COMPMID-908 - Merge Activation layer with Convolution Layer (NEON. CL, GLES)
Change-Id: Iab06d0768ecf805b841e601185608aae88cf9166
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120874
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/tests/benchmark/CL/ConvolutionLayer.cpp b/tests/benchmark/CL/ConvolutionLayer.cpp
index bad644f..ee25e12 100644
--- a/tests/benchmark/CL/ConvolutionLayer.cpp
+++ b/tests/benchmark/CL/ConvolutionLayer.cpp
@@ -56,69 +56,79 @@
TEST_SUITE(CL)
REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, CLGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(),
+ 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(datasets::LeNet5ConvolutionLayerDataset(),
+ 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(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(),
+ 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(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(),
+ 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(datasets::SqueezeNetConvolutionLayerDataset(),
+ 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(datasets::MobileNetConvolutionLayerDataset(),
+ 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(datasets::AlexNetConvolutionLayerDataset(),
+ 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(datasets::LeNet5ConvolutionLayerDataset(),
+ 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(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(),
+ 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(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(),
+ 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(datasets::SqueezeNetConvolutionLayerDataset(),
+ 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(datasets::VGG16ConvolutionLayerDataset(),
+ 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(datasets::YOLOV2ConvolutionLayerDataset(),
+ 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 })));
TEST_SUITE_END()
diff --git a/tests/benchmark/CL/DirectConvolutionLayer.cpp b/tests/benchmark/CL/DirectConvolutionLayer.cpp
index c7b0780..df63d3a 100644
--- a/tests/benchmark/CL/DirectConvolutionLayer.cpp
+++ b/tests/benchmark/CL/DirectConvolutionLayer.cpp
@@ -49,58 +49,67 @@
const auto data_types = framework::dataset::make("DataType", { DataType::F16, DataType::F32 });
} // namespace
-using CLConvolutionLayerFixture = DirectConvolutionLayerFixture<CLTensor, CLDirectConvolutionLayer, CLAccessor>;
+using CLDirectConvolutionLayerFixture = DirectConvolutionLayerFixture<CLTensor, CLDirectConvolutionLayer, CLAccessor>;
TEST_SUITE(CL)
-REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, CLDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, CLDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, CLDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo())),
data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, CLDirectConvolutionLayerFixture, 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)));
TEST_SUITE(NIGHTLY)
-REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, CLDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, CLDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, CLDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo())),
data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, CLDirectConvolutionLayerFixture, 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 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(VGG16DirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(VGG16DirectConvolutionLayer, CLDirectConvolutionLayerFixture, 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, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2DirectConvolutionLayer, CLConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2DirectConvolutionLayer, CLDirectConvolutionLayerFixture, 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 })));
TEST_SUITE_END()
diff --git a/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp
index 4cb020c..c79e015 100644
--- a/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp
+++ b/tests/benchmark/GLES_COMPUTE/ConvolutionLayer.cpp
@@ -55,64 +55,73 @@
TEST_SUITE(GC)
REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(),
+ 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)));
TEST_SUITE(NIGHTLY)
REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(),
+ 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, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(),
+ 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 })));
TEST_SUITE_END()
diff --git a/tests/benchmark/GLES_COMPUTE/DirectConvolutionLayer.cpp b/tests/benchmark/GLES_COMPUTE/DirectConvolutionLayer.cpp
index d319c41..66fd8fc 100644
--- a/tests/benchmark/GLES_COMPUTE/DirectConvolutionLayer.cpp
+++ b/tests/benchmark/GLES_COMPUTE/DirectConvolutionLayer.cpp
@@ -49,58 +49,68 @@
const auto data_types = framework::dataset::make("DataType", { DataType::F32, DataType::F16 });
} // namespace
-using GCConvolutionLayerFixture = DirectConvolutionLayerFixture<GCTensor, GCDirectConvolutionLayer, GCAccessor>;
+using GCDirectConvolutionLayerFixture = DirectConvolutionLayerFixture<GCTensor, GCDirectConvolutionLayer, GCAccessor>;
TEST_SUITE(GC)
-REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, GCDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, GCDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, GCDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo())),
data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, GCDirectConvolutionLayerFixture, 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)));
TEST_SUITE(NIGHTLY)
-REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, GCDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, GCDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, GCDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), framework::dataset::make("ActivationInfo",
+ ActivationLayerInfo())),
data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, GCDirectConvolutionLayerFixture, 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 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(VGG16DirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(VGG16DirectConvolutionLayer, GCDirectConvolutionLayerFixture, 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, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2DirectConvolutionLayer, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(),
+REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2DirectConvolutionLayer, GCDirectConvolutionLayerFixture, 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 })));
TEST_SUITE_END()
diff --git a/tests/benchmark/NEON/ConvolutionLayer.cpp b/tests/benchmark/NEON/ConvolutionLayer.cpp
index a425d95..e7f2788 100644
--- a/tests/benchmark/NEON/ConvolutionLayer.cpp
+++ b/tests/benchmark/NEON/ConvolutionLayer.cpp
@@ -65,91 +65,133 @@
using NEWinogradLayerFixture = WinogradLayerFixture<Tensor, NEWinogradLayer, Accessor>;
REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", 1)));
REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", 1)));
REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", 1)));
REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", 1)));
#endif /* __aarch64__ */
REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetConvolutionLayer, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::MobileNetConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::LeNet5ConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1ConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4ConvolutionLayerDataset(), data_types),
+ 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, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types),
+ 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 2GB of memory which is too much for most devices!
