[GitHub] [incubator-mxnet] zheshipinyinMc opened a new issue #15207: Mack-RCNN C++ Deployment Not Working in GPU Mode And CPU Mode

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zheshipinyinMc opened a new issue #15207: Mack-RCNN C++ Deployment Not Working in GPU Mode And CPU Mode
URL: https://github.com/apache/incubator-mxnet/issues/15207
 
 
   Thanks for the great work! I am pretty interested in C++ deployment so just tried for object detection, and then used maskrcnn for detection to see if it works.
   1、Train Mask R-CNN with COCO dataset.
   2、Test saved model in python is ok.
   3、Deploy mask R-CNN with gluoncv c++ deployment, the model is not working in GPU mode and CPU mode.
   
   MXNet: 1.4
   System: Ubuntu 16.04
   Gluon CV: 0.4.0
   
   errors:
   In GPU mode, the error is "incubator-mxnet/cpp-package/include/mxnet-cpp/ndarray.hpp:242: Check failed: MXNDArrayWaitAll() == 0 (-1 vs. 0) : [08:43:52] src/storage/./pooled_storage_manager.h:157: cudaMalloc failed: out of memory".
   In CPU mode, the error is "incubator-mxnet/cpp-package/include/mxnet-cpp/ndarray.hpp:242: Check failed: MXNDArrayWaitAll() == 0 (-1 vs. 0) : [08:46:48] src/ndarray/ndarray.cc:752: Check failed: !IsMKLDNNData(): We can't generate TBlob for MKLDNN data. Please use Reorder2Default() to generate a new NDArray first".
   
   My GPU has 6G memory, CPU has 32G memory.
   
   incubator-mxnet make command :
   "make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CPP_PACKAGE=1 USE_CUDA=1 USE_MKLDNN=1 USE_CUDNN=1 USE_CUDA_PATH=/usr/local/cuda"
   
   But gluoncv yolov3 is working in GPU mode and CPU mode.
    @zhreshold 

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