title: YOLO keep using CPU despite of specifying using GPU
body: <p>I'm using a laptop with GPU 0: Intel(R) HD Graphics 630 and GPU 1: GTX1050 TI. I just finished setting up YOLO environment in Anaconda Environment using the following tutorial: <a href="https://appliedmachinelearning.blog/2018/05/27/running-yolo-v2-for-real-time-object-detection-on-videos-images-via-darkflow/" rel="nofollow noreferrer">https://appliedmachinelearning.blog/2018/05/27/running-yolo-v2-for-real-time-object-detection-on-videos-images-via-darkflow/</a></p>
  
  <p>The problem is: whenever I try to render a video with YOLO in Anaconda environment using GPU</p>
  
  <pre><code>python flow --model cfg/yolo.cfg --load bin/yolov2.weights --demo videofile.mp4 --saveVideo --gpu 0.5
  </code></pre>
  
  <p>the video does render successfully, BUT, my CPU usage goes up to almost 100% (task manager), while my GPU is not used at all. I tried to specify GPU name by adding --gpuName /gpu:1 to the end, but still, CPU is used. Here are the output lines copied from Anaconda Prompt.</p>
  
  <pre><code>(df) C:\Users\User\Videos\PC-programming\darkflow-master&gt;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 ......
  </code></pre>
  
  <p>Then, if I try to render image instead, still, task manager shows that GPU is not used at all.</p>
  
  <pre><code>(df) C:\Users\User\Videos\PC-programming\darkflow-master&gt;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
  </code></pre>
  
  <p>What's wrong >_&lt; ??? Thanks in advance!</p>