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| author | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-14 12:10:28 +0000 |
|---|---|---|
| committer | Teresa Charlin <teresa.charlinreyes@arm.com> | 2023-03-28 11:41:55 +0100 |
| commit | ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9 (patch) | |
| tree | a5b8e1ad68a2437f007338f0b6195ca5ed2bddc3 /delegate/src | |
| parent | 9cb3466b677a1048b8abb24661e92c4c83fdda04 (diff) | |
| download | armnn-ad1b3d7518429e2d16a2695d9b0bbf81b6565ac9.tar.gz | |
IVGCVSW-7555 Restructure Delegate
* New folders created:
* common is for common code where TfLite API is not used
* classic is for existing delegate implementations
* opaque is for new opaque delegate implementation,
* tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed
Diffstat (limited to 'delegate/src')
122 files changed, 0 insertions, 33487 deletions
diff --git a/delegate/src/Activation.hpp b/delegate/src/Activation.hpp deleted file mode 100644 index 59066d23e3..0000000000 --- a/delegate/src/Activation.hpp +++ /dev/null @@ -1,133 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.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> - -namespace armnnDelegate -{ - -TfLiteStatus ValidateActivationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& outputInfo, - armnn::ActivationDescriptor& activationDesc) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("ACTIVATION", - tfLiteContext, - IsActivationSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo, - outputInfo, - activationDesc); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus VisitActivationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::ActivationDescriptor activationDesc; - switch(operatorCode) - { - case kTfLiteBuiltinRelu: - { - activationDesc.m_Function = armnn::ActivationFunction::ReLu; - break; - } - case kTfLiteBuiltinRelu6: - { - activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; - activationDesc.m_A = 6.0f; - break; - } - case kTfLiteBuiltinLogistic: - { - activationDesc.m_Function = armnn::ActivationFunction::Sigmoid; - break; - } - case kTfLiteBuiltinTanh: - { - activationDesc.m_Function = armnn::ActivationFunction::TanH; - activationDesc.m_A = 1.0f; - activationDesc.m_B = 1.0f; - break; - } - case kTfLiteBuiltinElu: - { - activationDesc.m_Function = armnn::ActivationFunction::Elu; - activationDesc.m_A = 1.0f; - break; - } - case kTfLiteBuiltinHardSwish: - { - activationDesc.m_Function = armnn::ActivationFunction::HardSwish; - break; - } - default: - { - return kTfLiteError; - } - } - if (!delegateData.m_Network) - { - return ValidateActivationOperator(delegateData, - tfLiteContext, - inputTensorInfo, - outputTensorInfo, - activationDesc); - } - armnn::IConnectableLayer* activationLayer = delegateData.m_Network->AddActivationLayer(activationDesc); - ARMNN_ASSERT(activationLayer != nullptr); - - armnn::IOutputSlot& outputSlot = activationLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(activationLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(activationLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/ArgMinMax.hpp b/delegate/src/ArgMinMax.hpp deleted file mode 100644 index 4e4a2a3f3a..0000000000 --- a/delegate/src/ArgMinMax.hpp +++ /dev/null @@ -1,132 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <tensorflow/lite/builtin_ops.h> -#include <tensorflow/lite/c/builtin_op_data.h> -#include <tensorflow/lite/c/common.h> -#include <tensorflow/lite/kernels/internal/tensor_ctypes.h> -#include <tensorflow/lite/minimal_logging.h> - -namespace armnnDelegate -{ - -TfLiteStatus VisitArgMinMaxOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t argMinMaxOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, argMinMaxOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, argMinMaxOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Get const axis value from model and set it to descriptor. - const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteAxisTensor, argMinMaxOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - armnn::ArgMinMaxDescriptor desc; - // Get the axis value from the input tensor - switch (tfLiteAxisTensor.type) - { - case kTfLiteInt32: - case kTfLiteInt64: - desc.m_Axis = tflite::GetTensorData<int>(&tfLiteAxisTensor)[0]; - break; - default: - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Axis value data type is not supported in operator #%d node #%d: ", - argMinMaxOperatorCode, nodeIndex); - return kTfLiteError; - } - - // If output_type is int32 then set Signed32 else Signed64. Default type is Signed64. - if (argMinMaxOperatorCode == kTfLiteBuiltinArgMax) - { - desc.m_Function = armnn::ArgMinMaxFunction::Max; - auto* argMaxParameters = reinterpret_cast<TfLiteArgMaxParams*>(tfLiteNode->builtin_data); - if (argMaxParameters->output_type != kTfLiteInt32 && argMaxParameters->output_type != kTfLiteInt64) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ", - argMinMaxOperatorCode, nodeIndex); - return kTfLiteError; - } - } - else - { - desc.m_Function = armnn::ArgMinMaxFunction::Min; - auto* argMinParameters = reinterpret_cast<TfLiteArgMinParams*>(tfLiteNode->builtin_data); - if (argMinParameters->output_type != kTfLiteInt32 && argMinParameters->output_type != kTfLiteInt64) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ", - argMinMaxOperatorCode, nodeIndex); - return kTfLiteError; - } - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("ARGMINMAX", - tfLiteContext, - IsArgMinMaxSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - desc); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add an ArgMinMax layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddArgMinMaxLayer(desc); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/BatchMatMul.hpp b/delegate/src/BatchMatMul.hpp deleted file mode 100644 index 49fba05238..0000000000 --- a/delegate/src/BatchMatMul.hpp +++ /dev/null @@ -1,107 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include <algorithm> -#include <iterator> -#include <string> -#include <vector> - -namespace armnnDelegate -{ - TfLiteStatus VisitBatchMatMulOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) - { - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& kTfLiteLHSInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - const TfLiteTensor& kTfLiteRHSInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - - if (!IsValid(tfLiteContext, kTfLiteLHSInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - if (!IsValid(tfLiteContext, kTfLiteRHSInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - if (IsDynamicTensor(kTfLiteLHSInputTensor) || IsDynamicTensor(kTfLiteRHSInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& kTfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (IsDynamicTensor(kTfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& armnnLHSInputTensorInfo = GetTensorInfoForTfLiteTensor(kTfLiteLHSInputTensor); - const armnn::TensorInfo& armnnRHSInputTensorInfo = GetTensorInfoForTfLiteTensor(kTfLiteRHSInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(kTfLiteOutputTensor, true); - - armnn::BatchMatMulDescriptor descriptor; - auto* params = reinterpret_cast<TfLiteBatchMatMulParams *>(tfLiteNode->builtin_data); - - // Tensorflow params are called adjoint, however they are actually just transposes behind the scene. They do - // not perform ajoint. - descriptor.m_TransposeX = params->adj_x; - descriptor.m_TransposeY = params->adj_y; - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("BATCH_MATMUL", - tfLiteContext, - IsBatchMatMulSupported, - delegateData.m_Backends, - isSupported, - setBackend, - armnnLHSInputTensorInfo, - armnnRHSInputTensorInfo, - outputTensorInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddBatchMatMulLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - return Connect(layer, tfLiteNode, delegateData); - } -} // namespace armnnDelegate diff --git a/delegate/src/BatchSpace.hpp b/delegate/src/BatchSpace.hpp deleted file mode 100644 index 30c6dbfc15..0000000000 --- a/delegate/src/BatchSpace.hpp +++ /dev/null @@ -1,216 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitBatchToSpaceNdOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteBlockShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteBlockShapeTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteCropsTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if (!IsValid(tfLiteContext, tfLiteCropsTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& blockShapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBlockShapeTensor); - const armnn::TensorInfo& cropsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteCropsTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements()); - ::memcpy(blockShape.data(), tfLiteBlockShapeTensor.data.data, blockShapeTensorInfo.GetNumBytes()); - - std::vector<unsigned int> cropsVector(cropsTensorInfo.GetNumElements()); - std::memcpy(cropsVector.data(), tfLiteCropsTensor.data.data, cropsTensorInfo.GetNumBytes()); - - size_t step = 2; - std::vector<std::pair<unsigned int, unsigned int>> crops; - for (unsigned int i = 0; i < cropsTensorInfo.GetNumElements() / step; ++i) - { - crops.emplace_back(cropsVector[i * step], cropsVector[i * step + 1]); - } - - armnn::BatchToSpaceNdDescriptor descriptor; - descriptor.m_BlockShape = blockShape; - descriptor.m_Crops = crops; - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("BATCH_TO_SPACE_ND", - tfLiteContext, - IsBatchToSpaceNdSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor); - }; - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitBatchToSpaceNdOperator will be called again to add the layer to the network as seen below - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a BatchToSpace layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddBatchToSpaceNdLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -TfLiteStatus VisitSpaceToBatchNdOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteBlockShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteBlockShapeTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLitePadListTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if (!IsValid(tfLiteContext, tfLitePadListTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& blockShapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBlockShapeTensor); - const armnn::TensorInfo& padListTensorInfo = GetTensorInfoForTfLiteTensor(tfLitePadListTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - std::vector<unsigned int> blockShape(blockShapeTensorInfo.GetNumElements()); - std::memcpy(blockShape.data(), tfLiteBlockShapeTensor.data.data, blockShapeTensorInfo.GetNumBytes()); - - std::vector<unsigned int> padListVector(padListTensorInfo.GetNumElements()); - std::memcpy(padListVector.data(), tfLitePadListTensor.data.data, padListTensorInfo.GetNumBytes()); - - size_t step = 2; - std::vector<std::pair<unsigned int, unsigned int>> padList; - for (unsigned int i = 0; i < padListTensorInfo.GetNumElements() / step; ++i) - { - padList.emplace_back(padListVector[i * step], padListVector[i * step + 1]); - } - - armnn::SpaceToBatchNdDescriptor descriptor; - descriptor.m_BlockShape = blockShape; - descriptor.m_PadList = padList; - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("SPACE_TO_BATCH_ND", - tfLiteContext, - IsSpaceToBatchNdSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor); - }; - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitSpaceToBatchNdOperator will be called again to add the layer to the network as seen below - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a SpaceToBatch layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddSpaceToBatchNdLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Comparison.hpp b/delegate/src/Comparison.hpp deleted file mode 100644 index 688f90c597..0000000000 --- a/delegate/src/Comparison.hpp +++ /dev/null @@ -1,135 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitComparisonOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteComparisonOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (IsDynamicTensor(tfLiteInputTensor0)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - tfLiteComparisonOperatorCode, nodeIndex); - - return kTfLiteError; - } - - const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (IsDynamicTensor(tfLiteInputTensor1)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - tfLiteComparisonOperatorCode, 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: ", - tfLiteComparisonOperatorCode, nodeIndex); - return kTfLiteError; - } - - armnn::TensorInfo inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); - armnn::TensorInfo inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Check if we need to expand the dims of any of the input tensor infos. - // This is required for a few of the backends. - if(inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions()) - { - ExpandTensorRankToEqual(inputTensorInfo0, inputTensorInfo1); - } - - armnn::ComparisonOperation comparisonOperation = armnn::ComparisonOperation::Equal; - switch(tfLiteComparisonOperatorCode) - { - case kTfLiteBuiltinEqual: - comparisonOperation = armnn::ComparisonOperation::Equal; - break; - case kTfLiteBuiltinGreater: - comparisonOperation = armnn::ComparisonOperation::Greater; - break; - case kTfLiteBuiltinGreaterEqual: - comparisonOperation = armnn::ComparisonOperation::GreaterOrEqual; - break; - case kTfLiteBuiltinLess: - comparisonOperation = armnn::ComparisonOperation::Less; - break; - case kTfLiteBuiltinLessEqual: - comparisonOperation = armnn::ComparisonOperation::LessOrEqual; - break; - case kTfLiteBuiltinNotEqual: - comparisonOperation = armnn::ComparisonOperation::NotEqual; - break; - default: - return kTfLiteError; - } - - armnn::ComparisonDescriptor descriptor(comparisonOperation); - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("COMPARISON", - tfLiteContext, - IsComparisonSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* comparisonLayer = delegateData.m_Network->AddComparisonLayer(descriptor); - comparisonLayer->SetBackendId(setBackend); - ARMNN_ASSERT(comparisonLayer != nullptr); - - armnn::IOutputSlot& outputSlot = comparisonLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(comparisonLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - return Connect(comparisonLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Control.hpp b/delegate/src/Control.hpp deleted file mode 100644 index a3ea6e92a7..0000000000 --- a/delegate/src/Control.hpp +++ /dev/null @@ -1,342 +0,0 @@ -// -// Copyright © 2022-2023 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/kernels/internal/tensor_ctypes.h> -#include <tensorflow/lite/minimal_logging.h> - -#include <algorithm> -#include <iterator> -#include <string> -#include <vector> - -namespace armnnDelegate -{ - -void SetupConcatViewOrigin(const armnn::TensorInfo& inputTensorInfo, - armnn::OriginsDescriptor& concatDescriptor, - const unsigned int concatAxis, - unsigned int inputIndex, - unsigned int& mergeDimOrigin) -{ - const uint32_t inputRank = concatDescriptor.GetNumDimensions(); - - // double check dimensions of the tensors - if (inputTensorInfo.GetNumDimensions() != inputRank) - { - throw armnn::ParseException("The number of dimensions for input tensors " - "of the concatenation operator should be: " + std::to_string(inputRank)); - } - - for (unsigned int j = 0; j < concatAxis; ++j) - { - concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); - } - - concatDescriptor.SetViewOriginCoord(inputIndex, concatAxis, mergeDimOrigin); - mergeDimOrigin += inputTensorInfo.GetShape()[concatAxis]; - - for (unsigned int j = concatAxis + 1; j < inputRank; ++j) - { - concatDescriptor.SetViewOriginCoord(inputIndex, j, 0); - } -} - -TfLiteStatus VisitConcatenationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteConcatOperatorCode) -{ - unsigned int numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - std::vector<armnn::TensorInfo> inputTensorInfos; - for (unsigned int i = 0; i < numInputs; ++i) - { - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[i]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteConcatOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - inputTensorInfos.emplace_back(inputTensorInfo); - } - - // Convert input tensors to const armnn::TensorInfo* type for FORWARD_LAYER_SUPPORT_FUNC. - std::vector<const armnn::TensorInfo*> inputConstTensorInfos; - std::transform(inputTensorInfos.begin(), - inputTensorInfos.end(), - std::back_inserter(inputConstTensorInfos), - [](armnn::TensorInfo& t)->const armnn::TensorInfo*{ return &t; }); - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteConcatOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - // Setup OriginsDescriptor, axis and view origin - unsigned int numConcatView = static_cast<unsigned int>(numInputs); - uint32_t inputRank = tfLiteTensors[tfLiteNode->inputs->data[0]].dims->size; - - auto* concatenationParameters = reinterpret_cast<TfLiteConcatenationParams*>(tfLiteNode->builtin_data); - - if(!concatenationParameters) - { - throw armnn::Exception(&"TfLiteArmnnDelegate: Concat parameters are null in: " [ nodeIndex]); - } - - const unsigned int concatDimInput = static_cast<unsigned int>( - (static_cast<int>(inputRank) + concatenationParameters->axis) % static_cast<int>(inputRank)); - - armnn::OriginsDescriptor concatDescriptor(static_cast<uint32_t>(numConcatView), inputRank); - concatDescriptor.SetConcatAxis(concatDimInput); - - unsigned int mergeDimOrigin = 0; - for (unsigned int viewIndex = 0; viewIndex < numConcatView; ++viewIndex) - { - armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor( - tfLiteTensors[tfLiteNode->inputs->data[viewIndex]]); - - // Sets up concatDescriptor view origin - SetupConcatViewOrigin(inputTensorInfo, concatDescriptor, concatDimInput, viewIndex, mergeDimOrigin); - } - - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Verify we support the fused activation before attempting to create a layer - TfLiteFusedActivation activationType = concatenationParameters->activation; - - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("CONCATENATION", - tfLiteContext, - IsConcatSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputConstTensorInfos, - outputTensorInfo, - concatDescriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Setup layer and connect. - armnn::IConnectableLayer* concatenationLayer = delegateData.m_Network->AddConcatLayer(concatDescriptor); - concatenationLayer->SetBackendId(setBackend); - ARMNN_ASSERT(concatenationLayer != nullptr); - - // Connect the Constant Inputs - auto inputsTensorsProcess = ProcessInputs(concatenationLayer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - armnn::IOutputSlot& outputSlot = concatenationLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - if(Connect(concatenationLayer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - if (activationType == kTfLiteActNone) - { - // No Activation - return kTfLiteOk; - } - - // Check and Create activation - return FusedActivation(tfLiteContext, tfLiteNode, activationType, concatenationLayer, 0, delegateData); -} - -TfLiteStatus VisitMeanOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteMeanOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if(!IsValid(&tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", - tfLiteMeanOperatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - tfLiteMeanOperatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if(!IsValid(&tfLiteAxisTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid axis tensor in operator #%d node #%d: ", - tfLiteMeanOperatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteAxisTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic axis tensors are not supported in operator #%d node #%d: ", - tfLiteMeanOperatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if(!IsValid(&tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", - tfLiteAxisTensor, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", - tfLiteMeanOperatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* axisTensorData = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); - - std::vector<int32_t> axis; - // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis. - for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i) - { - axis.emplace_back(axisTensorData[i]); - } - - // Convert the axis to unsigned int and remove duplicates. - unsigned int rank = inputTensorInfo.GetNumDimensions(); - std::set<unsigned int> uniqueAxis; - std::transform(axis.begin(), - axis.end(), - std::inserter(uniqueAxis, uniqueAxis.begin()), - [rank](int i)->unsigned int{ return (i + rank) % rank; }); - - // Setup MeanDescriptor and assign axis and keepDims - armnn::MeanDescriptor desc; - desc.m_Axis.assign(uniqueAxis.begin(), uniqueAxis.end()); - desc.m_KeepDims = inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ? true : false; - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("MEAN", - tfLiteContext, - IsMeanSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - desc); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Setup layer and connect. - armnn::IConnectableLayer* meanLayer = delegateData.m_Network->AddMeanLayer(desc); - meanLayer->SetBackendId(setBackend); - ARMNN_ASSERT(meanLayer != nullptr); - - armnn::IOutputSlot& outputSlot = meanLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(meanLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - return Connect(meanLayer, tfLiteNode, delegateData); -} - -TfLiteStatus VisitControlOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - armnn::IgnoreUnused(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - operatorCode); - - switch(operatorCode) - { - case kTfLiteBuiltinConcatenation: - return VisitConcatenationOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); - case kTfLiteBuiltinMean: - return VisitMeanOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); - default: - return kTfLiteError; - } -} - -} // namespace armnnDelegate diff --git a/delegate/src/Convolution.hpp b/delegate/src/Convolution.hpp deleted file mode 100644 index 31cb2ab9ac..0000000000 --- a/delegate/src/Convolution.hpp +++ /dev/null @@ -1,870 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include "SharedFunctions.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.h" - -namespace armnnDelegate -{ - -TfLiteStatus VisitConv2dOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - armnn::Convolution2dDescriptor descriptor; - const auto params = reinterpret_cast<TfLiteConvParams*>(tfLiteNode->builtin_data); - - bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); - descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); - descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if(!IsValid(&tfLiteTensors[tfLiteNode->inputs->data[0]])) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if(!IsValid(&tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if(!IsValid(&tfLiteFilterTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteFilterTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ", - nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConvParams*>(tfLiteNode->builtin_data); - TfLiteFusedActivation activationType=kTfLiteActNone; - if (tfLiteNodeParameters) - { - activationType = tfLiteNodeParameters->activation; - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - } - - const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); - - armnn::TensorInfo biasTensorInfo; - if(biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(!IsValid(&tfLiteBiasTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteBiasTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", - nodeIndex); - return kTfLiteError; - } - biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); - } - else - { - biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); - } - - armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); - - // TfLite uses NHWC tensors - const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; - const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; - - const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; - const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; - - // Calculate padding - CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, - descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); - CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, - descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("CONV2D", - tfLiteContext, - IsConvolution2dSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor, - filterTensorInfo, - optionalBiasInfo); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Set up filter and biases - armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution2dLayer(descriptor); - layer->SetBackendId(setBackend); - - if(filterTensorInfo.IsConstant()) - { - auto filter = - CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[1]], - filterTensorInfo); - - armnn::IConnectableLayer *weightsLayer = delegateData.m_Network->AddConstantLayer(filter); - weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); - weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); - } - - if (biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(biasTensorInfo.IsConstant()) - { - auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo); - armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); - ARMNN_ASSERT(biasLayer != nullptr); - biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); - biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); - } - } - - // The data input can also be constant, so we must check that this is also allocated to an input slot - if(inputTensorInfo.IsConstant()) - { - auto input = - CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], - inputTensorInfo); - - armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); - inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); - inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); - } - - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - if (!tfLiteNodeParameters) - { - // No Activation - return kTfLiteOk; - } - // Check and Create activation - return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); - -} - -// 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) -TfLiteStatus VisitConv3dOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - armnn::Convolution3dDescriptor descriptor; - const auto params = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); - - bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_DataLayout = armnn::DataLayout::NDHWC; - descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); - descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); - descriptor.m_StrideZ = NonNegative(params->stride_depth, nodeIndex); - descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); - descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); - descriptor.m_DilationZ = NonNegative(params->dilation_depth_factor, nodeIndex); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); - TfLiteFusedActivation activationType=kTfLiteActNone; - if (tfLiteNodeParameters) - { - activationType = tfLiteNodeParameters->activation; - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - } - - const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); - - armnn::TensorInfo biasTensorInfo; - if(biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); - } - else - { - biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); - } - - armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); - - // TfLite uses NDHWC tensors - const unsigned int inputDepth = inputTensorInfo.GetShape()[1]; - const unsigned int inputHeight = inputTensorInfo.GetShape()[2]; - const unsigned int inputWidth = inputTensorInfo.GetShape()[3]; - - // Assuming the filter is DHWIO : Depth, Height, Width, OutputChannels, InputChannels - const unsigned int filterDepth = filterTensorInfo.GetShape()[0]; - const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; - const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; - - // Calculate padding - CalcPadding(inputDepth, filterDepth, descriptor.m_StrideZ, descriptor.m_DilationZ, - descriptor.m_PadFront, descriptor.m_PadBack, params->padding); - CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, - descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); - CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, - descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitConvolutionOperator will be called again to add the layer to the network as seen below. - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("CONV3D", - tfLiteContext, - IsConvolution3dSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor, - filterTensorInfo, - optionalBiasInfo); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution3dLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - // Add a constant layer for weights and biases if inputs are constant, - // which are connected to the Convolution3d layer as inputs. - if (filterTensorInfo.IsConstant()) - { - auto filter = CreateConstTensor(&tfLiteFilterTensor, - filterTensorInfo); - - armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); - ARMNN_ASSERT(weightsLayer != nullptr); - - weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); - weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); - } - - if(biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(biasTensorInfo.IsConstant()) - { - auto biases = CreateConstTensor(&tfLiteBiasTensor, - biasTensorInfo); - - armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases); - ARMNN_ASSERT(biasLayer != nullptr); - - biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); - biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); - } - } - - // The data input can also be constant, so we must check that this is also allocated to an input slot - if(inputTensorInfo.IsConstant()) - { - auto input = - CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], - inputTensorInfo); - - armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); - inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); - inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); - } - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - if (!tfLiteNodeParameters) - { - // No Activation - return kTfLiteOk; - } - - // Check and create activation - return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); -} -#endif - -TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); - - armnn::DepthwiseConvolution2dDescriptor descriptor; - const auto params = reinterpret_cast<TfLiteDepthwiseConvParams*>(tfLiteNode->builtin_data); - - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); - descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); - descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if(!IsValid(&tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if(!IsValid(&tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if(!IsValid(&tfLiteFilterTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteFilterTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ", - nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* tfLiteNodeParameters = reinterpret_cast<TfLiteDepthwiseConvParams *>(tfLiteNode->builtin_data); - TfLiteFusedActivation activationType = kTfLiteActNone; - if (tfLiteNodeParameters) - { - activationType = tfLiteNodeParameters->activation; - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - } - - const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); - - // Assuming input is NHWC - unsigned int inputHeight = inputTensorInfo.GetShape()[1]; - unsigned int inputWidth = inputTensorInfo.GetShape()[2]; - - // TensorflowLite weights come in the format [1, H, W, I * M] - unsigned int filterHeight = filterTensorInfo.GetShape()[1]; - unsigned int filterWidth = filterTensorInfo.GetShape()[2]; - - // Calculate padding - CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, - descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); - CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, - descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); - - armnn::TensorInfo biasTensorInfo; - if(biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(!IsValid(&tfLiteBiasTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteBiasTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", - nodeIndex); - return kTfLiteError; - } - biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); - } - else - { - biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); - } - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("DEPTHWISE_CONV2D", - tfLiteContext, - IsDepthwiseConvolutionSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor, - filterTensorInfo, - armnn::Optional<armnn::TensorInfo>(biasTensorInfo)); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor); - layer->SetBackendId(setBackend); - - if(filterTensorInfo.IsConstant()) - { - // For depthwise the weights layout is the same as for tflite [1, H, W, I*M]. No permutation required. - auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo); - - armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); - weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); - weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); - } - - if (biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(biasTensorInfo.IsConstant()) - { - auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo); - armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); - ARMNN_ASSERT(biasLayer != nullptr); - biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); - biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); - } - } - - // The data input can also be constant, so we must check that this is also allocated to an input slot - if(inputTensorInfo.IsConstant()) - { - auto input = - CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], - inputTensorInfo); - - armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); - inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); - inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); - } - - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - if (!tfLiteNodeParameters) - { - // No Activation - return kTfLiteOk; - } - // Check and create activation - return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); -} - -TfLiteStatus VisitTransposeConv2dOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - armnn::TransposeConvolution2dDescriptor descriptor; - auto* parameters = reinterpret_cast<TfLiteTransposeConvParams*>(tfLiteNode->builtin_data); - descriptor.m_BiasEnabled = false; - descriptor.m_StrideX = NonNegative(parameters->stride_width, nodeIndex); - descriptor.m_StrideY = NonNegative(parameters->stride_height, nodeIndex); - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteOutputShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if(!IsValid(&tfLiteOutputShapeTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteOutputShapeTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo outputShapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputShapeTensor); - std::vector<int32_t> outputShape(outputShapeTensorInfo.GetNumElements()); - if (outputShapeTensorInfo.GetDataType() == armnn::DataType::Signed32) - { - for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); i++) - { - outputShape[i] = ::tflite::GetTensorData<int32_t>(&tfLiteOutputShapeTensor)[i]; - } - } - - if (outputShapeTensorInfo.GetDataType() == armnn::DataType::QAsymmU8) - { - for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); i++) - { - outputShape[i] = ::tflite::GetTensorData<uint8_t>(&tfLiteOutputShapeTensor)[i]; - } - } - // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor. - for (int dimension : outputShape) - { - descriptor.m_OutputShape.push_back(static_cast<unsigned int>(dimension)); - } - descriptor.m_OutputShapeEnabled = true; - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(!IsValid(&tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if(!IsValid(&tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if(!IsValid(&tfLiteFilterTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - if (IsDynamicTensor(tfLiteFilterTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); - - // TfLite uses NHWC tensors - const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; - const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; - - const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; - const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; - - // Calculate padding - CalcPadding(inputHeight, - filterHeight, - descriptor.m_StrideY, - 1, // dilation y - descriptor.m_PadTop, - descriptor.m_PadBottom, - parameters->padding); - CalcPadding(inputWidth, - filterWidth, - descriptor.m_StrideX, - 1, // dilation x - descriptor.m_PadLeft, - descriptor.m_PadRight, - parameters->padding); - - // Set up filter - auto filterTensor = CreateConstTensor(&tfLiteFilterTensor, - filterTensorInfo); - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("TRANSPOSE_CONV2D", - tfLiteContext, - IsTransposeConvolution2dSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor, - filterTensorInfo, - armnn::EmptyOptional()); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddTransposeConvolution2dLayer(descriptor, - filterTensor, - armnn::EmptyOptional()); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - // The data input can be constant, so we must check that this is allocated to an input slot - if(inputTensorInfo.IsConstant()) - { - auto input = - CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[2]], - inputTensorInfo); - - armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); - inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); - inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); - } - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // Connect - if (delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->inputs->data[2])] != nullptr) - { - delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->inputs->data[2])]-> - Connect(layer->GetInputSlot(0)); - } - - // Prepare output slots - for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) - { - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); - delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->outputs->data[outputIndex])] = - &outputSlot; - } - return kTfLiteOk; -} - -TfLiteStatus VisitConvolutionOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - switch(operatorCode) - { - case kTfLiteBuiltinConv2d: - return VisitConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); -// 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) - case kTfLiteBuiltinConv3d: - return VisitConv3dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); -#endif - case kTfLiteBuiltinDepthwiseConv2d: - return VisitDepthwiseConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); - case kTfLiteBuiltinTransposeConv: - return VisitTransposeConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); - default: - return kTfLiteError; - } -} - -} // namespace armnnDelegate diff --git a/delegate/src/DelegateOptions.cpp b/delegate/src/DelegateOptions.cpp deleted file mode 100644 index fc4858fa29..0000000000 --- a/delegate/src/DelegateOptions.cpp +++ /dev/null @@ -1,256 +0,0 @@ -// -// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <DelegateOptions.hpp> -#include <armnn/utility/NumericCast.hpp> -#include <armnn/utility/StringUtils.hpp> - -namespace armnnDelegate -{ - -DelegateOptions::DelegateOptions(armnn::Compute computeDevice, - const std::vector<armnn::BackendOptions>& backendOptions, - const armnn::Optional<armnn::LogSeverity> logSeverityLevel) - : m_Backends({computeDevice}), m_RuntimeOptions(), m_LoggingSeverity(logSeverityLevel) -{ - m_RuntimeOptions.m_BackendOptions = backendOptions; -} - -DelegateOptions::DelegateOptions(const std::vector<armnn::BackendId>& backends, - const std::vector<armnn::BackendOptions>& backendOptions, - const armnn::Optional<armnn::LogSeverity> logSeverityLevel) - : m_Backends(backends), m_RuntimeOptions(), m_LoggingSeverity(logSeverityLevel) -{ - m_RuntimeOptions.m_BackendOptions = backendOptions; -} - -DelegateOptions::DelegateOptions(armnn::Compute computeDevice, - const armnn::OptimizerOptions& optimizerOptions, - const armnn::Optional<armnn::LogSeverity>& logSeverityLevel, - const armnn::Optional<armnn::DebugCallbackFunction>& func) - : m_Backends({computeDevice}), - m_RuntimeOptions(), - m_OptimizerOptions(optimizerOptions), - m_LoggingSeverity(logSeverityLevel), - m_DebugCallbackFunc(func) -{ -} - -DelegateOptions::DelegateOptions(const std::vector<armnn::BackendId>& backends, - const armnn::OptimizerOptions& optimizerOptions, - const armnn::Optional<armnn::LogSeverity>& logSeverityLevel, - const armnn::Optional<armnn::DebugCallbackFunction>& func) - : m_Backends(backends), - m_RuntimeOptions(), - m_OptimizerOptions(optimizerOptions), - m_LoggingSeverity(logSeverityLevel), - m_DebugCallbackFunc(func) -{ -} - -DelegateOptions::DelegateOptions(char const* const* options_keys, - char const* const* options_values, - size_t num_options, - void (*report_error)(const char*)) -{ - armnn::IRuntime::CreationOptions runtimeOptions; - armnn::OptimizerOptions optimizerOptions; - bool internalProfilingState = false; - armnn::ProfilingDetailsMethod internalProfilingDetail = armnn::ProfilingDetailsMethod::DetailsWithEvents; - for (size_t i = 0; i < num_options; ++i) - { - // Process backends - if (std::string(options_keys[i]) == std::string("backends")) - { - // The backend option is a comma separated string of backendIDs that needs to be split - std::vector<armnn::BackendId> backends; - char* dup = strdup(options_values[i]); - char* pch = std::strtok(dup, ","); - while (pch != NULL) - { - backends.push_back(pch); - pch = strtok (NULL, ","); - } - this->SetBackends(backends); - } - // Process dynamic-backends-path - else if (std::string(options_keys[i]) == std::string("dynamic-backends-path")) - { - runtimeOptions.m_DynamicBackendsPath = std::string(options_values[i]); - } - // Process logging level - else if (std::string(options_keys[i]) == std::string("logging-severity")) - { - this->SetLoggingSeverity(options_values[i]); - } - // Process GPU backend options - else if (std::string(options_keys[i]) == std::string("gpu-tuning-level")) - { - armnn::BackendOptions option("GpuAcc", {{"TuningLevel", atoi(options_values[i])}}); - runtimeOptions.m_BackendOptions.push_back(option); - } - else if (std::string(options_keys[i]) == std::string("gpu-mlgo-tuning-file")) - { - armnn::BackendOptions