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-rw-r--r--delegate/src/test/ControlTest.cpp420
-rw-r--r--delegate/src/test/ControlTestHelper.hpp344
-rw-r--r--delegate/src/test/TestUtils.hpp33
3 files changed, 797 insertions, 0 deletions
diff --git a/delegate/src/test/ControlTest.cpp b/delegate/src/test/ControlTest.cpp
new file mode 100644
index 0000000000..43491be982
--- /dev/null
+++ b/delegate/src/test/ControlTest.cpp
@@ -0,0 +1,420 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ControlTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+// CONCATENATION Operator
+void ConcatUint8TwoInputsTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 2, 2 };
+ std::vector<int32_t> expectedOutputShape { 4, 2 };
+
+ // Set input and output data
+ std::vector<std::vector<uint8_t>> inputValues;
+ std::vector<uint8_t> inputValue1 { 0, 1, 2, 3 }; // Lower bounds
+ std::vector<uint8_t> inputValue2 { 252, 253, 254, 255 }; // Upper bounds
+ inputValues.push_back(inputValue1);
+ inputValues.push_back(inputValue2);
+
+ std::vector<uint8_t> expectedOutputValues { 0, 1, 2, 3, 252, 253, 254, 255 };
+
+ ConcatenationTest<uint8_t>(tflite::BuiltinOperator_CONCATENATION,
+ ::tflite::TensorType_UINT8,
+ backends,
+ inputShape,
+ expectedOutputShape,
+ inputValues,
+ expectedOutputValues);
+}
+
+void ConcatInt16TwoInputsTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 2, 2 };
+ std::vector<int32_t> expectedOutputShape { 4, 2 };
+
+ std::vector<std::vector<int16_t>> inputValues;
+ std::vector<int16_t> inputValue1 { -32768, -16384, -1, 0 };
+ std::vector<int16_t> inputValue2 { 1, 2, 16384, 32767 };
+ inputValues.push_back(inputValue1);
+ inputValues.push_back(inputValue2);
+
+ std::vector<int16_t> expectedOutputValues { -32768, -16384, -1, 0, 1, 2, 16384, 32767};
+
+ ConcatenationTest<int16_t>(tflite::BuiltinOperator_CONCATENATION,
+ ::tflite::TensorType_INT16,
+ backends,
+ inputShape,
+ expectedOutputShape,
+ inputValues,
+ expectedOutputValues);
+}
+
+void ConcatFloat32TwoInputsTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 2, 2 };
+ std::vector<int32_t> expectedOutputShape { 4, 2 };
+
+ std::vector<std::vector<float>> inputValues;
+ std::vector<float> inputValue1 { -127.f, -126.f, -1.f, 0.f };
+ std::vector<float> inputValue2 { 1.f, 2.f, 126.f, 127.f };
+ inputValues.push_back(inputValue1);
+ inputValues.push_back(inputValue2);
+
+ std::vector<float> expectedOutputValues { -127.f, -126.f, -1.f, 0.f, 1.f, 2.f, 126.f, 127.f };
+
+ ConcatenationTest<float>(tflite::BuiltinOperator_CONCATENATION,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ expectedOutputShape,
+ inputValues,
+ expectedOutputValues);
+}
+
+void ConcatThreeInputsTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 2, 2 };
+ std::vector<int32_t> expectedOutputShape { 6, 2 };
+
+ std::vector<std::vector<uint8_t>> inputValues;
+ std::vector<uint8_t> inputValue1 { 0, 1, 2, 3 };
+ std::vector<uint8_t> inputValue2 { 125, 126, 127, 128 };
+ std::vector<uint8_t> inputValue3 { 252, 253, 254, 255 };
+ inputValues.push_back(inputValue1);
+ inputValues.push_back(inputValue2);
+ inputValues.push_back(inputValue3);
+
+ std::vector<uint8_t> expectedOutputValues { 0, 1, 2, 3, 125, 126, 127, 128, 252, 253, 254, 255 };
+
