IVGCVSW-5396 TfLiteDelegate: Implement the Resize operators
* Added resize biliniear and nearest neighbour operator
support to the tflite delegate
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Id0113d6b865ea282c6f4de55e8419a6244a35f0e
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index 2ee00f3..0dc72c2 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -102,7 +102,10 @@
src/test/Pooling2dTest.cpp
src/test/Pooling2dTestHelper.hpp
src/test/QuantizationTest.cpp
- src/test/QuantizationTestHelper.hpp)
+ src/test/QuantizationTestHelper.hpp
+ src/test/ResizeTest.cpp
+ src/test/ResizeTestHelper.hpp
+ src/test/TestUtils.hpp)
add_executable(DelegateUnitTests ${armnnDelegate_unittest_sources})
target_include_directories(DelegateUnitTests PRIVATE third-party)
diff --git a/delegate/src/Resize.hpp b/delegate/src/Resize.hpp
index be40b64..f91cdb0 100644
--- a/delegate/src/Resize.hpp
+++ b/delegate/src/Resize.hpp
@@ -5,21 +5,194 @@
#pragma once
+#include "DelegateUtils.hpp"
+
+#include <armnn/Descriptors.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 <tensorflow/lite/kernels/internal/tensor_ctypes.h>
namespace armnnDelegate
{
+
+
+TfLiteStatus ValidateResizeOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ const armnn::TensorInfo& inputInfo,
+ const armnn::TensorInfo& outputInfo,
+ const armnn::ResizeDescriptor& descriptor)
+{
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ tfLiteContext,
+ IsResizeSupported,
+ delegateData.m_Backends,
+ isSupported,
+ inputInfo,
+ outputInfo,
+ descriptor);
+
+ return isSupported ? kTfLiteOk : kTfLiteError;
+}
+
TfLiteStatus VisitResizeOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t resizeOperatorCode)
{
- return kTfLiteError;
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+
+ // The first input contains the data of the image that should be resized [batch, height, width, channels]
+ const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+ if (IsDynamicTensor(tfLiteInputTensor))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The second input contains a size tensor. The size tensor contains two integer values
+ // that describe the new height and width of the image [new_height, new_width]
+ const TfLiteTensor& tfLiteSizeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+ if (IsDynamicTensor(tfLiteSizeTensor))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The output tensor should have the shape [batch, new_height, new_width, channels]
+ const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+ if (IsDynamicTensor(tfLiteOutputTensor))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+ resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+ const armnn::TensorInfo& sizeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteSizeTensor);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+ std::string layerName("Resize");
+
+ // Fill descriptor
+ armnn::ResizeDescriptor desc;
+ switch (resizeOperatorCode)
+ {
+ case kTfLiteBuiltinResizeBilinear:
+ {
+ desc.m_Method = armnn::ResizeMethod::Bilinear;
+
+ layerName += "Bilinear:" + nodeIndex;
+
+ TfLiteResizeBilinearParams* biliniarOptions =
+ reinterpret_cast<TfLiteResizeBilinearParams*>(tfLiteNode->builtin_data);
+
+ desc.m_AlignCorners = biliniarOptions->align_corners;
+ desc.m_HalfPixelCenters = biliniarOptions->half_pixel_centers;
+ break;
+ }
+ case kTfLiteBuiltinResizeNearestNeighbor:
+ {
+ desc.m_Method = armnn::ResizeMethod::NearestNeighbor;
+ layerName += "NearestNeighbor:" + nodeIndex;
+
+ TfLiteResizeNearestNeighborParams* nearestNeighborOptions =
+ reinterpret_cast<TfLiteResizeNearestNeighborParams*>(tfLiteNode->builtin_data);
+
+ desc.m_AlignCorners = nearestNeighborOptions->align_corners;
+ desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers;
+ break;
+ }
+ default:
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Unknown TfLite built in operation for Resize. Given operator: #%d node #%d: ",
+ resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+ }
+
+ // In armnn the values of the size input tensor [new_hight, new_width] is saved in the operator
+ // descriptor. We have to read it from the input tensor and write it to the descriptor.
