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-rw-r--r--delegate/src/test/ResizeTestHelper.hpp192
1 files changed, 192 insertions, 0 deletions
diff --git a/delegate/src/test/ResizeTestHelper.hpp b/delegate/src/test/ResizeTestHelper.hpp
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+++ b/delegate/src/test/ResizeTestHelper.hpp
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+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#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> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode,
+ tflite::TensorType inputTensorType,
+ const std::vector <int32_t>& inputTensorShape,
+ const std::vector <int32_t>& sizeTensorData,
+ const std::vector <int32_t>& sizeTensorShape,
+ const std::vector <int32_t>& outputTensorShape)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ buffers.push_back(CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t*>(sizeTensorData.data()),
+ sizeof(int32_t) * sizeTensorData.size())));
+
+ std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()),
+ inputTensorType,
+ 0,
+ flatBufferBuilder.CreateString("input_tensor"));
+
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(),
+ sizeTensorShape.size()),
+ TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("size_input_tensor"));
+
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ inputTensorType,
+ 0,
+ flatBufferBuilder.CreateString("output_tensor"));
+
+ // Create Operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+ flatbuffers::Offset<void> operatorBuiltinOption = 0;
+ switch (operatorCode)
+ {
+ case BuiltinOperator_RESIZE_BILINEAR:
+ {
+ operatorBuiltinOption = CreateResizeBilinearOptions(flatBufferBuilder, false, false).Union();
+ operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeBilinearOptions;
+ break;
+ }
+ case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR:
+ {
+ operatorBuiltinOption = CreateResizeNearestNeighborOptions(flatBufferBuilder, false, false).Union();
+ operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeNearestNeighborOptions;
+ break;
+ }
+ default:
+ break;
+ }
+
+ const std::vector<int> operatorInputs{{0, 1}};
+ const std::vector<int> operatorOutputs{{2}};
+ flatbuffers::Offset <Operator> resizeOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOption);
+
+ const std::vector<int> subgraphInputs{{0, 1}};
+ const std::vector<int> subgraphOutputs{{2}};
+ 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(&resizeOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model");
+ flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&opCode, 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 ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<float>& input1Values,
+ std::vector<int32_t> input1Shape,
+ std::vector<int32_t> input2NewShape,
+ std::vector<int32_t> input2Shape,
+ std::vector<float>& expectedOutputValues,
+ std::vector<int32_t> expectedOutputShape)
+{
+ using namespace tflite;
+
+ std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode,
+ ::tflite::TensorType_FLOAT32,
+ input1Shape,
+ input2NewShape,
+ input2Shape,
+ expectedOutputShape);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+
+ // The model will be executed using tflite and using the armnn delegate so that the outputs
+ // can be compared.
+
+ // Create TfLite Interpreter with armnn delegate
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ // Create TfLite Interpreter without armnn delegate
+ 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 for the armnn interpreter
+ armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input1Values);
+ armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input2NewShape);
+
+ // Set input data for the tflite interpreter
+ armnnDelegate::FillInput(tfLiteInterpreter, 0, input1Values);
+ armnnDelegate::FillInput(tfLiteInterpreter, 1, input2NewShape);
+
+ // Run EnqueWorkload
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+ CHECK(tfLiteInterpreter->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 < expectedOutputValues.size(); i++)
+ {
+ CHECK(expectedOutputValues[i] == doctest::Approx(armnnDelegateOutputData[i]));
+ CHECK(armnnDelegateOutputData[i] == doctest::Approx(tfLiteDelageOutputData[i]));
+ }
+
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace \ No newline at end of file
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-25 06:35:17 +0000