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/CMakeLists.txt b/delegate/CMakeLists.txt
index d488de4..523214b 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -1,5 +1,5 @@
#
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
# SPDX-License-Identifier: MIT
#
@@ -177,6 +177,8 @@
src/test/PadTestHelper.hpp
src/test/Pooling2dTest.cpp
src/test/Pooling2dTestHelper.hpp
+ src/test/Pooling3dTest.cpp
+ src/test/Pooling3dTestHelper.hpp
src/test/PreluTest.cpp
src/test/PreluTestHelper.hpp
src/test/QuantizationTest.cpp
diff --git a/delegate/src/Pooling.hpp b/delegate/src/Pooling.hpp
index 4095ac4..df8c3db 100644
--- a/delegate/src/Pooling.hpp
+++ b/delegate/src/Pooling.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -11,15 +11,16 @@
#include <tensorflow/lite/c/builtin_op_data.h>
#include <tensorflow/lite/c/common.h>
#include <tensorflow/lite/minimal_logging.h>
+#include <flatbuffers/flexbuffers.h>
namespace armnnDelegate
{
-TfLiteStatus VisitPoolingOperator(DelegateData& delegateData,
- TfLiteContext* tfLiteContext,
- TfLiteNode* tfLiteNode,
- int nodeIndex,
- int32_t tfLitePoolingOperatorCode)
+TfLiteStatus VisitPooling2dOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ TfLiteNode* tfLiteNode,
+ int nodeIndex,
+ int32_t tfLitePoolingOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
@@ -113,4 +114,167 @@
return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData);
}
+TfLiteStatus VisitPooling3dOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ TfLiteNode* tfLiteNode,
+ int nodeIndex,
+ std::string customOperatorName)
+{
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+ 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: ",
+ customOperatorName.c_str(), nodeIndex);
+ return kTfLiteError;
+ }
+
+ 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: ",
+ customOperatorName.c_str(), nodeIndex);
+ return kTfLiteError;
+ }
+ // Set the input and output info
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+ // Custom Operators are defined by the name string associated to the operator. Use this to determine
+ // which pooling algorithm to create the armnn operator with. L2 Pooling3D is unsupported in TfLite.
+ armnn::PoolingAlgorithm poolingAlgorithm;
+ if (customOperatorName == "MaxPool3D")
+ {
+ poolingAlgorithm = armnn::PoolingAlgorithm::Max;
+ }
+ else if (customOperatorName == "AveragePool3D")
+ {
+ poolingAlgorithm = armnn::PoolingAlgorithm::Average;
+ }
+ else
+ {
+ return kTfLiteError;
+ }
+ // Create the armnn pool3d descriptor and set the algorithm parsed above.
+ armnn::Pooling3dDescriptor descriptor;
+ descriptor.m_PoolType = poolingAlgorithm;
+
+ // custom_initial_data and custom_initial_data_size are void* variables defined in the tflite registration
+ // used to access the custom option buffer for the operator.
+ auto custom_data = tfLiteNode->custom_initial_data;
+ auto custom_data_size = tfLiteNode->custom_initial_data_size;
+ // Reinterpret the void* to a byte buffer to access the options data in the flexbuffers map.
+ const flexbuffers::Map& m = flexbuffers::GetRoot(reinterpret_cast<const uint8_t*>(custom_data),
+ custom_data_size).AsMap();
+ // poolDims is a vector of [ 1, Depth, Height, Width, 1 ]
+ const auto poolDims = m["ksize"].AsTypedVector();
+ descriptor.m_PoolWidth = poolDims[3].AsInt32();
+ descriptor.m_PoolHeight = poolDims[2].AsInt32();
+ descriptor.m_PoolDepth = poolDims[1].AsInt32();
+
+ // strideDimes is a vector of [ 1, Z, Y, X, 1]
+ const auto strideDims = m["strides"].AsTypedVector();
+ descriptor.m_StrideX = strideDims[3].AsInt32();
+ descriptor.m_StrideY = strideDims[2].AsInt32();
+ descriptor.m_StrideZ = strideDims[1].AsInt32();
+ descriptor.m_DataLayout = armnn::DataLayout::NDHWC;
+
+ unsigned int inputDepth = inputTensorInfo.GetShape()[1];
+ unsigned int inputHeight = inputTensorInfo.GetShape()[2];
+ unsigned int inputWidth = inputTensorInfo.GetShape()[3];
+
+ // CalcPadding expects a TfLitePadding type. Parse flexbuffers to extract padding string and create TfLitePadding.
