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#ifndef TIM_VX_OPS_CONV2D_H_
#define TIM_VX_OPS_CONV2D_H_

#include <array>

#include "tim/vx/builtin_op.h"

namespace tim {
namespace vx {
namespace ops {

/**
 * ## Conv2d
 *
 * Performs a 2-D convolution operation, include classic Conv2D /
 * Depthwise Conv2D / Group Conv2D / Dilation Conv2D.
 *
 * Input:
 * - input [WHCN or CWHN].
 * - kernel [ WHIcOc ] (Ic: Input Channels. Oc: Output Channels) normally,
 *   [WHIc(Oc)1] for Depthwise Conv.
 * - bias [ O ]. Optional.
 *
 * Attribute:
 * - weights : the output channel number for weight tensor.
 * - ksize : the height and width for weight tensor.
 * - padding : AUTO, VALID or SAME.
 * - pad : pad value for each spatial axis.
 * - stride : stride along each spatial axis.
 * - dilation : dilation value along each spatial axis of the filter.
 * - multiplier: function similar to group attribute on other framework,
 * but the value is different. multiplier = weights / group.
 * - layout : WHCN or CWHN.
 */

class Conv2d : public BuiltinOp {
 public:
  Conv2d(Graph* graph, PadType padding, const std::array<uint32_t, 2>& stride,
         const std::array<uint32_t, 2>& dilation, int32_t multiplier = 0,
         DataLayout input_layout = DataLayout::WHCN,
         DataLayout kernel_layout = DataLayout::WHIcOc);
  Conv2d(Graph* graph, const std::array<uint32_t, 4> pad,
         const std::array<uint32_t, 2>& stride,
         const std::array<uint32_t, 2>& dilation, int32_t multiplier = 0,
         DataLayout input_layout = DataLayout::WHCN,
         DataLayout kernel_layout = DataLayout::WHIcOc);
  Conv2d(Graph* graph, int32_t weights, PadType padding,
         const std::array<uint32_t, 2>& ksize,
         const std::array<uint32_t, 2>& stride,
         const std::array<uint32_t, 2>& dilation, int32_t multiplier = 0,
         DataLayout input_layout = DataLayout::WHCN,
         DataLayout kernel_layout = DataLayout::WHIcOc);
  Conv2d(Graph* graph, int32_t weights, PadType padding,
         const std::array<uint32_t, 2>& ksize,
         const std::array<uint32_t, 2>& stride,
         const std::array<uint32_t, 2>& dilation,
         const std::array<uint32_t, 4>& pad, int32_t multiplier = 0,
         DataLayout input_layout = DataLayout::WHCN,
         DataLayout kernel_layout = DataLayout::WHIcOc);

  DataLayout KernelDataLayout() { return kernel_layout_; }

  std::shared_ptr<Operation> Clone(
      std::shared_ptr<Graph>& graph) const override;

  const std::vector<std::shared_ptr<Tensor>> ConstantInputsTensor()
      const override;

 protected:
  const uint32_t weights_;
  const PadType padding_;
  const std::array<uint32_t, 2> ksize_;
  const std::array<uint32_t, 2> stride_;
  const std::array<uint32_t, 2> dilation_;
  const std::array<uint32_t, 4> pad_;
  const int32_t multiplier_;
  const DataLayout kernel_layout_;

 private:
  void OnBindInputPostProc(const std::shared_ptr<Tensor>& tensor,
                           int32_t input_idx) override;
};

}  // namespace ops
}  // namespace vx
}  // namespace tim

#endif /* TIM_VX_OPS_CONV2D_H_ */