From e855c237a5b61c4ed5a5ab79dd4af27385cf72f5 Mon Sep 17 00:00:00 2001 From: Stephen Li Date: Thu, 4 Jan 2018 14:13:22 +0800 Subject: APPBROWSER-377: GCConvoutionLayer support for FP16 Change-Id: I801b5e393a16a9f92c062826e6fcfd5982ca7bb3 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116584 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp | 119 ++++++++++++++++++++++ 1 file changed, 119 insertions(+) create mode 100644 tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp (limited to 'tests/benchmark') diff --git a/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp new file mode 100644 index 0000000000..0d8edb757d --- /dev/null +++ b/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp @@ -0,0 +1,119 @@ +/* + * Copyright (c) 2017-2018 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/GLES_COMPUTE/GCTensor.h" +#include "arm_compute/runtime/GLES_COMPUTE/GCTensorAllocator.h" +#include "arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h" +#include "tests/GLES_COMPUTE/GCAccessor.h" +#include "tests/benchmark/fixtures/ConvolutionLayerFixture.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/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" + +namespace arm_compute +{ +namespace test +{ +namespace +{ +const auto data_types = framework::dataset::make("DataType", { DataType::F16 }); +} // namespace + +using GCConvolutionLayerFixture = ConvolutionLayerFixture; + +TEST_SUITE(GC) + +REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL, + framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", 1))); + +REGISTER_FIXTURE_DATA_TEST_CASE(LeNet5ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL, + framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", 1))); + +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL, + framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", 1))); + +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL, + framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", 1))); + +REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL, + framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", 1))); + +TEST_SUITE(NIGHTLY) +REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", { 4, 8 }))); + +REGISTER_FIXTURE_DATA_TEST_CASE(LeNet5ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", { 4, 8 }))); + +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", { 4, 8 }))); + +REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", { 4, 8 }))); + +REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), + 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", { 1, 4 }))); + +REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2ConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, + framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), + data_types), + framework::dataset::make("Batches", { 1, 4, 8 }))); +TEST_SUITE_END() +TEST_SUITE_END() +} // namespace test +} // namespace arm_compute -- cgit v1.2.1