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author | Sadik Armagan <sadik.armagan@arm.com> | 2021-02-10 16:26:44 +0000 |
---|---|---|
committer | Sadik Armagan <sadik.armagan@arm.com> | 2021-02-10 16:26:44 +0000 |
commit | 788e2c644e16fa7043b9a647806df46fd18bc040 (patch) | |
tree | 72b7f2a0f70f807c788dd7850f876ea1309872c2 | |
parent | 29fde27f863ce82a2604270cfa7216904f1f171b (diff) | |
download | armnn-788e2c644e16fa7043b9a647806df46fd18bc040.tar.gz |
IVGCVSW-5400 'TfLiteDelegate: FLOOR operator support'
* Added FLOOR operator support to Arm NN TfLiteDelegate
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I986ce8c5a825f509e0f8b3d257fd5b60834c322f
-rw-r--r-- | delegate/CMakeLists.txt | 2 | ||||
-rw-r--r-- | delegate/src/Round.hpp | 56 | ||||
-rw-r--r-- | delegate/src/test/RoundTest.cpp | 72 | ||||
-rw-r--r-- | delegate/src/test/RoundTestHelper.hpp | 161 | ||||
-rw-r--r-- | docs/01_03_delegate.dox | 2 |
5 files changed, 285 insertions, 8 deletions
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt index 74390c8a93..11bff48a66 100644 --- a/delegate/CMakeLists.txt +++ b/delegate/CMakeLists.txt @@ -149,6 +149,8 @@ if(BUILD_UNIT_TESTS) src/test/ReshapeTest.cpp src/test/ResizeTest.cpp src/test/ResizeTestHelper.hpp + src/test/RoundTest.cpp + src/test/RoundTestHelper.hpp src/test/SoftmaxTest.cpp src/test/SoftmaxTestHelper.hpp src/test/SpaceDepthTest.cpp diff --git a/delegate/src/Round.hpp b/delegate/src/Round.hpp index 3335d0b337..1677607571 100644 --- a/delegate/src/Round.hpp +++ b/delegate/src/Round.hpp @@ -5,8 +5,6 @@ #pragma once -#include <armnn/utility/IgnoreUnused.hpp> - #include <tensorflow/lite/builtin_ops.h> #include <tensorflow/lite/c/builtin_op_data.h> #include <tensorflow/lite/c/common.h> @@ -21,13 +19,55 @@ TfLiteStatus VisitFloorOperator(DelegateData& delegateData, int nodeIndex, int32_t operatorCode) { - armnn::IgnoreUnused(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - 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); + + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsFloorSupported, + 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, VisitFloorOperator 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 Floor layer + armnn::IConnectableLayer* layer = delegateData.m_Network->AddFloorLayer(); + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); - return kTfLiteError; + // Connect + return Connect(layer, tfLiteNode, delegateData); } } // namespace armnnDelegate diff --git a/delegate/src/test/RoundTest.cpp b/delegate/src/test/RoundTest.cpp new file mode 100644 index 0000000000..9d323f3700 --- /dev/null +++ b/delegate/src/test/RoundTest.cpp @@ -0,0 +1,72 @@ +// +// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "RoundTestHelper.hpp" + +#include <armnn_delegate.hpp> + +#include <flatbuffers/flatbuffers.h> +#include <tensorflow/lite/schema/schema_generated.h> + +#include <doctest/doctest.h> + +namespace armnnDelegate +{ + +void FloorFp32Test(std::vector<armnn::BackendId>& backends) +{ + std::vector<int32_t> inputShape {1, 3, 2, 3}; + std::vector<int32_t> outputShape {1, 3, 2, 3}; + + std::vector<float> inputValues { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, + 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }; + + std::vector<float> expectedOutputValues { -38.0f, -16.0f, -9.0f, -2.0f, -2.0f, -2.0f, -1.0f, -1.0f, 0.0f, + 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 2.0f, 8.0f, 15.0f, 37.0f }; + + RoundTest<float>(tflite::BuiltinOperator_FLOOR, + ::tflite::TensorType_FLOAT32, + backends, + inputShape, + inputValues, + expectedOutputValues); +} + +// FLOOR Test Suite +TEST_SUITE("FLOOR_CpuRefTests") +{ + +TEST_CASE ("FLOOR_Fp32_CpuRef_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; + FloorFp32Test(backends); +} + +} + +TEST_SUITE("FLOOR_CpuAccTests") +{ + +TEST_CASE ("FLOOR_Fp32_CpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; + FloorFp32Test(backends); +} + +} + +TEST_SUITE("FLOOR_GpuAccTests") +{ + +TEST_CASE ("FLOOR_Fp32_GpuAcc_Test") +{ + std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; + FloorFp32Test(backends); +} + +} +// End of FLOOR Test Suite + +} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/RoundTestHelper.hpp b/delegate/src/test/RoundTestHelper.hpp new file mode 100644 index 0000000000..3a35ee0764 --- /dev/null +++ b/delegate/src/test/RoundTestHelper.hpp @@ -0,0 +1,161 @@ +// +// 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> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCode, + tflite::TensorType tensorType, + const std::vector <int32_t>& tensorShape, + 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>(tensorShape.data(), + tensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("input"), + quantizationParameters); + tensors[1] = CreateTensor(flatBufferBuilder, + flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), + tensorShape.size()), + tensorType, + 0, + flatBufferBuilder.CreateString("output"), + quantizationParameters); + + const std::vector<int32_t> operatorInputs({0}); + const std::vector<int32_t> operatorOutputs({1}); + + flatbuffers::Offset<Operator> roundOperator; + flatbuffers::Offset<flatbuffers::String> modelDescription; + flatbuffers::Offset<OperatorCode> operatorCode; + + switch (roundOperatorCode) + { + case tflite::BuiltinOperator_FLOOR: + default: + roundOperator = + CreateOperator(flatBufferBuilder, + 0, + flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), + flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); + modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Floor Operator Model"); + operatorCode = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_FLOOR); + break; + } + 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(&roundOperator, 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> +void RoundTest(tflite::BuiltinOperator roundOperatorCode, + tflite::TensorType tensorType, + std::vector<armnn::BackendId>& backends, + std::vector<int32_t>& shape, + std::vector<T>& inputValues, + std::vector<T>& expectedOutputValues, + float quantScale = 1.0f, + int quantOffset = 0) +{ + using namespace tflite; + std::vector<char> modelBuffer = CreateRoundTfLiteModel(roundOperatorCode, + tensorType, + shape, + 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<T>(tfLiteDelegate, + armnnDelegate, + shape, + expectedOutputValues, + 0); + + tfLiteDelegate.reset(nullptr); + armnnDelegate.reset(nullptr); +} + +} // anonymous namespace diff --git a/docs/01_03_delegate.dox b/docs/01_03_delegate.dox index 7c7763a923..73d869041e 100644 --- a/docs/01_03_delegate.dox +++ b/docs/01_03_delegate.dox @@ -63,6 +63,8 @@ The Arm NN SDK TensorFlow Lite delegate currently supports the following operato - FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE +- FLOOR + - GATHER - GREATER |