From d0bbf0351e560bfd35f248ac6137ce88fff23c30 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 16 Dec 2019 17:00:49 +0000 Subject: COMPMID-2932: Clear untracked benchmarks - Removes untracked benchmarks - Leaves a couple per backend for public show-case Signed-off-by: Georgios Pinitas Change-Id: I5fac2aec8617b03131ec0f3d64ed40fbbd4a63c2 Reviewed-on: https://review.mlplatform.org/c/2492 Tested-by: Arm Jenkins Reviewed-by: Giuseppe Rossini Reviewed-by: Michalis Spyrou Comments-Addressed: Michalis Spyrou Comments-Addressed: Arm Jenkins --- tests/benchmark/CL/ConvolutionLayer.cpp | 200 -------------------------------- 1 file changed, 200 deletions(-) delete mode 100644 tests/benchmark/CL/ConvolutionLayer.cpp (limited to 'tests/benchmark/CL/ConvolutionLayer.cpp') diff --git a/tests/benchmark/CL/ConvolutionLayer.cpp b/tests/benchmark/CL/ConvolutionLayer.cpp deleted file mode 100644 index 20828b7717..0000000000 --- a/tests/benchmark/CL/ConvolutionLayer.cpp +++ /dev/null @@ -1,200 +0,0 @@ -/* - * Copyright (c) 2017-2019 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 "arm_compute/core/TensorShape.h" -#include "arm_compute/core/Types.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/CL/CLTensorAllocator.h" -#include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h" -#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" -#include "tests/CL/CLAccessor.h" -#include "tests/benchmark/fixtures/ConvolutionLayerFixture.h" -#include "tests/benchmark/fixtures/FFTConvolutionLayerFixture.h" -#include "tests/benchmark/fixtures/WinogradConvolutionLayerFixture.h" -#include "tests/datasets/system_tests/alexnet/AlexNetConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/googlenet/inceptionv1/GoogLeNetInceptionV1ConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/googlenet/inceptionv4/GoogLeNetInceptionV4ConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/lenet5/LeNet5ConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/mobilenet/MobileNetConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/squeezenet/SqueezeNetConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/vgg/vgg16/VGG16ConvolutionLayerDataset.h" -#include "tests/datasets/system_tests/yolo/v2/YOLOV2ConvolutionLayerDataset.h" -#include "tests/framework/Macros.h" -#include "tests/framework/datasets/Datasets.h" -#include "utils/TypePrinter.h" -#include -#include -#include - -namespace arm_compute -{ -namespace test -{ -namespace benchmark -{ -namespace -{ -const auto data_types = framework::dataset::make("DataType", { DataType::F16, DataType::F32, DataType::QASYMM8 }); -} // namespace - -TEST_SUITE(CL) - -using CLGEMMConvolutionLayerFixture = ConvolutionLayerFixture; -using CLWinogradLayerFixture = WinogradConvolutionLayerFixture; -using CLFFTConvolutionLayerFixture = FFTConvolutionLayerFixture; - -REGISTER_FIXTURE_DATA_TEST_CASE(ResNet12FFTLayer, CLFFTConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::ResNet12FFTConvolutionLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("DataType", { DataType::F32 })), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(LeNet5ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - data_types), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo())), - data_types), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", 1))); - -REGISTER_FIXTURE_DATA_TEST_CASE(MobileNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::MobileNetConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", 1))); - -TEST_SUITE(NIGHTLY) -REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(LeNet5ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - data_types), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo())), - data_types), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", { 4, 8 }))); - -// 8 batches use about 1.8GB of memory which is too much for most devices! -REGISTER_FIXTURE_DATA_TEST_CASE(VGG16ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - data_types), - framework::dataset::make("Batches", { 1, 4 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2ConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - data_types), - framework::dataset::make("Batches", { 1, 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo())), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", { 4, 8 }))); - -REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, CLWinogradLayerFixture, framework::DatasetMode::NIGHTLY, - framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(), - framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), - framework::dataset::make("DataType", { DataType::F16, DataType::F32 })), - framework::dataset::make("Batches", { 4, 8 }))); -TEST_SUITE_END() -TEST_SUITE_END() -} // namespace benchmark -} // namespace test -} // namespace arm_compute -- cgit v1.2.1