| /* |
| * 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 "benchmark/Datasets.h" |
| #include "benchmark/Profiler.h" |
| #include "benchmark/WallClockTimer.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| |
| #include "benchmark/benchmark_api.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::test; |
| using namespace arm_compute::test::benchmark; |
| using namespace arm_compute::test::neon; |
| |
| #include "benchmark/common/ConvolutionLayer.h" |
| |
| namespace |
| { |
| using ConvolutionLayerAlexNetF32 = ConvolutionLayer<AlexNetConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer>; |
| using ConvolutionLayerAlexNetQS8 = ConvolutionLayer<AlexNetConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer, DataType::QS8>; |
| using ConvolutionLayerLeNet5 = ConvolutionLayer<LeNet5ConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer>; |
| using ConvolutionLayerGoogLeNet1 = ConvolutionLayer<GoogLeNetConvolutionLayerDataset1, Tensor, NEAccessor, NEConvolutionLayer>; |
| using ConvolutionLayerGoogLeNet2 = ConvolutionLayer<GoogLeNetConvolutionLayerDataset2, Tensor, NEAccessor, NEConvolutionLayer>; |
| } // namespace |
| |
| // F32 |
| BENCHMARK_DEFINE_F(ConvolutionLayerAlexNetF32, neon_alexnet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| conv_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 1, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 2, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 3, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 4, 1, 4, 8>); |
| |
| // QS8 |
| BENCHMARK_DEFINE_F(ConvolutionLayerAlexNetQS8, neon_alexnet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| conv_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 1, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 2, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 3, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 4, 1, 4, 8>); |
| |
| BENCHMARK_DEFINE_F(ConvolutionLayerLeNet5, neon_lenet5) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| conv_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(ConvolutionLayerLeNet5, neon_lenet5) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<LeNet5ConvolutionLayerDataset, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerLeNet5, neon_lenet5) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<LeNet5ConvolutionLayerDataset, 1, 1, 4, 8>); |
| |
| BENCHMARK_DEFINE_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| conv_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_DEFINE_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| conv_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 1, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 2, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 3, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 4, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 5, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 6, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 7, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 8, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 9, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 10, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 11, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 12, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 13, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 14, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 15, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 16, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 17, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 18, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 19, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 20, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 21, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 22, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 23, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 24, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 25, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 26, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 27, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 28, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 29, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 30, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 31, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 1, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 2, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 3, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 4, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 5, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 6, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 7, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 8, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 9, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 10, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 11, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 12, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 13, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 14, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 15, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 16, 1, 4, 8>); |