arm_compute/runtime/CL/functions/CLConvolutionLayer.h

/*
 * Copyright (c) 2017-2020 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
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 * The above copyright notice and this permission notice shall be included in all
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#ifndef ARM_COMPUTE_CLCONVOLUTIONLAYER_H
#define ARM_COMPUTE_CLCONVOLUTIONLAYER_H

#include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLFFTConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h"
#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"

#include <memory>

namespace arm_compute
{
/** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions:
 *
 * -# @ref CLGEMMConvolutionLayer
 * -# @ref CLWinogradConvolutionLayer
 * -# @ref CLDirectConvolutionLayer
 * -# @ref CLFFTConvolutionLayer
 *
 * The function selects one of the algorithms mentioned above based on:
 *      - The size of the kernel
 *      - Number of input/output feature maps
 *      - Amount of memory needed
 *
 * Generally GEMM-based convolution is executed when neither Winograd nor FFT nor Direct convolution can be performed.
 *
 * FP32 Algorithm| Filter Size                                                 |   Input/Output feature maps               |
 * --------------|-------------------------------------------------------------|-------------------------------------------|
 * Winograd      | 3x3 1x3 3x1 5x1 1x5 5x5(fast maths) 7x1 1x7                 |  Input channels is greater than 3         |
 * FFT           | Squared kernels and greater than 9x9                        |  Input feature maps > Output feature maps |
 * DirectConv    | 9x9                                                         |                                           |
 * GEMM          | Any size                                                    |                                           |
 *
 * Winograd 5x5 requires fast maths enabled.
 *
 * FP16 Algorithm| Filter Size                |   Input/Output feature maps               |
 * --------------|----------------------------|-------------------------------------------|
 * Winograd      | 3x3 1x3 3x1 5x1 1x5 5x5    |  Input channels is greater than 3         |
 * FFT           | Not supported              |                                           |
 * DirectConv    | 9x9                        |                                           |
 * GEMM          | Any size                   |                                           |
 *
 * Winograd FP16 requires fast maths enabled.
 *
 */
class CLConvolutionLayer : public IFunction
{
public:
    /** Default constructor */
    CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Set the input and output tensors.
     *
     * @param[in]  input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
     *                              while every optional dimension from 4 and above represent a batch of inputs.
     *                              Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
     * @param[in]  weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
     *                              Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
     * @param[in]  biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
     *                              Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
     * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                              Data types supported: Same as @p input.
     * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo.
     * @param[in]  weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input.
     * @param[in]  dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in]  enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
     *                              available which may introduce a drop of accuracy as well. Default is false
     * @param[in]  num_groups       (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
     */
    void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
                   const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1);
    /** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayer
     *
     * @param[in] input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
     *                             while every optional dimension from 4 and above represent a batch of inputs.
     *                             Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
     * @param[in] weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
     *                             Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
     * @param[in] biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input.
     * @param[in] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                             Data types supported: Same as @p input.
     * @param[in] conv_info        Contains padding and stride information described in @ref PadStrideInfo.
     * @param[in] weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel.
     * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
     *                             available which may introduce a drop of accuracy as well. Default is false
     * @param[in] num_groups       (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                           const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false,
                           unsigned int num_groups = 1);
    /** Static function to check if given info will return the convolution called by @ref CLConvolutionLayer
     *
     * @param[in] input            Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
     *                             while every optional dimension from 4 and above represent a batch of inputs.
     *                             Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
     * @param[in] weights          Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
     *                             Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
     * @param[in] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                             Data types supported: Same as @p input.
     * @param[in] conv_info        Contains padding and stride information described in @ref PadStrideInfo.
     * @param[in] weights_info     Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel.
     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in] gpu_target       Specifies the @p GPUTarget.
     * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
     *                             available which may introduce a drop of accuracy as well. Default is false
     *
     * @return a status
     */
    static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                                    const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U), bool enable_fast_math = false);
    // Inherited methods overridden:
    void run() override;
    void prepare() override;

private:
    std::shared_ptr<IMemoryManager> _memory_manager;
    std::unique_ptr<IFunction>      _function;
};
}
#endif /* ARM_COMPUTE_CLCONVOLUTIONLAYER_H */