IVGCVSW-8037 Add BROADCAST_TO to tflite classic and opaque delegate.
Signed-off-by: Idriss Chaouch <idriss.chaouch@arm.com>
Change-Id: Ibc145d0ea1ac9414b6a68b5b547bf2ea2852fd36
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index c1bf73a..d92611f 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -134,6 +134,8 @@
test/BatchMatMulTestHelper.hpp
test/BatchSpaceTest.cpp
test/BatchSpaceTestHelper.hpp
+ test/BroadcastToTest.cpp
+ test/BroadcastToTestHelper.hpp
test/CastTest.cpp
test/CastTestHelper.hpp
test/ComparisonTest.cpp
diff --git a/delegate/classic/CMakeLists.txt b/delegate/classic/CMakeLists.txt
index 7807153..dfd0cf9 100644
--- a/delegate/classic/CMakeLists.txt
+++ b/delegate/classic/CMakeLists.txt
@@ -13,6 +13,7 @@
src/ArgMinMax.hpp
src/BatchMatMul.hpp
src/BatchSpace.hpp
+ src/BroadcastTo.hpp
src/ClassicDelegateUtils.hpp
src/Comparison.hpp
src/Convolution.hpp
diff --git a/delegate/classic/src/BroadcastTo.hpp b/delegate/classic/src/BroadcastTo.hpp
new file mode 100644
index 0000000..92aed79
--- /dev/null
+++ b/delegate/classic/src/BroadcastTo.hpp
@@ -0,0 +1,122 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#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>
+#include <tensorflow/lite/minimal_logging.h>
+#include <tensorflow/lite/kernels/internal/tensor_ctypes.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <armnn_delegate.hpp>
+
+namespace armnnDelegate
+{
+ TfLiteStatus ValidateBroadcastToOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ const armnn::TensorInfo& inputInfo,
+ const armnn::TensorInfo& outputInfo,
+ const armnn::BroadcastToDescriptor& descriptor)
+ {
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC("BROADCAST_TO",
+ tfLiteContext,
+ IsBroadcastToSupported,
+ delegateData.m_Backends,
+ isSupported,
+ armnn::BackendId(),
+ inputInfo,
+ outputInfo,
+ descriptor);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ TfLiteStatus VisitBroadcastToOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ TfLiteNode* tfLiteNode,
+ int nodeIndex,
+ int32_t broadcastToOperatorCode)
+ {
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+
+ // The input contains the data that should be broadcasted
+ 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: ",
+ broadcastToOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The shape tensor contains the new shape to be applied on the input
+ const TfLiteTensor& tfLiteShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+ if (IsDynamicTensor(tfLiteShapeTensor))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ broadcastToOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The output tensor
+ 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: ",
+ broadcastToOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+ auto* shapeData = tflite::GetTensorData<int32_t>(&tfLiteShapeTensor);
+ auto shapeTensorNum = tfLiteShapeTensor.dims->data[0];
+
+ armnn::BroadcastToDescriptor broadcastToDescriptor;
+ broadcastToDescriptor.m_BroadcastToShape = armnn::TensorShape(shapeTensorNum,
+ shapeData);
+
+ // No network pointer indicates that only support for this operator should be checked
+ if (!delegateData.m_Network)
+ {
+ return ValidateBroadcastToOperator(delegateData,
+ tfLiteContext,
+ inputTensorInfo,
+ outputTensorInfo,
+ broadcastToDescriptor);
+ }
+
+ auto layerName = GetLayerName(armnn::LayerType::BroadcastTo, nodeIndex);
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddBroadcastToLayer(broadcastToDescriptor,
+ layerName.c_str());
+
+ if (layer == nullptr)
+ {
+ return kTfLiteError;
+ }
+
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
+ {
+ return kTfLiteError;
+ }
+
+ return Connect(layer, tfLiteNode, delegateData);
+ }
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/classic/src/armnn_delegate.cpp b/delegate/classic/src/armnn_delegate.cpp
index de2aa0c..c428d46 100644
--- a/delegate/classic/src/armnn_delegate.cpp
+++ b/delegate/classic/src/armnn_delegate.cpp