option("GpuAcc", {{"MLGOTuningFilePath", std::string(options_values[i])}}); - optimizerOptions.m_ModelOptions.push_back(option); - } - else if (std::string(options_keys[i]) == std::string("gpu-tuning-file")) - { - armnn::BackendOptions option("GpuAcc", {{"TuningFile", std::string(options_values[i])}}); - runtimeOptions.m_BackendOptions.push_back(option); - } - else if (std::string(options_keys[i]) == std::string("gpu-enable-profiling")) - { - runtimeOptions.m_EnableGpuProfiling = (*options_values[i] != '0'); - } - else if (std::string(options_keys[i]) == std::string("gpu-kernel-profiling-enabled")) - { - armnn::BackendOptions option("GpuAcc", {{"KernelProfilingEnabled", - armnn::stringUtils::StringToBool(options_values[i])}}); - runtimeOptions.m_BackendOptions.push_back(option); - } - else if (std::string(options_keys[i]) == std::string("save-cached-network")) - { - armnn::BackendOptions option("GpuAcc", {{"SaveCachedNetwork", - armnn::stringUtils::StringToBool(options_values[i])}}); - optimizerOptions.m_ModelOptions.push_back(option); - } - else if (std::string(options_keys[i]) == std::string("cached-network-filepath")) - { - armnn::BackendOptions option("GpuAcc", {{"CachedNetworkFilePath", std::string(options_values[i])}}); - optimizerOptions.m_ModelOptions.push_back(option); - } - // Process GPU & CPU backend options - else if (std::string(options_keys[i]) == std::string("enable-fast-math")) - { - armnn::BackendOptions modelOptionGpu("GpuAcc", {{"FastMathEnabled", - armnn::stringUtils::StringToBool(options_values[i])}}); - optimizerOptions.m_ModelOptions.push_back(modelOptionGpu); - - armnn::BackendOptions modelOptionCpu("CpuAcc", {{"FastMathEnabled", - armnn::stringUtils::StringToBool(options_values[i])}}); - optimizerOptions.m_ModelOptions.push_back(modelOptionCpu); - } - // Process CPU backend options - else if (std::string(options_keys[i]) == std::string("number-of-threads")) - { - unsigned int numberOfThreads = armnn::numeric_cast<unsigned int>(atoi(options_values[i])); - armnn::BackendOptions modelOption("CpuAcc", {{"NumberOfThreads", numberOfThreads}}); - optimizerOptions.m_ModelOptions.push_back(modelOption); - } - // Process reduce-fp32-to-fp16 option - else if (std::string(options_keys[i]) == std::string("reduce-fp32-to-fp16")) - { - optimizerOptions.m_ReduceFp32ToFp16 = armnn::stringUtils::StringToBool(options_values[i]); - } - // Process debug-data - else if (std::string(options_keys[i]) == std::string("debug-data")) - { - optimizerOptions.m_Debug = armnn::stringUtils::StringToBool(options_values[i]); - } - // Infer output-shape - else if (std::string(options_keys[i]) == std::string("infer-output-shape")) - { - armnn::BackendOptions backendOption("ShapeInferenceMethod", - { - { "InferAndValidate", armnn::stringUtils::StringToBool(options_values[i]) } - }); - optimizerOptions.m_ModelOptions.push_back(backendOption); - } - // Allow expanded dims - else if (std::string(options_keys[i]) == std::string("allow-expanded-dims")) - { - armnn::BackendOptions backendOption("AllowExpandedDims", - { - { "AllowExpandedDims", armnn::stringUtils::StringToBool(options_values[i]) } - }); - optimizerOptions.m_ModelOptions.push_back(backendOption); - } - // Process memory-import - else if (std::string(options_keys[i]) == std::string("memory-import")) - { - optimizerOptions.m_ImportEnabled = armnn::stringUtils::StringToBool(options_values[i]); - } - // Process enable-internal-profiling - else if (std::string(options_keys[i]) == std::string("enable-internal-profiling")) - { - internalProfilingState = *options_values[i] != '0'; - optimizerOptions.m_ProfilingEnabled = internalProfilingState; - } - // Process internal-profiling-detail - else if (std::string(options_keys[i]) == std::string("internal-profiling-detail")) - { - uint32_t detailLevel = static_cast<uint32_t>(std::stoul(options_values[i])); - switch (detailLevel) - { - case 1: - internalProfilingDetail = armnn::ProfilingDetailsMethod::DetailsWithEvents; - break; - case 2: - internalProfilingDetail = armnn::ProfilingDetailsMethod::DetailsOnly; - break; - default: - internalProfilingDetail = armnn::ProfilingDetailsMethod::Undefined; - break; - } - } - // Process enable-external-profiling - else if (std::string(options_keys[i]) == std::string("enable-external-profiling")) - { - runtimeOptions.m_ProfilingOptions.m_EnableProfiling = - armnn::stringUtils::StringToBool(options_values[i]); - } - // Process timeline-profiling - else if (std::string(options_keys[i]) == std::string("timeline-profiling")) - { - runtimeOptions.m_ProfilingOptions.m_TimelineEnabled = armnn::stringUtils::StringToBool(options_values[i]); - } - // Process outgoing-capture-file - else if (std::string(options_keys[i]) == std::string("outgoing-capture-file")) - { - runtimeOptions.m_ProfilingOptions.m_OutgoingCaptureFile = options_values[i]; - } - // Process incoming-capture-file - else if (std::string(options_keys[i]) == std::string("incoming-capture-file")) - { - runtimeOptions.m_ProfilingOptions.m_IncomingCaptureFile = options_values[i]; - } - // Process file-only-external-profiling - else if (std::string(options_keys[i]) == std::string("file-only-external-profiling")) - { - runtimeOptions.m_ProfilingOptions.m_FileOnly = armnn::stringUtils::StringToBool(options_values[i]); - } - // Process counter-capture-period - else if (std::string(options_keys[i]) == std::string("counter-capture-period")) - { - runtimeOptions.m_ProfilingOptions.m_CapturePeriod = static_cast<uint32_t>(std::stoul(options_values[i])); - } - // Process profiling-file-format - else if (std::string(options_keys[i]) == std::string("profiling-file-format")) - { - runtimeOptions.m_ProfilingOptions.m_FileFormat = options_values[i]; - } - // Process serialize-to-dot - else if (std::string(options_keys[i]) == std::string("serialize-to-dot")) - { - this->SetSerializeToDot(options_values[i]); - } - - // Process disable-tflite-runtime-fallback - else if (std::string(options_keys[i]) == std::string("disable-tflite-runtime-fallback")) - { - this->DisableTfLiteRuntimeFallback(armnn::stringUtils::StringToBool(options_values[i])); - } - else - { - throw armnn::Exception("Unknown option for the ArmNN Delegate given: " + std::string(options_keys[i])); - } - } - - this->SetRuntimeOptions(runtimeOptions); - this->SetOptimizerOptions(optimizerOptions); - this->SetInternalProfilingParams(internalProfilingState, internalProfilingDetail); -} -} // namespace armnnDelegate diff --git a/delegate/src/DelegateUtils.hpp b/delegate/src/DelegateUtils.hpp deleted file mode 100644 index 1aa9029271..0000000000 --- a/delegate/src/DelegateUtils.hpp +++ /dev/null @@ -1,639 +0,0 @@ -// -// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn_delegate.hpp> - -#include <armnn/ArmNN.hpp> -#include <armnn/BackendHelper.hpp> -#include <armnn/utility/Assert.hpp> -#include <armnn/utility/NumericCast.hpp> - -#include <armnnUtils/Permute.hpp> -#include <armnnUtils/TensorUtils.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/kernel_util.h" - -namespace -{ - -// Macro to call an Is<layer_name>Supported function and log caller name together with reason for lack of support -#define FORWARD_LAYER_SUPPORT_FUNC(opName, tfLiteContext, func, backends, supported, setBackend, ...) \ -try \ -{ \ - for (auto&& backendId : backends) \ - { \ - auto layerSupportObject = armnn::GetILayerSupportByBackendId(backendId); \ - if (layerSupportObject.IsBackendRegistered()) \ - { \ - std::string reasonIfUnsupported; \ - supported = \ - layerSupportObject.func(__VA_ARGS__, armnn::Optional<std::string&>(reasonIfUnsupported)); \ - if (supported) \ - { \ - setBackend = backendId; \ - break; \ - } \ - else \ - { \ - if (reasonIfUnsupported.size() > 0) \ - { \ - TFLITE_LOG_PROD(tflite::TFLITE_LOG_WARNING, \ - "%s: not supported by armnn: %s", opName, reasonIfUnsupported.c_str()); \ - } \ - else \ - { \ - TFLITE_LOG_PROD(tflite::TFLITE_LOG_WARNING, \ - "%s: not supported by armnn", opName); \ - } \ - } \ - } \ - else \ - { \ - TF_LITE_KERNEL_LOG(tfLiteContext, "%s: backend not registered: %s", opName, backendId.Get().c_str()); \ - } \ - } \ - if (!supported) \ - { \ - TF_LITE_KERNEL_LOG(tfLiteContext, "%s: not supported by any specified backend", opName); \ - } \ -} \ -catch (const armnn::InvalidArgumentException &e) \ -{ \ - throw armnn::InvalidArgumentException(e, "Failed to check layer support", CHECK_LOCATION()); \ -} - -TfLiteStatus ValidateNumInputs(TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - const unsigned int expectedSize, - int nodeIndex) -{ - auto numInputs = tfLiteNode->inputs->size; - if (static_cast<unsigned int >(numInputs) != expectedSize) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of inputs (%d != %d) in node #%d", - numInputs, expectedSize, nodeIndex); - return kTfLiteError; - } - return kTfLiteOk; -} - -TfLiteStatus ValidateNumOutputs(TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - const unsigned int expectedSize, - int nodeIndex) -{ - auto numOutputs = tfLiteNode->outputs->size; - if (static_cast<unsigned int >(numOutputs) != expectedSize) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Unexpected number of outputs (%d != %d) in node #%d", - numOutputs, expectedSize, nodeIndex); - return kTfLiteError; - } - return kTfLiteOk; -} - -bool IsDynamicTensor(const TfLiteTensor& tfLiteTensor) -{ - auto tensorAllocationType = tfLiteTensor.allocation_type; - if (tensorAllocationType == kTfLiteDynamic) - { - return true; - } - return false; -} - -bool IsValid(const TfLiteTensor* tfLiteTensor) -{ - return tfLiteTensor == nullptr ? false : true; -} - -bool IsValid(TfLiteContext* tfLiteContext, const TfLiteTensor& tfLiteTensor, int32_t operatorCode, int32_t nodeIndex) -{ - if(!IsValid(&tfLiteTensor)) - { - std::cout << "..Is Not Valid" << std::endl; - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Invalid TfLite tensor in operator #%d node #%d: ", - operatorCode, nodeIndex); - return false; - } - if (IsDynamicTensor(tfLiteTensor)) - { - std::cout << "..IsDynamicTensor" << std::endl; - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic tensors are not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return false; - } - return true; -} - -uint32_t NonNegative(int32_t value, int nodeIndex) -{ - if (value < 0) - { - throw armnn::Exception( - "TfLiteArmnnDelegate: Non-negative value in node " + std::to_string(static_cast<int>(nodeIndex))); - } - else - { - return static_cast<uint32_t>(value); - } -} - -bool IsAffineQuantization(const TfLiteTensor& tfLiteTensor) -{ - auto quantizationInfo = tfLiteTensor.quantization; - if (quantizationInfo.type == kTfLiteAffineQuantization) - { - return true; - } - return false; -} - -TfLiteStatus Connect(armnn::IConnectableLayer* layer, - TfLiteNode* tfLiteNode, - armnnDelegate::DelegateData& data) -{ - ARMNN_ASSERT(static_cast<unsigned int>(tfLiteNode->outputs->size) == layer->GetNumOutputSlots()); - - // Connect the input slots - for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex) - { - if (data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]] != nullptr) - { - data.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]]->Connect(layer->GetInputSlot(inputIndex)); - } - } - - // Prepare output slots - for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) - { - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); - data.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &outputSlot; - } - - return kTfLiteOk; -} - -void ExpandTensorRankToEqual(armnn::TensorInfo& inputInfo0, - armnn::TensorInfo& inputInfo1) -{ - unsigned int inputDimensions0 = inputInfo0.GetNumDimensions(); - unsigned int inputDimensions1 = inputInfo1.GetNumDimensions(); - - if (inputDimensions0 == inputDimensions1) - { - return; - } - - unsigned int biggerInputDimensions = std::max(inputDimensions0, inputDimensions1); - - bool input0IsSmaller = inputDimensions0 < inputDimensions1; - armnn::TensorInfo& smallInfo = input0IsSmaller ? inputInfo0 : inputInfo1; - const armnn::TensorShape& newShape = armnnUtils::ExpandDimsToRank(smallInfo.GetShape(), biggerInputDimensions); - - smallInfo.SetShape(newShape); - -} - -TfLiteStatus FusedActivation(TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - TfLiteFusedActivation activationType, - armnn::IConnectableLayer* prevLayer, - unsigned int outputSlotIndex, - armnnDelegate::DelegateData& data) -{ - - const armnn::TensorInfo& activationOutputInfo = prevLayer->GetOutputSlot(outputSlotIndex).GetTensorInfo(); - - armnn::ActivationDescriptor activationDesc; - - switch (activationType) - { - case kTfLiteActNone: - { - // No Activation - return kTfLiteOk; - } - case kTfLiteActRelu: - { - activationDesc.m_Function = armnn::ActivationFunction::ReLu; - break; - } -// The name of kTfLiteActRelu1 changed after TF Lite v2.3 -#if defined(ARMNN_POST_TFLITE_2_3) - case kTfLiteActReluN1To1: -#else - case kTfLiteActRelu1: -#endif - { - activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; - activationDesc.m_A = 1.0f; - activationDesc.m_B = -1.0f; - break; - } - case kTfLiteActRelu6: - { - activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; - activationDesc.m_A = 6.0f; - activationDesc.m_B = 0.0f; - break; - } - case kTfLiteActSigmoid: - { - activationDesc.m_Function = armnn::ActivationFunction::Sigmoid; - break; - } - case kTfLiteActTanh: - { - activationDesc.m_Function = armnn::ActivationFunction::TanH; - activationDesc.m_A = 1.0f; - activationDesc.m_B = 1.0f; - break; - } - default: - return kTfLiteError; - } - - bool isSupported = false; - armnn::BackendId setBackend; - FORWARD_LAYER_SUPPORT_FUNC("ACTIVATION", - tfLiteContext, - IsActivationSupported, - data.m_Backends, - isSupported, - setBackend, - activationOutputInfo, - activationOutputInfo, - activationDesc); - if (!isSupported) - { - return kTfLiteError; - } - armnn::IConnectableLayer* activationLayer = data.m_Network->AddActivationLayer(activationDesc); - activationLayer->SetBackendId(setBackend); - - ARMNN_ASSERT(activationLayer != nullptr); - activationLayer->GetOutputSlot(0).SetTensorInfo(activationOutputInfo); - - // Connect and prepare output slots - for (unsigned int outputIndex = 0; outputIndex < activationLayer->GetNumOutputSlots(); ++outputIndex) - { - data.m_OutputSlotForNode[static_cast<unsigned long>( - tfLiteNode->outputs->data[outputIndex])]->Connect(activationLayer->GetInputSlot(0)); - armnn::IOutputSlot& outputSlot = activationLayer->GetOutputSlot(outputIndex); - data.m_OutputSlotForNode[static_cast<unsigned long>( - tfLiteNode->outputs->data[outputIndex])] = &outputSlot; - } - return kTfLiteOk; -} - -armnn::IConnectableLayer* AddReshapeLayer(TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - armnn::IConnectableLayer* prevLayer, - armnn::TensorInfo reshapedOutputTensorInfo, - armnn::TensorInfo outputTensorInfo, - armnnDelegate::DelegateData& data) -{ - armnn::ReshapeDescriptor desc; - desc.m_TargetShape = outputTensorInfo.GetShape(); - - bool isSupported = false; - armnn::BackendId setBackend; - FORWARD_LAYER_SUPPORT_FUNC("RESHAPE", - tfLiteContext, - IsReshapeSupported, - data.m_Backends, - isSupported, - setBackend, - reshapedOutputTensorInfo, - outputTensorInfo, - desc); - - if (!isSupported) - { - return nullptr; - } - - armnn::IConnectableLayer* reshapeLayer = data.m_Network->AddReshapeLayer(desc); - reshapeLayer->SetBackendId(setBackend); - ARMNN_ASSERT(reshapeLayer != nullptr); - - prevLayer->GetOutputSlot(0).SetTensorInfo(reshapedOutputTensorInfo); - reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); - - // Connect and prepare output slots - for (unsigned int outputIndex = 0; outputIndex < reshapeLayer->GetNumOutputSlots(); ++outputIndex) - { - data.m_OutputSlotForNode[static_cast<unsigned long>( - tfLiteNode->outputs->data[outputIndex])]->Connect(reshapeLayer->GetInputSlot(0)); - armnn::IOutputSlot& outputSlot = reshapeLayer->GetOutputSlot(outputIndex); - data.m_OutputSlotForNode[static_cast<unsigned long>( - tfLiteNode->outputs->data[outputIndex])] = &outputSlot; - } - return reshapeLayer; -} - -armnn::DataType GetDataType(const TfLiteTensor& tfLiteTensor) -{ - switch (tfLiteTensor.type) - { - case kTfLiteBool: - return armnn::DataType::Boolean; - case kTfLiteFloat32: - return armnn::DataType::Float32; - case kTfLiteFloat16: - return armnn::DataType::Float16; - case kTfLiteUInt8: - return armnn::DataType::QAsymmU8; - case kTfLiteInt8: - { - auto quantizationInfo = tfLiteTensor.quantization; - if (quantizationInfo.type == kTfLiteAffineQuantization) - { - auto* quantization = - reinterpret_cast<TfLiteAffineQuantization*>(tfLiteTensor.quantization.params); - if (quantization->zero_point != nullptr && quantization->zero_point->size == 1) - { - return armnn::DataType::QAsymmS8; - } - else - { - return armnn::DataType::QSymmS8; - } - } - else - { - return armnn::DataType::QAsymmS8; - } - } - case kTfLiteInt16: - return armnn::DataType::QSymmS16; - case kTfLiteInt32: - return armnn::DataType::Signed32; - case kTfLiteInt64: - return armnn::DataType::Signed64; - default: - throw armnn::Exception(&"TfLiteArmnnDelegate: Unsupported data type: " [ tfLiteTensor.type]); - } -} - -armnn::TensorInfo GetTensorInfoForTfLiteTensor(const TfLiteTensor& tfLiteTensor, bool isOutput = false) -{ - armnn::DataType type = GetDataType(tfLiteTensor); - armnn::TensorInfo ret; - auto tensorDimensionSize = tfLiteTensor.dims->size; - if (tensorDimensionSize == 0) - { - // If input tensor does not have a shape - // assuming that it has 1D tensor - if (!isOutput) - { - std::vector<unsigned int> safeShape = { 1 }; - bool dimensionsSpecificity[1] = { true }; - armnn::TensorShape tensorShape(armnn::numeric_cast<unsigned int>(safeShape.size()), - safeShape.data(), - dimensionsSpecificity); - ret = armnn::TensorInfo(tensorShape, type); - if(tflite::IsConstantTensor(&tfLiteTensor)) - { - ret.SetConstant(true); - } - } - else - { - armnn::TensorShape tensorShape(armnn::Dimensionality::NotSpecified); - ret = armnn::TensorInfo(tensorShape, type); - } - } - else - { - std::vector<unsigned int> tensorDims(static_cast<unsigned int>(tensorDimensionSize)); - bool dimensionsSpecificity[5] = { true, true, true, true, true }; - for (unsigned int i = 0; i < static_cast<unsigned int>(tensorDimensionSize); ++i) { - auto dim = tfLiteTensor.dims->data[i]; - if (dim == 0) - { - dimensionsSpecificity[i] = false; - } - tensorDims[i] = static_cast<unsigned int>(dim); - } - armnn::TensorShape tensorShape(static_cast<unsigned int>(tensorDimensionSize), - tensorDims.data(), - dimensionsSpecificity); - - if(tflite::IsConstantTensor(&tfLiteTensor)) - { - ret = armnn::TensorInfo(tensorShape, type); - ret.SetConstant(true); - } - else - { - ret = armnn::TensorInfo(tensorShape, type); - } - } - - auto quantizationInfo = tfLiteTensor.quantization; - if (quantizationInfo.type == kTfLiteAffineQuantization) - { - // get per-channel quantization parameters - const auto* affineQuantization = - reinterpret_cast<TfLiteAffineQuantization*>(tfLiteTensor.quantization.params); - if (affineQuantization->scale->size > 1) - { - std::vector<float> quantizationScales; - for (unsigned int i = 0; i < static_cast<unsigned int>(affineQuantization->scale->size); ++i) - { - quantizationScales.push_back(affineQuantization->scale->data[i]); - } - ret.SetQuantizationScales(quantizationScales); - ret.SetQuantizationDim(armnn::numeric_cast<unsigned int>(affineQuantization->quantized_dimension)); - } - else - { - ret.SetQuantizationScale(affineQuantization->scale->data[0]); - ret.SetQuantizationOffset(affineQuantization->zero_point->data[0]); - } - } - else - { - auto quantizationParameters = tfLiteTensor.params; - ret.SetQuantizationScale(quantizationParameters.scale); - ret.SetQuantizationOffset(quantizationParameters.zero_point); - } - - return ret; -} - -armnn::ConstTensor CreateConstTensor(const TfLiteTensor* tfLiteTensor, - const armnn::TensorInfo& tensorInfo) -{ - if (tfLiteTensor->allocation_type != kTfLiteMmapRo) - { - throw armnn::Exception( - "TfLiteArmnnDelegate: Not constant allocation type: " + std::to_string(tfLiteTensor->allocation_type)); - } - - return armnn::ConstTensor(tensorInfo, tfLiteTensor->data.data); -} - -armnn::ConstTensor* GetConstTensorForTfLiteTensor(const TfLiteTensor* tfLiteTensors, TfLiteNode* tfLiteNode, int index) -{ - const TfLiteTensor &tfLiteTensor = tfLiteTensors[tfLiteNode->inputs->data[index]]; - armnn::TensorInfo tensorInfo = GetTensorInfoForTfLiteTensor(tfLiteTensor); - return new armnn::ConstTensor(tensorInfo, tfLiteTensor.data.data); -} - -void CalcPadding(uint32_t inputSize, - uint32_t filterSize, - uint32_t stride, - uint32_t dilation, - uint32_t& paddingFront, - uint32_t& paddingBack, - TfLitePadding padding) -{ - paddingFront = 0; - paddingBack = 0; - if (padding == kTfLitePaddingSame) - { - uint32_t outputSize = (inputSize + stride - 1) / stride; - uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1); - uint32_t temp = (outputSize - 1) * stride + dilatedSize; - if (temp > inputSize) - { - paddingFront = (temp - inputSize) / 2; - paddingBack = (temp - inputSize) - paddingFront; - } - } -} - -TfLiteStatus ConnectConstant(armnn::IConnectableLayer* layer, - const armnn::TensorInfo& constTensorInfo, - TfLiteContext* tfLiteContext, - const TfLiteTensor& tfLiteTensor, - armnnDelegate::DelegateData& data, - unsigned int slotIndex) -{ - IgnoreUnused(layer); - bool isSupported = false; - armnn::BackendId setBackend; - FORWARD_LAYER_SUPPORT_FUNC("CONSTANT", - tfLiteContext, - IsConstantSupported, - data.m_Backends, - isSupported, - setBackend, - constTensorInfo); - if (!isSupported) - { - return kTfLiteError; - } - - auto constantInput = CreateConstTensor(&tfLiteTensor, - constTensorInfo); - armnn::IConnectableLayer* constantLayer = data.m_Network->AddConstantLayer(constantInput); - constantLayer->SetBackendId(setBackend); - armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(constTensorInfo); - - data.m_OutputSlotForNode[static_cast<unsigned long>(slotIndex)] = &outputSlot; - - return kTfLiteOk; -} - -bool IsOptionalOperandPresent(TfLiteNode* tfLiteNode, const int operandIndex) -{ - // If the inputs array has fewer than operandIndex entries or if the entry at operandIndex has a value of -1 or - // less then the input is not present. - if (tfLiteNode->inputs->size > operandIndex && tfLiteNode->inputs->data[operandIndex] >= 0) - { - return true; - } - return false; -} - -TfLiteStatus ProcessInputs(armnn::IConnectableLayer* layer, - armnnDelegate::DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode) -{ - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - // Process input tensors - // If input tensor is a Constant tensor create a constant layer and connect it to the network - for (unsigned int inputIndex = 0; inputIndex < layer->GetNumInputSlots(); ++inputIndex) - { - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[inputIndex]]; - if (tflite::IsConstantTensor(&tfLiteInputTensor)) - { - armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - bool isSupported = false; - armnn::BackendId setBackend; - FORWARD_LAYER_SUPPORT_FUNC("CONSTANT", - tfLiteContext, - IsConstantSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo); - if (!isSupported) - { - return kTfLiteError; - } - auto constantInput = CreateConstTensor(&tfLiteInputTensor, - inputTensorInfo); - armnn::IConnectableLayer* constantLayer = delegateData.m_Network->AddConstantLayer(constantInput); - constantLayer->SetBackendId(setBackend); - armnn::IOutputSlot& outputSlot = constantLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(inputTensorInfo); - - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[inputIndex]] = &outputSlot; - } - } - return kTfLiteOk; -} - -unsigned int ComputeWrappedIndex(int index, unsigned int numDimensions) -{ - int numDims = armnn::numeric_cast<int>(numDimensions); - int wrappedIndex = index < 0 ? numDims + index : index; - ARMNN_ASSERT(wrappedIndex >= 0); - ARMNN_ASSERT(wrappedIndex < numDims); - - return static_cast<unsigned int>(wrappedIndex); -}; - -bool AreAllSigned32(const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - return (armnn::DataType::Signed32 == inputInfo1.GetDataType()) && - (armnn::DataType::Signed32 == inputInfo2.GetDataType()) && - (armnn::DataType::Signed32 == outputInfo.GetDataType()); -} - -void UpdateConstantTensorOutputs(const armnn::TensorInfo& inputInfo, armnn::TensorInfo& outputInfo) -{ - // If input tensor info is constant and output tensor info shape is not specified - // set the output shape from input shape - if (inputInfo.IsConstant() && outputInfo.GetShape().GetDimensionality() == armnn::Dimensionality::NotSpecified) - { - outputInfo.SetShape(inputInfo.GetShape()); - } - return; -} - -} // namespace anonymous diff --git a/delegate/src/ElementwiseBinary.hpp b/delegate/src/ElementwiseBinary.hpp deleted file mode 100644 index fa9021b5c1..0000000000 --- a/delegate/src/ElementwiseBinary.hpp +++ /dev/null @@ -1,401 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include "MultiLayerFacade.hpp" -#include "SharedFunctions.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/delegates/utils.h" - -namespace armnnDelegate -{ - -TfLiteStatus ValidateAddOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - std::vector<armnn::TensorInfo> infos { inputInfo1, inputInfo2, outputInfo }; - FORWARD_LAYER_SUPPORT_FUNC("ADD", - tfLiteContext, - IsElementwiseBinarySupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo1, - inputInfo2, - outputInfo, - armnn::BinaryOperation::Add); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - - -TfLiteStatus ValidateDivOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("DIV", - tfLiteContext, - IsElementwiseBinarySupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo1, - inputInfo2, - outputTensorInfo, - armnn::BinaryOperation::Div); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus ValidateFloorDivOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - // need first to validate that the div operator is supported - // then that the floor operator is supported - TfLiteStatus status = ValidateDivOperator(delegateData, tfLiteContext, inputInfo1, inputInfo2, outputInfo); - if (status != kTfLiteOk) - { - return status; - } - // if the inputs and output of the div are all Signed32 we don't need to add the floor operator afterward. - if (AreAllSigned32(inputInfo1, inputInfo2, outputInfo)) - { - return status; - } - // in case broadcasting is being done from one of the inputs to the div - // choose the full sized input tensor to pass to the floor validation routine - armnn::TensorInfo floorInputInfo = inputInfo1; - if (inputInfo1.GetNumDimensions() < inputInfo2.GetNumDimensions()) - { - floorInputInfo = inputInfo2; - } - status = ValidateFloorOperator(delegateData, tfLiteContext, floorInputInfo, outputInfo); - return status; -} - -TfLiteStatus ValidateMaximumOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("MAXIMUM", - tfLiteContext, - IsElementwiseBinarySupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo1, - inputInfo2, - outputTensorInfo, - armnn::BinaryOperation::Maximum); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus ValidateMinimumOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("MINIMUM", - tfLiteContext, - IsElementwiseBinarySupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo1, - inputInfo2, - outputTensorInfo, - armnn::BinaryOperation::Minimum); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus ValidateMulOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("MUL", - tfLiteContext, - IsElementwiseBinarySupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo1, - inputInfo2, - outputTensorInfo, - armnn::BinaryOperation::Mul); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus ValidateSubOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo1, - const armnn::TensorInfo& inputInfo2, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("SUB", - tfLiteContext, - IsElementwiseBinarySupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo1, - inputInfo2, - outputTensorInfo, - armnn::BinaryOperation::Sub); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -std::pair<armnn::IConnectableLayer*, armnn::IConnectableLayer*> AddFloorDivLayer( - DelegateData& delegateData, - const armnn::TensorInfo& outputTensorInfo) -{ - armnn::IConnectableLayer* divisionLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Div); - // if the output of the div is Signed32 the Floor layer is not required - if (armnn::DataType::Signed32 == outputTensorInfo.GetDataType()) - { - return std::make_pair(divisionLayer, divisionLayer); - } - armnn::IOutputSlot& outputSlot = divisionLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - armnn::IConnectableLayer* floorLayer = delegateData.m_Network->AddFloorLayer(); - outputSlot.Connect(floorLayer->GetInputSlot(0)); - return std::make_pair(divisionLayer, floorLayer); -} - -TfLiteStatus VisitElementwiseBinaryOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t elementwiseBinaryOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (IsDynamicTensor(tfLiteInputTensor0)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - elementwiseBinaryOperatorCode, nodeIndex); - return kTfLiteError; - } - - const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (IsDynamicTensor(tfLiteInputTensor1)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - elementwiseBinaryOperatorCode, 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: ", - elementwiseBinaryOperatorCode, nodeIndex); - return kTfLiteError; - } - - armnn::TensorInfo inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); - armnn::TensorInfo inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1); - - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Check if we need to expand the dims of the input tensor infos. - // This is required for a few of the backends. - if(inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions()) - { - ExpandTensorRankToEqual(inputTensorInfo0, inputTensorInfo1); - } - - auto* tfLiteNodeParameters = reinterpret_cast<TfLiteAddParams*>(tfLiteNode->builtin_data); - TfLiteFusedActivation activationType = kTfLiteActNone; - if (tfLiteNodeParameters) - { - activationType = tfLiteNodeParameters->activation; - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - } - - if (!delegateData.m_Network) - { - switch(elementwiseBinaryOperatorCode) - { - case kTfLiteBuiltinAdd: - return ValidateAddOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - case kTfLiteBuiltinDiv: - return ValidateDivOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - case kTfLiteBuiltinFloorDiv: - return ValidateFloorDivOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - case kTfLiteBuiltinMaximum: - return ValidateMaximumOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - case kTfLiteBuiltinMinimum: - return ValidateMinimumOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - case kTfLiteBuiltinMul: - return ValidateMulOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - case kTfLiteBuiltinSub: - return ValidateSubOperator(delegateData, - tfLiteContext, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo); - default: - return kTfLiteError; - } - } - - armnn::IConnectableLayer* elementwiseBinaryLayer = nullptr; - MultiLayerFacade multiLayer; - switch(elementwiseBinaryOperatorCode) - { - case kTfLiteBuiltinAdd: - elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Add); - break; - case kTfLiteBuiltinDiv: - elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Div); - break; - case kTfLiteBuiltinFloorDiv: - { - auto layers = AddFloorDivLayer(delegateData, outputTensorInfo); - multiLayer.AssignValues(layers.first, layers.second); - elementwiseBinaryLayer = &multiLayer; - } - break; - case kTfLiteBuiltinMaximum: - elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Maximum); - break; - case kTfLiteBuiltinMinimum: - elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Minimum); - break; - case kTfLiteBuiltinMul: - elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Mul); - break; - case kTfLiteBuiltinSub: - elementwiseBinaryLayer = delegateData.m_Network->AddElementwiseBinaryLayer( - armnn::BinaryOperation::Sub); - break; - default: - return kTfLiteError; - } - ARMNN_ASSERT(elementwiseBinaryLayer != nullptr); - armnn::IOutputSlot& outputSlot = elementwiseBinaryLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - auto inputsTensorsProcess = ProcessInputs(elementwiseBinaryLayer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - if(Connect(elementwiseBinaryLayer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - if (!tfLiteNodeParameters) - { - // No Activation - return kTfLiteOk; - } - // Check and Create Activation - return FusedActivation(tfLiteContext, tfLiteNode, activationType, elementwiseBinaryLayer, 0, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/ElementwiseUnary.hpp b/delegate/src/ElementwiseUnary.hpp deleted file mode 100644 index 4be6fba82e..0000000000 --- a/delegate/src/ElementwiseUnary.hpp +++ /dev/null @@ -1,91 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" - -#include <armnn/utility/Assert.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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitElementwiseUnaryOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - armnn::UnaryOperation unaryOperation) -{ - 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 node #%d: ", - 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 node #%d: ", - nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::ElementwiseUnaryDescriptor descriptor(unaryOperation); - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("ELEMENTWISE_UNARY", - tfLiteContext, - IsElementwiseUnarySupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddElementwiseUnaryLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Fill.hpp b/delegate/src/Fill.hpp deleted file mode 100644 index e79133e15c..0000000000 --- a/delegate/src/Fill.hpp +++ /dev/null @@ -1,114 +0,0 @@ -// -// Copyright © 2022 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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitFillOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteFillOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - switch(tfLiteFillOperatorCode) - { - case kTfLiteBuiltinFill: - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - break; - default: - return kTfLiteError; - } - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteFillOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteFillTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteFillTensor, tfLiteFillOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteFillOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::FillDescriptor descriptor; - switch (tfLiteFillTensor.type) - { - case kTfLiteFloat32: - descriptor.m_Value = tflite::GetTensorData<float>(&tfLiteFillTensor)[0]; - break; - case kTfLiteInt32: - descriptor.m_Value = tflite::GetTensorData<int32_t>(&tfLiteFillTensor)[0]; - break; - default: - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: FILL value data type is not supported in operator #%d node #%d: ", - tfLiteFillOperatorCode, nodeIndex); - return kTfLiteError; - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("FILL", - tfLiteContext, - IsFillSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddFillLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - auto inputsTensorsProcess = ProcessInputs(layer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/FullyConnected.hpp b/delegate/src/FullyConnected.hpp deleted file mode 100644 index 1129951104..0000000000 --- a/delegate/src/FullyConnected.hpp +++ /dev/null @@ -1,275 +0,0 @@ -// -// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include "armnnUtils/TensorUtils.hpp" -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteWeightsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteWeightsTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& weightsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteWeightsTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Check that we support fused activation before we attempt to create a layer - auto* tfLiteNodeParameters = reinterpret_cast<TfLiteFullyConnectedParams *>(tfLiteNode->builtin_data); - TfLiteFusedActivation activationType=kTfLiteActNone; - if (tfLiteNodeParameters) - { - activationType = tfLiteNodeParameters->activation; - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - } - - // Fully Connected Layer accepts two dimensional weights input - int32_t weightsDimension = static_cast<int32_t>(weightsTensorInfo.GetNumDimensions()); - if (weightsDimension != 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn" - " in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex); - return kTfLiteError; - } - - armnn::TensorInfo biasTensorInfo; - if (biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); - } - else - { - biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); - } - - armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - if (inputTensorInfo.GetNumDimensions() > 2) - { - // Calculate reshape to flatten to 2D [batch_size, input_size] - std::vector<unsigned int> reshapedDimensions(2); - reshapedDimensions[1] = weightsTensorInfo.GetShape()[1]; - reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1]; - - if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ", - reshapedDimensions[1], operatorCode, nodeIndex); - return kTfLiteError; - } - - reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); - } - armnn::TensorInfo reshapedOutputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); - - if (outputTensorInfo.GetNumDimensions() > 2) - { - // Calculate reshape to flatten to 2D [batch_size, input_size] - std::vector<unsigned int> reshapedDimensions(2); - reshapedDimensions[1] = weightsTensorInfo.GetShape()[0]; - reshapedDimensions[0] = outputTensorInfo.GetNumElements() / reshapedDimensions[1]; - - if (outputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Failed to deduce output tensor shape from filter size #%d #%d node #%d: ", - reshapedDimensions[1], operatorCode, nodeIndex); - return kTfLiteError; - } - reshapedOutputTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); - } - - armnn::FullyConnectedDescriptor descriptor; - descriptor.m_TransposeWeightMatrix = true; - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_ConstantWeights = weightsTensorInfo.IsConstant(); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - - FORWARD_LAYER_SUPPORT_FUNC("FULLY_CONNECTED", - tfLiteContext, - IsFullyConnectedSupported, - delegateData.m_Backends, - isSupported, - setBackend, - reshapedTensorInfo, - outputTensorInfo, - weightsTensorInfo, - biasTensorInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(reshapedOutputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - // Add a constant layer for weights and biases if inputs are constant. - if (weightsTensorInfo.IsConstant()) - { - auto weightsTensor = CreateConstTensor(&tfLiteWeightsTensor, - weightsTensorInfo); - - armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(weightsTensor); - - weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); - weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsTensorInfo); - } - - if (biasEnabled) - { - const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if(biasTensorInfo.IsConstant()) - { - auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, - biasTensorInfo); - - armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); - ARMNN_ASSERT(biasLayer != nullptr); - - biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); - biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); - } - } - - // The data input can also be constant, so we must check that this is also allocated to an input slot - if(inputTensorInfo.IsConstant()) - { - auto input = - CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], - inputTensorInfo); - - armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); - inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); - inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); - } - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - armnn::IConnectableLayer* reshapeLayer = nullptr; - if (inputTensorInfo.GetNumDimensions() > 2) - { - // Add reshape to flatten to 2D [batch_size, input_size] - armnn::ReshapeDescriptor reshapeDescriptor; - reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape(); - reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor); - ARMNN_ASSERT(reshapeLayer != nullptr); - - reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); - - // Connect - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0)); - reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); - - if (!descriptor.m_ConstantWeights) - { - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(layer->GetInputSlot(1)); - } - - if (biasEnabled && !biasTensorInfo.IsConstant()) - { - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[2]]->Connect(layer->GetInputSlot(2)); - } - delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[0]] = &outputSlot; - } - - if (reshapeLayer == nullptr) - { - if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - } - - if (outputTensorInfo.GetNumDimensions() > 2) - { - layer = AddReshapeLayer(tfLiteContext, tfLiteNode, layer, reshapedOutputTensorInfo, outputTensorInfo, - delegateData); - if (!layer) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Failed to add reshape for FullyConnected #%d node #%d: ", - operatorCode, - nodeIndex); - return kTfLiteError; - } - } - - if (!tfLiteNodeParameters) - { - // No Activation - return kTfLiteOk; - } - - // Check and Create Activation - return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/Gather.hpp b/delegate/src/Gather.hpp deleted file mode 100644 index 9125997417..0000000000 --- a/delegate/src/Gather.hpp +++ /dev/null @@ -1,106 +0,0 @@ -// -// Copyright © 2020,2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include <algorithm> -#include <iterator> -#include <string> -#include <vector> - -namespace armnnDelegate -{ -TfLiteStatus VisitGatherOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteIndicesTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteIndicesTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - auto* gatherParameters = reinterpret_cast<TfLiteGatherParams*>(tfLiteNode->builtin_data); - auto axis = gatherParameters->axis; - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& indicesTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteIndicesTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - armnn::GatherDescriptor