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path: root/src/armnnDeserializer/Deserializer.cpp
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-rw-r--r--src/armnnDeserializer/Deserializer.cpp152
1 files changed, 105 insertions, 47 deletions
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 93fa99dcc3..704b6c35c1 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -1372,11 +1372,48 @@ void IDeserializer::DeserializerImpl::ParseConstant(GraphPtr graph, unsigned int
auto serializerInput = serializerLayer->input();
armnn::ConstTensor input = ToConstTensor(serializerInput);
+ IConnectableLayer* layer;
- IConnectableLayer* layer = m_Network->AddConstantLayer(input, layerName.c_str());
+ // Required for when Constant Layer is used as an inputs to DepthwiseConvolution2d Layer.
+ // Running a model that was created before weights layout scheme version was added to our flatbuffers
+ // file ensuring older models can still be read and executed. featureVersion weights layout scheme 1
+ // indicates a change in the depthwise weights layout within ArmNN from [M,I,H,W] --> [1,H,W,I*M]
+ if (this->GetFeatureVersions(graph).m_WeightsLayoutScheme <= 0)
+ {
+ // Permute weights [ H, W, M, I ] --> [ 1, H, W, I*M ]
+ // Step1: [ M, I, H, W ] --> [ H, W, I, M]
+ PermutationVector permutationVector = { 3, 2, 0, 1 };
+ armnn::TensorInfo weightsInfo = input.GetInfo();
+ std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightsInfo.GetNumBytes()]);
+ weightsInfo = armnnUtils::Permuted(weightsInfo, permutationVector);
+ armnnUtils::Permute(weightsInfo.GetShape(), permutationVector,
+ input.GetMemoryArea(), permuteBuffer.get(),
+ GetDataTypeSize(weightsInfo.GetDataType()));
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
- layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ // Step2: Reshape [ H, W, I, M] --> [ 1, H, W, I*M ]
+ auto weightsShape = weightsInfo.GetShape();
+ weightsInfo.SetShape({1,
+ weightsShape[0],
+ weightsShape[1],
+ weightsShape[2]*weightsShape[3]});
+
+ armnn::ConstTensor weightsPermuted(weightsInfo, permuteBuffer.get());
+
+ layer = m_Network->AddConstantLayer(weightsPermuted, layerName.c_str());
+
+ layer->GetOutputSlot(0).SetTensorInfo(weightsPermuted.GetInfo());
+
+ RegisterOutputSlots(graph, layerIndex, layer);
+
+ return;
+ }
+ else
+ {
+ layer = m_Network->AddConstantLayer(input, layerName.c_str());
+
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ }
RegisterOutputSlots(graph, layerIndex, layer);
}
@@ -1499,7 +1536,6 @@ void IDeserializer::DeserializerImpl::ParseDepthwiseConvolution2d(GraphPtr graph
CHECK_LAYERS(graph, 0, layerIndex);
auto inputs = GetInputs(graph, layerIndex);
CHECK_LOCATION();
- CHECK_VALID_SIZE(inputs.size(), 1);
auto outputs = GetOutputs(graph, layerIndex);
CHECK_VALID_SIZE(outputs.size(), 1);
@@ -1509,67 +1545,89 @@ void IDeserializer::DeserializerImpl::ParseDepthwiseConvolution2d(GraphPtr graph
auto serializerDescriptor = serializerLayer->descriptor();
armnn::DepthwiseConvolution2dDescriptor descriptor;
- descriptor.m_PadLeft = serializerDescriptor->padLeft();
- descriptor.m_PadRight = serializerDescriptor->padRight();
- descriptor.m_PadTop = serializerDescriptor->padTop();
- descriptor.m_PadBottom = serializerDescriptor->padBottom();
- descriptor.m_StrideX = serializerDescriptor->strideX();
- descriptor.m_StrideY = serializerDescriptor->strideY();
- descriptor.m_DilationX = serializerDescriptor->dilationX();
- descriptor.m_DilationY = serializerDescriptor->dilationY();
- descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();;
- descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout());
+ descriptor.m_PadLeft = serializerDescriptor->padLeft();
+ descriptor.m_PadRight = serializerDescriptor->padRight();
+ descriptor.m_PadTop = serializerDescriptor->padTop();
+ descriptor.m_PadBottom = serializerDescriptor->padBottom();
+ descriptor.m_StrideX = serializerDescriptor->strideX();
+ descriptor.m_StrideY = serializerDescriptor->strideY();
+ descriptor.m_DilationX = serializerDescriptor->dilationX();
+ descriptor.m_DilationY = serializerDescriptor->dilationY();
+ descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();
+ descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout());
IConnectableLayer* layer;
+ std::vector<unsigned int> ignoreSlots {};
- armnn::Optional<armnn::ConstTensor> optionalBiases = armnn::EmptyOptional();
- if (descriptor.m_BiasEnabled)
- {
- armnn::ConstTensor biases = ToConstTensor(serializerLayer->biases());
- optionalBiases = armnn::Optional<armnn::ConstTensor>(biases);
- }
-
- armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights());
- // The data layout for weights in ArmNN used to be [M,I,H,W] but now it's changed to [1,H,W,I*M]
- // When reading older flatbuffer files we need to add a permutation to get to the new layout.