@@ -11,6 +11,7 @@
#include "ArgMinMax.hpp"
#include "BatchMatMul.hpp"
#include "BatchSpace.hpp"
+#include "BroadcastTo.hpp"
#include "Comparison.hpp"
#include "Convolution.hpp"
#include "Control.hpp"
@@ -603,6 +604,12 @@
tfLiteNode,
nodeIndex,
kTfLiteBuiltinBatchToSpaceNd);
+ case kTfLiteBuiltinBroadcastTo:
+ return VisitBroadcastToOperator(delegateData,
+ tfLiteContext,
+ tfLiteNode,
+ nodeIndex,
+ kTfLiteBuiltinBroadcastTo);
case kTfLiteBuiltinCast:
return VisitCastOperator(delegateData,
tfLiteContext,
diff --git a/delegate/opaque/CMakeLists.txt b/delegate/opaque/CMakeLists.txt
index c05bccf..365e016 100644
--- a/delegate/opaque/CMakeLists.txt
+++ b/delegate/opaque/CMakeLists.txt
@@ -13,6 +13,7 @@
src/armnn_external_delegate.cpp
src/BatchMatMul.hpp
src/BatchSpace.hpp
+ src/BroadcastTo.hpp
src/Comparison.hpp
src/Control.hpp
src/Convolution.hpp
diff --git a/delegate/opaque/src/BroadcastTo.hpp b/delegate/opaque/src/BroadcastTo.hpp
new file mode 100644
index 0000000..3795875
--- /dev/null
+++ b/delegate/opaque/src/BroadcastTo.hpp
@@ -0,0 +1,141 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include <OpaqueDelegateUtils.hpp>
+
+namespace armnnOpaqueDelegate
+{
+ TfLiteStatus ValidateBroadcastToOperator(DelegateData& delegateData,
+ TfLiteOpaqueContext *tfLiteContext,
+ const armnn::TensorInfo& inputInfo,
+ const armnn::TensorInfo& outputInfo,
+ const armnn::BroadcastToDescriptor& descriptor)
+ {
+ bool isSupported = false;
+ FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("BROADCAST_TO",
+ tfLiteContext,
+ IsBroadcastToSupported,
+ delegateData.m_Backends,
+ isSupported,
+ armnn::BackendId(),
+ inputInfo,
+ outputInfo,
+ descriptor);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ TfLiteStatus VisitBroadcastToOperator(DelegateData& delegateData,
+ TfLiteOpaqueContext* tfLiteContext,
+ TfLiteOpaqueNode* tfLiteNode,
+ int nodeIndex,
+ int32_t broadcastToOperatorCode)
+ {
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ // Gather input tensors
+ auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
+ const int* inputTensors;
+ if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+ {
+ TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+ nodeIndex);
+ return kTfLiteError;
+ }
+
+ // Gather output tensors
+ int numOutputs = 0;
+ const int* outputTensors;
+ if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors,
+ &numOutputs) != kTfLiteOk)
+ {
+ TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+ nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The input contains the data
+ const TfLiteOpaqueTensor* tfLiteInputTensor =
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
+ if (IsDynamicTensor(tfLiteInputTensor))
+ {
+ TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ broadcastToOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The shape tensor
+ const TfLiteOpaqueTensor* tfLiteShapeTensor =
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);;
+ if (IsDynamicTensor(tfLiteShapeTensor))
+ {
+ TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ broadcastToOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ // The output tensor
+ const TfLiteOpaqueTensor* tfLiteOutputTensor =
+ TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+ if (IsDynamicTensor(tfLiteOutputTensor))
+ {
+ TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+ broadcastToOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor,
+ true);
+
+ auto* shapeData = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteShapeTensor));
+ int32_t shapeTensorNum = TfLiteOpaqueTensorDim(tfLiteShapeTensor, 0);
+
+ armnn::BroadcastToDescriptor broadcastToDescriptor;
+ broadcastToDescriptor.m_BroadcastToShape = armnn::TensorShape(shapeTensorNum,
+ shapeData);
+
+ // No network pointer indicates that only support for this operator should be checked
+ if (!delegateData.m_Network)
+ {
+ return ValidateBroadcastToOperator(delegateData,
+ tfLiteContext,
