IVGCVSW-2556 Add Layer implementation for Detection PostProcess
* Add DetectionPostProcessDescriptor.
* Add implementation for DetectionPostProcessLayer.
* Unit test to validate output.
Change-Id: If63e83eb2a2978c549071c7aeb272906e7c35fe9
diff --git a/include/armnn/Descriptors.hpp b/include/armnn/Descriptors.hpp
index 44235c7..29d294e 100644
--- a/include/armnn/Descriptors.hpp
+++ b/include/armnn/Descriptors.hpp
@@ -329,6 +329,42 @@
struct DetectionPostProcessDescriptor
{
+ DetectionPostProcessDescriptor()
+ : m_MaxDetections(0)
+ , m_MaxClassesPerDetection(1)
+ , m_DetectionsPerClass(100)
+ , m_NmsScoreThreshold(0)
+ , m_NmsIouThreshold(0)
+ , m_NumClasses(0)
+ , m_UseRegularNms(false)
+ , m_ScaleX(0)
+ , m_ScaleY(0)
+ , m_ScaleW(0)
+ , m_ScaleH(0)
+ {}
+
+ /// Maximum numbers of detections.
+ uint32_t m_MaxDetections;
+ /// Maximum numbers of classes per detection, used in Fast NMS.
+ uint32_t m_MaxClassesPerDetection;
+ /// Detections per classes, used in Regular NMS.
+ uint32_t m_DetectionsPerClass;
+ /// NMS score threshold.
+ float m_NmsScoreThreshold;
+ /// Intersection over union threshold.
+ float m_NmsIouThreshold;
+ /// Number of classes.
+ int32_t m_NumClasses;
+ /// Use Regular NMS.
+ bool m_UseRegularNms;
+ /// Center size encoding scale x.
+ float m_ScaleX;
+ /// Center size encoding scale y.
+ float m_ScaleY;
+ /// Center size encoding scale weight.
+ float m_ScaleW;
+ /// Center size encoding scale height.
+ float m_ScaleH;
};
/// A NormalizationDescriptor for the NormalizationLayer.
diff --git a/src/armnn/layers/DetectionPostProcessLayer.cpp b/src/armnn/layers/DetectionPostProcessLayer.cpp
index 7858922..3eea198 100644
--- a/src/armnn/layers/DetectionPostProcessLayer.cpp
+++ b/src/armnn/layers/DetectionPostProcessLayer.cpp
@@ -8,6 +8,7 @@
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
+#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/WorkloadData.hpp>
#include <backendsCommon/WorkloadFactory.hpp>
@@ -34,6 +35,39 @@
void DetectionPostProcessLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(2, CHECK_LOCATION());
+
+ // on this level constant data should not be released.
+ BOOST_ASSERT_MSG(m_Anchors != nullptr, "DetectionPostProcessLayer: Anchors data should not be null.");
+
+ BOOST_ASSERT_MSG(GetNumOutputSlots() == 4, "DetectionPostProcessLayer: The layer should return 4 outputs.");
+
+ unsigned int detectedBoxes = m_Param.m_MaxDetections * m_Param.m_MaxClassesPerDetection;
+
+ const TensorShape& inferredDetectionBoxes = TensorShape({ 1, detectedBoxes, 4 });
+ const TensorShape& inferredDetectionScores = TensorShape({ 1, detectedBoxes });
+ const TensorShape& inferredNumberDetections = TensorShape({ 1 });
+
+ ConditionalThrowIfNotEqual<LayerValidationException>(
+ "DetectionPostProcessLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+ GetOutputSlot(0).GetTensorInfo().GetShape(),
+ inferredDetectionBoxes);
+ ConditionalThrowIfNotEqual<LayerValidationException>(
+ "DetectionPostProcessLayer: TensorShape set on OutputSlot[1] does not match the inferred shape.",
+ GetOutputSlot(1).GetTensorInfo().GetShape(),
+ inferredDetectionScores);
+ ConditionalThrowIfNotEqual<LayerValidationException>(
+ "DetectionPostProcessLayer: TensorShape set on OutputSlot[2] does not match the inferred shape.",
+ GetOutputSlot(2).GetTensorInfo().GetShape(),
+ inferredDetectionScores);
+ ConditionalThrowIfNotEqual<LayerValidationException>(
+ "DetectionPostProcessLayer: TensorShape set on OutputSlot[3] does not match the inferred shape.",
+ GetOutputSlot(3).GetTensorInfo().GetShape(),
+ inferredNumberDetections);
+}
+
+Layer::ConstantTensors DetectionPostProcessLayer::GetConstantTensorsByRef()
+{
+ return { m_Anchors };
}
void DetectionPostProcessLayer::Accept(ILayerVisitor& visitor) const
diff --git a/src/armnn/layers/DetectionPostProcessLayer.hpp b/src/armnn/layers/DetectionPostProcessLayer.hpp
index 629e386..a1c499e 100644
--- a/src/armnn/layers/DetectionPostProcessLayer.hpp
+++ b/src/armnn/layers/DetectionPostProcessLayer.hpp
@@ -10,10 +10,15 @@
namespace armnn
{
+class ScopedCpuTensorHandle;
+
/// This layer represents a detection postprocess operator.
