1 files changed, 33 insertions, 55 deletions

diff --git a/delegate/test/UnidirectionalSequenceLstmTestHelper.hpp b/delegate/test/UnidirectionalSequenceLstmTestHelper.hpp
index 0ff04e7949..c058d83bc6 100644
--- a/delegate/test/UnidirectionalSequenceLstmTestHelper.hpp
+++ b/delegate/test/UnidirectionalSequenceLstmTestHelper.hpp
@@ -8,14 +8,13 @@
 #include "TestUtils.hpp"
 
 #include <armnn_delegate.hpp>
+#include <DelegateTestInterpreter.hpp>
 
 #include <flatbuffers/flatbuffers.h>
-#include <tensorflow/lite/interpreter.h>
 #include <tensorflow/lite/kernels/register.h>
-#include <tensorflow/lite/model.h>
-#include <schema_generated.h>
 #include <tensorflow/lite/version.h>
-#include <tensorflow/lite/c/common.h>
+
+#include <schema_generated.h>
 
 #include <doctest/doctest.h>
 
@@ -569,7 +568,7 @@ std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType
                                                modelDescription,
                                                flatBufferBuilder.CreateVector(buffers));
 
-    flatBufferBuilder.Finish(flatbufferModel);
+    flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
 
     return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
                              flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
@@ -624,7 +623,7 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends,
                                         bool isTimeMajor,
                                         float quantScale = 0.1f)
 {
-    using namespace tflite;
+    using namespace delegateTestInterpreter;
 
     std::vector<char> modelBuffer = CreateUnidirectionalSequenceLstmTfLiteModel(tensorType,
                                                                                 batchSize,
@@ -671,72 +670,51 @@ void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends,
                                                                                 isTimeMajor,
                                                                                 quantScale);
 
-    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
-    auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
-    auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId);
-    for (unsigned int i = 0; i < inputValues.size(); ++i)
+    std::vector<int32_t> outputShape;
+    if (isTimeMajor)
     {
-        tfLiteDelageInputData[i] = inputValues[i];
+        outputShape = {timeSize, batchSize, outputSize};
     }
-
-    auto armnnDelegateInputId   = armnnDelegateInterpreter->inputs()[0];
-    auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
-    for (unsigned int i = 0; i < inputValues.size(); ++i)
+    else
     {
-        armnnDelegateInputData[i] = inputValues[i];
+        outputShape = {batchSize, timeSize, outputSize};
     }
 
-    // Run EnqueueWorkload
-    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
-    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+    // Setup interpreter with just TFLite Runtime.
+    auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
+    CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
+    CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
+    CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
+    std::vector<float>   tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0);
+    std::vector<int32_t> tfLiteOutputShape  = tfLiteInterpreter.GetOutputShape(0);
+
+    // Setup interpreter with Arm NN Delegate applied.
+    auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
+    CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
+    CHECK(armnnInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
+    CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
+    std::vector<float>   armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
+    std::vector<int32_t> armnnOutputShape  = armnnInterpreter.GetOutputShape(0);
 
-    // Compare output data
-    auto tfLiteDelegateOutputId   = tfLiteInterpreter->outputs()[0];
-    auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
-    auto armnnDelegateOutputId    = armnnDelegateInterpreter->outputs()[0];
-    auto armnnDelegateOutputData  = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+    armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
 
     if (tensorType == ::tflite::TensorType_INT8)
     {
         // Allow 2% tolerance for Quantized weights
-        armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData,
+        armnnDelegate::CompareData(expectedOutputValues.data(), armnnOutputValues.data(),
                                    expectedOutputValues.size(), 2);
-        armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData,
+        armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteOutputValues.data(),
                                    expectedOutputValues.size(), 2);
-        armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData,
+        armnnDelegate::CompareData(tfLiteOutputValues.data(), armnnOutputValues.data(),
                                    expectedOutputValues.size(), 2);
     }
     else
     {
-        armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData,
-                                   expectedOutputValues.size());
-        armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData,
-                                   expectedOutputValues.size());
-        armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
+        armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
     }
+
+    tfLiteInterpreter.Cleanup();
+    armnnInterpreter.Cleanup();
 }
 
 } // anonymous namespace
\ No newline at end of file