gatherDescriptor; - gatherDescriptor.m_Axis = axis; - - auto inputDimensions = static_cast<int32_t>(inputTensorInfo.GetNumDimensions()); - auto indicesDimensions = indicesTensorInfo.GetNumDimensions(); - auto outputDimensions = outputTensorInfo.GetNumDimensions(); - if (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0))) - { - TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, - "TfLiteArmnnDelegate: Operation has invalid axis: %d. It is out of bounds [-%d, %d))", - axis, inputDimensions, inputDimensions); - return kTfLiteError; - } - if (outputDimensions != static_cast<unsigned int>(inputDimensions) + indicesDimensions - 1) - { - TF_LITE_MAYBE_KERNEL_LOG( tfLiteContext, - "Operation has invalid output dimensions: %d. Output must be an (%d + %d - 1)-D tensor", - outputDimensions, inputDimensions, indicesDimensions); - return kTfLiteError; - } - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - // Check if supported - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("GATHER", - tfLiteContext, - IsGatherSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - indicesTensorInfo, - outputTensorInfo, - gatherDescriptor); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddGatherLayer(gatherDescriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); - - auto inputsTensorsProcess = ProcessInputs(layer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - return Connect(layer, tfLiteNode, delegateData); -} -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/GatherNd.hpp b/delegate/src/GatherNd.hpp deleted file mode 100644 index cf526e1995..0000000000 --- a/delegate/src/GatherNd.hpp +++ /dev/null @@ -1,82 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include <algorithm> -#include <iterator> -#include <string> -#include <vector> - -namespace armnnDelegate -{ -TfLiteStatus VisitGatherNdOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteIndicesTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteIndicesTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& indicesTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteIndicesTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - // Check if supported - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("GATHER_ND", - tfLiteContext, - IsGatherNdSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - indicesTensorInfo, - outputTensorInfo); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddGatherNdLayer(); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); - - auto inputsTensorsProcess = ProcessInputs(layer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - return Connect(layer, tfLiteNode, delegateData); -} -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/LogicalBinary.hpp b/delegate/src/LogicalBinary.hpp deleted file mode 100644 index d71618ee9c..0000000000 --- a/delegate/src/LogicalBinary.hpp +++ /dev/null @@ -1,102 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitLogicalBinaryOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t logicalOperatorCode, - armnn::LogicalBinaryOperation binaryOperation) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor0, logicalOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor1, logicalOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, logicalOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - armnn::TensorInfo inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); - armnn::TensorInfo inputTensorInfo1 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor1); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Check if we need to expand the dims of any of the input tensor infos. - // This is required for a few of the backends. - if(inputTensorInfo0.GetNumDimensions() != inputTensorInfo1.GetNumDimensions()) - { - ExpandTensorRankToEqual(inputTensorInfo0, inputTensorInfo1); - } - - // Setup descriptor and assign operation - armnn::LogicalBinaryDescriptor desc; - desc.m_Operation = binaryOperation; - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("LOGICAL_BINARY", - tfLiteContext, - IsLogicalBinarySupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo0, - inputTensorInfo1, - outputTensorInfo, - desc); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* logicalBinaryLayer = delegateData.m_Network->AddLogicalBinaryLayer(desc); - logicalBinaryLayer->SetBackendId(setBackend); - ARMNN_ASSERT(logicalBinaryLayer != nullptr); - - armnn::IOutputSlot& outputSlot = logicalBinaryLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - auto inputsTensorsProcess = ProcessInputs(logicalBinaryLayer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - return Connect(logicalBinaryLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Lstm.hpp b/delegate/src/Lstm.hpp deleted file mode 100644 index 8c1f877ec9..0000000000 --- a/delegate/src/Lstm.hpp +++ /dev/null @@ -1,268 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" - -#include <armnn/LstmParams.hpp> -#include <armnn/Tensor.hpp> -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitLstmOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - - const auto nodeParams = reinterpret_cast<TfLiteLSTMParams*>(tfLiteNode->builtin_data); - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - // Set the params structure for the AddLstmLayer call - armnn::LstmInputParams params; - - if (IsOptionalOperandPresent(tfLiteNode, 1)) - { - params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 1); - } - - params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 2); - params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 3); - params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 4); - - // Recurrent weight tensors of size {n_cell, n_output} - if (IsOptionalOperandPresent(tfLiteNode, 5)) - { - params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 5); - } - - params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 6); - params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 7); - params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 8); - - // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. - if (IsOptionalOperandPresent(tfLiteNode, 9)) - { - params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 9); - } - - if (IsOptionalOperandPresent(tfLiteNode, 10)) - { - params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 10); - } - - if (IsOptionalOperandPresent(tfLiteNode, 11)) - { - params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 11); - } - - // Gates bias tensors of size {n_cell} - if (IsOptionalOperandPresent(tfLiteNode, 12)) - { - params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 12); - } - - params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 13); - params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 14); - params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 15); - - // Projection weight tensor of size {n_output, n_cell} - if (IsOptionalOperandPresent(tfLiteNode, 16)) - { - params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 16); - } - // Projection bias tensor of size {n_output} - if (IsOptionalOperandPresent(tfLiteNode, 17)) - { - params.m_ProjectionBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 17); - } - - // These state tensors are defined as variable tensors, and will be modified by this op. - armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[18]]); - armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[19]]); - - // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix. - if (IsOptionalOperandPresent(tfLiteNode, 20)) - { - params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 20); - } - - if (IsOptionalOperandPresent(tfLiteNode, 21)) - { - params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 21); - } - - if (IsOptionalOperandPresent(tfLiteNode, 22)) - { - params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 22); - } - - if (IsOptionalOperandPresent(tfLiteNode, 23)) - { - params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 23); - } - - // set the layer descriptor - armnn::LstmDescriptor desc; - desc.m_ActivationFunc = NonNegative(nodeParams->activation, nodeIndex); - desc.m_ClippingThresCell = nodeParams->cell_clip; - desc.m_ClippingThresProj = nodeParams->proj_clip; - desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr - || params.m_RecurrentToInputWeights == nullptr - || params.m_InputGateBias == nullptr); - desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || params.m_CellToOutputWeights != nullptr); - desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); - desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr - || params.m_ForgetLayerNormWeights != nullptr - || params.m_CellLayerNormWeights != nullptr - || params.m_OutputLayerNormWeights != nullptr); - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - unsigned int batchSize = inputTensorInfo.GetShape()[0]; - unsigned int outputSize = outputTensorInfo.GetShape()[1]; - unsigned int numUnits = cellStateInInfo.GetShape()[1]; - - armnn::DataType dataType = inputTensorInfo.GetDataType(); - float qScale = inputTensorInfo.GetQuantizationScale(); - float qOffset = inputTensorInfo.GetQuantizationOffset(); - - armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, dataType, qScale, qOffset); - if (!desc.m_CifgEnabled) - { - scratchBufferTensorInfo = armnn::TensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset); - } - armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, dataType, qScale, qOffset); - armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset); - - armnn::LstmInputParamsInfo paramsInfo; - paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); - paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); - paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); - paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); - paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); - paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); - paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); - paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); - paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); - - if (!desc.m_CifgEnabled) - { - paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); - paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); - if (params.m_CellToInputWeights != nullptr) - { - paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); - } - paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); - } - - if (desc.m_ProjectionEnabled) - { - paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); - if (params.m_ProjectionBias != nullptr) - { - paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); - } - } - - if (desc.m_PeepholeEnabled) - { - paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); - paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); - } - - if (desc.m_LayerNormEnabled) - { - if(!desc.m_CifgEnabled) - { - paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); - } - paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); - paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); - paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("LSTM", - tfLiteContext, - IsLstmSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputStateInInfo, - cellStateInInfo, - scratchBufferTensorInfo, - outputStateOutTensorInfo, - cellStateOutTensorInfo, - outputInfo, - desc, - paramsInfo); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddLstmLayer(desc, params); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - layer->GetOutputSlot(0).SetTensorInfo(scratchBufferTensorInfo); - layer->GetOutputSlot(1).SetTensorInfo(outputStateOutTensorInfo); - layer->GetOutputSlot(2).SetTensorInfo(cellStateOutTensorInfo); - layer->GetOutputSlot(3).SetTensorInfo(outputTensorInfo); - - // Connect the inputs - // input_layer - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(layer->GetInputSlot(0)); - // cellStateIn - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[18]]->Connect(layer->GetInputSlot(1)); - //outputStateIn - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[19]]->Connect(layer->GetInputSlot(2)); - - // In the test_model there is only 1 Output - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(1); - delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[0])] = &outputSlot; - return kTfLiteOk; -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/MultiLayerFacade.hpp b/delegate/src/MultiLayerFacade.hpp deleted file mode 100644 index 31a7354382..0000000000 --- a/delegate/src/MultiLayerFacade.hpp +++ /dev/null @@ -1,136 +0,0 @@ -// -// Copyright © 2021,2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -// NOTE: the MultiLayerFacade class is a utility class which makes a chain -// of operators look like a single IConnectableLayer with the first -// layer in the chain supplying the input slots and the last supplying -// the output slots. It enables us, for example, to simulate a -// Tensorflow Lite FloorDiv operator by chaining a Div layer followed -// by a Floor layer and pass them as a single unit to the code that -// connects up the graph as the delegate proceeds to build up the -// Arm NN subgraphs. -// - -#include <common/include/ProfilingGuid.hpp> -#include <armnn/INetwork.hpp> - -namespace armnnDelegate -{ - -class MultiLayerFacade : public armnn::IConnectableLayer -{ -public: - MultiLayerFacade() : - m_FirstLayer(nullptr), m_LastLayer(nullptr) {} - - MultiLayerFacade(armnn::IConnectableLayer* firstLayer, armnn::IConnectableLayer* lastLayer) : - m_FirstLayer(firstLayer), m_LastLayer(lastLayer) {} - - MultiLayerFacade(const MultiLayerFacade& obj) : - m_FirstLayer(obj.m_FirstLayer), m_LastLayer(obj.m_LastLayer) {} - - ~MultiLayerFacade() {} // we don't own the pointers - - MultiLayerFacade& operator=(const MultiLayerFacade& obj) - { - m_FirstLayer = obj.m_FirstLayer; - m_LastLayer = obj.m_LastLayer; - return *this; - } - - void AssignValues(armnn::IConnectableLayer* firstLayer, armnn::IConnectableLayer* lastLayer) - { - m_FirstLayer = firstLayer; - m_LastLayer = lastLayer; - } - - virtual const char* GetName() const override - { - return m_FirstLayer->GetName(); - } - - virtual unsigned int GetNumInputSlots() const override - { - return m_FirstLayer->GetNumInputSlots(); - } - - virtual unsigned int GetNumOutputSlots() const override - { - return m_LastLayer->GetNumOutputSlots(); - } - - virtual const armnn::IInputSlot& GetInputSlot(unsigned int index) const override - { - return m_FirstLayer->GetInputSlot(index); - } - - virtual armnn::IInputSlot& GetInputSlot(unsigned int index) override - { - return m_FirstLayer->GetInputSlot(index); - } - - virtual const armnn::IOutputSlot& GetOutputSlot(unsigned int index) const override - { - return m_LastLayer->GetOutputSlot(index); - } - - virtual armnn::IOutputSlot& GetOutputSlot(unsigned int index) override - { - return m_LastLayer->GetOutputSlot(index); - } - - virtual std::vector<armnn::TensorShape> InferOutputShapes( - const std::vector<armnn::TensorShape>& inputShapes) const override - { - // NOTE: do not expect this function to be used. Likely that if it is it might need to be overridden - // for particular sequences of operators. - return m_FirstLayer->InferOutputShapes(inputShapes); - } - - virtual LayerGuid GetGuid() const override - { - return m_FirstLayer->GetGuid(); - } - - virtual void ExecuteStrategy(armnn::IStrategy& strategy) const override - { - // Do not expect this function to be used so not providing an implementation - // if an implementation is required and the chain contains more than two operators - // would have to provide a way to record the intermediate layers so they could be - // visited... the same applies to the BackendSelectionHint - // below. - } - - virtual void BackendSelectionHint(armnn::Optional<armnn::BackendId> backend) override - { - // Do not expect this function to be used so not providing an implementation - } - - virtual armnn::LayerType GetType() const override - { - return m_FirstLayer->GetType(); - } - - virtual const armnn::BaseDescriptor& GetParameters() const override { return m_NullDescriptor; } - - void SetBackendId(const armnn::BackendId& id) override {} - -protected: - /// Retrieve the handles to the constant values stored by the layer. - /// @return A vector of the constant tensors stored by this layer. - ConstantTensors GetConstantTensorsByRef() override { return {}; } - ImmutableConstantTensors GetConstantTensorsByRef() const override { return {}; } - -private: - armnn::IConnectableLayer* m_FirstLayer; - armnn::IConnectableLayer* m_LastLayer; - - // to satisfy the GetParameters method need to hand back a NullDescriptor - armnn::NullDescriptor m_NullDescriptor; -}; - -} // namespace armnnDelegate diff --git a/delegate/src/Normalization.hpp b/delegate/src/Normalization.hpp deleted file mode 100644 index ef2e524369..0000000000 --- a/delegate/src/Normalization.hpp +++ /dev/null @@ -1,162 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitL2NormalizationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::L2NormalizationDescriptor descriptor; - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("L2_NORMALIZATION", - tfLiteContext, - IsL2NormalizationSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a L2Normalization layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddL2NormalizationLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - - -TfLiteStatus VisitLocalResponseNormalizationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t normalizationOperatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, normalizationOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, normalizationOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::NormalizationDescriptor descriptor; - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - descriptor.m_NormChannelType = armnn::NormalizationAlgorithmChannel::Across; - descriptor.m_NormMethodType = armnn::NormalizationAlgorithmMethod::LocalBrightness; - - auto* params = reinterpret_cast<TfLiteLocalResponseNormParams*>(tfLiteNode->builtin_data); - descriptor.m_NormSize = params->radius; - descriptor.m_K = params->bias; - descriptor.m_Alpha = params->alpha; - descriptor.m_Beta = params->beta; - - // ArmNN expects normSize to be the full size of the normalization window - descriptor.m_NormSize = 1 + (2 * descriptor.m_NormSize); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("NORMALIZATION", - tfLiteContext, - IsNormalizationSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a Normalization layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddNormalizationLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Pack.hpp b/delegate/src/Pack.hpp deleted file mode 100644 index 57d3b460f5..0000000000 --- a/delegate/src/Pack.hpp +++ /dev/null @@ -1,122 +0,0 @@ -// -// Copyright © 2021,2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitPackOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - unsigned int numInputs = tfLiteNode->inputs->size; - if (numInputs < 1) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Must have at least one input in (%d != %d) in node #%d", - 1, numInputs, nodeIndex); - return kTfLiteError; - } - - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - // Validate all inputs and get TensorInfo - std::vector<armnn::TensorInfo> inputTensorInfos; - for (unsigned int i = 0; i < numInputs; ++i) - { - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[i]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - armnn::TensorInfo inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - inputTensorInfos.emplace_back(inputTensorInfo); - } - - // Convert input tensors to const armnn::TensorInfo* type for FORWARD_LAYER_SUPPORT_FUNC. - std::vector<const armnn::TensorInfo*> inputConstTensorInfos; - std::transform(inputTensorInfos.begin(), - inputTensorInfos.end(), - std::back_inserter(inputConstTensorInfos), - [](armnn::TensorInfo& t)->const armnn::TensorInfo*{ return &t; }); - - // Validate output and get TensorInfo - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::StackDescriptor desc; - desc.m_NumInputs = static_cast<uint32_t>(numInputs); - - // Get axis from TfLite parameters - auto* params = reinterpret_cast<TfLitePackParams*>(tfLiteNode->builtin_data); - desc.m_Axis = static_cast<uint32_t>(params->axis); - - // Use the tensor shape of the first input as the "correct" input shape in the descriptor - desc.m_InputShape = inputTensorInfos[0].GetShape(); - - // Check if supported - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("STACK", - tfLiteContext, - IsStackSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputConstTensorInfos, - outputTensorInfo, - desc); - }; - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitPackOperator will be called again to add the layer to the network as seen below - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // The TfLite Pack operator is equivalent to the ArmNN Stack operator - armnn::IConnectableLayer* layer = delegateData.m_Network->AddStackLayer(desc); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - // Connect the Constant Inputs - auto inputsTensorsProcess = ProcessInputs(layer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Pad.hpp b/delegate/src/Pad.hpp deleted file mode 100644 index 2ecf2a06d7..0000000000 --- a/delegate/src/Pad.hpp +++ /dev/null @@ -1,179 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitPadOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLitePadOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - switch(tfLitePadOperatorCode) - { - case kTfLiteBuiltinMirrorPad: - case kTfLiteBuiltinPad: - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - break; - case kTfLiteBuiltinPadv2: - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); - break; - default: - return kTfLiteError; - } - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - const TfLiteTensor& tfLitepaddingTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - - if (IsDynamicTensor(tfLiteInputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", - tfLitePadOperatorCode, 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: ", - tfLitePadOperatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& paddingTensorInfo = GetTensorInfoForTfLiteTensor(tfLitepaddingTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Get the padding data from the input tensor - auto* paddingData = tflite::GetTensorData<int32_t>(&tfLitepaddingTensor); - - size_t step = 2; - armnn::PadDescriptor descriptor; - for (unsigned int i = 0; i < paddingTensorInfo.GetNumElements() / step; ++i) - { - descriptor.m_PadList.emplace_back(paddingData[i * step], paddingData[i * step + 1]); - } - - if (tfLitePadOperatorCode == kTfLiteBuiltinPad && inputTensorInfo.IsQuantized()) - { - descriptor.m_PadValue = inputTensorInfo.GetQuantizationOffset(); - } - else if (tfLitePadOperatorCode == kTfLiteBuiltinPadv2) - { - const TfLiteTensor& tfLitepaddingValue = tfLiteTensors[tfLiteNode->inputs->data[2]]; - armnn::TensorInfo paddingValueTensorInfo = GetTensorInfoForTfLiteTensor(tfLitepaddingValue); - if (paddingValueTensorInfo.GetNumElements() != 1) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Multiple padding value are not supported in operator #%d node #%d: ", - tfLitePadOperatorCode, nodeIndex); - return kTfLiteError; - } - // Get the padding value from the input tensor - switch (tfLitepaddingValue.type) - { - case kTfLiteFloat32: - descriptor.m_PadValue = tflite::GetTensorData<float>(&tfLitepaddingValue)[0]; - break; - case kTfLiteUInt8: - descriptor.m_PadValue = tflite::GetTensorData<uint8>(&tfLitepaddingValue)[0]; - break; - case kTfLiteInt8: - descriptor.m_PadValue = tflite::GetTensorData<int8>(&tfLitepaddingValue)[0]; - break; - default: - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Padding value datatype is not supported in operator #%d node #%d: ", - tfLitePadOperatorCode, nodeIndex); - return kTfLiteError; - } - } - else if (tfLitePadOperatorCode == kTfLiteBuiltinMirrorPad) - { - TfLiteMirrorPaddingParams* options = reinterpret_cast<TfLiteMirrorPaddingParams*>(tfLiteNode->builtin_data); - - - if (options->mode == TfLiteMirrorPaddingMode::kTfLiteMirrorPaddingReflect) - { - descriptor.m_PaddingMode = armnn::PaddingMode::Reflect; - } - else if (options->mode == TfLiteMirrorPaddingMode::kTfLiteMirrorPaddingSymmetric) - { - descriptor.m_PaddingMode = armnn::PaddingMode::Symmetric; - } - else - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: PaddingMode must be either REFLECT or SYMMETRIC in operator #%d node #%d: ", - tfLitePadOperatorCode, nodeIndex); - } - - // If padding mode is Reflect then both paddings must be no greater than inputShape(i) - 1. - // If padding mode is Symmetric then both paddings must be no greater than inputShape(i). - auto inputShape = inputTensorInfo.GetShape(); - auto padList = descriptor.m_PadList; - - const unsigned int isReflect = - static_cast<unsigned int>(descriptor.m_PaddingMode == armnn::PaddingMode::Reflect); - for(unsigned int i = 0; i < padList.size(); ++i) - { - if(padList.at(i).first > (inputShape[i] - isReflect) || - padList.at(i).second > (inputShape[i] - isReflect)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Padding values must be less (Reflect) or " - "equal (Symmetric) to the dimension size in operator #%d node #%d: ", - tfLitePadOperatorCode, nodeIndex); - } - } - } - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("PAD", - tfLiteContext, - IsPadSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor); - - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* padLayer = delegateData.m_Network->AddPadLayer(descriptor); - padLayer->SetBackendId(setBackend); - ARMNN_ASSERT(padLayer != nullptr); - - armnn::IOutputSlot& outputSlot = padLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - return Connect(padLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Pooling.hpp b/delegate/src/Pooling.hpp deleted file mode 100644 index 1178b6d8dc..0000000000 --- a/delegate/src/Pooling.hpp +++ /dev/null @@ -1,327 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.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 <flatbuffers/flexbuffers.h> - -namespace armnnDelegate -{ - -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)); - - 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: ", - tfLitePoolingOperatorCode, 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: ", - tfLitePoolingOperatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* tfLiteNodeParameters = reinterpret_cast<TfLitePoolParams*>(tfLiteNode->builtin_data); - TfLiteFusedActivation activationType = kTfLiteActNone; - if (tfLiteNodeParameters) - { - activationType = tfLiteNodeParameters->activation; - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - } - - armnn::PoolingAlgorithm poolingAlgorithm; - switch(tfLitePoolingOperatorCode) - { - case kTfLiteBuiltinAveragePool2d: - poolingAlgorithm = armnn::PoolingAlgorithm::Average; - break; - case kTfLiteBuiltinL2Pool2d: - poolingAlgorithm = armnn::PoolingAlgorithm::L2; - break; - case kTfLiteBuiltinMaxPool2d: - poolingAlgorithm = armnn::PoolingAlgorithm::Max; - break; - default: - return kTfLiteError; - } - - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = poolingAlgorithm; - - descriptor.m_PoolWidth = tfLiteNodeParameters->filter_width; - descriptor.m_PoolHeight = tfLiteNodeParameters->filter_height; - descriptor.m_StrideX = tfLiteNodeParameters->stride_width; - descriptor.m_StrideY = tfLiteNodeParameters->stride_height; - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - - unsigned int inputHeight = inputTensorInfo.GetShape()[1]; - unsigned int inputWidth = inputTensorInfo.GetShape()[2]; - - CalcPadding(inputHeight, descriptor.m_PoolHeight, descriptor.m_StrideY, 1u, - descriptor.m_PadTop, descriptor.m_PadBottom, tfLiteNodeParameters->padding); - CalcPadding(inputWidth, descriptor.m_PoolWidth, descriptor.m_StrideX, 1u, - descriptor.m_PadLeft, descriptor.m_PadRight, tfLiteNodeParameters->padding); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("POOLING_2D", - tfLiteContext, - IsPooling2dSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling2dLayer(descriptor); - poolingLayer->SetBackendId(setBackend); - ARMNN_ASSERT(poolingLayer != nullptr); - - armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(poolingLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - if(Connect(poolingLayer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - // Check and create activation - 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, true); - - // 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); - - - // Check activation by parsing the string from the flexbuffer map - std::string activationTypeStr = m["activation"].AsString().str(); - TfLiteFusedActivation activationType = kTfLiteActNone; - - 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; - } - - TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, - outputTensorInfo, activationType); - if(activationStatus != kTfLiteOk) - { - return kTfLiteError; - } - - - // Validate the output info. - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) { - FORWARD_LAYER_SUPPORT_FUNC("POOLING_3D", - tfLiteContext, - IsPooling3dSupported, - delegateData.m_Backends, - isSupported, - setBackend, - 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); - poolingLayer->SetBackendId(setBackend); - ARMNN_ASSERT(poolingLayer != nullptr); - - // Create and set output slots - armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(poolingLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - if(Connect(poolingLayer, tfLiteNode, delegateData) != kTfLiteOk) - { - return kTfLiteError; - } - - return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Prelu.hpp b/delegate/src/Prelu.hpp deleted file mode 100644 index 06e74ed635..0000000000 --- a/delegate/src/Prelu.hpp +++ /dev/null @@ -1,108 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.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> - -namespace armnnDelegate -{ - -TfLiteStatus ValidatePreluOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& alphaInfo, - const armnn::TensorInfo& outputInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("PRELU", - tfLiteContext, - IsPreluSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo, - alphaInfo, - outputInfo); - }; - - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus VisitPreluOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteAlphaTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteAlphaTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& alphaTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAlphaTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - if (!delegateData.m_Network) - { - return ValidatePreluOperator(delegateData, - tfLiteContext, - inputTensorInfo, - alphaTensorInfo, - outputTensorInfo); - } - - armnn::IConnectableLayer* preluLayer = delegateData.m_Network->AddPreluLayer(); - ARMNN_ASSERT(preluLayer != nullptr); - - bool isConstantAlpha = tflite::IsConstantTensor(&tfLiteAlphaTensor); - - // Add constant layer for constant alpha - if (isConstantAlpha) - { - auto constAlphaTensor = armnn::ConstTensor(alphaTensorInfo, tfLiteAlphaTensor.data.data); - - armnn::IConnectableLayer* constLayer = delegateData.m_Network->AddConstantLayer(constAlphaTensor); - ARMNN_ASSERT(constLayer != nullptr); - - constLayer->GetOutputSlot(0).SetTensorInfo(alphaTensorInfo); - constLayer->GetOutputSlot(0).Connect(preluLayer->GetInputSlot(1)); - } - - armnn::IOutputSlot& outputSlot = preluLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // Connect - return Connect(preluLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/Quantization.hpp b/delegate/src/Quantization.hpp deleted file mode 100644 index f1192960e4..0000000000 --- a/delegate/src/Quantization.hpp +++ /dev/null @@ -1,171 +0,0 @@ -// -// Copyright © 2022-2023 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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitDequantizeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteDequantizeOperatorCode) -{ - 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: ", - tfLiteDequantizeOperatorCode, 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: ", - tfLiteDequantizeOperatorCode, nodeIndex); - - return kTfLiteError; - } - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - armnn::TensorInfo outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - UpdateConstantTensorOutputs(inputTensorInfo, outputTensorInfo); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("DEQUANTIZE", - tfLiteContext, - IsDequantizeSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* dequantizeLayer = delegateData.m_Network->AddDequantizeLayer(); - dequantizeLayer->SetBackendId(setBackend); - ARMNN_ASSERT(dequantizeLayer != nullptr); - - armnn::IOutputSlot& outputSlot = dequantizeLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - auto inputsTensorsProcess = ProcessInputs(dequantizeLayer, - delegateData, - tfLiteContext, - tfLiteNode); - if (inputsTensorsProcess == kTfLiteError) - { - return inputsTensorsProcess; - } - - return Connect(dequantizeLayer, tfLiteNode, delegateData); -} - -TfLiteStatus VisitQuantizeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteQuantizeOperatorCode) -{ - 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: ", - tfLiteQuantizeOperatorCode, 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: ", - tfLiteQuantizeOperatorCode, nodeIndex); - return kTfLiteError; - } - - // Only affine per-layer quantization is supported. - if (!IsAffineQuantization(tfLiteOutputTensor)) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Only affine per-layer quantization is supported in operator #%d node #%d: ", - tfLiteQuantizeOperatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("QUANTIZE", - tfLiteContext, - IsQuantizeSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfo); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* quantizeLayer = delegateData.m_Network->AddQuantizeLayer(); - quantizeLayer->SetBackendId(setBackend); - ARMNN_ASSERT(quantizeLayer != nullptr); - - armnn::IOutputSlot& outputSlot = quantizeLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(quantizeLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - return Connect(quantizeLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Redefine.hpp b/delegate/src/Redefine.hpp deleted file mode 100644 index 864fb7af67..0000000000 --- a/delegate/src/Redefine.hpp +++ /dev/null @@ -1,289 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/utility/IgnoreUnused.hpp> - -#include "DelegateUtils.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 <numeric> - -namespace armnnDelegate -{ - -TfLiteStatus VisitCastOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("CAST", - tfLiteContext, - IsCastSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo); - }; - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitCastOperator will be called again to add the layer to the network as seen further below - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a Cast layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddCastLayer(); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - - -TfLiteStatus CreateOutputTensorShape(const armnn::TensorInfo& inputTensorInfo, - const std::vector<int32_t>& targetShape, - armnn::ReshapeDescriptor& reshapeDesc) -{ - std::vector<unsigned int> outputDims(targetShape.begin(), targetShape.end()); - const auto stretchDim = std::find(targetShape.begin(), targetShape.end(), -1); - - if (stretchDim != targetShape.end()) - { - if (std::find(std::next(stretchDim), targetShape.end(), -1) != targetShape.end()) - { - // Return kTfLiteError and log the error after returning - return kTfLiteError; - } - - auto targetNumElements = - armnn::numeric_cast<unsigned int>( - std::accumulate(targetShape.begin(), targetShape.end(), -1, std::multiplies<int32_t>())); - - auto stretchIndex = static_cast<size_t>(std::distance(targetShape.begin(), stretchDim)); - outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements; - } - - armnn::TensorShape outputShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()), - outputDims.data()); - reshapeDesc.m_TargetShape = outputShape; - return kTfLiteOk; -} - -TfLiteStatus VisitReshapeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - - if (numInputs == 2) - { - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - } - else - { - 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& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor0, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::ReshapeDescriptor reshapeDesc; - std::vector<int32_t> targetShape; - - TfLiteReshapeParams* reshapeOptions = reinterpret_cast<TfLiteReshapeParams*>(tfLiteNode->builtin_data); - - // The new shape can be defined by either a second input tensor or by a builtin option, we need to check for both. - // Options might be set without valid data. we need to check the dimensions are in a valid range. - if (reshapeOptions && reshapeOptions->num_dimensions > 0 && reshapeOptions->num_dimensions <= 8) - { - for (int i=0; i < reshapeOptions->num_dimensions; ++i) - { - targetShape.push_back(reshapeOptions->shape[i]); - } - } - else if (numInputs == 2) - { - // Get shape from the second input tensor - const TfLiteTensor& tfLiteShapeInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteShapeInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - if (tfLiteShapeInputTensor.dims->size != 1) - { - TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, - "TfLiteArmnnDelegate: Target 'shape' input is not a 1D tensor in " - "operator #%d node #%d: Falling back to TfLiteOptions.", - operatorCode, nodeIndex); - } - else - { - // Get the shape data out of the input tensor - auto* shapeTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteShapeInputTensor); - auto shapeTensorNumValues = tfLiteShapeInputTensor.dims->data[0]; - for (auto i=0; i < shapeTensorNumValues; ++i) - { - targetShape.push_back(*(shapeTensorDataPtr+i)); - } - } - } - else - { - TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, - "Target shape not defined in reshape parameters or input tensor. " - "At least one method required in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - // Use the data to create the required tensor shape. - if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk) - { - TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, - "TfLiteArmnnDelegate: At most one component of shape can be -1 in: " - "operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - if (reshapeDesc.m_TargetShape.GetNumElements() != inputTensorInfo0.GetNumElements()) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Reshape, number of elements in output shape does not match input " - "operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("RESHAPE", - tfLiteContext, - IsReshapeSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo0, - outInfo, - reshapeDesc); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - armnn::IgnoreUnused(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - operatorCode); - - return kTfLiteError; -} - -TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - armnn::IgnoreUnused(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - operatorCode); - - return kTfLiteError; -} - -} // namespace armnnDelegate diff --git a/delegate/src/Reduce.hpp b/delegate/src/Reduce.hpp deleted file mode 100644 index 2d8b462cd2..0000000000 --- a/delegate/src/Reduce.hpp +++ /dev/null @@ -1,146 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <tensorflow/lite/builtin_ops.h> -#include <tensorflow/lite/c/builtin_op_data.h> -#include <tensorflow/lite/c/common.h> -#include <tensorflow/lite/kernels/internal/tensor_ctypes.h> -#include <tensorflow/lite/minimal_logging.h> - -namespace armnnDelegate -{ - -TfLiteStatus VisitReduceOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t reduceOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, reduceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, reduceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // Get const axis value from model and set it to descriptor. - const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteAxisTensor, reduceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& axisTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteAxisTensor); - auto* axisTensorData = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); - - std::vector<int32_t> axis; - // Add axis data to vector to be converter to unsigned int and assigned to descriptor axis. - if (axisTensorData != nullptr) - { - for (unsigned int i = 0; i < axisTensorInfo.GetNumElements(); ++i) - { - axis.emplace_back(axisTensorData[i]); - } - } - else - { - for (unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); ++i) - { - axis.push_back(i); - } - } - - // Convert the axis to unsigned int and remove duplicates. - unsigned int rank = inputTensorInfo.GetNumDimensions(); - std::set<unsigned int> uniqueAxis; - std::transform(axis.begin(), - axis.end(), - std::inserter(uniqueAxis, uniqueAxis.begin()), - [rank](int i)->unsigned int{ return (i + rank) % rank; }); - - armnn::ReduceDescriptor desc; - desc.m_vAxis.assign(uniqueAxis.begin(), uniqueAxis.end()); - - auto* reducerParameters = reinterpret_cast<TfLiteReducerParams*>(tfLiteNode->builtin_data); - desc.m_KeepDims = reducerParameters->keep_dims; - if (reduceOperatorCode == kTfLiteBuiltinReduceMax) - { - desc.m_ReduceOperation = armnn::ReduceOperation::Max; - } - else if (reduceOperatorCode == kTfLiteBuiltinReduceMin) - { - desc.m_ReduceOperation = armnn::ReduceOperation::Min; - } - else if (reduceOperatorCode == kTfLiteBuiltinSum) - { - desc.m_ReduceOperation = armnn::ReduceOperation::Sum; - } - else if (reduceOperatorCode == kTfLiteBuiltinReduceProd) - { - desc.m_ReduceOperation = armnn::ReduceOperation::Prod; - } - else - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Unsupported Reduction Operator #%d node #%d: ", - reduceOperatorCode, nodeIndex); - return kTfLiteError; - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("REDUCE", - tfLiteContext, - IsReduceSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - desc); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add an Reduce layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddReduceLayer(desc); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Resize.hpp b/delegate/src/Resize.hpp deleted file mode 100644 index 370f1ab2d2..0000000000 --- a/delegate/src/Resize.hpp +++ /dev/null @@ -1,205 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" -#include <armnn/utility/IgnoreUnused.