#include "workloads/NeonPadWorkload.hpp"
#include "workloads/NeonPermuteWorkload.hpp"
#include "workloads/NeonPooling2dWorkload.hpp"
+#include "workloads/NeonPreluWorkload.hpp"
#include "workloads/NeonQuantizeWorkload.hpp"
#include "workloads/NeonResizeBilinearWorkload.hpp"
#include "workloads/NeonSoftmaxBaseWorkload.hpp"
FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPooling2dWorkloadValidate, reasonIfUnsupported, input, output, descriptor);
}
+bool NeonLayerSupport::IsPreluSupported(const armnn::TensorInfo &input,
+ const armnn::TensorInfo &alpha,
+ const armnn::TensorInfo &output,
+ armnn::Optional<std::string &> reasonIfUnsupported) const
+{
+ FORWARD_WORKLOAD_VALIDATE_FUNC(NeonPreluWorkloadValidate, reasonIfUnsupported, input, alpha, output);
+}
+
bool NeonLayerSupport::IsQuantizeSupported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported) const
const Pooling2dDescriptor& descriptor,
Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
+ bool IsPreluSupported(const TensorInfo& input,
+ const TensorInfo& alpha,
+ const TensorInfo& output,
+ Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
+
bool IsQuantizeSupported(const TensorInfo& input,
const TensorInfo& output,
Optional<std::string&> reasonIfUnsupported = EmptyOptional()) const override;
return std::make_unique<NeonPooling2dWorkload>(descriptor, info);
}
+std::unique_ptr<armnn::IWorkload> NeonWorkloadFactory::CreatePrelu(const armnn::PreluQueueDescriptor &descriptor,
+ const armnn::WorkloadInfo &info) const
+{
+ return std::make_unique<NeonPreluWorkload>(descriptor, info);
+}
+
std::unique_ptr<armnn::IWorkload> NeonWorkloadFactory::CreateConvolution2d(
const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) const
{
std::unique_ptr<IWorkload> CreatePooling2d(const Pooling2dQueueDescriptor& descriptor,
const WorkloadInfo& info) const override;
+ std::unique_ptr<IWorkload> CreatePrelu(const PreluQueueDescriptor& descriptor,
+ const WorkloadInfo& info) const override;
+
std::unique_ptr<IWorkload> CreateConvolution2d(const Convolution2dQueueDescriptor& descriptor,
const WorkloadInfo& info) const override;
workloads/NeonPadWorkload.cpp \
workloads/NeonPermuteWorkload.cpp \
workloads/NeonPooling2dWorkload.cpp \
+ workloads/NeonPreluWorkload.cpp \
workloads/NeonQuantizeWorkload.cpp \
workloads/NeonReshapeWorkload.cpp \
workloads/NeonResizeBilinearWorkload.cpp \
NeonCreatePooling2dWorkloadTest<DataType::QuantisedAsymm8>(DataLayout::NHWC);
}
+static void NeonCreatePreluWorkloadTest(const armnn::TensorShape& inputShape,
+ const armnn::TensorShape& alphaShape,
+ const armnn::TensorShape& outputShape,
+ armnn::DataType dataType)
+{
+ Graph graph;
+ NeonWorkloadFactory factory =
+ NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager());
+
+ auto workload = CreatePreluWorkloadTest<NeonPreluWorkload>(factory,
+ graph,
+ inputShape,
+ alphaShape,
+ outputShape,
+ dataType);
+
+ // Checks that outputs and inputs are as we expect them (see definition of CreateReshapeWorkloadTest).
