From e339bf681f13990c7db7c656b75c011e84c290a9 Mon Sep 17 00:00:00 2001 From: Jan Eilers Date: Tue, 10 Nov 2020 18:43:23 +0000 Subject: IVGCVSW-5396 TfLiteDelegate: Implement the Resize operators * Added resize biliniear and nearest neighbour operator support to the tflite delegate Signed-off-by: Jan Eilers Change-Id: Id0113d6b865ea282c6f4de55e8419a6244a35f0e --- delegate/CMakeLists.txt | 5 +- delegate/src/Resize.hpp | 175 +++++++++++++++++++++++++++++- delegate/src/test/ResizeTest.cpp | 134 +++++++++++++++++++++++ delegate/src/test/ResizeTestHelper.hpp | 192 +++++++++++++++++++++++++++++++++ delegate/src/test/TestUtils.hpp | 26 +++++ 5 files changed, 530 insertions(+), 2 deletions(-) create mode 100644 delegate/src/test/ResizeTest.cpp create mode 100644 delegate/src/test/ResizeTestHelper.hpp create mode 100644 delegate/src/test/TestUtils.hpp diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt index 2ee00f3887..0dc72c2af6 100644 --- a/delegate/CMakeLists.txt +++ b/delegate/CMakeLists.txt @@ -102,7 +102,10 @@ if(BUILD_UNIT_TESTS) src/test/Pooling2dTest.cpp src/test/Pooling2dTestHelper.hpp src/test/QuantizationTest.cpp - src/test/QuantizationTestHelper.hpp) + src/test/QuantizationTestHelper.hpp + src/test/ResizeTest.cpp + src/test/ResizeTestHelper.hpp + src/test/TestUtils.hpp) add_executable(DelegateUnitTests ${armnnDelegate_unittest_sources}) target_include_directories(DelegateUnitTests PRIVATE third-party) diff --git a/delegate/src/Resize.hpp b/delegate/src/Resize.hpp index be40b64ad4..f91cdb04a0 100644 --- a/delegate/src/Resize.hpp +++ b/delegate/src/Resize.hpp @@ -5,21 +5,194 @@ #pragma once +#include "DelegateUtils.hpp" + +#include + #include #include #include #include +#include namespace armnnDelegate { + + +TfLiteStatus ValidateResizeOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + const armnn::TensorInfo& inputInfo, + const armnn::TensorInfo& outputInfo, + const armnn::ResizeDescriptor& descriptor) +{ + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsResizeSupported, + delegateData.m_Backends, + isSupported, + inputInfo, + outputInfo, + descriptor); + + return isSupported ? kTfLiteOk : kTfLiteError; +} + TfLiteStatus VisitResizeOperator(DelegateData& delegateData, TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode, int nodeIndex, int32_t resizeOperatorCode) { - return kTfLiteError; + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + + // The first input contains the data of the image that should be resized [batch, height, width, channels] + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + // The second input contains a size tensor. The size tensor contains two integer values + // that describe the new height and width of the image [new_height, new_width] + const TfLiteTensor& tfLiteSizeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (IsDynamicTensor(tfLiteSizeTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + // The output tensor should have the shape [batch, new_height, new_width, channels] + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& sizeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteSizeTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + std::string layerName("Resize"); + + // Fill descriptor + armnn::ResizeDescriptor desc; + switch (resizeOperatorCode) + { + case kTfLiteBuiltinResizeBilinear: + { + desc.m_Method = armnn::ResizeMethod::Bilinear; + + layerName += "Bilinear:" + nodeIndex; + + TfLiteResizeBilinearParams* biliniarOptions = + reinterpret_cast(tfLiteNode->builtin_data); + + desc.m_AlignCorners = biliniarOptions->align_corners; + desc.m_HalfPixelCenters = biliniarOptions->half_pixel_centers; + break; + } + case kTfLiteBuiltinResizeNearestNeighbor: + { + desc.m_Method = armnn::ResizeMethod::NearestNeighbor; + layerName += "NearestNeighbor:" + nodeIndex; + + TfLiteResizeNearestNeighborParams* nearestNeighborOptions = + reinterpret_cast(tfLiteNode->builtin_data); + + desc.m_AlignCorners = nearestNeighborOptions->align_corners; + desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers; + break; + } + default: + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Unknown TfLite built in operation for Resize. Given operator: #%d node #%d: ", + resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + } + + // In armnn the values of the size input tensor [new_hight, new_width] is saved in the operator + // descriptor. We have to read it from the input tensor and write it to the descriptor. + + auto* sizeTensorDataPtr = tflite::GetTensorData(&tfLiteSizeTensor); + auto