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("RESIZE", - tfLiteContext, - IsResizeSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo, - outputInfo, - descriptor); - - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus VisitResizeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t resizeOperatorCode) -{ - 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& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - std::string layerName("Resize"); - - // Fill descriptor - armnn::ResizeDescriptor desc; - switch (resizeOperatorCode) - { - case kTfLiteBuiltinResizeBilinear: - { - desc.m_Method = armnn::ResizeMethod::Bilinear; - - layerName += "Bilinear:" + std::to_string(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:" + std::to_string(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); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(resizeLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - ARMNN_ASSERT(resizeLayer != nullptr); - - return Connect(resizeLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Round.hpp b/delegate/src/Round.hpp deleted file mode 100644 index 7a060b1d8f..0000000000 --- a/delegate/src/Round.hpp +++ /dev/null @@ -1,71 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "SharedFunctions.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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitFloorOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - // NOTE: looks like the outputTensorInfo is the only thing that is required for the case - // where we are adding the floor layer so maybe move the other stuff inside the - // if !delegateData block for efficiency. - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitFloorOperator will be called again to add the layer to the network as seen further below - if (!delegateData.m_Network) - { - return ValidateFloorOperator(delegateData, tfLiteContext, inputTensorInfo, outputTensorInfo); - } - - // Add a Floor layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddFloorLayer(); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Shape.hpp b/delegate/src/Shape.hpp deleted file mode 100644 index d797563ab5..0000000000 --- a/delegate/src/Shape.hpp +++ /dev/null @@ -1,95 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.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 <numeric> - -namespace armnnDelegate -{ - -TfLiteStatus VisitShapeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* shapeParameters = reinterpret_cast<TfLiteShapeParams*>(tfLiteNode->builtin_data); - if ( shapeParameters->out_type != kTfLiteInt32 && shapeParameters->out_type != kTfLiteInt64 ) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("SHAPE", - tfLiteContext, - IsShapeSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo); - }; - - // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the - // support for the operator - // If supported, VisitShapeOperator will be called again to add the layer to the network as seen further below - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a Shape layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddShapeLayer(); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/SharedFunctions.cpp b/delegate/src/SharedFunctions.cpp deleted file mode 100644 index fef970173e..0000000000 --- a/delegate/src/SharedFunctions.cpp +++ /dev/null @@ -1,116 +0,0 @@ -// -// Copyright © 2021-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - - -#include "SharedFunctions.hpp" - -#include "DelegateUtils.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> - -namespace armnnDelegate -{ - -TfLiteStatus ValidateFloorOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputTensorInfo, - const armnn::TensorInfo& outputTensorInfo) -{ - bool isSupported = false; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("FLOOR", - tfLiteContext, - IsFloorSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputTensorInfo, - outInfo); - }; - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus ValidateFusedActivationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& outputInfo, - TfLiteFusedActivation activationType) -{ - armnn::ActivationDescriptor activationDesc; - - switch (activationType) - { - case kTfLiteActNone: - { - // No Activation - return kTfLiteOk; - } - case kTfLiteActRelu: - { - activationDesc.m_Function = armnn::ActivationFunction::ReLu; - break; - } -// The name of kTfLiteActRelu1 changed after TF Lite v2.3 -#if defined(ARMNN_POST_TFLITE_2_3) - case kTfLiteActReluN1To1: -#else - case kTfLiteActRelu1: -#endif - { - activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; - activationDesc.m_A = 1.0f; - activationDesc.m_B = -1.0f; - break; - } - case kTfLiteActRelu6: - { - activationDesc.m_Function = armnn::ActivationFunction::BoundedReLu; - activationDesc.m_A = 6.0f; - activationDesc.m_B = 0.0f; - break; - } - case kTfLiteActSigmoid: - { - activationDesc.m_Function = armnn::ActivationFunction::Sigmoid; - break; - } - case kTfLiteActTanh: - { - activationDesc.m_Function = armnn::ActivationFunction::TanH; - activationDesc.m_A = 1.0f; - activationDesc.m_B = 1.0f; - break; - } - default: - return kTfLiteError; - } - - bool isSupported = false; - armnn::BackendId setBackend; - - auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("ACTIVATION", - tfLiteContext, - IsActivationSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo, - outputInfo, - activationDesc); - }; - validateFunc(outputInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; -} - - -} // namespace armnnDelegate - diff --git a/delegate/src/SharedFunctions.hpp b/delegate/src/SharedFunctions.hpp deleted file mode 100644 index b03a63ded9..0000000000 --- a/delegate/src/SharedFunctions.hpp +++ /dev/null @@ -1,25 +0,0 @@ -// -// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn_delegate.hpp> - -namespace armnnDelegate -{ - -TfLiteStatus ValidateFloorOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputTensorInfo, - const armnn::TensorInfo& outputTensorInfo); - -TfLiteStatus ValidateFusedActivationOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& outputInfo, - TfLiteFusedActivation activationType); - -} // namespace armnnDelegate - diff --git a/delegate/src/Slice.hpp b/delegate/src/Slice.hpp deleted file mode 100644 index f19e3327e4..0000000000 --- a/delegate/src/Slice.hpp +++ /dev/null @@ -1,141 +0,0 @@ -// -// Copyright © 2022-2023 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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitSliceOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t sliceOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - // Read inputs [input, begin, size] - int numInputs = tfLiteNode->inputs->size; - std::vector<const TfLiteTensor*> tfLiteInputs; - tfLiteInputs.reserve(numInputs); - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - for (int i = 0; i < numInputs; i++) - { - const TfLiteTensor* inputTensor = &tfLiteTensors[tfLiteNode->inputs->data[i]]; - tfLiteInputs.push_back(inputTensor); - if (!IsValid(tfLiteContext, *inputTensor, sliceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - } - - // We save the begin and size tensors in our descriptor. Therefore we have to read those values from inputs - int inputRank = tfLiteInputs[0]->dims->size; - auto ReadInt32Input = [&](int inputIndex, std::vector<uint32_t>& outputData) -> TfLiteStatus - { - if (tfLiteInputs[inputIndex]->type != kTfLiteInt32) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The Begin- and Size-Tensors of the Slice operation need to " - "be of type int32. Operator: #%d node #%d: ", - sliceOperatorCode, nodeIndex); - return kTfLiteError; - } - int rank = tfLiteInputs[inputIndex]->dims->size; - if (rank != 1) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The Begin- and Size-Tensors of the Slice operation need to " - "be a 1D-Tensor. Operator: #%d node #%d: ", - sliceOperatorCode, nodeIndex); - return kTfLiteError; - } - int numValues = tfLiteInputs[inputIndex]->dims->data[0]; - if (numValues != inputRank) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The number of values in the Begin- and Size-Tensors of the " - "Slice operation need to be equal to the rank of the Input-Tensor. Operator: #%d node #%d: ", - sliceOperatorCode, nodeIndex); - return kTfLiteError; - } - // return tensor data - auto* tensorDataPtr = tflite::GetTensorData<uint32_t>(tfLiteInputs[inputIndex]); - outputData.assign(tensorDataPtr, tensorDataPtr+numValues); - return kTfLiteOk; - }; - - std::vector<uint32_t> begin; - if (ReadInt32Input(1, begin) != kTfLiteOk) - return kTfLiteError; - std::vector<uint32_t> size; - if (ReadInt32Input(2, size) != kTfLiteOk) - return kTfLiteError; - - // Write all data to the descriptor - armnn::SliceDescriptor descriptor(begin, size); - - // Validate output - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, sliceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(*tfLiteInputs[0]); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("SLICE", - tfLiteContext, - IsSliceSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a Slice layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddSliceLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate - diff --git a/delegate/src/Softmax.hpp b/delegate/src/Softmax.hpp deleted file mode 100644 index 31c6ac3677..0000000000 --- a/delegate/src/Softmax.hpp +++ /dev/null @@ -1,155 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.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> - -namespace armnnDelegate -{ - -TfLiteStatus ValidateSoftmaxOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& outputTensorInfo, - const armnn::SoftmaxDescriptor& descriptor) -{ - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("SOFTMAX", - tfLiteContext, - IsSoftmaxSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo, - outputTensorInfo, - descriptor); - return isSupported ? kTfLiteOk : kTfLiteError; -} - - -TfLiteStatus ValidateLogSoftmaxOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const armnn::TensorInfo& inputInfo, - const armnn::TensorInfo& outputTensorInfo, - const armnn::LogSoftmaxDescriptor& descriptor) -{ - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("LOG_SOFTMAX", - tfLiteContext, - IsLogSoftmaxSupported, - delegateData.m_Backends, - isSupported, - armnn::BackendId(), - inputInfo, - outputTensorInfo, - descriptor); - return isSupported ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus VisitSoftmaxOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t softmaxOperatorCode) -{ - 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 node #%d: ", - 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 node #%d: ", - nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - - if (!delegateData.m_Network) - { - switch(softmaxOperatorCode) - { - case kTfLiteBuiltinSoftmax: - { - armnn::SoftmaxDescriptor descriptor; - auto* params = reinterpret_cast<TfLiteSoftmaxParams*>(tfLiteNode->builtin_data); - descriptor.m_Beta = params->beta; - return ValidateSoftmaxOperator(delegateData, - tfLiteContext, - inputTensorInfo, - outputTensorInfo, - descriptor); - } - case kTfLiteBuiltinLogSoftmax: - { - armnn::LogSoftmaxDescriptor descriptor; - return ValidateLogSoftmaxOperator(delegateData, - tfLiteContext, - inputTensorInfo, - outputTensorInfo, - descriptor); - } - default: - return kTfLiteError; - } - } - - armnn::IConnectableLayer* softmaxLayer = nullptr; - - switch(softmaxOperatorCode) - { - case kTfLiteBuiltinSoftmax: - { - armnn::SoftmaxDescriptor descriptor; - auto* params = reinterpret_cast<TfLiteSoftmaxParams*>(tfLiteNode->builtin_data); - descriptor.m_Beta = params->beta; - softmaxLayer = delegateData.m_Network->AddSoftmaxLayer(descriptor); - break; - } - case kTfLiteBuiltinLogSoftmax: - { - armnn::LogSoftmaxDescriptor descriptor; - softmaxLayer = delegateData.m_Network->AddLogSoftmaxLayer(descriptor); - break; - } - default: - return kTfLiteError; - } - ARMNN_ASSERT(softmaxLayer != nullptr); - - armnn::IOutputSlot& outputSlot = softmaxLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(softmaxLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(softmaxLayer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/SpaceDepth.hpp b/delegate/src/SpaceDepth.hpp deleted file mode 100644 index cc7f03413d..0000000000 --- a/delegate/src/SpaceDepth.hpp +++ /dev/null @@ -1,152 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitSpaceToDepthOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::SpaceToDepthDescriptor descriptor; - auto* params = reinterpret_cast<TfLiteSpaceToDepthParams*>(tfLiteNode->builtin_data); - descriptor.m_BlockSize = params->block_size; - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("SPACE_TO_DEPTH", - tfLiteContext, - IsSpaceToDepthSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a SpaceToDepth layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddSpaceToDepthLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -TfLiteStatus VisitDepthToSpaceOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - 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 (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - armnn::DepthToSpaceDescriptor descriptor; - auto* params = reinterpret_cast<TfLiteDepthToSpaceParams*>(tfLiteNode->builtin_data); - descriptor.m_BlockSize = params->block_size; - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("DEPTH_TO_SPACE", - tfLiteContext, - IsDepthToSpaceSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a DepthToSpace layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthToSpaceLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate diff --git a/delegate/src/Split.hpp b/delegate/src/Split.hpp deleted file mode 100644 index b183b55c54..0000000000 --- a/delegate/src/Split.hpp +++ /dev/null @@ -1,347 +0,0 @@ -// -// Copyright © 2020,2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" - -#include <algorithm> -#include <iterator> -#include <vector> - -namespace armnnDelegate -{ - -constexpr unsigned int MaxNumOfTensorDimensions = 5U; - -TfLiteStatus VisitSplitOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteSplitOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - - auto* splitParameters = reinterpret_cast<TfLiteSplitParams*>(tfLiteNode->builtin_data); - const unsigned int numSplits = NonNegative(splitParameters->num_splits, nodeIndex); - - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, numSplits, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteAxisTensor, tfLiteSplitOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteSplitOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - - ARMNN_ASSERT(GetTensorInfoForTfLiteTensor(tfLiteAxisTensor).GetNumElements() == 1); - auto* axisTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); - std::vector<int32_t> axisTensorData(axisTensorDataPtr, axisTensorDataPtr + 1); - int32_t axis = axisTensorData[0]; - - auto inputDimensions = static_cast<int32_t>(inputTensorInfo.GetNumDimensions()); - if (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0))) - { - // Square bracket denotes inclusive n while parenthesis denotes exclusive n - // E.g. Rank 4 tensor can have axis in range [-4, 3) - // -1 == 3, -2 == 2, -3 == 1, -4 == 0 - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Operation has invalid axis: #%d. Axis must be in range [-n, n) in node #%d:", - axis, nodeIndex); - } - const unsigned int splitDim = ComputeWrappedIndex(axis, inputTensorInfo.GetNumDimensions()); - - std::vector<armnn::TensorInfo> outputs; - for (unsigned int i = 0; i < numSplits; ++i) - { - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[i]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteSplitOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - outputs.push_back(GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true)); - } - const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end()); - - auto inputDimSize = inputTensorInfo.GetNumDimensions(); - if (inputDimSize > MaxNumOfTensorDimensions) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The number of dimensions: #%d for input tensors of the split op cannot be greater " - "than #%d in node #%d: ", inputDimSize, MaxNumOfTensorDimensions, nodeIndex); - return kTfLiteError; - } - - std::vector<unsigned int> splitterDimSizes(inputDimSize); - - // Add current input shape to splitterDimSizes - for (unsigned int i = 0; i < inputDimSize; ++i) - { - splitterDimSizes[i] = inputTensorInfo.GetShape()[i]; - } - - if (splitterDimSizes[splitDim] % numSplits != 0) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Number of splits #%d must evenly divide the dimension #%d in node #%d: ", - numSplits, splitterDimSizes[splitDim], nodeIndex); - return kTfLiteError; - } - splitterDimSizes[splitDim] /= numSplits; - - armnn::SplitterDescriptor splitDescriptor(numSplits, inputDimSize); - for (unsigned int j = 0; j < numSplits; ++j) - { - // Set the size of the views. - for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx) - { - splitDescriptor.SetViewSize(j, dimIdx, splitterDimSizes[dimIdx]); - } - splitDescriptor.SetViewOriginCoord(j, splitDim, splitterDimSizes[splitDim] * j); - } - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - // Check if supported - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("SPLIT", - tfLiteContext, - IsSplitterSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfos, - splitDescriptor); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddSplitterLayer(splitDescriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) - { - layer->GetOutputSlot(k).SetTensorInfo(outputs[k]); - } - - // Connect the input slots - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[1]]->Connect(layer->GetInputSlot(0)); - - // Prepare output slots - for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) - { - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); - delegateData.m_OutputSlotForNode[ - static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &outputSlot; - } - - return kTfLiteOk; -} - -TfLiteStatus VisitSplitVOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfLiteSplitVOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, tfLiteSplitVOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteSplitsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (!IsValid(tfLiteContext, tfLiteSplitsTensor, tfLiteSplitVOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; - if (!IsValid(tfLiteContext, tfLiteAxisTensor, tfLiteSplitVOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& splitsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteSplitsTensor); - ARMNN_ASSERT(splitsTensorInfo.GetNumDimensions() == 1); - ARMNN_ASSERT(GetTensorInfoForTfLiteTensor(tfLiteAxisTensor).GetNumElements() == 1); - - auto* axisTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor); - std::vector<int32_t> axisTensorData(axisTensorDataPtr, axisTensorDataPtr + 1); - int32_t axis = axisTensorData[0]; - - auto inputDimensions = static_cast<int32_t>(inputTensorInfo.GetNumDimensions()); - if (((axis < -inputDimensions) && (axis < 0)) || ((axis >= inputDimensions) && (axis > 0))) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Operation has invalid axis: #%d. Axis must be in range [-n, n) in node #%d:", - axis, nodeIndex); - } - const unsigned int splitDim = ComputeWrappedIndex(axisTensorData[0], inputTensorInfo.GetNumDimensions()); - - auto* splitVParameters = reinterpret_cast<TfLiteSplitVParams*>(tfLiteNode->builtin_data); - unsigned int numSplits = 0; - if (splitVParameters) - { - numSplits = NonNegative(splitVParameters->num_splits, nodeIndex); - } - else - { - numSplits = splitsTensorInfo.GetNumElements(); - } - - if (numSplits <= 0) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Invalid number of splits %d in node #%d", - numSplits, nodeIndex); - return kTfLiteError; - } - - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, numSplits, nodeIndex)); - std::vector<armnn::TensorInfo> outputs; - for (unsigned int i = 0; i < numSplits; ++i) - { - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[i]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, tfLiteSplitVOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - outputs.push_back(GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true)); - } - const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end()); - - auto inputDimSize = inputTensorInfo.GetNumDimensions(); - if (inputDimSize > MaxNumOfTensorDimensions) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The number of dimensions: #%d for input tensors of the split op cannot be greater " - "than #%d in node #%d: ", inputDimSize, MaxNumOfTensorDimensions, nodeIndex); - return kTfLiteError; - } - - std::vector<int32_t> splitsTensorData(numSplits); - std::memcpy(splitsTensorData.data(), tfLiteSplitsTensor.data.data, splitsTensorInfo.GetNumBytes()); - - - unsigned int index = 0; - unsigned int inferredIndex = 0; - int numberOfInferred = 0; - int splitSum = 0; - - for (auto splitData : splitsTensorData) - { - if (splitData < 0) - { - ++numberOfInferred; - inferredIndex = index; - } - else - { - splitSum += splitData; - } - ++index; - } - - // Check for inferred axis - if (numberOfInferred == 0) - { - if (splitSum != armnn::numeric_cast<int>(inputTensorInfo.GetShape()[splitDim])) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: SplitV split_sizes does not sum to the dimension of value along" - " split_dim in node #%d", nodeIndex); - return kTfLiteError; - } - } - else if (numberOfInferred == 1) - { - splitsTensorData[inferredIndex] = armnn::numeric_cast<int>(inputTensorInfo.GetShape()[splitDim]) - splitSum; - } - else - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: SplitV cannot infer split size for more than one split in node #%d", - nodeIndex); - return kTfLiteError; - } - - armnn::SplitterDescriptor splitDescriptor(numSplits, inputDimSize); - unsigned int accumSplit = 0; - for (unsigned int j = 0; j < numSplits; ++j) - { - unsigned int splitSize = armnn::numeric_cast<unsigned int>(splitsTensorData[j]); - - // Set the size of the views. - for (unsigned int dimIdx = 0; dimIdx < inputTensorInfo.GetNumDimensions(); ++dimIdx) - { - unsigned int dimSize = inputTensorInfo.GetShape()[dimIdx]; - if (dimIdx == splitDim) - { - dimSize = splitSize; - } - splitDescriptor.SetViewSize(j, dimIdx, dimSize); - } - - splitDescriptor.SetViewOriginCoord(j, splitDim, accumSplit); - accumSplit += splitSize; - } - - armnn::BackendId setBackend; - if (!delegateData.m_Network) - { - // Check if supported - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("SPLIT", - tfLiteContext, - IsSplitterSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputTensorInfos, - splitDescriptor); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddSplitterLayer(splitDescriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k) - { - layer->GetOutputSlot(k).SetTensorInfo(outputs[k]); - } - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/StridedSlice.hpp b/delegate/src/StridedSlice.hpp deleted file mode 100644 index 998e3d3e14..0000000000 --- a/delegate/src/StridedSlice.hpp +++ /dev/null @@ -1,156 +0,0 @@ -// -// Copyright © 2022-2023 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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitStridedSliceOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t sliceOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 4, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - // Read inputs [input, begin, end, strides] - int numInputs = tfLiteNode->inputs->size; - std::vector<const TfLiteTensor*> tfLiteInputs; - tfLiteInputs.reserve(numInputs); - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - for (int i = 0; i < numInputs; i++) - { - const TfLiteTensor* inputTensor = &tfLiteTensors[tfLiteNode->inputs->data[i]]; - tfLiteInputs.push_back(inputTensor); - if (!IsValid(tfLiteContext, *inputTensor, sliceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - } - - // We save the begin, end and strides tensors in our descriptor. Therefore we have to read those values from inputs - int inputRank = tfLiteInputs[0]->dims->size; - auto ReadInt32Input = [&](int inputIndex, std::vector<int32_t>& outputData) -> TfLiteStatus - { - if (tfLiteInputs[inputIndex]->type != kTfLiteInt32) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to " - "be of type int32. Operator: #%d node #%d: ", - sliceOperatorCode, nodeIndex); - return kTfLiteError; - } - int rank = tfLiteInputs[inputIndex]->dims->size; - if (rank != 1) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to " - "be a 1D-Tensor. Operator: #%d node #%d: ", - sliceOperatorCode, nodeIndex); - return kTfLiteError; - } - int numValues = tfLiteInputs[inputIndex]->dims->data[0]; - if (numValues != inputRank) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The number of values in the Begin-, End- and Stride-Tensors of the " - "StridedSlice operation need to be equal to the rank of the Input-Tensor. Operator: #%d node #%d: ", - sliceOperatorCode, nodeIndex); - return kTfLiteError; - } - // return tensor data - auto* tensorDataPtr = tflite::GetTensorData<int32_t>(tfLiteInputs[inputIndex]); - outputData.assign(tensorDataPtr, tensorDataPtr+numValues); - return kTfLiteOk; - }; - - std::vector<int32_t> beginData; - if (ReadInt32Input(1, beginData) != kTfLiteOk) - return kTfLiteError; - std::vector<int32_t> endData; - if (ReadInt32Input(2, endData) != kTfLiteOk) - return kTfLiteError; - std::vector<int32_t> strideData; - if (ReadInt32Input(3, strideData) != kTfLiteOk) - return kTfLiteError; - - // parse built in options - auto* stridedSliceParams = reinterpret_cast<TfLiteStridedSliceParams*>(tfLiteNode->builtin_data); - - // Write all data to the descriptor - armnn::StridedSliceDescriptor descriptor; - descriptor.m_Begin = std::move(beginData); - descriptor.m_End = std::move(endData); - descriptor.m_Stride = std::move(strideData); - descriptor.m_BeginMask = stridedSliceParams->begin_mask; - descriptor.m_EllipsisMask = stridedSliceParams->ellipsis_mask; - descriptor.m_EndMask = stridedSliceParams->end_mask; - descriptor.m_NewAxisMask = stridedSliceParams->new_axis_mask; - descriptor.m_ShrinkAxisMask = stridedSliceParams->shrink_axis_mask; - descriptor.m_DataLayout = armnn::DataLayout::NHWC; - - // Validate output - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, sliceOperatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(*tfLiteInputs[0]); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("STRIDED_SLICE", - tfLiteContext, - IsStridedSliceSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Add a StridedSlice layer - armnn::IConnectableLayer* layer = delegateData.m_Network->AddStridedSliceLayer(descriptor); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - // Connect - return Connect(layer, tfLiteNode, delegateData); -} - -} // namespace armnnDelegate - diff --git a/delegate/src/Transpose.hpp b/delegate/src/Transpose.hpp deleted file mode 100644 index 41178d0b59..0000000000 --- a/delegate/src/Transpose.hpp +++ /dev/null @@ -1,110 +0,0 @@ -// -// Copyright © 2022-2023 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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitTransposeOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t tfliteTransposeOperatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex)); - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor *tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (IsDynamicTensor(tfLiteInputTensor0)) - { - TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in " - "operator #%d node #%d: ", - tfliteTransposeOperatorCode, nodeIndex); - - return kTfLiteError; - } - - const TfLiteTensor& tfLiteInputTensor1 = tfLiteTensors[tfLiteNode->inputs->data[1]]; - if (IsDynamicTensor(tfLiteInputTensor1)) - { - TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, - "TfLiteArmnnDelegate: Dynamic input tensors are not supported in " - "operator #%d node #%d: ", - tfliteTransposeOperatorCode, 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: ", - tfliteTransposeOperatorCode, nodeIndex); - return kTfLiteError; - } - - const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - auto* permTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteInputTensor1); - unsigned int numEl = tfLiteInputTensor1.dims->data[0]; - - ARMNN_ASSERT( numEl <= static_cast<int>(armnn::MaxNumOfTensorDimensions)); - ARMNN_ASSERT( tfLiteInputTensor1.dims->size == 1); // ensure only single dimension to the permutation tensor - - armnn::TransposeDescriptor descriptor(armnn::PermutationVector( - reinterpret_cast<const armnn::PermutationVector::ValueType *> (permTensorDataPtr), - static_cast<armnn::PermutationVector::SizeType>(numEl))); - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("TRANSPOSE", - tfLiteContext, - IsTransposeSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo0, - outputTensorInfo, - descriptor); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* transposeLayer = delegateData.m_Network->AddTransposeLayer(descriptor); - transposeLayer->SetBackendId(setBackend); - ARMNN_ASSERT(transposeLayer != nullptr); - ARMNN_ASSERT(transposeLayer->GetNumInputSlots() == 1); // permutation vector given to descriptor object - - armnn::IOutputSlot& outputSlot = transposeLayer->GetOutputSlot(0); - outputSlot.SetTensorInfo(outputTensorInfo); - - // try to connect the Constant Inputs if there are any - if(ProcessInputs(transposeLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) - { - return kTfLiteError; - } - - return Connect(transposeLayer, tfLiteNode, delegateData); -} -} // namespace armnnDelegate diff --git a/delegate/src/UnidirectionalSequenceLstm.hpp b/delegate/src/UnidirectionalSequenceLstm.hpp deleted file mode 100644 index 9408397587..0000000000 --- a/delegate/src/UnidirectionalSequenceLstm.hpp +++ /dev/null @@ -1,302 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "DelegateUtils.hpp" - -#include <armnn/LstmParams.hpp> -#include <armnn/Tensor.hpp> -#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> - -namespace armnnDelegate -{ - -TfLiteStatus VisitUnidirectionalSequenceLstmOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - auto numInputs = tfLiteNode->inputs->size; - if (numInputs < 2) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", - 2, numInputs, nodeIndex); - return kTfLiteError; - } - - const auto nodeParams = reinterpret_cast<TfLiteUnidirectionalSequenceLSTMParams *>(tfLiteNode->builtin_data); - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - // Set the params structure for the AddUnidirectionalSequenceLstmLayer call - // Please refer to each operand at - // https://www.tensorflow.org/mlir/tfl_ops#tflunidirectional_sequence_lstm_tflunidirectionalsequencelstmop - armnn::LstmInputParams params; - - if (IsOptionalOperandPresent(tfLiteNode, 1)) - { - params.m_InputToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 1); - } - - params.m_InputToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 2); - params.m_InputToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 3); - params.m_InputToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 4); - - // Recurrent weight tensors of size {n_cell, n_output} - if (IsOptionalOperandPresent(tfLiteNode, 5)) - { - params.m_RecurrentToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 5); - } - - params.m_RecurrentToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 6); - params.m_RecurrentToCellWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 7); - params.m_RecurrentToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 8); - - // Peephole weights tensors of size {n_cell}, representing a diagonal matrix. - if (IsOptionalOperandPresent(tfLiteNode, 9)) - { - params.m_CellToInputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 9); - } - - if (IsOptionalOperandPresent(tfLiteNode, 10)) - { - params.m_CellToForgetWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 10); - } - - if (IsOptionalOperandPresent(tfLiteNode, 11)) - { - params.m_CellToOutputWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 11); - } - - // Gates bias tensors of size {n_cell} - if (IsOptionalOperandPresent(tfLiteNode, 12)) - { - params.m_InputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 12); - } - - params.m_ForgetGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 13); - params.m_CellBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 14); - params.m_OutputGateBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 15); - - // Projection weight tensor of size {n_output, n_cell} - if (IsOptionalOperandPresent(tfLiteNode, 16)) - { - params.m_ProjectionWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 16); - } - // Projection bias tensor of size {n_output} - if (IsOptionalOperandPresent(tfLiteNode, 17)) - { - params.m_ProjectionBias = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 17); - } - - // These state tensors are defined as variable tensors, and will be modified by this op. - armnn::TensorInfo outputStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[18]]); - armnn::TensorInfo cellStateInInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->inputs->data[19]]); - - // Layer norm coefficient tensors of size {n_cell}, representing a diagonal matrix. - if (IsOptionalOperandPresent(tfLiteNode, 20)) - { - params.m_InputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 20); - } - - if (IsOptionalOperandPresent(tfLiteNode, 21)) - { - params.m_ForgetLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 21); - } - - if (IsOptionalOperandPresent(tfLiteNode, 22)) - { - params.m_CellLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 22); - } - - if (IsOptionalOperandPresent(tfLiteNode, 23)) - { - params.m_OutputLayerNormWeights = GetConstTensorForTfLiteTensor(tfLiteTensors, tfLiteNode, 23); - } - - // set the layer descriptor - armnn::UnidirectionalSequenceLstmDescriptor desc; - desc.m_ActivationFunc = NonNegative(nodeParams->activation, nodeIndex); - desc.m_ClippingThresCell = nodeParams->cell_clip; - desc.m_ClippingThresProj = nodeParams->proj_clip; - desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr - || params.m_RecurrentToInputWeights == nullptr - || params.m_InputGateBias == nullptr); - desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr || params.m_CellToOutputWeights != nullptr); - desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr); - desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr - || params.m_ForgetLayerNormWeights != nullptr - || params.m_CellLayerNormWeights != nullptr - || params.m_OutputLayerNormWeights != nullptr); - desc.m_TimeMajor = nodeParams->time_major; - - if (tfLiteNode->intermediates->size > 3 && desc.m_LayerNormEnabled) - { - auto inputIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( - tfLiteTensors[tfLiteNode->intermediates->data[0]]); - auto forgetIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( - tfLiteTensors[tfLiteNode->intermediates->data[1]]); - auto cellIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( - tfLiteTensors[tfLiteNode->intermediates->data[2]]); - auto outputIntermediateTensorInfo = GetTensorInfoForTfLiteTensor( - tfLiteTensors[tfLiteNode->intermediates->data[3]]); - - desc.m_InputIntermediateScale = inputIntermediateTensorInfo.GetQuantizationScale(); - desc.m_ForgetIntermediateScale = forgetIntermediateTensorInfo.GetQuantizationScale(); - desc.m_CellIntermediateScale = cellIntermediateTensorInfo.GetQuantizationScale(); - desc.m_OutputIntermediateScale = outputIntermediateTensorInfo.GetQuantizationScale(); - } - else - { - float defaultIntermediate = std::pow(2, -12); - desc.m_InputIntermediateScale = defaultIntermediate; - desc.m_ForgetIntermediateScale = defaultIntermediate; - desc.m_CellIntermediateScale = defaultIntermediate; - desc.m_OutputIntermediateScale = defaultIntermediate; - } - if (tfLiteNode->intermediates->size > 4) - { - auto hiddentensorInfo = GetTensorInfoForTfLiteTensor(tfLiteTensors[tfLiteNode->intermediates->data[4]]); - desc.m_HiddenStateScale = hiddentensorInfo.GetQuantizationScale(); - desc.m_HiddenStateZeroPoint = hiddentensorInfo.GetQuantizationOffset(); - } - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); - - unsigned int batchSize = inputTensorInfo.GetShape()[0]; - unsigned int outputSize = outputTensorInfo.GetShape()[2]; - unsigned int numUnits = cellStateInInfo.GetShape()[1]; - - armnn::DataType dataType = inputTensorInfo.GetDataType(); - float qScale = inputTensorInfo.GetQuantizationScale(); - float qOffset = inputTensorInfo.GetQuantizationOffset(); - - armnn::TensorInfo scratchBufferTensorInfo({batchSize, numUnits * 3}, dataType, qScale, qOffset); - if (!desc.m_CifgEnabled) - { - scratchBufferTensorInfo = armnn::TensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset); - } - armnn::TensorInfo cellStateOutTensorInfo({batchSize, numUnits}, - cellStateInInfo.GetDataType(), - cellStateInInfo.GetQuantizationScale(), - cellStateInInfo.GetQuantizationOffset()); - - armnn::TensorInfo outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset); - - armnn::LstmInputParamsInfo paramsInfo; - paramsInfo.m_InputToForgetWeights = &(params.m_InputToForgetWeights->GetInfo()); - paramsInfo.m_InputToCellWeights = &(params.m_InputToCellWeights->GetInfo()); - paramsInfo.m_InputToOutputWeights = &(params.m_InputToOutputWeights->GetInfo()); - paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo()); - paramsInfo.m_RecurrentToCellWeights = &(params.m_RecurrentToCellWeights->GetInfo()); - paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo()); - paramsInfo.m_ForgetGateBias = &(params.m_ForgetGateBias->GetInfo()); - paramsInfo.m_CellBias = &(params.m_CellBias->GetInfo()); - paramsInfo.m_OutputGateBias = &(params.m_OutputGateBias->GetInfo()); - - if (!desc.m_CifgEnabled) - { - paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo()); - paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo()); - if (params.m_CellToInputWeights != nullptr) - { - paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo()); - } - paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo()); - } - - if (desc.m_ProjectionEnabled) - { - paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo()); - if (params.m_ProjectionBias != nullptr) - { - paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo()); - } - } - - if (desc.m_PeepholeEnabled) - { - paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo()); - paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo()); - } - - if (desc.m_LayerNormEnabled) - { - if(!desc.m_CifgEnabled) - { - paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo()); - } - paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo()); - paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo()); - paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo()); - } - - bool isSupported = false; - armnn::BackendId setBackend; - auto validateFunc = [&](const armnn::TensorInfo& outputInfo, bool& isSupported) - { - FORWARD_LAYER_SUPPORT_FUNC("UNIDIRECTIONAL_SEQUENCE_LSTM", - tfLiteContext, - IsUnidirectionalSequenceLstmSupported, - delegateData.m_Backends, - isSupported, - setBackend, - inputTensorInfo, - outputStateInInfo, - cellStateInInfo, - outputStateOutTensorInfo, - cellStateOutTensorInfo, - outputInfo, - desc, - paramsInfo); - }; - - if (!delegateData.m_Network) - { - validateFunc(outputTensorInfo, isSupported); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - armnn::IConnectableLayer* layer = delegateData.m_Network->AddUnidirectionalSequenceLstmLayer(desc, params); - layer->SetBackendId(setBackend); - ARMNN_ASSERT(layer != nullptr); - - layer->GetOutputSlot(0).SetTensorInfo(outputStateOutTensorInfo); - layer->GetOutputSlot(1).SetTensorInfo(cellStateOutTensorInfo); - layer->GetOutputSlot(2).SetTensorInfo(outputTensorInfo); - - // Connect the inputs - // input_layer - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(layer->GetInputSlot(0)); - // cellStateIn - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[18]]->Connect(layer->GetInputSlot(1)); - //outputStateIn - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[19]]->Connect(layer->GetInputSlot(2)); - - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(2); - delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tfLiteNode->outputs->data[0])] = &outputSlot; - return kTfLiteOk; -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/Unpack.hpp b/delegate/src/Unpack.hpp deleted file mode 100644 index ad541adccc..0000000000 --- a/delegate/src/Unpack.hpp +++ /dev/null @@ -1,214 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn/utility/IgnoreUnused.hpp> - -#include "DelegateUtils.