// See docs in ../ops/image_ops.cc
#define EIGEN_USE_THREADS

#include <memory>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/public/status.h"
#include "tensorflow/core/public/tensor.h"
#include "tensorflow/core/public/tensor_shape.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"

namespace tensorflow {

typedef Eigen::ThreadPoolDevice CPUDevice;

template <typename Device, typename T>
class ResizeBilinearOp : public OpKernel {
 public:
  explicit ResizeBilinearOp(OpKernelConstruction* context)
      : OpKernel(context) {}

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    OP_REQUIRES(context, input.dims() == 4,
                errors::InvalidArgument("input must be 4-dimensional",
                                        input.shape().ShortDebugString()));
    const Tensor& shape_t = context->input(1);
    OP_REQUIRES(context, shape_t.dims() == 1,
                errors::InvalidArgument("shape_t must be 1-dimensional",
                                        shape_t.shape().ShortDebugString()));
    OP_REQUIRES(context, shape_t.NumElements() == 2,
                errors::InvalidArgument("shape_t must have two elements",
                                        shape_t.shape().ShortDebugString()));

    auto Svec = shape_t.vec<int32>();
    // Initialize shape to the batch size of the input, then add
    // the rest of the dimensions
    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(
                                0, TensorShape({input.dim_size(0), Svec(0),
                                                Svec(1), input.dim_size(3)}),
                                &output));

    const int64 batch_size = input.dim_size(0);
    const int64 in_height = input.dim_size(1);
    const int64 in_width = input.dim_size(2);
    const int64 channels = input.dim_size(3);
    const int64 out_height = output->dim_size(1);
    const int64 out_width = output->dim_size(2);

    typename TTypes<T, 4>::ConstTensor input_data = input.tensor<T, 4>();
    typename TTypes<float, 4>::Tensor output_data = output->tensor<float, 4>();

    const float height_scale = in_height / static_cast<float>(out_height);
    const float width_scale = in_width / static_cast<float>(out_width);

    for (int b = 0; b < batch_size; ++b) {
      for (int y = 0; y < out_height; ++y) {
        const float in_y = y * height_scale;
        const int top_y_index = static_cast<int>(floorf(in_y));
        const int bottom_y_index =
            std::min(static_cast<int64>(ceilf(in_y)), (in_height - 1));
        const float y_lerp = in_y - top_y_index;
        const float inverse_y_lerp = (1.0f - y_lerp);
        for (int x = 0; x < out_width; ++x) {
          const float in_x = x * width_scale;
          const int left_x_index = static_cast<int>(floorf(in_x));
          const int right_x_index =
              std::min(static_cast<int64>(ceilf(in_x)), (in_width - 1));
          const float x_lerp = in_x - left_x_index;
          const float inverse_x_lerp = (1.0f - x_lerp);
          for (int c = 0; c < channels; ++c) {
            const float top_left = input_data(b, top_y_index, left_x_index, c);
            const float top_right =
                input_data(b, top_y_index, right_x_index, c);
            const float bottom_left =
                input_data(b, bottom_y_index, left_x_index, c);
            const float bottom_right =
                input_data(b, bottom_y_index, right_x_index, c);
            const float top =
                (top_left * inverse_x_lerp) + (top_right * x_lerp);
            const float bottom =
                (bottom_left * inverse_x_lerp) + (bottom_right * x_lerp);
            output_data(b, y, x, c) =
                (top * inverse_y_lerp) + (bottom * y_lerp);
          }
        }
      }
    }
  }
};

#define REGISTER_KERNEL(T)                            \
  REGISTER_KERNEL_BUILDER(Name("ResizeBilinear")      \
                              .Device(DEVICE_CPU)     \
                              .TypeConstraint<T>("T") \
                              .HostMemory("size"),    \
                          ResizeBilinearOp<CPUDevice, T>);

REGISTER_KERNEL(uint8);
REGISTER_KERNEL(int8);
REGISTER_KERNEL(int32);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
#undef REGISTER_KERNEL

}  // namespace tensorflow