sizeTensorNumDimensions = tfLiteSizeTensor.dims->size; + // The size tensor is only a 1D tensor -> [new_hight, new width] + if (sizeTensorNumDimensions != 1) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a " + "dynamic tensor. Operator: #%d node #%d: ", + resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + // Get number of values in the size tensor + auto sizeTensorNumValues = tfLiteSizeTensor.dims->data[0]; + if (sizeTensorNumValues == 0) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a " + "dynamic tensor. Operator: #%d node #%d: ", + resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + else if (sizeTensorNumValues != 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation requires to " + "have a dimension of 2 [new_hight, new width] but a tensor with a dimension of #%d was given. " + "Operator: #%d node #%d: ", + sizeTensorNumValues, resizeOperatorCode, nodeIndex); + return kTfLiteError; + } + // get size tensor data + std::vector sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues); + + desc.m_TargetHeight = static_cast (sizeTensorData[0]); + desc.m_TargetWidth = static_cast (sizeTensorData[1]); + desc.m_DataLayout = armnn::DataLayout::NHWC; + + // No network pointer indicates that only support for this operator should be checked + if (!delegateData.m_Network) + { + return ValidateResizeOperator(delegateData, + tfLiteContext, + inputTensorInfo, + outputTensorInfo, + desc); + } + + + armnn::IConnectableLayer* resizeLayer = nullptr; + resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str()); + + armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + ARMNN_ASSERT(resizeLayer != nullptr); + + return Connect(resizeLayer, tfLiteNode, delegateData); } } // namespace armnnDelegate diff --git a/delegate/src/test/ResizeTest.cpp b/delegate/src/test/ResizeTest.cpp new file mode 100644 index 0000000000..394ad6c7ae --- /dev/null +++ b/delegate/src/test/ResizeTest.cpp @@ -0,0 +1,134 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ResizeTestHelper.hpp" + +#include + +#include +#include +#include +#include +#include +#include + +#include + +namespace armnnDelegate +{ + +void ResizeBiliniarFloat32Test(std::vector& backends) +{ + // Set input data + std::vector input1Values + { + 0.0f, 1.0f, 2.0f, + 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f + }; + const std::vector input2NewShape { 5, 5 }; + + // Calculate output data + std::vector expectedOutputValues + { + 0.0f, 0.6f, 1.2f, 1.8f, 2.0f, + 1.8f, 2.4f, 3.0f, 3.6f, 3.8f, + 3.6f, 4.2f, 4.8f, 5.4f, 5.6f, + 5.4f, 6.0f, 6.6f, 7.2f, 7.4f, + 6.0f, 6.6f, 7.2f, 7.8f, 8.0f + }; + + const std::vector input1Shape { 1, 3, 3, 1 }; + const std::vector input2Shape { 2 }; + const std::vector expectedOutputShape = input2NewShape; + + ResizeFP32TestImpl(tflite::BuiltinOperator_RESIZE_BILINEAR, + backends, + input1Values, + input1Shape, + input2NewShape, + input2Shape, + expectedOutputValues, + expectedOutputShape); +} + +void ResizeNearestNeighbourFloat32Test(std::vector& backends) +{ + // Set input data + std::vector input1Values { 1.0f, 2.0f, 3.0f, 4.0f } + ; + const std::vector input2NewShape { 1, 1 }; + + // Calculate output data + std::vector expectedOutputValues { 1.0f }; + + const std::vector input1Shape { 1, 2, 2, 1 }; + const std::vector input2Shape { 2 }; + const std::vector expectedOutputShape = input2NewShape; + + ResizeFP32TestImpl(tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, + backends, + input1Values, + input1Shape, + input2NewShape, + input2Shape, + expectedOutputValues, + expectedOutputShape); +} + +TEST_SUITE("ResizeTests_GpuAccTests") +{ + +TEST_CASE ("Resize_Biliniar_Float32_GpuAcc_Test") +{ + std::vector backends = { armnn::Compute::GpuAcc }; + ResizeBiliniarFloat32Test(backends); +} + +TEST_CASE ("Resize_NearestNeighbour_Float32_GpuAcc_Test") +{ + std::vector backends = { armnn::Compute::GpuAcc }; + ResizeNearestNeighbourFloat32Test(backends); +} + +} // TEST_SUITE("ResizeTests_GpuAccTests") + + +TEST_SUITE("ResizeTests_CpuAccTests") +{ + +TEST_CASE ("Resize_Biliniar_Float32_CpuAcc_Test") +{ + std::vector backends = { armnn::Compute::CpuAcc }; + ResizeBiliniarFloat32Test(backends); +} + +TEST_CASE ("Resize_NearestNeighbour_Float32_CpuAcc_Test") +{ + std::vector backends = { armnn::Compute::CpuAcc }; + ResizeNearestNeighbourFloat32Test(backends); +} + +} // TEST_SUITE("ResizeTests_CpuAccTests") + + +TEST_SUITE("ResizeTests_CpuRefTests") +{ + +TEST_CASE ("Resize_Biliniar_Float32_CpuRef_Test") +{ + std::vector