+
+ auto* sizeTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteSizeTensor);
+ auto sizeTensorNumDimensions = tfLiteSizeTensor.dims->size;
+ // The size tensor is only a 1D tensor -> [new_hight, new width]
+ if (sizeTensorNumDimensions != 1)
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
+ "dynamic tensor. Operator: #%d node #%d: ",
+ resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // Get number of values in the size tensor
+ auto sizeTensorNumValues = tfLiteSizeTensor.dims->data[0];
+ if (sizeTensorNumValues == 0)
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
+ "dynamic tensor. Operator: #%d node #%d: ",
+ resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+ else if (sizeTensorNumValues != 2)
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation requires to "
+ "have a dimension of 2 [new_hight, new width] but a tensor with a dimension of #%d was given. "
+ "Operator: #%d node #%d: ",
+ sizeTensorNumValues, resizeOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+ // get size tensor data
+ std::vector<int32_t> sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues);
+
+ desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]);
+ desc.m_TargetWidth = static_cast<uint32_t> (sizeTensorData[1]);
+ desc.m_DataLayout = armnn::DataLayout::NHWC;
+
+ // No network pointer indicates that only support for this operator should be checked
+ if (!delegateData.m_Network)
+ {
+ return ValidateResizeOperator(delegateData,
+ tfLiteContext,
+ inputTensorInfo,
+ outputTensorInfo,
+ desc);
+ }
+
+
+ armnn::IConnectableLayer* resizeLayer = nullptr;
+ resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str());
+
+ armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0);
+ outputSlot.SetTensorInfo(outputTensorInfo);
+
+ ARMNN_ASSERT(resizeLayer != nullptr);
+
+ return Connect(resizeLayer, tfLiteNode, delegateData);
}
} // namespace armnnDelegate
diff --git a/delegate/src/test/ResizeTest.cpp b/delegate/src/test/ResizeTest.cpp
new file mode 100644
index 0000000..394ad6c
--- /dev/null
+++ b/delegate/src/test/ResizeTest.cpp
@@ -0,0 +1,134 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ResizeTestHelper.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 armnnDelegate
+{
+
+void ResizeBiliniarFloat32Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data
+ std::vector<float> input1Values
+ {
+ 0.0f, 1.0f, 2.0f,
+ 3.0f, 4.0f, 5.0f,
+ 6.0f, 7.0f, 8.0f
+ };
+ const std::vector<int32_t> input2NewShape { 5, 5 };
+
+ // Calculate output data
+ std::vector<float> expectedOutputValues
+ {
+ 0.0f, 0.6f, 1.2f, 1.8f, 2.0f,
+ 1.8f, 2.4f, 3.0f, 3.6f, 3.8f,
+ 3.6f, 4.2f, 4.8f, 5.4f, 5.6f,
+ 5.4f, 6.0f, 6.6f, 7.2f, 7.4f,
+ 6.0f, 6.6f, 7.2f, 7.8f, 8.0f
+ };
+
+ const std::vector<int32_t> input1Shape { 1, 3, 3, 1 };
+ const std::vector<int32_t> input2Shape { 2 };
+ const std::vector<int32_t> expectedOutputShape = input2NewShape;
+
+ ResizeFP32TestImpl(tflite::BuiltinOperator_RESIZE_BILINEAR,
+ backends,
+ input1Values,
+ input1Shape,
+ input2NewShape,
+ input2Shape,
+ expectedOutputValues,
+ expectedOutputShape);
+}
+
+void ResizeNearestNeighbourFloat32Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data
+ std::vector<float> input1Values { 1.0f, 2.0f, 3.0f, 4.0f }
+ ;
+ const std::vector<int32_t> input2NewShape { 1, 1 };
+
+ // Calculate output data
+ std::vector<float> expectedOutputValues { 1.0f };
+
+ const std::vector<int32_t> input1Shape { 1, 2, 2, 1 };
+ const std::vector<int32_t> input2Shape { 2 };
+ const std::vector<int32_t> expectedOutputShape = input2NewShape;
+
+ ResizeFP32TestImpl(tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR,
+ backends,
+ input1Values,
+ input1Shape,
+ input2NewShape,
+ input2Shape,
+ expectedOutputValues,
+ expectedOutputShape);
+}
+
+TEST_SUITE("ResizeTests_GpuAccTests")
+{
+
+TEST_CASE ("Resize_Biliniar_Float32_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ ResizeBiliniarFloat32Test(backends);
+}
+
+TEST_CASE ("Resize_NearestNeighbour_Float32_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ ResizeNearestNeighbourFloat32Test(backends);
+}
+
+} // TEST_SUITE("ResizeTests_GpuAccTests")
+
+
+TEST_SUITE("ResizeTests_CpuAccTests")
+{
+
+TEST_CASE ("Resize_Biliniar_Float32_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ ResizeBiliniarFloat32Test(backends);
+}
+
+TEST_CASE ("Resize_NearestNeighbour_Float32_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ ResizeNearestNeighbourFloat32Test(backends);
+}
+
+} // TEST_SUITE("ResizeTests_CpuAccTests")
+
+
+TEST_SUITE("ResizeTests_CpuRefTests")
+{
+
+TEST_CASE ("Resize_Biliniar_Float32_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ ResizeBiliniarFloat32Test(backends);
+}
+
+TEST_CASE ("Resize_NearestNeighbour_Float32_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ ResizeNearestNeighbourFloat32Test(backends);
+}
+
+} // TEST_SUITE("ResizeTests_CpuRefTests")
+
+} // namespace armnnDelegate
