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704 lines
38 KiB
704 lines
38 KiB
//
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// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
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// SPDX-License-Identifier: MIT
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//
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#pragma once
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#include "TestUtils.hpp"
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#include <armnn_delegate.hpp>
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#include <DelegateTestInterpreter.hpp>
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#include <flatbuffers/flatbuffers.h>
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#include <tensorflow/lite/interpreter.h>
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#include <tensorflow/lite/kernels/register.h>
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#include <tensorflow/lite/model.h>
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#include <schema_generated.h>
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#include <tensorflow/lite/version.h>
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#include <doctest/doctest.h>
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namespace
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{
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template <typename T, typename B = float>
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std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode,
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tflite::TensorType tensorType,
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uint32_t strideX,
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uint32_t strideY,
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uint32_t dilationX,
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uint32_t dilationY,
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tflite::Padding padding,
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tflite::ActivationFunctionType fused_activation_function,
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const std::vector <int32_t>& inputTensorShape,
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const std::vector <int32_t>& filterTensorShape,
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const std::vector <int32_t>& biasTensorShape,
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const std::vector <int32_t>& outputTensorShape,
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const std::vector <T>& filterData,
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const std::vector <B>& biasData,
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const std::vector<float> biasScales = {1.0f},
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const std::vector<int64_t> biasOffsets = {0},
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const std::vector<float> filterScales = {1.0f},
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const std::vector<int64_t> filterOffsets = {0},
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float outputQuantScale = 2.0f,
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int outputQuantOffset = 0,
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float quantScale = 1.0f,
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int quantOffset = 0,
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int32_t depth_multiplier = 1,
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int32_t filterQuantizationDim = 0)
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{
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using namespace tflite;
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flatbuffers::FlatBufferBuilder flatBufferBuilder;
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std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers;
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buffers[0] = CreateBuffer(flatBufferBuilder);
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buffers[1] = CreateBuffer(flatBufferBuilder);
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buffers[2] = CreateBuffer(flatBufferBuilder,
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flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()),
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sizeof(T) * filterData.size()));
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buffers[3] = CreateBuffer(flatBufferBuilder,
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flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()),
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sizeof(B) * biasData.size()));
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buffers[4] = CreateBuffer(flatBufferBuilder);
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auto quantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({ quantScale }),
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flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
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auto outputQuantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
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flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
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auto filterQuantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>(filterScales),
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flatBufferBuilder.CreateVector<int64_t>(filterOffsets),
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tflite::QuantizationDetails_NONE,
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0,
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filterQuantizationDim);
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auto biasQuantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>(biasScales),
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flatBufferBuilder.CreateVector<int64_t>(biasOffsets));
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std::array<flatbuffers::Offset<Tensor>, 4> tensors;
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tensors[0] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
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inputTensorShape.size()),
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tensorType,
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1,
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flatBufferBuilder.CreateString("input"),
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quantizationParameters);
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tensors[1] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(),
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filterTensorShape.size()),
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tensorType,
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2,
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flatBufferBuilder.CreateString("filter"),
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filterQuantizationParameters);
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auto biasTensorType = ::tflite::TensorType_FLOAT32;
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if (tensorType == ::tflite::TensorType_INT8 || tensorType == ::tflite::TensorType_UINT8)
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{
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biasTensorType = ::tflite::TensorType_INT32;
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}
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tensors[2] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()),
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biasTensorType,
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3,
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flatBufferBuilder.CreateString("bias"),
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biasQuantizationParameters);
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tensors[3] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
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outputTensorShape.size()),
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tensorType,
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4,
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flatBufferBuilder.CreateString("output"),
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outputQuantizationParameters);
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flatbuffers::Offset<void> operatorBuiltinOptions;
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tflite::BuiltinOptions operatorBuiltinOptionsType;
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if(convolutionOperatorCode == tflite::BuiltinOperator_DEPTHWISE_CONV_2D)
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{
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operatorBuiltinOptionsType = tflite::BuiltinOptions_DepthwiseConv2DOptions;
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operatorBuiltinOptions = CreateDepthwiseConv2DOptions(flatBufferBuilder,
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padding,
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strideX,
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strideY,
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depth_multiplier,
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fused_activation_function,
