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-rw-r--r--delegate/src/test/ConvolutionTestHelper.hpp258
1 files changed, 258 insertions, 0 deletions
diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp
index 1b33c1d74d..ce1f951d21 100644
--- a/delegate/src/test/ConvolutionTestHelper.hpp
+++ b/delegate/src/test/ConvolutionTestHelper.hpp
@@ -5,6 +5,8 @@
#pragma once
+#include "TestUtils.hpp"
+
#include <armnn_delegate.hpp>
#include <flatbuffers/flatbuffers.h>
@@ -221,6 +223,7 @@ void ConvolutionTest(tflite::BuiltinOperator convolutionOperatorCode,
using namespace tflite;
std::vector<char> modelBuffer;
+
modelBuffer = CreateConv2dTfLiteModel(convolutionOperatorCode,
tensorType,
strideX,
@@ -301,6 +304,261 @@ void ConvolutionTest(tflite::BuiltinOperator convolutionOperatorCode,
}
}
+// Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5.
+#if defined(ARMNN_POST_TFLITE_2_5)
+template <typename T, typename B = float>
+std::vector<char> CreateConv3dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<uint32_t> strides,
+ std::vector<uint32_t> dilation,
+ tflite::Padding padding,
+ tflite::ActivationFunctionType fused_activation_function,
+ const std::vector<int32_t>& inputTensorShape,
+ const std::vector<int32_t>& filterTensorShape,
+ const std::vector<int32_t>& biasTensorShape,
+ const std::vector<int32_t>& outputTensorShape,
+ const std::vector<T>& filterData,
+ const std::vector<B>& biasData,
+ const std::vector<float> biasScales = {1.0f},
+ const std::vector<int64_t> biasOffsets = {0},
+ const std::vector<float> filterScales = {1.0f},
+ const std::vector<int64_t> filterOffsets = {0},
+ float outputQuantScale = 2.0f,
+ int outputQuantOffset = 0,
+ float quantScale = 1.0f,
+ int quantOffset = 0,
+ int32_t depth_multiplier = 1,
+ int32_t filterQuantizationDim = 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*>(filterData.data()),
+ sizeof(T) * filterData.size()));
+
+ buffers[2] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()),
+ sizeof(B) * biasData.size()));
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+ auto outputQuantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
+
+ auto filterQuantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>(filterScales),
+ flatBufferBuilder.CreateVector<int64_t>(filterOffsets),
+ tflite::QuantizationDetails_NONE,
+ 0,
+ filterQuantizationDim);
+
+ auto biasQuantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>(biasScales),
+ flatBufferBuilder.CreateVector<int64_t>(biasOffsets));
+
+ std::array<flatbuffers::Offset<Tensor>, 4> 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>(filterTensorShape.data(),
+ filterTensorShape.size()),
+ tensorType,
+ 1,
+ flatBufferBuilder.CreateString("filter"),
+ filterQuantizationParameters);
+
+ auto biasTensorType = ::tflite::TensorType_FLOAT32;
+ if (tensorType == ::tflite::TensorType_INT8 || tensorType == ::tflite::TensorType_UINT8)
+ {
+ biasTensorType = ::tflite::TensorType_INT32;
+ }
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), biasTensorShape.size()),
+ biasTensorType,
+ 2,
+ flatBufferBuilder.CreateString("bias"),
+ biasQuantizationParameters);
+ tensors[3] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"),
+ outputQuantizationParameters);
+
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv3DOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateConv3DOptions(flatBufferBuilder,
+ padding,
+ strides[2], // Depth
+ strides[0], // Width
+ strides[1], // Height
+ fused_activation_function,
+ dilation[2],
+ dilation[0],
+ dilation[1]).Union();
+
+ // Create operator
+ const std::vector<int> operatorInputs{0, 1, 2};
+ const std::vector<int> operatorOutputs{3};
+ flatbuffers::Offset <Operator> convolutionOperator =
+ 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};
+ 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(&convolutionOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Convolution 3d Operator Model");
+
+ // If using an operator with a code greater than 127 then the enum value should be passed as the fifth
+ // parameter rather than the second like in other tests.
+ flatbuffers::Offset <OperatorCode> operatorCode =
+ CreateOperatorCode(flatBufferBuilder, 0, 0, 1, tflite::BuiltinOperator_CONV_3D);
+
+ 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, typename B = float>
+void Convolution3dTest(tflite::BuiltinOperator convolutionOperatorCode,
+ tflite::TensorType tensorType,
+ std::vector<uint32_t> strides,
+ std::vector<uint32_t> dilation,
+ tflite::Padding padding,
+ tflite::ActivationFunctionType fused_activation_function,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<int32_t>& inputShape,
+ std::vector<int32_t>& filterShape,
+ std::vector<int32_t>& outputShape,
+ std::vector<T>& inputValues,
+ std::vector<T>& filterValues,
+ std::vector<T>& expectedOutputValues,
+ const std::vector<int32_t>& biasShape = {},
+ const std::vector<B>& biasValues = {},
+ const std::vector<float> biasScales = {1.0f},
+ const std::vector<int64_t> biasOffsets = {0},
+ const std::vector<float> filterScales = {1.0f},
+ const std::vector<int64_t> filterOffsets = {0},
+ float outputQuantScale = 2.0f,
+ int outputQuantOffset = 0,
+ float quantScale = 1.0f,
+ int quantOffset = 0,
+ int32_t depth_multiplier = 1,
+ int32_t filterQuantizationDim = 3)
+{
+ using namespace tflite;
+
+ std::vector<char> modelBuffer;
+ modelBuffer = CreateConv3dTfLiteModel(convolutionOperatorCode,
+ tensorType,
+ strides,
+ dilation,
+ padding,
+ fused_activation_function,
+ inputShape,
+ filterShape,
+ biasShape,
+ outputShape,
+ filterValues,
+ biasValues,
+ biasScales,
+ biasOffsets,
+ filterScales,
+ filterOffsets,
+ outputQuantScale,
+ outputQuantOffset,
+ quantScale,
+ quantOffset,
+ depth_multiplier,
+ filterQuantizationDim);
+
+ 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
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues);
+
+ // Run EnqueueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+ auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
+ auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+ auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
+
+ armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size(), 1);
+ armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size(), 1);
+ armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size(), 1);
+}
+#endif
+
template <typename T>
std::vector<char> CreateTransposeConvTfLiteModel(tflite::TensorType tensorType,
uint32_t strideX,
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-01 16:23:24 +0000