| /* |
| * 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 "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/NENormalizationLayer.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/NormalizationLayer.h" |
| |
| namespace |
| { |
| using NormalizationLayerAlexNetF32 = NormalizationLayer<AlexNetNormalizationLayerDataset, Tensor, NEAccessor, NENormalizationLayer>; |
| using NormalizationLayerAlexNetQS8 = NormalizationLayer<AlexNetNormalizationLayerDataset, Tensor, NEAccessor, NENormalizationLayer, DataType::QS8>; |
| using NormalizationLayerGoogLeNet = NormalizationLayer<GoogLeNetNormalizationLayerDataset, Tensor, NEAccessor, NENormalizationLayer>; |
| } // namespace |
| |
| // F32 |
| BENCHMARK_DEFINE_F(NormalizationLayerAlexNetF32, neon_alexnet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| norm_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(NormalizationLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetNormalizationLayerDataset, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(NormalizationLayerAlexNetF32, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetNormalizationLayerDataset, 1, 1, 4, 8>); |
| |
| // QS8 |
| BENCHMARK_DEFINE_F(NormalizationLayerAlexNetQS8, neon_alexnet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| norm_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(NormalizationLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetNormalizationLayerDataset, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(NormalizationLayerAlexNetQS8, neon_alexnet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<AlexNetNormalizationLayerDataset, 1, 1, 4, 8>); |
| |
| BENCHMARK_DEFINE_F(NormalizationLayerGoogLeNet, neon_googlenet) |
| (::benchmark::State &state) |
| { |
| while(state.KeepRunning()) |
| { |
| // Run function |
| profiler.start(); |
| norm_layer->run(); |
| profiler.stop(); |
| } |
| } |
| |
| BENCHMARK_REGISTER_F(NormalizationLayerGoogLeNet, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetNormalizationLayerDataset, 0, 1, 4, 8>); |
| BENCHMARK_REGISTER_F(NormalizationLayerGoogLeNet, neon_googlenet) |
| ->Threads(1) |
| ->Apply(DataSetArgBatched<GoogLeNetNormalizationLayerDataset, 1, 1, 4, 8>); |