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dilationX,
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dilationY).Union();
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}
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if(convolutionOperatorCode == tflite::BuiltinOperator_CONV_2D)
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{
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operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv2DOptions;
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operatorBuiltinOptions = CreateConv2DOptions(flatBufferBuilder,
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padding,
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strideX,
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strideY,
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fused_activation_function,
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dilationX,
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dilationY).Union();
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}
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// create operator
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const std::vector<int> operatorInputs{0, 1, 2};
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const std::vector<int> operatorOutputs{3};
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flatbuffers::Offset <Operator> convolutionOperator =
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CreateOperator(flatBufferBuilder,
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0,
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flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
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operatorBuiltinOptionsType,
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operatorBuiltinOptions);
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const std::vector<int> subgraphInputs{0, 1, 2};
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const std::vector<int> subgraphOutputs{3};
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flatbuffers::Offset <SubGraph> subgraph =
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CreateSubGraph(flatBufferBuilder,
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flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
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flatBufferBuilder.CreateVector(&convolutionOperator, 1));
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flatbuffers::Offset <flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: Convolution2d Operator Model");
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flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, convolutionOperatorCode);
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flatbuffers::Offset <Model> flatbufferModel =
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CreateModel(flatBufferBuilder,
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TFLITE_SCHEMA_VERSION,
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flatBufferBuilder.CreateVector(&operatorCode, 1),
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flatBufferBuilder.CreateVector(&subgraph, 1),
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modelDescription,
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flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
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flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
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return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
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flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
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}
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template <typename T, typename B = float>
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void ConvolutionTest(tflite::BuiltinOperator convolutionOperatorCode,
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tflite::TensorType tensorType,
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uint32_t strideX,
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uint32_t strideY,
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uint32_t dilationX,
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uint32_t dilationY,
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tflite::Padding padding,
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tflite::ActivationFunctionType fused_activation_function,
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std::vector<armnn::BackendId>& backends,
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std::vector<int32_t>& inputShape,
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std::vector<int32_t>& filterShape,
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std::vector<int32_t>& outputShape,
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std::vector<T>& inputValues,
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std::vector<T>& filterValues,
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std::vector<T>& expectedOutputValues,
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const std::vector<int32_t>& biasShape = {},
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const std::vector<B>& biasValues = {},
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const std::vector<float> biasScales = {1.0f},
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const std::vector<int64_t> biasOffsets = {0},
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const std::vector<float> filterScales = {1.0f},
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const std::vector<int64_t> filterOffsets = {0},
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float outputQuantScale = 2.0f,
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int outputQuantOffset = 0,
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float quantScale = 1.0f,
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int quantOffset = 0,
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int32_t depth_multiplier = 1,
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int32_t filterQuantizationDim = 3)
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{
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using namespace delegateTestInterpreter;
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std::vector<char> modelBuffer;
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modelBuffer = CreateConv2dTfLiteModel(convolutionOperatorCode,
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tensorType,
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strideX,
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strideY,
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dilationX,
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dilationY,
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padding,
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fused_activation_function,
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inputShape,
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filterShape,
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biasShape,
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outputShape,
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filterValues,
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biasValues,
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biasScales,
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biasOffsets,
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filterScales,
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filterOffsets,
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outputQuantScale,
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outputQuantOffset,
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quantScale,
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quantOffset,
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depth_multiplier,
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filterQuantizationDim);
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// Setup interpreter with just TFLite Runtime.
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auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
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CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
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CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
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// Setup interpreter with Arm NN Delegate applied.
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auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
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CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
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CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
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armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
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armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
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tfLiteInterpreter.Cleanup();
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armnnInterpreter.Cleanup();
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}
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// Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5.
