IVGCVSW-6946 Add Pool3D to tflite delegate
* Add new test and test helper for Pool3d
* Add new custom operator to switch in armnn_delegate.cpp
* Add new pool3d function to pooling.hpp
* Update doxygen
Signed-off-by: Ryan OShea <ryan.oshea3@arm.com>
Change-Id: I77a541bf423b337c749e70c564cdd727efe2fd05
diff --git a/delegate/src/test/Pooling3dTestHelper.hpp b/delegate/src/test/Pooling3dTestHelper.hpp
new file mode 100644
index 0000000..f5f5cc3
--- /dev/null
+++ b/delegate/src/test/Pooling3dTestHelper.hpp
@@ -0,0 +1,295 @@
+//
+// Copyright © 2022 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 <flatbuffers/flexbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/custom_ops_register.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
+{
+#if defined(ARMNN_POST_TFLITE_2_5)
+
+std::vector<uint8_t> CreateCustomOptions(int, int, int, int, int, int, TfLitePadding);
+
+std::vector<char> CreatePooling3dTfLiteModel(
+ std::string poolType,
+ tflite::TensorType tensorType,
+ const std::vector<int32_t>& inputTensorShape,
+ const std::vector<int32_t>& outputTensorShape,
+ TfLitePadding padding = kTfLitePaddingSame,
+ int32_t strideWidth = 0,
+ int32_t strideHeight = 0,
+ int32_t strideDepth = 0,
+ int32_t filterWidth = 0,
+ int32_t filterHeight = 0,
+ int32_t filterDepth = 0,
+ tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
+ 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 }));
+
+ // Create the input and output tensors
+ std::array<flatbuffers::Offset<Tensor>, 2> 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>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+
+ // Create the custom options from the function below
+ std::vector<uint8_t> customOperatorOptions = CreateCustomOptions(strideHeight, strideWidth, strideDepth,
+ filterHeight, filterWidth, filterDepth, padding);
+ // opCodeIndex is created as a uint8_t to avoid map lookup
+ uint8_t opCodeIndex = 0;
+ // Set the operator name based on the PoolType passed in from the test case
+ std::string opName = "";
+ if (poolType == "kMax")
+ {
+ opName = "MaxPool3D";
+ }
+ else
+ {
+ opName = "AveragePool3D";
+ }
+ // To create a custom operator code you pass in the builtin code for custom operators and the name of the custom op
+ flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCodeDirect(flatBufferBuilder,
+ tflite::BuiltinOperator_CUSTOM,
+ opName.c_str());
+
+ // Create the Operator using the opCodeIndex and custom options. Also sets builtin options to none.
+ const std::vector<int32_t> operatorInputs{ 0 };
+ const std::vector<int32_t> operatorOutputs{ 1 };
+ flatbuffers::Offset<Operator> poolingOperator =
+ CreateOperator(flatBufferBuilder,
+ opCodeIndex,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ tflite::BuiltinOptions_NONE,
+ 0,
+ flatBufferBuilder.CreateVector<uint8_t>(customOperatorOptions),
+ tflite::CustomOptionsFormat_FLEXBUFFERS);
+
+ // Create the subgraph using the operator created above.
+ const std::vector<int> subgraphInputs{ 0 };
+ const std::vector<int> 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(&poolingOperator, 1));
+
+ flatbuffers::Offset<flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Pooling3d Operator Model");
+
+ // Create the model using operatorCode and the subgraph.
