COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/tests/validation/NEON/ConvolutionLayerDirect.cpp b/tests/validation/NEON/ConvolutionLayerDirect.cpp
new file mode 100644
index 0000000..4e36e33
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+++ b/tests/validation/NEON/ConvolutionLayerDirect.cpp
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+/*
+ * 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 "Globals.h"
+#include "NEON/Helper.h"
+#include "NEON/NEAccessor.h"
+#include "TensorLibrary.h"
+#include "TypePrinter.h"
+#include "Utils.h"
+#include "validation/Datasets.h"
+#include "validation/Reference.h"
+#include "validation/Validation.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
+#include "arm_compute/runtime/Tensor.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+
+#include "boost_wrapper.h"
+
+#include <random>
+#include <string>
+#include <tuple>
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+using namespace arm_compute::test::neon;
+using namespace arm_compute::test::validation;
+
+namespace
+{
+const float tolerance_fp = 1e-3f; /**< Tolerance for floating point tests */
+const float tolerance_qs8 = 1; /**< Tolerance for fixed point tests */
+
+/** Compute NEON direct convolution layer function.
+ *
+ * @param[in] src_shape Shape of the input tensor.
+ * @param[in] weights_shape Shape of the weights.
+ * @param[in] bias_shape Shape of the bias tensor.
+ * @param[in] dst_shape Shape of the output tensor.
+ * @param[in] dt Data type of input, convolution matrix and output tensors.
+ * @param[in] conv_info Padding and stride information.
+ * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers
+ *
+ * @return Computed output tensor.
+*/
+Tensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape,
+ DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0)
+{
+ // Create tensors
+ Tensor src = create_tensor(src_shape, dt, 1, fixed_point_position);
+ Tensor weights = create_tensor(weights_shape, dt, 1, fixed_point_position);
+ Tensor bias = create_tensor(bias_shape, dt, 1, fixed_point_position);
+ Tensor dst = create_tensor(dst_shape, dt, 1, fixed_point_position);
+
+ // Create and configure function
+ NEDirectConvolutionLayer conv_layer;
+ conv_layer.configure(&src, &weights, &bias, &dst, conv_info);
+
+ // Allocate tensors
+ src.allocator()->allocate();
+ weights.allocator()->allocate();
+ bias.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ BOOST_TEST(!src.info()->is_resizable());
+ BOOST_TEST(!weights.info()->is_resizable());
+ BOOST_TEST(!bias.info()->is_resizable());
+ BOOST_TEST(!dst.info()->is_resizable());
+
+ // Fill tensors
+ if(dt == DataType::F32)
+ {
+ std::uniform_real_distribution<> distribution(-1.f, 1.f);
+ library->fill(NEAccessor(src), distribution, 0);
+ library->fill(NEAccessor(weights), distribution, 1);
+ library->fill(NEAccessor(bias), distribution, 2);
+ }
+ else
+ {
+ library->fill_tensor_uniform(NEAccessor(src), 0);
+ library->fill_tensor_uniform(NEAccessor(weights), 1);
+ library->fill_tensor_uniform(NEAccessor(bias), 2);
+ }
+
+ // Compute function
+ conv_layer.run();
+
+ return dst;
+}
+
+TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info)
+{
+ TensorShape out_shape(in_shape);
+ const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(),
+ in_shape.y(),
+ kernel_shape.x(),
+ conv_info.stride().first, conv_info.stride().second,
+ conv_info.pad().first, conv_info.pad().second,
+ conv_info.round());
+ out_shape.set(0, scaled_dims.first);
+ out_shape.set(1, scaled_dims.second);
+ out_shape.set(2, kernel_shape[3]);
+ return out_shape;
+}
+
+} // namespace
+
+#ifndef DOXYGEN_SKIP_THIS
+BOOST_AUTO_TEST_SUITE(NEON)
+BOOST_AUTO_TEST_SUITE(ConvolutionLayer)
+BOOST_AUTO_TEST_SUITE(Direct)
+
+BOOST_AUTO_TEST_SUITE(Float)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W1x1,
+ DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
+ input_shape, dt, sx, sy, num_kernels)
+{
+ const unsigned int kernel_size = 1;
+ const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR);
+ const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+ const TensorShape b_shape(static_cast<unsigned int>(num_kernels));
+ const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+ Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
+
+ RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
+
+ // Validate output
+ validate(NEAccessor(dst), ref);
+}
+
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2,
+ 1)
+ * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }),
+ input_shape, dt, sx, sy, px, py, num_kernels)
+{
+ const unsigned int kernel_size = 3;
+ const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR);
+ const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+ const TensorShape b_shape(static_cast<unsigned int>(num_kernels));
+ const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+ Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info);
+
+ RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0);
+
+ // Validate output
+ validate(NEAccessor(dst), ref, tolerance_fp);
+}
+BOOST_AUTO_TEST_SUITE_END()
+
+BOOST_AUTO_TEST_SUITE(Quantized)
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W1x1,
+ DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }),
+ input_shape, sx, sy, num_kernels, fixed_point_position)
+{
+ const unsigned int kernel_size = 1;
+ const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR);
+ const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+ const TensorShape b_shape(static_cast<unsigned int>(num_kernels));
+ const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+ Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+ RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+ // Validate output
+ validate(NEAccessor(dst), ref);
+}
+
+BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit"))
+BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, 1)
+ * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }),
+ input_shape, sx, sy, px, py, num_kernels, fixed_point_position)
+{
+ const unsigned int kernel_size = 3;
+ const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR);
+ const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels));
+ const TensorShape b_shape(static_cast<unsigned int>(num_kernels));
+ const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info));
+
+ Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+ RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position);
+
+ // Validate output
+ validate(NEAccessor(dst), ref, tolerance_qs8);
+}
+BOOST_AUTO_TEST_SUITE_END()
+
+BOOST_AUTO_TEST_SUITE_END()
+BOOST_AUTO_TEST_SUITE_END()
+BOOST_AUTO_TEST_SUITE_END()
+#endif
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