+ inputTensorInfo,
+ outputTensorInfo,
+ broadcastToDescriptor);
+ }
+
+ std::string layerName("BroadcastTo");
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddBroadcastToLayer(broadcastToDescriptor,
+ layerName.c_str());
+
+ if (layer == nullptr)
+ {
+ return kTfLiteError;
+ }
+
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ if (ProcessInputs(layer, delegateData, tfLiteContext, tfLiteNode, nodeIndex) != kTfLiteOk)
+ {
+ return kTfLiteError;
+ }
+
+ return Connect(layer, tfLiteContext, tfLiteNode, delegateData);
+ }
+
+} // namespace armnnOpaqueDelegate
\ No newline at end of file
diff --git a/delegate/opaque/src/armnn_delegate.cpp b/delegate/opaque/src/armnn_delegate.cpp
index bad1aba..08b1504 100644
--- a/delegate/opaque/src/armnn_delegate.cpp
+++ b/delegate/opaque/src/armnn_delegate.cpp
@@ -10,6 +10,7 @@
#include "ArgMinMax.hpp"
#include "BatchMatMul.hpp"
#include "BatchSpace.hpp"
+#include "BroadcastTo.hpp"
#include "Comparison.hpp"
#include "Convolution.hpp"
#include "Control.hpp"
@@ -654,6 +655,12 @@
tfLiteNode,
nodeIndex,
kTfLiteBuiltinBatchMatmul);
+ case kTfLiteBuiltinBroadcastTo:
+ return VisitBroadcastToOperator(delegateData,
+ tfLiteContext,
+ tfLiteNode,
+ nodeIndex,
+ kTfLiteBuiltinBroadcastTo);
case kTfLiteBuiltinBatchToSpaceNd:
return VisitBatchToSpaceNdOperator(delegateData,
tfLiteContext,
diff --git a/delegate/test/BroadcastToTest.cpp b/delegate/test/BroadcastToTest.cpp
new file mode 100644
index 0000000..f4692cf
--- /dev/null
+++ b/delegate/test/BroadcastToTest.cpp
@@ -0,0 +1,80 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "BroadcastToTestHelper.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 <schema_generated.h>
+#include <tensorflow/lite/version.h>
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+template<typename T>
+void BroadcastToTest(std::vector<armnn::BackendId> &backends, tflite::TensorType inputTensorType)
+{
+ // Set input data
+ std::vector<T> inputValues = {
+ 0, 1, 2, 3
+ };
+ // Set output data
+ std::vector<T> expectedOutputValues = {
+ 0, 1, 2, 3,
+ 0, 1, 2, 3,
+ 0, 1, 2, 3
+ };
+
+ // The shape data
+ const std::vector<int32_t> shapeData = {3, 4};
+
+ // Set shapes
+ const std::vector<int32_t> inputShape = {1, 4};
+ const std::vector<int32_t> shapeShape = {2};
+ const std::vector<int32_t> expectedOutputShape = {3, 4};
+
+ BroadcastToTestImpl<T>(inputTensorType,
+ tflite::BuiltinOperator_BROADCAST_TO,
+ backends,
+ inputValues,
+ inputShape,
+ shapeShape,
+ shapeData,
+ expectedOutputValues,
+ expectedOutputShape);
+}
+
+TEST_SUITE("BroadcastToTests_CpuRefTests")
+{
+
+ TEST_CASE ("BroadcastTo_int_CpuRef_Test")
+ {
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ BroadcastToTest<int32_t>(backends, ::tflite::TensorType::TensorType_INT32);
+ }
+
+ TEST_CASE ("BroadcastTo_Float32_CpuRef_Test")
+ {
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ BroadcastToTest<float>(backends, ::tflite::TensorType::TensorType_FLOAT32);
+ }
+
+ TEST_CASE ("BroadcastTo_Uint8_t_CpuRef_Test")
+ {
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ BroadcastToTest<uint8_t>(backends, ::tflite::TensorType::TensorType_UINT8);
+ }
+
+ TEST_CASE ("BroadcastTo_Int8_t_CpuRef_Test")
+ {
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ BroadcastToTest<int8_t>(backends, ::tflite::TensorType::TensorType_INT8);
+ }
+
+} // TEST_SUITE("BroadcastToTests_CpuRefTests")
+}
\ No newline at end of file
diff --git a/delegate/test/BroadcastToTestHelper.hpp b/delegate/test/BroadcastToTestHelper.hpp
new file mode 100644
index 0000000..630fe3a
--- /dev/null
+++ b/delegate/test/BroadcastToTestHelper.hpp
@@ -0,0 +1,167 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+#include <DelegateTestInterpreter.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/version.h>
+
+#include <schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+ std::vector<char> CreateBroadcastToTfLiteModel(tflite::BuiltinOperator operatorCode,