REGISTER_FIXTURE_DATA_TEST_CASE(VGG16ConvolutionLayer, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), data_types),
+ 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, 2 })));
REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2ConvolutionLayer, NEGEMMConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), data_types),
+ 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 })));
#if defined(__aarch64__)
REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetWinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", { 4, 8 })));
REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1WinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", { 4, 8 })));
REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4WinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4WinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", { 4, 8 })));
REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetWinogradLayer, NEWinogradLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(), framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetWinogradLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("Batches", { 4, 8 })));
#endif /* __aarch64__ */
diff --git a/tests/benchmark/NEON/DirectConvolutionLayer.cpp b/tests/benchmark/NEON/DirectConvolutionLayer.cpp
index 8a17f3c..22f1bd2 100644
--- a/tests/benchmark/NEON/DirectConvolutionLayer.cpp
+++ b/tests/benchmark/NEON/DirectConvolutionLayer.cpp
@@ -55,49 +55,69 @@
#endif /* ARM_COMPUTE_ENABLE_F16 */
} // namespace
-using NEConvolutionLayerFixture = DirectConvolutionLayerFixture<Tensor, NEDirectConvolutionLayer, Accessor>;
+using NEDirectConvolutionLayerFixture = DirectConvolutionLayerFixture<Tensor, NEDirectConvolutionLayer, Accessor>;
TEST_SUITE(NEON)
-REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, NEDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, NEDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, NEDirectConvolutionLayerFixture, framework::DatasetMode::ALL,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
+ data_types),
framework::dataset::make("Batches", 1)));
-REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, NEDirectConvolutionLayerFixture, 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)));
TEST_SUITE(NIGHTLY)
-REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(AlexNetDirectConvolutionLayer, NEDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::AlexNetDirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV1DirectConvolutionLayer, NEDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV1DirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))),
+ data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(GoogLeNetInceptionV4DirectConvolutionLayer, NEDirectConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
+ framework::dataset::combine(framework::dataset::combine(framework::dataset::combine(datasets::GoogLeNetInceptionV4DirectConvolutionLayerDataset(),
+ framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
+ data_types),
framework::dataset::make("Batches", { 4, 8 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::SqueezeNetConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(SqueezeNetDirectConvolutionLayer, NEDirectConvolutionLayerFixture, 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 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(VGG16DirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::VGG16ConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(VGG16DirectConvolutionLayer, NEDirectConvolutionLayerFixture, 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, 2 })));
-REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2DirectConvolutionLayer, NEConvolutionLayerFixture, framework::DatasetMode::NIGHTLY,
- framework::dataset::combine(framework::dataset::combine(datasets::YOLOV2ConvolutionLayerDataset(), data_types),
+REGISTER_FIXTURE_DATA_TEST_CASE(YOLOV2DirectConvolutionLayer, NEDirectConvolutionLayerFixture, 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 })));
TEST_SUITE_END()
diff --git a/tests/benchmark/fixtures/ConvolutionLayerFixture.h b/tests/benchmark/fixtures/ConvolutionLayerFixture.h
index 7558b4c..511daf7 100644
--- a/tests/benchmark/fixtures/ConvolutionLayerFixture.h
+++ b/tests/benchmark/fixtures/ConvolutionLayerFixture.h
@@ -42,7 +42,8 @@
{
public:
template <typename...>
- void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, DataType data_type, int batches)
+ void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, ActivationLayerInfo act_info, DataType data_type,
+ int batches)
{
// Set batched in source and destination shapes
const unsigned int fixed_point_position = 4;
@@ -57,7 +58,7 @@
dst = create_tensor<TensorType>(dst_shape, data_type, 1, fixed_point_position);
// Create and configure function
- conv_layer.configure(&src, &weights, &biases, &dst, info, WeightsInfo(), dilation);
+ conv_layer.configure(&src, &weights, &biases, &dst, info, WeightsInfo(), dilation, act_info);
// Allocate tensors
src.allocator()->allocate();
diff --git a/tests/benchmark/fixtures/DirectConvolutionLayerFixture.h b/tests/benchmark/fixtures/DirectConvolutionLayerFixture.h
index e3289b7..419f6dd 100644
--- a/tests/benchmark/fixtures/DirectConvolutionLayerFixture.h
+++ b/tests/benchmark/fixtures/DirectConvolutionLayerFixture.h
@@ -43,7 +43,8 @@
{
