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author | Keith Davis <keith.davis@arm.com> | 2021-06-01 17:36:32 +0100 |
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committer | KeithARM <keith.davis@arm.com> | 2021-06-18 09:35:52 +0000 |
commit | 0176fd81b3f6a82ddc89e016cb634010f5397425 (patch) | |
tree | 93f140f935a9ff312276fe663279402af53f9b03 /delegate/src | |
parent | 32b6af5d0bd85a06b3400f22a58d0eeaba04ba32 (diff) | |
download | armnn-0176fd81b3f6a82ddc89e016cb634010f5397425.tar.gz |
MLCE-510 Add CpuRef Shape Operator to ArmNN
* Add TfLiteParser and delegate support
Signed-off-by: Keith Davis <keith.davis@arm.com>
Change-Id: Id3219ba7cc7128b5e73de2c7d8d076a40dcce9c5
Diffstat (limited to 'delegate/src')
-rw-r--r-- | delegate/src/Shape.hpp | 86 | ||||
-rw-r--r-- | delegate/src/armnn_delegate.cpp | 7 | ||||
-rw-r--r-- | delegate/src/test/ShapeTest.cpp | 45 | ||||
-rw-r--r-- | delegate/src/test/ShapeTestHelper.hpp | 171 |
4 files changed, 309 insertions, 0 deletions
diff --git a/delegate/src/Shape.hpp b/delegate/src/Shape.hpp new file mode 100644 index 0000000000..b173299a62 --- /dev/null +++ b/delegate/src/Shape.hpp @@ -0,0 +1,86 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "DelegateUtils.hpp" + +#include <tensorflow/lite/builtin_ops.h> +#include <tensorflow/lite/c/builtin_op_data.h> +#include <tensorflow/lite/c/common.h> +#include <tensorflow/lite/minimal_logging.h> +#include <numeric> + +namespace armnnDelegate +{ + +TfLiteStatus VisitShapeOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + auto* shapeParameters = reinterpret_cast<TfLiteShapeParams*>(tfLiteNode->builtin_data); + if ( shapeParameters->out_type != kTfLiteInt32 && shapeParameters->out_type != kTfLiteInt64 ) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsShapeSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outInfo); + }; + + // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the + // support for the operator + // If supported, VisitShapeOperator will be called again to add the layer to the network as seen further below + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + // Add a Shape layer + armnn::IConnectableLayer* layer = delegateData.m_Network->AddShapeLayer(); + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + // Connect + return Connect(layer, tfLiteNode, delegateData); +} + +} // namespace armnnDelegate diff --git a/delegate/src/armnn_delegate.cpp b/delegate/src/armnn_delegate.cpp index 0c984ecc82..0ac33808b0 100644 --- a/delegate/src/armnn_delegate.cpp +++ b/delegate/src/armnn_delegate.cpp @@ -30,6 +30,7 @@ #include "Reduce.hpp" #include "Resize.hpp" #include "Round.hpp" +#include "Shape.hpp" #include "Slice.hpp" #include "Softmax.hpp" #include "SpaceDepth.hpp" @@ -805,6 +806,12 @@ TfLiteStatus ArmnnSubgraph::VisitNode(DelegateData& delegateData, tfLiteNode, nodeIndex, armnn::UnaryOperation::Rsqrt); + case kTfLiteBuiltinShape: + return VisitShapeOperator(delegateData, + tfLiteContext, + tfLiteNode, + nodeIndex, + kTfLiteBuiltinShape); case kTfLiteBuiltinSplit: return VisitSplitOperator(delegateData, tfLiteContext, diff --git a/delegate/src/test/ShapeTest.cpp b/delegate/src/test/ShapeTest.cpp new file mode 100644 index 0000000000..b49910adf6 --- /dev/null +++ b/delegate/src/test/ShapeTest.cpp @@ -0,0 +1,45 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ShapeTestHelper.hpp" + +#include <doctest/doctest.h> + +namespace armnnDelegate +{ + +void ShapeSimpleTest(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape{ 1, 3, 2, 3 }; + + std::vector<int32_t> inputValues{ 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, }; + + std::vector<int32_t> expectedOutputShape{ 4 }; + std::vector<int32_t> expectedOutputValues{ 1, 3, 2, 3 }; + + ShapeTest<int32_t, int32_t>(::tflite::TensorType_INT32, + ::tflite::TensorType_INT32, + backends, + inputShape, + inputValues, + expectedOutputValues, + expectedOutputShape); +} + +// SHAPE Test Suite +TEST_SUITE("SHAPE_CpuRefTests") +{ + +TEST_CASE("SHAPE_Simple_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; + ShapeSimpleTest(backends); +} + +} +// End of SHAPE Test Suite + +} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ShapeTestHelper.hpp b/delegate/src/test/ShapeTestHelper.hpp new file mode 100644 index 0000000000..854c5084aa --- /dev/null +++ b/delegate/src/test/ShapeTestHelper.hpp @@ -0,0 +1,171 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TestUtils.