imdb voc_2012_test --cfg experiments/cfgs/faster_rcnn_end2end.yml

问题：

wrote gt roidb to /home/jz/py-faster-rcnn/data/cache/voc_2007_test_gt_roidb.pkl
Traceback (most recent call last):
File "./tools/test_net.py", line 90, in <module>
test_net(net, imdb, max_per_image=args.max_per_image, vis=args.vis)
File "/home/jz/py-faster-rcnn/tools/../lib/fast_rcnn/test.py", line 242, in test_net
roidb = imdb.roidb
File "/home/jz/py-faster-rcnn/tools/../lib/datasets/imdb.py", line 67, in roidb
self._roidb = self.roidb_handler()
File "/home/jz/py-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 128, in selective_search_roidb
ss_roidb = self._load_selective_search_roidb(gt_roidb)
File "/home/jz/py-faster-rcnn/tools/../lib/datasets/pascal_voc.py", line 162, in _load_selective_search_roidb
'Selective search data not found at: {}'.format(filename)
AssertionError: Selective search data not found at: /home/jz/py-faster-rcnn/data/selective_search_data/voc_2007_test.mat
Command exited with non-zero status 1
1.39user 2.38system 0:06.47elapsed 58%CPU (0avgtext+0avgdata 1815028maxresident)k
1263144inputs+48outputs (589major+388691minor)pagefaults 0swaps

解决：You can modify the following flag in "lib/fast-rcnn/config.py"

# Propose boxes

__C.TEST.HAS_RPN = True

问题：
    1. 如果在最后出现 KeyError: 'xxxxxxxxxx'，请删除 $FRCN_ROOT/data/VOCdevkit2007/annotations_cache/annots.pkl
    2. 如果中途发现标错了数据，重新标注数据后，请删除 $FRCN_ROOT/data/cache/voc_2007_trainval_gt_roidb.pkl
    3. 如果最后测试出现IndexError: too many indices for array，那是因为你的测试数据中缺少了某些类别。请根据错误提示，找到对应的代码（$FRCN_ROOT/lib/datasets/voc_eval.py第148行），前面加上一个if语句：
      if len(BB) != 0:
      BB = BB[sorted_ind, :]

使用py-faster-rcnn训练VOC2007数据集时遇到问题

====================================================================================================================================================================

视频检测：

sudo python ./tools/demo_video1.py --net zf

10.结果

在py-faster-rcnn下，

执行：

  1. ./tools/demo.py --net zf

或者将默认的模型改为zf：

  1. parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
  2. choices=NETS.keys(), default='vgg16')

修改：

  1. default='zf'

执行：

    1. ./tools/demo.py
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