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>
diff --git a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
index c4ae574..7115710 100644
--- a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
@@ -48,22 +48,30 @@
~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 15cd6e2..2d1eadf 100644
--- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
@@ -46,28 +46,38 @@
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 35117c6..b0d0bbe 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -48,31 +48,39 @@
~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;