Vfrdtyky

I got tensorflow binaries (already compiled)
I have added to tensorflow source:
tensorflowcoreuser_opsicp_op_kernel.cc - contains:
https://github.com/tensorflow/models/blob/master/research/vid2depth/ops/icp_op_kernel.cc
tensorflowcoreuser_opsBUILD - contains:

load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")



tf_custom_op_library(

    name = "icp_op_kernel.so",

    srcs = ["icp_op_kernel.cc"],

)

I am trying to build with:

bazel build --config opt //tensorflow/core/user_ops:icp_op_kernel.so

And I get:

tensorflow/core/user_ops/icp_op_kernel.cc(16): fatal error C1083: Cannot open include file: 'pcl/point_types.h': No such file or directory

Because bazel don't know where the pcl include files are.
I have installed pcl and the include directory is in:

C:Program FilesPCL 1.6.0includepcl-1.6

How do I tell bazel to also include this directory?

Also I will probably need to add C:Program FilesPCL 1.6.0lib to the link, How do I do that?

You don't need bazel for building ops if it fails.

I have implemented customized ops both in CPU and GPU, and basically follow the two Tensorflow tutorials.

For CPU ops, follow Tensorflow tutorial on Build the op library:

TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )

TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )

g++ -std=c++11 -shared zero_out.cc -o zero_out.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2

Note on gcc version >=5: gcc uses the new C++ ABI since version 5. The binary pip packages available on the TensorFlow website are built with gcc4 that uses the older ABI. If you compile your op library with gcc>=5, add -D_GLIBCXX_USE_CXX11_ABI=0 to the command line to make the library compatible with the older abi.

For GPU ops, check the current official GPU ops building instructions on Tensorflow adding GPU op support

nvcc -std=c++11 -c -o cuda_op_kernel.cu.o cuda_op_kernel.cu.cc 

  ${TF_CFLAGS[@]} -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC



g++ -std=c++11 -shared -o cuda_op_kernel.so cuda_op_kernel.cc 

  cuda_op_kernel.cu.o ${TF_CFLAGS[@]} -fPIC -lcudart ${TF_LFLAGS[@]}

As it says, Note that if your CUDA libraries are not installed in /usr/local/lib64, you'll need to specify the path explicitly in the second (g++) command above. For example, add -L /usr/local/cuda-8.0/lib64/ if your CUDA is installed in /usr/local/cuda-8.0.

Also, Note in some linux settings, additional options to nvcc compiling step are needed. Add -D_MWAITXINTRIN_H_INCLUDED to the nvcc command line to avoid errors from mwaitxintrin.h.