public:
template <typename...>
- void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, DataType data_type, int batches)
+ void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, ActivationLayerInfo act_info, DataType data_type,
+ int batches)
{
ARM_COMPUTE_UNUSED(dilation);
@@ -60,7 +61,7 @@
dst = create_tensor<TensorType>(dst_shape, data_type, 1, fixed_point_position);
// Create and configure function
- conv_layer.configure(&src, &weights, &biases, &dst, info);
+ conv_layer.configure(&src, &weights, &biases, &dst, info, act_info);
// Allocate tensors
src.allocator()->allocate();
diff --git a/tests/benchmark/fixtures/WinogradLayerFixture.h b/tests/benchmark/fixtures/WinogradLayerFixture.h
index 31a1eb8..0be535f 100644
--- a/tests/benchmark/fixtures/WinogradLayerFixture.h
+++ b/tests/benchmark/fixtures/WinogradLayerFixture.h
@@ -42,7 +42,8 @@
{
public:
template <typename...>
- void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, DataType data_type, int batches)
+ void setup(TensorShape src_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape dst_shape, PadStrideInfo info, Size2D dilation, ActivationLayerInfo act_info, DataType data_type,
+ int batches)
{
ARM_COMPUTE_UNUSED(dilation);
@@ -59,7 +60,7 @@
dst = create_tensor<TensorType>(dst_shape, data_type, 1, fixed_point_position);
// Create and configure function
- conv_layer.configure(&src, &weights, &biases, &dst, info);
+ conv_layer.configure(&src, &weights, &biases, &dst, info, act_info);
// Allocate tensors
src.allocator()->allocate();
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index c50519b..b50bb94 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -60,6 +60,13 @@
DataType::QS16,
DataType::QASYMM8,
});
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
+});
} // namespace
TEST_SUITE(CL)
@@ -109,18 +116,18 @@
ConvolutionMethod is_valid = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(false),
&weights_info.clone()->set_is_resizable(false),
&biases_info.clone()->set_is_resizable(false),
- &output_info.clone()->set_is_resizable(false), conv_info, WeightsInfo(), gpu_target);
+ &output_info.clone()->set_is_resizable(false), conv_info, WeightsInfo(), ActivationLayerInfo(), gpu_target);
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
TEST_SUITE_END()
TEST_SUITE(GEMMConvolutionLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), CNNDataTypes),
- input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()),
+ CNNDataTypes),
+ ActivationFunctionsDataset),
+ input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, act_info)
{
- ARM_COMPUTE_UNUSED(dilation);
-
// Set fixed point position data type allowed
int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0;
@@ -142,7 +149,7 @@
// Create and configure function
CLGEMMConvolutionLayer conv;
- conv.configure(&src, &weights, &bias, &dst, info);
+ conv.configure(&src, &weights, &bias, &dst, info, WeightsInfo(), dilation, act_info);
// Validate valid region
const ValidRegion src_valid_region = shape_to_valid_region(input_shape);
@@ -168,18 +175,22 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
- DataType::F16)))
+ DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::F16)))
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
@@ -187,18 +198,22 @@
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
- DataType::F32)))
+ DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::F32)))
+ framework::dataset::make("DataType",
+ DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -212,20 +227,23 @@
TEST_SUITE(FixedPoint)
TEST_SUITE(QS8)
// We test for fixed point precision [4,6]
-FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+ framework::dataset::make("FractionalBits", 4, 7)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+ framework::dataset::make("FractionalBits", 4, 7)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
@@ -234,20 +252,23 @@
TEST_SUITE(QS16)
// Testing for fixed point position [1,14)
-FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+ framework::dataset::make("FractionalBits", 1, 14)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+ framework::dataset::make("FractionalBits", 1, 14)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
@@ -258,20 +279,30 @@
template <typename T>
using CLGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>;
+const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
+});
+
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) })))
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) })),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
diff --git a/tests/validation/CL/DilatedConvolutionLayer.cpp b/tests/validation/CL/DilatedConvolutionLayer.cpp
index dadae22..532d04b 100644
--- a/tests/validation/CL/DilatedConvolutionLayer.cpp
+++ b/tests/validation/CL/DilatedConvolutionLayer.cpp
@@ -113,7 +113,7 @@
ConvolutionMethod is_valid = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(false),
&weights_info.clone()->set_is_resizable(false),
&biases_info.clone()->set_is_resizable(false),
- &output_info.clone()->set_is_resizable(false), conv_info, WeightsInfo(), gpu_target, dilation);
+ &output_info.clone()->set_is_resizable(false), conv_info, WeightsInfo(), ActivationLayerInfo(), gpu_target, dilation);
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
TEST_SUITE_END()
@@ -171,18 +171,18 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::F16)))
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::F16)))
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
@@ -190,18 +190,18 @@
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::F32)))
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::F32)))
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -215,20 +215,24 @@
TEST_SUITE(FixedPoint)
TEST_SUITE(QS8)
// We test for fixed point precision [4,6]
-FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 4, 7)),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 4, 7)),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
@@ -237,20 +241,24 @@
TEST_SUITE(QS16)
// Testing for fixed point position [1,14)
-FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+FIXTURE_DATA_TEST_CASE(RunTiny, CLGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType",
- DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fixed);
@@ -263,18 +271,22 @@
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) })))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
index bf8b405..4564c64 100644
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ b/tests/validation/CL/DirectConvolutionLayer.cpp
@@ -73,6 +73,14 @@
combine(framework::dataset::make("PadY", 0, 2),
framework::dataset::make("KernelSize", { 3 })))),
framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
+/** Activation function Dataset*/
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
+});
} // namespace
TEST_SUITE(CL)
@@ -82,7 +90,7 @@
// *INDENT-OFF*
// clang-format off
-DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width
@@ -143,10 +151,14 @@
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
})),
- framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true })),
- input_info, weights_info, biases_info, output_info, conv_info, expected)
+ framework::dataset::make("ActivationInfo",
{
- bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info));
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
+})),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true })),
+ input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
+{
+ bool is_valid = bool(CLDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -159,7 +171,7 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::F16)), ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
@@ -167,7 +179,8 @@
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fp32);
@@ -175,8 +188,9 @@
TEST_SUITE_END()
TEST_SUITE(FP32_CustomDataset)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::ALL, combine(datasets::DirectConvolutionLayerDataset(),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesFixture<float>, framework::DatasetMode::ALL, combine(combine(datasets::DirectConvolutionLayerDataset(),
+ framework::dataset::make("DataType", DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_fp32);
@@ -189,8 +203,10 @@
TEST_SUITE(FixedPoint)
TEST_SUITE(QS8)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(data_fixed_point, framework::dataset::make("DataType", DataType::QS8)),
- framework::dataset::make("FractionalBits", 2, 7)))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(data_fixed_point, framework::dataset::make("DataType",
+ DataType::QS8)),
+ framework::dataset::make("FractionalBits", 2, 7)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qs8);
@@ -198,8 +214,10 @@
TEST_SUITE_END()
TEST_SUITE(QS16)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(data_fixed_point, framework::dataset::make("DataType", DataType::QS16)),
- framework::dataset::make("FractionalBits", 2, 15)))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(combine(data_fixed_point, framework::dataset::make("DataType",
+ DataType::QS16)),
+ framework::dataset::make("FractionalBits", 2, 15)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qs16);
@@ -212,10 +230,17 @@
template <typename T>
using CLDirectConvolutionValidationWithTensorShapesQuantizedFixture = DirectConvolutionValidationWithTensorShapesQuantizedFixture<CLTensor, CLAccessor, CLDirectConvolutionLayer, T>;
+const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
+});
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(data, framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10) })),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
@@ -223,9 +248,10 @@
TEST_SUITE_END()
TEST_SUITE(QASYMM8_CustomDataset)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(datasets::DirectConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127) })))
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127) })),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qasymm8);
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index 07a52f8..9aba8f7 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -373,14 +373,17 @@
// *INDENT-ON*
using CLWinogradConvolutionLayerFixture = WinogradConvolutionLayerValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>;
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
- framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
diff --git a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
index aaa0d15..8808d82 100644
--- a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
@@ -53,13 +53,21 @@
DataType::F16,
// DataType::F32,
});
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f)
+});
} // namespace
