From 84ce1f9d5d63c7fcfa5ac3f52e4de5bbb9ccb886 Mon Sep 17 00:00:00 2001 From: Vidhya Sudhan Loganathan Date: Wed, 25 Apr 2018 13:00:09 +0100 Subject: COMPMID-718 : Winograd: add validate method and tests Changed API's to use winograd_info struct instead of individual params Modified validation to test Validate API Change-Id: I796650092165069e2067e02ace3f42a43f545779 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/128991 Reviewed-by: Anthony Barbier Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- src/runtime/NEON/functions/NEWinogradLayer.cpp | 33 ++++++++++++-------------- 1 file changed, 15 insertions(+), 18 deletions(-) (limited to 'src/runtime') diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp index 7f4761020c..264b97f7c1 100644 --- a/src/runtime/NEON/functions/NEWinogradLayer.cpp +++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp @@ -270,31 +270,32 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we // Get indices for the width and height const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + // Input shape + const TensorShape input_shape = input->tensor_shape(); // Kernel size const unsigned int kernel_w = weights->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->tensor_shape()[idx_height]; - // Number of tiles along the X and Y direction - const unsigned int num_tiles_x = std::ceil((input->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); - const unsigned int num_tiles_y = std::ceil((input->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); - - // Compute output shape - const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); + const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + Size2D(kernel_w, kernel_h), + Size2D(input_shape[idx_width], input_shape[idx_height]), + conv_info, + input->data_layout()); // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, Size2D(kernel_w, kernel_h)); + const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); switch(weights->dimension(0)) { case 3: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); break; } case 5: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); break; } default: @@ -304,19 +305,19 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we } } // Validate filter transform - const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, Size2D(2U, 2U)); + const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); switch(weights->dimension(0)) { case 3: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, Size2D(2U, 2U)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); break; } case 5: { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, Size2D(2U, 2U)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); break; } default: @@ -336,9 +337,7 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)))); // Validate output transform - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], - output_convolved_shape[idx_height]), - Size2D(num_tiles_x, num_tiles_y)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); break; } case 5: @@ -346,9 +345,7 @@ Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *we ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)))); // Validate output transform - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], - output_convolved_shape[idx_height]), - Size2D(num_tiles_x, num_tiles_y)))); + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); break; } default: -- cgit v1.2.1