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author | Sadik Armagan <sadik.armagan@arm.com> | 2020-11-13 17:51:56 +0000 |
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committer | Jim Flynn <jim.flynn@arm.com> | 2020-11-16 10:08:49 +0000 |
commit | 32ca144fc8b4f0a1e2eda274da55ffd0a6016c02 (patch) | |
tree | 774754819eb4a01ed21be1166e60fa79dce8899d /delegate/src/test/ConvolutionTestHelper.hpp | |
parent | 33d2c785c01c682c6a32e0de34088729f7593c19 (diff) | |
download | armnn-32ca144fc8b4f0a1e2eda274da55ffd0a6016c02.tar.gz |
IVGCVSW-5338 TfLiteDelegate: Implement the Convolution operators
* Add Convolution, DepthwiseConvolution and TransposeConvolution
Signed-off-by: Kevin May <kevin.may@arm.com>
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I797e42844dfee0cc80beb64eabc3111b96320daf
Diffstat (limited to 'delegate/src/test/ConvolutionTestHelper.hpp')
-rw-r--r-- | delegate/src/test/ConvolutionTestHelper.hpp | 504 |
1 files changed, 504 insertions, 0 deletions
diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp new file mode 100644 index 0000000000..b7705cc904 --- /dev/null +++ b/delegate/src/test/ConvolutionTestHelper.hpp @@ -0,0 +1,504 @@ +// +// 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 +{ + +template <typename T, typename B = float> +std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode, + tflite::TensorType tensorType, + uint32_t strideX, + uint32_t strideY, + uint32_t dilationX, + uint32_t dilationY, + tflite::Padding padding, + tflite::ActivationFunctionType fused_activation_function, + const std::vector <int32_t>& inputTensorShape, + const std::vector <int32_t>& filterTensorShape, + const std::vector <int32_t>& biasTensorShape, + const std::vector <int32_t>& outputTensorShape, + const std::vector <T>& filterData, + const std::vector <B>& biasData, + float filterScale = 1.0f, + int filterOffset = 0, + float outputQuantScale = 2.0f, + int outputQuantOffset = 0, + float quantScale = 1.0f, + int quantOffset = 0, + int32_t depth_multiplier = 1) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; + buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); + buffers[1] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), + sizeof(T) * filterData.size())); + + buffers[2] = CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), + sizeof(B) * biasData.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>(inputTensorShape.data(), + inputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), + filterTensorShape.size()), + tensorType, + 1, + flatBufferBuilder.CreateString("filter"), + filterQuantizationParameters); + + auto biasTensorType = ::tflite::TensorType_FLOAT32; + if (tensorType == ::tflite::TensorType_UINT8) + { + biasTensorType = ::tflite::TensorType_INT32; + } + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()), + biasTensorType, + 2, + flatBufferBuilder.CreateString("bias"), + quantizationParameters); + tensors[3] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + outputQuantizationParameters); + + flatbuffers::Offset<void> operatorBuiltinOptions; + tflite::BuiltinOptions operatorBuiltinOptionsType; + + if(convolutionOperatorCode == tflite::BuiltinOperator_DEPTHWISE_CONV_2D) + { + operatorBuiltinOptionsType = tflite::BuiltinOptions_DepthwiseConv2DOptions; + operatorBuiltinOptions = CreateDepthwiseConv2DOptions(flatBufferBuilder, + padding, + strideX, + strideY, + depth_multiplier, + fused_activation_function, + dilationX, + dilationY).Union(); + } + if(convolutionOperatorCode == tflite::BuiltinOperator_CONV_2D) + { + operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv2DOptions; + operatorBuiltinOptions = CreateConv2DOptions(flatBufferBuilder, + padding, + strideX, + strideY, + fused_activation_function, + dilationX, + dilationY).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: Convolution2d Operator Model"); + flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, convolutionOperatorCode); + + 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, typename B = float> +void ConvolutionTest(tflite::BuiltinOperator convolutionOperatorCode, + tflite::TensorType tensorType, + uint32_t strideX, + uint32_t strideY, + uint32_t dilationX, + uint32_t dilationY, + tflite::Padding padding, + tflite::ActivationFunctionType fused_activation_function, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputShape, + std::vector<int32_t>& filterShape, + std::vector<int32_t>& outputShape, + std::vector<T>& inputValues, + std::vector<T>& filterValues, + std::vector<T>& expectedOutputValues, + const std::vector<int32_t>& biasShape = {}, + const std::vector<B>& biasValues = {}, + float filterScale = 1.0f, + int filterOffset = 0, + float outputQuantScale = 2.0f, + int outputQuantOffset = 0, + float quantScale = 1.0f, + int quantOffset = 0, + int32_t depth_multiplier = 1) + +{ + using namespace tflite; + + std::vector<char> modelBuffer; + modelBuffer = CreateConv2dTfLiteModel(convolutionOperatorCode, + tensorType, + strideX, + strideY, + dilationX, + dilationY, + padding, + fused_activation_function, + inputShape, + filterShape, + biasShape, + outputShape, + filterValues, + biasValues, + filterScale, + filterOffset, + outputQuantScale, + outputQuantOffset, + quantScale, + quantOffset, + depth_multiplier); + + + 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<T>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelageInputData[i] = inputValues[i]; + } + + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; + auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + armnnDelegateInputData[i] = inputValues[i]; + } + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); + for (size_t i = 0; i < expectedOutputValues.size(); i++) + { + CHECK(tfLiteDelagateOutputData[i] == armnnDelegateOutputData[i]); + CHECK(doctest::Approx(tfLiteDelagateOutputData[i]).epsilon(0.000001f) == expectedOutputValues[i]); + CHECK(doctest::Approx(armnnDelegateOutputData[i]).epsilon(0.000001f) == expectedOutputValues[i]); + } +} + +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, flatBufferBuilder.CreateVector({})); + 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); + + 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 tflite; + + std::vector<char> modelBuffer; + modelBuffer = CreateTransposeConvTfLiteModel<T>(tensorType, + strideX, + strideY, + padding, + transposeTensorShape, + filterTensorShape, + inputTensorShape, + outputTensorShape, + transposeData, + filterData, + filterScale, + filterOffset, + outputQuantScale, + outputQuantOffset, + quantScale, + quantOffset); + + + 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()[2]; + auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelageInputData[i] = inputValues[i]; + } + + auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[2]; + auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + armnnDelegateInputData[i] = inputValues[i]; + } + // Run EnqueueWorkload + CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); + CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); + + // Compare output data + auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; + auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); + for (size_t i = 0; i < expectedOutputValues.size(); i++) + { + CHECK(armnnDelegateOutputData[i] == expectedOutputValues[i]); + CHECK(tfLiteDelagateOutputData[i] == expectedOutputValues[i]); + CHECK(tfLiteDelagateOutputData[i] == armnnDelegateOutputData[i]); + } +} + +} // anonymous namespace + + + + |