COMPMID-935 Implementing Convolution with Winograd on OpenCL (part 3)
Change-Id: I51f92f30602fb0a02314f344fa67061f448694bf
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/122793
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
new file mode 100644
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+++ b/tests/validation/reference/Winograd.cpp
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+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "Winograd.h"
+
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/Utils.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+namespace
+{
+template <typename T>
+void winograd_input_transform3x3(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const PadStrideInfo &conv_info)
+{
+ TensorShape shape4x4(4u, 4u);
+
+ // Simple tensor for the 4x4 input tile
+ SimpleTensor<T> src_tile{ shape4x4, src.data_type() };
+
+ // Simple tensor for the 4x4 temporary tile
+ SimpleTensor<T> tmp_tile{ shape4x4, src.data_type() };
+
+ // Simple tensor for the 4x4 output tile
+ SimpleTensor<T> dst_tile{ shape4x4, src.data_type() };
+
+ // Simple tensor for the transformation matrix
+ SimpleTensor<T> matrix{ shape4x4, src.data_type() };
+
+ // Simple tensor for the transformation matrix transposed
+ SimpleTensor<T> 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
+ };
+
+ for(int i = 0; i < matrix.num_elements(); ++i)
+ {
+ matrix[i] = matrix_values[i];
+ }
+
+ 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);
+
+ ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(dst.shape().y()));
+
+ for(int b = 0; b < num_batches; ++b)
+ {
+ for(int z = 0; z < in_d; ++z)
+ {
+ for(int y = 0; y < num_tiles_y; ++y)
+ {
+ 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();
+
+ // Get the 4x4 tile from the input tensor
+ get_tile(src, 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)
+ {
+ int xo = z;
+ int yo = x + y * num_tiles_x;
+ dst[coords2index(dst.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i];
+ }
+ }
+ }
+ }
+ }
+}
+} // 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 SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute