From 0534e0364473c0b1244f96462cbde1808e92ce81 Mon Sep 17 00:00:00 2001
From: Sadik Armagan <sadik.armagan@arm.com>
Date: Tue, 27 Oct 2020 17:30:18 +0000
Subject: IVGCVSW-5378 'TfLiteDelegate: Implement the ElementWiseUnary
 operators '

* Moved ElementwiseUnary operators tests into single file
* Implemented FP32 test for supported ElementwiseUnary operators

Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I4b7eab190c3c8edb50927b8e1e94dd353597efcb
---
 delegate/src/test/ElementwiseUnaryTestHelper.hpp | 141 +++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 delegate/src/test/ElementwiseUnaryTestHelper.hpp

(limited to 'delegate/src/test/ElementwiseUnaryTestHelper.hpp')

diff --git a/delegate/src/test/ElementwiseUnaryTestHelper.hpp b/delegate/src/test/ElementwiseUnaryTestHelper.hpp
new file mode 100644
index 0000000000..4d45f4e964
--- /dev/null
+++ b/delegate/src/test/ElementwiseUnaryTestHelper.hpp
@@ -0,0 +1,141 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#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>
+
+namespace
+{
+
+std::vector<char> CreateElementwiseUnaryTfLiteModel(tflite::BuiltinOperator unaryOperatorCode,
+                                                    tflite::TensorType tensorType,
+                                                    const std::vector <int32_t>& tensorShape)
+{
+    using namespace tflite;
+    flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+    std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers;
+    buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
+
+    std::array<flatbuffers::Offset<Tensor>, 2> tensors;
+    tensors[0] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()),
+                              tensorType);
+    tensors[1] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()),
+                              tensorType);
+
+    // create operator
+    const std::vector<int> operatorInputs{{0}};
+    const std::vector<int> operatorOutputs{{1}};
+    flatbuffers::Offset <Operator> unaryOperator =
+        CreateOperator(flatBufferBuilder,
+                       0,
+                       flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+                       flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()));
+
+    const std::vector<int> subgraphInputs{{0}};
+    const std::vector<int> subgraphOutputs{{1}};
+    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(&unaryOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Unary Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, unaryOperatorCode);
+
+    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());
+}
+
+void ElementwiseUnaryFP32Test(tflite::BuiltinOperator unaryOperatorCode,
+                              std::vector<armnn::BackendId>& backends,
+                              std::vector<float>& inputValues,
+                              std::vector<float>& expectedOutputValues)
+{
+    using namespace tflite;
+    const std::vector<int32_t> inputShape  { { 3, 1, 2} };
+    std::vector<char> modelBuffer = CreateElementwiseUnaryTfLiteModel(unaryOperatorCode,
+                                                                      ::tflite::TensorType_FLOAT32,
+                                                                      inputShape);
+
+    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);
+    auto armnnDelegate = TfLiteArmnnDelegateCreate(delegateOptions);
+    CHECK(armnnDelegate != nullptr);
+    // Modify armnnDelegateInterpreter to use armnnDelegate
+    CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(armnnDelegate) == 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)
+    {
+        tfLiteDelageInputData[i] = inputValues[i];
+    }
+
+    auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
+    auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
+    for (unsigned int i = 0; i < inputValues.size(); ++i)
+    {
+        armnnDelegateInputData[i] = inputValues[i];
+    }
+    // Run EnqueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    // Compare output data
+    auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+    auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
+    auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+    auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+    for (size_t i = 0; i < inputValues.size(); i++)
+    {
+        CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]);
+        CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]);
+    }
+}
+
+} // anonymous namespace
+
+
+
+
-- 
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