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 <numeric> - -namespace armnnDelegate -{ - -TfLiteStatus VisitUnpackOperator(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteNode* tfLiteNode, - int nodeIndex, - int32_t operatorCode) -{ - TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); - - const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; - const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; - - if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - - // Get Unpack Axis - const auto params = reinterpret_cast<TfLiteUnpackParams*>(tfLiteNode->builtin_data); - - const unsigned int unpackAxis = NonNegative(params->axis, nodeIndex); - - const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); - - if (unpackAxis >= inputTensorInfo.GetNumDimensions()) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: The unpack axis #%d cannot be greater than or equal to " - "the number of input dimensions #%d in operator #%d node #%d", - unpackAxis, inputTensorInfo.GetNumDimensions(), operatorCode, nodeIndex); - return kTfLiteError; - } - - // Get Unpack Num - unsigned int unpackNum = NonNegative(params->num, nodeIndex); - - // If num is not defined, automatically infer from the length of the dimension axis. - if(unpackNum == 0) - { - unpackNum = inputTensorInfo.GetShape()[unpackAxis]; - } - - // If unpack number cannot be inferred and is still zero, return kTfLiteError. - if(unpackNum == 0) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Number to unpack must greater than zero in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - // Check outputs - TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, unpackNum, nodeIndex)); - - - auto inputDimSize = inputTensorInfo.GetNumDimensions(); - std::vector<unsigned int> unpackDimSizes(inputDimSize); - - // Add current input shape to unpackDimSizes - for (unsigned int i = 0; i < inputDimSize; ++i) - { - unpackDimSizes[i] = inputTensorInfo.GetShape()[i]; - } - - if (unpackDimSizes[unpackAxis] != unpackNum) - { - TF_LITE_MAYBE_KERNEL_LOG( - tfLiteContext, - "TfLiteArmnnDelegate: Number to unpack must be the same as length " - "of the dimension to unpack along in operator #%d node #%d: ", - operatorCode, nodeIndex); - return kTfLiteError; - } - - unpackDimSizes[unpackAxis] /= unpackNum; - - armnn::SplitterDescriptor splitDesc(unpackNum, static_cast<unsigned int>(unpackDimSizes.size())); - for (unsigned int j = 0; j < unpackNum; ++j) - { - // Set the size of the views. - for (unsigned int dimIdx = 0; dimIdx < unpackDimSizes.size(); ++dimIdx) - { - splitDesc.SetViewSize(j, dimIdx, unpackDimSizes[dimIdx]); - } - splitDesc.SetViewOriginCoord(j, unpackAxis, unpackDimSizes[unpackAxis] * j); - } - - std::vector<armnn::TensorInfo> outputs; - for (unsigned int i = 0; i < unpackNum; ++i) - { - const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[i]]; - if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) - { - return kTfLiteError; - } - outputs.push_back(GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true)); - } - const std::vector<std::reference_wrapper<armnn::TensorInfo>> outputTensorInfos(outputs.begin(), outputs.end()); - - // Determine the shape of the Splitter layer outputs for validation - armnn::TensorShape splitOutShape = armnn::TensorShape(static_cast<unsigned int>(unpackDimSizes.size()), - unpackDimSizes.data()); - - std::vector<armnn::TensorInfo> splitterOutputs; - for (unsigned int outputIndex = 0; outputIndex < outputTensorInfos.size(); ++outputIndex) - { - splitterOutputs.push_back(armnn::TensorInfo(splitOutShape, - outputTensorInfos[outputIndex].get().GetDataType(), - outputTensorInfos[outputIndex].get().GetQuantizationScale(), - outputTensorInfos[outputIndex].get().GetQuantizationOffset())); - } - std::vector<std::reference_wrapper<armnn::TensorInfo>> splitterOutputTensorInfos(splitterOutputs.begin(), - splitterOutputs.end()); - - armnn::BackendId setBackendSplit; - if (!delegateData.m_Network) - { - // Check if splitter is supported - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("UNPACK", - tfLiteContext, - IsSplitterSupported, - delegateData.m_Backends, - isSupported, - setBackendSplit, - inputTensorInfo, - splitterOutputTensorInfos, - splitDesc); - return isSupported ? kTfLiteOk : kTfLiteError; - } - - // Create Reshape descriptor from the first outputTensorInfo to validate a single Reshape layer - // Use this descriptor later when creating every ReshapeLayer as all Reshape Layers should be the same - armnn::ReshapeDescriptor reshapeDescriptor; - reshapeDescriptor.m_TargetShape = outputTensorInfos[0].get().GetShape(); - - armnn::BackendId setBackendReshape; - if (!delegateData.m_Network) - { - bool isSupported = false; - FORWARD_LAYER_SUPPORT_FUNC("RESHAPE", - tfLiteContext, - IsReshapeSupported, - delegateData.m_Backends, - isSupported, - setBackendReshape, - splitterOutputTensorInfos[0], - outputTensorInfos[0], - reshapeDescriptor); - return isSupported ? kTfLiteOk : kTfLiteError; - }; - - std::string splitterLayerName("Unpack Splitter"); - - armnn::IConnectableLayer* splitterLayer = delegateData.m_Network->AddSplitterLayer(splitDesc, - splitterLayerName.c_str()); - splitterLayer->SetBackendId(setBackendSplit); - ARMNN_ASSERT(splitterLayer != nullptr); - - for (unsigned int k = 0; k < splitterLayer->GetNumOutputSlots(); ++k) - { - splitterLayer->GetOutputSlot(k).SetTensorInfo(outputs[k]); - } - - // Connect the input slots - delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(splitterLayer->GetInputSlot(0)); - - // Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter. - for (unsigned int outputIndex = 0; outputIndex < splitterLayer->GetNumOutputSlots(); ++outputIndex) - { - std::string reshapeLayerName("Unpack Reshape"); - armnn::IConnectableLayer* reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor, - reshapeLayerName.c_str()); - reshapeLayer->SetBackendId(setBackendReshape); - ARMNN_ASSERT(reshapeLayer != nullptr); - - splitterLayer->GetOutputSlot(outputIndex).SetTensorInfo(splitterOutputTensorInfos[outputIndex]); - splitterLayer->GetOutputSlot(outputIndex).Connect(reshapeLayer->GetInputSlot(0)); - - armnn::TensorInfo outputTensorInfo = outputTensorInfos[outputIndex]; - reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); - - armnn::IOutputSlot& slot = reshapeLayer->GetOutputSlot(0); - - delegateData.m_OutputSlotForNode[ - static_cast<unsigned long>(tfLiteNode->outputs->data[outputIndex])] = &slot; - - } - - return kTfLiteOk; -} - -} // namespace armnnDelegate diff --git a/delegate/src/armnn_delegate.cpp b/delegate/src/armnn_delegate.cpp deleted file mode 100644 index 4ddfc1a35f..0000000000 --- a/delegate/src/armnn_delegate.cpp +++ /dev/null @@ -1,1059 +0,0 @@ -// -// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <armnn_delegate.hpp> - -#include "Version.hpp" - -#include "Activation.hpp" -#include "ArgMinMax.hpp" -#include "BatchMatMul.hpp" -#include "BatchSpace.hpp" -#include "Comparison.hpp" -#include "Convolution.hpp" -#include "Control.hpp" -#include "ElementwiseBinary.hpp" -#include "ElementwiseUnary.hpp" -#include "Fill.hpp" -#include "FullyConnected.hpp" -#include "Gather.hpp" -#include "GatherNd.hpp" -#include "LogicalBinary.hpp" -#include "Lstm.hpp" -#include "Normalization.hpp" -#include "Pack.hpp" -#include "Pad.hpp" -#include "Pooling.hpp" -#include "Prelu.hpp" -#include "Quantization.hpp" -#include "Redefine.hpp" -#include "Reduce.hpp" -#include "Resize.hpp" -#include "Round.hpp" -#include "Shape.hpp" -#include "Slice.hpp" -#include "StridedSlice.hpp" -#include "Softmax.hpp" -#include "SpaceDepth.hpp" -#include "Split.hpp" -#include "Transpose.hpp" -#include "UnidirectionalSequenceLstm.hpp" -#include "Unpack.hpp" - -#include <armnnUtils/Filesystem.hpp> -#include <armnn/utility/Timer.hpp> -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/context_util.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <algorithm> -#include <iostream> -#include <sstream> - -namespace armnnDelegate -{ - -DelegateOptions TfLiteArmnnDelegateOptionsDefault() -{ - DelegateOptions options(armnn::Compute::CpuRef); - return options; -} - -TfLiteDelegate* TfLiteArmnnDelegateCreate(armnnDelegate::DelegateOptions options) -{ - auto* armnnDelegate = new ::armnnDelegate::Delegate(options); - return armnnDelegate->GetDelegate(); -} - -void TfLiteArmnnDelegateDelete(TfLiteDelegate* tfLiteDelegate) -{ - if (tfLiteDelegate != nullptr) - { - delete static_cast<::armnnDelegate::Delegate*>(tfLiteDelegate->data_); - } -} - -TfLiteStatus DoPrepare(TfLiteContext* tfLiteContext, TfLiteDelegate* tfLiteDelegate) -{ - TfLiteIntArray* supportedOperators = - static_cast<::armnnDelegate::Delegate*>(tfLiteDelegate->data_)->IdentifyOperatorsToDelegate(tfLiteContext); - - // ArmNN Delegate Registration - static const TfLiteRegistration kArmnnSubgraphRegistration = { - // ArmnnSubgraph Init - .init = [](TfLiteContext* tfLiteContext, const char* buffer, size_t length) -> void* { - armnn::IgnoreUnused(length); - const TfLiteDelegateParams* parameters = reinterpret_cast<const TfLiteDelegateParams*>(buffer); - - return static_cast<void*>(ArmnnSubgraph::Create( - tfLiteContext, parameters, static_cast<::armnnDelegate::Delegate*>(parameters->delegate->data_))); - }, - // ArmnnSubgraph Free - .free = [](TfLiteContext* tfLiteContext, void* buffer) -> void { - armnn::IgnoreUnused(tfLiteContext); - if (buffer != nullptr) - { - delete static_cast<ArmnnSubgraph*>(buffer); - } - }, - // ArmnnSubgraph Prepare - .prepare = [](TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode) -> TfLiteStatus { - if (tfLiteNode->user_data == nullptr) - { - return kTfLiteError; - } - return static_cast<ArmnnSubgraph*>(tfLiteNode->user_data)->Prepare(tfLiteContext); - }, - // ArmnnSubgraph Invoke - .invoke = [](TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode) -> TfLiteStatus { - if (tfLiteNode->user_data == nullptr) - { - return kTfLiteError; - } - - return static_cast<ArmnnSubgraph*>(tfLiteNode->user_data)->Invoke(tfLiteContext, tfLiteNode); - }, - - .profiling_string = nullptr, - .builtin_code = kTfLiteBuiltinDelegate, - .custom_name = "TfLiteArmNnDelegate", - .version = 1, - .registration_external = nullptr, - }; - - const TfLiteStatus status = - tfLiteContext->ReplaceNodeSubsetsWithDelegateKernels( - tfLiteContext, kArmnnSubgraphRegistration, supportedOperators, tfLiteDelegate); - - TfLiteIntArrayFree(supportedOperators); - return status; - -} - -Delegate::Delegate(armnnDelegate::DelegateOptions options) - : m_Options(std::move(options)) -{ - // Configures logging for ARMNN - if (m_Options.IsLoggingEnabled()) - { - armnn::ConfigureLogging(true, true, m_Options.GetLoggingSeverity()); - } - // Create/Get the static ArmNN Runtime. Note that the m_Runtime will be shared by all armnn_delegate - // instances so the RuntimeOptions cannot be altered for different armnn_delegate instances. - m_Runtime = GetRuntime(m_Options.GetRuntimeOptions()); - std::vector<armnn::BackendId> backends; - if (m_Runtime) - { - const armnn::BackendIdSet supportedDevices = m_Runtime->GetDeviceSpec().GetSupportedBackends(); - for (auto& backend : m_Options.GetBackends()) - { - if (std::find(supportedDevices.cbegin(), supportedDevices.cend(), backend) == supportedDevices.cend()) - { - TFLITE_LOG_PROD(tflite::TFLITE_LOG_INFO, - "TfLiteArmnnDelegate: Requested unknown backend %s", backend.Get().c_str()); - } - else - { - backends.push_back(backend); - } - } - } - - if (backends.empty()) - { - // No known backend specified - throw armnn::InvalidArgumentException("TfLiteArmnnDelegate: No known backend specified."); - } - m_Options.SetBackends(backends); - - TFLITE_LOG_PROD_ONCE(tflite::TFLITE_LOG_INFO, "TfLiteArmnnDelegate: Created TfLite ArmNN delegate."); -} - -TfLiteIntArray* Delegate::IdentifyOperatorsToDelegate(TfLiteContext* tfLiteContext) -{ - TfLiteIntArray* executionPlan = nullptr; - if (tfLiteContext->GetExecutionPlan(tfLiteContext, &executionPlan) != kTfLiteOk) - { - TF_LITE_KERNEL_LOG(tfLiteContext, "TfLiteArmnnDelegate: Unable to get graph execution plan."); - return nullptr; - } - - // Delegate data with null network - DelegateData delegateData(m_Options.GetBackends()); - - TfLiteIntArray* nodesToDelegate = TfLiteIntArrayCreate(executionPlan->size); - nodesToDelegate->size = 0; - - std::set<int32_t> unsupportedOperators; - - for (int i = 0; i < executionPlan->size; ++i) - { - const int nodeIndex = executionPlan->data[i]; - - // If TfLite nodes can be delegated to ArmNN - TfLiteNode* tfLiteNode = nullptr; - TfLiteRegistration* tfLiteRegistration = nullptr; - if (tfLiteContext->GetNodeAndRegistration( - tfLiteContext, nodeIndex, &tfLiteNode, &tfLiteRegistration) != kTfLiteOk) - { - TF_LITE_KERNEL_LOG(tfLiteContext, - "TfLiteArmnnDelegate: Unable to get node and registration for node %d.", - nodeIndex); - continue; - } - - TfLiteStatus visitStatus; - - try - { - visitStatus = ArmnnSubgraph::VisitNode( - delegateData, tfLiteContext, tfLiteRegistration, tfLiteNode, nodeIndex); - } - catch(std::exception& ex) - { - ARMNN_LOG(error) << "ArmNN Failed to visit node with error: " << ex.what(); - visitStatus = kTfLiteError; - } - - if ( visitStatus != kTfLiteOk) - { - // node is not supported by ArmNN - unsupportedOperators.insert(tfLiteRegistration->builtin_code); - continue; - } - - nodesToDelegate->data[nodesToDelegate->size++] = nodeIndex; - } - - for (std::set<int32_t>::iterator it=unsupportedOperators.begin(); it!=unsupportedOperators.end(); ++it) - { - TF_LITE_KERNEL_LOG(tfLiteContext, - "Operator %s [%d] is not supported by armnn_delegate.", - tflite::EnumNameBuiltinOperator(tflite::BuiltinOperator(*it)), - *it); - } - - if (!unsupportedOperators.empty() && m_Options.TfLiteRuntimeFallbackDisabled()) - { - std::stringstream exMessage; - exMessage << "TfLiteArmnnDelegate: There are unsupported operators in the model. "; - exMessage << "Not falling back to TfLite Runtime as fallback is disabled. "; - exMessage << "This should only be disabled under test conditions."; - throw armnn::Exception(exMessage.str()); - } - if (nodesToDelegate->size == 0) - { - ARMNN_LOG(info) << "No operators in this model are supported by the Arm NN TfLite delegate." << - " The model will be executed entirely by TfLite runtime."; - } - - std::sort(&nodesToDelegate->data[0], &nodesToDelegate->data[nodesToDelegate->size]); - return nodesToDelegate; -} - -TfLiteDelegate* Delegate::GetDelegate() -{ - return &m_Delegate; -} - -const std::string Delegate::GetVersion() -{ - return DELEGATE_VERSION; -} - -TfLiteStatus ArmnnSubgraph::AddInputLayer(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const TfLiteIntArray* inputs, - std::vector<armnn::BindingPointInfo>& inputBindings) -{ - const size_t numInputs = static_cast<size_t>(inputs->size); - for (unsigned int i = 0; i < numInputs; ++i) - { - const int32_t tensorId = inputs->data[i]; - const TfLiteTensor tensor = tfLiteContext->tensors[tensorId]; - // Do not create bindings for constant inputs - if (tensor.allocation_type == kTfLiteMmapRo) - { - continue; - } - - auto bindingId = static_cast<armnn::LayerBindingId>((tensorId)); - armnn::IConnectableLayer* layer = delegateData.m_Network->AddInputLayer(bindingId); - - auto tensorInfo = GetTensorInfoForTfLiteTensor(tensor); - armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); - outputSlot.SetTensorInfo(tensorInfo); - - // Store for creating connections - delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tensorId)] = &outputSlot; - - inputBindings.push_back(std::make_pair(bindingId, tensorInfo)); - } - - return kTfLiteOk; -} - -TfLiteStatus ArmnnSubgraph::AddOutputLayer(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - const TfLiteIntArray* outputs, - std::vector<armnn::BindingPointInfo>& outputBindings) -{ - const size_t numOutputs = static_cast<size_t>(outputs->size); - for (unsigned int i = 0; i < numOutputs; ++i) - { - const int32_t tensorId = outputs->data[i]; - const TfLiteTensor tensor = tfLiteContext->tensors[tensorId]; - - auto bindingId = static_cast<armnn::LayerBindingId>((tensorId)); - armnn::IConnectableLayer* layer = delegateData.m_Network->AddOutputLayer(bindingId); - - auto tensorInfo = GetTensorInfoForTfLiteTensor(tensor); - ARMNN_ASSERT(delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tensorId)] != nullptr); - delegateData.m_OutputSlotForNode[static_cast<unsigned long>(tensorId)]->Connect(layer->GetInputSlot(0)); - outputBindings.push_back(std::make_pair(bindingId, tensorInfo)); - } - - return kTfLiteOk; -} - -ArmnnSubgraph* ArmnnSubgraph::Create(TfLiteContext* tfLiteContext, - const TfLiteDelegateParams* parameters, - const Delegate* delegate) -{ - const auto startTime = armnn::GetTimeNow(); - ARMNN_LOG(info) << "ArmnnSubgraph creation"; - - TfLiteIntArray* executionPlan; - if (tfLiteContext->GetExecutionPlan(tfLiteContext, &executionPlan) != kTfLiteOk) - { - return nullptr; - } - - // Initialize DelegateData holds network and output slots information - DelegateData delegateData(delegate->m_Options.GetBackends()); - - // Build ArmNN Network - armnn::NetworkOptions networkOptions = delegate->m_Options.GetOptimizerOptions().m_ModelOptions; - armnn::NetworkId networkId; - delegateData.m_Network = armnn::INetwork::Create(networkOptions); - - delegateData.m_OutputSlotForNode = std::vector<armnn::IOutputSlot*>(tfLiteContext->tensors_size, nullptr); - - std::vector<armnn::BindingPointInfo> inputBindings; - std::vector<armnn::BindingPointInfo> outputBindings; - - // Add input layer - auto status = AddInputLayer(delegateData, tfLiteContext, parameters->input_tensors, inputBindings); - if (status != kTfLiteOk) - { - throw armnn::Exception("TfLiteArmnnDelegate: Unable to add Inputs to the network!"); - } - - // Parse TfLite delegate nodes to ArmNN - const auto parseStartTime = armnn::GetTimeNow(); - for (int i = 0; i < parameters->nodes_to_replace->size; ++i) - { - const int nodeIndex = parameters->nodes_to_replace->data[i]; - - TfLiteNode* tfLiteNode = nullptr; - TfLiteRegistration* tfLiteRegistration = nullptr; - if (tfLiteContext->GetNodeAndRegistration( - tfLiteContext, nodeIndex, &tfLiteNode, &tfLiteRegistration) != kTfLiteOk) - { - throw armnn::Exception(&"TfLiteArmnnDelegate: Unable to get node registration: " [ nodeIndex]); - } - - if (VisitNode(delegateData, tfLiteContext, tfLiteRegistration, tfLiteNode, nodeIndex) != kTfLiteOk) - { - throw armnn::Exception(&"TfLiteArmnnDelegate: Unable to parse node: " [ nodeIndex]); - } - } - ARMNN_LOG(info) << "Parse nodes to ArmNN time: " << std::setprecision(2) - << std::fixed << armnn::GetTimeDuration(parseStartTime).count() << " ms"; - - // Add Output layer - status = AddOutputLayer(delegateData, tfLiteContext, parameters->output_tensors, outputBindings); - if (status != kTfLiteOk) - { - throw armnn::Exception("TfLiteArmnnDelegate: Unable to add Outputs to the network!"); - } - - // Optimize ArmNN network - armnn::IOptimizedNetworkPtr optNet(nullptr, nullptr); - try - { - const auto optimizeStartTime = armnn::GetTimeNow(); - optNet = armnn::Optimize(*(delegateData.m_Network.get()), - delegate->m_Options.GetBackends(), - delegate->m_Runtime->GetDeviceSpec(), - delegate->m_Options.GetOptimizerOptions()); - ARMNN_LOG(info) << "Optimize ArmnnSubgraph time: " << std::setprecision(2) - << std::fixed << armnn::GetTimeDuration(optimizeStartTime).count() << " ms"; - } - catch (std::exception& ex) - { - std::stringstream exMessage; - exMessage << "TfLiteArmnnDelegate: Exception (" << ex.what() << ") caught from optimize."; - throw armnn::Exception(exMessage.str()); - } - if (!optNet) - { - // Optimize failed - throw armnn::Exception("TfLiteArmnnDelegate: Unable to optimize the network!"); - } - - // If set, we will serialize the optimized model into a dot file. - const std::string serializeToDotFile = delegate->m_Options.GetSerializeToDot(); - if (!serializeToDotFile.empty()) - { - ARMNN_LOG(info) << "Writing graph to dot file: " << serializeToDotFile; - fs::path filename = serializeToDotFile; - std::fstream file(filename.c_str(), std::ios_base::out); - optNet->SerializeToDot(file); - } - - try - { - const auto loadStartTime = armnn::GetTimeNow(); - - // Load graph into runtime - std::string errorMessage; - armnn::Status loadingStatus; - armnn::MemorySource inputSource = armnn::MemorySource::Undefined; - armnn::MemorySource outputSource = armnn::MemorySource::Undefined; - // There's a bit of an assumption here that the delegate will only support Malloc memory source. - if (delegate->m_Options.GetOptimizerOptions().m_ImportEnabled) - { - inputSource = armnn::MemorySource::Malloc; - } - if (delegate->m_Options.GetOptimizerOptions().m_ExportEnabled) - { - outputSource = armnn::MemorySource::Malloc; - } - armnn::INetworkProperties networkProperties(false, - inputSource, - outputSource, - delegate->m_Options.GetInternalProfilingState(), - delegate->m_Options.GetInternalProfilingDetail()); - loadingStatus = delegate->m_Runtime->LoadNetwork(networkId, - std::move(optNet), - errorMessage, - networkProperties); - if (loadingStatus != armnn::Status::Success) - { - // Network load failed. - throw armnn::Exception("TfLiteArmnnDelegate: Network could not be loaded: " + errorMessage); - } - - ARMNN_LOG(info) << "Load ArmnnSubgraph time: " << std::setprecision(2) - << std::fixed << armnn::GetTimeDuration(loadStartTime).count() << " ms"; - } - catch (std::exception& ex) - { - std::stringstream exMessage; - exMessage << "TfLiteArmnnDelegate: Exception (" << ex.what() << ") caught from LoadNetwork."; - throw armnn::Exception(exMessage.str()); - } - - // Register debug callback function - if (delegate->m_Options.GetDebugCallbackFunction().has_value()) - { - delegate->m_Runtime->RegisterDebugCallback(networkId, delegate->m_Options.GetDebugCallbackFunction().value()); - } - - ARMNN_LOG(info) << "Overall ArmnnSubgraph creation time: " << std::setprecision(2) - << std::fixed << armnn::GetTimeDuration(startTime).count() << " ms\n"; - - // Create a new SubGraph with networkId and runtime - return new ArmnnSubgraph(networkId, delegate->m_Runtime, inputBindings, outputBindings); -} - -TfLiteStatus ArmnnSubgraph::Prepare(TfLiteContext* tfLiteContext) -{ - armnn::IgnoreUnused(tfLiteContext); - return kTfLiteOk; -} - -TfLiteStatus ArmnnSubgraph::Invoke(TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode) -{ - // Prepare inputs - armnn::InputTensors inputTensors; - size_t inputIndex = 0; - for (auto inputIdx : tflite::TfLiteIntArrayView(tfLiteNode->inputs)) - { - TfLiteTensor* tensor = &tfLiteContext->tensors[inputIdx]; - if (tensor->allocation_type != kTfLiteMmapRo) - { - const armnn::BindingPointInfo& inputBinding = m_InputBindings[inputIndex]; - armnn::TensorInfo inputTensorInfo = inputBinding.second; - inputTensorInfo.SetConstant(true); - const armnn::ConstTensor inputTensor(inputTensorInfo, tensor->data.data); - inputTensors.emplace_back(inputIdx, inputTensor); - - ++inputIndex; - } - } - - // Prepare outputs - armnn::OutputTensors outputTensors; - size_t outputIndex = 0; - for (auto outputIdx : tflite::TfLiteIntArrayView(tfLiteNode->outputs)) - { - const armnn::BindingPointInfo& outputBinding = m_OutputBindings[outputIndex]; - TfLiteTensor* tensor = &tfLiteContext->tensors[outputIdx]; - const armnn::Tensor outputTensor(outputBinding.second, tensor->data.data); - outputTensors.emplace_back(outputIdx, outputTensor); - - ++outputIndex; - } - - // Run graph - auto status = m_Runtime->EnqueueWorkload(m_NetworkId, inputTensors, outputTensors); - // The delegate holds its own Arm NN runtime so this is our last chance to print internal profiling data. - std::shared_ptr<armnn::IProfiler> profiler = m_Runtime->GetProfiler(m_NetworkId); - if (profiler && profiler->IsProfilingEnabled()) - { - profiler->Print(std::cout); - } - return (status == armnn::Status::Success) ? kTfLiteOk : kTfLiteError; -} - -TfLiteStatus ArmnnSubgraph::VisitNode(DelegateData& delegateData, - TfLiteContext* tfLiteContext, - TfLiteRegistration* tfLiteRegistration, - TfLiteNode* tfLiteNode, - int nodeIndex) -{ - 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, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Abs); - case kTfLiteBuiltinAdd: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinAdd); - case kTfLiteBuiltinArgMax: - return VisitArgMinMaxOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinArgMax); - case kTfLiteBuiltinArgMin: - return VisitArgMinMaxOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinArgMin); - case kTfLiteBuiltinAveragePool2d: - return VisitPooling2dOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinAveragePool2d); - case kTfLiteBuiltinBatchMatmul: - return VisitBatchMatMulOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinBatchMatmul); - case kTfLiteBuiltinBatchToSpaceNd: - return VisitBatchToSpaceNdOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinBatchToSpaceNd); - case kTfLiteBuiltinCast: - return VisitCastOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinCast); - case kTfLiteBuiltinConcatenation: - return VisitControlOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinConcatenation); - case kTfLiteBuiltinConv2d: - return VisitConvolutionOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinConv2d); -// 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) - case kTfLiteBuiltinConv3d: - return VisitConvolutionOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinConv3d); -#endif - case kTfLiteBuiltinDepthToSpace: - return VisitDepthToSpaceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinDepthToSpace); - case kTfLiteBuiltinDepthwiseConv2d: - return VisitConvolutionOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinDepthwiseConv2d); - case kTfLiteBuiltinDequantize: - return VisitDequantizeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinDequantize); - case kTfLiteBuiltinDiv: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinDiv); - case kTfLiteBuiltinElu: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinElu); - case kTfLiteBuiltinEqual: - return VisitComparisonOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinEqual); - case kTfLiteBuiltinExp: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Exp); - case kTfLiteBuiltinExpandDims: - return VisitExpandDimsOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinExpandDims); - case kTfLiteBuiltinFill: - return VisitFillOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinFill); - case kTfLiteBuiltinFloor: - return VisitFloorOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinFloor); - case kTfLiteBuiltinFloorDiv: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinFloorDiv); - case kTfLiteBuiltinFullyConnected: - return VisitFullyConnectedOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinFullyConnected); - case kTfLiteBuiltinGather: - return VisitGatherOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinGather); - case kTfLiteBuiltinGatherNd: - return VisitGatherNdOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinGatherNd); - case kTfLiteBuiltinGreater: - return VisitComparisonOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinGreater); - case kTfLiteBuiltinGreaterEqual: - return VisitComparisonOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinGreaterEqual); - case kTfLiteBuiltinHardSwish: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinHardSwish); - case kTfLiteBuiltinL2Normalization: - return VisitL2NormalizationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinL2Normalization); - case kTfLiteBuiltinL2Pool2d: - return VisitPooling2dOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinL2Pool2d); - case kTfLiteBuiltinLess: - return VisitComparisonOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLess); - case kTfLiteBuiltinLessEqual: - return VisitComparisonOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLessEqual); - case kTfLiteBuiltinLocalResponseNormalization: - return VisitLocalResponseNormalizationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLocalResponseNormalization); - case kTfLiteBuiltinLog: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Log); - case kTfLiteBuiltinLogicalAnd: - return VisitLogicalBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLogicalAnd, - armnn::LogicalBinaryOperation::LogicalAnd); - case kTfLiteBuiltinLogicalNot: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::LogicalNot); - case kTfLiteBuiltinLogicalOr: - return VisitLogicalBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLogicalOr, - armnn::LogicalBinaryOperation::LogicalOr); - case kTfLiteBuiltinLogistic: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLogistic); - case kTfLiteBuiltinLogSoftmax: - return VisitSoftmaxOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLogSoftmax); - case kTfLiteBuiltinLstm: - return VisitLstmOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinLstm); - case kTfLiteBuiltinMaxPool2d: - return VisitPooling2dOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinMaxPool2d); - case kTfLiteBuiltinMaximum: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinMaximum); - case kTfLiteBuiltinMean: - return VisitControlOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinMean); - case kTfLiteBuiltinMinimum: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinMinimum); - case kTfLiteBuiltinMirrorPad: - return VisitPadOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinMirrorPad); - case kTfLiteBuiltinMul: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinMul); - case kTfLiteBuiltinNeg: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Neg); - case kTfLiteBuiltinNotEqual: - return VisitComparisonOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinNotEqual); - case kTfLiteBuiltinPack: - return VisitPackOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinPack); - case kTfLiteBuiltinPad: - return VisitPadOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinPad); - case kTfLiteBuiltinPadv2: - return VisitPadOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinPadv2); - case kTfLiteBuiltinPrelu: - return VisitPreluOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinPrelu); - case kTfLiteBuiltinQuantize: - return VisitQuantizeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinQuantize); - case kTfLiteBuiltinRank: - return VisitControlOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinRank); - case kTfLiteBuiltinReduceMax: - return VisitReduceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinReduceMax); - case kTfLiteBuiltinReduceMin: - return VisitReduceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinReduceMin); - case kTfLiteBuiltinReduceProd: - return VisitReduceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinReduceProd); - case kTfLiteBuiltinRelu: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinRelu); - case kTfLiteBuiltinReluN1To1: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinReluN1To1); - case kTfLiteBuiltinRelu6: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinRelu6); - case kTfLiteBuiltinReshape: - return VisitReshapeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinReshape); - case kTfLiteBuiltinResizeBilinear: - return VisitResizeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinResizeBilinear); - case kTfLiteBuiltinResizeNearestNeighbor: - return VisitResizeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinResizeNearestNeighbor); - case kTfLiteBuiltinRsqrt: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Rsqrt); - case kTfLiteBuiltinShape: - return VisitShapeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinShape); - case kTfLiteBuiltinSin: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Sin); - case kTfLiteBuiltinSplit: - return VisitSplitOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSplit); - case kTfLiteBuiltinSplitV: - return VisitSplitVOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSplitV); - case kTfLiteBuiltinSqrt: - return VisitElementwiseUnaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - armnn::UnaryOperation::Sqrt); - case kTfLiteBuiltinSqueeze: - return VisitSqueezeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSqueeze); - case kTfLiteBuiltinSlice: - return VisitSliceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSlice); - case kTfLiteBuiltinStridedSlice: - return VisitStridedSliceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinStridedSlice); - case kTfLiteBuiltinSum: - return VisitReduceOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSum); - case kTfLiteBuiltinTranspose: - return VisitTransposeOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinTranspose); - case kTfLiteBuiltinTransposeConv: - return VisitConvolutionOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinTransposeConv); - case kTfLiteBuiltinSoftmax: - return VisitSoftmaxOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSoftmax); - case kTfLiteBuiltinSpaceToBatchNd: - return VisitSpaceToBatchNdOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSpaceToBatchNd); - case kTfLiteBuiltinSpaceToDepth: - return VisitSpaceToDepthOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSpaceToDepth); - case kTfLiteBuiltinSub: - return VisitElementwiseBinaryOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinSub); - case kTfLiteBuiltinTanh: - return VisitActivationOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinTanh); - case kTfLiteBuiltinUnidirectionalSequenceLstm: - return VisitUnidirectionalSequenceLstmOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinUnidirectionalSequenceLstm); - case kTfLiteBuiltinUnpack: - return VisitUnpackOperator(delegateData, - tfLiteContext, - tfLiteNode, - nodeIndex, - kTfLiteBuiltinUnpack); - default: - return kTfLiteError; - } -} - -} // armnnDelegate namespace
\ No newline at end of file diff --git a/delegate/src/armnn_external_delegate.cpp b/delegate/src/armnn_external_delegate.cpp deleted file mode 100644 index c3875740e1..0000000000 --- a/delegate/src/armnn_external_delegate.cpp +++ /dev/null @@ -1,68 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include "armnn_delegate.hpp" -#include <armnn/Logging.hpp> -#include <armnn/utility/NumericCast.hpp> - -#include <iostream> -#include <tensorflow/lite/minimal_logging.h> - -namespace tflite -{ - -/** - * This file defines two symbols that need to be exported to use the TFLite external delegate provider. This is a plugin - * that can be used for fast integration of delegates into benchmark tests and other tools. It allows loading of - * a dynamic delegate library at runtime. - * - * The external delegate also has Tensorflow Lite Python bindings. Therefore the dynamic external delegate - * can be directly used with Tensorflow Lite Python APIs. - * - * See tensorflow/lite/delegates/external for details or visit the tensorflow guide - * [here](https://www.tensorflow.org/lite/performance/implementing_delegate#option_2_leverage_external_delegate) - */ - -extern "C" -{ - -/** - * Implementation of the TfLite external delegate plugin - * - * For details about what options_keys and option_values are supported please see: - * armnnDelegate::DelegateOptions::DelegateOptions(char const* const*, char const* const*,size_t,void (*)(const char*)) - */ -TfLiteDelegate* tflite_plugin_create_delegate(char** options_keys, - char** options_values, - size_t num_options, - void (*report_error)(const char*)) -{ - // Returning null indicates an error during delegate creation, we initialize with that - TfLiteDelegate* delegate = nullptr; - try - { - armnnDelegate::DelegateOptions options (options_keys, options_values, num_options, (*report_error)); - delegate = TfLiteArmnnDelegateCreate(options); - } - catch (const std::exception& ex) - { - if(report_error) - { - report_error(ex.what()); - } - } - return delegate; -} - -/** Destroy a given delegate plugin - * - * @param[in] delegate Delegate to destruct - */ -void tflite_plugin_destroy_delegate(TfLiteDelegate* delegate) -{ - armnnDelegate::TfLiteArmnnDelegateDelete(delegate); -} - -} // extern "C" -} // namespace tflite
\ No newline at end of file diff --git a/delegate/src/test/ActivationTest.cpp b/delegate/src/test/ActivationTest.cpp deleted file mode 100644 index 69041d77a2..0000000000 --- a/delegate/src/test/ActivationTest.cpp +++ /dev/null @@ -1,299 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ActivationTestHelper.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 ActivationReLuTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<float> inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - // Calculate output values for input. - auto f = [](float value) - { - return std::fmax(0.0f, value); - }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - ActivationTest(tflite::BuiltinOperator_RELU, - backends, - inputData, - outputExpectedData); -} - -void ActivationBoundedReluTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<float> inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - const float a = 6.0f; - const float b = 0.0f; - // Calculate output values for input. - auto f = [a, b](float value) - { - return std::min(a, std::max(b, value)); - }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - ActivationTest(tflite::BuiltinOperator_RELU6, - backends, - inputData, - outputExpectedData); -} - -void ActivationSigmoidTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<float> inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - // Calculate output values for input. - auto f = [](float value) - { - return 1.0f / (1.0f + std::exp(-value)); - }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - ActivationTest(tflite::BuiltinOperator_LOGISTIC, - backends, - inputData, - outputExpectedData); -} - - -void ActivationTanHTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<float> inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - // Calculate output values for input. - auto f = [](float value) - { - return tanhf(value); - }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - ActivationTest(tflite::BuiltinOperator_TANH, - backends, - inputData, - outputExpectedData); -} - -void ActivationEluTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<float> inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - // Calculate output values for input. - auto f = [](float value) - { - if (value < 0) - { - // alpha * (exp(x) - 1) - return 1 * (std::exp(value) - 1); - } - return value; - }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - ActivationTest(tflite::BuiltinOperator_ELU, - backends, - inputData, - outputExpectedData); -} - -void ActivationHardSwishTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<float> inputData = { - -0.1f, -0.2f, -0.3f, -0.4f, - 0.1f, 0.2f, 0.3f, 0.4f, - -1.0f, -2.0f, -3.0f, -4.0f, - 1.0f, 2.0f, 3.0f, 4.0f - }; - - // Calculate output values for input. - auto f = [](float x) - { - // Break down the calculation to help with verification. - // hard_swish(x) = x * relu6(x+3) / 6 - // relu6(x) = min(max(x,0),6) - float reLu6_step1 = std::max((x + 3),0.0f); - float reLu6Complete = std::min(reLu6_step1, 6.0f); - float hardSwish_step1 = x * reLu6Complete; - float result = hardSwish_step1 / 6; - return result; - }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); - - ActivationTest(tflite::BuiltinOperator_HARD_SWISH, - backends, - inputData, - outputExpectedData); -} - -TEST_SUITE("Activation_CpuRefTests") -{ - -TEST_CASE ("Activation_ReLu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ActivationReLuTest(backends); -} - -TEST_CASE ("Activation_Bounded_Relu6_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ActivationBoundedReluTest(backends); -} - -TEST_CASE ("Activation_Sigmoid_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ActivationSigmoidTest(backends); -} - -TEST_CASE ("Activation_TanH_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ActivationTanHTest(backends); -} - -TEST_CASE ("Activation_Elu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ActivationEluTest(backends); -} - -TEST_CASE ("Activation_HardSwish_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ActivationHardSwishTest(backends); -} - -} - -TEST_SUITE("Activation_CpuAccTests") -{ - -TEST_CASE ("Activation_ReLu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ActivationReLuTest(backends); -} - -TEST_CASE ("Activation_Bounded_Relu6_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ActivationBoundedReluTest(backends); -} - -TEST_CASE ("Activation_Sigmoid_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ActivationSigmoidTest(backends); -} - -TEST_CASE ("Activation_TanH_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ActivationTanHTest(backends); -} - -TEST_CASE ("Activation_Elu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ActivationEluTest(backends); -} - -TEST_CASE ("Activation_HardSwish_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ActivationHardSwishTest(backends); -} - -} - -TEST_SUITE("Activation_GpuAccTests") -{ - -TEST_CASE ("Activation_ReLu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ActivationReLuTest(backends); -} - -TEST_CASE ("Activation_Bounded_Relu6_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ActivationBoundedReluTest(backends); -} - -TEST_CASE ("Activation_Sigmoid_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ActivationSigmoidTest(backends); -} - -TEST_CASE ("Activation_TanH_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ActivationTanHTest(backends); -} - -TEST_CASE ("Activation_Elu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ActivationEluTest(backends); -} - -TEST_CASE ("Activation_HardSwish_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ActivationHardSwishTest(backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ActivationTestHelper.hpp b/delegate/src/test/ActivationTestHelper.hpp deleted file mode 100644 index 6475083da0..0000000000 --- a/delegate/src/test/ActivationTestHelper.hpp +++ /dev/null @@ -1,130 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateActivationTfLiteModel(tflite::BuiltinOperator activationOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& tensorShape) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), - tensorType); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), - tensorType); - - // create operator - const std::vector<int> operatorInputs{0}; - const std::vector<int> operatorOutputs{1}; - flatbuffers::Offset <Operator> unaryOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); - - 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(&unaryOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Activation Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, activationOperatorCode); - - 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()); -} - -void ActivationTest(tflite::BuiltinOperator activationOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<float>& inputValues, - std::vector<float>& expectedOutputValues) -{ - using namespace tflite; - std::vector<int32_t> inputShape { { 4, 1, 4} }; - std::vector<char> modelBuffer = CreateActivationTfLiteModel(activationOperatorCode, - ::tflite::TensorType_FLOAT32, - inputShape); - - 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<float>(tfLiteInterpreter, 0, inputValues); - armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 0, inputValues); - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<float>(tfLiteInterpreter, - armnnDelegateInterpreter, - inputShape, - expectedOutputValues); - - tfLiteInterpreter.reset(nullptr); - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/ArgMinMaxTest.cpp b/delegate/src/test/ArgMinMaxTest.cpp deleted file mode 100644 index bf60a77cb2..0000000000 --- a/delegate/src/test/ArgMinMaxTest.cpp +++ /dev/null @@ -1,174 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ArgMinMaxTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void ArgMaxFP32Test(std::vector<armnn::BackendId>& backends, int axisValue) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 2, 4 }; - std::vector<int32_t> outputShape { 1, 3, 4 }; - std::vector<int32_t> axisShape { 1 }; - - std::vector<float> inputValues = { 1.0f, 2.0f, 3.0f, 4.0f, - 5.0f, 6.0f, 7.0f, 8.0f, - - 10.0f, 20.0f, 30.0f, 40.0f, - 50.0f, 60.0f, 70.0f, 80.0f, - - 100.0f, 200.0f, 300.0f, 400.0f, - 500.0f, 600.0f, 700.0f, 800.0f }; - - std::vector<int32_t> expectedOutputValues = { 1, 1, 1, 1, - 1, 1, 1, 1, - 1, 1, 1, 1 }; - - ArgMinMaxTest<float, int32_t>(tflite::BuiltinOperator_ARG_MAX, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - axisShape, - outputShape, - inputValues, - expectedOutputValues, - axisValue, - ::tflite::TensorType_INT32); -} - -void ArgMinFP32Test(std::vector<armnn::BackendId>& backends, int axisValue) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 2, 4 }; - std::vector<int32_t> outputShape { 1, 3, 2 }; - std::vector<int32_t> axisShape { 1 }; - - std::vector<float> inputValues = { 1.0f, 2.0f, 3.0f, 4.0f, - 5.0f, 6.0f, 7.0f, 8.0f, - - 10.0f, 20.0f, 30.0f, 40.0f, - 50.0f, 60.0f, 70.0f, 80.0f, - - 100.0f, 200.0f, 300.0f, 400.0f, - 500.0f, 600.0f, 700.0f, 800.0f }; - - std::vector<int32_t> expectedOutputValues = { 0, 0, - 0, 0, - 0, 0 }; - - ArgMinMaxTest<float, int32_t>(tflite::BuiltinOperator_ARG_MIN, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - axisShape, - outputShape, - inputValues, - expectedOutputValues, - axisValue, - ::tflite::TensorType_INT32); -} - -void ArgMaxUint8Test(std::vector<armnn::BackendId>& backends, int axisValue) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 1, 1, 5 }; - std::vector<int32_t> outputShape { 1, 1, 1 }; - std::vector<int32_t> axisShape { 1 }; - - std::vector<uint8_t> inputValues = { 5, 2, 8, 10, 9 }; - - std::vector<int32_t> expectedOutputValues = { 3 }; - - ArgMinMaxTest<uint8_t, int32_t>(tflite::BuiltinOperator_ARG_MAX, - ::tflite::TensorType_UINT8, - backends, - inputShape, - axisShape, - outputShape, - inputValues, - expectedOutputValues, - axisValue, - ::tflite::TensorType_INT32); -} - -TEST_SUITE("ArgMinMax_CpuRefTests") -{ - -TEST_CASE ("ArgMaxFP32Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ArgMaxFP32Test(backends, 2); -} - -TEST_CASE ("ArgMinFP32Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ArgMinFP32Test(backends, 3); -} - -TEST_CASE ("ArgMaxUint8Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ArgMaxUint8Test(backends, -1); -} - -} // TEST_SUITE("ArgMinMax_CpuRefTests") - -TEST_SUITE("ArgMinMax_CpuAccTests") -{ - -TEST_CASE ("ArgMaxFP32Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ArgMaxFP32Test(backends, 2); -} - -TEST_CASE ("ArgMinFP32Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ArgMinFP32Test(backends, 3); -} - -TEST_CASE ("ArgMaxUint8Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ArgMaxUint8Test(backends, -1); -} - -} // TEST_SUITE("ArgMinMax_CpuAccTests") - -TEST_SUITE("ArgMinMax_GpuAccTests") -{ - -TEST_CASE ("ArgMaxFP32Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ArgMaxFP32Test(backends, 2); -} - -TEST_CASE ("ArgMinFP32Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ArgMinFP32Test(backends, 3); -} - -TEST_CASE ("ArgMaxUint8Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ArgMaxUint8Test(backends, -1); -} - -} // TEST_SUITE("ArgMinMax_GpuAccTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ArgMinMaxTestHelper.hpp b/delegate/src/test/ArgMinMaxTestHelper.hpp deleted file mode 100644 index 3e607d6b2b..0000000000 --- a/delegate/src/test/ArgMinMaxTestHelper.hpp +++ /dev/null @@ -1,199 +0,0 @@ -// -// Copyright © 2021, 2023 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 -{ - -template <typename InputT, typename OutputT> -std::vector<char> CreateArgMinMaxTfLiteModel(tflite::BuiltinOperator argMinMaxOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& axisTensorShape, - const std::vector<int32_t>& outputTensorShape, - const std::vector<OutputT> axisValue, - tflite::TensorType outputType, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - auto inputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - auto axisTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), - axisTensorShape.size()), - tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("axis")); - - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - outputType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, axisTensor, outputTensor }; - - 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*>(axisValue.data()), - sizeof(OutputT)))); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - std::vector<int32_t> operatorInputs = {{ 0, 1 }}; - std::vector<int> subgraphInputs = {{ 0, 1 }}; - - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ArgMaxOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateArgMaxOptions(flatBufferBuilder, outputType).Union(); - - if (argMinMaxOperatorCode == tflite::BuiltinOperator_ARG_MIN) - { - operatorBuiltinOptionsType = BuiltinOptions_ArgMinOptions; - operatorBuiltinOptions = CreateArgMinOptions(flatBufferBuilder, outputType).Union(); - } - - // create operator - const std::vector<int32_t> operatorOutputs{ 2 }; - flatbuffers::Offset <Operator> argMinMaxOperator = - 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> 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(&argMinMaxOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: ArgMinMax Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - argMinMaxOperatorCode); - - 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 InputT, typename OutputT> -void ArgMinMaxTest(tflite::BuiltinOperator argMinMaxOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - const std::vector<int32_t>& inputShape, - const std::vector<int32_t>& axisShape, - std::vector<int32_t>& outputShape, - std::vector<InputT>& inputValues, - std::vector<OutputT>& expectedOutputValues, - OutputT axisValue, - tflite::TensorType outputType, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateArgMinMaxTfLiteModel<InputT, OutputT>(argMinMaxOperatorCode, - tensorType, - inputShape, - axisShape, - outputShape, - {axisValue}, - outputType, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - - 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<InputT>(tfLiteInterpreter, 0, inputValues); - armnnDelegate::FillInput<InputT>(armnnDelegateInterpreter, 0, inputValues); - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<OutputT>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<OutputT>(armnnDelegateOutputId); - - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); - } -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/ArmnnDelegateTest.cpp b/delegate/src/test/ArmnnDelegateTest.cpp deleted file mode 100644 index bc73dde2ef..0000000000 --- a/delegate/src/test/ArmnnDelegateTest.cpp +++ /dev/null @@ -1,93 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#define DOCTEST_CONFIG_IMPLEMENT_WITH_MAIN -#include <doctest/doctest.h> - -#include <armnn_delegate.hpp> - -#include "tensorflow/lite/kernels/builtin_op_kernels.h" -#include <tensorflow/lite/interpreter.h> -#include <tensorflow/lite/kernels/register.h> - -namespace armnnDelegate -{ - -TEST_SUITE("ArmnnDelegate") -{ - -TEST_CASE ("ArmnnDelegate Registered") -{ - using namespace tflite; - auto tfLiteInterpreter = std::make_unique<Interpreter>(); - - tfLiteInterpreter->AddTensors(3); - tfLiteInterpreter->SetInputs({0, 1}); - tfLiteInterpreter->SetOutputs({2}); - - tfLiteInterpreter->SetTensorParametersReadWrite(0, kTfLiteFloat32, "input1", {1,2,2,1}, TfLiteQuantization()); - tfLiteInterpreter->SetTensorParametersReadWrite(1, kTfLiteFloat32, "input2", {1,2,2,1}, TfLiteQuantization()); - tfLiteInterpreter->SetTensorParametersReadWrite(2, kTfLiteFloat32, "output", {1,2,2,1}, TfLiteQuantization()); - - tflite::ops::builtin::BuiltinOpResolver opResolver; - const TfLiteRegistration* opRegister = opResolver.FindOp(BuiltinOperator_ADD, 1); - tfLiteInterpreter->AddNodeWithParameters({0, 1}, {2}, "", 0, nullptr, opRegister); - - // Create the Armnn Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<armnn::BackendOptions> backendOptions; - backendOptions.emplace_back( - armnn::BackendOptions{ "BackendName", - { - { "Option1", 42 }, - { "Option2", true } - }} - ); - - armnnDelegate::DelegateOptions delegateOptions(backends, backendOptions); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - - auto status = tfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate)); - CHECK(status == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); -} - -TEST_CASE ("ArmnnDelegateOptimizerOptionsRegistered") -{ - using namespace tflite; - auto tfLiteInterpreter = std::make_unique<Interpreter>(); - - tfLiteInterpreter->AddTensors(3); - tfLiteInterpreter->SetInputs({0, 1}); - tfLiteInterpreter->SetOutputs({2}); - - tfLiteInterpreter->SetTensorParametersReadWrite(0, kTfLiteFloat32, "input1", {1,2,2,1}, TfLiteQuantization()); - tfLiteInterpreter->SetTensorParametersReadWrite(1, kTfLiteFloat32, "input2", {1,2,2,1}, TfLiteQuantization()); - tfLiteInterpreter->SetTensorParametersReadWrite(2, kTfLiteFloat32, "output", {1,2,2,1}, TfLiteQuantization()); - - tflite::ops::builtin::BuiltinOpResolver opResolver; - const TfLiteRegistration* opRegister = opResolver.FindOp(BuiltinOperator_ADD, 1); - tfLiteInterpreter->AddNodeWithParameters({0, 1}, {2}, "", 0, nullptr, opRegister); - - // Create the Armnn Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - - armnn::OptimizerOptions optimizerOptions(true, true, false, true); - - armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - - auto status = tfLiteInterpreter->ModifyGraphWithDelegate(std::move(theArmnnDelegate)); - CHECK(status == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); -} - -} - -} // namespace armnnDelegate diff --git a/delegate/src/test/BatchMatMulTest.cpp b/delegate/src/test/BatchMatMulTest.cpp deleted file mode 100644 index 06ad2c3be2..0000000000 --- a/delegate/src/test/BatchMatMulTest.cpp +++ /dev/null @@ -1,689 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "BatchMatMulTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - - void BatchMatMul2DFp32SimpleTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2, 2 }; - std::vector<int32_t> RHSInputShape { 2, 2 }; - std::vector<int32_t> outputShape { 2, 2 }; - - std::vector<float> LHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<float> RHSInputValues = { 5, 6, - 7, 8 }; - - std::vector<float> expectedOutputValues = { 19, 22, - 43, 50 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - void BatchMatMul2DInt8SimpleTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2, 2 }; - std::vector<int32_t> RHSInputShape { 2, 2 }; - std::vector<int32_t> outputShape { 2, 2 }; - - std::vector<int8_t> LHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<int8_t> RHSInputValues = { 5, 6, - 7, 8 }; - - std::vector<int8_t> expectedOutputValues = { 19, 22, - 43, 50 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3DFp32SimpleTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 1,2,2 }; - std::vector<int32_t> RHSInputShape { 1,2,2 }; - std::vector<int32_t> outputShape { 1,2,2 }; - - std::vector<float> LHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<float> RHSInputValues = { 5, 6, - 7, 8 }; - - std::vector<float> expectedOutputValues = { 19, 22, - 43, 50 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3DInt8SimpleTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 1,2,2 }; - std::vector<int32_t> RHSInputShape { 1,2,2 }; - std::vector<int32_t> outputShape { 1,2,2 }; - - std::vector<int8_t> LHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<int8_t> RHSInputValues = { 5, 6, - 7, 8 }; - - std::vector<int8_t> expectedOutputValues = { 19, 22, - 43, 50 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul4DFp32SimpleTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 1,1,2,2 }; - std::vector<int32_t> RHSInputShape { 1,1,2,2 }; - std::vector<int32_t> outputShape { 1,1,2,2 }; - - std::vector<float> LHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<float> RHSInputValues = { 5, 6, - 7, 8 }; - - std::vector<float> expectedOutputValues = { 19, 22, - 43, 50 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul4DInt8SimpleTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 1,1,2,2}; - std::vector<int32_t> RHSInputShape { 1,1,2,2 }; - std::vector<int32_t> outputShape { 1,1,2,2 }; - - std::vector<int8_t> LHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<int8_t> RHSInputValues = { 5, 6, - 7, 8 }; - - std::vector<int8_t> expectedOutputValues = { 19, 22, - 43, 50 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3DFp32BatchTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,2,2 }; - std::vector<int32_t> RHSInputShape { 2,2,2 }; - std::vector<int32_t> outputShape { 2,2,2 }; - - std::vector<float> LHSInputValues = { 1, 2, - 3, 4, - - 9, 10, - 11, 12 }; - - std::vector<float> RHSInputValues = { 5, 6, - 7, 8, - - 13, 14, - 15, 16 }; - - std::vector<float> expectedOutputValues = { 19, 22, - 43, 50, - - 267, 286, - 323, 346 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3DInt8BatchTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,2,2 }; - std::vector<int32_t> RHSInputShape { 2,2,2 }; - std::vector<int32_t> outputShape { 2,2,2 }; - - std::vector<int8_t> LHSInputValues = { 1, 2, - 3, 4, - - 9, 10, - 11, 12 }; - - std::vector<int8_t> RHSInputValues = { 5, 6, - 7, 8, - - 1, 2, - 3, 4 }; - - std::vector<int8_t> expectedOutputValues = { 19, 22, - 43, 50, - - 39, 58, - 47, 70 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3DFp32BroadcastTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,2,2 }; - std::vector<int32_t> RHSInputShape { 2,2 }; - std::vector<int32_t> outputShape { 2,2,2 }; - - std::vector<float> LHSInputValues = { 1, 2, - 3, 4, - - 9, 10, - 11, 12 }; - - std::vector<float> RHSInputValues = { 13, 14, - 15, 16 }; - - std::vector<float> expectedOutputValues = { 43, 46, - 99, 106, - - 267, 286, - 323, 346 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3DInt8BroadcastTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,2,2 }; - std::vector<int32_t> RHSInputShape { 1,2,2 }; - std::vector<int32_t> outputShape { 2,2,2 }; - - std::vector<int8_t> LHSInputValues = { 1, 2, - 3, 4, - - 9, 10, - 11, 12 }; - - std::vector<int8_t> RHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<int8_t> expectedOutputValues = { 7, 10, - 15, 22, - - 39, 58, - 47, 70 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3D2DFp32BroadcastTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,2,2 }; - std::vector<int32_t> RHSInputShape { 2,2 }; - std::vector<int32_t> outputShape { 2,2,2 }; - - std::vector<float> LHSInputValues = { 1, 2, - 3, 4, - - 9, 10, - 11, 12 }; - - std::vector<float> RHSInputValues = { 13, 14, - 15, 16 }; - - std::vector<float> expectedOutputValues = { 43, 46, - 99, 106, - - 267, 286, - 323, 346 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul3D2DInt8BroadcastTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,2,2 }; - std::vector<int32_t> RHSInputShape { 2,2 }; - std::vector<int32_t> outputShape { 2,2,2 }; - - std::vector<int8_t> LHSInputValues = { 1, 2, - 3, 4, - - 9, 10, - 11, 12 }; - - std::vector<int8_t> RHSInputValues = { 1, 2, - 3, 4 }; - - std::vector<int8_t> expectedOutputValues = { 7, 10, - 15, 22, - - 39, 58, - 47, 70 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul2DFp32TinyTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 1,1 }; - std::vector<int32_t> RHSInputShape { 1,1 }; - std::vector<int32_t> outputShape { 1,1 }; - - std::vector<float> LHSInputValues = { 3 }; - - std::vector<float> RHSInputValues = { 5 }; - - std::vector<float> expectedOutputValues = { 15 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - void BatchMatMul2DInt8TinyTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 1,1 }; - std::vector<int32_t> RHSInputShape { 1,1 }; - std::vector<int32_t> outputShape { 1,1 }; - - std::vector<int8_t> LHSInputValues = { 3 }; - - std::vector<int8_t> RHSInputValues = { 5 }; - - std::vector<int8_t> expectedOutputValues = { 15 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMulNonSquareFp32Test(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,5,3 }; - std::vector<int32_t> RHSInputShape { 2,3,4 }; - std::vector<int32_t> outputShape { 2,5,4 }; - - std::vector<float> LHSInputValues = { 8, 8, 4, - 6, 1, 3, - 8, 8, 3, - 8, 9, 8, - 5, 4, 4, - - 1, 8, 5, - 7, 1, 1, - 8, 7, 9, - 3, 2, 7, - 8, 5, 3 }; - - std::vector<float> RHSInputValues = { 6, 2, 3, 2, - 6, 2, 2, 8, - 3, 7, 8, 1, - - 7, 2, 9, 5, - 2, 3, 1, 3, - 2, 7, 7, 5 }; - - std::vector<float> expectedOutputValues = { 108, 60, 72, 84, - 51, 35, 44, 23, - 105, 53, 64, 83, - 126, 90, 106, 96, - 66, 46, 55, 46, - - 33, 61, 52, 54, - 53, 24, 71, 43, - 88, 100, 142, 106, - 39, 61, 78, 56, - 72, 52, 98, 70 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMulNonSquareInt8Test(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 2,5,3 }; - std::vector<int32_t> RHSInputShape { 2,3,4 }; - std::vector<int32_t> outputShape { 2,5,4 }; - - std::vector<int8_t> LHSInputValues = { 8, 8, 4, - 6, 1, 3, - 8, 8, 3, - 8, 9, 8, - 5, 4, 4, - - 1, 8, 5, - 7, 1, 1, - 8, 7, 9, - 3, 2, 7, - 8, 5, 3 }; - - std::vector<int8_t> RHSInputValues = { 6, 2, 3, 2, - 6, 2, 2, 8, - 3, 7, 8, 1, - - 7, 2, 3, 5, - 2, 3, 1, 3, - 2, 7, 7, 5 }; - - std::vector<int8_t> expectedOutputValues = { 108, 60, 72, 84, - 51, 35, 44, 23, - 105, 53, 64, 83, - 126, 90, 106, 96, - 66, 46, 55, 46, - - 33, 61, 46, 54, - 53, 24, 29, 43, - 88, 100, 94, 106, - 39, 61, 60, 56, - 72, 52, 50, 70 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - false, - false); - } - - void BatchMatMul2DFp32SimpleAdjointTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 3,3 }; - std::vector<int32_t> RHSInputShape { 3,3 }; - std::vector<int32_t> outputShape { 3,3 }; - - std::vector<float> LHSInputValues = { 3, 1, 1, - 1, 3, -1, - 2, 4, 1 }; - - std::vector<float> RHSInputValues = { 1, 0, 0, - 0, 1, 0, - 0, 0, 1 }; - - std::vector<float> expectedOutputValues = { 3, 1, 2, - 1, 3, 4, - 1, -1, 1 }; - - BatchMatMulTest<float>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_FLOAT32, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - true, - false); - } - - void BatchMatMul2DInt8SimpleAdjointTest(std::vector<armnn::BackendId>& backends) - { - // Set input data - std::vector<int32_t> LHSInputShape { 3,3 }; - std::vector<int32_t> RHSInputShape { 3,3 }; - std::vector<int32_t> outputShape { 3,3 }; - - std::vector<int8_t> LHSInputValues = { 3, 1, 1, - 1, 3, -1, - 2, 4, 1 }; - - std::vector<int8_t> RHSInputValues = { 1, 0, 0, - 0, 1, 0, - 0, 0, 1 }; - - std::vector<int8_t> expectedOutputValues = { 3, 1, 2, - 1, 3, 4, - 1, -1, 1 }; - - BatchMatMulTest<int8_t>(tflite::BuiltinOperator_BATCH_MATMUL, - ::tflite::TensorType_INT8, - backends, - LHSInputShape, - RHSInputShape, - outputShape, - LHSInputValues, - RHSInputValues, - expectedOutputValues, - true, - false); - } - - TEST_SUITE("BATCH_MATMUL_CpuRefTests") - { - TEST_CASE("BATCH_MATMUL_Fp32_CpuRefTests") - { - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - BatchMatMul2DFp32SimpleTest (backends); - BatchMatMul3DFp32SimpleTest (backends); - BatchMatMul4DFp32SimpleTest (backends); - BatchMatMul3DFp32BatchTest (backends); - BatchMatMul3DFp32BroadcastTest (backends); - BatchMatMul3D2DFp32BroadcastTest (backends); - BatchMatMul2DFp32TinyTest (backends); - BatchMatMulNonSquareFp32Test (backends); - BatchMatMul2DFp32SimpleAdjointTest(backends); - } - - TEST_CASE("BATCH_MATMUL_Int8_CpuRefTests") - { - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - BatchMatMul2DInt8SimpleTest (backends); - BatchMatMul3DInt8SimpleTest (backends); - BatchMatMul4DInt8SimpleTest (backends); - BatchMatMul3DInt8BatchTest (backends); - BatchMatMul3DInt8BroadcastTest (backends); - BatchMatMul3D2DInt8BroadcastTest (backends); - BatchMatMul2DInt8TinyTest (backends); - BatchMatMulNonSquareInt8Test (backends); - BatchMatMul2DInt8SimpleAdjointTest(backends); - } - } - - TEST_SUITE("BATCH_MATMUL_CpuAccTests") - { - TEST_CASE("BATCH_MATMUL_Fp32_CpuAccTests") - { - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - BatchMatMul2DFp32SimpleTest (backends); - BatchMatMul3DFp32SimpleTest (backends); - BatchMatMul4DFp32SimpleTest (backends); - BatchMatMul3DFp32BatchTest (backends); - BatchMatMul3DFp32BroadcastTest (backends); - BatchMatMul3D2DFp32BroadcastTest (backends); - BatchMatMul2DFp32TinyTest (backends); - BatchMatMulNonSquareFp32Test (backends); - BatchMatMul2DFp32SimpleAdjointTest(backends); - } - } - TEST_SUITE("BATCH_MATMUL_GpuAccTests") - { - TEST_CASE("BATCH_MATMUL_Fp32_GpuAccTests") - { - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - BatchMatMul2DFp32SimpleTest (backends); - BatchMatMul3DFp32SimpleTest (backends); - BatchMatMul4DFp32SimpleTest (backends); - BatchMatMul3DFp32BatchTest (backends); - BatchMatMul3DFp32BroadcastTest (backends); - BatchMatMul3D2DFp32BroadcastTest (backends); - BatchMatMul2DFp32TinyTest (backends); - BatchMatMulNonSquareFp32Test (backends); - BatchMatMul2DFp32SimpleAdjointTest(backends); - } - } -} diff --git a/delegate/src/test/BatchMatMulTestHelper.hpp b/delegate/src/test/BatchMatMulTestHelper.hpp deleted file mode 100644 index 7437064a42..0000000000 --- a/delegate/src/test/BatchMatMulTestHelper.hpp +++ /dev/null @@ -1,208 +0,0 @@ -// -// Copyright © 2022-2023 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> CreateBatchMatMulTfLiteModel( - tflite::BuiltinOperator bmmOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& LHSInputTensorShape, - const std::vector <int32_t>& RHSInputTensorShape, - const std::vector <int32_t>& outputTensorShape, - bool adjX = false, - bool adjY = false, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(LHSInputTensorShape.data(), - LHSInputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("LHSInput"), - quantizationParameters); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(), - RHSInputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("RHSInput"), - quantizationParameters); - - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_BatchMatMulOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateBatchMatMulOptions(flatBufferBuilder, - adjX, - adjY).Union(); - - const std::vector<int32_t> operatorInputs{{0, 1}}; - const std::vector<int32_t> operatorOutputs{2}; - flatbuffers::Offset <Operator> bmmOperator = - 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}; - 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(&bmmOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: BatchMatMul Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, bmmOperatorCode); - - 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 BatchMatMulTest(tflite::BuiltinOperator bmmOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& LHSInputShape, - std::vector<int32_t>& RHSInputShape, - std::vector<int32_t>& outputShape, - std::vector<T>& LHSInputValues, - std::vector<T>& RHSInputValues, - std::vector<T>& expectedOutputValues, - bool adjX = false, - bool adjY = false, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateBatchMatMulTfLiteModel(bmmOperatorCode, - tensorType, - LHSInputShape, - RHSInputShape, - outputShape, - adjX, - adjY, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - // 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 - auto tfLiteDelegateLHSInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelegateLHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateLHSInputId); - auto tfLiteDelegateRHSInputId = tfLiteInterpreter->inputs()[1]; - auto tfLiteDelegateRHSInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateRHSInputId); - for (unsigned int i = 0; i < LHSInputValues.size(); ++i) - { - tfLiteDelegateLHSInputData[i] = LHSInputValues[i]; - } - for (unsigned int i = 0; i < RHSInputValues.size(); ++i) - { - tfLiteDelegateRHSInputData[i] = RHSInputValues[i]; - } - - auto armnnDelegateLHSInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateLHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateLHSInputId); - auto armnnDelegateRHSInputId = armnnDelegateInterpreter->inputs()[1]; - auto armnnDelegateRHSInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateRHSInputId); - for (unsigned int i = 0; i < LHSInputValues.size(); ++i) - { - armnnDelegateLHSInputData[i] = LHSInputValues[i]; - } - for (unsigned int i = 0; i < RHSInputValues.size(); ++i) - { - armnnDelegateRHSInputData[i] = RHSInputValues[i]; - } - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, - outputShape, expectedOutputValues); -} - -} // anonymous namespace - - - - diff --git a/delegate/src/test/BatchSpaceTest.cpp b/delegate/src/test/BatchSpaceTest.cpp deleted file mode 100644 index 47eba452e7..0000000000 --- a/delegate/src/test/BatchSpaceTest.cpp +++ /dev/null @@ -1,299 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "BatchSpaceTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -// BatchToSpaceND Operator -void BatchToSpaceNDFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 4, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f }; - std::vector<float> expectedOutputValues { 1.0f, 2.0f, 3.0f, 4.0f }; - - std::vector<unsigned int> blockShape({2, 2}); - std::vector<std::pair<unsigned int, unsigned int>> crops = {{0, 0}, {0, 0}}; - - BatchSpaceTest<float>(tflite::BuiltinOperator_BATCH_TO_SPACE_ND, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - expectedOutputShape, - inputValues, - blockShape, - crops, - expectedOutputValues); -} - -void BatchToSpaceNDFp32BatchOneTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f }; - std::vector<float> expectedOutputValues { 1.0f, 2.0f, 3.0f, 4.0f }; - - std::vector<unsigned int> blockShape({1, 1}); - std::vector<std::pair<unsigned int, unsigned int>> crops = {{0, 0}, {0, 0}}; - - BatchSpaceTest<float>(tflite::BuiltinOperator_BATCH_TO_SPACE_ND, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - expectedOutputShape, - inputValues, - blockShape, - crops, - expectedOutputValues); -} - -void BatchToSpaceNDUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 4, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<uint8_t> inputValues { 1, 2, 3, 4, 5, 6, 7 }; - std::vector<uint8_t> expectedOutputValues { 1, 2, 3, 4, 5, 6, 7 }; - - std::vector<unsigned int> blockShape({2, 2}); - std::vector<std::pair<unsigned int, unsigned int>> crops = {{0, 0}, {0, 0}}; - - BatchSpaceTest<uint8_t>(tflite::BuiltinOperator_BATCH_TO_SPACE_ND, - ::tflite::TensorType_UINT8, - backends, - inputShape, - expectedOutputShape, - inputValues, - blockShape, - crops, - expectedOutputValues); -} - -// SpaceToBatchND Operator -void SpaceToBatchNDFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 4, 1, 1, 1 }; - - std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f }; - std::vector<float> expectedOutputValues { 1.0f, 2.0f, 3.0f, 4.0f }; - - std::vector<unsigned int> blockShape({2, 2}); - std::vector<std::pair<unsigned int, unsigned int>> padding = {{0, 0}, {0, 0}}; - - BatchSpaceTest<float>(tflite::BuiltinOperator_SPACE_TO_BATCH_ND, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - expectedOutputShape, - inputValues, - blockShape, - padding, - expectedOutputValues); -} - -void SpaceToBatchNDFp32PaddingTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 2, 4, 1 }; - std::vector<int32_t> expectedOutputShape { 8, 1, 3, 1 }; - - std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f, - 5.0f, 6.0f, 7.0f, 8.0f, - 9.0f, 10.0f, 11.0f, 12.0f, - 13.0f, 14.0f, 15.0f, 16.0f }; - - std::vector<float> expectedOutputValues { 0.0f, 1.0f, 3.0f, 0.0f, 9.0f, 11.0f, - 0.0f, 2.0f, 4.0f, 0.0f, 10.0f, 12.0f, - 0.0f, 5.0f, 7.0f, 0.0f, 13.0f, 15.0f, - 0.0f, 6.0f, 8.0f, 0.0f, 14.0f, 16.0f }; - - std::vector<unsigned int> blockShape({2, 2}); - std::vector<std::pair<unsigned int, unsigned int>> padding = {{0, 0}, {2, 0}}; - - BatchSpaceTest<float>(tflite::BuiltinOperator_SPACE_TO_BATCH_ND, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - expectedOutputShape, - inputValues, - blockShape, - padding, - expectedOutputValues); -} - -void SpaceToBatchNDUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 2, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 4, 1, 1, 3 }; - - std::vector<uint8_t> inputValues { 1, 2, 3, 4, 5, 6, 7 }; - std::vector<uint8_t> expectedOutputValues { 1, 2, 3, 4, 5, 6, 7 }; - - std::vector<unsigned int> blockShape({2, 2}); - std::vector<std::pair<unsigned int, unsigned int>> padding = {{0, 0}, {0, 0}}; - - BatchSpaceTest<uint8_t>(tflite::BuiltinOperator_SPACE_TO_BATCH_ND, - ::tflite::TensorType_UINT8, - backends, - inputShape, - expectedOutputShape, - inputValues, - blockShape, - padding, - expectedOutputValues); -} - -// BatchToSpaceND Tests -TEST_SUITE("BatchToSpaceND_CpuAccTests") -{ - -TEST_CASE ("BatchToSpaceND_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - BatchToSpaceNDFp32Test(backends); -} - -TEST_CASE ("BatchToSpaceND_Fp32_BatchOne_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - BatchToSpaceNDFp32BatchOneTest(backends); -} - -TEST_CASE ("BatchToSpaceND_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - BatchToSpaceNDUint8Test(backends); -} - -} - -TEST_SUITE("BatchToSpaceND_GpuAccTests") -{ - -TEST_CASE ("BatchToSpaceND_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - BatchToSpaceNDFp32Test(backends); -} - -TEST_CASE ("BatchToSpaceND_Fp32_BatchOne_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - BatchToSpaceNDFp32BatchOneTest(backends); -} - -TEST_CASE ("BatchToSpaceND_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - BatchToSpaceNDUint8Test(backends); -} - -} - -TEST_SUITE("BatchToSpaceND_CpuRefTests") -{ - -TEST_CASE ("BatchToSpaceND_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - BatchToSpaceNDFp32Test(backends); -} - -TEST_CASE ("BatchToSpaceND_Fp32_BatchOne_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - BatchToSpaceNDFp32BatchOneTest(backends); -} - -TEST_CASE ("BatchToSpaceND_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - BatchToSpaceNDUint8Test(backends); -} - -} - -// SpaceToBatchND Tests -TEST_SUITE("SpaceToBatchND_CpuAccTests") -{ - -TEST_CASE ("SpaceToBatchND_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SpaceToBatchNDFp32Test(backends); -} - -TEST_CASE ("SpaceToBatchND_Fp32_Padding_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SpaceToBatchNDFp32PaddingTest(backends); -} - -TEST_CASE ("SpaceToBatchND_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SpaceToBatchNDUint8Test(backends); -} - -} - -TEST_SUITE("SpaceToBatchND_GpuAccTests") -{ - -TEST_CASE ("SpaceToBatchND_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SpaceToBatchNDFp32Test(backends); -} - -TEST_CASE ("SpaceToBatchND_Fp32_Padding_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SpaceToBatchNDFp32PaddingTest(backends); -} - -TEST_CASE ("SpaceToBatchND_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SpaceToBatchNDUint8Test(backends); -} - -} - -TEST_SUITE("SpaceToBatchND_CpuRefTests") -{ - -TEST_CASE ("SpaceToBatchND_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SpaceToBatchNDFp32Test(backends); -} - -TEST_CASE ("SpaceToBatchND_Fp32_Padding_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SpaceToBatchNDFp32PaddingTest(backends); -} - -TEST_CASE ("SpaceToBatchND_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SpaceToBatchNDUint8Test(backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/BatchSpaceTestHelper.hpp b/delegate/src/test/BatchSpaceTestHelper.hpp deleted file mode 100644 index d4fa9837e8..0000000000 --- a/delegate/src/test/BatchSpaceTestHelper.hpp +++ /dev/null @@ -1,218 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateBatchSpaceTfLiteModel(tflite::BuiltinOperator batchSpaceOperatorCode, - tflite::TensorType tensorType, - std::vector<int32_t>& inputTensorShape, - std::vector <int32_t>& outputTensorShape, - std::vector<unsigned int>& blockData, - std::vector<std::pair<unsigned int, unsigned int>>& cropsPadData, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder); - buffers[1] = CreateBuffer(flatBufferBuilder); - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(blockData.data()), - sizeof(int32_t) * blockData.size())); - buffers[3] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cropsPadData.data()), - sizeof(int64_t) * cropsPadData.size())); - buffers[4] = CreateBuffer(flatBufferBuilder); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::string cropsOrPadding = - batchSpaceOperatorCode == tflite::BuiltinOperator_BATCH_TO_SPACE_ND ? "crops" : "padding"; - - std::vector<int32_t> blockShape { 2 }; - std::vector<int32_t> cropsOrPaddingShape { 2, 2 }; - - std::array<flatbuffers::Offset<Tensor>, 4> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(blockShape.data(), - blockShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("block"), - quantizationParameters); - - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(cropsOrPaddingShape.data(), - cropsOrPaddingShape.size()), - ::tflite::TensorType_INT32, - 3, - flatBufferBuilder.CreateString(cropsOrPadding), - quantizationParameters); - - // Create output tensor - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 4, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // Create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; - flatbuffers::Offset<void> operatorBuiltinOptions = 0; - switch (batchSpaceOperatorCode) - { - case tflite::BuiltinOperator_BATCH_TO_SPACE_ND: - { - operatorBuiltinOptionsType = tflite::BuiltinOptions_BatchToSpaceNDOptions; - operatorBuiltinOptions = CreateBatchToSpaceNDOptions(flatBufferBuilder).Union(); - break; - } - case tflite::BuiltinOperator_SPACE_TO_BATCH_ND: - { - operatorBuiltinOptionsType = tflite::BuiltinOptions_SpaceToBatchNDOptions; - operatorBuiltinOptions = CreateSpaceToBatchNDOptions(flatBufferBuilder).Union(); - break; - } - default: - break; - } - - const std::vector<int> operatorInputs{ {0, 1, 2} }; - const std::vector<int> operatorOutputs{ 3 }; - flatbuffers::Offset <Operator> batchSpaceOperator = - 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(&batchSpaceOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: BatchSpace Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, batchSpaceOperatorCode); - - 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 BatchSpaceTest(tflite::BuiltinOperator controlOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& inputValues, - std::vector<unsigned int>& blockShapeValues, - std::vector<std::pair<unsigned int, unsigned int>>& cropsPaddingValues, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateBatchSpaceTfLiteModel(controlOperatorCode, - tensorType, - inputShape, - expectedOutputShape, - blockShapeValues, - cropsPaddingValues, - quantScale, - quantOffset); - - 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 EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - expectedOutputShape, - expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); - tfLiteInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/CastTest.cpp b/delegate/src/test/CastTest.cpp deleted file mode 100644 index a637071ffc..0000000000 --- a/delegate/src/test/CastTest.cpp +++ /dev/null @@ -1,95 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "CastTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void CastUint8ToFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape {1, 3, 2, 3}; - - std::vector<uint8_t> inputValues { 1, 3, 1, 3, 1, 3, 1, 3, 1, - 3, 1, 3, 1, 2, 1, 3, 1, 3 }; - - std::vector<float> expectedOutputValues { 1.0f, 3.0f, 1.0f, 3.0f, 1.0f, 3.0f, 1.0f, 3.0f, 1.0f, - 3.0f, 1.0f, 3.0f, 1.0f, 2.0f, 1.0f, 3.0f, 1.0f, 3.0f }; - - CastTest<uint8_t, float>(::tflite::TensorType_UINT8, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - inputValues, - expectedOutputValues); -} - -void CastInt32ToFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape {1, 3, 2, 3}; - - std::vector<int32_t> inputValues { -1, -3, -1, -3, -1, -3, -1, -3, 1, - 3, 1, 3, 1, 2, 1, 3, 1, 3 }; - - std::vector<float> expectedOutputValues { -1.0f, -3.0f, -1.0f, -3.0f, -1.0f, -3.0f, -1.0f, -3.0f, 1.0f, - 3.0f, 1.0f, 3.0f, 1.0f, 2.0f, 1.0f, 3.0f, 1.0f, 3.0f }; - - CastTest<int32_t, float>(::tflite::TensorType_INT32, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - inputValues, - expectedOutputValues); -} - -// CAST Test Suite -TEST_SUITE("CAST_CpuRefTests") -{ - -TEST_CASE ("CAST_UINT8_TO_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - CastUint8ToFp32Test(backends); -} - -TEST_CASE ("CAST_INT32_TO_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - CastInt32ToFp32Test(backends); -} - -} - -TEST_SUITE("CAST_CpuAccTests") -{ - -TEST_CASE ("CAST_INT32_TO_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - CastInt32ToFp32Test(backends); -} - -} - -TEST_SUITE("CAST_GpuAccTests") -{ - -TEST_CASE ("CAST_INT32_TO_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - CastInt32ToFp32Test(backends); -} - -} -// End of CAST Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/CastTestHelper.hpp b/delegate/src/test/CastTestHelper.hpp deleted file mode 100644 index 0448e65856..0000000000 --- a/delegate/src/test/CastTestHelper.hpp +++ /dev/null @@ -1,159 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateCastTfLiteModel(tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - const std::vector <int32_t>& tensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - inputTensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - outputTensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - const std::vector<int32_t> operatorInputs({0}); - const std::vector<int32_t> operatorOutputs({1}); - - flatbuffers::Offset<Operator> castOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_CastOptions, - CreateCastOptions(flatBufferBuilder).Union()); - - flatbuffers::Offset<flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: CAST Operator Model"); - flatbuffers::Offset<OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_CAST); - - const std::vector<int32_t> subgraphInputs({0}); - const std::vector<int32_t> 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(&castOperator, 1)); - - 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 K> -void CastTest(tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& shape, - std::vector<T>& inputValues, - std::vector<K>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateCastTfLiteModel(inputTensorType, - outputTensorType, - shape, - 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 - armnnDelegate::CompareOutputData<K>(tfLiteDelegate, - armnnDelegate, - shape, - expectedOutputValues, - 0); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} - -} // anonymous namespace diff --git a/delegate/src/test/ComparisonTest.cpp b/delegate/src/test/ComparisonTest.cpp deleted file mode 100644 index 95bfe21d27..0000000000 --- a/delegate/src/test/ComparisonTest.cpp +++ /dev/null @@ -1,844 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ComparisonTestHelper.