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#if defined(ARMNN_POST_TFLITE_2_5)
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template <typename T, typename B = float>
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std::vector<char> CreateConv3dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode,
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tflite::TensorType tensorType,
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std::vector<uint32_t> strides,
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std::vector<uint32_t> dilation,
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tflite::Padding padding,
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tflite::ActivationFunctionType fused_activation_function,
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const std::vector<int32_t>& inputTensorShape,
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const std::vector<int32_t>& filterTensorShape,
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const std::vector<int32_t>& biasTensorShape,
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const std::vector<int32_t>& outputTensorShape,
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const std::vector<T>& filterData,
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const std::vector<B>& biasData,
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const std::vector<float> biasScales = {1.0f},
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const std::vector<int64_t> biasOffsets = {0},
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const std::vector<float> filterScales = {1.0f},
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const std::vector<int64_t> filterOffsets = {0},
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float outputQuantScale = 2.0f,
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int outputQuantOffset = 0,
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float quantScale = 1.0f,
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int quantOffset = 0,
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int32_t depth_multiplier = 1,
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int32_t filterQuantizationDim = 0)
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{
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using namespace tflite;
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flatbuffers::FlatBufferBuilder flatBufferBuilder;
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std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers;
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buffers[0] = CreateBuffer(flatBufferBuilder);
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buffers[1] = CreateBuffer(flatBufferBuilder,
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flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()),
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sizeof(T) * filterData.size()));
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buffers[2] = CreateBuffer(flatBufferBuilder,
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flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()),
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sizeof(B) * biasData.size()));
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auto quantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({ quantScale }),
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flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
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auto outputQuantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
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flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
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auto filterQuantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>(filterScales),
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flatBufferBuilder.CreateVector<int64_t>(filterOffsets),
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tflite::QuantizationDetails_NONE,
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0,
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filterQuantizationDim);
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auto biasQuantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>(biasScales),
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flatBufferBuilder.CreateVector<int64_t>(biasOffsets));
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std::array<flatbuffers::Offset<Tensor>, 4> tensors;
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tensors[0] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
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inputTensorShape.size()),
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tensorType,
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0,
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flatBufferBuilder.CreateString("input"),
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quantizationParameters);
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tensors[1] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(),
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filterTensorShape.size()),
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tensorType,
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1,
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flatBufferBuilder.CreateString("filter"),
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filterQuantizationParameters);
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auto biasTensorType = ::tflite::TensorType_FLOAT32;
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if (tensorType == ::tflite::TensorType_INT8 || tensorType == ::tflite::TensorType_UINT8)
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{
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biasTensorType = ::tflite::TensorType_INT32;
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}
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tensors[2] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()),
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biasTensorType,
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2,
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flatBufferBuilder.CreateString("bias"),
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biasQuantizationParameters);
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tensors[3] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
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outputTensorShape.size()),
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tensorType,
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0,
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flatBufferBuilder.CreateString("output"),
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outputQuantizationParameters);
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tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv3DOptions;
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flatbuffers::Offset<void> operatorBuiltinOptions = CreateConv3DOptions(flatBufferBuilder,
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padding,
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strides[2], // Depth
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strides[0], // Width
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strides[1], // Height
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fused_activation_function,
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dilation[2],
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dilation[0],
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dilation[1]).Union();
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// Create operator
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const std::vector<int> operatorInputs{0, 1, 2};
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const std::vector<int> operatorOutputs{3};
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flatbuffers::Offset <Operator> convolutionOperator =
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CreateOperator(flatBufferBuilder,
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0,
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flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
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operatorBuiltinOptionsType,
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operatorBuiltinOptions);
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const std::vector<int> subgraphInputs{0, 1, 2};
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const std::vector<int> subgraphOutputs{3};
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flatbuffers::Offset <SubGraph> subgraph =
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CreateSubGraph(flatBufferBuilder,
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flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
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flatBufferBuilder.CreateVector(&convolutionOperator, 1));
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flatbuffers::Offset <flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: Convolution 3d Operator Model");
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// If using an operator with a code greater than 127 then the enum value should be passed as the fifth
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// parameter rather than the second like in other tests.