TEST_SUITE(GC)
TEST_SUITE(ConvolutionLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), CNNDataTypes),
- input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()),
+ CNNDataTypes),
+ ActivationFunctionsDataset),
+ input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, act_info)
{
// Set fixed point position data type allowed
int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0;
@@ -82,7 +90,7 @@
// Create and configure function
GCConvolutionLayer conv;
- conv.configure(&src, &weights, &bias, &dst, info, WeightsInfo(), dilation);
+ conv.configure(&src, &weights, &bias, &dst, info, WeightsInfo(), dilation, act_info);
// Validate valid region
const ValidRegion src_valid_region = shape_to_valid_region(input_shape);
@@ -108,18 +116,20 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType",
- DataType::F16)))
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType",
- DataType::F16)))
+ framework::dataset::make("DataType",
+ DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_f16, tolerance_num);
diff --git a/tests/validation/GLES_COMPUTE/DirectConvolutionLayer.cpp b/tests/validation/GLES_COMPUTE/DirectConvolutionLayer.cpp
index 153b060..47c35f9 100644
--- a/tests/validation/GLES_COMPUTE/DirectConvolutionLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/DirectConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -68,6 +68,13 @@
combine(framework::dataset::make("PadY", 0, 2),
framework::dataset::make("KernelSize", { 3, 5 })))),
framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))));
+/** Activation function Dataset*/
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+});
} // namespace
TEST_SUITE(GC)
@@ -80,7 +87,8 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(Run, GCDirectConvolutionLayerFixture<half_float::half>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(Run, GCDirectConvolutionLayerFixture<half_float::half>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_fp16, tolerance_num);
@@ -88,7 +96,8 @@
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(Run, GCDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(data, framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(Run, GCDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(data, framework::dataset::make("DataType", DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_fp32);
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 313e4bc..27216af 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -65,6 +65,12 @@
DataType::QS16,
DataType::QASYMM8,
});
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f)
+});
} // namespace
TEST_SUITE(NEON)
@@ -112,9 +118,11 @@
using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerValidationFixture<Tensor, Accessor, NEWinogradLayer, T>;
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
- datasets::SmallWinogradConvolutionLayer5x5Dataset()),
- framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
+ datasets::SmallWinogradConvolutionLayer5x5Dataset()),
+ framework::dataset::make("DataType", { DataType::F32 })),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
@@ -125,11 +133,12 @@
TEST_SUITE(GEMMConvolutionLayer)
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()), CNNDataTypes),
- input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(datasets::SmallConvolutionLayerDataset(), datasets::LargeConvolutionLayerDataset()),
+ CNNDataTypes),
+ framework::dataset::make("ActivationInfo",
+{ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU) })),
+input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, act_info)
{
- ARM_COMPUTE_UNUSED(dilation);
-
// Set fixed point position data type allowed
int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0;
@@ -151,7 +160,7 @@
// Create and configure function
NEGEMMConvolutionLayer conv;
- conv.configure(&src, &weights, &bias, &dst, info);
+ conv.configure(&src, &weights, &bias, &dst, info, WeightsInfo(), dilation, act_info);
// Validate valid region
const ValidRegion src_valid_region = shape_to_valid_region(input_shape);
@@ -178,16 +187,18 @@
TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F16)))
+ framework::dataset::make("DataType", DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F16)))
+ framework::dataset::make("DataType", DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
@@ -196,16 +207,18 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("DataType", DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("DataType", DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
@@ -219,18 +232,20 @@
TEST_SUITE(FixedPoint)
TEST_SUITE(QS8)
// We test for fixed point precision [4,6]
-FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
framework::dataset::make("DataType", DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+ framework::dataset::make("FractionalBits", 4, 7)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
framework::dataset::make("DataType", DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+ framework::dataset::make("FractionalBits", 4, 7)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
@@ -239,18 +254,20 @@
TEST_SUITE(QS16)
// Testing for fixed point position [1,14)
-FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
framework::dataset::make("DataType", DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+ framework::dataset::make("FractionalBits", 1, 14)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
