IVGCVSW-3370 Add broadcasting support to PReLU to properly run the Android VTS/NN
tests
* Updated ConvertPrelu to support input broadcasting
* Updated the BroadcastTensor utility function so that it preserves
the order of the inputs
* Updated the documentation
Signed-off-by: Matteo Martincigh <matteo.martincigh@arm.com>
Change-Id: Ibbac6f7161132740e61c85f597f8be70cd5d7325
diff --git a/ConversionUtils.hpp b/ConversionUtils.hpp
index 36bc4ae..9a711cb 100644
--- a/ConversionUtils.hpp
+++ b/ConversionUtils.hpp
@@ -201,55 +201,91 @@
#endif
-void BroadcastTensor(LayerInputHandle& input0, LayerInputHandle& input1, armnn::IConnectableLayer* startLayer,
- armnn::INetwork& network)
+template<typename LayerHandleType>
+armnn::IConnectableLayer& AddReshapeLayer(armnn::INetwork& network, LayerHandleType& inputLayer,
+ armnn::TensorInfo reshapeInfo)
+{
+ armnn::ReshapeDescriptor reshapeDescriptor;
+ reshapeDescriptor.m_TargetShape = reshapeInfo.GetShape();
+
+ armnn::IConnectableLayer* reshapeLayer = network.AddReshapeLayer(reshapeDescriptor);
+ BOOST_ASSERT(reshapeLayer != nullptr);
+
+ // Attach the input layer to the reshape layer
+ inputLayer.Connect(reshapeLayer->GetInputSlot(0));
+ reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapeInfo);
+
+ return *reshapeLayer;
+}
+
+void BroadcastTensor(LayerInputHandle& input0, LayerInputHandle& input1,
+ armnn::IConnectableLayer* startLayer, armnn::INetwork& network)
{
BOOST_ASSERT(startLayer != nullptr);
- const armnn::TensorInfo& inputTensorInfo0 = input0.GetTensorInfo();
- const armnn::TensorInfo& inputTensorInfo1 = input1.GetTensorInfo();
- if (inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions())
+ const armnn::TensorInfo& inputInfo0 = input0.GetTensorInfo();
+ const armnn::TensorInfo& inputInfo1 = input1.GetTensorInfo();
+
+ unsigned int inputDimensions0 = inputInfo0.GetNumDimensions();
+ unsigned int inputDimensions1 = inputInfo1.GetNumDimensions();
+
+ if (inputDimensions0 == inputDimensions1)
{
- // If the number of dimensions do not match then we need to add degenerate dimensions
- // to the "smaller" tensor using a reshape:
- // Small Big
+ // The inputs have the same number of dimensions, simply connect them to the given layer as they are
+ input0.Connect(startLayer->GetInputSlot(0));
+ input1.Connect(startLayer->GetInputSlot(1));
+
+ return;
+ }
+
+ // Since the number of dimensions do not match then we need to add degenerate dimensions
+ // to the "smaller" tensor using a reshape, while keeping the order of the inputs.