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flatbuffers::Offset <OperatorCode> operatorCode =
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CreateOperatorCode(flatBufferBuilder, 0, 0, 1, tflite::BuiltinOperator_CONV_3D);
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flatbuffers::Offset <Model> flatbufferModel =
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CreateModel(flatBufferBuilder,
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TFLITE_SCHEMA_VERSION,
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flatBufferBuilder.CreateVector(&operatorCode, 1),
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flatBufferBuilder.CreateVector(&subgraph, 1),
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modelDescription,
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flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
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flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
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return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
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flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
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}
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template <typename T, typename B = float>
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void Convolution3dTest(tflite::BuiltinOperator convolutionOperatorCode,
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tflite::TensorType tensorType,
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std::vector<uint32_t> strides,
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std::vector<uint32_t> dilation,
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tflite::Padding padding,
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tflite::ActivationFunctionType fused_activation_function,
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std::vector<armnn::BackendId>& backends,
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std::vector<int32_t>& inputShape,
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std::vector<int32_t>& filterShape,
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std::vector<int32_t>& outputShape,
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std::vector<T>& inputValues,
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std::vector<T>& filterValues,
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std::vector<T>& expectedOutputValues,
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const std::vector<int32_t>& biasShape = {},
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const std::vector<B>& biasValues = {},
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const std::vector<float> biasScales = {1.0f},
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const std::vector<int64_t> biasOffsets = {0},
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const std::vector<float> filterScales = {1.0f},
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const std::vector<int64_t> filterOffsets = {0},
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float outputQuantScale = 2.0f,
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int outputQuantOffset = 0,
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float quantScale = 1.0f,
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int quantOffset = 0,
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int32_t depth_multiplier = 1,
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int32_t filterQuantizationDim = 3)
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{
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using namespace delegateTestInterpreter;
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std::vector<char> modelBuffer;
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modelBuffer = CreateConv3dTfLiteModel(convolutionOperatorCode,
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tensorType,
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strides,
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dilation,
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padding,
|
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fused_activation_function,
|
|
inputShape,
|
|
filterShape,
|
|
biasShape,
|
|
outputShape,
|
|
filterValues,
|
|
biasValues,
|
|
biasScales,
|
|
biasOffsets,
|
|
filterScales,
|
|
filterOffsets,
|
|
outputQuantScale,
|
|
outputQuantOffset,
|
|
quantScale,
|
|
quantOffset,
|
|
depth_multiplier,
|
|
filterQuantizationDim);
|
|
|
|
// Setup interpreter with just TFLite Runtime.
|
|
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
|
|
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
|
|
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
|
|
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
|
|
std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(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<T>(inputValues, 0) == kTfLiteOk);
|
|
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
|
|
std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
|
|
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
|
|
|
|
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape);
|
|
|
|
armnnDelegate::CompareData(expectedOutputValues.data(), armnnOutputValues.data(), expectedOutputValues.size(), 1);
|
|
armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteOutputValues.data(), expectedOutputValues.size(), 1);
|
|
armnnDelegate::CompareData(tfLiteOutputValues.data(), armnnOutputValues.data(), expectedOutputValues.size(), 1);
|
|
|
|
tfLiteInterpreter.Cleanup();
|
|
armnnInterpreter.Cleanup();
|
|
}
|
|
#endif
|
|
|
|
template <typename T>
|
|
std::vector<char> CreateTransposeConvTfLiteModel(tflite::TensorType tensorType,
|
|
uint32_t strideX,
|
|
uint32_t strideY,
|
|