framework::dataset::make("DataType", DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+ framework::dataset::make("FractionalBits", 1, 14)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
@@ -261,20 +278,28 @@
template <typename T>
using NEGEMMConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEGEMMConvolutionLayer, T>;
+const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
+});
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_qasymm8);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_qasymm8);
diff --git a/tests/validation/NEON/DilatedConvolutionLayer.cpp b/tests/validation/NEON/DilatedConvolutionLayer.cpp
index c0a72ec..1e8c19f 100644
--- a/tests/validation/NEON/DilatedConvolutionLayer.cpp
+++ b/tests/validation/NEON/DilatedConvolutionLayer.cpp
@@ -163,16 +163,18 @@
TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F16)))
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F16)))
+ framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
@@ -181,16 +183,18 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::F32)))
+ framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
@@ -204,18 +208,22 @@
TEST_SUITE(FixedPoint)
TEST_SUITE(QS8)
// We test for fixed point precision [4,6]
-FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("DataType", DataType::QS8)),
+ framework::dataset::make("FractionalBits", 4, 7)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("DataType", DataType::QS8)),
+ framework::dataset::make("FractionalBits", 4, 7)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
@@ -224,18 +232,22 @@
TEST_SUITE(QS16)
// Testing for fixed point position [1,14)
-FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("DataType", DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
}
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
- framework::dataset::make("DataType", DataType::QS16)),
- framework::dataset::make("FractionalBits", 1, 14)))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("DataType", DataType::QS16)),
+ framework::dataset::make("FractionalBits", 1, 14)),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_q);
@@ -248,18 +260,22 @@
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT,
+ combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_qasymm8);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true })),
- framework::dataset::make("DataType", DataType::QASYMM8)),
- framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })))
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
+ framework::dataset::make("DataType", DataType::QASYMM8)),
+ framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })),
+ framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_qasymm8);
diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp
index 57e030c..b6f9f62 100644
--- a/tests/validation/NEON/DirectConvolutionLayer.cpp
+++ b/tests/validation/NEON/DirectConvolutionLayer.cpp
@@ -86,6 +86,14 @@
combine(framework::dataset::make("PadY", 0),
combine(framework::dataset::make("KernelSize", 1),
framework::dataset::make("NumKernels", { 1, 4, 8, 16 })))))));
+/** Activation function Dataset*/
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
+});
} // namespace
TEST_SUITE(NEON)
@@ -93,7 +101,7 @@
// *INDENT-OFF*
// clang-format off
-DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
+DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching data type input/weights
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Mismatching input feature maps
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32, 0), // Unsupported kernel width
@@ -144,10 +152,15 @@
PadStrideInfo(1, 1, 0, 0),
PadStrideInfo(1, 1, 0, 0),
})),
- framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })),
- input_info, weights_info, biases_info, output_info, conv_info, expected)
+ framework::dataset::make("ActivationInfo",
{
- bool is_valid = bool(NEDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info));
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)
+})),
+ framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })),
+ input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
+{
+ bool is_valid = bool(NEDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
@@ -161,7 +174,8 @@
TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(data_f32, framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(data_f32, framework::dataset::make("DataType", DataType::F16)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_fp16);
@@ -170,7 +184,8 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(data_f32, framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(data_f32, framework::dataset::make("DataType", DataType::F32)),
+ ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_fp32);
@@ -181,11 +196,19 @@
template <typename T>
using NEDirectConvolutionLayerFixedPointFixture = DirectConvolutionValidationFixedPointFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>;
+const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+ ActivationLayerInfo(),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
+});
+
TEST_SUITE(Quantized)
TEST_SUITE(QS8)
// We test for fixed point precision [4,6]
-FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(data_qs8, framework::dataset::make("DataType", DataType::QS8)),
