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authorTeresa Charlin <teresa.charlinreyes@arm.com>2023-03-14 12:10:28 +0000
committerTeresa Charlin <teresa.charlinreyes@arm.com>2023-03-28 11:41:55 +0100
commitad1b3d7518429e2d16a2695d9b0bbf81b6565ac9 (patch)
treea5b8e1ad68a2437f007338f0b6195ca5ed2bddc3 /delegate/test/TestUtils.hpp
parent9cb3466b677a1048b8abb24661e92c4c83fdda04 (diff)
downloadarmnn-ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9.tar.gz
IVGCVSW-7555 Restructure Delegate
* New folders created: * common is for common code where TfLite API is not used * classic is for existing delegate implementations * opaque is for new opaque delegate implementation, * tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use. * Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so * Opaque structure is introduced but no API is added yet. * CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added * Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE * Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com> Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed
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+//
+// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <tensorflow/lite/c/common.h>
+#include <tensorflow/lite/interpreter.h>
+
+#include <doctest/doctest.h>
+
+#include <half/half.hpp>
+
+using Half = half_float::half;
+
+namespace armnnDelegate
+{
+
+/// Can be used to assign input data from a vector to a model input.
+/// Example usage can be found in ResizeTesthelper.hpp
+template <typename T>
+void FillInput(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<T>& inputValues)
+{
+ auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex];
+ auto tfLiteDelageInputData = interpreter->typed_tensor<T>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelageInputData[i] = inputValues[i];
+ }
+}
+
+template <>
+void FillInput(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<Half>& inputValues);
+
+/// Can be used to compare bool data coming from a tflite interpreter
+/// Boolean types get converted to a bit representation in a vector. vector.data() returns a void pointer
+/// instead of a pointer to bool. Therefore a special function to compare to vector of bool is required
+void CompareData(std::vector<bool>& tensor1, bool tensor2[], size_t tensorSize);
+void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize);
+
+/// Can be used to compare float data coming from a tflite interpreter with a tolerance of limit_of_float*100
+void CompareData(float tensor1[], float tensor2[], size_t tensorSize);
+
+/// Can be used to compare float data coming from a tflite interpreter with a given percentage tolerance
+void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance);
+
+/// Can be used to compare int8_t data coming from a tflite interpreter with a tolerance of 1
+void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize);
+
+/// Can be used to compare uint8_t data coming from a tflite interpreter with a tolerance of 1
+void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize);
+
+/// Can be used to compare int16_t data coming from a tflite interpreter with a tolerance of 1
+void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize);
+
+/// Can be used to compare int32_t data coming from a tflite interpreter with a tolerance of 1
+void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize);
+
+/// Can be used to compare Half (Float16) data with a tolerance of limit_of_float*100
+void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize);
+
+/// Can be used to compare TfLiteFloat16 data coming from a tflite interpreter
+void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize);
+
+/// Can be used to compare Half (Float16) data and TfLiteFloat16 data coming from a tflite interpreter
+void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize);
+
+/// 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,
+ unsigned int outputIndex = 0)
+{
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex];
+ auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId);
+ auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex];
+ auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId);
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId);
+
+ CHECK(expectedOutputShape.size() == tfLiteDelegateOutputTensor->dims->size);
+ CHECK(expectedOutputShape.size() == armnnDelegateOutputTensor->dims->size);
+
+ 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]);
+ }
+
+ armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData , expectedOutputValues.size());
+ armnnDelegate::CompareData(tfLiteDelegateOutputData , expectedOutputValues.data(), expectedOutputValues.size());
+ armnnDelegate::CompareData(tfLiteDelegateOutputData , armnnDelegateOutputData , expectedOutputValues.size());
+}
+
+} // namespace armnnDelegate