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-rw-r--r--delegate/src/test/PadTestHelper.hpp224
1 files changed, 0 insertions, 224 deletions
diff --git a/delegate/src/test/PadTestHelper.hpp b/delegate/src/test/PadTestHelper.hpp
deleted file mode 100644
index e96bc4bfe3..0000000000
--- a/delegate/src/test/PadTestHelper.hpp
+++ /dev/null
@@ -1,224 +0,0 @@
-//
-// Copyright © 2020, 2023 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
-{
-
-template <typename T>
-std::vector<char> CreatePadTfLiteModel(
- tflite::BuiltinOperator padOperatorCode,
- tflite::TensorType tensorType,
- tflite::MirrorPadMode paddingMode,
- const std::vector<int32_t>& inputTensorShape,
- const std::vector<int32_t>& paddingTensorShape,
- const std::vector<int32_t>& outputTensorShape,
- const std::vector<int32_t>& paddingDim,
- const std::vector<T> paddingValue,
- float quantScale = 1.0f,
- int quantOffset = 0)
-{
- using namespace tflite;
- flatbuffers::FlatBufferBuilder flatBufferBuilder;
-
- auto quantizationParameters =
- CreateQuantizationParameters(flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({ quantScale }),
- flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
-
- auto inputTensor = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
- inputTensorShape.size()),
- tensorType,
- 0,
- flatBufferBuilder.CreateString("input"),
- quantizationParameters);
-
- auto paddingTensor = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(paddingTensorShape.data(),
- paddingTensorShape.size()),
- tflite::TensorType_INT32,
- 1,
- flatBufferBuilder.CreateString("padding"));
-
- auto outputTensor = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
- outputTensorShape.size()),
- tensorType,
- 2,
- flatBufferBuilder.CreateString("output"),
- quantizationParameters);
-
- std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor};
-
- std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
- buffers.push_back(CreateBuffer(flatBufferBuilder));
- buffers.push_back(
- CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()),
- sizeof(int32_t) * paddingDim.size())));
- buffers.push_back(CreateBuffer(flatBufferBuilder));
-
- std::vector<int32_t> operatorInputs;
- std::vector<int> subgraphInputs;
-
- tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_PadOptions;
- flatbuffers::Offset<void> operatorBuiltinOptions;
-
- if (padOperatorCode == tflite::BuiltinOperator_PAD)
- {
- operatorInputs = {{ 0, 1 }};
- subgraphInputs = {{ 0, 1 }};
- operatorBuiltinOptions = CreatePadOptions(flatBufferBuilder).Union();
- }
- else if(padOperatorCode == tflite::BuiltinOperator_MIRROR_PAD)
- {
- operatorInputs = {{ 0, 1 }};
- subgraphInputs = {{ 0, 1 }};
-
- operatorBuiltinOptionsType = BuiltinOptions_MirrorPadOptions;
- operatorBuiltinOptions = CreateMirrorPadOptions(flatBufferBuilder, paddingMode).Union();
- }
- else if (padOperatorCode == tflite::BuiltinOperator_PADV2)
- {
- buffers.push_back(
- CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingValue.data()),
- sizeof(T))));
-
- const std::vector<int32_t> shape = { 1 };
- auto padValueTensor = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(shape.data(),
- shape.size()),
- tensorType,
- 3,
- flatBufferBuilder.CreateString("paddingValue"),
- quantizationParameters);
-
- tensors.push_back(padValueTensor);
-
- operatorInputs = {{ 0, 1, 3 }};
- subgraphInputs = {{ 0, 1, 3 }};
-
- operatorBuiltinOptionsType = BuiltinOptions_PadV2Options;
- operatorBuiltinOptions = CreatePadV2Options(flatBufferBuilder).Union();
- }
-
- // create operator
- const std::vector<int32_t> operatorOutputs{ 2 };
- flatbuffers::Offset <Operator> paddingOperator =
- CreateOperator(flatBufferBuilder,
- 0,
- flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
- flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
- operatorBuiltinOptionsType,
- operatorBuiltinOptions);
-
- 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(&paddingOperator, 1));
-
- flatbuffers::Offset <flatbuffers::String> modelDescription =
- flatBufferBuilder.CreateString("ArmnnDelegate: Pad Operator Model");
- flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
- padOperatorCode);
-
- 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());
-}
-
-template <typename T>
-void PadTest(tflite::BuiltinOperator padOperatorCode,
- tflite::TensorType tensorType,
- const std::vector<armnn::BackendId>& backends,
- const std::vector<int32_t>& inputShape,
- const std::vector<int32_t>& paddingShape,
- std::vector<int32_t>& outputShape,
- std::vector<T>& inputValues,
- std::vector<int32_t>& paddingDim,
- std::vector<T>& expectedOutputValues,
- T paddingValue,
- float quantScale = 1.0f,
- int quantOffset = 0,
- tflite::MirrorPadMode paddingMode = tflite::MirrorPadMode_SYMMETRIC)
-{
- using namespace tflite;
- std::vector<char> modelBuffer = CreatePadTfLiteModel<T>(padOperatorCode,
- tensorType,
- paddingMode,
- inputShape,
- paddingShape,
- outputShape,
- paddingDim,
- {paddingValue},
- quantScale,
- quantOffset);
-
- const Model* tfLiteModel = GetModel(modelBuffer.data());
- CHECK(tfLiteModel != nullptr);
-
- 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
- armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues);
- armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues);
-
- // Run EnqueueWorkload
- CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
- CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
- armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
-}
-
-} // anonymous namespace
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-25 18:33:04 +0000