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Diffstat (limited to 'tests/validation/NEON/ConvolutionLayerDirect.cpp')
| -rw-r--r-- | tests/validation/NEON/ConvolutionLayerDirect.cpp | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/tests/validation/NEON/ConvolutionLayerDirect.cpp b/tests/validation/NEON/ConvolutionLayerDirect.cpp new file mode 100644 index 0000000000..4e36e331bd --- /dev/null +++ b/tests/validation/NEON/ConvolutionLayerDirect.cpp @@ -0,0 +1,219 @@ +/* + * 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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