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 EqualFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - 1.f, 1.f, 1.f, 1.f, 5.f, 5.f, 5.f, 5.f, - 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f - }; - - std::vector<float> input1Values = - { - 1.f, 1.f, 1.f, 1.f, 3.f, 3.f, 3.f, 3.f, - 5.f, 5.f, 5.f, 5.f, 4.f, 4.f, 4.f, 4.f - }; - - std::vector<bool> expectedOutputValues = - { - 1, 1, 1, 1, 0, 0, 0, 0, - 0, 0, 0, 0, 1, 1, 1, 1 - }; - - - ComparisonTest<float>(tflite::BuiltinOperator_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void EqualBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<float> input0Values - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input1Values { 4.f, 5.f, 6.f }; - // Set output data - std::vector<bool> expectedOutputValues - { - 0, 0, 0, 1, 1, 1, - 0, 0, 0, 0, 0, 0 - }; - ComparisonTest<float>(tflite::BuiltinOperator_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void EqualInt32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<int32_t> input0Values = { 1, 5, 6, 4 }; - - std::vector<int32_t> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { 1, 0, 0, 1 }; - - ComparisonTest<int32_t>(tflite::BuiltinOperator_EQUAL, - ::tflite::TensorType_INT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void NotEqualFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - 1.f, 1.f, 1.f, 1.f, 5.f, 5.f, 5.f, 5.f, - 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f - }; - - std::vector<float> input1Values = - { - 1.f, 1.f, 1.f, 1.f, 3.f, 3.f, 3.f, 3.f, - 5.f, 5.f, 5.f, 5.f, 4.f, 4.f, 4.f, 4.f - }; - - std::vector<bool> expectedOutputValues = - { - 0, 0, 0, 0, 1, 1, 1, 1, - 1, 1, 1, 1, 0, 0, 0, 0 - }; - - ComparisonTest<float>(tflite::BuiltinOperator_NOT_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void NotEqualBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<float> input0Values - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input1Values { 4.f, 5.f, 6.f }; - // Set output data - std::vector<bool> expectedOutputValues - { - 1, 1, 1, 0, 0, 0, - 1, 1, 1, 1, 1, 1 - }; - ComparisonTest<float>(tflite::BuiltinOperator_NOT_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void NotEqualInt32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<int32_t> input0Values = { 1, 5, 6, 4 }; - - std::vector<int32_t> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { 0, 1, 1, 0 }; - - ComparisonTest<int32_t>(tflite::BuiltinOperator_NOT_EQUAL, - ::tflite::TensorType_INT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void GreaterFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> input0Values = { 1, 5, 6, 4 }; - - std::vector<float> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { 0, 1, 0, 0 }; - - ComparisonTest<float>(tflite::BuiltinOperator_GREATER, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void GreaterBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<float> input0Values - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input1Values { 4.f, 5.f, 6.f }; - - std::vector<bool> expectedOutputValues - { - 0, 0, 0, 0, 0, 0, - 1, 1, 1, 1, 1, 1 - }; - ComparisonTest<float>(tflite::BuiltinOperator_GREATER, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void GreaterInt32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<int32_t> input0Values = { 1, 5, 6, 4 }; - - std::vector<int32_t> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { 0, 1, 0, 0 }; - - ComparisonTest<int32_t>(tflite::BuiltinOperator_GREATER, - ::tflite::TensorType_INT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void GreaterEqualFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> input0Values = { 1.f, 5.f, 6.f, 4.f }; - - std::vector<float> input1Values = { 1.f, 3.f, 9.f, 4.f }; - - std::vector<bool> expectedOutputValues = { true, true, false, true }; - - ComparisonTest<float>(tflite::BuiltinOperator_GREATER_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void GreaterEqualBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<float> input0Values - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input1Values { 4.f, 5.f, 6.f }; - // Set output data - std::vector<bool> expectedOutputValues - { - 0, 0, 0, 1, 1, 1, - 1, 1, 1, 1, 1, 1 - }; - - ComparisonTest<float>(tflite::BuiltinOperator_GREATER_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void GreaterEqualInt32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<int32_t> input0Values = { 1, 5, 6, 3 }; - - std::vector<int32_t> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { 1, 1, 0, 0 }; - - ComparisonTest<int32_t>(tflite::BuiltinOperator_GREATER_EQUAL, - ::tflite::TensorType_INT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LessFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> input0Values = { 1.f, 5.f, 6.f, 4.f }; - - std::vector<float> input1Values = { 1.f, 3.f, 9.f, 4.f }; - - std::vector<bool> expectedOutputValues = { false, false, true, false }; - - ComparisonTest<float>(tflite::BuiltinOperator_LESS, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LessBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<float> input0Values - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input1Values { 4.f, 5.f, 6.f }; - - std::vector<bool> expectedOutputValues - { - true, true, true, false, false, false, - false, false, false, false, false, false - }; - - ComparisonTest<float>(tflite::BuiltinOperator_LESS, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LessInt32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<int32_t> input0Values = { 1, 5, 6, 3 }; - - std::vector<int32_t> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { false, false, true, true }; - - ComparisonTest<int32_t>(tflite::BuiltinOperator_LESS, - ::tflite::TensorType_INT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LessEqualFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> input0Values = { 1.f, 5.f, 6.f, 4.f }; - - std::vector<float> input1Values = { 1.f, 3.f, 9.f, 4.f }; - - std::vector<bool> expectedOutputValues = { true, false, true, true }; - - ComparisonTest<float>(tflite::BuiltinOperator_LESS_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LessEqualBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<float> input0Values - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f, 10.f, 11.f, 12.f - }; - std::vector<float> input1Values { 4.f, 5.f, 6.f }; - - std::vector<bool> expectedOutputValues - { - true, true, true, true, true, true, - false, false, false, false, false, false - }; - - ComparisonTest<float>(tflite::BuiltinOperator_LESS_EQUAL, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LessEqualInt32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<int32_t> input0Values = { 1, 5, 6, 3 }; - - std::vector<int32_t> input1Values = { 1, 3, 9, 4 }; - - std::vector<bool> expectedOutputValues = { true, false, true, true }; - - ComparisonTest<int32_t>(tflite::BuiltinOperator_LESS_EQUAL, - ::tflite::TensorType_INT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -TEST_SUITE("Comparison_CpuRefTests") -{ - -TEST_CASE ("EQUAL_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - EqualFP32Test(backends); -} - -TEST_CASE ("EQUAL_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - EqualBroadcastTest(backends); -} - -TEST_CASE ("EQUAL_INT32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - EqualInt32Test(backends); -} - -TEST_CASE ("NOT_EQUAL_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - NotEqualFP32Test(backends); -} - -TEST_CASE ("NOT_EQUAL_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - NotEqualBroadcastTest(backends); -} - -TEST_CASE ("NOT_EQUAL_INT32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - NotEqualInt32Test(backends); -} - -TEST_CASE ("GREATER_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - GreaterFP32Test(backends); -} - -TEST_CASE ("GREATER_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - GreaterBroadcastTest(backends); -} - -TEST_CASE ("GREATER_INT32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - GreaterInt32Test(backends); -} - -TEST_CASE ("GREATER_EQUAL_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - GreaterEqualFP32Test(backends); -} - -TEST_CASE ("GREATER_EQUAL_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - GreaterEqualBroadcastTest(backends); -} - -TEST_CASE ("GREATER_EQUAL_INT32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - GreaterEqualInt32Test(backends); -} - -TEST_CASE ("LESS_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LessFP32Test(backends); -} - -TEST_CASE ("LESS_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LessBroadcastTest(backends); -} - -TEST_CASE ("LESS_INT32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LessInt32Test(backends); -} - -TEST_CASE ("LESS_EQUAL_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LessEqualFP32Test(backends); -} - -TEST_CASE ("LESS_EQUAL_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LessEqualBroadcastTest(backends); -} - -TEST_CASE ("LESS_EQUAL_INT32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LessEqualInt32Test(backends); -} -} // End TEST_SUITE("Comparison_CpuRefTests") - - - -TEST_SUITE("Comparison_GpuAccTests") -{ - -TEST_CASE ("EQUAL_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - EqualFP32Test(backends); -} - -TEST_CASE ("EQUAL_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - EqualBroadcastTest(backends); -} - -TEST_CASE ("EQUAL_INT32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - EqualInt32Test(backends); -} - -TEST_CASE ("NOT_EQUAL_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - NotEqualFP32Test(backends); -} - -TEST_CASE ("NOT_EQUAL_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - NotEqualBroadcastTest(backends); -} - -TEST_CASE ("NOT_EQUAL_INT32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - NotEqualInt32Test(backends); -} - -TEST_CASE ("GREATER_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, - armnn::Compute::CpuRef }; - GreaterFP32Test(backends); -} - -TEST_CASE ("GREATER_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, - armnn::Compute::CpuRef }; - GreaterBroadcastTest(backends); -} - -TEST_CASE ("GREATER_INT32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc, - armnn::Compute::CpuRef }; - GreaterInt32Test(backends); -} - -TEST_CASE ("GREATER_EQUAL_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - GreaterEqualFP32Test(backends); -} - -TEST_CASE ("GREATER_EQUAL_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - GreaterEqualBroadcastTest(backends); -} - -TEST_CASE ("GREATER_EQUAL_INT32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - GreaterEqualInt32Test(backends); -} - -TEST_CASE ("LESS_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LessFP32Test(backends); -} - -TEST_CASE ("LESS_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LessBroadcastTest(backends); -} - -TEST_CASE ("LESS_INT32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LessInt32Test(backends); -} - -TEST_CASE ("LESS_EQUAL_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LessEqualFP32Test(backends); -} - -TEST_CASE ("LESS_EQUAL_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LessEqualBroadcastTest(backends); -} - -TEST_CASE ("LESS_EQUAL_INT32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LessEqualInt32Test(backends); -} - -} // End TEST_SUITE("Comparison_GpuAccTests") - - -TEST_SUITE("Comparison_CpuAccTests") -{ - -TEST_CASE ("EQUAL_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - EqualFP32Test(backends); -} - -TEST_CASE ("EQUAL_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - EqualBroadcastTest(backends); -} - -TEST_CASE ("EQUAL_INT32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - EqualInt32Test(backends); -} - -TEST_CASE ("NOT_EQUAL_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - NotEqualFP32Test(backends); -} - -TEST_CASE ("NOT_EQUAL_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - NotEqualBroadcastTest(backends); -} - -TEST_CASE ("NOT_EQUAL_INT32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - NotEqualInt32Test(backends); -} - -TEST_CASE ("GREATER_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - GreaterFP32Test(backends); -} - -TEST_CASE ("GREATER_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - GreaterBroadcastTest(backends); -} - -TEST_CASE ("GREATER_INT32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - GreaterInt32Test(backends); -} - -TEST_CASE ("GREATER_EQUAL_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - GreaterEqualFP32Test(backends); -} - -TEST_CASE ("GREATER_EQUAL_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - GreaterEqualBroadcastTest(backends); -} - -TEST_CASE ("GREATER_EQUAL_INT32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - GreaterEqualInt32Test(backends); -} - -TEST_CASE ("LESS_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LessFP32Test(backends); -} - -TEST_CASE ("LESS_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LessBroadcastTest(backends); -} - -TEST_CASE ("LESS_INT32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LessInt32Test(backends); -} - -TEST_CASE ("LESS_EQUAL_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LessEqualFP32Test(backends); -} - -TEST_CASE ("LESS_EQUAL_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LessEqualBroadcastTest(backends); -} - -TEST_CASE ("LESS_EQUAL_INT32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LessEqualInt32Test(backends); -} - -} // End TEST_SUITE("Comparison_CpuAccTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ComparisonTestHelper.hpp b/delegate/src/test/ComparisonTestHelper.hpp deleted file mode 100644 index db337f9f8a..0000000000 --- a/delegate/src/test/ComparisonTestHelper.hpp +++ /dev/null @@ -1,238 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateComparisonTfLiteModel(tflite::BuiltinOperator comparisonOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& input0TensorShape, - const std::vector <int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), - input1TensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("input_1"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - ::tflite::TensorType_BOOL, - 3); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_EqualOptions;; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateEqualOptions(flatBufferBuilder).Union(); - switch (comparisonOperatorCode) - { - case BuiltinOperator_EQUAL: - { - operatorBuiltinOptionsType = BuiltinOptions_EqualOptions; - operatorBuiltinOptions = CreateEqualOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_NOT_EQUAL: - { - operatorBuiltinOptionsType = BuiltinOptions_NotEqualOptions; - operatorBuiltinOptions = CreateNotEqualOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_GREATER: - { - operatorBuiltinOptionsType = BuiltinOptions_GreaterOptions; - operatorBuiltinOptions = CreateGreaterOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_GREATER_EQUAL: - { - operatorBuiltinOptionsType = BuiltinOptions_GreaterEqualOptions; - operatorBuiltinOptions = CreateGreaterEqualOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_LESS: - { - operatorBuiltinOptionsType = BuiltinOptions_LessOptions; - operatorBuiltinOptions = CreateLessOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_LESS_EQUAL: - { - operatorBuiltinOptionsType = BuiltinOptions_LessEqualOptions; - operatorBuiltinOptions = CreateLessEqualOptions(flatBufferBuilder).Union(); - break; - } - default: - break; - } - const std::vector<int32_t> operatorInputs{0, 1}; - const std::vector<int32_t> operatorOutputs{2}; - flatbuffers::Offset <Operator> comparisonOperator = - 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}; - 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(&comparisonOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Comparison Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, comparisonOperatorCode); - - 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 ComparisonTest(tflite::BuiltinOperator comparisonOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& outputShape, - std::vector<T>& input0Values, - std::vector<T>& input1Values, - std::vector<bool>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateComparisonTfLiteModel(comparisonOperatorCode, - tensorType, - input0Shape, - input1Shape, - outputShape, - quantScale, - quantOffset); - - 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 - auto tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput0Id); - for (unsigned int i = 0; i < input0Values.size(); ++i) - { - tfLiteDelageInput0Data[i] = input0Values[i]; - } - - auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1]; - auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput1Id); - for (unsigned int i = 0; i < input1Values.size(); ++i) - { - tfLiteDelageInput1Data[i] = input1Values[i]; - } - - auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput0Id); - for (unsigned int i = 0; i < input0Values.size(); ++i) - { - armnnDelegateInput0Data[i] = input0Values[i]; - } - - auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; - auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput1Id); - for (unsigned int i = 0; i < input1Values.size(); ++i) - { - armnnDelegateInput1Data[i] = input1Values[i]; - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<bool>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<bool>(armnnDelegateOutputId); - - armnnDelegate::CompareData(expectedOutputValues , armnnDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues , tfLiteDelageOutputData , expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelageOutputData, armnnDelegateOutputData, expectedOutputValues.size()); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/ControlTest.cpp b/delegate/src/test/ControlTest.cpp deleted file mode 100644 index 18bbc5a9a8..0000000000 --- a/delegate/src/test/ControlTest.cpp +++ /dev/null @@ -1,420 +0,0 @@ -// -// Copyright © 2020,2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ControlTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -// CONCATENATION Operator -void ConcatUint8TwoInputsTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 2 }; - std::vector<int32_t> expectedOutputShape { 4, 2 }; - - // Set input and output data - std::vector<std::vector<uint8_t>> inputValues; - std::vector<uint8_t> inputValue1 { 0, 1, 2, 3 }; // Lower bounds - std::vector<uint8_t> inputValue2 { 252, 253, 254, 255 }; // Upper bounds - inputValues.push_back(inputValue1); - inputValues.push_back(inputValue2); - - std::vector<uint8_t> expectedOutputValues { 0, 1, 2, 3, 252, 253, 254, 255 }; - - ConcatenationTest<uint8_t>(tflite::BuiltinOperator_CONCATENATION, - ::tflite::TensorType_UINT8, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues); -} - -void ConcatInt16TwoInputsTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 2 }; - std::vector<int32_t> expectedOutputShape { 4, 2 }; - - std::vector<std::vector<int16_t>> inputValues; - std::vector<int16_t> inputValue1 { -32768, -16384, -1, 0 }; - std::vector<int16_t> inputValue2 { 1, 2, 16384, 32767 }; - inputValues.push_back(inputValue1); - inputValues.push_back(inputValue2); - - std::vector<int16_t> expectedOutputValues { -32768, -16384, -1, 0, 1, 2, 16384, 32767}; - - ConcatenationTest<int16_t>(tflite::BuiltinOperator_CONCATENATION, - ::tflite::TensorType_INT16, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues); -} - -void ConcatFloat32TwoInputsTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 2 }; - std::vector<int32_t> expectedOutputShape { 4, 2 }; - - std::vector<std::vector<float>> inputValues; - std::vector<float> inputValue1 { -127.f, -126.f, -1.f, 0.f }; - std::vector<float> inputValue2 { 1.f, 2.f, 126.f, 127.f }; - inputValues.push_back(inputValue1); - inputValues.push_back(inputValue2); - - std::vector<float> expectedOutputValues { -127.f, -126.f, -1.f, 0.f, 1.f, 2.f, 126.f, 127.f }; - - ConcatenationTest<float>(tflite::BuiltinOperator_CONCATENATION, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues); -} - -void ConcatThreeInputsTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 2 }; - std::vector<int32_t> expectedOutputShape { 6, 2 }; - - std::vector<std::vector<uint8_t>> inputValues; - std::vector<uint8_t> inputValue1 { 0, 1, 2, 3 }; - std::vector<uint8_t> inputValue2 { 125, 126, 127, 128 }; - std::vector<uint8_t> inputValue3 { 252, 253, 254, 255 }; - inputValues.push_back(inputValue1); - inputValues.push_back(inputValue2); - inputValues.push_back(inputValue3); - - std::vector<uint8_t> expectedOutputValues { 0, 1, 2, 3, 125, 126, 127, 128, 252, 253, 254, 255 }; - - ConcatenationTest<uint8_t>(tflite::BuiltinOperator_CONCATENATION, - ::tflite::TensorType_UINT8, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues); -} - -void ConcatAxisTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 4 }; - - std::vector<std::vector<uint8_t>> inputValues; - std::vector<uint8_t> inputValue1 { 0, 1, 2, 3 }; - std::vector<uint8_t> inputValue3 { 252, 253, 254, 255 }; - inputValues.push_back(inputValue1); - inputValues.push_back(inputValue3); - - std::vector<uint8_t> expectedOutputValues { 0, 1, 252, 253, 2, 3, 254, 255 }; - - ConcatenationTest<uint8_t>(tflite::BuiltinOperator_CONCATENATION, - ::tflite::TensorType_UINT8, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 2); -} - -// MEAN Operator -void MeanUint8KeepDimsTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 3 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1 }; - - std::vector<uint8_t> input0Values { 5, 10, 15 }; // Inputs - std::vector<int32_t> input1Values { 1 }; // Axis - - std::vector<uint8_t> expectedOutputValues { 10 }; - - MeanTest<uint8_t>(tflite::BuiltinOperator_MEAN, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - true); -} - -void MeanUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 2, 2 }; - - std::vector<uint8_t> input0Values { 5, 10, 15, 20 }; // Inputs - std::vector<int32_t> input1Values { 0 }; // Axis - - std::vector<uint8_t> expectedOutputValues { 5, 10, 15, 20 }; - - MeanTest<uint8_t>(tflite::BuiltinOperator_MEAN, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - false); -} - -void MeanFp32KeepDimsTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 2 }; - - std::vector<float> input0Values { 1.0f, 1.5f, 2.0f, 2.5f }; // Inputs - std::vector<int32_t> input1Values { 1 }; // Axis - - std::vector<float> expectedOutputValues { 1.5f, 2.0f }; - - MeanTest<float>(tflite::BuiltinOperator_MEAN, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - true); -} - -void MeanFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 1 }; - - std::vector<float> input0Values { 1.0f, 1.5f, 2.0f, 2.5f }; // Inputs - std::vector<int32_t> input1Values { 2 }; // Axis - - std::vector<float> expectedOutputValues { 1.25f, 2.25f }; - - MeanTest<float>(tflite::BuiltinOperator_MEAN, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - false); -} - -// CONCATENATION Tests. -TEST_SUITE("Concatenation_CpuAccTests") -{ - -TEST_CASE ("Concatenation_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - ConcatUint8TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Int16_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - ConcatInt16TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Float32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - ConcatFloat32TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Three_Inputs_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - ConcatThreeInputsTest(backends); -} - -TEST_CASE ("Concatenation_Axis_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - ConcatAxisTest(backends); -} - -} - -TEST_SUITE("Concatenation_GpuAccTests") -{ - -TEST_CASE ("Concatenation_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - ConcatUint8TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Int16_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - ConcatInt16TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Float32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - ConcatFloat32TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Three_Inputs_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - ConcatThreeInputsTest(backends); -} - -TEST_CASE ("Concatenation_Axis_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - ConcatAxisTest(backends); -} - -} - -TEST_SUITE("Concatenation_CpuRefTests") -{ - -TEST_CASE ("Concatenation_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConcatUint8TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Int16_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConcatInt16TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Float32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConcatFloat32TwoInputsTest(backends); -} - -TEST_CASE ("Concatenation_Three_Inputs_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConcatThreeInputsTest(backends); -} - -TEST_CASE ("Concatenation_Axis_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - ConcatAxisTest(backends); -} - -} - -// MEAN Tests -TEST_SUITE("Mean_CpuAccTests") -{ - -TEST_CASE ("Mean_Uint8_KeepDims_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - MeanUint8KeepDimsTest(backends); -} - -TEST_CASE ("Mean_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - MeanUint8Test(backends); -} - -TEST_CASE ("Mean_Fp32_KeepDims_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - MeanFp32KeepDimsTest(backends); -} - -TEST_CASE ("Mean_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - MeanFp32Test(backends); -} - -} - -TEST_SUITE("Mean_GpuAccTests") -{ - -TEST_CASE ("Mean_Uint8_KeepDims_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - MeanUint8KeepDimsTest(backends); -} - -TEST_CASE ("Mean_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - MeanUint8Test(backends); -} - -TEST_CASE ("Mean_Fp32_KeepDims_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - MeanFp32KeepDimsTest(backends); -} - -TEST_CASE ("Mean_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - MeanFp32Test(backends); -} - -} - -TEST_SUITE("Mean_CpuRefTests") -{ - -TEST_CASE ("Mean_Uint8_KeepDims_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - MeanUint8KeepDimsTest(backends); -} - -TEST_CASE ("Mean_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - MeanUint8Test(backends); -} - -TEST_CASE ("Mean_Fp32_KeepDims_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - MeanFp32KeepDimsTest(backends); -} - -TEST_CASE ("Mean_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - MeanFp32Test(backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ControlTestHelper.hpp b/delegate/src/test/ControlTestHelper.hpp deleted file mode 100644 index 3e427e60c5..0000000000 --- a/delegate/src/test/ControlTestHelper.hpp +++ /dev/null @@ -1,346 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateConcatTfLiteModel(tflite::BuiltinOperator controlOperatorCode, - tflite::TensorType tensorType, - std::vector<int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - const int32_t inputTensorNum, - int32_t axis = 0, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::vector<int32_t> operatorInputs{}; - const std::vector<int32_t> operatorOutputs{inputTensorNum}; - std::vector<int> subgraphInputs{}; - const std::vector<int> subgraphOutputs{inputTensorNum}; - - std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1); - for (int i = 0; i < inputTensorNum; ++i) - { - tensors[i] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input" + std::to_string(i)), - quantizationParameters); - - // Add number of inputs to vector. - operatorInputs.push_back(i); - subgraphInputs.push_back(i); - } - - // Create output tensor - tensors[inputTensorNum] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ConcatenationOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateConcatenationOptions(flatBufferBuilder, axis).Union(); - - 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); - - 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: Concatenation Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); - - 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()); -} - -std::vector<char> CreateMeanTfLiteModel(tflite::BuiltinOperator controlOperatorCode, - tflite::TensorType tensorType, - std::vector<int32_t>& input0TensorShape, - std::vector<int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape, - std::vector<int32_t>& axisData, - const bool keepDims, - 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); - 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>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), - input1TensorShape.size()), - ::tflite::TensorType_INT32, - 1, - flatBufferBuilder.CreateString("axis"), - quantizationParameters); - - // Create output tensor - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator. Mean uses ReducerOptions. - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union(); - - const std::vector<int> operatorInputs{ {0, 1} }; - const std::vector<int> operatorOutputs{ 2 }; - 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 }; - 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: Mean Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, controlOperatorCode); - - 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 ConcatenationTest(tflite::BuiltinOperator controlOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShapes, - std::vector<int32_t>& expectedOutputShape, - std::vector<std::vector<T>>& inputValues, - std::vector<T>& expectedOutputValues, - int32_t axis = 0, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateConcatTfLiteModel(controlOperatorCode, - tensorType, - inputShapes, - expectedOutputShape, - inputValues.size(), - axis, - quantScale, - quantOffset); - - 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 for all input tensors. - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - // Get single input tensor and assign to interpreters. - auto inputTensorValues = inputValues[i]; - armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues); - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - expectedOutputShape, - expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); -} - -template <typename T> -void MeanTest(tflite::BuiltinOperator controlOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& input0Values, - std::vector<int32_t>& input1Values, - std::vector<T>& expectedOutputValues, - const bool keepDims, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateMeanTfLiteModel(controlOperatorCode, - tensorType, - input0Shape, - input1Shape, - expectedOutputShape, - input1Values, - keepDims, - quantScale, - quantOffset); - - 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, input0Values); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - expectedOutputShape, - expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/Convolution2dTest.cpp b/delegate/src/test/Convolution2dTest.cpp deleted file mode 100644 index 10510792a1..0000000000 --- a/delegate/src/test/Convolution2dTest.cpp +++ /dev/null @@ -1,489 +0,0 @@ -// -// Copyright © 2020,2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvolutionTestHelper.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 Conv2DWithBiasesFp32Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 5, 5, 1 }; - std::vector<int32_t> filterShape { 1, 3, 3, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 3, 3, 1 }; - - static std::vector<float> inputValues = - { - 1, 5, 2, 3, 5, - 8, 7, 3, 6, 3, - 3, 3, 9, 1, 9, - 4, 1, 8, 1, 3, - 6, 8, 1, 9, 2 - }; - - std::vector<float> filterValues = - { - 4, 5, 6, - 0, 0, 0, - 3, 2, 1 - }; - - std::vector<float> biasValues = { 0 }; - - std::vector<float> expectedOutputValues = - { - 23, 33, 24, - 91, 99, 48, - 26, 50, 19 - }; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<float>(tflite::BuiltinOperator_CONV_2D, - ::tflite::TensorType_FLOAT32, - 2, // strideX - 2, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - -void Conv2DWithBiasesInt8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> filterShape { 1, 2, 2, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - static std::vector<int8_t> inputValues = { 1, 2, 3, 4 }; - - std::vector<int8_t> filterValues = { 2, 1, 0, 6 }; - - std::vector<int32_t> biasValues = { 10 }; - - std::vector<int8_t> expectedOutputValues = - { - (1 * 2 + 2 * 1 + 3 * 0 + 4 * 6 + 10) / 2, // 19 - (2 * 2 + 0 * 1 + 4 * 0 + 0 * 6 + 10) / 2, // 7 - (3 * 2 + 4 * 1 + 0 * 0 + 0 * 6 + 10) / 2, // 10 - (4 * 2 + 0 * 1 + 0 * 0 + 0 * 6 + 10) / 2, // 9 - }; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, - ::tflite::TensorType_INT8, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - -void Conv2DWithBiasesReluUint8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> filterShape { 1, 2, 2, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - static std::vector<uint8_t> inputValues = { 1, 2, 4, 8 }; - - std::vector<uint8_t> filterValues = { 2, 1, 0, 6 }; - - std::vector<int32_t> biasValues = { 16 }; - - // factors to consider: - // - the filter zero point is non zero, hence the (x-fz) - // - the output scale is 2 hence the /2 - // - output zero point is non zero, hence the +outZero - // - RELU cuts negative values and then we add the output zero point - uint8_t bias = 16; - uint8_t outZero = 20; - uint8_t fz = 4; // filter zero point - - std::vector<uint8_t> expectedOutputValues = - { - std::max(outZero, static_cast<uint8_t>((1*(2-fz) + 2*(1-fz) + 4*(0-fz) + 8*(6-fz) + bias)/2 + outZero)), - std::max(outZero, static_cast<uint8_t>((2*(2-fz) + 0*(1-fz) + 8*(0-fz) + 0*(6-fz) + bias)/2 + outZero)), - std::max(outZero, static_cast<uint8_t>((4*(2-fz) + 8*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)), - std::max(outZero, static_cast<uint8_t>((8*(2-fz) + 0*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)) - }; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, - ::tflite::TensorType_UINT8, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_RELU, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues, - {1.0f}, // biasScale - {0}, // biasOffset - {1.0f}, // filterScale - {4}, // filterOffsets - 2, // output scale - 20); // output offset -} - -void Conv2DWithBiasesRelu6Uint8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> filterShape { 1, 2, 2, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - static std::vector<uint8_t> inputValues = { 1, 2, 4, 1 }; - - std::vector<uint8_t> filterValues = { 2, 1, 0, 6 }; - - std::vector<int32_t> biasValues = { 0 }; - - // factors to consider: - // - the output scale is 2 hence the /2 - // - RELU6 cuts output values at +6 - uint8_t relu6Min = 6 / 2; // divide by output scale - - std::vector<uint8_t> expectedOutputValues = - { - std::min(relu6Min, static_cast<uint8_t>((1 * 2 + 2 * 1 + 4 * 0 + 1 * 6) / 2)), - std::min(relu6Min, static_cast<uint8_t>((2 * 2 + 0 * 1 + 1 * 0 + 0 * 6) / 2)), - std::min(relu6Min, static_cast<uint8_t>((4 * 2 + 1 * 1 + 0 * 0 + 0 * 6) / 2)), - std::min(relu6Min, static_cast<uint8_t>((1 * 2 + 0 * 1 + 0 * 0 + 0 * 6) / 2)) - }; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, - ::tflite::TensorType_UINT8, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_RELU6, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - - -void Conv2DPerChannelInt8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1,4,4,2 }; - std::vector<int32_t> filterShape { 4,2,2,2 }; - std::vector<int32_t> biasShape { 4 }; - std::vector<int32_t> outputShape { 1,4,4,4 }; - - static std::vector<int8_t> inputValues = - { - -11, 40,-26, 11,-28, 8, 0, -8, - -10, 34, 47, 0,-33,-14, 28, 35, - 6,-28,-26, 8, 13, 33,-31,-41, - 31,-20,-31,-16, 8,-18,-44, 0 - }; - - std::vector<float> filterScales = { 1.858268, 2.0, 1.992126, 1.905512 }; - int32_t filterQuantizationDim = 0; - std::vector<int8_t> filterValues = - { - 13,-44, 5,-14, 21,-45, 36,-25, - -42, -2, 24,-30,-31, 35, 43,-30, - -20, -5, 25, 17, 18, 20, 4,-46, - -49, 9, -3,-20, 46, 5, 7,-15 - }; - - std::vector<int32_t> biasValues = { 0,0,0,0 }; - std::vector<float> biasScales = { 0.721445, 0.7764700055, 0.773414, 0.739787 }; - - std::vector<int8_t> expectedOutputValues = - { - -1, 9, 3, 5, 1, -1, 5, 9, - 2, 7, -1, 2, 2, 4, 5, 6, - 1, 1, 4, 4, 2, 0, -4, -3, - 0, 6, 12, 6, 3, 0, -1, -2, - 7, -4, 4, 4, 3, 6, 6, 2, - 0, -3, -1, 4, 4, 8, 3, 1, - 5, 0, 0, 1, 4, 7, 4, 6, - 4, 0, 1, 2, 2, 7, 5, 7 - }; - float outputQuantScale = 401.960785f; - int outputQuantOffset = 3; - float inputQuantScale = 0.388235f; - int inputQuantOffset = 1; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, - ::tflite::TensorType_INT8, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues, - biasScales, - {0,0,0,0}, - filterScales, - {0,0,0,0}, - outputQuantScale, - outputQuantOffset, - inputQuantScale, - inputQuantOffset, - 1, // depth_multiplier is ignored for conv2d value doesn't matter - filterQuantizationDim); -} - -TEST_SUITE("Convolution2dTest_CpuRefTests") -{ - -TEST_CASE ("Conv2DWithBiases_Fp32_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv2DWithBiasesFp32Test(backends); -} - -TEST_CASE ("Conv2DWithBiases_Int8_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv2DWithBiasesInt8Test(backends); -} - -TEST_CASE ("Conv2DPerChannel_Int8_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv2DPerChannelInt8Test(backends); -} - -} //End of TEST_SUITE("Convolution2dTest_CpuRef") - -TEST_SUITE("Convolution2dTest_CpuAccTests") -{ - -TEST_CASE ("Conv2DWithBiases_Fp32_CpuAcc_Test") -{ -std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; -Conv2DWithBiasesFp32Test(backends); -} - -TEST_CASE ("Conv2DWithBiases_Int8_CpuAcc_Test") -{ -std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; -Conv2DWithBiasesInt8Test(backends); -} - -TEST_CASE ("Conv2DPerChannel_Int8_CpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - Conv2DPerChannelInt8Test(backends); -} - -} //End of TEST_SUITE("Convolution2dTest_CpuAcc") - -TEST_SUITE("Convolution2dTest_GpuAccTests") -{ - -TEST_CASE ("Conv2DWithBiases_Fp32_GpuAcc_Test") -{ -std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; -Conv2DWithBiasesFp32Test(backends); -} - -TEST_CASE ("Conv2DWithBiases_Int8_GpuAcc_Test") -{ -std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; -Conv2DWithBiasesInt8Test(backends); -} - -TEST_CASE ("Conv2DPerChannel_Int8_GpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - Conv2DPerChannelInt8Test(backends); -} - -} //End of TEST_SUITE("Convolution2dTest_GpuAcc") - -void TransposeConvInt8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> transposeTensorShape { 4 }; - std::vector<int32_t> filterShape { 1, 2, 2, 1 }; - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> outputShape { 1, 3, 3, 1 }; - - std::vector<int32_t> transposeData = { 1, 3, 3, 1 }; - static std::vector<int8_t> inputValues = { 1, 2, 3, 4 }; - std::vector<int8_t> filterValues = { 0, 1, 2, 4 }; - std::vector<int8_t> expectedOutputValues = - { - 0, 1, 2, - 2, 11, 12, - 6, 20, 16 - }; - - tflite::Padding padding = tflite::Padding_VALID; - TransposeConvTest<int8_t>(backends, - ::tflite::TensorType_INT8, - 1, // strideX - 1, // strideY - padding, - transposeTensorShape, - filterShape, - inputShape, - outputShape, - transposeData, - filterValues, - inputValues, - expectedOutputValues); -} - -void TransposeConvFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> transposeTensorShape { 4 }; - std::vector<int32_t> filterShape { 1, 2, 2, 1 }; - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> outputShape { 1, 3, 3, 1 }; - - std::vector<int32_t> transposeData = { 1, 3, 3, 1 }; - static std::vector<float> inputValues = { 1, 2, 3, 4 }; - std::vector<float> filterValues = { 0, 1, 2, 4 }; - std::vector<float> expectedOutputValues = - { - 0, 1, 2, - 2, 11, 12, - 6, 20, 16 - }; - - tflite::Padding padding = tflite::Padding_VALID; - TransposeConvTest<float>(backends, - ::tflite::TensorType_FLOAT32, - 1, // strideX - 1, // strideY - padding, - transposeTensorShape, - filterShape, - inputShape, - outputShape, - transposeData, - filterValues, - inputValues, - expectedOutputValues); -} - -TEST_SUITE("TransposeConv_CpuRef_Test") -{ - -TEST_CASE ("TransposeConv_CpuRef_Fp32_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - TransposeConvFp32Test(backends); -} - -TEST_CASE ("TransposeConv_CpuRef_Int8_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - TransposeConvInt8Test(backends); -} - -} // End of TEST_SUITE(TransposeConv_CpuRef_Test) - -TEST_SUITE("TransposeConv_CpuAcc_Test") -{ - -TEST_CASE ("TransposeConv_CpuAcc_Fp32_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - TransposeConvFp32Test(backends); -} - -TEST_CASE ("TransposeConv_CpuAcc_Int8_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - TransposeConvInt8Test(backends); -} - -} // End of TEST_SUITE(TransposeConv_CpuAcc_Test) - -TEST_SUITE("TransposeConv_GpuAcc_Test") -{ - -TEST_CASE ("TransposeConv_GpuAcc_Fp32_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - TransposeConvFp32Test(backends); -} - -TEST_CASE ("TransposeConv_GpuAcc_Int8_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - TransposeConvInt8Test(backends); -} - -} // End of TEST_SUITE(TransposeConv_GpuAcc_Test) - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/Convolution3dTest.cpp b/delegate/src/test/Convolution3dTest.cpp deleted file mode 100644 index 06883f186d..0000000000 --- a/delegate/src/test/Convolution3dTest.cpp +++ /dev/null @@ -1,318 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvolutionTestHelper.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 <doctest/doctest.h> - -namespace armnnDelegate -{ - -// Conv3d is currently only supports Float32 inputs, filter, bias and outputs in TFLite. -// 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) - -// Create a vector from 0 to size divided to create smaller floating point values. -template <typename T> -std::vector<T> CreateFloatData(int32_t size, float divisor) -{ - std::vector<float> data; - for (int32_t i = 0; i < size; ++i) - { - float value = static_cast<float>(i); - data.push_back(value/divisor); - } - return data; -} - -void Conv3DWithBiasesSimpleWithPaddingFp32Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 2, 1 }; - std::vector<int32_t> filterShape { 2, 2, 2, 1, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 2, 1 }; - - static std::vector<float> inputValues = - { - 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f - }; - - std::vector<float> filterValues = - { - 2.f,1.f, 1.f,0.f, 0.f,1.f, 1.f,1.f - }; - - std::vector<float> biasValues = { 5.f }; - - std::vector<float> expectedOutputValues = - { - 33.f, 21.f, 23.f, 13.f, 28.f, 25.f, 27.f, 21.f - }; - - Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, - ::tflite::TensorType_FLOAT32, - { 1, 1, 1 }, // strideX, strideY, strideZ - { 1, 1, 1 }, // dilationX, dilationY, dilationZ - tflite::Padding_SAME, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - -void Conv3DWithBiasesStridesFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 3, 10, 10, 1 }; - std::vector<int32_t> filterShape { 3, 5, 5, 1, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 1, 3, 3, 1 }; - - std::vector<float> inputValues = CreateFloatData<float>(300, 1.0f); - - std::vector<float> filterValues = - { - 1.f, 1.f, 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f, 1.f, 1.f, - - 0.f, 0.f, 0.f, 0.f, 0.f, - 0.f, 0.f, 0.f, 0.f, 0.f, - 0.f, 0.f, 0.f, 0.f, 0.f, - 0.f, 0.f, 0.f, 0.f, 0.f, - 0.f, 0.f, 0.f, 0.f, 0.f, - - 2.f, 2.f, 2.f, 2.f, 2.f, - 2.f, 2.f, 2.f, 2.f, 2.f, - 2.f, 2.f, 2.f, 2.f, 2.f, - 2.f, 2.f, 2.f, 2.f, 2.f, - 2.f, 2.f, 2.f, 2.f, 2.f - }; - - std::vector<float> biasValues = { 10.f }; - - std::vector<float> expectedOutputValues = - { - 11660.f, 11810.f, 11960.f, - - 13160.f, 13310.f, 13460.f, - - 14660.f, 14810.f, 14960.f - }; - - Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, - ::tflite::TensorType_FLOAT32, - { 2, 2, 2 }, // strideX, strideY, strideZ - { 1, 1, 1 }, // dilationX, dilationY, dilationZ - tflite::Padding_VALID, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - - -void Conv3DWithBiasesDilationFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 5, 5, 5, 2 }; - std::vector<int32_t> filterShape { 2, 2, 2, 2, 2 }; - std::vector<int32_t> biasShape { 2 }; - std::vector<int32_t> outputShape { 1, 2, 2, 2, 2 }; - - std::vector<float> inputValues = CreateFloatData<float>(250, 1.0f); - - std::vector<float> filterValues = - { - -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, -1.f, 1.f, 1.f, 1.f, -1.f, -1.f, - 1.f, 1.f, -1.f, 1.f, -1.f, 1.f, -1.f, 1.f, -1.f, -1.f, -1.f, 1.f, -1.f, 1.f, -1.f, 1.f, - }; - - std::vector<float> biasValues = { 0.f, 2.f }; - - // Since the dilation rate is 3 this will dilate the kernel to be 4x4, - // therefore the output will be 2x2 - std::vector<float> expectedOutputValues = - { - -1124.f, 976.f, - -1148.f, 980.f, - - -1244.f, 996.f, - -1268.f, 1000.f, - - -1724.f, 1076.f, - -1748.f, 1080.f, - - -1844.f, 1096.f, - -1868.f, 1100.f - }; - - Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, - ::tflite::TensorType_FLOAT32, - { 1, 1, 1 }, // strideX, strideY, strideZ - { 3, 3, 3 }, // dilationX, dilationY, dilationZ - tflite::Padding_VALID, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - -void Conv3DFp32SmallTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 3, 10, 10, 1 }; - std::vector<int32_t> filterShape { 3, 3, 3, 1, 1 }; - std::vector<int32_t> biasShape { 1 }; - std::vector<int32_t> outputShape { 1, 1, 4, 4, 1 }; - - std::vector<float> inputValues = CreateFloatData<float>(300, 100.0f); - - std::vector<float> filterValues = - { - 0.125977f, 0.150391f, 0.101562f, - 0.0585938f, 0.0864258f, 0.043457f, - 0.034668f, 0.0322266f, 0.0385742f, - - 0.125977f, 0.150391f, -0.101562f, - -0.0585938f,-0.0864258f,-0.043457f, - -0.0104630f, 0.0154114f, 0.0013768f, - - 0.0344238f, 0.035644f, 0.0495605f, - 0.0683594f, 0.099121f, -0.0461426f, - -0.0996094f,-0.126953f, -0.043457f, - }; - - std::vector<float> biasValues = { 0 }; - - std::vector<float> expectedOutputValues = - { - -0.08156067f, -0.06891209f, -0.05589598f, -0.04310101f, - 0.04584253f, 0.05855697f, 0.07129729f, 0.08325434f, - 0.17304349f, 0.18521416f, 0.19818866f, 0.21096253f, - 0.29965734f, 0.312698f, 0.32547557f, 0.33818722f - }; - - Convolution3dTest<float>(tflite::BuiltinOperator_CONV_3D, - ::tflite::TensorType_FLOAT32, - { 2, 2, 2 }, // strideX, strideY, strideZ - { 1, 1, 1 }, // dilationX, dilationY, dilationZ - tflite::Padding_VALID, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - -TEST_SUITE("Convolution3dTest_CpuRefTests") -{ - -TEST_CASE ("Conv3DWithBiasesSimpleWithPadding_Fp32_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv3DWithBiasesSimpleWithPaddingFp32Test(backends); -} - -TEST_CASE ("Conv3DWithBiasesStrides_Fp32_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv3DWithBiasesStridesFp32Test(backends); -} - -TEST_CASE ("Conv3DWithBiasesDilation_Fp32_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv3DWithBiasesDilationFp32Test(backends); -} - -TEST_CASE ("Conv3DFp32Small_Fp32_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - Conv3DFp32SmallTest(backends); -} - -} //End of TEST_SUITE("Convolution3dTest_CpuRefTests") - -TEST_SUITE("Convolution3dTest_CpuAccTests") -{ - -TEST_CASE ("Conv3DWithBiasesSimpleWithPadding_Fp32_CpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - Conv3DWithBiasesSimpleWithPaddingFp32Test(backends); -} - -TEST_CASE ("Conv3DWithBiasesStrides_Fp32_CpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - Conv3DWithBiasesStridesFp32Test(backends); -} - -TEST_CASE ("Conv3DFp32Small_Fp32_CpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - Conv3DFp32SmallTest(backends); -} - -} //End of TEST_SUITE("Convolution3dTest_CpuAccTests") - -TEST_SUITE("Convolution3dTest_GpuAccTests") -{ - -TEST_CASE ("Conv3DWithBiasesSimpleWithPadding_Fp32_GpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - Conv3DWithBiasesSimpleWithPaddingFp32Test(backends); -} - -TEST_CASE ("Conv3DWithBiasesStrides_Fp32_GpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - Conv3DWithBiasesStridesFp32Test(backends); -} - -TEST_CASE ("Conv3DFp32Small_Fp32_GpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - Conv3DFp32SmallTest(backends); -} - -} //End of TEST_SUITE("Convolution3dTest_GpuAccTests") - -#endif - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp deleted file mode 100644 index 70c1da6dce..0000000000 --- a/delegate/src/test/ConvolutionTestHelper.hpp +++ /dev/null @@ -1,784 +0,0 @@ -// -// Copyright © 2020, 2023 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 -{ - -template <typename T, typename B = float> -std::vector<char> CreateConv2dTfLiteModel(tflite::BuiltinOperator convolutionOperatorCode, - tflite::TensorType tensorType, - uint32_t strideX, - uint32_t strideY, - uint32_t dilationX, - uint32_t dilationY, - 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>, 5> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder); - buffers[1] = CreateBuffer(flatBufferBuilder); - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), - sizeof(T) * filterData.size())); - - buffers[3] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), - sizeof(B) * biasData.size())); - buffers[4] = CreateBuffer(flatBufferBuilder); - - 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, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), - filterTensorShape.size()), - tensorType, - 2, - 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, - 3, - flatBufferBuilder.CreateString("bias"), - biasQuantizationParameters); - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 4, - flatBufferBuilder.CreateString("output"), - outputQuantizationParameters); - - flatbuffers::Offset<void> operatorBuiltinOptions; - tflite::BuiltinOptions operatorBuiltinOptionsType; - - if(convolutionOperatorCode == tflite::BuiltinOperator_DEPTHWISE_CONV_2D) - { - operatorBuiltinOptionsType = tflite::BuiltinOptions_DepthwiseConv2DOptions; - operatorBuiltinOptions = CreateDepthwiseConv2DOptions(flatBufferBuilder, - padding, - strideX, - strideY, - depth_multiplier, - fused_activation_function, - dilationX, - dilationY).Union(); - } - if(convolutionOperatorCode == tflite::BuiltinOperator_CONV_2D) - { - operatorBuiltinOptionsType = tflite::BuiltinOptions_Conv2DOptions; - operatorBuiltinOptions = CreateConv2DOptions(flatBufferBuilder, - padding, - strideX, - strideY, - fused_activation_function, - dilationX, - dilationY).