1 files changed, 101 insertions, 0 deletions
diff --git a/delegate/test/TestUtils.hpp b/delegate/test/TestUtils.hpp
new file mode 100644
index 0000000000..95dd257c92
--- /dev/null
+++ b/delegate/test/TestUtils.hpp
@@ -0,0 +1,101 @@
+//
+// 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