backends = { armnn::Compute::CpuRef }; + ResizeBiliniarFloat32Test(backends); +} + +TEST_CASE ("Resize_NearestNeighbour_Float32_CpuRef_Test") +{ + std::vector backends = { armnn::Compute::CpuRef }; + ResizeNearestNeighbourFloat32Test(backends); +} + +} // TEST_SUITE("ResizeTests_CpuRefTests") + +} // namespace armnnDelegate diff --git a/delegate/src/test/ResizeTestHelper.hpp b/delegate/src/test/ResizeTestHelper.hpp new file mode 100644 index 0000000000..1e9d3bcb3b --- /dev/null +++ b/delegate/src/test/ResizeTestHelper.hpp @@ -0,0 +1,192 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TestUtils.hpp" + +#include + +#include +#include +#include +#include +#include +#include + +#include + +namespace +{ + +std::vector CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode, + tflite::TensorType inputTensorType, + const std::vector & inputTensorShape, + const std::vector & sizeTensorData, + const std::vector & sizeTensorShape, + const std::vector & outputTensorShape) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + buffers.push_back(CreateBuffer(flatBufferBuilder, + flatBufferBuilder.CreateVector( + reinterpret_cast(sizeTensorData.data()), + sizeof(int32_t) * sizeTensorData.size()))); + + std::array, 3> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(inputTensorShape.data(), inputTensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("input_tensor")); + + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(sizeTensorShape.data(), + sizeTensorShape.size()), + TensorType_INT32, + 1, + flatBufferBuilder.CreateString("size_input_tensor")); + + tensors[2] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector(outputTensorShape.data(), + outputTensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("output_tensor")); + + // Create Operator + tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; + flatbuffers::Offset 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 operatorInputs{{0, 1}}; + const std::vector operatorOutputs{{2}}; + flatbuffers::Offset resizeOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector(operatorOutputs.data(), operatorOutputs.size()), + operatorBuiltinOptionsType, + operatorBuiltinOption); + + const std::vector subgraphInputs{{0, 1}}; + const std::vector subgraphOutputs{{2}}; + flatbuffers::Offset subgraph = + CreateSubGraph(flatBufferBuilder, + flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), + flatBufferBuilder.CreateVector(subgraphInputs.data(), subgraphInputs.size()), + flatBufferBuilder.CreateVector(subgraphOutputs.data(), subgraphOutputs.size()), + flatBufferBuilder.CreateVector(&resizeOperator, 1)); + + flatbuffers::Offset modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model"); + flatbuffers::Offset opCode = CreateOperatorCode(flatBufferBuilder, operatorCode); + + flatbuffers::Offset 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(flatBufferBuilder.GetBufferPointer(), + flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); +} + +void ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode, + std::vector& backends, + std::vector& input1Values, + std::vector input1Shape, + std::vector input2NewShape, + std::vector input2Shape, + std::vector& expectedOutputValues, + std::vector expectedOutputShape) +{ + using namespace tflite; + + std::vector 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 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 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 + 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(tfLiteDelegateOutputId); + auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; + auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor(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 diff --git a/delegate/src/test/TestUtils.hpp b/delegate/src/test/TestUtils.hpp new file mode 100644 index 0000000000..162d62f3bb --- /dev/null +++ b/delegate/src/test/TestUtils.hpp @@ -0,0 +1,26 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include + +namespace armnnDelegate +{ + +/// Can be used to assign input data from a vector to a model input. +/// Example usage can be found in ResizeTesthelper.hpp +template +void FillInput(std::unique_ptr& interpreter, int inputIndex, std::vector& inputValues) +{ + auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex]; + auto tfLiteDelageInputData = interpreter->typed_tensor(tfLiteDelegateInputId); + for (unsigned int i = 0; i < inputValues.size(); ++i) + { + tfLiteDelageInputData[i] = inputValues[i]; + } +} + +} // namespace armnnDelegate -- cgit v1.2.1