class DetectionPostProcessLayer : public LayerWithParameters<DetectionPostProcessDescriptor>
{
public:
+ /// A unique pointer to store Anchor values.
+ std::unique_ptr<ScopedCpuTensorHandle> m_Anchors;
+
/// Makes a workload for the DetectionPostProcess type.
/// @param [in] graph The graph where this layer can be found.
/// @param [in] factory The workload factory which will create the workload.
@@ -39,6 +44,10 @@
/// Default destructor
~DetectionPostProcessLayer() = default;
+
+ /// Retrieve the handles to the constant values stored by the layer.
+ /// @return A vector of the constant tensors stored by this layer.
+ ConstantTensors GetConstantTensorsByRef() override;
};
} // namespace armnn
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp
index 3b07986..f40a78a 100644
--- a/src/armnn/test/OptimizerTests.cpp
+++ b/src/armnn/test/OptimizerTests.cpp
@@ -1050,4 +1050,39 @@
BOOST_CHECK_NO_THROW(graph.InferTensorInfos());
}
+BOOST_AUTO_TEST_CASE(DetectionPostProcessValidateTensorShapes)
+{
+ Graph graph;
+ armnn::TensorInfo boxEncodingsInfo({1, 10, 4}, DataType::QuantisedAsymm8);
+ armnn::TensorInfo scoresInfo({1, 10, 4}, DataType::QuantisedAsymm8);
+ std::vector<uint8_t> anchorsVector(40);
+ armnn::ConstTensor anchors(armnn::TensorInfo({10, 4}, armnn::DataType::QuantisedAsymm8), anchorsVector);
+
+ armnn::TensorInfo detectionBoxesInfo({1, 3, 4}, DataType::QuantisedAsymm8);
+ armnn::TensorInfo detectionScoresInfo({1, 3}, DataType::QuantisedAsymm8);
+ armnn::TensorInfo detectionClassesInfo({1, 3}, DataType::QuantisedAsymm8);
+ armnn::TensorInfo numDetectionInfo({1}, DataType::QuantisedAsymm8);
+
+ Layer* input0 = graph.AddLayer<InputLayer>(0, "boxEncodings");
+ input0->GetOutputSlot().SetTensorInfo(boxEncodingsInfo);
+
+ Layer* input1 = graph.AddLayer<InputLayer>(1, "score");
+ input1->GetOutputSlot().SetTensorInfo(scoresInfo);
+
+ DetectionPostProcessDescriptor descriptor;
+ descriptor.m_MaxDetections = 3;
+
+ DetectionPostProcessLayer* layer = graph.AddLayer<DetectionPostProcessLayer>(descriptor, "detectionPostProcess");
+ layer->m_Anchors = std::make_unique<armnn::ScopedCpuTensorHandle>(anchors);
+ layer->GetOutputSlot(0).SetTensorInfo(detectionBoxesInfo);
+ layer->GetOutputSlot(1).SetTensorInfo(detectionScoresInfo);
+ layer->GetOutputSlot(2).SetTensorInfo(detectionClassesInfo);
+ layer->GetOutputSlot(3).SetTensorInfo(numDetectionInfo);
+
+ input0->GetOutputSlot().Connect(layer->GetInputSlot(0));
+ input1->GetOutputSlot().Connect(layer->GetInputSlot(1));
+
+ BOOST_CHECK_NO_THROW(graph.InferTensorInfos());
+}
+
BOOST_AUTO_TEST_SUITE_END()