- if (this->GetFeatureVersions(graph).m_WeightsLayoutScheme <= 0)
+ // Weights and biases used to be always constant and were stored as members of the layer. This has changed and
+ // they are now passed as inputs. If they are constant then they will be stored in a ConstantLayer.
+ if (this->GetFeatureVersions(graph).m_ConstTensorsAsInputs <= 0)
{
- // Permute weights [ H, W, M, I ] --> [ 1, H, W, I*M ]
- // Step1: [ M, I, H, W ] --> [ H, W, I, M]
- PermutationVector permutationVector = { 3, 2, 0, 1 };
- armnn::TensorInfo weightsInfo = weights.GetInfo();
- std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightsInfo.GetNumBytes()]);
- weightsInfo = armnnUtils::Permuted(weightsInfo, permutationVector);
- armnnUtils::Permute(weightsInfo.GetShape(), permutationVector,
- weights.GetMemoryArea(), permuteBuffer.get(),
- GetDataTypeSize(weightsInfo.GetDataType()));
-
- // Step2: Reshape [ H, W, I, M] --> [ 1, H, W, I*M ]
- auto weightsShape = weightsInfo.GetShape();
- weightsInfo.SetShape({1,
- weightsShape[0],
- weightsShape[1],
- weightsShape[2]*weightsShape[3]});
+ CHECK_VALID_SIZE(inputs.size(), 1);
- armnn::ConstTensor weightsPermuted(weightsInfo, permuteBuffer.get());
+ // If the model stores weights and biases as members of the layer we have to read them from there
+ // but add them to their own ConstantLayer for compatibility
+ armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights());
+ ignoreSlots.emplace_back(1u);
layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
- weightsPermuted,
- optionalBiases,
layerName.c_str());
+
+ armnn::Optional<armnn::ConstTensor> optionalBiases = armnn::EmptyOptional();
+ if (descriptor.m_BiasEnabled)
+ {
+ armnn::ConstTensor biases = ToConstTensor(serializerLayer->biases());
+ ignoreSlots.emplace_back(2u);
+
+ auto biasLayer = m_Network->AddConstantLayer(biases);
+ biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biases.GetInfo());
+ }
+
+ if (this->GetFeatureVersions(graph).m_WeightsLayoutScheme <= 0)
+ {
+ // Permute weights [ H, W, M, I ] --> [ 1, H, W, I*M ]
+ // Step1: [ M, I, H, W ] --> [ H, W, I, M]
+ PermutationVector permutationVector = { 3, 2, 0, 1 };
+ armnn::TensorInfo weightsInfo = weights.GetInfo();
+ std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightsInfo.GetNumBytes()]);
+ weightsInfo = armnnUtils::Permuted(weightsInfo, permutationVector);
+ armnnUtils::Permute(weightsInfo.GetShape(), permutationVector,
+ weights.GetMemoryArea(), permuteBuffer.get(),
+ GetDataTypeSize(weightsInfo.GetDataType()));
+
+ // Step2: Reshape [ H, W, I, M] --> [ 1, H, W, I*M ]
+ auto weightsShape = weightsInfo.GetShape();
+ weightsInfo.SetShape({1,
+ weightsShape[0],
+ weightsShape[1],
+ weightsShape[2]*weightsShape[3]});
+
+ armnn::ConstTensor weightsPermuted(weightsInfo, permuteBuffer.get());
+
+ auto weightsLayer = m_Network->AddConstantLayer(weightsPermuted);
+ weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsPermuted.GetInfo());
+ }
+ else
+ {
+ auto weightsLayer = m_Network->AddConstantLayer(weights);
+ weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weights.GetInfo());
+ }
}
else
{
layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
- weights,
- optionalBiases,
layerName.c_str());
+ uint32_t numInputs = descriptor.GetNumInputs();
+ CHECK_VALID_SIZE(inputs.size(), numInputs);
}
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
- RegisterInputSlots(graph, layerIndex, layer);
+ RegisterInputSlots(graph, layerIndex, layer, ignoreSlots);
RegisterOutputSlots(graph, layerIndex, layer);
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-16 15:00:03 +0000