17 #include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h> 21 using namespace armcomputetensorutils;
30 if (activationDescriptor)
33 if (std::find(activations.begin(), activations.end(), activationDescriptor->
m_Function) == activations.end())
36 arm_compute::ErrorCode::RUNTIME_ERROR,
"NeonFullyConnectedWorkload :Unsupported Activation Function"};
40 const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input);
41 const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output);
42 const arm_compute::TensorInfo aclWeights = BuildArmComputeTensorInfo(weights);
44 arm_compute::TensorInfo aclBiases;
45 arm_compute::TensorInfo *optionalAclBiases =
nullptr;
48 aclBiases = BuildArmComputeTensorInfo(biases);
49 optionalAclBiases = &aclBiases;
52 const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo =
55 return arm_compute::NEFullyConnectedLayer::validate(&aclInput,
59 fullyConnectedLayerInfo);
63 const WorkloadInfo&
info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
68 arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(
m_Data.
m_Inputs[0])->GetTensor();
69 arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(
m_Data.
m_Outputs[0])->GetTensor();
71 m_WeightsTensor = std::make_unique<arm_compute::Tensor>();
76 m_BiasesTensor = std::make_unique<arm_compute::Tensor>();
82 arm_compute::FullyConnectedLayerInfo fc_info =
85 auto layer = std::make_unique<arm_compute::NEFullyConnectedLayer>(memoryManager);
86 layer->configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info);
87 m_FullyConnectedLayer.reset(layer.release());
113 m_FullyConnectedLayer->prepare();
120 m_FullyConnectedLayer->run();
123 void NeonFullyConnectedWorkload::FreeUnusedTensors()
125 FreeTensorIfUnused(m_WeightsTensor);
126 FreeTensorIfUnused(m_BiasesTensor);
const ConstCpuTensorHandle * m_Weight
const FullyConnectedQueueDescriptor m_Data
#define ARMNN_SCOPED_PROFILING_EVENT_NEON(name)
arm_compute::ActivationLayerInfo ConvertAdditionalInfoToAclActivationLayerInfo(const QueueDescriptor &queueDescriptor)
NeonFullyConnectedWorkload(const FullyConnectedQueueDescriptor &descriptor, const WorkloadInfo &info, std::shared_ptr< arm_compute::MemoryManagerOnDemand > &memoryManager)
arm_compute::Status NeonFullyConnectedWorkloadValidate(const TensorInfo &input, const TensorInfo &output, const TensorInfo &weights, const TensorInfo &biases, const FullyConnectedDescriptor &descriptor, const ActivationDescriptor *activationDescriptor)
void ValidateInputsOutputs(const std::string &descName, unsigned int numExpectedIn, unsigned int numExpectedOut) const
Copyright (c) 2020 ARM Limited.
LayerDescriptor m_Parameters
DataType GetDataType() const
A FullyConnectedDescriptor for the FullyConnectedLayer.
bool m_BiasEnabled
Enable/disable bias.
arm_compute::FullyConnectedLayerInfo ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(const FullyConnectedDescriptor &fullyConnectedDesc, const ActivationDescriptor *activationDesc)
An ActivationDescriptor for the ActivationLayer.
min(a, max(b, input)) ReLu1 & ReLu6.
void InitializeArmComputeTensorData(arm_compute::Tensor &tensor, const ConstCpuTensorHandle *handle)
std::vector< ITensorHandle * > m_Outputs
Contains information about inputs and outputs to a layer.
std::vector< ITensorHandle * > m_Inputs
const ConstCpuTensorHandle * m_Bias
virtual void Execute() const override
ActivationFunction m_Function
The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square, Elu).
const TensorInfo & GetTensorInfo() const