IVGCVSW-1770: Refactor ModelToINetworkConverter to allow conversion of HAL1.1 operators
Change-Id: I9b10f0a9c88344df108b2325c0233f9660fa6b7c
diff --git a/ModelToINetworkConverter.cpp b/ModelToINetworkConverter.cpp
index 95ee8ef..4a7b401 100644
--- a/ModelToINetworkConverter.cpp
+++ b/ModelToINetworkConverter.cpp
@@ -24,6 +24,7 @@
namespace armnn_driver
{
+
class LayerInputHandle
{
public:
@@ -55,10 +56,12 @@
bool m_Valid;
armnn::TensorInfo m_TensorInfo;
};
-} // armnn_driver
+
+} // namespace armnn_driver
namespace
{
+
using namespace armnn_driver;
using namespace android::nn;
@@ -210,7 +213,7 @@
const armnn::PermutationVector RotateTensorLeft({ 2U, 0U, 1U });
const armnn::PermutationVector RotateTensorRight({ 1U, 2U, 0U });
-template <typename OSlot>
+template<typename OSlot>
armnn::IConnectableLayer& AddPermuteLayer(armnn::INetwork& network, OSlot& input,
const armnn::PermutationVector& mappings)
{
@@ -309,7 +312,7 @@
return inputShape.GetNumDimensions() < 3;
}
-template <typename OSlot>
+template<typename OSlot>
armnn::IConnectableLayer& AddReshapeLayer(armnn::INetwork& network, OSlot& inputLayer,
armnn::TensorInfo reshapeInfo)
{
@@ -393,8 +396,7 @@
}
}
-
-} // namespace
+} // anonymous namespace
namespace armnn_driver
{
@@ -456,8 +458,9 @@
bool m_Optional;
};
-ModelToINetworkConverter::ModelToINetworkConverter(armnn::Compute compute,
- const neuralnetworks::V1_0::Model& model,
+template<typename HalVersion>
+ModelToINetworkConverter<HalVersion>::ModelToINetworkConverter(armnn::Compute compute,
+ const HalModel& model,
const std::set<unsigned int>& forcedUnsupportedOperations)
: m_Compute(compute)
, m_Model(model)
@@ -477,9 +480,11 @@
}
}
-void ModelToINetworkConverter::Convert()
+template<typename HalVersion>
+void ModelToINetworkConverter<HalVersion>::Convert()
{
- ALOGV("ModelToINetworkConverter::Convert(): %s", GetModelSummary(m_Model).c_str());
+ using Model = typename HalVersion::Model;
+ ALOGV("ModelToINetworkConverter::Convert(): %s", GetModelSummary<Model>(m_Model).c_str());
// map the memory pool into shared pointers
m_MemPools.clear();
@@ -594,7 +599,8 @@
}
}
-bool ModelToINetworkConverter::ConvertOperation(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertOperation(const neuralnetworks::V1_0::Operation& operation)
{
switch (operation.type)
{
@@ -646,7 +652,30 @@
}
}
-bool ModelToINetworkConverter::ConvertAdd(const neuralnetworks::V1_0::Operation& operation)
+#if defined(ARMNN_ANDROID_NN_V1_1)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertOperation(const neuralnetworks::V1_1::Operation& operation)
+{
+ if (compliantWithV1_0(operation))
+ {
+ neuralnetworks::V1_0::Operation v1Operation = convertToV1_0(operation);
+ return ConvertOperation(v1Operation);
+ }
+ else
+ {
+ switch (operation.type)
+ {
+ // TODO: provide cases for converting the new v1.1 operations
+ default:
+ return Fail("%s: Operation type %s not supported in ArmnnDriver",
+ __func__, toString(operation.type).c_str());
+ }
+ }
+}
+#endif
+
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertAdd(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
@@ -699,12 +728,14 @@
}
}
-bool ModelToINetworkConverter::ConvertAveragePool2d(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertAveragePool2d(const neuralnetworks::V1_0::Operation& operation)
{
return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Average);
}
-bool ModelToINetworkConverter::ConvertConcatenation(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertConcatenation(const neuralnetworks::V1_0::Operation& operation)
{
// The first N (0..N-1) inputs are tensors. The Nth input is the concatenation axis.
if (operation.inputs.size() <= 1)
@@ -919,7 +950,8 @@
return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
}
-bool ModelToINetworkConverter::ConvertConv2d(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertConv2d(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1022,7 +1054,8 @@
}
}
-bool ModelToINetworkConverter::ConvertDepthwiseConv2d(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertDepthwiseConv2d(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1144,7 +1177,8 @@
}
}
-bool ModelToINetworkConverter::ConvertFloor(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertFloor(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1174,7 +1208,8 @@
return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
}
-bool ModelToINetworkConverter::ConvertFullyConnected(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertFullyConnected(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1277,7 +1312,9 @@
}
}
-bool ModelToINetworkConverter::ConvertLocalResponseNormalization(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertLocalResponseNormalization(
+ const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1335,7 +1372,8 @@
return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
}
-bool ModelToINetworkConverter::ConvertLogistic(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertLogistic(const neuralnetworks::V1_0::Operation& operation)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::Sigmoid;
@@ -1343,7 +1381,8 @@
return ConvertToActivation(operation, __func__, desc);
}
-bool ModelToINetworkConverter::ConvertL2Normalization(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertL2Normalization(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1381,17 +1420,20 @@
return SetupAndTrackLayerOutputSlot(operation, 0, outSwizzleLayer);
}
-bool ModelToINetworkConverter::ConvertL2Pool2d(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertL2Pool2d(const neuralnetworks::V1_0::Operation& operation)
{
return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::L2);
}
-bool ModelToINetworkConverter::ConvertMaxPool2d(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertMaxPool2d(const neuralnetworks::V1_0::Operation& operation)
{
return ConvertPooling2d(operation, __func__, armnn::PoolingAlgorithm::Max);
}
-bool ModelToINetworkConverter::ConvertMul(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertMul(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input0 = ConvertToLayerInputHandle(operation, 0);
LayerInputHandle input1 = ConvertToLayerInputHandle(operation, 1);
@@ -1445,7 +1487,8 @@
}
}
-bool ModelToINetworkConverter::ConvertReLu(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertReLu(const neuralnetworks::V1_0::Operation& operation)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::ReLu;
@@ -1453,7 +1496,8 @@
return ConvertToActivation(operation, __func__, desc);
}
-bool ModelToINetworkConverter::ConvertReLu1(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertReLu1(const neuralnetworks::V1_0::Operation& operation)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::BoundedReLu;
@@ -1463,7 +1507,8 @@
return ConvertToActivation(operation, __func__, desc);
}
-bool ModelToINetworkConverter::ConvertReLu6(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertReLu6(const neuralnetworks::V1_0::Operation& operation)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::BoundedReLu;
@@ -1472,7 +1517,8 @@
return ConvertToActivation(operation, __func__, desc);
}
-bool ModelToINetworkConverter::ConvertSoftmax(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertSoftmax(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1511,7 +1557,8 @@
return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
}
-bool ModelToINetworkConverter::ConvertTanH(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertTanH(const neuralnetworks::V1_0::Operation& operation)
{
armnn::ActivationDescriptor desc;
desc.m_Function = armnn::ActivationFunction::TanH;
@@ -1521,7 +1568,8 @@
return ConvertToActivation(operation, __func__, desc);
}
-bool ModelToINetworkConverter::ConvertReshape(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertReshape(const neuralnetworks::V1_0::Operation& operation)
{
const Operand* inputOperand = GetInputOperand(operation, 0);
const Operand* requestedShapeOperand = GetInputOperand(operation, 1);
@@ -1589,7 +1637,8 @@
return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
}
-bool ModelToINetworkConverter::ConvertResizeBilinear(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertResizeBilinear(const neuralnetworks::V1_0::Operation& operation)
{
LayerInputHandle input = ConvertToLayerInputHandle(operation, 0);
if (!input.IsValid())
@@ -1635,7 +1684,8 @@
}
-bool ModelToINetworkConverter::ConvertLstm(const neuralnetworks::V1_0::Operation& operation)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertLstm(const neuralnetworks::V1_0::Operation& operation)
{
// Inputs:
// 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where
@@ -1935,7 +1985,8 @@
SetupAndTrackLayerOutputSlot(operation, 3, *layer, 3));
}
-bool ModelToINetworkConverter::ConvertToActivation(const neuralnetworks::V1_0::Operation& operation,
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertToActivation(const neuralnetworks::V1_0::Operation& operation,
const char* operationName,
const armnn::ActivationDescriptor& activationDesc)
{
@@ -1968,7 +2019,8 @@
return SetupAndTrackLayerOutputSlot(operation, 0, *layer);
}
-bool ModelToINetworkConverter::ConvertPooling2d(const neuralnetworks::V1_0::Operation& operation,
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::ConvertPooling2d(const neuralnetworks::V1_0::Operation& operation,
const char* operationName,
armnn::PoolingAlgorithm poolType)
{
@@ -2088,7 +2140,8 @@
}
}
-const void* ModelToINetworkConverter::GetOperandValueReadOnlyAddress(const Operand& operand) const
+template<typename HalVersion>
+const void* ModelToINetworkConverter<HalVersion>::GetOperandValueReadOnlyAddress(const Operand& operand) const
{
const void* valueStart = nullptr;
@@ -2118,8 +2171,9 @@
return valueStart;
}
-const Operand* ModelToINetworkConverter::GetInputOperand(const neuralnetworks::V1_0::Operation& operation,
- uint32_t inputIndex) const
+template<typename HalVersion>
+const Operand* ModelToINetworkConverter<HalVersion>::GetInputOperand(const neuralnetworks::V1_0::Operation& operation,
+ uint32_t inputIndex) const
{
if (inputIndex >= operation.inputs.size())
{
@@ -2131,8 +2185,9 @@
return &m_Model.operands[operation.inputs[inputIndex]];
}
-const Operand* ModelToINetworkConverter::GetOutputOperand(const neuralnetworks::V1_0::Operation& operation,
- uint32_t outputIndex) const
+template<typename HalVersion>
+const Operand* ModelToINetworkConverter<HalVersion>::GetOutputOperand(const neuralnetworks::V1_0::Operation& operation,
+ uint32_t outputIndex) const
{
if (outputIndex >= operation.outputs.size())
{
@@ -2144,8 +2199,10 @@
return &m_Model.operands[operation.outputs[outputIndex]];
}
+template<typename HalVersion>
template<typename T>
-bool ModelToINetworkConverter::GetInputScalar(const neuralnetworks::V1_0::Operation& operation, uint32_t inputIndex,
+bool ModelToINetworkConverter<HalVersion>::GetInputScalar(const neuralnetworks::V1_0::Operation& operation,
+ uint32_t inputIndex,
OperandType type, T& outValue) const
{
const Operand* operand = GetInputOperand(operation, inputIndex);
@@ -2176,22 +2233,26 @@
return true;
}
-bool ModelToINetworkConverter::GetInputInt32(const neuralnetworks::V1_0::Operation& operation,
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetInputInt32(const neuralnetworks::V1_0::Operation& operation,
uint32_t inputIndex, int32_t& outValue) const
{
return GetInputScalar(operation, inputIndex, OperandType::INT32, outValue);
}
-bool ModelToINetworkConverter::GetInputFloat32(const neuralnetworks::V1_0::Operation& operation,
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetInputFloat32(const neuralnetworks::V1_0::Operation& operation,
uint32_t inputIndex, float& outValue) const
{
return GetInputScalar(operation, inputIndex, OperandType::FLOAT32, outValue);
}
-bool ModelToINetworkConverter::GetInputActivationFunctionImpl(const neuralnetworks::V1_0::Operation& operation,
- uint32_t inputIndex,
- OperandType type,
- ActivationFn& outActivationFunction) const
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetInputActivationFunctionImpl(
+ const neuralnetworks::V1_0::Operation& operation,
+ uint32_t inputIndex,
+ OperandType type,
+ ActivationFn& outActivationFunction) const
{
if (type != OperandType::INT32 && type != OperandType::TENSOR_INT32)
{
@@ -2211,24 +2272,29 @@
return true;
}
-bool ModelToINetworkConverter::GetInputActivationFunction(const neuralnetworks::V1_0::Operation& operation,
- uint32_t inputIndex,
- ActivationFn& outActivationFunction) const
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetInputActivationFunction(
+ const neuralnetworks::V1_0::Operation& operation,
+ uint32_t inputIndex,
+ ActivationFn& outActivationFunction) const
{
return GetInputActivationFunctionImpl(operation, inputIndex, OperandType::INT32, outActivationFunction);
}
-bool ModelToINetworkConverter::GetInputActivationFunctionFromTensor(const neuralnetworks::V1_0::Operation& operation,
- uint32_t inputIndex,
- ActivationFn& outActivationFunction) const
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetInputActivationFunctionFromTensor(
+ const neuralnetworks::V1_0::Operation& operation,
+ uint32_t inputIndex,
+ ActivationFn& outActivationFunction) const
{
// This only accepts a 1-D tensor of size 1
