Question

我有张量流二进制文件（已编译）我已经添加到张量流源： tensorflow\core\user_ops\icp_op_kernel.cc-包含： https://github.com/tensorflow/models/blob/master/research/vid2depth/ops/icp_op_kernel.cc tensorflow\core\user_ops\BUILD-包含：

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

tf_custom_op_library(
    name = "icp_op_kernel.so",
    srcs = ["icp_op_kernel.cc"],
)

我正在尝试使用：

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

我得到：

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

因为bazel不知道pcl包含文件在哪里。我已经安装了pcl，并且include目录位于：

C:\Program Files\PCL 1.6.0\include\pcl-1.6

如何告诉bazel也包含此目录？

我可能还需要在链接中添加C:\Program Files\PCL 1.6.0\lib，我该怎么做？

Answer 1

如果操作失败，则不需要Object.values。

我已经在CPU和GPU中实现了自定义操作，并且基本上遵循了两个Tensorflow教程。

对于CPU操作，请遵循Build the op library上的Tensorflow教程：

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关于bazel版本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的注释：gcc从版本5开始使用新的gcc。TensorFlow网站上可用的二进制pip包是使用旧版ABI的gcc4构建的。如果您使用>=5编译操作库，请将C++ ABI添加到命令行以使该库与较早的abi兼容。

对于GPU操作，请查看Tensorflow adding GPU op support上当前的官方GPU操作构建说明

gcc>=5

如前所述，请注意，如果您的CUDA库未安装在-D_GLIBCXX_USE_CXX11_ABI=0中，则需要在上面的第二个（g ++）命令中显式指定路径。例如，如果您的CUDA已安装在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[@]}中，则添加/usr/local/lib64。

此外，请注意，在某些Linux设置中，需要nvcc编译步骤的其他选项。将-L /usr/local/cuda-8.0/lib64/添加到nvcc命令行中，以避免来自/usr/local/cuda-8.0的错误。

如何使用带有附加include目录的bazel构建Tensorflow Op

1 个答案: