IVGCVSW-5969 TfLiteDelegate: Add PACK operator Support
* Added support for PACK which is equivalent to Arm NN STACK
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I9ea134d0310eeea1caba30a8b9221712e9487c75
diff --git a/delegate/src/test/PackTestHelper.hpp b/delegate/src/test/PackTestHelper.hpp
new file mode 100644
index 0000000..0869228
--- /dev/null
+++ b/delegate/src/test/PackTestHelper.hpp
@@ -0,0 +1,185 @@
+//
+// 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>
+
+#include <string>
+
+namespace
+{
+
+std::vector<char> CreatePackTfLiteModel(tflite::BuiltinOperator packOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<int32_t>& inputTensorShape,
+ const std::vector <int32_t>& outputTensorShape,
+ const int32_t inputTensorNum,
+ unsigned int axis = 0,
+ 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::vector<int32_t> operatorInputs{};
+ const std::vector<int32_t> operatorOutputs{inputTensorNum};
+ std::vector<int> subgraphInputs{};
+ const std::vector<int> subgraphOutputs{inputTensorNum};
+
+ std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1);
+ for (int i = 0; i < inputTensorNum; ++i)
+ {
+ tensors[i] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("input" + std::to_string(i)),
+ quantizationParameters);
+
+ // Add number of inputs to vector.
+ operatorInputs.push_back(i);
+ subgraphInputs.push_back(i);
+ }
+
+ // Create output tensor
+ tensors[inputTensorNum] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_PackOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions =
+ CreatePackOptions(flatBufferBuilder, inputTensorNum, axis).Union();
+
+ flatbuffers::Offset <Operator> packOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ 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(&packOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Pack Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, packOperatorCode);
+
+ 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 PackTest(tflite::BuiltinOperator packOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& inputShape,
+ std::vector<int32_t>& expectedOutputShape,
+ std::vector<std::vector<T>>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ unsigned int axis = 0,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreatePackTfLiteModel(packOperatorCode,
+ tensorType,
+ inputShape,
+ expectedOutputShape,
+ inputValues.size(),
+ axis,
+ 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 for all input tensors.
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ // Get single input tensor and assign to interpreters.
+ auto inputTensorValues = inputValues[i];
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues);
+ }
+
+ // Run EnqueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
+ armnnDelegateInterpreter,
+ expectedOutputShape,
+ expectedOutputValues);
+
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace
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