return GetInputActivationFunctionImpl(operation, inputIndex, OperandType::INT32, outActivationFunction);
}
-bool ModelToINetworkConverter::GetOptionalInputActivation(const neuralnetworks::V1_0::Operation& operation,
- uint32_t inputIndex,
- ActivationFn& activationFunction) const
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetOptionalInputActivation(const neuralnetworks::V1_0::Operation& operation,
+ uint32_t inputIndex,
+ ActivationFn& activationFunction) const
{
if (operation.inputs.size() <= inputIndex)
{
@@ -2244,7 +2310,8 @@
return true;
}
-bool ModelToINetworkConverter::GetInputPaddingScheme(const neuralnetworks::V1_0::Operation& operation,
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetInputPaddingScheme(const neuralnetworks::V1_0::Operation& operation,
uint32_t inputIndex,
android::nn::PaddingScheme& outPaddingScheme) const
{
@@ -2258,7 +2325,8 @@
return true;
}
-LayerInputHandle ModelToINetworkConverter::ConvertToLayerInputHandle(
+template<typename HalVersion>
+LayerInputHandle ModelToINetworkConverter<HalVersion>::ConvertToLayerInputHandle(
const neuralnetworks::V1_0::Operation& operation,
uint32_t inputIndex)
{
@@ -2328,7 +2396,8 @@
}
}
-ConstTensorPin ModelToINetworkConverter::ConvertOperationInputToConstTensorPin(
+template<typename HalVersion>
+ConstTensorPin ModelToINetworkConverter<HalVersion>::ConvertOperationInputToConstTensorPin(
const neuralnetworks::V1_0::Operation& operation,
uint32_t inputIndex, const armnn::PermutationVector& dimensionMappings,
const armnn::TensorShape* overrideTensorShape, bool optional)
@@ -2342,7 +2411,8 @@
return ConvertOperandToConstTensorPin(*operand, dimensionMappings, overrideTensorShape, optional);
}
-ConstTensorPin ModelToINetworkConverter::ConvertOperandToConstTensorPin(const Operand& operand,
+template<typename HalVersion>
+ConstTensorPin ModelToINetworkConverter<HalVersion>::ConvertOperandToConstTensorPin(const Operand& operand,
const armnn::PermutationVector& dimensionMappings, const armnn::TensorShape* overrideTensorShape, bool optional)
{
if (!IsOperandTypeSupportedForTensors(operand.type))
@@ -2378,7 +2448,9 @@
return ConstTensorPin(tensorInfo, valueStart, operand.location.length, dimensionMappings);
}
-bool ModelToINetworkConverter::GetTensorInt32Values(const Operand& operand, std::vector<int32_t>& outValues) const
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::GetTensorInt32Values(const Operand& operand,
+ std::vector<int32_t>& outValues) const
{
if (operand.type != OperandType::TENSOR_INT32)
{
@@ -2410,7 +2482,8 @@
// activation function or nullptr if an error occurred (e.g. unsupported activation).
// Note that the end layer matches the input layer if no activation is required
// (the sequence of layers has length 1).
-armnn::IConnectableLayer* ModelToINetworkConverter::ProcessActivation(const armnn::TensorInfo& tensorInfo,
+template<typename HalVersion>
+armnn::IConnectableLayer* ModelToINetworkConverter<HalVersion>::ProcessActivation(const armnn::TensorInfo& tensorInfo,
ActivationFn activation, armnn::IConnectableLayer* prevLayer)
{
assert(prevLayer->GetNumOutputSlots() == 1);
@@ -2476,10 +2549,12 @@
return activationLayer;
}
-bool ModelToINetworkConverter::SetupAndTrackLayerOutputSlot(const neuralnetworks::V1_0::Operation& operation,
- uint32_t operationOutputIndex,
- armnn::IConnectableLayer& layer,
- uint32_t layerOutputIndex)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::SetupAndTrackLayerOutputSlot(
+ const neuralnetworks::V1_0::Operation& operation,
+ uint32_t operationOutputIndex,
+ armnn::IConnectableLayer& layer,
+ uint32_t layerOutputIndex)
{
const Operand* outputOperand = GetOutputOperand(operation, operationOutputIndex);
@@ -2498,19 +2573,27 @@
return true;
}
-bool ModelToINetworkConverter::SetupAndTrackLayerOutputSlot(const neuralnetworks::V1_0::Operation& operation,
- uint32_t outputIndex,
- armnn::IConnectableLayer& layer)
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::SetupAndTrackLayerOutputSlot(
+ const neuralnetworks::V1_0::Operation& operation,
+ uint32_t outputIndex,
+ armnn::IConnectableLayer& layer)
{
return SetupAndTrackLayerOutputSlot(operation, outputIndex, layer, outputIndex);
}
-bool ModelToINetworkConverter::IsOperationSupported(uint32_t operationIndex) const
+template<typename HalVersion>
+bool ModelToINetworkConverter<HalVersion>::IsOperationSupported(uint32_t operationIndex) const
{
std::map<uint32_t, bool>::const_iterator it = m_OperationSupported.find(operationIndex);
assert(it != m_OperationSupported.end());
return it->second;
}
+template class ModelToINetworkConverter<HalVersion_1_0>;
-} // armnn_driver
+#if defined(ARMNN_ANDROID_NN_V1_1)
+template class ModelToINetworkConverter<HalVersion_1_1>;
+#endif
+
+} // armnn_driver
\ No newline at end of file