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
diff --git a/delegate/test/TestUtils.cpp b/delegate/test/TestUtils.cpp
new file mode 100644
index 0000000..2689c2e
--- /dev/null
+++ b/delegate/test/TestUtils.cpp
@@ -0,0 +1,152 @@
+//
+// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "TestUtils.hpp"
+
+namespace armnnDelegate
+{
+
+void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize)
+{
+ auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));};
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(compareBool(tensor1[i], tensor2[i]));
+ }
+}
+
+void CompareData(std::vector<bool>& tensor1, bool tensor2[], size_t tensorSize)
+{
+ auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));};
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(compareBool(tensor1[i], tensor2[i]));
+ }
+}
+
+void CompareData(float tensor1[], float tensor2[], size_t tensorSize)
+{
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(tensor1[i] == doctest::Approx( tensor2[i] ));
+ }
+}
+
+void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance)
+{
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <=
+ std::abs(tensor1[i]*percentTolerance/100));
+ }
+}
+
+void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize)
+{
+ uint8_t tolerance = 1;
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
+ }
+}
+
+void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize)
+{
+ int16_t tolerance = 1;
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
+ }
+}
+
+void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize)
+{
+ int32_t tolerance = 1;
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
+ }
+}
+
+void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize)
+{
+ int8_t tolerance = 1;
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
+ }
+}
+
+void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize)
+{
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ CHECK(tensor1[i] == doctest::Approx( tensor2[i] ));
+ }
+}
+
+void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize)
+{
+ uint16_t tolerance = 1;
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ uint16_t tensor1Data = tensor1[i].data;
+ uint16_t tensor2Data = tensor2[i].data;
+ CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance);
+ }
+}
+
+void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize) {
+ uint16_t tolerance = 1;
+ for (size_t i = 0; i < tensorSize; i++)
+ {
+ uint16_t tensor1Data = tensor1[i].data;
+ uint16_t tensor2Data = half_float::detail::float2half<std::round_indeterminate, float>(tensor2[i]);
+ CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance);
+ }
+}
+
+template <>
+void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter,
+ std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter,
+ std::vector<int32_t>& expectedOutputShape,
+ std::vector<Half>& expectedOutputValues,
+ unsigned int outputIndex)
+{
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex];
+ auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId);
+ auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex];
+ auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId);
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<TfLiteFloat16>(armnnDelegateOutputId);
+
+ CHECK(expectedOutputShape.size() == tfLiteDelegateOutputTensor->dims->size);
+ CHECK(expectedOutputShape.size() == armnnDelegateOutputTensor->dims->size);
+
+ for (size_t i = 0; i < expectedOutputShape.size(); i++)
+ {
+ CHECK(armnnDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]);
+ CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]);
+ CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]);
+ }
+
+ armnnDelegate::CompareData(armnnDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size());
+ armnnDelegate::CompareData(tfLiteDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size());
+ armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
+}
+
+template <>
+void FillInput<Half>(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<Half>& inputValues)
+{
+ auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex];
+ auto tfLiteDelageInputData = interpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelageInputData[i].data = half_float::detail::float2half<std::round_indeterminate, float>(inputValues[i]);
+
+ }
+}
+
+} // namespace armnnDelegate
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