Comments (2)
Hello,
from your post, I cannot find the problem directly. Further information is required, on which kind of architecture your test is running (CNN, RNN)? Have you checked the MXNet's version under BMXNet , and the version in your MXNet? If the version of BMXNet is behind the MXNet, the problem might be solved by next upgrade. Pls make sure CuDNN works for both framework equally, and pls make sure you built both projects in the same building mode "Release or debug" etc.
Basically, if you use BMXNet specific layers such as "q_cudnn_convolution*", it will be slower than the original Conv layer (but obviously, this is not your case). Otherwise, there should be no difference...
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Hi haojin,
Thanks for your reply. I changed the cmake mode to release and the speed was as fast as mxnet. Many thanks.
from bmxnet.
Related Issues (20)
- Error while doing inference on binarized model. HOT 12
- What is the location of the implement of QActivation, QConvolution, and QFullyConneted? HOT 2
- encounter errors durring complie HOT 2
- cudnn_off=False has some problems with mx.mon.Monitor HOT 4
- Where do you implement XNOR networks? HOT 1
- Build failing
- How to integrate a trained binary model in iOS? HOT 3
- No difference in binarized and full-precision LeNet model size HOT 2
- Can an existing MXNet 1.0.0 installation be used to set up BMXNet ? HOT 10
- Order of QActivation, QConvolution layers HOT 1
- Quantization method HOT 3
- depth-wise convolution support HOT 2
- less forward speed-up when batch size is larger HOT 1
- mxnet error: redeclaration of 'kNone' when setting USE_CPP_PACKAGE = 1 HOT 1
- Any pre-built dynamic library or executable file for andriod? HOT 2
- No speedup or even slower on CPU with 1-bit QConvolution and QActivation HOT 4
- ImportError: cannot import name 'transforms' HOT 2
- How do I get the model-convertor executable file ?
- XNOR-Net implementation HOT 1
- Regarding pretrained binary models
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