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/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index ad0dcbd..604e252 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -28,6 +28,8 @@
#include "arm_compute/core/Types.h"
+#include <algorithm>
+
namespace arm_compute
{
namespace test
@@ -39,153 +41,155 @@
namespace
{
template <typename T>
-void winograd_filter_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out, const Size2D &output_tile)
+void initialize_matrix_transform(SimpleTensor<T> &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<T> input_tile{ TensorShape(3u, 3u), in.data_type(), 1 };
-
- // Simple tensor for the transformation matrix
- SimpleTensor<T> trans_matrix{ TensorShape(3u, transf_side), in.data_type(), 1 };
-
- // Simple tensor for the transformation matrix transpose
- SimpleTensor<T> trans_matrix_transposed{ TensorShape(transf_side, 3u), in.data_type(), 1 };
-
- // Simple tensor for the 3xSide temporary tile
- SimpleTensor<T> tmp_tile{ TensorShape(3u, transf_side), in.data_type(), 1 };
-
- // Simple tensor for the SidexSide output tile
- SimpleTensor<T> transf_tile{ TensorShape(transf_side, transf_side), in.data_type(), 1 };
-
- if(is_2x2)
+ // Winograd input transform matrices
+ static const float imatrix2x2_3x3[] =
{
- // 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;
+ 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
+ };
+
+ 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
+ };
+
+ 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
+ };
+
+ // ------------------------------------------
+
+ using WinogradKey = std::tuple<std::pair<int, int>, std::pair<int, int>, WinogradTransformType>;
+
+ // Key = (Output tile size, Kernel size, Winograd transform type)
+ static std::map<WinogradKey, const float *> matrix_map =
+ {
+ { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix2x2_3x3 },
+ { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix4x4_3x3 },
+ { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
+ { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
+ { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
+ { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3 },
+ };
+
+ // Find input matrix transform
+ std::map<WinogradKey, const float *>::iterator it;
+
+ it = matrix_map.find(WinogradKey(std::pair<int, int>(output_tile_size.width, output_tile_size.height),
+ std::pair<int, int>(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);
-
- 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);
-
- 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));
-
- // First transformation
- matrix_multiply(trans_matrix, input_tile, tmp_tile);
-
- // Second transformation
- matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile);
-
- // Store the 4x4 output tile across the 16 channels
- const int output_offset = w + z * num_filters;
-
- 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];
- }
- }
- }
- }
- }
+ // Copy values
+ std::copy(&matrix_values[0], &matrix_values[0] + src.num_elements(), &src[0]);
}
+} // namespace
template <typename T>
-void winograd_input_transform3x3(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const PadStrideInfo &conv_info)
+SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info)
{
- TensorShape shape4x4(4u, 4u);
+ ARM_COMPUTE_ERROR_ON(in.data_layout() != DataLayout::NCHW);
- // Simple tensor for the 4x4 input tile
- SimpleTensor<T> src_tile{ shape4x4, src.data_type() };
+ 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;
- // Simple tensor for the 4x4 temporary tile
- SimpleTensor<T> tmp_tile{ shape4x4, src.data_type() };
+ SimpleTensor<T> out{ output_shape, in.data_type() };
- // Simple tensor for the 4x4 output tile
- SimpleTensor<T> dst_tile{ shape4x4, src.data_type() };
+ // 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;
+
+ TensorShape tile_dims(tile_w, tile_h);
+
+ // Simple tensor for the input tile
+ SimpleTensor<T> src_tile{ tile_dims, in.data_type() };
+
+ // Simple tensor for the temporary tile
+ SimpleTensor<T> tmp_tile{ tile_dims, in.data_type() };
+
+ // Simple tensor for the output tile
+ SimpleTensor<T> dst_tile{ tile_dims, in.data_type() };
// Simple tensor for the transformation matrix
- SimpleTensor<T> matrix{ shape4x4, src.data_type() };
+ SimpleTensor<T> matrix{ tile_dims, in.data_type() };
// Simple tensor for the transformation matrix transposed
- SimpleTensor<T> matrix_transposed{ shape4x4, src.data_type() };
+ SimpleTensor<T> matrix_transposed{ tile_dims, in.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
- };
+ // Initialize matrix for the input transform
+ initialize_matrix_transform(matrix, output_tile_size, kernel_size, WinogradTransformType::INPUT);
- for(int i = 0; i < matrix.num_elements(); ++i)
