IVGCVSW-1806: Refactor Android-NN-Driver ModelToINetworkConverter
* Moved conversion logic into new V1_0 and V1_1 HalPolicy classes
* Extracted common helper functions into ConversionUtils class
Change-Id: I1ab50edc266dd528c0cb22a5cd1aa65e103674d9
diff --git a/ConversionUtils.cpp b/ConversionUtils.cpp
new file mode 100644
index 0000000..60d1a1f
--- /dev/null
+++ b/ConversionUtils.cpp
@@ -0,0 +1,172 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ConversionUtils.hpp"
+
+///
+/// Helper classes
+///
+
+namespace armnn_driver
+{
+
+LayerInputHandle::LayerInputHandle()
+ : m_OutputSlot(nullptr)
+ , m_Valid(false)
+{}
+
+LayerInputHandle::LayerInputHandle(bool valid, armnn::IOutputSlot* outputSlot, armnn::TensorInfo tensorInfo)
+ : m_OutputSlot(outputSlot)
+ , m_Valid(valid)
+ , m_TensorInfo(tensorInfo)
+{}
+
+bool LayerInputHandle::IsValid() const
+{
+ return m_Valid;
+}
+
+void LayerInputHandle::Connect(armnn::IInputSlot& inputSlot)
+{
+ BOOST_ASSERT(IsValid());
+ if (m_OutputSlot)
+ {
+ m_OutputSlot->Connect(inputSlot);
+ }
+}
+
+const armnn::TensorInfo& LayerInputHandle::GetTensorInfo() const
+{
+ return m_TensorInfo;
+}
+
+ConstTensorPin::ConstTensorPin(bool optional)
+ : m_Optional(optional)
+{}
+
+ConstTensorPin::ConstTensorPin(const armnn::TensorInfo& tensorInfo,
+ const void* valueStart,
+ uint32_t numBytes,
+ const armnn::PermutationVector& mappings)
+{
+ boost::ignore_unused(numBytes);
+ assert(tensorInfo.GetNumBytes() == numBytes);
+
+ const bool needsSwizzling = (mappings.GetSize() > 0);
+ if (needsSwizzling)
+ {
+ m_SwizzledTensorData.resize(tensorInfo.GetNumBytes());
+ SwizzleAndroidNn4dTensorToArmNn(tensorInfo, valueStart, m_SwizzledTensorData.data(), mappings);
+
+ m_ConstTensor = armnn::ConstTensor(armnnUtils::Permuted(tensorInfo, mappings), m_SwizzledTensorData.data());
+ }
+ else
+ {
+ m_ConstTensor = armnn::ConstTensor(tensorInfo, valueStart);
+ }
+}
+
+bool ConstTensorPin::IsValid() const
+{
+ return m_ConstTensor.GetMemoryArea() != nullptr;
+}
+
+bool ConstTensorPin::IsOptional() const
+{
+ return m_Optional;
+}
+
+const armnn::ConstTensor& ConstTensorPin::GetConstTensor() const
+{
+ return m_ConstTensor;
+}
+
+const armnn::ConstTensor* ConstTensorPin::GetConstTensorPtr() const
+{
+ if (IsValid() && m_ConstTensor.GetNumElements() > 0)
+ {
+ return &m_ConstTensor;
+ }
+ // tensor is either invalid, or has no elements (indicating an optional tensor that was not provided)
+ return nullptr;
+}
+
+///
+/// Utility functions
+///
+
+armnn::IConnectableLayer* ProcessActivation(const armnn::TensorInfo& tensorInfo,
+ ActivationFn activation,
+ armnn::IConnectableLayer* prevLayer,
+ ConversionData& data)
+{
+ BOOST_ASSERT(prevLayer->GetNumOutputSlots() == 1);
+
+ prevLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+ armnn::IConnectableLayer* activationLayer = prevLayer;
+
+ if (activation != ActivationFn::kActivationNone)
+ {
+ armnn::ActivationDescriptor activationDesc;
+ switch (activation)
+ {
+ case ActivationFn::kActivationRelu:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::ReLu;
+ break;
+ }
+ case ActivationFn::kActivationRelu1:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
+ activationDesc.m_A = 1.0f;
+ activationDesc.m_B = -1.0f;
+ break;
+ }
+ case ActivationFn::kActivationRelu6:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu;
+ activationDesc.m_A = 6.0f;
+ break;
+ }
+ case ActivationFn::kActivationSigmoid:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::Sigmoid;
+ break;
+ }
+ case ActivationFn::kActivationTanh:
+ {
+ activationDesc.m_Function = armnn::ActivationFunction::TanH;
+ activationDesc.m_A = 1.0f;
+ activationDesc.m_B = 1.0f;
+ break;
+ }
+ default:
+ {
+ Fail("%s: Invalid activation enum value %i", __func__, activation);
+ return nullptr;
+ }
+ }
+
+ if (!IsLayerSupported(__func__,
+ armnn::IsActivationSupported,
+ data.m_Compute,
+ prevLayer->GetOutputSlot(0).GetTensorInfo(),
+ tensorInfo,
+ activationDesc))
+ {
+ return nullptr;
+ }
+
+ activationLayer = data.m_Network->AddActivationLayer(activationDesc);
+
+ prevLayer->GetOutputSlot(0).Connect(activationLayer->GetInputSlot(0));
+ activationLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ }
+
+ return activationLayer;
+}
+
+} // namespace armnn_driver
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