+ tflite::TensorType inputTensorType,
+ const std::vector<int32_t>& inputTensorShape,
+ const std::vector<int32_t>& shapeTensorShape,
+ const std::vector<int32_t>& shapeTensorData,
+ const std::vector<int32_t>& outputTensorShape)
+ {
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ buffers.push_back(CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t*>(shapeTensorData.data()),
+ sizeof(int32_t) * shapeTensorData.size())));
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+
+ float qScale = 1.0f;
+ int32_t qOffset = 0;
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ qScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ qOffset }));
+
+ std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ inputTensorType,
+ 1,
+ flatBufferBuilder.CreateString("input_tensor"),
+ quantizationParameters);
+
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(shapeTensorShape.data(),
+ shapeTensorShape.size()),
+ TensorType_INT32,
+ 2,
+ flatBufferBuilder.CreateString("shape_input_tensor"),
+ quantizationParameters);
+
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ inputTensorType,
+ 3,
+ flatBufferBuilder.CreateString("output_tensor"),
+ quantizationParameters);
+
+ // Create Operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_BroadcastToOptions;
+ flatbuffers::Offset<void> operatorBuiltinOption = 0;
+
+ const std::vector<int> operatorInputs {0, 1};
+ const std::vector<int> operatorOutputs {2};
+
+ flatbuffers::Offset<Operator> broadcastOperator =
+ 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(&broadcastOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: BrodacastTo Operator Model");
+ flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder,0,
+ 0, 2,
+ tflite::BuiltinOperator_BROADCAST_TO);
+
+ 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, armnnDelegate::FILE_IDENTIFIER);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+ }
+
+ template<typename T>
+ void BroadcastToTestImpl(tflite::TensorType inputTensorType,
+ tflite::BuiltinOperator operatorCode,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<T>& inputValues,
+ std::vector<int32_t> inputShape,
+ std::vector<int32_t> shapeShapes,
+ std::vector<int32_t> shapeData,
+ std::vector<T>& expectedOutputValues,
+ std::vector<int32_t> expectedOutputShape)
+ {
+ using namespace delegateTestInterpreter;
+
+ std::vector<char> modelBuffer = CreateBroadcastToTfLiteModel(operatorCode,
+ inputTensorType,
+ inputShape,
+ shapeShapes,
+ shapeData,
+ expectedOutputShape);
+
+
+ // Setup interpreter with just TFLite Runtime.
+ auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
+ CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
+ CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
+ CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(shapeData, 1) == kTfLiteOk);
+ CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
+ std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
+ std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
+
+ // Setup interpreter with Arm NN Delegate applied.
+ auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
+ CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
+ CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
+ CHECK(armnnInterpreter.FillInputTensor<int32_t>(shapeData, 1) == kTfLiteOk);
+ CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
+ std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
+ std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
+
+ armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
+ armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);
+
+ tfLiteInterpreter.Cleanup();
+ armnnInterpreter.Cleanup();
+ }
+
+} // anonymous namespace
\ No newline at end of file
diff --git a/docs/05_03_delegate.dox b/docs/05_03_delegate.dox
index 624f06f..9a40a8a 100644
--- a/docs/05_03_delegate.dox
+++ b/docs/05_03_delegate.dox
@@ -46,6 +46,8 @@
- BATCH_TO_SPACE_ND
+- BROADCAST_TO
+
- CAST
- CEIL