| /* |
| * 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/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<Tensor>(src_shape, dt, 1, fixed_point_position); |
| Tensor weights = create_tensor<Tensor>(weights_shape, dt, 1, fixed_point_position); |
| Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position); |
| Tensor dst = create_tensor<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(), |
| kernel_shape.y(), |
| conv_info); |
| 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 /* DOXYGEN_SKIP_THIS */ |