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 --- tests/validation/reference/Winograd.cpp | 504 +++++++++++++++++--------------- 1 file changed, 271 insertions(+), 233 deletions(-) (limited to 'tests/validation/reference/Winograd.cpp') diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp index ad0dcbd958..604e25214b 100644 --- a/tests/validation/reference/Winograd.cpp +++ b/tests/validation/reference/Winograd.cpp @@ -28,6 +28,8 @@ #include "arm_compute/core/Types.h" +#include + namespace arm_compute { namespace test @@ -39,153 +41,155 @@ namespace reference namespace { template -void winograd_filter_transform3x3(const SimpleTensor &in, SimpleTensor &out, const Size2D &output_tile) +void initialize_matrix_transform(SimpleTensor &src, const Size2D &output_tile_size, const Size2D &kernel_size, WinogradTransformType winograd_transform_type) { - const bool is_2x2 = (output_tile.width == 2); - const unsigned int transf_side = is_2x2 ? 4u : 6u; + ARM_COMPUTE_ERROR_ON((output_tile_size != Size2D(2U, 2U)) && (output_tile_size != Size2D(4U, 4U))); + ARM_COMPUTE_ERROR_ON(kernel_size != Size2D(3U, 3U)); - // Simple tensor for the 3x3 input tile - SimpleTensor input_tile{ TensorShape(3u, 3u), in.data_type(), 1 }; + // Winograd input transform matrices + static const float imatrix2x2_3x3[] = + { + 1.0f, 0.0f, -1.0f, 0.0f, + 0.0f, 1.0f, 1.0f, 0.0f, + 0.0f, -1.0f, 1.0f, 0.0f, + 0.0f, 1.0f, 0.0f, -1.0f + }; - // Simple tensor for the transformation matrix - SimpleTensor trans_matrix{ TensorShape(3u, transf_side), in.data_type(), 1 }; + static const float imatrix4x4_3x3[] = + { + 4.0f, 0.0f, -5.0f, 0.0f, 1.0f, 0.0f, + 0.0f, -4.0f, -4.0f, 1.0f, 1.0f, 0.0f, + 0.0f, 4.0f, -4.0f, -1.0f, 1.0f, 0.0f, + 0.0f, -2.0f, -1.0f, 2.0f, 1.0f, 0.0f, + 0.0f, 2.0f, -1.0f, -2.0f, 1.0f, 0.0f, + 0.0f, 4.0f, 0.0f, -5.0f, 0.0f, 1.0f, + }; + + // ------------------------------------------ + + // Winograd filter transform matrices + static const float fmatrix2x2_3x3[] = + { + 1.0f, 0.0f, 0.0f, + 0.5f, 0.5f, 0.5f, + 0.5f, -0.5f, 0.5f, + 0.0f, 0.0f, 1.0f + }; - // Simple tensor for the transformation matrix transpose - SimpleTensor trans_matrix_transposed{ TensorShape(transf_side, 3u), in.data_type(), 1 }; + static const float fmatrix4x4_3x3[] = + { + 0.25f, 0.0f, 0.0f, + -1.0f / 6.0f, -1.0f / 6.0f, -1.0f / 6.0f, + -1.0f / 6.0f, 1.0f / 6.0f, -1.0f / 6.0f, + 1.0f / 24.0f, 1.0f / 12.0f, 1.0f / 6.0f, + 1.0f / 24.0f, -1.0f / 12.0f, 1.0f / 6.0f, + 0.0f, 0.0f, 1.0f + }; + + // ------------------------------------------ + + // Winograd output transform matrices + static const float omatrix2x2_3x3[] = + { + 1.0f, 1.0f, 1.0f, 0.0f, + 0.0f, 1.0f, -1.0f, -1.0f + }; + + static const float omatrix4x4_3x3[] = + { + 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.0f, + 0.0f, 1.0f, -1.0f, 2.0f, -2.0f, 0.0f, + 0.0f, 1.0f, 1.0f, 4.0f, 4.0f, 0.0f, + 0.0f, 1.0f, -1.0f, 8.0f, -8.0f, 1.0f + }; - // Simple tensor for the 3xSide temporary tile - SimpleTensor tmp_tile{ TensorShape(3u, transf_side), in.data_type(), 1 }; + // ------------------------------------------ - // Simple tensor for the SidexSide output tile - SimpleTensor transf_tile{ TensorShape(transf_side, transf_side), in.data_type(), 1 }; + using WinogradKey = std::tuple, std::pair, WinogradTransformType>; - if(is_2x2) + // Key = (Output tile size, Kernel size, Winograd transform type) + static std::map matrix_map = { - // Initialize 3x4 transformation matrix - // 1 | 0 | 0 - // 0.5 | 0.5 | 0.5 - // 0.5 |-0.5 | 0.5 - // 0 | 0 | 1 - trans_matrix[0 + 0 * 3] = 1.0f; - trans_matrix[1 + 0 * 3] = 0.0f; - trans_matrix[2 + 0 * 3] = 0.0f; - trans_matrix[0 + 1 * 3] = 