diff options
Diffstat (limited to 'delegate/src/test/ElementwiseBinaryTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/ElementwiseBinaryTestHelper.hpp | 89 |
1 files changed, 61 insertions, 28 deletions
diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp index 72f9f850c8..60092726a2 100644 --- a/delegate/src/test/ElementwiseBinaryTestHelper.hpp +++ b/delegate/src/test/ElementwiseBinaryTestHelper.hpp @@ -24,7 +24,9 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin tflite::TensorType tensorType, const std::vector <int32_t>& input0TensorShape, const std::vector <int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape) + const std::vector <int32_t>& outputTensorShape, + float quantScale = 1.0f, + int quantOffset = 0) { using namespace tflite; flatbuffers::FlatBufferBuilder flatBufferBuilder; @@ -32,19 +34,36 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 3> tensors; tensors[0] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), input0TensorShape.size()), - tensorType, 0); + tensorType, + 0, + flatBufferBuilder.CreateString("input_0"), + quantizationParameters); tensors[1] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), input1TensorShape.size()), - tensorType, 0); + tensorType, + 0, + flatBufferBuilder.CreateString("input_1"), + quantizationParameters); tensors[2] = CreateTensor(flatBufferBuilder, flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), outputTensorShape.size()), - tensorType); + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); // create operator tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; @@ -63,6 +82,18 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union(); break; } + case BuiltinOperator_MAXIMUM: + { + operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; + operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); + break; + } + case BuiltinOperator_MINIMUM: + { + operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; + operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); + break; + } case BuiltinOperator_MUL: { operatorBuiltinOptionsType = BuiltinOptions_MulOptions; @@ -115,23 +146,29 @@ std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator bin flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); } -void ElementwiseBinaryFP32Test(tflite::BuiltinOperator binaryOperatorCode, - tflite::ActivationFunctionType activationType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& outputShape, - std::vector<float>& input0Values, - std::vector<float>& input1Values, - std::vector<float>& expectedOutputValues) +template <typename T> +void ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode, + tflite::ActivationFunctionType activationType, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& input0Shape, + std::vector<int32_t>& input1Shape, + std::vector<int32_t>& outputShape, + std::vector<T>& input0Values, + std::vector<T>& input1Values, + std::vector<T>& expectedOutputValues, + float quantScale = 1.0f, + int quantOffset = 0) { using namespace tflite; std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel(binaryOperatorCode, activationType, - ::tflite::TensorType_FLOAT32, - input0Shape, - input1Shape, - outputShape); + tensorType, + input0Shape, + input1Shape, + outputShape, + quantScale, + quantOffset); const Model* tfLiteModel = GetModel(modelBuffer.data()); // Create TfLite Interpreters @@ -158,28 +195,28 @@ void ElementwiseBinaryFP32Test(tflite::BuiltinOperator binaryOperatorCode, // Set input data auto tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInput0Id); + auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput0Id); for (unsigned int i = 0; i < input0Values.size(); ++i) { tfLiteDelageInput0Data[i] = input0Values[i]; } auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1]; - auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInput1Id); + auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput1Id); for (unsigned int i = 0; i < input1Values.size(); ++i) { tfLiteDelageInput1Data[i] = input1Values[i]; } auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput0Id); + auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput0Id); for (unsigned int i = 0; i < input0Values.size(); ++i) { armnnDelegateInput0Data[i] = input0Values[i]; } auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; - auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput1Id); + auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput1Id); for (unsigned int i = 0; i < input1Values.size(); ++i) { armnnDelegateInput1Data[i] = input1Values[i]; @@ -191,9 +228,9 @@ void ElementwiseBinaryFP32Test(tflite::BuiltinOperator binaryOperatorCode, // Compare output data auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); + auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); for (size_t i = 0; i < expectedOutputValues.size(); i++) { CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); @@ -204,8 +241,4 @@ void ElementwiseBinaryFP32Test(tflite::BuiltinOperator binaryOperatorCode, armnnDelegateInterpreter.reset(nullptr); } -} // anonymous namespace - - - - +} // anonymous namespace
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