+ ConcatenationTest<uint8_t>(tflite::BuiltinOperator_CONCATENATION,
+ ::tflite::TensorType_UINT8,
+ backends,
+ inputShape,
+ expectedOutputShape,
+ inputValues,
+ expectedOutputValues);
+}
+
+void ConcatAxisTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 1, 2, 2 };
+ std::vector<int32_t> expectedOutputShape { 1, 2, 4 };
+
+ std::vector<std::vector<uint8_t>> inputValues;
+ std::vector<uint8_t> inputValue1 { 0, 1, 2, 3 };
+ std::vector<uint8_t> inputValue3 { 252, 253, 254, 255 };
+ inputValues.push_back(inputValue1);
+ inputValues.push_back(inputValue3);
+
+ std::vector<uint8_t> expectedOutputValues { 0, 1, 252, 253, 2, 3, 254, 255 };
+
+ ConcatenationTest<uint8_t>(tflite::BuiltinOperator_CONCATENATION,
+ ::tflite::TensorType_UINT8,
+ backends,
+ inputShape,
+ expectedOutputShape,
+ inputValues,
+ expectedOutputValues,
+ 2);
+}
+
+// MEAN Operator
+void MeanUint8KeepDimsTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> input0Shape { 1, 3 };
+ std::vector<int32_t> input1Shape { 1 };
+ std::vector<int32_t> expectedOutputShape { 1, 1 };
+
+ std::vector<uint8_t> input0Values { 5, 10, 15 }; // Inputs
+ std::vector<int32_t> input1Values { 1 }; // Axis
+
+ std::vector<uint8_t> expectedOutputValues { 10 };
+
+ MeanTest<uint8_t>(tflite::BuiltinOperator_MEAN,
+ ::tflite::TensorType_UINT8,
+ backends,
+ input0Shape,
+ input1Shape,
+ expectedOutputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues,
+ true);
+}
+
+void MeanUint8Test(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> input0Shape { 1, 2, 2 };
+ std::vector<int32_t> input1Shape { 1 };
+ std::vector<int32_t> expectedOutputShape { 2, 2 };
+
+ std::vector<uint8_t> input0Values { 5, 10, 15, 20 }; // Inputs
+ std::vector<int32_t> input1Values { 0 }; // Axis
+
+ std::vector<uint8_t> expectedOutputValues { 5, 10, 15, 20 };
+
+ MeanTest<uint8_t>(tflite::BuiltinOperator_MEAN,
+ ::tflite::TensorType_UINT8,
+ backends,
+ input0Shape,
+ input1Shape,
+ expectedOutputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues,
+ false);
+}
+
+void MeanFp32KeepDimsTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> input0Shape { 1, 2, 2 };
+ std::vector<int32_t> input1Shape { 1 };
+ std::vector<int32_t> expectedOutputShape { 1, 1, 2 };
+
+ std::vector<float> input0Values { 1.0f, 1.5f, 2.0f, 2.5f }; // Inputs
+ std::vector<int32_t> input1Values { 1 }; // Axis
+
+ std::vector<float> expectedOutputValues { 1.5f, 2.0f };
+
+ MeanTest<float>(tflite::BuiltinOperator_MEAN,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ input0Shape,
+ input1Shape,
+ expectedOutputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues,
+ true);
+}
+
+void MeanFp32Test(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> input0Shape { 1, 2, 2, 1 };
+ std::vector<int32_t> input1Shape { 1 };
+ std::vector<int32_t> expectedOutputShape { 1, 2, 1 };
+
+ std::vector<float> input0Values { 1.0f, 1.5f, 2.0f, 2.5f }; // Inputs
+ std::vector<int32_t> input1Values { 2 }; // Axis
+
+ std::vector<float> expectedOutputValues { 1.25f, 2.25f };
+
+ MeanTest<float>(tflite::BuiltinOperator_MEAN,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ input0Shape,
+ input1Shape,
+ expectedOutputShape,
+ input0Values,
+ input1Values,
+ expectedOutputValues,
+ false);
+}
+
+// CONCATENATION Tests.
+TEST_SUITE("Concatenation_CpuAccTests")
+{
+
+TEST_CASE ("Concatenation_Uint8_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ ConcatUint8TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Int16_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ ConcatInt16TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Float32_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ ConcatFloat32TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Three_Inputs_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ ConcatThreeInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Axis_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ ConcatAxisTest(backends);
+}
+
+}
+
+TEST_SUITE("Concatenation_GpuAccTests")
+{
+
+TEST_CASE ("Concatenation_Uint8_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ ConcatUint8TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Int16_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ ConcatInt16TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Float32_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ ConcatFloat32TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Three_Inputs_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ ConcatThreeInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Axis_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ ConcatAxisTest(backends);
+}
+
+}
+
+TEST_SUITE("Concatenation_CpuRefTests")
+{
+
+TEST_CASE ("Concatenation_Uint8_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ ConcatUint8TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Int16_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ ConcatInt16TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Float32_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ ConcatFloat32TwoInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Three_Inputs_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ ConcatThreeInputsTest(backends);
+}
+
+TEST_CASE ("Concatenation_Axis_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ ConcatAxisTest(backends);
+}
+
+}
+
+// MEAN Tests
+TEST_SUITE("Mean_CpuAccTests")
+{
+
+TEST_CASE ("Mean_Uint8_KeepDims_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ MeanUint8KeepDimsTest(backends);
+}
+
+TEST_CASE ("Mean_Uint8_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ MeanUint8Test(backends);
+}
+
+TEST_CASE ("Mean_Fp32_KeepDims_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ MeanFp32KeepDimsTest(backends);
+}
+
+TEST_CASE ("Mean_Fp32_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ MeanFp32Test(backends);
+}
+
+}
+
+TEST_SUITE("Mean_GpuAccTests")
+{
+
+TEST_CASE ("Mean_Uint8_KeepDims_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ MeanUint8KeepDimsTest(backends);
+}
+
+TEST_CASE ("Mean_Uint8_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ MeanUint8Test(backends);
+}
+
+TEST_CASE ("Mean_Fp32_KeepDims_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ MeanFp32KeepDimsTest(backends);
+}
+
+TEST_CASE ("Mean_Fp32_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ MeanFp32Test(backends);
+}
+
+}
+
+TEST_SUITE("Mean_CpuRefTests")
+{
+
+TEST_CASE ("Mean_Uint8_KeepDims_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ MeanUint8KeepDimsTest(backends);
+}
+
+TEST_CASE ("Mean_Uint8_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ MeanUint8Test(backends);
+}
+
+TEST_CASE ("Mean_Fp32_KeepDims_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ MeanFp32KeepDimsTest(backends);
+}
+
+TEST_CASE ("Mean_Fp32_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ MeanFp32Test(backends);
+}
+
+}
+
+} // namespace armnnDelegate \ No newline at end of file
diff --git a/delegate/src/test/ControlTestHelper.hpp b/delegate/src/test/ControlTestHelper.hpp
new file mode 100644
index 0000000000..0c9796170d
--- /dev/null
+++ b/delegate/src/test/ControlTestHelper.hpp
@@ -0,0 +1,344 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+#include <string>
+
+namespace
+{
+
+std::vector<char> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<int32_t>& inputTensorShape,
+ const std::vector <int32_t>& outputTensorShape,
+ const int32_t inputTensorNum,
+ int32_t axis = 0,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ 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::vector<int32_t> operatorInputs{};
+ const std::vector<int32_t> operatorOutputs{inputTensorNum};
+ std::vector<int> subgraphInputs{};
+ const std::vector<int> subgraphOutputs{inputTensorNum};
+
+ std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1);
+ for (int i = 0; i < inputTensorNum; ++i)
+ {
+ tensors[i] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("input" + std::to_string(i)),
+ quantizationParameters);
+
+ // Add number of inputs to vector.
+ operatorInputs.push_back(i);
+ subgraphInputs.push_back(i);
+ }
+
+ // Create output tensor
+ tensors[inputTensorNum] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union();
+
+ flatbuffers::Offset <Operator> controlOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ flatbuffers::Offset <SubGraph> subgraph =
+ CreateSubGraph(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+ flatBufferBuilder.CreateVector(&controlOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Concatenation Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&operatorCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+ flatBufferBuilder.Finish(flatbufferModel);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<int32_t>& input0TensorShape,
+ std::vector<int32_t>& input1TensorShape,
+ const std::vector <int32_t>& outputTensorShape,
+ std::vector<int32_t>& axisData,
+ const bool keepDims,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers;
+ buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
+ buffers[1] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
+ sizeof(int32_t) * axisData.size()));
+
+ 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,
+ flatBufferBuilder.CreateString("input"),
+ quantizationParameters);
+
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
+ input1TensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("axis"),
+ quantizationParameters);
+
+ // Create output tensor
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+
+ // create operator. Mean uses ReducerOptions.
