text: /*
   * 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
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, 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,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #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);
      /** Default Destructor */
      ~CLConvolutionLayer();
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      CLConvolutionLayer(const CLConvolutionLayer &) = delete;
      /** Default move constructor */
      CLConvolutionLayer(CLConvolutionLayer &&) = default;
      /** Prevent instances of this class from being copied (As this class contains pointers) */
      CLConvolutionLayer &operator=(const CLConvolutionLayer &) = delete;
      /** Default move assignment operator */
      CLConvolutionLayer &operator=(CLConvolutionLayer &&) = default;
      /** 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);
      /** Set the input and output tensors.
       *
       * @param[in]  compile_context  The compile context to be used.
       * @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(const CLCompileContext &compile_context, 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 */
  