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| author | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | 2019-01-25 10:46:40 +0000 |
|---|---|---|
| committer | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | 2019-01-25 16:59:16 +0000 |
| commit | a0d56c77a53f5f991565041927003ce7460730ce (patch) | |
| tree | de08bfa6f95752ec8e7b5b58d0b689288f44b36a /src/armnn/test/OptimizerTests.cpp | |
| parent | 12055747d47657a89d60748a078897f6436e6aa0 (diff) | |
| download | armnn-a0d56c77a53f5f991565041927003ce7460730ce.tar.gz | |
IVGCVSW-2556 Add Layer implementation for Detection PostProcess
* Add DetectionPostProcessDescriptor.
* Add implementation for DetectionPostProcessLayer.
* Unit test to validate output.
Change-Id: If63e83eb2a2978c549071c7aeb272906e7c35fe9
Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
| -rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 35 |
1 files changed, 35 insertions, 0 deletions
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index 3b079864c2..f40a78a0d9 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -1050,4 +1050,39 @@ BOOST_AUTO_TEST_CASE(GatherValidateTensorShapesFromInputsMultiDimIndices) 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() |