+ std::string paddingStr = m["padding"].AsString().str();
+ TfLitePadding padding;
+ if (paddingStr == "VALID")
+ {
+ padding = kTfLitePaddingValid;
+ }
+ else if (paddingStr == "SAME")
+ {
+ padding = kTfLitePaddingSame;
+ }
+ else
+ {
+ padding = kTfLitePaddingUnknown;
+ }
+ // Calculates padding for each pooling dimension separately
+ CalcPadding(inputHeight, descriptor.m_PoolHeight, descriptor.m_StrideY, 1u,
+ descriptor.m_PadTop, descriptor.m_PadBottom, padding);
+ CalcPadding(inputWidth, descriptor.m_PoolWidth, descriptor.m_StrideX, 1u,
+ descriptor.m_PadLeft, descriptor.m_PadRight, padding);
+ CalcPadding(inputDepth, descriptor.m_PoolDepth, descriptor.m_StrideZ, 1u,
+ descriptor.m_PadFront, descriptor.m_PadBack, padding);
+
+ // Validate the output info.
+ bool isSupported = false;
+ auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) {
+ FORWARD_LAYER_SUPPORT_FUNC("POOLING_3D",
+ tfLiteContext,
+ IsPooling3dSupported,
+ delegateData.m_Backends,
+ isSupported,
+ inputTensorInfo,
+ outputTensorInfo,
+ descriptor);
+ };
+
+ if (!delegateData.m_Network)
+ {
+ validateFunc(outputTensorInfo, isSupported);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ // Create the Layer
+ armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling3dLayer(descriptor);
+ ARMNN_ASSERT(poolingLayer != nullptr);
+
+ // Create and set output slots
+ armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0);
+ outputSlot.SetTensorInfo(outputTensorInfo);
+ Connect(poolingLayer, tfLiteNode, delegateData);
+
+ // Check activation by parsing the string from the flexbuffer map
+ std::string activationTypeStr = m["activation"].AsString().str();
+ TfLiteFusedActivation activationType;
+
+ if (activationTypeStr == "kTfLiteActRelu")
+ {
+ activationType = kTfLiteActRelu;
+ }
+ else if (activationTypeStr == "kTfLiteActReluN1To1")
+ {
+ activationType = kTfLiteActReluN1To1;
+ }
+ else if (activationTypeStr == "kTfLiteActRelu6")
+ {
+ activationType = kTfLiteActRelu6;
+ }
+ else if (activationTypeStr == "kTfLiteActTanh")
+ {
+ activationType = kTfLiteActTanh;
+ }
+ else if (activationTypeStr == "kTfLiteActSignBit")
+ {
+ activationType = kTfLiteActSignBit;
+ }
+ else if (activationTypeStr == "kTfLiteActSigmoid")
+ {
+ activationType = kTfLiteActSigmoid;
+ }
+ else
+ {
+ activationType = kTfLiteActNone;
+ }
+
+ return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData);
+}
+
} // namespace armnnDelegate
diff --git a/delegate/src/armnn_delegate.cpp b/delegate/src/armnn_delegate.cpp
index 6e1a91f..bb2f3c3 100644
--- a/delegate/src/armnn_delegate.cpp
+++ b/delegate/src/armnn_delegate.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -495,6 +495,32 @@
{
switch (tfLiteRegistration->builtin_code)
{
+ case kTfLiteBuiltinCustom:
+ {
+#if defined(ARMNN_POST_TFLITE_2_5)
+ // Custom operators are defined by the name rather than the builtin code.
+ // Parse the custom_name param in the registration to point to the correct visitor function.