+ PreluQueueDescriptor queueDescriptor = workload->GetData();
+ auto inputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[0]);
+ auto alphaHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Inputs[1]);
+ auto outputHandle = boost::polymorphic_downcast<IAclTensorHandle*>(queueDescriptor.m_Outputs[0]);
+ BOOST_TEST(TestNeonTensorHandleInfo(inputHandle, TensorInfo(inputShape, dataType)));
+ BOOST_TEST(TestNeonTensorHandleInfo(alphaHandle, TensorInfo(alphaShape, dataType)));
+ BOOST_TEST(TestNeonTensorHandleInfo(outputHandle, TensorInfo(outputShape, dataType)));
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ BOOST_AUTO_TEST_CASE(CreatePreluFloat16Workload)
+{
+ NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::Float16);
+}
+#endif
+
+BOOST_AUTO_TEST_CASE(CreatePreluFloatWorkload)
+{
+ NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::Float32);
+}
+
+BOOST_AUTO_TEST_CASE(CreatePreluUint8Workload)
+{
+ NeonCreatePreluWorkloadTest({ 1, 4, 1, 2 }, { 5, 4, 3, 1 }, { 5, 4, 3, 2 }, DataType::QuantisedAsymm8);
+}
+
template <typename armnn::DataType DataType>
static void NeonCreateReshapeWorkloadTest()
{
ARMNN_AUTO_TEST_CASE(QuantizeSimpleUint8, QuantizeSimpleUint8Test)
ARMNN_AUTO_TEST_CASE(QuantizeClampUint8, QuantizeClampUint8Test)
+// PReLU
+ARMNN_AUTO_TEST_CASE(PreluFloat32, PreluTest<armnn::DataType::Float32>)
+ARMNN_AUTO_TEST_CASE(PreluUint8, PreluTest<armnn::DataType::QuantisedAsymm8>)
+
// ============================================================================
// COMPARE tests
NeonPermuteWorkload.hpp
NeonPooling2dWorkload.cpp
NeonPooling2dWorkload.hpp
+ NeonPreluWorkload.cpp
+ NeonPreluWorkload.hpp
NeonQuantizeWorkload.cpp
NeonQuantizeWorkload.hpp
NeonReshapeWorkload.cpp
--- /dev/null
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NeonPreluWorkload.hpp"
+#include "NeonWorkloadUtils.hpp"
+#include <aclCommon/ArmComputeUtils.hpp>
+
+#include <arm_compute/runtime/NEON/functions/NEPReluLayer.h>
+
+namespace armnn
+{
+
+arm_compute::Status NeonPreluWorkloadValidate(const TensorInfo& input,
+ const TensorInfo& alpha,
+ const TensorInfo& output)
+{
+ const arm_compute::TensorInfo aclInput = armcomputetensorutils::BuildArmComputeTensorInfo(input);
+ const arm_compute::TensorInfo aclAlpha = armcomputetensorutils::BuildArmComputeTensorInfo(alpha);
+ const arm_compute::TensorInfo aclOutput = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+
+ return arm_compute::NEPReluLayer::validate(&aclInput,
+ &aclAlpha,
+ &aclOutput);
+}
+
+NeonPreluWorkload::NeonPreluWorkload(const PreluQueueDescriptor& descriptor,
+ const WorkloadInfo& info)
+ : BaseWorkload<PreluQueueDescriptor>(descriptor, info)
+{
+ m_Data.ValidateInputsOutputs("NeonPreluWorkload", 1, 1);
+
+ arm_compute::ITensor& input = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+ arm_compute::ITensor& alpha = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
+ arm_compute::ITensor& output = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+
+ auto layer = std::make_unique<arm_compute::NEPReluLayer>();
+ layer->configure(&input, &alpha, &output);
+
+ m_PreluLayer.reset(layer.release());
+}
+
+void NeonPreluWorkload::Execute() const
+{
+ ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonPreluWorkload_Execute");
+ m_PreluLayer->run();
+}
+
+} //namespace armnn
\ No newline at end of file
--- /dev/null
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <backendsCommon/Workload.hpp>
+#include <arm_compute/core/Error.h>
+#include <arm_compute/runtime/IFunction.h>
+
+namespace armnn
+{
+
+arm_compute::Status NeonPreluWorkloadValidate(const TensorInfo& input,
+ const TensorInfo& alpha,
+ const TensorInfo& output);
+
+class NeonPreluWorkload : public BaseWorkload<PreluQueueDescriptor>
+{
+public:
+ NeonPreluWorkload(const PreluQueueDescriptor& descriptor, const WorkloadInfo& info);
+ void Execute() const override;
+
+private:
+ std::unique_ptr<arm_compute::IFunction> m_PreluLayer;
+};
+
+} //namespace armnn
#include "NeonPadWorkload.hpp"
#include "NeonPermuteWorkload.hpp"
#include "NeonPooling2dWorkload.hpp"
+#include "NeonPreluWorkload.hpp"
#include "NeonQuantizeWorkload.hpp"
#include "NeonReshapeWorkload.hpp"
#include "NeonResizeBilinearWorkload.hpp"