TIM-VX/include/tim/vx/ops/conv2d.h

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4.3 KiB
C++

/****************************************************************************
*
* Copyright (c) 2020-2023 Vivante Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
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* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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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).
* - 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_;
#if defined(__clang__) && (__clang_major__ >= 15)
#define TIM_VX_OPS_CONV2D_WITH_F16BIAS 1
private:
void OnBindInputPostProc(const std::shared_ptr<Tensor>& tensor,
int32_t input_idx) override;
#endif
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_CONV2D_H_ */