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/interpreter.h> +#include <tensorflow/lite/kernels/register.h> +#include <tensorflow/lite/model.h> +#include <tensorflow/lite/schema/schema_generated.h> +#include <tensorflow/lite/version.h> + +#include <doctest/doctest.h> + +namespace +{ +std::vector<char> CreateShapeTfLiteModel(tflite::TensorType inputTensorType, + tflite::TensorType outputTensorType, + const std::vector<int32_t>& inputTensorShape, + const std::vector<int32_t>& outputTensorShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + flatbuffers::FlatBufferBuilder flatBufferBuilder; + + std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; + buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}))); + + auto quantizationParameters = + CreateQuantizationParameters(flatBufferBuilder, + 0, + 0, + flatBufferBuilder.CreateVector<float>({ quantScale }), + flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); + + std::array<flatbuffers::Offset<Tensor>, 2> tensors; + tensors[0] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), + inputTensorShape.size()), + inputTensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), + outputTensorShape.size()), + outputTensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + const std::vector<int32_t> operatorInputs({ 0 }); + const std::vector<int32_t> operatorOutputs({ 1 }); + + flatbuffers::Offset<Operator> shapeOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), + operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), + operatorOutputs.size()), + BuiltinOptions_ShapeOptions, + CreateShapeOptions(flatBufferBuilder, outputTensorType).Union()); + + flatbuffers::Offset<flatbuffers::String> modelDescription = + flatBufferBuilder.CreateString("ArmnnDelegate: SHAPE Operator Model"); + + flatbuffers::Offset<OperatorCode> operatorCode = + CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_SHAPE); + + const std::vector<int32_t> subgraphInputs({ 0 }); + const std::vector<int32_t> subgraphOutputs({ 1 }); + + 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(&shapeOperator, 1)); + + 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 K> +void ShapeTest(tflite::TensorType inputTensorType, + tflite::TensorType outputTensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& inputShape, + std::vector<T>& inputValues, + std::vector<K>& expectedOutputValues, + std::vector<int32_t>& expectedOutputShape, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateShapeTfLiteModel(inputTensorType, + outputTensorType, + inputShape, + expectedOutputShape, + quantScale, + quantOffset); + + const Model* tfLiteModel = GetModel(modelBuffer.data()); + + // Create TfLite Interpreters + std::unique_ptr<Interpreter> armnnDelegate; + + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&armnnDelegate) == kTfLiteOk); + CHECK(armnnDelegate != nullptr); + CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk); + + std::unique_ptr<Interpreter> tfLiteDelegate; + + CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) + (&tfLiteDelegate) == kTfLiteOk); + CHECK(tfLiteDelegate != nullptr); + CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); + + // Set input data + armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues); + armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues); + + // Run EnqueWorkload + CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); + CHECK(armnnDelegate->Invoke() == kTfLiteOk); + + // Compare output data + armnnDelegate::CompareOutputData<K>(tfLiteDelegate, + armnnDelegate, + expectedOutputShape, + expectedOutputValues, + 0); + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} + +} // anonymous namespace |