diff --git a/delegate/src/test/ResizeTestHelper.hpp b/delegate/src/test/ResizeTestHelper.hpp
new file mode 100644
index 0000000..1e9d3bc
--- /dev/null
+++ b/delegate/src/test/ResizeTestHelper.hpp
@@ -0,0 +1,192 @@
+//
+// 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>
+
+namespace
+{
+
+std::vector<char> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode,
+ tflite::TensorType inputTensorType,
+ const std::vector <int32_t>& inputTensorShape,
+ const std::vector <int32_t>& sizeTensorData,
+ const std::vector <int32_t>& sizeTensorShape,
+ const std::vector <int32_t>& outputTensorShape)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ buffers.push_back(CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t*>(sizeTensorData.data()),
+ sizeof(int32_t) * sizeTensorData.size())));
+
+ std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()),
+ inputTensorType,
+ 0,
+ flatBufferBuilder.CreateString("input_tensor"));
+
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(),
+ sizeTensorShape.size()),
+ TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("size_input_tensor"));
+
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ inputTensorType,
+ 0,
+ flatBufferBuilder.CreateString("output_tensor"));
+
+ // Create Operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+ flatbuffers::Offset<void> operatorBuiltinOption = 0;
+ switch (operatorCode)
+ {
+ case BuiltinOperator_RESIZE_BILINEAR:
+ {
+ operatorBuiltinOption = CreateResizeBilinearOptions(flatBufferBuilder, false, false).Union();
+ operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeBilinearOptions;
+ break;
+ }
+ case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR:
+ {
+ operatorBuiltinOption = CreateResizeNearestNeighborOptions(flatBufferBuilder, false, false).Union();
+ operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeNearestNeighborOptions;
+ break;
+ }
+ default:
+ break;
+ }
+
+ const std::vector<int> operatorInputs{{0, 1}};
+ const std::vector<int> operatorOutputs{{2}};
+ flatbuffers::Offset <Operator> resizeOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOption);
+
+ const std::vector<int> subgraphInputs{{0, 1}};
+ const std::vector<int> subgraphOutputs{{2}};
+ 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(&resizeOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model");
+ flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&opCode, 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());
+}
+
+void ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<float>& input1Values,
+ std::vector<int32_t> input1Shape,
+ std::vector<int32_t> input2NewShape,
+ std::vector<int32_t> input2Shape,
+ std::vector<float>& expectedOutputValues,
+ std::vector<int32_t> expectedOutputShape)
+{
+ using namespace tflite;
+
+ std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode,
+ ::tflite::TensorType_FLOAT32,
+ input1Shape,
+ input2NewShape,
+ input2Shape,
+ expectedOutputShape);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+
+ // The model will be executed using tflite and using the armnn delegate so that the outputs
+ // can be compared.
+
+ // Create TfLite Interpreter with armnn delegate
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ // Create TfLite Interpreter without armnn delegate
+ 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 the armnn interpreter
+ armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input1Values);
+ armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input2NewShape);
+
+ // Set input data for the tflite interpreter
+ armnnDelegate::FillInput(tfLiteInterpreter, 0, input1Values);
+ armnnDelegate::FillInput(tfLiteInterpreter, 1, input2NewShape);
+
+ // Run EnqueWorkload
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+ for (size_t i = 0; i < expectedOutputValues.size(); i++)
+ {
+ CHECK(expectedOutputValues[i] == doctest::Approx(armnnDelegateOutputData[i]));
+ CHECK(armnnDelegateOutputData[i] == doctest::Approx(tfLiteDelageOutputData[i]));
+ }
+
+ armnnDelegateInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace
\ No newline at end of file
diff --git a/delegate/src/test/TestUtils.hpp b/delegate/src/test/TestUtils.hpp
new file mode 100644
index 0000000..162d62f
--- /dev/null
+++ b/delegate/src/test/TestUtils.hpp
@@ -0,0 +1,26 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <tensorflow/lite/interpreter.h>
+
+namespace armnnDelegate
+{
+
+/// Can be used to assign input data from a vector to a model input.
+/// Example usage can be found in ResizeTesthelper.hpp
+template <typename T>
+void FillInput(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<T>& inputValues)
+{
+ auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex];
+ auto tfLiteDelageInputData = interpreter->typed_tensor<T>(tfLiteDelegateInputId);
+ for (unsigned int i = 0; i < inputValues.size(); ++i)
+ {
+ tfLiteDelageInputData[i] = inputValues[i];
+ }
+}
+
+} // namespace armnnDelegate