0.5f; - trans_matrix[1 + 1 * 3] = 0.5f; - trans_matrix[2 + 1 * 3] = 0.5f; - trans_matrix[0 + 2 * 3] = 0.5f; - trans_matrix[1 + 2 * 3] = -0.5f; - trans_matrix[2 + 2 * 3] = 0.5f; - trans_matrix[0 + 3 * 3] = 0.0f; - trans_matrix[1 + 3 * 3] = 0.0f; - trans_matrix[2 + 3 * 3] = 1.0f; + { WinogradKey(std::pair(2, 2), std::pair(3, 3), WinogradTransformType::INPUT), imatrix2x2_3x3 }, + { WinogradKey(std::pair(4, 4), std::pair(3, 3), WinogradTransformType::INPUT), imatrix4x4_3x3 }, + { WinogradKey(std::pair(2, 2), std::pair(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 }, + { WinogradKey(std::pair(4, 4), std::pair(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 }, + { WinogradKey(std::pair(2, 2), std::pair(3, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3 }, + { WinogradKey(std::pair(4, 4), std::pair(3, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3 }, + }; + + // Find input matrix transform + std::map::iterator it; + + it = matrix_map.find(WinogradKey(std::pair(output_tile_size.width, output_tile_size.height), + std::pair(kernel_size.width, kernel_size.height), + winograd_transform_type)); + + float const *matrix_values = nullptr; + if(it != matrix_map.end()) + { + // Get matrix pointer + matrix_values = it->second; } else { - // Initialize 3x6 transformation matrix - // 1/4 | 0 | 0 - // -1/6 | -1/6 | -1/6 - // -1/6 | 1/6 | -1/6 - // 1/24 | 1/12 | 1/6 - // 1/24 | -1/12 | 1/6 - // 0 | 0 | 1 - trans_matrix[0 + 0 * 3] = 1.0f / 4.0f; - trans_matrix[1 + 0 * 3] = 0.0f; - trans_matrix[2 + 0 * 3] = 0.0f; - trans_matrix[0 + 1 * 3] = -1.0f / 6.0f; - trans_matrix[1 + 1 * 3] = -1.0f / 6.0f; - trans_matrix[2 + 1 * 3] = -1.0f / 6.0f; - trans_matrix[0 + 2 * 3] = -1.0f / 6.0f; - trans_matrix[1 + 2 * 3] = 1.0f / 6.0f; - trans_matrix[2 + 2 * 3] = -1.0f / 6.0f; - trans_matrix[0 + 3 * 3] = 1.0f / 24.0f; - trans_matrix[1 + 3 * 3] = 1.0f / 12.0f; - trans_matrix[2 + 3 * 3] = 1.0f / 6.0f; - trans_matrix[0 + 4 * 3] = 1.0f / 24.0f; - trans_matrix[1 + 4 * 3] = -1.0f / 12.0f; - trans_matrix[2 + 4 * 3] = 1.0f / 6.0f; - trans_matrix[0 + 5 * 3] = 0.0f; - trans_matrix[1 + 5 * 3] = 0.0f; - trans_matrix[2 + 5 * 3] = 1.0f; + ARM_COMPUTE_ERROR("Winograd configuration not supported"); } - // Transpose the transformation matrix - transpose_matrix(trans_matrix, trans_matrix_transposed); + // Copy values + std::copy(&matrix_values[0], &matrix_values[0] + src.num_elements(), &src[0]); +} +} // namespace - const int num_channels = in.shape()[2]; - const int num_filters = in.shape()[3]; - const int num_batches = in.shape().total_size() / (9 * num_channels * num_filters); +template +SimpleTensor winograd_input_transform(const SimpleTensor &in, const TensorShape &output_shape, const WinogradInfo &winograd_info) +{ + ARM_COMPUTE_ERROR_ON(in.data_layout() != DataLayout::NCHW); - for(int n = 0; n < num_batches; ++n) - { - for(int w = 0; w < num_filters; ++w) - { - for(int z = 0; z < num_channels; ++z) - { - // Load the 3x3 tile from the input tensor - get_tile(in, input_tile, Coordinates(0, 0, z, w, n)); + const PadStrideInfo conv_info = winograd_info.convolution_info; + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D kernel_size = winograd_info.kernel_size; - // First transformation - matrix_multiply(trans_matrix, input_tile, tmp_tile); + SimpleTensor out{ output_shape, in.data_type() }; - // Second transformation - matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile); + // Calculate dimensions for the tile + const unsigned int tile_w = output_tile_size.width + kernel_size.width - 1; + const unsigned int tile_h = output_tile_size.height + kernel_size.height - 1; - // Store the 