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;