| /* |
| * 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 "NEON/NEAccessor.h" |
| #include "TypePrinter.h" |
| #include "dataset/ConvolutionLayerDataset.h" |
| #include "tests/Globals.h" |
| #include "tests/Utils.h" |
| #include "validation/Datasets.h" |
| #include "validation/Reference.h" |
| #include "validation/Validation.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" |
| |
| #include <random> |
| |
| 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_f32 = 1e-03f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| const float tolerance_f16 = 0.01f; /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| const float tolerance_q = 1.0f; /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ |
| |
| Tensor compute_convolution_layer(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, DataType dt, |
| const PadStrideInfo &conv_info, int fixed_point_position) |
| { |
| // Create tensors |
| Tensor src = create_tensor<Tensor>(input_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>(output_shape, dt, 1, fixed_point_position); |
| |
| // Create and configure function |
| NEConvolutionLayer conv; |
| conv.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::F16 || dt == DataType::F32) |
| { |
| std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| 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 NEConvolutionLayer function |
| conv.run(); |
| |
| return dst; |
| } |
| } // namespace |
| |
| #ifndef DOXYGEN_SKIP_THIS |
| BOOST_AUTO_TEST_SUITE(NEON) |
| BOOST_AUTO_TEST_SUITE(ConvolutionLayer) |
| BOOST_AUTO_TEST_SUITE(GEMM) |
| |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) |
| BOOST_DATA_TEST_CASE(Configuration, |
| AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::F32, DataType::QS8, DataType::QS16 }), |
| conv_set, dt) |
| { |
| // Set fixed point position data type allowed |
| int fixed_point_position = (dt == DataType::F32) ? 0 : 3; |
| |
| // Create tensors |
| Tensor src = create_tensor<Tensor>(conv_set.src_shape, dt, 1, fixed_point_position); |
| Tensor weights = create_tensor<Tensor>(conv_set.weights_shape, dt, 1, fixed_point_position); |
| Tensor bias = create_tensor<Tensor>(conv_set.bias_shape, dt, 1, fixed_point_position); |
| Tensor dst = create_tensor<Tensor>(conv_set.dst_shape, dt, 1, fixed_point_position); |
| |
| BOOST_TEST(src.info()->is_resizable()); |
| BOOST_TEST(weights.info()->is_resizable()); |
| BOOST_TEST(bias.info()->is_resizable()); |
| BOOST_TEST(dst.info()->is_resizable()); |
| |
| // Create and configure function |
| NEConvolutionLayer conv; |
| conv.configure(&src, &weights, &bias, &dst, conv_set.info); |
| |
| // Validate valid region |
| const ValidRegion src_valid_region = shape_to_valid_region(conv_set.src_shape); |
| const ValidRegion weights_valid_region = shape_to_valid_region(conv_set.weights_shape); |
| const ValidRegion bias_valid_region = shape_to_valid_region(conv_set.bias_shape); |
| const ValidRegion dst_valid_region = shape_to_valid_region(conv_set.dst_shape); |
| |
| validate(src.info()->valid_region(), src_valid_region); |
| validate(weights.info()->valid_region(), weights_valid_region); |
| validate(bias.info()->valid_region(), bias_valid_region); |
| validate(dst.info()->valid_region(), dst_valid_region); |
| } |
| |
| #ifdef ARM_COMPUTE_ENABLE_FP16 |
| BOOST_AUTO_TEST_SUITE(Float16) |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| BOOST_DATA_TEST_CASE(SmallConvolutionLayer, |
| SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F16), |
| conv_set, dt) |
| { |
| // Compute function |
| Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_f16); |
| } |
| BOOST_AUTO_TEST_SUITE_END() |
| #endif /* ARM_COMPUTE_ENABLE_FP16 */ |
| |
| BOOST_AUTO_TEST_SUITE(Float) |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| BOOST_DATA_TEST_CASE(SmallConvolutionLayer, |
| SmallConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32), |
| conv_set, dt) |
| { |
| // Compute function |
| Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_f32); |
| } |
| |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) |
| BOOST_DATA_TEST_CASE(LargeConvolutionLayer, |
| AlexNetConvolutionLayerDataset() * boost::unit_test::data::make(DataType::F32), |
| conv_set, dt) |
| { |
| // Compute function |
| Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, 0); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_f32); |
| } |
| BOOST_AUTO_TEST_SUITE_END() |
| |
| BOOST_AUTO_TEST_SUITE(Quantized) |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| BOOST_DATA_TEST_CASE(SmallConvolutionLayer, |
| SmallConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), |
| conv_set, dt, fixed_point_position) |
| { |
| // Compute function |
| Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_q); |
| } |
| |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) |
| BOOST_DATA_TEST_CASE(LargeConvolutionLayer, |
| AlexNetConvolutionLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16 }) * boost::unit_test::data::xrange(4, 7), |
| conv_set, dt, fixed_point_position) |
| { |
| // Compute function |
| Tensor dst = compute_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_convolution_layer(conv_set.src_shape, conv_set.weights_shape, conv_set.bias_shape, conv_set.dst_shape, dt, conv_set.info, fixed_point_position); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_q); |
| } |
| BOOST_AUTO_TEST_SUITE_END() |
| |
| BOOST_AUTO_TEST_SUITE_END() |
| BOOST_AUTO_TEST_SUITE_END() |
| BOOST_AUTO_TEST_SUITE_END() |
| #endif /* DOXYGEN_SKIP_THIS */ |