tflite::Padding padding,
|
|
const std::vector <int32_t>& transposeTensorShape,
|
|
const std::vector <int32_t>& filterTensorShape,
|
|
const std::vector <int32_t>& inputTensorShape,
|
|
const std::vector <int32_t>& outputTensorShape,
|
|
const std::vector <int32_t>& transposeData,
|
|
const std::vector <T>& filterData,
|
|
float filterScale = 1.0f,
|
|
int filterOffset = 0,
|
|
float outputQuantScale = 2.0f,
|
|
int outputQuantOffset = 0,
|
|
float quantScale = 1.0f,
|
|
int quantOffset = 0)
|
|
{
|
|
using namespace tflite;
|
|
flatbuffers::FlatBufferBuilder flatBufferBuilder;
|
|
|
|
std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers;
|
|
buffers[0] = CreateBuffer(flatBufferBuilder);
|
|
buffers[1] = CreateBuffer(flatBufferBuilder,
|
|
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(transposeData.data()),
|
|
sizeof(int32_t) * transposeData.size()));
|
|
buffers[2] = CreateBuffer(flatBufferBuilder,
|
|
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()),
|
|
sizeof(T) * filterData.size()));
|
|
|
|
auto quantizationParameters =
|
|
CreateQuantizationParameters(flatBufferBuilder,
|
|
0,
|
|
0,
|
|
flatBufferBuilder.CreateVector<float>({ quantScale }),
|
|
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
|
|
auto outputQuantizationParameters =
|
|
CreateQuantizationParameters(flatBufferBuilder,
|
|
0,
|
|
0,
|
|
flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
|
|
flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
|
|
auto filterQuantizationParameters =
|
|
CreateQuantizationParameters(flatBufferBuilder,
|
|
0,
|
|
0,
|
|
flatBufferBuilder.CreateVector<float>({ filterScale }),
|
|
flatBufferBuilder.CreateVector<int64_t>({ filterOffset }));
|
|
|
|
std::array<flatbuffers::Offset<Tensor>, 4> tensors;
|
|
tensors[0] = CreateTensor(flatBufferBuilder,
|
|
flatBufferBuilder.CreateVector<int32_t>(transposeTensorShape.data(),
|
|
transposeTensorShape.size()),
|
|
tflite::TensorType_INT32,
|
|
1);
|
|
tensors[1] = CreateTensor(flatBufferBuilder,
|
|
flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(),
|
|
filterTensorShape.size()),
|
|
tensorType,
|
|
2,
|
|
flatBufferBuilder.CreateString("filter"),
|
|
filterQuantizationParameters);
|
|
tensors[2] = CreateTensor(flatBufferBuilder,
|
|
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
|
|
inputTensorShape.size()),
|
|
tensorType,
|
|
0,
|
|
flatBufferBuilder.CreateString("input"),
|
|
quantizationParameters);
|
|
tensors[3] = CreateTensor(flatBufferBuilder,
|
|
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
|
|
outputTensorShape.size()),
|
|
tensorType,
|
|
0,
|
|
flatBufferBuilder.CreateString("output"),
|
|
outputQuantizationParameters);
|
|
|
|
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_TransposeConvOptions;
|
|
flatbuffers::Offset<void> operatorBuiltinOptions =
|
|
CreateTransposeConvOptions(flatBufferBuilder, padding, strideX, strideY).Union();
|
|
|
|
// create operator
|
|
const std::vector<int> operatorInputs{0, 1, 2};
|
|
const std::vector<int> operatorOutputs{3};
|
|
flatbuffers::Offset <Operator> convolutionOperator =
|
|
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> subgraphInputs{0, 1, 2};
|
|
const std::vector<int> subgraphOutputs{3};
|
|
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(&convolutionOperator, 1));
|
|
|
|
flatbuffers::Offset <flatbuffers::String> modelDescription =
|
|
flatBufferBuilder.CreateString("ArmnnDelegate: TransposeConv Operator Model");
|
|
flatbuffers::Offset <OperatorCode> operatorCode =
|
|
CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_TRANSPOSE_CONV);
|
|
|
|
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, armnnDelegate::FILE_IDENTIFIER);
|
|
|
|
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
|
|
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
|
|
}
|
|
|
|
template <typename T>
|
|
void TransposeConvTest(std::vector<armnn::BackendId>& backends,
|
|
tflite::TensorType tensorType,
|
|
uint32_t strideX,
|
|
uint32_t strideY,
|
|
tflite::Padding padding,
|
|
const std::vector <int32_t>& transposeTensorShape,
|
|
const std::vector <int32_t>& filterTensorShape,
|
|
const std::vector <int32_t>& inputTensorShape,
|
|
const std::vector <int32_t>& outputTensorShape,
|
|
const std::vector <int32_t>& transposeData,
|
|
const std::vector <T>& filterData,
|
|
std::vector<T>& inputValues,
|
|
std::vector<T>& expectedOutputValues,
|
|
float filterScale = 1.0f,
|
|
int filterOffset = 0,
|
|
float outputQuantScale = 1.0f,
|
|
int outputQuantOffset = 0,
|
|
float quantScale = 1.0f,
|
|
int quantOffset = 0)
|
|
{
|
|
using namespace delegateTestInterpreter;
|
|
|
|
std::vector<char> modelBuffer;
|
|
modelBuffer = CreateTransposeConvTfLiteModel<T>(tensorType,
|
|
strideX,
|
|
strideY,
|
|
padding,
|
|
transposeTensorShape,
|
|
filterTensorShape,
|
|
inputTensorShape,
|
|
outputTensorShape,
|
|
transposeData,
|
|
filterData,
|
|
filterScale,
|
|
filterOffset,
|
|
outputQuantScale,
|
|
outputQuantOffset,
|
|
quantScale,
|
|
quantOffset);
|
|
|
|
|
|
// Setup interpreter with just TFLite Runtime.
|
|
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
|
|
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
|
|
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 2) == kTfLiteOk);
|
|
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
|
|
std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(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<T>(inputValues, 2) == kTfLiteOk);
|
|
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
|
|
std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
|
|
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
|
|
|
|
armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
|
|
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShape);
|
|
|
|
tfLiteInterpreter.Cleanup();
|
|
armnnInterpreter.Cleanup();
|
|
}
|
|
|
|
} // anonymous namespace
|
|
|
|
|
|
|
|
|