- {
- matrix[i] = matrix_values[i];
- }
-
+ // 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<float>(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<float>(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<int>(dst.shape().y()));
+ ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(out.shape().y()));
for(int b = 0; b < num_batches; ++b)
{
@@ -195,61 +199,154 @@
{
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 <typename T>
-void winograd_output_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out, int num_tiles_x)
+SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info)
{
- ARM_COMPUTE_ERROR_ON(in.shape()[2] != 16);
- ARM_COMPUTE_ERROR_ON(in.shape()[0] != out.shape()[2]);
+ ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format");
- // Simple tensor for the 3x3 input tile
- SimpleTensor<T> input_tile{ TensorShape(4u, 4u), in.data_type(), 1 };
+ // Create reference
+ SimpleTensor<T> 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<T> input_tile{ kernel_tile_dims, in.data_type(), 1 };
// Simple tensor for the transformation matrix
- SimpleTensor<T> trans_matrix{ TensorShape(4u, 2u), in.data_type(), 1 };
+ SimpleTensor<T> trans_matrix{ TensorShape(kernel_tile_dims[0], input_tile_w), in.data_type(), 1 };
// Simple tensor for the transformation matrix transpose
- SimpleTensor<T> trans_matrix_transposed{ TensorShape(2u, 4u), in.data_type(), 1 };
+ SimpleTensor<T> trans_matrix_transposed{ TensorShape(input_tile_w, kernel_tile_dims[0]), in.data_type(), 1 };
- // Simple tensor for the 4x3 temporary tile
- SimpleTensor<T> tmp_tile{ TensorShape(4u, 2u), in.data_type(), 1 };
+ // Simple tensor for the temporary tile
+ SimpleTensor<T> tmp_tile{ TensorShape(kernel_tile_dims[0], input_tile_w), in.data_type(), 1 };
- // Simple tensor for the 4x4 output tile
- SimpleTensor<T> output_tile{ TensorShape(2u, 2u), in.data_type(), 1 };
+ // Simple tensor for the output tile
+ SimpleTensor<T> transf_tile{ TensorShape(input_tile_w, input_tile_w), 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;
+ // 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 <typename T>
+SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &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<T> 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]);
+
+ // 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<T> input_tile{ in_tile_dims, in.data_type(), 1 };
+
+ // Simple tensor for the transformation matrix
+ SimpleTensor<T> trans_matrix{ tr_tile_dims, in.data_type(), 1 };
+
+ // Simple tensor for the transformation matrix transpose
+ SimpleTensor<T> trans_matrix_transposed{ TensorShape(tr_tile_dims[1], tr_tile_dims[0]), in.data_type(), 1 };
+
+ // Simple tensor for the temporary tile
+ SimpleTensor<T> tmp_tile{ tr_tile_dims, in.data_type(), 1 };
+
+ // Simple tensor for the output tile
+ SimpleTensor<T> 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 @@
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<float>(output_tile_size.width));
+ const int num_tiles_y = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height));
+
+ ARM_COMPUTE_UNUSED(num_tiles_y);
+ ARM_COMPUTE_ERROR_ON(in.shape()[1] != static_cast<unsigned int>(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 @@
// 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];
+ const int output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out);
- // Check out-of-bound writes
- if(xo + 1 < w_out)
+ for(int yi = 0; yi < static_cast<int>(out_tile_h); ++yi)
{
- 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];
- }
-
- if((yo + 1 < h_out) && (xo + 1 < w_out))
- {
- out[output_offset + 1 * stridey_out + 1] = output_tile[1 + 1 * 2];
+ for(int xi = 0; xi < static_cast<int>(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 <typename T>
-SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &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<T> 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 <typename T>
-SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &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<T> 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 <typename T>
-SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &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<T> 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<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims);
-template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const Size2D &output_tile);
-template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles);
+template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
+template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info);
} // namespace reference
} // namespace validation
} // namespace test