From 247f52cfe337f7b2542b900e3d8cf122e9d4f11c Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Thu, 22 Mar 2018 11:24:56 +0000 Subject: 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 Reviewed-by: Anthony Barbier --- .../CL/functions/CLWinogradConvolutionLayer.cpp | 53 +++++++++++----------- 1 file changed, 26 insertions(+), 27 deletions(-) (limited to 'src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index 7af36bf06b..0aa7f8d1b5 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -39,21 +39,22 @@ CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptrinfo()->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + // Input shape + const TensorShape input_shape = input->info()->tensor_shape(); + // Kernel size const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width]; const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height]; - // Number of tiles along the X and Y direction - const unsigned int num_tiles_x = std::ceil((input->info()->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->info()->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->info(), *weights->info(), 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->info()->data_layout()); // Manage intermediate tensors _memory_group.manage(&_input0); @@ -62,17 +63,16 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we // Do not manage _input1 as it contains the weights // Configure input transform - _input_transform.configure(input, &_input0, conv_info, Size2D(kernel_w, kernel_h)); + _input_transform.configure(input, &_input0, winograd_info); // Configure filter transform - _filter_transform.configure(weights, &_input1, Size2D(2U, 2U)); + _filter_transform.configure(weights, &_input1, winograd_info); // Configure batched matrix multiply _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); // Configure output transform - _output_transform.configure(&_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)); + _output_transform.configure(&_batched_mm_output, biases, output, winograd_info); // Configure activation layer _is_activationlayer_enabled = act_info.enabled(); @@ -90,31 +90,32 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) { - // TODO(COMPMID-1013): This part will be removed // Get indeces 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); - ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h))); + ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, winograd_info)); // 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); - ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, Size2D(2U, 2U))); + ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, winograd_info)); // Validate batched matrix multiply TensorShape batched_mm_output_shape = input0.tensor_shape(); @@ -122,10 +123,8 @@ Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::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(CLWinogradOutputTransformKernel::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))); + // Configure output transform + ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, winograd_info)); // Validate Activation Layer if(act_info.enabled()) -- cgit v1.2.1