+ std::string customOperatorName = tfLiteRegistration->custom_name;
+ if ( customOperatorName == "AveragePool3D" )
+ {
+ return VisitPooling3dOperator(delegateData,
+ tfLiteContext,
+ tfLiteNode,
+ nodeIndex,
+ customOperatorName);
+ }
+ else if (customOperatorName == "MaxPool3D")
+ {
+ return VisitPooling3dOperator(delegateData,
+ tfLiteContext,
+ tfLiteNode,
+ nodeIndex,
+ customOperatorName);
+ }
+#endif
+ // Invalid or unsupported custom operator
+ return kTfLiteError;
+ }
case kTfLiteBuiltinAbs:
return VisitElementwiseUnaryOperator(delegateData,
tfLiteContext,
@@ -520,7 +546,7 @@
nodeIndex,
kTfLiteBuiltinArgMin);
case kTfLiteBuiltinAveragePool2d:
- return VisitPoolingOperator(delegateData,
+ return VisitPooling2dOperator(delegateData,
tfLiteContext,
tfLiteNode,
nodeIndex,
@@ -667,7 +693,7 @@
nodeIndex,
kTfLiteBuiltinL2Normalization);
case kTfLiteBuiltinL2Pool2d:
- return VisitPoolingOperator(delegateData,
+ return VisitPooling2dOperator(delegateData,
tfLiteContext,
tfLiteNode,
nodeIndex,
@@ -729,7 +755,7 @@
nodeIndex,
kTfLiteBuiltinLstm);
case kTfLiteBuiltinMaxPool2d:
- return VisitPoolingOperator(delegateData,
+ return VisitPooling2dOperator(delegateData,
tfLiteContext,
tfLiteNode,
nodeIndex,
diff --git a/delegate/src/test/Pooling3dTest.cpp b/delegate/src/test/Pooling3dTest.cpp
new file mode 100644
index 0000000..c0a1862
--- /dev/null
+++ b/delegate/src/test/Pooling3dTest.cpp
@@ -0,0 +1,431 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Pooling3dTestHelper.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
+{
+
+// Pool3D custom op was only added in tflite r2.6.
+#if defined(ARMNN_POST_TFLITE_2_5)
+
+void MaxPool3dFP32PaddingValidTest(std::vector<armnn::BackendId>& backends)
+{
+ // Set input and expected output data
+ std::vector<int32_t> inputShape = { 1, 2, 3, 4, 1 };
+ std::vector<int32_t> outputShape = { 1, 1, 2, 3, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 6, 6, 4 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kMax";
+ TfLitePadding padding = kTfLitePaddingValid;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 1,
+ 1,
+ 1,
+ 2,
+ 2,
+ 2);
+}
+
+void MaxPool3dFP32PaddingSameTest(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data
+ std::vector<int32_t> inputShape = { 1, 2, 3, 4, 1 };
+ std::vector<int32_t> outputShape = { 1, 2, 3, 4, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 6, 6, 4, 4, 6, 6, 6, 6, 4, 5, 6, 6, 6, 6, 4, 4 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kMax";
+ TfLitePadding padding = kTfLitePaddingSame;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 1,
+ 1,
+ 1,
+ 2,
+ 2,
+ 2);
+}
+
+void MaxPool3dFP32H1Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data
+ std::vector<int32_t> inputShape = { 1, 2, 3, 4, 1 };
+ std::vector<int32_t> outputShape = { 1, 1, 3, 3, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 2, 3 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kMax";
+ TfLitePadding padding = kTfLitePaddingValid;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 1,
+ 1,
+ 1,
+ 2,
+ 1,
+ 2);
+}
+
+void MaxPool3dFP32Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data
+ std::vector<int32_t> inputShape = { 1, 2, 3, 4, 1 };
+ std::vector<int32_t> outputShape = { 1, 2, 3, 4, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 6, 6 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kMax";
+ TfLitePadding padding = kTfLitePaddingUnknown;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 1,
+ 1,
+ 1,
+ 2,
+ 2,
+ 2);
+}
+
+void AveragePool3dFP32PaddingValidTest(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data.
