Thanks for your reply but that is not my problem.
To begin with, I intended to ask the way to check the performance of faster rcnn using nvcaffe fp16 branch not their own caffe branch on TX1.
In my case, I already succeeded to implement faster rcnn using nvcaffe with cudnn5.1 by adding things related to roi_pooling layer and modifying thins related to cudnn5.1 so I obtained the performance of faster rcnn using nvcaffe branch whose type is float. But there is a problem with using nvcaffe float16 not float.
That is, in order to check the inference time of faster rcnn using nvcaffe float16, I modified the _caffe.cpp(python/caffe/) as follows:
(originally the type of Dtype and Mtype is float not float16 in _caffe.cpp)
typedef float16 Dtype;
typedef float16 Mtype;
const int NPY_DTYPE = NPY_FLOAT16;
After this, I did pycaffe build.
Then I tried to check the inference time on python but the error messages occurs as follows:
File “/home/ubuntu/nvcaffe/python/caffe/__init__py”, line 1, in
from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
File “/home/ubuntu/nvcaffe/python/caffe/pycaffe.py”, line 13. in
from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver,
ImportError: /home/ubuntu/nvcaffe/python/caffe/_caffe.so: undefined symbol: _ZN5caffe6SolverINS_7float16ES1_E5SolveEPKc
If you have the solution to fix this problem, please help me and let me know.