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union();
+
+ const std::vector<int> operatorInputs{ {0, 1} };
+ const std::vector<int> operatorOutputs{ 2 };
+ flatbuffers::Offset <Operator> controlOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ const std::vector<int> subgraphInputs{ {0, 1} };
+ const std::vector<int> subgraphOutputs{ 2 };
+ flatbuffers::Offset <SubGraph> subgraph =
+ CreateSubGraph(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+ flatBufferBuilder.CreateVector(&controlOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Mean Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&operatorCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+ flatBufferBuilder.Finish(flatbufferModel);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+template <typename T>
+void ConcatenationTest(tflite::BuiltinOperator controlOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& inputShapes,
+ std::vector<int32_t>& expectedOutputShape,
+ std::vector<std::vector<T>>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ int32_t axis = 0,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateConcatTfLiteModel(controlOperatorCode,
+ tensorType,
+ inputShapes,
+ expectedOutputShape,
+ inputValues.size(),
+ axis,
+ quantScale,
+ quantOffset);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+
+ // Create TfLite Interpreters
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&tfLiteInterpreter) == kTfLiteOk);
+ CHECK(tfLiteInterpreter != nullptr);
+ CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
+ // Create the ArmNN Delegate
+ armnnDelegate::DelegateOptions delegateOptions(backends);
+ std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
+ CHECK(theArmnnDelegate != nullptr);
+
+ // Modify armnnDelegateInterpreter to use armnnDelegate
+ CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+ // Set input data for all input tensors.
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ // Get single input tensor and assign to interpreters.
+ auto inputTensorValues = inputValues[i];
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues);
+ }
+
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
+ armnnDelegateInterpreter,
+ expectedOutputShape,
+ expectedOutputValues);
+
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+template <typename T>
+void MeanTest(tflite::BuiltinOperator controlOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& input0Shape,
+ std::vector<int32_t>& input1Shape,
+ std::vector<int32_t>& expectedOutputShape,
+ std::vector<T>& input0Values,
+ std::vector<int32_t>& input1Values,
+ std::vector<T>& expectedOutputValues,
+ const bool keepDims,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateMeanTfLiteModel(controlOperatorCode,
+ tensorType,
+ input0Shape,
+ input1Shape,
+ expectedOutputShape,
+ input1Values,
+ keepDims,
+ quantScale,
+ quantOffset);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+
+ // Create TfLite Interpreters
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&tfLiteInterpreter) == kTfLiteOk);
+ CHECK(tfLiteInterpreter != nullptr);
+ CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
+ // Create the ArmNN Delegate
+ armnnDelegate::DelegateOptions delegateOptions(backends);
+ std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
+ CHECK(theArmnnDelegate != nullptr);
+
+ // Modify armnnDelegateInterpreter to use armnnDelegate
+ CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+ // Set input data
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, input0Values);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values);
+
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
+ armnnDelegateInterpreter,
+ expectedOutputShape,
+ expectedOutputValues);
+
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace \ No newline at end of file
diff --git a/delegate/src/test/TestUtils.hpp b/delegate/src/test/TestUtils.hpp
index 162d62f3bb..9bbab8f62b 100644
--- a/delegate/src/test/TestUtils.hpp
+++ b/delegate/src/test/TestUtils.hpp
@@ -7,6 +7,8 @@
#include <tensorflow/lite/interpreter.h>
+#include <doctest/doctest.h>
+
namespace armnnDelegate
{
@@ -23,4 +25,35 @@ void FillInput(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex
}
}
+// Can be used to compare the output tensor shape and values
+// from armnnDelegateInterpreter and tfLiteInterpreter.
+// Example usage can be found in ControlTestHelper.hpp
+template <typename T>
+void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter,
+ std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter,
+ std::vector<int32_t>& expectedOutputShape,
+ std::vector<T>& expectedOutputValues)
+{
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId);
+ auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId);
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId);
+
+ for (size_t i = 0; i < expectedOutputShape.size(); i++)
+ {
+ CHECK(expectedOutputShape[i] == armnnDelegateOutputTensor->dims->data[i]);
+ CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]);
+ CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]);
+ }
+
+ for (size_t i = 0; i < expectedOutputValues.size(); i++)
+ {
+ CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]);
+ CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]);
+ CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]);
+ }
+}
+
} // namespace armnnDelegate
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-25 10:12:04 +0000