4x4 output tile across the 16 channels - const int output_offset = w + z * num_filters; + TensorShape tile_dims(tile_w, tile_h); - for(unsigned int out_h = 0, out_pos = 0; out_h < transf_side; ++out_h) - { - for(unsigned int out_w = 0; out_w < transf_side; ++out_w, ++out_pos) - { - out[output_offset + out_pos * num_filters * num_channels] = transf_tile[out_w + out_h * transf_side]; - } - } - } - } - } -} + // Simple tensor for the input tile + SimpleTensor src_tile{ tile_dims, in.data_type() }; -template -void winograd_input_transform3x3(const SimpleTensor &src, SimpleTensor &dst, const PadStrideInfo &conv_info) -{ - TensorShape shape4x4(4u, 4u); - - // Simple tensor for the 4x4 input tile - SimpleTensor src_tile{ shape4x4, src.data_type() }; + // Simple tensor for the temporary tile + SimpleTensor tmp_tile{ tile_dims, in.data_type() }; - // Simple tensor for the 4x4 temporary tile - SimpleTensor tmp_tile{ shape4x4, src.data_type() }; - - // Simple tensor for the 4x4 output tile - SimpleTensor dst_tile{ shape4x4, src.data_type() }; + // Simple tensor for the output tile + SimpleTensor dst_tile{ tile_dims, in.data_type() }; // Simple tensor for the transformation matrix - SimpleTensor matrix{ shape4x4, src.data_type() }; + SimpleTensor matrix{ tile_dims, in.data_type() }; // Simple tensor for the transformation matrix transposed - SimpleTensor matrix_transposed{ shape4x4, src.data_type() }; - - const float matrix_values[] = { 1.f, 0.f, -1.f, 0.f, - 0.f, 1.f, 1.f, 0.f, - 0.f, -1.f, 1.f, 0.f, - 0.f, 1.f, 0.f, -1.f - }; + SimpleTensor matrix_transposed{ tile_dims, in.data_type() }; - for(int i = 0; i < matrix.num_elements(); ++i) - { - matrix[i] = matrix_values[i]; - } + // Initialize matrix for the input transform + initialize_matrix_transform(matrix, output_tile_size, kernel_size, WinogradTransformType::INPUT); + // Transpose matrix transpose_matrix(matrix, matrix_transposed); - const int in_w = src.shape().x(); - const int in_h = src.shape().y(); - const int in_d = src.shape().z(); - const int num_batches = src.shape().total_size() / (in_w * in_h * in_d); - const int num_tiles_x = std::ceil((in_w - 2 + conv_info.pad_left() + conv_info.pad_right()) / 2.0f); - const int num_tiles_y = std::ceil((in_h - 2 + conv_info.pad_top() + conv_info.pad_bottom()) / 2.0f); + const int in_w = in.shape().x(); + const int in_h = in.shape().y(); + const int in_d = in.shape().z(); + const int out_d = out.shape().z(); + const int num_batches = in.shape().total_size() / (in_w * in_h * in_d); + const int num_tiles_x = std::ceil((in_w - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast(output_tile_size.width)); + const int num_tiles_y = std::ceil((in_h - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast(output_tile_size.height)); + const int step_x = output_tile_size.width; + const int step_y = output_tile_size.height; - ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast(dst.shape().y())); + ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast(out.shape().y())); for(int b = 0; b < num_batches; ++b) { @@ -195,61 +199,154 @@ void winograd_input_transform3x3(const SimpleTensor &src, SimpleTensor &ds { for(int x = 0; x < num_tiles_x; ++x) { - int xi = x * 2 - conv_info.pad_left(); - int yi = y * 2 - conv_info.pad_top(); + int xi = x * step_x - conv_info.pad_left(); + int yi = y * step_y - conv_info.pad_top(); - // Get the 4x4 tile from the input tensor - get_tile(src, src_tile, Coordinates(xi, yi, z, b)); + // Get the tile from the input tensor + get_tile(in, src_tile, Coordinates(xi, yi, z, b)); // Compute the transformation matrix_multiply(matrix, src_tile, tmp_tile); matrix_multiply(tmp_tile, matrix_transposed, dst_tile); - // Store the 4x4 output tile across the 16 channels - for(int i = 0; i < 16; ++i) + // Store the output tile across the channels + for(int i = 0; i < out_d; ++i) { int xo = z; int yo = x + y * num_tiles_x; - dst[coords2index(dst.