+
+ unsigned int maxInputDimensions = std::max(inputDimensions0, inputDimensions1);
+ unsigned int sizeDifference = std::abs(boost::numeric_cast<int>(inputDimensions0) -
+ boost::numeric_cast<int>(inputDimensions1));
+
+ bool input0IsSmaller = inputDimensions0 < inputDimensions1;
+ LayerInputHandle& smallInputHandle = input0IsSmaller ? input0 : input1;
+ const armnn::TensorInfo& smallInfo = smallInputHandle.GetTensorInfo();
+
+ const armnn::TensorShape& smallShape = smallInfo.GetShape();
+ std::vector<unsigned int> reshapedDimensions(maxInputDimensions, 1);
+ for (unsigned int i = sizeDifference; i < maxInputDimensions; i++)
+ {
+ reshapedDimensions[i] = smallShape[i - sizeDifference];
+ }
+
+ armnn::TensorInfo reshapedInfo = smallInfo;
+ reshapedInfo.SetShape(armnn::TensorShape{ boost::numeric_cast<unsigned int>(reshapedDimensions.size()),
+ reshapedDimensions.data() });
+ armnn::IConnectableLayer& reshapeLayer = AddReshapeLayer(network, smallInputHandle, reshapedInfo);
+
+ if (input0IsSmaller)
+ {
+ // Input0 is the "smaller" tensor, connect the reshape layer as follows:
+ //
+ // Input0 Input1
// | |
// Reshape |
// \ /
- // Add
- bool input0IsBigger = inputTensorInfo0.GetNumDimensions() > inputTensorInfo1.GetNumDimensions();
+ // StartLayer
- LayerInputHandle& smallTensorHandle = input0IsBigger ? input1 : input0;
- const armnn::TensorInfo& smallTensorDims = smallTensorHandle.GetTensorInfo();
-
- LayerInputHandle& bigTensorHandle = input0IsBigger ? input0 : input1;
- const armnn::TensorInfo& bigTensorDims = bigTensorHandle.GetTensorInfo();
-
- const unsigned int bigTensorDimsNumber = bigTensorDims.GetNumDimensions();
- std::vector<unsigned int> reshapedDims(bigTensorDimsNumber, 1);
- unsigned int sizeDifference = bigTensorDimsNumber - smallTensorDims.GetNumDimensions();
- for (unsigned i = sizeDifference; i < bigTensorDimsNumber; ++i)
- {
- reshapedDims[i] = smallTensorDims.GetShape()[i-sizeDifference];
- }
- armnn::TensorInfo reshapedInfo = smallTensorDims;
- reshapedInfo.SetShape(armnn::TensorShape{ static_cast<unsigned int>(reshapedDims.size()),
- reshapedDims.data() });
-
- armnn::ReshapeDescriptor reshapeDesc;
- reshapeDesc.m_TargetShape = reshapedInfo.GetShape();
- armnn::IConnectableLayer* const reshapeLayer = network.AddReshapeLayer(reshapeDesc);
- smallTensorHandle.Connect(reshapeLayer->GetInputSlot(0));
- reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedInfo);
-
- // Connect the outputs from new reshape and original input layer
- reshapeLayer->GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
- bigTensorHandle.Connect(startLayer->GetInputSlot(1));
+ reshapeLayer.GetOutputSlot(0).Connect(startLayer->GetInputSlot(0));
+ input1.Connect(startLayer->GetInputSlot(1));
}
else
{
+ // Input1 is the "smaller" tensor, connect the reshape layer as follows:
+ //
+ // Input0 Input1
+ // | |
+ // | Reshape
+ // \ /
+ // StartLayer
+
input0.Connect(startLayer->GetInputSlot(0));
- input1.Connect(startLayer->GetInputSlot(1));
+ reshapeLayer.GetOutputSlot(0).Connect(startLayer->GetInputSlot(1));
}
}
@@ -402,23 +438,6 @@
return inputShape.GetNumDimensions() < 3;
}
-template<typename OSlot>
-armnn::IConnectableLayer& AddReshapeLayer(armnn::INetwork& network, OSlot& inputLayer,
- armnn::TensorInfo reshapeInfo)
-{
- armnn::ReshapeDescriptor reshapeDescriptor;
- reshapeDescriptor.m_TargetShape = reshapeInfo.GetShape();
-
- armnn::IConnectableLayer* reshapeLayer = network.AddReshapeLayer(reshapeDescriptor);
- BOOST_ASSERT(reshapeLayer != nullptr);
-
- // Attach the input layer to the reshape layer
- inputLayer.Connect(reshapeLayer->GetInputSlot(0));
- reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapeInfo);
-
- return *reshapeLayer;
-}
-
void SwizzleInputs(armnn::INetwork& network,
std::vector<LayerInputHandle>& inputs,
std::vector<armnn::TensorShape>& inputShapes,