anaconda - 尽管指定使用 GPU，YOLO 仍继续使用 CPU

Question

我正在使用带有 GPU 0：Intel(R) HD Graphics 630 和 GPU 1：GTX1050 TI 的笔记本电脑。我刚刚使用以下教程在 Anaconda 环境中设置了 YOLO 环境：https ://appliedmachinelearning.blog/2018/05/27/running-yolo-v2-for-real-time-object-detection-on-videos-images -通过-暗流/

问题是：每当我尝试使用 GPU 在 Anaconda 环境中使用 YOLO 渲染视频时

python flow --model cfg/yolo.cfg --load bin/yolov2.weights --demo videofile.mp4 --saveVideo --gpu 0.5

视频确实渲染成功，但是，我的 CPU 使用率几乎上升到 100%（任务管理器），而我的 GPU 根本没有使用。我尝试通过在末尾添加 --gpuName /gpu:1 来指定 GPU 名称，但仍然使用 CPU。这是从 Anaconda Prompt 复制的输出行。

(df) C:\Users\User\Videos\PC-programming\darkflow-master>python flow --model cfg/yolo.cfg --load bin/yolov2.weights --demo videofile.mp4 --saveVideo --gpu 0.5

Parsing ./cfg/yolov2.cfg
Parsing cfg/yolo.cfg
Loading bin/yolov2.weights ...
Successfully identified 203934260 bytes
Finished in 0.022666454315185547s
Model has a coco model name, loading coco labels.

Building net ...
Source | Train? | Layer description                | Output size
-------+--------+----------------------------------+---------------
       |        | input                            | (?, 608, 608, 3)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 608, 608, 32)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 304, 304, 32)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 304, 304, 64)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 152, 152, 64)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 152, 152, 64)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 76, 76, 128)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 76, 76, 128)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 38, 38, 256)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 19, 19, 512)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | concat [16]                      | (?, 38, 38, 512)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 64)
 Load  |  Yep!  | local flatten 2x2                | (?, 19, 19, 256)
 Load  |  Yep!  | concat [27, 24]                  | (?, 19, 19, 1280)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 1x1p0_1    linear           | (?, 19, 19, 425)
-------+--------+----------------------------------+---------------
GPU mode with 0.5 usage
2018-10-16 17:21:18.897583: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-10-16 17:21:18.904824: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
Finished in 4.7458178997039795s

Press [ESC] to quit demo
0.719 FPS ......

然后，如果我尝试改为渲染图像，任务管理器仍然显示根本没有使用 GPU。

(df) C:\Users\User\Videos\PC-programming\darkflow-master>python flow --model cfg/yolo.cfg --load bin/yolov2.weights --imgdir sample_img --gpu 0.9

Parsing ./cfg/yolov2.cfg
Parsing cfg/yolo.cfg
Loading bin/yolov2.weights ...
Successfully identified 203934260 bytes
Finished in 0.021943330764770508s
Model has a coco model name, loading coco labels.

Building net ...
Source | Train? | Layer description                | Output size
-------+--------+----------------------------------+---------------
       |        | input                            | (?, 608, 608, 3)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 608, 608, 32)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 304, 304, 32)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 304, 304, 64)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 152, 152, 64)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 152, 152, 64)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 152, 152, 128)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 76, 76, 128)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 76, 76, 128)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 76, 76, 256)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 38, 38, 256)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 256)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 38, 38, 512)
 Load  |  Yep!  | maxp 2x2p0_2                     | (?, 19, 19, 512)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 19, 19, 512)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | concat [16]                      | (?, 38, 38, 512)
 Load  |  Yep!  | conv 1x1p0_1  +bnorm  leaky      | (?, 38, 38, 64)
 Load  |  Yep!  | local flatten 2x2                | (?, 19, 19, 256)
 Load  |  Yep!  | concat [27, 24]                  | (?, 19, 19, 1280)
 Load  |  Yep!  | conv 3x3p1_1  +bnorm  leaky      | (?, 19, 19, 1024)
 Load  |  Yep!  | conv 1x1p0_1    linear           | (?, 19, 19, 425)
-------+--------+----------------------------------+---------------
GPU mode with 0.9 usage
2018-10-16 17:07:30.439641: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-10-16 17:07:30.449381: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
Finished in 5.261923789978027s

Forwarding 8 inputs ...
Total time = 10.975605964660645s / 8 inps = 0.7288891406778334 ips
Post processing 8 inputs ...
Total time = 0.48075294494628906s / 8 inps = 16.640563690969756 ips

怎么了>_<？？？提前致谢！

Answer 1

您是否对 Makefile 进行了一些更改，在 Makefile 中启用 GPU 和 CUDNN。小心路径。

Answer 2

克隆https://github.com/thtrieu/darkflow，从https://pjreddie.com/darknet/yolo/
下载必要的 .cfg 和权重， 将它们分别保存在 darkflow-master 内的 cfg 文件夹和新的 bin 文件夹中。

conda create -n darkflow-env python=3.6

activate darkflow-env

pip install tensorflow-gpu（pip，不是 conda。这一步也应该自动安装 CUDA 和 cuDNN，不需要单独下载。）

conda install cython numpy

conda config --add channels conda-forge

conda install opencv

转到您的 darkflow-master 文件夹并复制路径

cd 到路径（仍然使用 Anaconda Prompt）

python setup.py build_ext --inplace

python flow --model cfg/yolo.cfg --load bin/yolov2.weights --demo videofile.mp4 --saveVideo --gpu 0.7（videofile.mp4是要渲染的视频，我直接放在darkflow-master文件夹里面）

稍等一下，你会在 darkflow-master 文件夹中看到输出视频（在我的带有 GTX1050 TI 显卡的笔记本电脑上，渲染速度约为 8.5 FPS）。

如果您遇到任何问题，例如需要 Microsoft Visual C++ Build Tools，那么只需从 Microsoft 网站下载，记得在安装的同时安装 SDK。有关此问题的更多信息，您可以参考https://github.com/thtrieu/darkflow/issues/788。