+ std::vector<int32_t> inputShape = { 1, 2, 3, 4, 1 };
+ std::vector<int32_t> outputShape = { 1, 1, 2, 3, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 3.5, 3, 2.5 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kAverage";
+ TfLitePadding padding = kTfLitePaddingValid;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 1,
+ 1,
+ 1,
+ 2,
+ 2,
+ 2);
+}
+
+void AveragePool3dFP32PaddingSameTest(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data
+ std::vector<int32_t> inputShape = { 4, 2, 3, 1, 1 };
+ std::vector<int32_t> outputShape = { 4, 2, 3, 1, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 3, 4, 4.5, 4.5, 5.5, 6, 3, 4, 4.5, 4.5, 5.5, 6, 3, 4, 4.5, 4.5 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kAverage";
+ TfLitePadding padding = kTfLitePaddingSame;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 1,
+ 1,
+ 1,
+ 2,
+ 2,
+ 2);
+}
+
+void AveragePool3dFP32H1Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data
+ std::vector<int32_t> inputShape = { 1, 2, 3, 4, 1 };
+ std::vector<int32_t> outputShape = { 1, 1, 2, 2, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 1.5, 3.5 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kAverage";
+ TfLitePadding padding = kTfLitePaddingUnknown;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 2,
+ 2,
+ 2,
+ 2,
+ 1,
+ 2);
+}
+
+void AveragePool3dFP32Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data and expected output data
+ std::vector<int32_t> inputShape = { 4, 3, 2, 1, 1 };
+ std::vector<int32_t> outputShape = { 1, 2, 2, 4, 1 };
+
+ std::vector<float> inputValues = { 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6,
+ 1, 2, 3, 4, 5, 6 };
+ std::vector<float> expectedOutputValues = { 3.125, 4.25 };
+
+ // poolType string required to create the correct pooling operator
+ // Padding type required to create the padding in custom options
+ std::string poolType = "kMax";
+ TfLitePadding padding = kTfLitePaddingUnknown;
+
+ Pooling3dTest<float>(poolType,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ outputShape,
+ inputValues,
+ expectedOutputValues,
+ padding,
+ 2,
+ 2,
+ 2,
+ 2,
+ 2,
+ 2);
+}
+
+TEST_SUITE("Pooling3d_GpuAccTests")
+{
+
+TEST_CASE ("MaxPooling3d_FP32_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ MaxPool3dFP32Test(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_PaddingValid_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ MaxPool3dFP32PaddingValidTest(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_PaddingSame_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ MaxPool3dFP32PaddingSameTest(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_H1_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ MaxPool3dFP32H1Test(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_PaddingValid_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ AveragePool3dFP32PaddingValidTest(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_PaddingSame_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ AveragePool3dFP32PaddingSameTest(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_H1_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ AveragePool3dFP32H1Test(backends);
+}
+
+} // TEST_SUITE("Pooling3d_GpuAccTests")
+
+TEST_SUITE("Pooling3d_CpuAccTests")
+{
+
+TEST_CASE ("MaxPooling3d_FP32_PaddingValid_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ MaxPool3dFP32PaddingValidTest(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_PaddingSame_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ MaxPool3dFP32PaddingSameTest(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ MaxPool3dFP32Test(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_H1_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ MaxPool3dFP32H1Test(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_PaddingValid_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ AveragePool3dFP32PaddingValidTest(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_PaddingSame_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ AveragePool3dFP32PaddingSameTest(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_H1_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ AveragePool3dFP32H1Test(backends);
+}
+
+} // TEST_SUITE("Pooling3d_CpuAccTests")
+
+TEST_SUITE("Pooling3d_CpuRefTests")
+{
+TEST_CASE ("MaxPooling3d_FP32_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ MaxPool3dFP32Test(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_PaddingValid_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ MaxPool3dFP32PaddingValidTest(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_PaddingSame_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ MaxPool3dFP32PaddingSameTest(backends);
+}
+
+TEST_CASE ("MaxPooling3d_FP32_H1_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ MaxPool3dFP32H1Test(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_PaddingValid_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ AveragePool3dFP32PaddingValidTest(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_PaddingSame_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ AveragePool3dFP32PaddingSameTest(backends);
+}
+
+TEST_CASE ("AveragePooling3d_FP32_H1_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ AveragePool3dFP32H1Test(backends);
+}
+
+} // TEST_SUITE("Pooling3d_CpuRefTests")
+
+#endif
+
+}
\ No newline at end of file
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
+
+
+
+
diff --git a/docs/05_03_delegate.dox b/docs/05_03_delegate.dox
index 625b253..d1c41fe 100644
--- a/docs/05_03_delegate.dox
+++ b/docs/05_03_delegate.dox
@@ -43,6 +43,8 @@
- AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
+- AVERAGE_POOL_3D
+
- BATCH_TO_SPACE_ND
- CAST
@@ -107,6 +109,8 @@
- MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
+- MAX_POOL_3D
+
- MEAN
- MINIMUM