COMPMID-1013 - Create WinogradInfo data structure

COMPMID-1014 - Refactoring Winograd's dataset

Change-Id: I6abdcbf9a90d663f4db666cd410afece9f1d034d
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125899
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

3 files changed, 60 insertions, 34 deletions

diff --git a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
index c4ae5745b8..7115710d59 100644
--- a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
@@ -48,22 +48,30 @@ public:
     ~CLWinogradFilterTransformKernel() = default;
     /** Set the input and output tensor.
      *
-     * @param[in]  input       Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
-     *                         kernel_x must be 3 and equal to kernel_y. Data types supported: F32.
-     * @param[out] output      Destination tensor. The output is a 3D tensor with dimensions [OFM, IFM, 16]. Data type supported: same as @p input
-     * @param[in]  output_tile Output tile. Currently only 2x2 and 4x4 tiles are supported.
+     * @note Winograd filter transform supports the following configurations:
+     *       Output tile size: 2x2, 4x4
+     *       Kernel size: 3x3
+     *       Strides: only unit strides
+     *
+     * @param[in]  input         Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). Data types supported: F32.
+     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. Data types supported: Same as @p input
+     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
      */
-    void configure(const ICLTensor *input, ICLTensor *output, const Size2D &output_tile);
+    void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradFilterTransformKernel
      *
-     * @param[in] input       Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
-     *                        kernel_x must be 3 and equal to kernel_y. Data types supported: F32.
-     * @param[in] output      Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16]. Data type supported: same as @p input
-     * @param[in] output_tile Output tile. Currently only 2x2 and 4x4 tiles are supported.
+     * @note Winograd filter transform supports the following configurations:
+     *       Output tile size: 2x2, 4x4
+     *       Kernel size: 3x3
+     *       Strides: only unit strides
+     *
+     * @param[in]  input         Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). Data types supported: F32.
+     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_filter_transform_shape. Data types supported: Same as @p input
+     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &output_tile);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
diff --git a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
index 15cd6e2649..2d1eadf3cf 100644
--- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
@@ -46,28 +46,38 @@ public:
     CLWinogradInputTransformKernel &operator=(CLWinogradInputTransformKernel &&) = default;
     /** Set the input and output of the kernel.
      *
-     * @param[in] input       The input tensor to permute. Data types supported: F32
-     * @param[in] output      The output tensor. Data types supported: Same as @p input
-     * @param[in] conv_info   Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
-     * @param[in] kernel_dims Kernel dimensions. Currently only 3x3 kernels are supported
+     * @note Winograd input transform supports the following configurations:
+     *       Output tile size: 2x2
+     *       Kernel size: 3x3
+     *       Strides: only unit strides
+     *
+     * @param[in] input         The input tensor to transform. Data types supported: F32
+     * @param[in] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
+     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo.
      */
-    void configure(const ICLTensor *input, ICLTensor *output, const PadStrideInfo &conv_info, const Size2D &kernel_dims);
+    void configure(const ICLTensor *input, ICLTensor *output, const WinogradInfo &winograd_info);
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransformKernel
      *
-     * @param[in] input       First tensor input info. Data types supported: F32.
-     * @param[in] output      Output tensor info. Data types supported: same as @p input.
-     * @param[in] conv_info   Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
-     * @param[in] kernel_dims Kernel dimensions. Currently only 3x3 kernels are supported
+     * @note Winograd input transform supports the following configurations:
+     *       Output tile size: 2x2
+     *       Kernel size: 3x3
+     *       Strides: only unit strides
+     *
+     * @param[in] input         The input tensor to transform. Data types supported: F32
+     * @param[in] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_input_transform_shape. Data types supported: Same as @p input
+     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo.
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PadStrideInfo &conv_info, const Size2D &kernel_dims);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
     BorderSize border_size() const override;
 
 private:
+    using WinogradKey = std::pair<std::pair<int, int>, std::pair<int, int>>;
+
     BorderSize       _border_size;
     const ICLTensor *_input;
     ICLTensor       *_output;
diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
index 35117c65db..b0d0bbeeaa 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -48,31 +48,39 @@ public:
     ~CLWinogradOutputTransformKernel() = default;
     /** Set the input and output tensor.
      *
-     * @param[in]  input                 Source tensor with shape [C, N, 16, batches]. Data types supported: F32.
-     * @param[in]  bias                  Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
-     * @param[out] output                Destination tensor with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
-     * @param[in]  kernel_dims           Kernel dimensions (Width and height). Currently only supported 3x3 kernels
-     * @param[in]  output_convolved_dims Output dimensions after the convolution (Width and height)
-     * @param[in]  num_tiles             Number of tiles of size 2x2 in the output tensor along the X and Y direction
+     * @note Winograd output transform supports the following configurations:
+     *       Output tile size: 2x2
+     *       Kernel size: 3x3
+     *       Strides: only unit strides
+     *
+     * @param[in]  input         Source tensor with shape [C, N, 16, batches]. Data types supported: F32.
+     * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
+     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
+     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
      */
-    void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const Size2D &kernel_dims, const Size2D &output_convolved_dims, const Size2D &num_tiles);
+    void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info);
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel
      *
-     * @param[in]  input                 Source tensor with shape [C, N, 16, batches]. Data types supported: F32.
-     * @param[in]  bias                  Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
-     * @param[out] output                Destination tensor with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
-     * @param[in]  kernel_dims           Kernel dimensions (Width and height). Currently only supported 3x3 kernels
-     * @param[in]  output_convolved_dims Output dimensions after the convolution (Width and height)
-     * @param[in]  num_tiles             Number of tiles of size 2x2 in the output tensor along the X and Y direction
+     * @note Winograd output transform supports the following configurations:
+     *       Output tile size: 2x2
+     *       Kernel size: 3x3
+     *       Strides: only unit strides
+     *
+     * @param[in]  input         Source tensor with shape [C, N, 16, batches]. Data types supported: F32.
+     * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
+     * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
+     * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const Size2D &kernel_dims, const Size2D &output_convolved_dims, const Size2D &num_tiles);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
 
 private:
+    using WinogradKey = std::pair<std::pair<int, int>, std::pair<int, int>>;
+
     const ICLTensor *_input;
     const ICLTensor *_bias;
     ICLTensor       *_output;