- framework::dataset::make("FractionalBits", 4, 7)))
+FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(data_qs8, framework::dataset::make("DataType", DataType::QS8)),
+ framework::dataset::make("FractionalBits", 4, 7)),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_qs);
@@ -194,8 +217,9 @@
TEST_SUITE(QS16)
// We test for fixed point precision [4,13]
-FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(data_qs16, framework::dataset::make("DataType", DataType::QS16)),
- framework::dataset::make("FractionalBits", 4, 14)))
+FIXTURE_DATA_TEST_CASE(Run, NEDirectConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(combine(data_qs16, framework::dataset::make("DataType", DataType::QS16)),
+ framework::dataset::make("FractionalBits", 4, 14)),
+ QuantizedActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_qs);
diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h
index 3d073e3..1bcffed 100644
--- a/tests/validation/fixtures/ConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/ConvolutionLayerFixture.h
@@ -33,6 +33,7 @@
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ActivationLayer.h"
#include "tests/validation/reference/ConvolutionLayer.h"
#include "tests/validation/reference/Utils.h"
@@ -55,7 +56,7 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights,
- DataType data_type, int fractional_bits, QuantizationInfo quantization_info)
+ DataType data_type, int fractional_bits, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
_data_type = data_type;
_is_quantized = is_data_type_quantized_asymmetric(data_type);
@@ -63,8 +64,8 @@
_fractional_bits = fractional_bits;
_quantization_info = quantization_info;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation, act_info);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation, act_info);
}
protected:
@@ -98,7 +99,7 @@
}
TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- bool reshape_weights, const Size2D &dilation)
+ bool reshape_weights, const Size2D &dilation, const ActivationLayerInfo act_info)
{
const bool is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && _data_type == DataType::F32;
@@ -140,7 +141,7 @@
// Create and configure function
FunctionType conv;
- conv.configure(&src, &weights, &bias, &dst, info, weights_info, dilation);
+ conv.configure(&src, &weights, &bias, &dst, info, weights_info, dilation, act_info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -210,7 +211,7 @@
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- const Size2D &dilation)
+ const Size2D &dilation, const ActivationLayerInfo act_info)
{
// Create reference
SimpleTensor<T> src{ input_shape, _data_type, 1, _fractional_bits, _quantization_info };
@@ -222,7 +223,9 @@
fill(weights, 1);
fill(bias, 2);
- return reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation);
+ return (act_info.enabled()) ? reference::activation_layer<T>(reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation),
+ act_info) :
+ reference::convolution_layer<T>(src, weights, bias, output_shape, info, dilation);
}
TensorType _target{};
@@ -283,10 +286,12 @@
{
public:
template <typename...>
- void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type)
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
+ ActivationLayerInfo act_info)
{
ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, 0,
- QuantizationInfo());
+ QuantizationInfo(),
+ act_info);
}
};
@@ -296,10 +301,11 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
- int fractional_bits)
+ int fractional_bits,
+ ActivationLayerInfo act_info)
{
ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, fractional_bits,
- QuantizationInfo());
+ QuantizationInfo(), act_info);
}
};
@@ -309,10 +315,10 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
- QuantizationInfo quantization_info)
+ QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, 0,
- quantization_info);
+ quantization_info, act_info);
}
};
} // namespace validation
diff --git a/tests/validation/fixtures/DirectConvolutionLayerFixture.h b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
index fef9d2d..ef7721d 100644
--- a/tests/validation/fixtures/DirectConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
@@ -49,7 +49,7 @@
public:
template <typename...>
void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
- DataType data_type, int fractional_bits, QuantizationInfo quantization_info)
+ DataType data_type, int fractional_bits, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
_fractional_bits = fractional_bits;
_quantization_info = quantization_info;
@@ -61,13 +61,13 @@
const TensorShape output_shape = get_output_shape(input_shape, weights_shape, info);
const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info, act_info);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info, act_info);
}
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
- DataType data_type, int fractional_bits, QuantizationInfo quantization_info)
+ DataType data_type, int fractional_bits, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
ARM_COMPUTE_UNUSED(dilation);
@@ -77,8 +77,8 @@
const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info, act_info);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, fractional_bits, quantization_info, act_info);
}
protected:
@@ -112,7 +112,7 @@
}
TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- DataType data_type, DataType bias_data_type, int fixed_point_position, QuantizationInfo quantization_info)
+ DataType data_type, DataType bias_data_type, int fixed_point_position, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position, quantization_info);
@@ -122,7 +122,7 @@
// Create and configure function
FunctionType conv;
- conv.configure(&src, &weights, &bias, &dst, info);
+ conv.configure(&src, &weights, &bias, &dst, info, act_info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -152,7 +152,7 @@
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- DataType data_type, DataType bias_data_type, int fixed_point_position, QuantizationInfo quantization_info)
+ DataType data_type, DataType bias_data_type, int fixed_point_position, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
// Create reference
SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position, quantization_info };
@@ -164,7 +164,9 @@
fill(weights, 1);
fill(bias, 2);
- return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
+ return (act_info.enabled()) ? reference::activation_layer<T>(reference::convolution_layer<T>(src, weights, bias, output_shape, info),
+ act_info) :
+ reference::convolution_layer<T>(src, weights, bias, output_shape, info);
}
TensorType _target{};
@@ -194,9 +196,10 @@
{
public:
template <typename...>
- void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type)
+ void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, ActivationLayerInfo act_info)
{
- DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, 0, QuantizationInfo());
+ DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, 0, QuantizationInfo(),
+ act_info);
}
};
@@ -205,10 +208,11 @@
{
public:
template <typename...>
- void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, int fractional_bits)
+ void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, int fractional_bits,
+ ActivationLayerInfo act_info)
{
DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, fractional_bits,
- QuantizationInfo());
+ QuantizationInfo(), act_info);
}
};
@@ -217,9 +221,11 @@
{
public:
template <typename...>
- void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info)
+ void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info,
+ ActivationLayerInfo act_info)
{
- DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, 0, quantization_info);
+ DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, 0, quantization_info,
+ act_info);
}
};
@@ -229,9 +235,10 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
- DataType data_type, QuantizationInfo quantization_info)
+ DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
- DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, 0, quantization_info);
+ DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, 0, quantization_info,
+ act_info);
}
};
@@ -241,9 +248,10 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
- DataType data_type)
+ DataType data_type, ActivationLayerInfo act_info)
{
- DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, 0, QuantizationInfo());
+ DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, 0, QuantizationInfo(),
+ act_info);
}
};
diff --git a/tests/validation/fixtures/WinogradLayerFixture.h b/tests/validation/fixtures/WinogradLayerFixture.h
index a86f24f..5210cbf 100644
--- a/tests/validation/fixtures/WinogradLayerFixture.h
+++ b/tests/validation/fixtures/WinogradLayerFixture.h
@@ -33,6 +33,7 @@
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
+#include "tests/validation/reference/ActivationLayer.h"
#include "tests/validation/reference/ConvolutionLayer.h"
#include "tests/validation/reference/Utils.h"
#include "tests/validation/reference/Winograd.h"
@@ -52,12 +53,12 @@
{
public:
template <typename...>
- void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type)
+ void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, DataType data_type, ActivationLayerInfo act_info)
{
ARM_COMPUTE_UNUSED(dilation);
- _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type);
- _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type);
+ _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info);
+ _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, act_info);
}
protected:
@@ -82,7 +83,7 @@
}
TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- DataType data_type)
+ DataType data_type, ActivationLayerInfo act_info)
{
// Create tensors
TensorType src = create_tensor<TensorType>(input_shape, data_type, 1);
@@ -92,7 +93,7 @@
// Create and configure function
FunctionType conv;
- conv.configure(&src, &weights, &bias, &dst, info);
+ conv.configure(&src, &weights, &bias, &dst, info, act_info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -122,7 +123,7 @@
}
SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
- DataType data_type)
+ DataType data_type, ActivationLayerInfo act_info)
{
// Create reference
SimpleTensor<T> src{ input_shape, data_type, 1 };
@@ -134,7 +135,8 @@
fill(weights, 1, -1.f, 1.f);
fill(bias, 2, -1.f, 1.f);
- return reference::convolution_layer<T>(src, weights, bias, output_shape, info);
+ return (act_info.enabled()) ? reference::activation_layer<T>(reference::convolution_layer<T>(src, weights, bias, output_shape, info), act_info) : reference::convolution_layer<T>(src, weights, bias,
+ output_shape, info);
}
TensorType _target{};