COMPMID-556: Rename CPP folder to reference
Change-Id: I147644349547c4e3804a80b564a9ad95131ad2d0
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111560
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.cpp b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
new file mode 100644
index 0000000..0e88d3d
--- /dev/null
+++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
@@ -0,0 +1,195 @@
+/*
+ * Copyright (c) 2017 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 "DepthwiseConvolutionLayer.h"
+
+#include "ConvolutionLayer.h"
+#include "Utils.h"
+
+#include "tests/validation/FixedPoint.h"
+#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/Utils.h"
+#include "tests/validation/reference/UtilsQuantizedAsymm.h"
+
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace reference
+{
+/** Perform a depthwise convolution
+ *
+ * - Three dimensions tensors
+ * - Third dimention is number of channels
+ * - Depths of input tensor and filter are equals
+ * - Padding, stride and output shape "match"
+ *
+ */
+template <typename T, typename TB>
+SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &biases, const TensorShape &dst_shape, const PadStrideInfo &conv_info)
+{
+ // Create reference
+ SimpleTensor<T> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position() };
+
+ // Compute reference
+ const int filter_width = weights.shape().x();
+ const int filter_height = weights.shape().y();
+ const int filter_plane = filter_width * filter_height;
+ const int input_width = src.shape().x();
+ const int input_height = src.shape().y();
+ const int input_depth = src.shape().z();
+ const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);
+
+ const int filter_half_width = filter_width / 2;
+ const int filter_half_height = filter_height / 2;
+
+ const int pad_left = std::min(static_cast<int>(conv_info.pad_left()), filter_half_width);
+ const int pad_top = std::min(static_cast<int>(conv_info.pad_top()), filter_half_height);
+ const int pad_right = std::min(static_cast<int>(conv_info.pad_right()), filter_half_width);
+ const int pad_bottom = std::min(static_cast<int>(conv_info.pad_bottom()), filter_half_height);
+
+ const int minimum_x = -pad_left + filter_half_width;
+ const int minimum_y = -pad_top + filter_half_height;
+ const int maximum_x = input_width + pad_left - filter_half_width + pad_right - filter_half_width;
+ const int maximum_y = input_height + pad_top - filter_half_height + pad_bottom - filter_half_height;
+
+ int out_pos = 0;
+ for(int r = 0; r < num_batches; ++r)
+ {
+ for(int z = 0; z < input_depth; ++z)
+ {
+ for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second)
+ {
+ for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first)
+ {
+ Coordinates coords(static_cast<int>(x), static_cast<int>(y), static_cast<int>(z), static_cast<int>(r));
+ size_t filter_offset = filter_plane * z;
+
+ T val = 0;
+ for(int j = y - filter_half_height; j <= static_cast<int>(y + filter_half_height); ++j)
+ {
+ for(int i = x - filter_half_width; i <= static_cast<int>(x + filter_half_width); ++i)
+ {
+ coords.set(0, i);
+ coords.set(1, j);
+ val += *(weights.data() + filter_offset) * tensor_elem_at(src, coords, BorderMode::CONSTANT, 0.f);
+ ++filter_offset;
+ }
+ }
+ coords.set(0, x);
+ coords.set(1, y);
+ dst[out_pos++] = saturate_cast<T>(val + *static_cast<const TB *>(biases(Coordinates(z))));
+ }
+ }
+ }
+ }
+
+ return dst;
+}
+
+template <>
+SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, const SimpleTensor<uint8_t> &weights, const SimpleTensor<int32_t> &biases, const TensorShape &dst_shape,
+ const PadStrideInfo &conv_info)
+{
+ // Create reference
+ SimpleTensor<uint8_t> dst{ dst_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
+
+ const int input_offset = -src.quantization_info().offset;
+ const float input_scale = src.quantization_info().scale;
+ const int weights_offset = -weights.quantization_info().offset;
+ const float weights_scale = weights.quantization_info().scale;
+ const int output_offset = dst.quantization_info().offset;
+ const float output_scale = dst.quantization_info().scale;
+
+ int output_multiplier;
+ int output_shift;
+ const float multiplier = input_scale * weights_scale / output_scale;
+ arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ // Compute reference
+ const int filter_width = weights.shape().x();
+ const int filter_height = weights.shape().y();
+ const int filter_plane = filter_width * filter_height;
+ const int input_width = src.shape().x();
+ const int input_height = src.shape().y();
+ const int input_depth = src.shape().z();
+ const int num_batches = src.shape().total_size() / (input_width * input_height * input_depth);
+
+ const int filter_half_size = filter_width / 2;
+ const int pad_x = std::min(filter_half_size, static_cast<int>(conv_info.pad().first));
+ const int pad_y = std::min(filter_half_size, static_cast<int>(conv_info.pad().second));
+ const int minimum_x = -pad_x + filter_half_size;
+ const int minimum_y = -pad_y + filter_half_size;
+
+ int out_pos = 0;
+ for(int r = 0; r < num_batches; ++r)
+ {
+ for(int z = 0; z < input_depth; ++z)
+ {
+ int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(z)));
+ for(int y = minimum_y; y < input_height + pad_y - filter_half_size; y += conv_info.stride().second)
+ {
+ for(int x = minimum_x; x < input_width + pad_x - filter_half_size; x += conv_info.stride().first)
+ {
+ Coordinates coords(x, y, z);
+ int filter_offset = filter_plane * z;
+
+ uint32_t val = 0;
+ for(int j = y - filter_half_size; j <= (y + filter_half_size); ++j)
+ {
+ for(int i = x - filter_half_size; i <= (x + filter_half_size); ++i)
+ {
+ coords.set(0, i);
+ coords.set(1, j);
+ auto in_val = tensor_elem_at<uint8_t>(src, coords, BorderMode::CONSTANT, 0);
+ uint8_t w_val = *(weights.data() + filter_offset);
+ val += (in_val + input_offset) * (w_val + weights_offset);
+ ++filter_offset;
+ }
+ }
+ val += bias_val;
+ val = asymm_rounding_divide_by_pow2(asymm_int_mult(val, output_multiplier), output_shift);
+ val += output_offset;
+ val = std::max<int32_t>(val, 0);
+ val = std::min<int32_t>(val, 255);
+
+ // Store the result
+ dst[out_pos++] = val;
+ }
+ }
+ }
+ }
+
+ return dst;
+}
+
+template SimpleTensor<float> depthwise_convolution(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &biases, const TensorShape &dst_shape,
+ const PadStrideInfo &conv_info);
+} // namespace reference
+} // namespace validation
+} // namespace test
+} // namespace arm_compute