IVGCVSW-2429 Add Detection PostProcess Parser to TensorFlow Lite Parser
* Added parser function in TFLiteParser
* Removed custom options gating
* Added unit test
* Removed template usage in VerifyTensorInfo for DeserializeParser
Change-Id: If198654ed70060855a05f8aaed010293405bd103
Signed-off-by: keidav01 <keith.davis@arm.com>
diff --git a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
index 8d0ee01..50e674e 100644
--- a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
+++ b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
@@ -116,7 +116,7 @@
}
/// Executes the network with the given input tensor and checks the result against the given output tensor.
- /// This overload assumes the network has a single input and a single output.
+ /// This assumes the network has a single input and a single output.
template <std::size_t NumOutputDimensions,
armnn::DataType ArmnnType,
typename DataType = armnn::ResolveType<ArmnnType>>
@@ -133,6 +133,32 @@
const std::map<std::string, std::vector<DataType>>& inputData,
const std::map<std::string, std::vector<DataType>>& expectedOutputData);
+ /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
+ /// Executes the network with the given input tensors and checks the results against the given output tensors.
+ /// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
+ /// the input datatype to be different to the output
+ template <std::size_t NumOutputDimensions,
+ armnn::DataType ArmnnType1,
+ armnn::DataType ArmnnType2,
+ typename DataType1 = armnn::ResolveType<ArmnnType1>,
+ typename DataType2 = armnn::ResolveType<ArmnnType2>>
+ void RunTest(size_t subgraphId,
+ const std::map<std::string, std::vector<DataType1>>& inputData,
+ const std::map<std::string, std::vector<DataType2>>& expectedOutputData);
+
+
+ /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
+ /// Executes the network with the given input tensors and checks the results against the given output tensors.
+ /// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
+ /// the input datatype to be different to the output
+ template<armnn::DataType ArmnnType1,
+ armnn::DataType ArmnnType2,
+ typename DataType1 = armnn::ResolveType<ArmnnType1>,
+ typename DataType2 = armnn::ResolveType<ArmnnType2>>
+ void RunTest(std::size_t subgraphId,
+ const std::map<std::string, std::vector<DataType1>>& inputData,
+ const std::map<std::string, std::vector<DataType2>>& expectedOutputData);
+
void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape,
tflite::TensorType tensorType, uint32_t buffer, const std::string& name,
const std::vector<float>& min, const std::vector<float>& max,
@@ -157,25 +183,47 @@
}
};
+/// Single Input, Single Output
+/// Executes the network with the given input tensor and checks the result against the given output tensor.
+/// This overload assumes the network has a single input and a single output.
template <std::size_t NumOutputDimensions,
- armnn::DataType ArmnnType,
+ armnn::DataType armnnType,
typename DataType>
void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
const std::vector<DataType>& inputData,
const std::vector<DataType>& expectedOutputData)
{
- RunTest<NumOutputDimensions, ArmnnType>(subgraphId,
+ RunTest<NumOutputDimensions, armnnType>(subgraphId,
{ { m_SingleInputName, inputData } },
{ { m_SingleOutputName, expectedOutputData } });
}
+/// Multiple Inputs, Multiple Outputs
+/// Executes the network with the given input tensors and checks the results against the given output tensors.
+/// This overload supports multiple inputs and multiple outputs, identified by name.
template <std::size_t NumOutputDimensions,
- armnn::DataType ArmnnType,
+ armnn::DataType armnnType,
typename DataType>
void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
const std::map<std::string, std::vector<DataType>>& inputData,
const std::map<std::string, std::vector<DataType>>& expectedOutputData)
{
+ RunTest<NumOutputDimensions, armnnType, armnnType>(subgraphId, inputData, expectedOutputData);
+}
+
+/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes
+/// Executes the network with the given input tensors and checks the results against the given output tensors.
+/// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
+/// the input datatype to be different to the output
+template <std::size_t NumOutputDimensions,
+ armnn::DataType armnnType1,
+ armnn::DataType armnnType2,
+ typename DataType1,
+ typename DataType2>
+void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
+ const std::map<std::string, std::vector<DataType1>>& inputData,
+ const std::map<std::string, std::vector<DataType2>>& expectedOutputData)
+{
using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>;
// Setup the armnn input tensors from the given vectors.
@@ -183,18 +231,18 @@
for (auto&& it : inputData)
{
BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
- armnn::VerifyTensorInfoDataType<ArmnnType>(bindingInfo.second);
+ armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType1);
inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
}
// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
- std::map<std::string, boost::multi_array<DataType, NumOutputDimensions>> outputStorage;
+ std::map<std::string, boost::multi_array<DataType2, NumOutputDimensions>> outputStorage;
armnn::OutputTensors outputTensors;
for (auto&& it : expectedOutputData)
{
BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
- armnn::VerifyTensorInfoDataType<ArmnnType>(bindingInfo.second);
- outputStorage.emplace(it.first, MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second));
+ armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType2);
+ outputStorage.emplace(it.first, MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second));
outputTensors.push_back(
{ bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) });
}
@@ -205,7 +253,61 @@
for (auto&& it : expectedOutputData)
{
BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
- auto outputExpected = MakeTensor<DataType, NumOutputDimensions>(bindingInfo.second, it.second);
+ auto outputExpected = MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second, it.second);
BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
}
}
+
+/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
+/// Executes the network with the given input tensors and checks the results against the given output tensors.
+/// This overload supports multiple inputs and multiple outputs, identified by name along with the allowance for
+/// the input datatype to be different to the output.
+template <armnn::DataType armnnType1,
+ armnn::DataType armnnType2,
+ typename DataType1,
+ typename DataType2>
+void ParserFlatbuffersFixture::RunTest(std::size_t subgraphId,
+ const std::map<std::string, std::vector<DataType1>>& inputData,
+ const std::map<std::string, std::vector<DataType2>>& expectedOutputData)
+{
+ using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>;
+
+ // Setup the armnn input tensors from the given vectors.
+ armnn::InputTensors inputTensors;
+ for (auto&& it : inputData)
+ {
+ BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
+ armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType1);
+
+ inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
+ }
+
+ armnn::OutputTensors outputTensors;
+ outputTensors.reserve(expectedOutputData.size());
+ std::map<std::string, std::vector<DataType2>> outputStorage;
+ for (auto&& it : expectedOutputData)
+ {
+ BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
+ armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType2);
+
+ std::vector<DataType2> out(it.second.size());
+ outputStorage.emplace(it.first, out);
+ outputTensors.push_back({ bindingInfo.first,
+ armnn::Tensor(bindingInfo.second,
+ outputStorage.at(it.first).data()) });
+ }
+
+ m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
+
+ // Checks the results.
+ for (auto&& it : expectedOutputData)
+ {
+ std::vector<DataType2> out = outputStorage.at(it.first);
+ {
+ for (unsigned int i = 0; i < out.size(); ++i)
+ {
+ BOOST_TEST(it.second[i] == out[i], boost::test_tools::tolerance(0.000001f));
+ }
+ }
+ }
+}