MLCE-335 'DEPTH_TO_SPACE and GATHER operator support in TfLiteParser'
* Added GATHER operator support to TfLiteParser
* Added DEPTH_TO_SPACE operator support to TfLiteParser
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Id5d3b54e2d850eb9f19417029efbeb73a3029e69
diff --git a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
index 50a312f..fc1d94e 100644
--- a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
+++ b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
@@ -152,6 +152,18 @@
const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData,
bool isDynamic = false);
+ /// Multiple Inputs with different DataTypes, 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 inputType1,
+ armnn::DataType inputType2,
+ armnn::DataType outputType>
+ void RunTest(size_t subgraphId,
+ const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data,
+ const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data,
+ const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& 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.
@@ -212,8 +224,30 @@
tensors->quantization.get()->zero_point.begin(),
tensors->quantization.get()->zero_point.end());
}
+
+private:
+ /// Fills the InputTensors with given input data
+ template <armnn::DataType dataType>
+ void FillInputTensors(armnn::InputTensors& inputTensors,
+ const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData,
+ size_t subgraphId);
};
+/// Fills the InputTensors with given input data
+template <armnn::DataType dataType>
+void ParserFlatbuffersFixture::FillInputTensors(
+ armnn::InputTensors& inputTensors,
+ const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData,
+ size_t subgraphId)
+{
+ for (auto&& it : inputData)
+ {
+ armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first);
+ armnn::VerifyTensorInfoDataType(bindingInfo.second, dataType);
+ inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) });
+ }
+}
+
/// 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.
@@ -256,12 +290,7 @@
// Setup the armnn input tensors from the given vectors.
armnn::InputTensors inputTensors;
- for (auto&& it : inputData)
- {
- armnn::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()) });
- }
+ FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId);
// Allocate storage for the output tensors to be written to and setup the armnn output tensors.
std::map<std::string, boost::multi_array<DataType2, NumOutputDimensions>> outputStorage;
@@ -310,13 +339,7 @@
// Setup the armnn input tensors from the given vectors.
armnn::InputTensors inputTensors;
- for (auto&& it : inputData)
- {
- armnn::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()) });
- }
+ FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId);
armnn::OutputTensors outputTensors;
outputTensors.reserve(expectedOutputData.size());
@@ -347,3 +370,56 @@
}
}
}
+
+/// Multiple Inputs with different DataTypes, 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 inputType1,
+ armnn::DataType inputType2,
+ armnn::DataType outputType>
+void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
+ const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data,
+ const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data,
+ const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& expectedOutputData)
+{
+ using DataType2 = armnn::ResolveType<outputType>;
+
+ // Setup the armnn input tensors from the given vectors.
+ armnn::InputTensors inputTensors;
+ FillInputTensors<inputType1>(inputTensors, input1Data, subgraphId);
+ FillInputTensors<inputType2>(inputTensors, input2Data, subgraphId);
+
+ // Allocate storage for the output tensors to be written to and setup the armnn output tensors.
+ std::map<std::string, boost::multi_array<DataType2, NumOutputDimensions>> outputStorage;
+ armnn::OutputTensors outputTensors;
+ for (auto&& it : expectedOutputData)
+ {
+ armnn::LayerBindingId outputBindingId = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first).first;
+ armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkIdentifier, outputBindingId);
+
+ // Check that output tensors have correct number of dimensions (NumOutputDimensions specified in test)
+ auto outputNumDimensions = outputTensorInfo.GetNumDimensions();
+ BOOST_CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions),
+ fmt::format("Number of dimensions expected {}, but got {} for output layer {}",
+ NumOutputDimensions,
+ outputNumDimensions,
+ it.first));
+
+ armnn::VerifyTensorInfoDataType(outputTensorInfo, outputType);
+ outputStorage.emplace(it.first, MakeTensor<DataType2, NumOutputDimensions>(outputTensorInfo));
+ outputTensors.push_back(
+ { outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) });
+ }
+
+ m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
+
+ // Compare each output tensor to the expected values
+ for (auto&& it : expectedOutputData)
+ {
+ armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
+ auto outputExpected = MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second, it.second);
+ BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first], false));
+ }
+}
\ No newline at end of file