IVGCVSW-5393 'TfLiteDelegate: Implement the split operators'
* Added SPLIT and SPLIT_V support to armnn_delegate
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: I2def9b8be783b25ef17a997e521c6027553035d3
diff --git a/delegate/src/test/SplitTestHelper.hpp b/delegate/src/test/SplitTestHelper.hpp
new file mode 100644
index 0000000..31fc7d5
--- /dev/null
+++ b/delegate/src/test/SplitTestHelper.hpp
@@ -0,0 +1,368 @@
+//
+// Copyright © 2020 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> CreateSplitTfLiteModel(tflite::TensorType tensorType,
+ std::vector<int32_t>& axisTensorShape,
+ std::vector<int32_t>& inputTensorShape,
+ const std::vector<std::vector<int32_t>>& outputTensorShapes,
+ std::vector<int32_t>& axisData,
+ const int32_t numSplits,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::array<flatbuffers::Offset<tflite::Buffer>, 2> buffers;
+ buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
+ buffers[1] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
+ sizeof(int32_t) * axisData.size()));
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+ std::array<flatbuffers::Offset<Tensor>, 4> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
+ axisTensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("axis"),
+ quantizationParameters);
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("input"),
+ quantizationParameters);
+
+ // Create output tensor
+ for (unsigned int i = 0; i < outputTensorShapes.size(); ++i)
+ {
+ tensors[i + 2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(),
+ outputTensorShapes[i].size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+ }
+
+ // create operator. Mean uses ReducerOptions.
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union();
+
+ const std::vector<int> operatorInputs{ {0, 1} };
+ const std::vector<int> operatorOutputs{ {2, 3} };
+ flatbuffers::Offset <Operator> controlOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ const std::vector<int> subgraphInputs{ {0, 1} };
+ const std::vector<int> subgraphOutputs{ {2, 3} };
+ 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(&controlOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT);
+
+ 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 SplitTest(tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& axisTensorShape,
+ std::vector<int32_t>& inputTensorShape,
+ std::vector<std::vector<int32_t>>& outputTensorShapes,
+ std::vector<int32_t>& axisData,
+ std::vector<T>& inputValues,
+ std::vector<std::vector<T>>& expectedOutputValues,
+ const int32_t numSplits,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType,
+ axisTensorShape,
+ inputTensorShape,
+ outputTensorShapes,
+ axisData,
+ numSplits,
+ 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, 1, inputValues);
+ armnnDelegate::FillInput<T>(armnnDelegate, 1, inputValues);
+
+ // Run EnqueWorkload
+ CHECK(tfLiteDelegate->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegate->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ for (unsigned int i = 0; i < expectedOutputValues.size(); ++i)
+ {
+ armnnDelegate::CompareOutputData<T>(tfLiteDelegate,
+ armnnDelegate,
+ outputTensorShapes[i],
+ expectedOutputValues[i],
+ i);
+ }
+
+ tfLiteDelegate.reset(nullptr);
+ armnnDelegate.reset(nullptr);
+} // End of SPLIT Test
+
+std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType,
+ std::vector<int32_t>& inputTensorShape,
+ std::vector<int32_t>& splitsTensorShape,
+ std::vector<int32_t>& axisTensorShape,
+ const std::vector<std::vector<int32_t>>& outputTensorShapes,
+ std::vector<int32_t>& splitsData,
+ std::vector<int32_t>& axisData,
+ const int32_t numSplits,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers;
+ buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
+ buffers[1] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()),
+ sizeof(int32_t) * splitsData.size()));
+ buffers[2] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
+ sizeof(int32_t) * axisData.size()));
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+ std::array<flatbuffers::Offset<Tensor>, 5> 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>(splitsTensorShape.data(),
+ splitsTensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("splits"),
+ quantizationParameters);
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
+ axisTensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 2,
+ flatBufferBuilder.CreateString("axis"),
+ quantizationParameters);
+
+ // Create output tensor
+ for (unsigned int i = 0; i < outputTensorShapes.size(); ++i)
+ {
+ tensors[i + 3] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(),
+ outputTensorShapes[i].size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+ }
+
+ // create operator. Mean uses ReducerOptions.
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union();
+
+ const std::vector<int> operatorInputs{ {0, 1, 2} };
+ const std::vector<int> operatorOutputs{ {3, 4} };
+ flatbuffers::Offset <Operator> controlOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ const std::vector<int> subgraphInputs{ {0, 1, 2} };
+ const std::vector<int> subgraphOutputs{ {3, 4} };
+ 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(&controlOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V);
+
+ 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 SplitVTest(tflite::TensorType tensorType,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& inputTensorShape,
+ std::vector<int32_t>& splitsTensorShape,
+ std::vector<int32_t>& axisTensorShape,
+ std::vector<std::vector<int32_t>>& outputTensorShapes,
+ std::vector<T>& inputValues,
+ std::vector<int32_t>& splitsData,
+ std::vector<int32_t>& axisData,
+ std::vector<std::vector<T>>& expectedOutputValues,
+ const int32_t numSplits,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType,
+ inputTensorShape,
+ splitsTensorShape,
+ axisTensorShape,
+ outputTensorShapes,
+ splitsData,
+ axisData,
+ numSplits,
+ 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
+ for (unsigned int i = 0; i < expectedOutputValues.size(); ++i)
+ {
+ armnnDelegate::CompareOutputData<T>(tfLiteDelegate,
+ armnnDelegate,
+ outputTensorShapes[i],
+ expectedOutputValues[i],
+ i);
+ }
+
+ tfLiteDelegate.reset(nullptr);
+ armnnDelegate.reset(nullptr);
+} // End of SPLIT_V Test
+
+} // anonymous namespace
\ No newline at end of file