+ 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 Pooling3dTest(std::string poolType,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& inputShape,
+ std::vector<int32_t>& outputShape,
+ std::vector<T>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ TfLitePadding padding = kTfLitePaddingSame,
+ int32_t strideWidth = 0,
+ int32_t strideHeight = 0,
+ int32_t strideDepth = 0,
+ int32_t filterWidth = 0,
+ int32_t filterHeight = 0,
+ int32_t filterDepth = 0,
+ tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ // Create the single op model buffer
+ std::vector<char> modelBuffer = CreatePooling3dTfLiteModel(poolType,
+ tensorType,
+ inputShape,
+ outputShape,
+ padding,
+ strideWidth,
+ strideHeight,
+ strideDepth,
+ filterWidth,
+ filterHeight,
+ filterDepth,
+ fusedActivation,
+ quantScale,
+ quantOffset);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+ CHECK(tfLiteModel != nullptr);
+ // Create TfLite Interpreters
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+
+ // Custom ops need to be added to the BuiltinOp resolver before the interpreter is created
+ // Based on the poolType from the test case add the custom operator using the name and the tflite
+ // registration function
+ tflite::ops::builtin::BuiltinOpResolver armnn_op_resolver;
+ if (poolType == "kMax")
+ {
+ armnn_op_resolver.AddCustom("MaxPool3D", tflite::ops::custom::Register_MAX_POOL_3D());
+ }
+ else
+ {
+ armnn_op_resolver.AddCustom("AveragePool3D", tflite::ops::custom::Register_AVG_POOL_3D());
+ }
+
+ CHECK(InterpreterBuilder(tfLiteModel, armnn_op_resolver)
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteInterpreter;
+
+ // Custom ops need to be added to the BuiltinOp resolver before the interpreter is created
+ // Based on the poolType from the test case add the custom operator using the name and the tflite
+ // registration function
+ tflite::ops::builtin::BuiltinOpResolver tflite_op_resolver;
+ if (poolType == "kMax")
+ {
+ tflite_op_resolver.AddCustom("MaxPool3D", tflite::ops::custom::Register_MAX_POOL_3D());
+ }
+ else
+ {
+ tflite_op_resolver.AddCustom("AveragePool3D", tflite::ops::custom::Register_AVG_POOL_3D());
+ }
+
+ CHECK(InterpreterBuilder(tfLiteModel, tflite_op_resolver)
+ (&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 tfLiteDelegateInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelegateInputData[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);
+
+ armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
+}
+
+// Function to create the flexbuffer custom options for the custom pooling3d operator.
+std::vector<uint8_t> CreateCustomOptions(int strideHeight, int strideWidth, int strideDepth,
+ int filterHeight, int filterWidth, int filterDepth, TfLitePadding padding)
+{
+ auto flex_builder = std::make_unique<flexbuffers::Builder>();
+ size_t map_start = flex_builder->StartMap();
+ flex_builder->String("data_format", "NDHWC");
+ // Padding is created as a key and padding type. Only VALID and SAME supported
+ if (padding == kTfLitePaddingValid)
+ {
+ flex_builder->String("padding", "VALID");
+ }
+ else
+ {
+ flex_builder->String("padding", "SAME");
+ }
+
+ // Vector of filter dimensions in order ( 1, Depth, Height, Width, 1 )
+ auto start = flex_builder->StartVector("ksize");
+ flex_builder->Add(1);
+ flex_builder->Add(filterDepth);
+ flex_builder->Add(filterHeight);
+ flex_builder->Add(filterWidth);
+ flex_builder->Add(1);
+ // EndVector( start, bool typed, bool fixed)
+ flex_builder->EndVector(start, true, false);
+
+ // Vector of stride dimensions in order ( 1, Depth, Height, Width, 1 )
+ auto stridesStart = flex_builder->StartVector("strides");
+ flex_builder->Add(1);
+ flex_builder->Add(strideDepth);
+ flex_builder->Add(strideHeight);
+ flex_builder->Add(strideWidth);
+ flex_builder->Add(1);
+ // EndVector( stridesStart, bool typed, bool fixed)
+ flex_builder->EndVector(stridesStart, true, false);
+
+ flex_builder->EndMap(map_start);
+ flex_builder->Finish();
+
+ return flex_builder->GetBuffer();
+}
+#endif
+} // anonymous namespace
+
+
+
+