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 --- arm_compute/core/utils/misc/ShapeCalculator.h | 50 +++++++++++++++++---------- 1 file changed, 31 insertions(+), 19 deletions(-) (limited to 'arm_compute/core/utils') diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 8816819bcd..c3d5b64a92 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -196,31 +196,35 @@ inline TensorShape compute_fully_connected_reshaped_weights_shape(const ITensorI return output_shape; } -inline TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const Size2D &output_tile) +inline TensorShape compute_winograd_filter_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info) { TensorShape tensor_shape{ input.tensor_shape() }; - tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH)); - tensor_shape.set(Window::DimY, input.dimension(2)); - tensor_shape.set(Window::DimZ, (output_tile.width == 2) ? 16 : 36); + const Size2D kernel_size = winograd_info.kernel_size; + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1); - if(input.data_layout() == DataLayout::NCHW) - { - tensor_shape.set(Window::DimX, input.dimension(3)); - } + tensor_shape.remove_dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::WIDTH)); + tensor_shape.set(Window::DimX, input.dimension(3)); + tensor_shape.set(Window::DimY, input.dimension(get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL))); + tensor_shape.set(Window::DimZ, input_tile_size.area()); return tensor_shape; } - -inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const PadStrideInfo &conv_info, const Size2D &kernel_size) +inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info) { + const PadStrideInfo conv_info = winograd_info.convolution_info; + const Size2D kernel_size = winograd_info.kernel_size; + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D input_tile_size = Size2D(output_tile_size.width + kernel_size.width - 1, output_tile_size.height + kernel_size.height - 1); + // Compute height - const unsigned int num_tiles_x = std::ceil((input.tensor_shape().x() - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); - const unsigned int num_tiles_y = std::ceil((input.tensor_shape().y() - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); + const unsigned int num_tiles_x = std::ceil((input.tensor_shape().x() - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast(output_tile_size.width)); + const unsigned int num_tiles_y = std::ceil((input.tensor_shape().y() - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast(output_tile_size.height)); const unsigned int width = input.tensor_shape()[get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL)]; const unsigned int height = num_tiles_x * num_tiles_y; - const unsigned int depth = 16; // COMPMID-990 + const unsigned int depth = input_tile_size.area(); TensorShape output_shape{ input.tensor_shape() }; output_shape.set(0, width); @@ -229,14 +233,24 @@ inline TensorShape compute_winograd_input_transform_shape(const ITensorInfo &inp return output_shape; } - -inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &input, const Size2D &output_convolved_dims, DataLayout data_layout) +inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &input, const WinogradInfo &winograd_info) { + const PadStrideInfo conv_info = winograd_info.convolution_info; + const Size2D kernel_size = winograd_info.kernel_size; + const Size2D input_dimensions = winograd_info.input_dimensions; + const DataLayout data_layout = winograd_info.output_data_layout; + + // Compute output shape + unsigned int output_width = 0; + unsigned int output_height = 0; + std::tie(output_width, output_height) = scaled_dimensions(input_dimensions.width, input_dimensions.height, + kernel_size.width, kernel_size.height, conv_info); + TensorShape tensor_shape{ input.tensor_shape() }; // Output dimension - const unsigned int out_w = output_convolved_dims.width; - const unsigned int out_h = output_convolved_dims.height; + const unsigned int out_w = output_width; + const unsigned int out_h = output_height; const unsigned int out_c = input.dimension(0); tensor_shape.set(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH), out_w); @@ -245,7 +259,6 @@ inline TensorShape compute_winograd_output_transform_shape(const ITensorInfo &in return tensor_shape; } - inline TensorShape compute_deep_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info) { const TensorShape input_shape{ input.tensor_shape() }; @@ -271,7 +284,6 @@ inline TensorShape compute_deep_convolution_shape(const ITensorInfo &input, cons return output_shape; } - inline TensorShape compute_min_max_shape(const ITensorInfo *input) { TensorShape output_shape{ input->tensor_shape() }; -- cgit v1.2.1