Class EAbsOp

en_ops/e_abs.cc:36–73

using namespace tensorflow;

class EAbsOp : public OpKernel
{
public:
    explicit EAbsOp(OpKernelConstruction *context) : OpKernel(context)
    {
        OP_REQUIRES_OK(context, context->GetAttr("eid_low", &eid_low_));
        OP_REQUIRES_OK(context, context->GetAttr("eid_high", &eid_high_));
        OP_REQUIRES_OK(context, context->GetAttr("times", &times_));
        lib = dlopen("/home/zhangyan/jhrsgx/privacy_test/privacy_tf/sgx_tf_ops/sgx.so", RTLD_LAZY);
        OP_REQUIRES(context, lib != NULL, errors::Unknown("Unable to load sgx.so!"));
    }
    void Compute(OpKernelContext *context) override
    {
        const Tensor &input = context->input(0);
        auto input_flat = input.flat<float>();

        const TensorShape &input_shape = input.shape();

        Tensor *output = NULL;
        OP_REQUIRES_OK(context, context->allocate_output(0, input_shape, &output));
        auto output_flat = output->flat<float>();
        int N = input_flat.size() / times_;

        unsigned long int eid_ = (eid_high_ << 32) + eid_low_;
        typedef void (*function)(unsigned long int eid, float *input, int N, float *output);
        dlerror();
        function abs_kernel = (function)dlsym(lib, "abs1");
        const char *dlsym_error = dlerror();
        OP_REQUIRES(context, !dlsym_error, errors::Unknown("loading of abs failed: ", dlsym_error));
        abs_kernel(eid_, (float *)input_flat.data(), N, (float *)output_flat.data());
    };

private:
    void *lib;
    int64 eid_low_;
    int64 eid_high_;
    int64 times_;
};
REGISTER_KERNEL_BUILDER(Name("EAbs").Device(DEVICE_CPU), EAbsOp);

class EAbsGradOp : public OpKernel