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// See docs in ../ops/nn_ops.cc.
#define EIGEN_USE_THREADS
#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/l2loss_op.h"
#include "tensorflow/core/public/tensor.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
template <typename Device, typename T>
class L2LossOp : public OpKernel {
public:
explicit L2LossOp(OpKernelConstruction* context) : OpKernel(context) {}
void Compute(OpKernelContext* context) override {
// The input tensor can be of any number of dimensions, even though it's
// 2D in most typical applications.
const Tensor& input = context->input(0);
// The output is a single number.
Tensor* output = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, TensorShape({}), &output));
functor::L2Loss<Device, T>()(context->eigen_device<Device>(),
input.flat<T>(), output->scalar<T>());
}
};
#define REGISTER_KERNEL(T) \
REGISTER_KERNEL_BUILDER( \
Name("L2Loss").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
L2LossOp<CPUDevice, T>);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
#undef REGISTER_KERNEL
#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
namespace functor {
#define DECLARE_GPU_SPEC(T) \
template <> \
void L2Loss<GPUDevice, T>::operator()(const GPUDevice& d, \
typename TTypes<T>::ConstTensor input, \
typename TTypes<T>::Scalar output); \
extern template struct L2Loss<GPUDevice, T>;
DECLARE_GPU_SPEC(float);
#undef DECLARE_GPU_SPEC
} // namespace functor
// Registration of the GPU implementations.
#define REGISTER_GPU_KERNEL(T) \
REGISTER_KERNEL_BUILDER( \
Name("L2Loss").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
L2LossOp<GPUDevice, T>);
REGISTER_GPU_KERNEL(float);
#undef REGISTER_GPU_KERNEL
#endif // GOOGLE_CUDA
} // namespace tensorflow
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