[GitHub] Joke09 commented on issue #13545: For inference, I have the same problem. The client send jpg to server, then the server use cv2 to do resize. When put the image data into the mx.nd.array, it's very slow. And the Utilization of GPU is low too. How to solve it? Thank you!

[GitBox <gi...@apache.org>]

Joke09 commented on issue #13545: For inference, I have the same problem. The client send jpg to server, then the server use cv2 to do resize. When put the image data into the mx.nd.array, it's very slow. And the Utilization of GPU is low too. How to solve it? Thank you!
URL: https://github.com/apache/incubator-mxnet/issues/13545#issuecomment-444717557
 
 
   > Hi @Joke09 ,
   > 
   > When you put your image data into the NDArray, can you also try to copy it to GPU using `as_in_context` method before passing it to your model ?
   > 
   > http://mxnet.incubator.apache.org/test/api/python/ndarray.html#mxnet.ndarray.NDArray.as_in_context
   
   Thank you! It works, But not much.
   Before:
       data = [mx.nd.array(im_arrays), mx.nd.array(im_infos)]
   The shape of im_arrays is [8,3,896,1024]. It take 104ms.
   After:
       data = [mx.nd.array(im_arrays, ctx=mx.gpu(1)), mx.nd.array(im_infos)]
   It take 68ms. I think it's not fast enough. 

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