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i]; + out[coords2index(out.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i]; } } } } } + + return out; } template -void winograd_output_transform3x3(const SimpleTensor &in, SimpleTensor &out, int num_tiles_x) +SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const WinogradInfo &winograd_info) { - ARM_COMPUTE_ERROR_ON(in.shape()[2] != 16); + ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); + + // Create reference + SimpleTensor out{ output_shape, in.data_type(), 1 }; + + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D kernel_size = winograd_info.kernel_size; + + TensorShape kernel_tile_dims(kernel_size.width, kernel_size.height); + + // Calculate dimensions for the tile + const unsigned int input_tile_w = output_tile_size.width + kernel_size.width - 1; + const unsigned int input_tile_h = output_tile_size.height + kernel_size.height - 1; + const unsigned int input_tile_area = input_tile_w * input_tile_h; + + // Simple tensor for the input tile + SimpleTensor input_tile{ kernel_tile_dims, in.data_type(), 1 }; + + // Simple tensor for the transformation matrix + SimpleTensor trans_matrix{ TensorShape(kernel_tile_dims[0], input_tile_w), in.data_type(), 1 }; + + // Simple tensor for the transformation matrix transpose + SimpleTensor trans_matrix_transposed{ TensorShape(input_tile_w, kernel_tile_dims[0]), in.data_type(), 1 }; + + // Simple tensor for the temporary tile + SimpleTensor tmp_tile{ TensorShape(kernel_tile_dims[0], input_tile_w), in.data_type(), 1 }; + + // Simple tensor for the output tile + SimpleTensor transf_tile{ TensorShape(input_tile_w, input_tile_w), in.data_type(), 1 }; + + // Initialize matrix for the filter transform + initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::FILTER); + + // Transpose the transformation matrix + transpose_matrix(trans_matrix, trans_matrix_transposed); + + const int num_channels = in.shape()[2]; + const int num_filters = in.shape()[3]; + const int num_batches = in.shape().total_size() / (kernel_size.area() * num_channels * num_filters); + + for(int n = 0; n < num_batches; ++n) + { + for(int w = 0; w < num_filters; ++w) + { + for(int z = 0; z < num_channels; ++z) + { + // Load the tile from the input tensor + get_tile(in, input_tile, Coordinates(0, 0, z, w, n)); + + // First transformation + matrix_multiply(trans_matrix, input_tile, tmp_tile); + + // Second transformation + matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile); + + // Store the output tile across the channels + const int output_offset = w + z * num_filters; + + // Store the values across the channels + for(unsigned int i = 0; i < input_tile_area; ++i) + { + out[output_offset + i * num_filters * num_channels] = transf_tile[i]; + } + } + } + } + + return out; +} + +template +SimpleTensor winograd_output_transform(const SimpleTensor &in, const TensorShape &output_shape, const WinogradInfo &winograd_info) +{ + ARM_COMPUTE_ERROR_ON_MSG(winograd_info.output_data_layout != DataLayout::NCHW, "Only supported NCHW data format"); + + const PadStrideInfo conv_info = winograd_info.convolution_info; + const Size2D input_dimensions = winograd_info.input_dimensions; + const Size2D output_tile_size = winograd_info.output_tile_size; + const Size2D kernel_size = winograd_info.kernel_size; + + // Create reference + SimpleTensor out{ output_shape, in.data_type(), 1 }; + + // Calculate dimensions for the tiles + const unsigned int in_tile_w = output_tile_size.width + kernel_size.width - 1; + const unsigned int in_tile_h = output_tile_size.height + kernel_size.height - 1; + const unsigned int out_tile_w = output_tile_size.width; + const unsigned int out_tile_h = output_tile_size.height; + + ARM_COMPUTE_ERROR_ON(in.shape()[2] != (in_tile_w * in_tile_h)); ARM_COMPUTE_ERROR_ON(in.shape()[0] != out.shape()[2]); - // Simple tensor for the 3x3 input tile - SimpleTensor input_tile{ TensorShape(4u, 4u), in.data_type(), 1 }; + // Compute tile dimensions + // Input tile dimensions + TensorShape in_tile_dims(in_tile_w, in_tile_h); + + // Output tile dimensions + TensorShape out_tile_dims(output_tile_size.width, output_tile_size.height); + + // Transformation matrix dimensions + TensorShape tr_tile_dims(in_tile_w, output_tile_size.width); + + // Create tensors + // Simple tensor for the input tile + SimpleTensor input_tile{ in_tile_dims, in.data_type(), 1 }; // Simple tensor for the transformation matrix - SimpleTensor trans_matrix{ TensorShape(4u, 2u), in.data_type(), 1 }; + SimpleTensor trans_matrix{ tr_tile_dims, in.data_type(), 1 }; // Simple tensor for the transformation matrix transpose - SimpleTensor trans_matrix_transposed{ TensorShape(2u, 4u), in.data_type(), 1 }; - - // Simple tensor for the 4x3 temporary tile - SimpleTensor tmp_tile{ TensorShape(4u, 2u), in.data_type(), 1 }; - - // Simple tensor for the 4x4 output tile - SimpleTensor output_tile{ TensorShape(2u, 2u), in.data_type(), 1 }; - - // Initialize transformation matrix - // 1 | 1 | 1 | 1 - // 0 | 1 | -1 | -1 - trans_matrix[0 + 0 * 4] = 1.0f; - trans_matrix[1 + 0 * 4] = 1.0f; - trans_matrix[2 + 0 * 4] = 1.0f; - trans_matrix[3 + 0 * 4] = 0.0f; - trans_matrix[0 + 1 * 4] = 0.0f; - trans_matrix[1 + 1 * 4] = 1.0f; - trans_matrix[2 + 1 * 4] = -1.0f; - trans_matrix[3 + 1 * 4] = -1.0f; + SimpleTensor trans_matrix_transposed{ TensorShape(tr_tile_dims[1], tr_tile_dims[0]), in.data_type(), 1 }; + + // Simple tensor for the temporary tile + SimpleTensor tmp_tile{ tr_tile_dims, in.data_type(), 1 }; + + // Simple tensor for the output tile + SimpleTensor output_tile{ out_tile_dims, in.data_type(), 1 }; + + // Initialize matrix for the output transform + initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::OUTPUT); // Transpose the transformation matrix transpose_matrix(trans_matrix, trans_matrix_transposed); @@ -272,13 +369,22 @@ void winograd_output_transform3x3(const SimpleTensor &in, SimpleTensor &ou const int stridez_out = stridey_out * h_out; const int stridew_out = stridez_out * c_out; + // Compute number of elements to process in the X and Y direction + const int num_elements_x = input_dimensions.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right(); + const int num_elements_y = input_dimensions.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom(); + const int num_tiles_x = std::ceil(num_elements_x / static_cast(output_tile_size.width)); + const int num_tiles_y = std::ceil(num_elements_y / static_cast(output_tile_size.height)); + + ARM_COMPUTE_UNUSED(num_tiles_y); + ARM_COMPUTE_ERROR_ON(in.shape()[1] != static_cast(num_tiles_x * num_tiles_y)); + for(int n = 0; n < num_batches; ++n) { for(int y = 0; y < h_in; ++y) { for(int x = 0; x < w_in; ++x) { - // Load the 4x4 tile across the 16 channels of the input tensor + // Load the input tile tile across the channels of the input tensor for(int z = 0; z < c_in; ++z) { input_tile[z] = in[x + (y * stridey_in) + (z * stridez_in) + (n * stridew_in)]; @@ -290,102 +396,34 @@ void winograd_output_transform3x3(const SimpleTensor &in, SimpleTensor &ou // Second transformation matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile); - // Store the 2x2 output tile - const int xo = (y % num_tiles_x) * 2; - const int yo = (y / num_tiles_x) * 2; + // Store the output tile + const int xo = (y % num_tiles_x) * out_tile_w; + const int yo = (y / num_tiles_x) * out_tile_h; const int zo = x; - const int output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out); - out[output_offset + 0 * stridey_out + 0] = output_tile[0 + 0 * 2]; - - // Check out-of-bound writes - if(xo + 1 < w_out) - { - out[output_offset + 0 * stridey_out + 1] = output_tile[1 + 0 * 2]; - } - - if(yo + 1 < h_out) - { - out[output_offset + 1 * stridey_out + 0] = output_tile[0 + 1 * 2]; - } + const int output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out); - if((yo + 1 < h_out) && (xo + 1 < w_out)) + for(int yi = 0; yi < static_cast(out_tile_h); ++yi) { - out[output_offset + 1 * stridey_out + 1] = output_tile[1 + 1 * 2]; + for(int xi = 0; xi < static_cast(out_tile_w); ++xi) + { + // Check out-of-bound writes + if((xo + xi < w_out) && (yo + yi < h_out)) + { + out[output_offset + yi * stridey_out + xi] = output_tile[xi + yi * out_tile_w]; + } + } } } } } -} -} // namespace - -template -SimpleTensor winograd_input_transform(const SimpleTensor &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims) -{ - ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height); - ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NCHW); - - SimpleTensor dst{ dst_shape, src.data_type() }; - - switch(kernel_dims.width) - { - case 3: - winograd_input_transform3x3(src, dst, conv_info); - break; - default: - ARM_COMPUTE_ERROR("Only 3x3 kernels are supported"); - } - - return dst; -} - -template -SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &output_tile) -{ - ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); - - // Create reference - SimpleTensor out{ output_shape, in.data_type(), 1 }; - - switch(in.shape()[0]) - { - case 3: - winograd_filter_transform3x3(in, out, output_tile); - break; - default: - ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); - break; - } - - return out; -} - -template -SimpleTensor winograd_output_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles) -{ - ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); - ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height); - ARM_COMPUTE_ERROR_ON(in.shape()[1] != num_tiles.area()); - - // Create reference - SimpleTensor out{ output_shape, in.data_type(), 1 }; - - switch(kernel_dims.width) - { - case 3: - winograd_output_transform3x3(in, out, num_tiles.width); - break; - default: - ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); - break; - } return out; } -template SimpleTensor winograd_input_transform(const SimpleTensor &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); -template SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &output_tile); -template SimpleTensor winograd_output_transform(const SimpleTensor &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles); +template SimpleTensor winograd_filter_transform(const SimpleTensor &in, const TensorShape &output_shape, const WinogradInfo &winograd_info); +template SimpleTensor winograd_input_transform(const SimpleTensor &in, const TensorShape &output_shape, const WinogradInfo &winograd_info); +template SimpleTensor winograd_output_transform(const SimpleTensor &in, const TensorShape &output_shape, const WinogradInfo &winograd_info); } // namespace reference } // namespace validation } // namespace test -- cgit v1.2.1