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: Convolution2d Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, convolutionOperatorCode); - - 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 ConvolutionTest(tflite::BuiltinOperator convolutionOperatorCode, - tflite::TensorType tensorType, - uint32_t strideX, - uint32_t strideY, - uint32_t dilationX, - uint32_t dilationY, - 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 = CreateConv2dTfLiteModel(convolutionOperatorCode, - tensorType, - strideX, - strideY, - dilationX, - dilationY, - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[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); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(tfLiteDelagateOutputData[i] == armnnDelegateOutputData[i]); - CHECK(doctest::Approx(tfLiteDelagateOutputData[i]).epsilon(0.000001f) == expectedOutputValues[i]); - CHECK(doctest::Approx(armnnDelegateOutputData[i]).epsilon(0.000001f) == expectedOutputValues[i]); - } -} - -// 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); - 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, - uint32_t strideY, - tflite::Padding padding, - const std::vector <int32_t>& transposeTensorShape, - const std::vector <int32_t>& filterTensorShape, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - const std::vector <int32_t>& transposeData, - const std::vector <T>& filterData, - float filterScale = 1.0f, - int filterOffset = 0, - float outputQuantScale = 2.0f, - int outputQuantOffset = 0, - 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); - buffers[1] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(transposeData.data()), - sizeof(int32_t) * transposeData.size())); - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(filterData.data()), - sizeof(T) * filterData.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>({ filterScale }), - flatBufferBuilder.CreateVector<int64_t>({ filterOffset })); - - std::array<flatbuffers::Offset<Tensor>, 4> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(transposeTensorShape.data(), - transposeTensorShape.size()), - tflite::TensorType_INT32, - 1); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(filterTensorShape.data(), - filterTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("filter"), - filterQuantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - outputQuantizationParameters); - - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_TransposeConvOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateTransposeConvOptions(flatBufferBuilder, padding, strideX, strideY).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: TransposeConv Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_TRANSPOSE_CONV); - - 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 TransposeConvTest(std::vector<armnn::BackendId>& backends, - tflite::TensorType tensorType, - uint32_t strideX, - uint32_t strideY, - tflite::Padding padding, - const std::vector <int32_t>& transposeTensorShape, - const std::vector <int32_t>& filterTensorShape, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - const std::vector <int32_t>& transposeData, - const std::vector <T>& filterData, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - float filterScale = 1.0f, - int filterOffset = 0, - float outputQuantScale = 1.0f, - int outputQuantOffset = 0, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - - std::vector<char> modelBuffer; - modelBuffer = CreateTransposeConvTfLiteModel<T>(tensorType, - strideX, - strideY, - padding, - transposeTensorShape, - filterTensorShape, - inputTensorShape, - outputTensorShape, - transposeData, - filterData, - filterScale, - filterOffset, - outputQuantScale, - outputQuantOffset, - quantScale, - quantOffset); - - - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[2]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[2]; - 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); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(armnnDelegateOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelagateOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelagateOutputData[i] == armnnDelegateOutputData[i]); - } -} - -} // anonymous namespace - - - - diff --git a/delegate/src/test/DelegateOptionsTest.cpp b/delegate/src/test/DelegateOptionsTest.cpp deleted file mode 100644 index 98323131f9..0000000000 --- a/delegate/src/test/DelegateOptionsTest.cpp +++ /dev/null @@ -1,372 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "DelegateOptionsTestHelper.hpp" -#include <common/include/ProfilingGuid.hpp> -#include <armnnUtils/Filesystem.hpp> - -namespace armnnDelegate -{ - -TEST_SUITE("DelegateOptions") -{ - -TEST_CASE ("ArmnnDelegateOptimizerOptionsReduceFp32ToFp16") -{ - std::stringstream ss; - { - StreamRedirector redirect(std::cout, ss.rdbuf()); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 1, 2, 3, 4 }; - std::vector<float> divData = { 2, 2, 3, 4 }; - std::vector<float> expectedResult = { 1, 2, 2, 2 }; - - // Enable ReduceFp32ToFp16 - armnn::OptimizerOptions optimizerOptions(true, true, false, false); - armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); - - DelegateOptionTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); - } - // ReduceFp32ToFp16 option is enabled - CHECK(ss.str().find("convert_fp32_to_fp16") != std::string::npos); - CHECK(ss.str().find("convert_fp16_to_fp32") != std::string::npos); -} - -TEST_CASE ("ArmnnDelegateOptimizerOptionsDebug") -{ - std::stringstream ss; - { - StreamRedirector redirect(std::cout, ss.rdbuf()); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 1, 2, 3, 4 }; - std::vector<float> divData = { 2, 2, 3, 4 }; - std::vector<float> expectedResult = { 1, 2, 2, 2 }; - - // Enable Debug - armnn::OptimizerOptions optimizerOptions(false, true, false, false); - armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); - - DelegateOptionTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); - } - // Debug option triggered. - CHECK(ss.str().find("layerGuid") != std::string::npos); - CHECK(ss.str().find("layerName") != std::string::npos); - CHECK(ss.str().find("outputSlot") != std::string::npos); - CHECK(ss.str().find("shape") != std::string::npos); - CHECK(ss.str().find("data") != std::string::npos); -} - -TEST_CASE ("ArmnnDelegateOptimizerOptionsDebugFunction") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 1, 2, 3, 4 }; - std::vector<float> divData = { 2, 2, 3, 4 }; - std::vector<float> expectedResult = { 1, 2, 2, 2 }; - - // Enable debug with debug callback function - armnn::OptimizerOptions optimizerOptions(false, true, false, false); - bool callback = false; - auto mockCallback = [&](LayerGuid guid, unsigned int slotIndex, armnn::ITensorHandle* tensor) - { - armnn::IgnoreUnused(guid); - armnn::IgnoreUnused(slotIndex); - armnn::IgnoreUnused(tensor); - callback = true; - }; - - armnn::INetworkProperties networkProperties(false, armnn::MemorySource::Undefined, armnn::MemorySource::Undefined); - armnnDelegate::DelegateOptions delegateOptions(backends, - optimizerOptions, - armnn::EmptyOptional(), - armnn::Optional<armnn::DebugCallbackFunction>(mockCallback)); - - CHECK(!callback); - - DelegateOptionTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); - - // Check that the debug callback function was called. - CHECK(callback); -} - -TEST_CASE ("ArmnnDelegateOptimizerOptionsImport") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc, armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<uint8_t> inputData = { 1, 2, 3, 4 }; - std::vector<uint8_t> divData = { 2, 2, 3, 4 }; - std::vector<uint8_t> expectedResult = { 1, 2, 2, 2 }; - - armnn::OptimizerOptions optimizerOptions(false, false, false, true); - armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); - - DelegateOptionTest<uint8_t>(::tflite::TensorType_UINT8, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); -} - -TEST_CASE ("ArmnnDelegateStringParsingOptionDisableTfLiteRuntimeFallback") -{ - std::stringstream stringStream; - std::vector<std::string> keys { "backends", "debug-data", "disable-tflite-runtime-fallback"}; - std::vector<std::string> values { "CpuRef", "1", "1"}; - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 0.1f, -2.1f, 3.0f, -4.6f }; - std::vector<float> expectedResult = { 1.0f, -2.0f, 3.0f, -4.0f }; - - // Create options_keys and options_values char array - size_t num_options = keys.size(); - std::unique_ptr<const char*> options_keys = - std::unique_ptr<const char*>(new const char*[num_options + 1]); - std::unique_ptr<const char*> options_values = - std::unique_ptr<const char*>(new const char*[num_options + 1]); - for (size_t i=0; i<num_options; ++i) - { - options_keys.get()[i] = keys[i].c_str(); - options_values.get()[i] = values[i].c_str(); - } - - StreamRedirector redirect(std::cout, stringStream.rdbuf()); - - armnnDelegate::DelegateOptions delegateOptions(options_keys.get(), options_values.get(), num_options, nullptr); - DelegateOptionNoFallbackTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - expectedResult, - delegateOptions); - CHECK(stringStream.str().find("TfLiteArmnnDelegate: There are unsupported operators in the model") - != std::string::npos); -} - -TEST_CASE ("ArmnnDelegateStringParsingOptionEnableTfLiteRuntimeFallback") -{ - std::stringstream stringStream; - std::vector<std::string> keys { "backends", "debug-data", "disable-tflite-runtime-fallback"}; - std::vector<std::string> values { "CpuRef", "1", "0"}; - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 0.1f, -2.1f, 3.0f, -4.6f }; - std::vector<float> expectedResult = { 1.0f, -2.0f, 3.0f, -4.0f }; - - // Create options_keys and options_values char array - size_t num_options = keys.size(); - std::unique_ptr<const char*> options_keys = - std::unique_ptr<const char*>(new const char*[num_options + 1]); - std::unique_ptr<const char*> options_values = - std::unique_ptr<const char*>(new const char*[num_options + 1]); - for (size_t i=0; i<num_options; ++i) - { - options_keys.get()[i] = keys[i].c_str(); - options_values.get()[i] = values[i].c_str(); - } - - StreamRedirector redirect(std::cout, stringStream.rdbuf()); - - armnnDelegate::DelegateOptions delegateOptions(options_keys.get(), options_values.get(), num_options, nullptr); - DelegateOptionNoFallbackTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - expectedResult, - delegateOptions); - - CHECK(stringStream.str().find("TfLiteArmnnDelegate: There are unsupported operators in the model") - == std::string::npos); -} - -} - -TEST_SUITE("DelegateOptions_CpuAccTests") -{ - -TEST_CASE ("ArmnnDelegateModelOptions_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 1, 2, 3, 4 }; - std::vector<float> divData = { 2, 2, 3, 4 }; - std::vector<float> expectedResult = { 1, 2, 2, 2 }; - - unsigned int numberOfThreads = 2; - - armnn::ModelOptions modelOptions; - armnn::BackendOptions cpuAcc("CpuAcc", - { - { "FastMathEnabled", true }, - { "NumberOfThreads", numberOfThreads } - }); - modelOptions.push_back(cpuAcc); - - armnn::OptimizerOptions optimizerOptions(false, false, false, false, modelOptions, false); - armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); - - DelegateOptionTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); -} - -TEST_CASE ("ArmnnDelegateSerializeToDot") -{ - const fs::path filename(fs::temp_directory_path() / "ArmnnDelegateSerializeToDot.dot"); - if ( fs::exists(filename) ) - { - fs::remove(filename); - } - std::stringstream ss; - { - StreamRedirector redirect(std::cout, ss.rdbuf()); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 1, 2, 3, 4 }; - std::vector<float> divData = { 2, 2, 3, 4 }; - std::vector<float> expectedResult = { 1, 2, 2, 2 }; - - armnn::OptimizerOptions optimizerOptions(false, false, false, false); - armnnDelegate::DelegateOptions delegateOptions(backends, optimizerOptions); - // Enable serialize to dot by specifying the target file name. - delegateOptions.SetSerializeToDot(filename); - DelegateOptionTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); - } - CHECK(fs::exists(filename)); - // The file should have a size greater than 0 bytes. - CHECK(fs::file_size(filename) > 0); - // Clean up. - fs::remove(filename); -} - -void CreateFp16StringParsingTestRun(std::vector<std::string>& keys, - std::vector<std::string>& values, - std::stringstream& ss) -{ - StreamRedirector redirect(std::cout, ss.rdbuf()); - - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<int32_t> tensorShape { 1, 2, 2, 1 }; - std::vector<float> inputData = { 1, 2, 3, 4 }; - std::vector<float> divData = { 2, 2, 3, 4 }; - std::vector<float> expectedResult = { 1, 2, 2, 2 }; - - // Create options_keys and options_values char array - size_t num_options = keys.size(); - std::unique_ptr<const char*> options_keys = - std::unique_ptr<const char*>(new const char*[num_options + 1]); - std::unique_ptr<const char*> options_values = - std::unique_ptr<const char*>(new const char*[num_options + 1]); - for (size_t i=0; i<num_options; ++i) - { - options_keys.get()[i] = keys[i].c_str(); - options_values.get()[i] = values[i].c_str(); - } - - armnnDelegate::DelegateOptions delegateOptions(options_keys.get(), options_values.get(), num_options, nullptr); - DelegateOptionTest<float>(::tflite::TensorType_FLOAT32, - backends, - tensorShape, - inputData, - inputData, - divData, - expectedResult, - delegateOptions); -} - -TEST_CASE ("ArmnnDelegateStringParsingOptionReduceFp32ToFp16") -{ - SUBCASE("Fp16=1") - { - std::stringstream ss; - std::vector<std::string> keys { "backends", "debug-data", "reduce-fp32-to-fp16", "logging-severity"}; - std::vector<std::string> values { "CpuRef", "1", "1", "info"}; - CreateFp16StringParsingTestRun(keys, values, ss); - CHECK(ss.str().find("convert_fp32_to_fp16") != std::string::npos); - CHECK(ss.str().find("convert_fp16_to_fp32") != std::string::npos); - } - SUBCASE("Fp16=true") - { - std::stringstream ss; - std::vector<std::string> keys { "backends", "debug-data", "reduce-fp32-to-fp16"}; - std::vector<std::string> values { "CpuRef", "TRUE", "true"}; - CreateFp16StringParsingTestRun(keys, values, ss); - CHECK(ss.str().find("convert_fp32_to_fp16") != std::string::npos); - CHECK(ss.str().find("convert_fp16_to_fp32") != std::string::npos); - } - SUBCASE("Fp16=True") - { - std::stringstream ss; - std::vector<std::string> keys { "backends", "debug-data", "reduce-fp32-to-fp16"}; - std::vector<std::string> values { "CpuRef", "true", "True"}; - CreateFp16StringParsingTestRun(keys, values, ss); - CHECK(ss.str().find("convert_fp32_to_fp16") != std::string::npos); - CHECK(ss.str().find("convert_fp16_to_fp32") != std::string::npos); - } - SUBCASE("Fp16=0") - { - std::stringstream ss; - std::vector<std::string> keys { "backends", "debug-data", "reduce-fp32-to-fp16"}; - std::vector<std::string> values { "CpuRef", "true", "0"}; - CreateFp16StringParsingTestRun(keys, values, ss); - CHECK(ss.str().find("convert_fp32_to_fp16") == std::string::npos); - CHECK(ss.str().find("convert_fp16_to_fp32") == std::string::npos); - } - SUBCASE("Fp16=false") - { - std::stringstream ss; - std::vector<std::string> keys { "backends", "debug-data", "reduce-fp32-to-fp16"}; - std::vector<std::string> values { "CpuRef", "1", "false"}; - CreateFp16StringParsingTestRun(keys, values, ss); - CHECK(ss.str().find("convert_fp32_to_fp16") == std::string::npos); - CHECK(ss.str().find("convert_fp16_to_fp32") == std::string::npos); - } -} - -} - -} // namespace armnnDelegate diff --git a/delegate/src/test/DelegateOptionsTestHelper.hpp b/delegate/src/test/DelegateOptionsTestHelper.hpp deleted file mode 100644 index 00a3d95904..0000000000 --- a/delegate/src/test/DelegateOptionsTestHelper.hpp +++ /dev/null @@ -1,344 +0,0 @@ -// -// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn_delegate.hpp> - -#include "ConvolutionTestHelper.hpp" -#include "TestUtils.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 -{ - -struct StreamRedirector -{ -public: - StreamRedirector(std::ostream &stream, std::streambuf *newStreamBuffer) - : m_Stream(stream), m_BackupBuffer(m_Stream.rdbuf(newStreamBuffer)) {} - - ~StreamRedirector() { m_Stream.rdbuf(m_BackupBuffer); } - -private: - std::ostream &m_Stream; - std::streambuf *m_BackupBuffer; -}; - -std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType, - const std::vector<int32_t>& tensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - 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>(tensorShape.data(), - tensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("input_1"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("input_2"), - quantizationParameters); - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 4, - flatBufferBuilder.CreateString("add"), - quantizationParameters); - tensors[4] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 5, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions addBuiltinOptionsType = tflite::BuiltinOptions_AddOptions; - flatbuffers::Offset<void> addBuiltinOptions = - CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union(); - - tflite::BuiltinOptions divBuiltinOptionsType = tflite::BuiltinOptions_DivOptions; - flatbuffers::Offset<void> divBuiltinOptions = - CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union(); - - std::array<flatbuffers::Offset<Operator>, 2> operators; - const std::vector<int32_t> addInputs{0, 1}; - const std::vector<int32_t> addOutputs{3}; - operators[0] = CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(addInputs.data(), addInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(addOutputs.data(), addOutputs.size()), - addBuiltinOptionsType, - addBuiltinOptions); - const std::vector<int32_t> divInputs{3, 2}; - const std::vector<int32_t> divOutputs{4}; - operators[1] = CreateOperator(flatBufferBuilder, - 1, - flatBufferBuilder.CreateVector<int32_t>(divInputs.data(), divInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(divOutputs.data(), divOutputs.size()), - divBuiltinOptionsType, - divBuiltinOptions); - - const std::vector<int> subgraphInputs{0, 1, 2}; - const std::vector<int> subgraphOutputs{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(operators.data(), operators.size())); - - flatbuffers::Offset<flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Add and Div Operator Model"); - - std::array<flatbuffers::Offset<OperatorCode>, 2> codes; - codes[0] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_ADD); - codes[1] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_DIV); - - flatbuffers::Offset<Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(codes.data(), codes.size()), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -std::vector<char> CreateCeilTfLiteModel(tflite::TensorType tensorType, - const std::vector <int32_t>& tensorShape, - 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)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - const std::vector<int32_t> operatorInputs({0}); - const std::vector<int32_t> operatorOutputs({1}); - - flatbuffers::Offset<Operator> ceilOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_NONE); - - flatbuffers::Offset<flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: CEIL Operator Model"); - flatbuffers::Offset<OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_CEIL); - - const std::vector<int32_t> subgraphInputs({0}); - const std::vector<int32_t> 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(&ceilOperator, 1)); - - 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 DelegateOptionTest(tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& tensorShape, - std::vector<T>& input0Values, - std::vector<T>& input1Values, - std::vector<T>& input2Values, - std::vector<T>& expectedOutputValues, - const armnnDelegate::DelegateOptions& delegateOptions, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateAddDivTfLiteModel(tensorType, - tensorShape, - quantScale, - quantOffset); - - 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 - 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(tfLiteInterpreter, 0, input0Values); - armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values); - armnnDelegate::FillInput(tfLiteInterpreter, 2, input2Values); - - armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values); - armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values); - armnnDelegate::FillInput(armnnDelegateInterpreter, 2, input2Values); - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); -} - -template <typename T> -void DelegateOptionNoFallbackTest(tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& tensorShape, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - const armnnDelegate::DelegateOptions& delegateOptions, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateCeilTfLiteModel(tensorType, - tensorShape, - quantScale, - quantOffset); - - 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 - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - CHECK(theArmnnDelegate != nullptr); - // Modify armnnDelegateInterpreter to use armnnDelegate - try - { - armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()); - } - catch (const armnn::Exception& e) - { - // Forward the exception message to std::cout - std::cout << e.what() << std::endl; - } - - // Set input data - armnnDelegate::FillInput(tfLiteInterpreter, 0, inputValues); - armnnDelegate::FillInput(armnnDelegateInterpreter, 0, inputValues); - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/DepthwiseConvolution2dTest.cpp b/delegate/src/test/DepthwiseConvolution2dTest.cpp deleted file mode 100644 index ca10f2c0cb..0000000000 --- a/delegate/src/test/DepthwiseConvolution2dTest.cpp +++ /dev/null @@ -1,282 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ConvolutionTestHelper.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 DepthwiseConv2dValidReluFp32Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 2, 2 }; - std::vector<int32_t> filterShape { 1, 2, 2, 4 }; - std::vector<int32_t> biasShape { 4 }; - std::vector<int32_t> outputShape { 1, 3, 3, 1 }; - - static std::vector<float> inputValues = - { - 1, 2, 7, 8, - 3, 4, 9, 10, - 5, 6, 11, 12 - }; - - std::vector<float> filterValues = - { - 1, 2, 3, 4, - -9, 10, -11, 12, - 5, 6, 7, 8, - 13, -14, 15, -16 - }; - - std::vector<float> biasValues = { 1, 2, 3, 4 }; - - std::vector<float> expectedOutputValues = - { - 71, 0, 99, 0, - 91, 0, 127, 0 - }; - - tflite::Padding padding = tflite::Padding_VALID; - int32_t depth_multiplier = 2; - - ConvolutionTest<float>(tflite::BuiltinOperator_DEPTHWISE_CONV_2D, - ::tflite::TensorType_FLOAT32, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_RELU, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues, - {1.0f}, // biasScale - {0}, // biasOffset - {1.0f}, // filterScale - {0}, // filterOffsets - 2.0f, // outputQuantScale - 0, // outputQuantOffset - 1.0f, // quantScale - 0, // quantOffset - depth_multiplier); -} - -void DepthwiseConv2dSameUint8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 3, 1 }; - std::vector<int32_t> filterShape { 1, 3, 3, 1 }; - std::vector<int32_t> biasShape { 1 } ; - std::vector<int32_t> outputShape { 1, 3, 3, 1 }; - - static std::vector<uint8_t> inputValues = - { - 0, 1, 2, - 3, 4, 5, - 6, 7, 8 - }; - - std::vector<uint8_t> filterValues = { 9, 8, 7, 6, 5, 4, 3, 2, 1 }; - - std::vector<int32_t> biasValues = { 10 }; - - std::vector<uint8_t> expectedOutputValues = - { - 12, 23, 24, // ( 14+10)/2, ( 35+10)/2, ( 38+10)/2, - 34, 65, 61, // ( 57+10)/2, (120+10)/2, (111+10)/2, - 60, 104, 84 // (110+10)/2, (197+10)/2, (158+10)/2 - }; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_DEPTHWISE_CONV_2D, - ::tflite::TensorType_UINT8, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues); -} - -void DepthwiseConv2dSameInt8PerChannelTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 4, 4, 4 }; - std::vector<int32_t> filterShape { 1, 2, 2, 16 }; - std::vector<int32_t> biasShape {16} ; - std::vector<int32_t> outputShape { 1, 4, 4, 16 }; - - static std::vector<int8_t> inputValues = - { - 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3, - 3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4, - 3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0, - 4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2 - }; - - std::vector<int8_t> filterValues = { 12,20,10, 3, 2,24, 9,10, 5,16,30,12, 3,10, 4,32, - 8, 0,30, 3, 0,16,12,15,20,12, 0, 3, 9,20, 8, 8, - 12,15,20, 0, 0, 0, 3,15,15, 8,40,12, 9, 5, 2,24, - 4, 0, 0, 6, 6, 0, 3, 5,20, 8,20, 3, 6,15, 4, 0 }; - std::vector<float> filterScales = { 0.25, 0.2, 0.1, 0.3333333333, - 0.5, 0.125, 0.33333333, 0.2, - 0.2, 0.25, 0.1, 0.333333333, - 0.3333333333, 0.2, 0.5, 0.125 }; - - int32_t filterQuantizationDim = 3; - - int32_t depth_multiplier = 4; - - std::vector<int32_t> biasValues = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }; - - float inputScale = 1.0f; - std::vector<float> biasScales {}; - std::vector<int64_t> biasOffsets {}; - std::vector<int64_t> filterOffsets {}; - for (const auto& filterScale: filterScales) - { - biasScales.push_back(inputScale * filterScale); - // filter and bias offset always needs to be zero for per channel. We don't support anything else - biasOffsets.push_back(0); - filterOffsets.push_back(0); - } - - std::vector<int8_t> expectedOutputValues = - { - 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16, - 16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6, - 12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21, - 0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24, - 20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7, - 18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18, - 27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24, - 9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16, - 26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24, - 20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29, - 28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25, - 12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6, - 14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19, - 9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15, - 11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14, - 3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8 - }; - - tflite::Padding padding = tflite::Padding_SAME; - - ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_DEPTHWISE_CONV_2D, - ::tflite::TensorType_INT8, - 1, // strideX - 1, // strideY - 1, // dilationX - 1, // dilationY - padding, - tflite::ActivationFunctionType_NONE, - backends, - inputShape, - filterShape, - outputShape, - inputValues, - filterValues, - expectedOutputValues, - biasShape, - biasValues, - biasScales, - biasOffsets, - filterScales, - filterOffsets, - 1.0f, - 0, - inputScale, - 0, - depth_multiplier, - filterQuantizationDim); -} - -TEST_SUITE("DepthwiseConv2d_CpuRef_Tests") -{ - -TEST_CASE ("DepthwiseConv2d_Valid_Relu_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - DepthwiseConv2dValidReluFp32Test(backends); -} - -TEST_CASE ("DepthwiseConv2d_Same_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - DepthwiseConv2dSameUint8Test(backends); -} - -TEST_CASE ("DepthwiseConv2d_Same_Int8_PerChannelQuantization_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - DepthwiseConv2dSameInt8PerChannelTest(backends); -} - -}//End of TEST_SUITE("DepthwiseConv2d_CpuRef_Tests") - -TEST_SUITE("DepthwiseConv2d_CpuAcc_Tests") -{ - -TEST_CASE ("DepthwiseConv2d_Valid_Relu_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - DepthwiseConv2dValidReluFp32Test(backends); -} - -TEST_CASE ("DepthwiseConv2d_Same_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - DepthwiseConv2dSameUint8Test(backends); -} - -}//End of TEST_SUITE("DepthwiseConv2d_CpuAcc_Tests") - -TEST_SUITE("DepthwiseConv2d_GpuAcc_Tests") -{ - -TEST_CASE ("DepthwiseConv2d_Valid_Relu_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - DepthwiseConv2dValidReluFp32Test(backends); -} - -TEST_CASE ("DepthwiseConv2d_Same_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - DepthwiseConv2dSameUint8Test(backends); -} - -}//End of TEST_SUITE("DepthwiseConv2d_GpuAcc_Tests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ElementwiseBinaryTest.cpp b/delegate/src/test/ElementwiseBinaryTest.cpp deleted file mode 100644 index 8099efebff..0000000000 --- a/delegate/src/test/ElementwiseBinaryTest.cpp +++ /dev/null @@ -1,1136 +0,0 @@ -// -// Copyright © 2020-2021, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ElementwiseBinaryTestHelper.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 AddFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 3 }; - - std::vector<float> input0Values = - { - 0.0f, 2.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - - 1.0f, 2.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - - 0.0f, 2.0f, 1.0f, - 4.2f, 1.0f, 2.0f, - - 0.0f, 0.0f, 1.0f, - 0.2f, 1.0f, 2.0f, - }; - - std::vector<float> input1Values = - { - 1.0f, 2.0f, 1.0f, - 0.0f, 1.0f, 2.0f, - - 1.0f, 2.0f, -2.0f, - 0.2f, 1.0f, 2.0f, - - 0.0f, 2.0f, 1.0f, - 4.2f, 0.0f, -3.0f, - - 0.0f, 0.0f, 1.0f, - 0.7f, 1.0f, 5.0f, - }; - - std::vector<float> expectedOutputValues = - { - 1.0f, 4.0f, 2.0f, - 0.2f, 2.0f, 4.0f, - - 2.0f, 4.0f, -1.0f, - 0.4f, 2.0f, 4.0f, - - 0.0f, 4.0f, 2.0f, - 8.4f, 1.0f, -1.0f, - - 0.0f, 0.0f, 2.0f, - 0.9f, 2.0f, 7.0f, - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_ADD, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void AddBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 3, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 1, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 3, 2, 3 }; - - std::vector<float> input0Values - { - 0.0f, - 1.0f, - - 2.0f, - 3.0f, - - 4.0f, - 5.0f, - }; - std::vector<float> input1Values - { - 0.5f, 1.5f, 2.5f, - 3.5f, 4.5f, 5.5f, - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.5f, 1.5f, 2.5f, - 4.5f, 5.5f, 6.5f, - - 2.5f, 3.5f, 4.5f, - 6.5f, 7.5f, 8.5f, - - 4.5f, 5.5f, 6.5f, - 8.5f, 9.5f, 10.5f, - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_ADD, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void AddConstInputTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 3, 2, 1 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 3, 2, 1 }; - - std::vector<float> input0Values - { - 0.0f, - 1.0f, - - 2.0f, - 3.0f, - - 4.0f, - 5.0f, - }; - std::vector<float> input1Values - { - 0.5f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.5f, - 1.5f, - - 2.5f, - 3.5f, - - 4.5f, - 5.5f, - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_ADD, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - 1.0f, - 0, - true); -} - -void AddActivationTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> input0Values { 4.0f, 0.8f, 0.7f, -0.8f }; - std::vector<float> input1Values { 0.7f, -1.2f, 0.8f, 0.5f }; - std::vector<float> expectedOutputValues { 4.7f, 0.0f, 1.5f, 0.0f }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_ADD, - tflite::ActivationFunctionType_RELU, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void AddUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<uint8_t> input0Values = - { - 63, 35, 77, 70, 56, 112, - 203, 28, 252, 168, 245, 91 - }; - - std::vector<uint8_t> input1Values = - { - 21, 7, 175, 231, 175, 210, - 126, 161, 63, 21, 105, 126 - }; - - std::vector<uint8_t> expectedOutputValues = - { - 81, 39, 249, 255, 228, 255, - 255, 186, 255, 186, 255, 214, - }; - - ElementwiseBinaryTest<uint8_t>(tflite::BuiltinOperator_ADD, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, 7.0f, 3); -} - -void DivFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - 2.f, 2.f, 2.f, 2.f, 3.f, 3.f, 3.f, 3.f, - 4.f, 4.f, 4.f, 4.f, 5.f, 5.f, 5.f, 5.f - - }; - - std::vector<float> input1Values = - { - 1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f, - 4.f, 4.f, 4.f, 4.f, 4.f, 4.f, 4.f, 4.f - }; - - std::vector<float> expectedOutputValues = - { - 2.f, 2.f, 2.f, 2.f, 1.50f, 1.50f, 1.50f, 1.50f, - 1.f, 1.f, 1.f, 1.f, 1.25f, 1.25f, 1.25f, 1.25f - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_DIV, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void DivBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 2 }; - - std::vector<float> input0Values = { 2, 4, 6, 8, 10, 12, 14, 16 }; - std::vector<float> input1Values = { 2 }; - std::vector<float> expectedOutputValues = { 1, 2, 3, 4, 5, 6, 7, 8 }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_DIV, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void DivUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<uint8_t> input0Values = - { - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 - - }; - - std::vector<uint8_t> input1Values = - { - 1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 - }; - - std::vector<uint8_t> expectedOutputValues = - { - 8, 8, 8, 8, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5 - }; - - ElementwiseBinaryTest<uint8_t>(tflite::BuiltinOperator_DIV, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, 0.25f, 0); -} - -void FloorDivFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - -37.5f, -15.2f, -8.76f, -2.0f, -2.6f, -1.0f, -0.8f, 0.0f, - 4.0f, 1.6f, 2.0f, 5.2f, 6.0f, 35.04f, 60.8f, 150.0f - }; - - std::vector<float> input1Values = - { - 1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f, - 4.f, 4.f, 4.f, 4.f, 4.f, 4.f, 4.f, 4.f - }; - - std::vector<float> expectedOutputValues = - { - -38.0f, -16.0f, -9.0f, -2.0f, -2.0f, -1.0f, -1.0f, 0.0f, - 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 8.0f, 15.0f, 37.0f - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_FLOOR_DIV, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); - -} - -void MaxFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - 1.f, 1.f, 5.f, 1.f, 2.f, 2.f, 7.f, 2.f, - 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f - - }; - - std::vector<float> input1Values = - { - 2.f, 2.f, 2.f, 2.f, 3.f, 3.f, 3.f, 3.f, - 4.f, 4.f, 4.f, 4.f, 5.f, 5.f, 5.f, 5.f - }; - - std::vector<float> expectedOutputValues = - { - 2.f, 2.f, 5.f, 2.f, 3.f, 3.f, 7.f, 3.f, - 4.f, 4.f, 4.f, 4.f, 5.f, 5.f, 5.f, 5.f - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MAXIMUM, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void MaxBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 2 }; - - std::vector<float> input0Values = { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f }; - std::vector<float> input1Values = { 4.f }; - std::vector<float> expectedOutputValues = { 4.f, 4.f, 4.f, 4.f, 5.f, 6.f, 7.f, 8.f }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MAXIMUM, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void MaxUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<uint8_t> input0Values = - { - 1, 1, 1, 1, 7, 8, 9, 9, - 3, 3, 3, 3, 4, 4, 4, 4 - - }; - - std::vector<uint8_t> input1Values = - { - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 - }; - - std::vector<uint8_t> expectedOutputValues = - { - 2, 2, 2, 2, 7, 8, 9, 9, - 4, 4, 4, 4, 5, 5, 5, 5 - }; - - ElementwiseBinaryTest<uint8_t>(tflite::BuiltinOperator_MAXIMUM, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, 1.0f, 0); -} - -void MinFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - 1.f, 1.f, 5.f, 1.f, 2.f, 2.f, 7.f, 2.f, - 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f - - }; - - std::vector<float> input1Values = - { - 2.f, 2.f, 2.f, 2.f, 3.f, 3.f, 3.f, 3.f, - 1.f, 1.f, 1.f, 1.f, 5.f, 5.f, 5.f, 5.f - }; - - std::vector<float> expectedOutputValues = - { - 1.f, 1.f, 2.f, 1.f, 2.f, 2.f, 3.f, 2.f, - 1.f, 1.f, 1.f, 1.f, 4.f, 4.f, 4.f, 4.f - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MINIMUM, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void MinBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 2 }; - - std::vector<float> input0Values = { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f }; - - std::vector<float> input1Values = { 4.f }; - - std::vector<float> expectedOutputValues = { 1.f, 2.f, 3.f, 4.f, 4.f, 4.f, 4.f, 4.f }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MINIMUM, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void MinUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<uint8_t> input0Values = - { - 1, 1, 1, 1, 7, 8, 9, 9, - 3, 3, 3, 3, 4, 4, 4, 4 - - }; - - std::vector<uint8_t> input1Values = - { - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 - }; - - std::vector<uint8_t> expectedOutputValues = - { - 1, 1, 1, 1, 3, 3, 3, 3, - 3, 3, 3, 3, 4, 4, 4, 4 - }; - - ElementwiseBinaryTest<uint8_t>(tflite::BuiltinOperator_MINIMUM, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, 1.0f, 0); -} - -void MulFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 2, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 2, 2, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 2, 2, 2, 2 }; - - std::vector<float> input0Values = - { - 1.f, 1.f, 1.f, 1.f, 2.f, 2.f, 2.f, 2.f, - 3.f, 3.f, 3.f, 3.f, 4.f, 4.f, 4.f, 4.f - - }; - - std::vector<float> input1Values = - { - 2.f, 2.f, 2.f, 2.f, 3.f, 3.f, 3.f, 3.f, - 4.f, 4.f, 4.f, 4.f, 5.f, 5.f, 5.f, 5.f - }; - - std::vector<float> expectedOutputValues = - { - 2.f, 2.f, 2.f, 2.f, 6.f, 6.f, 6.f, 6.f, - 12.f, 12.f, 12.f, 12.f, 20.f, 20.f, 20.f, 20.f - }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MUL, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void MulBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 2 }; - - std::vector<float> input0Values = { 2, 4, 6, 8, 10, 12, 14, 16 }; - std::vector<float> input1Values = { 2 }; - std::vector<float> expectedOutputValues = { 4, 8, 12, 16, 20, 24, 28, 32 }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MUL, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void MulUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 3 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 3 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 3 }; - - std::vector<uint8_t> input0Values = - { - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - - }; - - std::vector<uint8_t> input1Values = { 1, 2, 3 }; - - std::vector<uint8_t> expectedOutputValues = - { - 1, 4, 9, 4, 10, 18, - 7, 16, 27, 10, 22, 36 - }; - - ElementwiseBinaryTest<uint8_t>(tflite::BuiltinOperator_MUL, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, 1.0f, 0); -} - -void MulActivationTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2, 1 }; - std::vector<int32_t> input1Shape { 1, 2, 2, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2, 1 }; - - std::vector<float> input0Values { 4.0f, 0.0f, 1.0f, 0.5f }; - std::vector<float> input1Values { -2.0f, -1.2f, 2.5f, 2.0f }; - std::vector<float> expectedOutputValues { 0.0f, 0.0f, 2.5f, 1.0f }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_MUL, - tflite::ActivationFunctionType_RELU, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void SubFP32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 2, 2 }; - - std::vector<float> input0Values = { 1, 3, 3, -7 }; - std::vector<float> input1Values = { 1, -1, 0, -2 }; - std::vector<float> expectedOutputValues = { 0, 4, 3, -5 }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_SUB, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void SubBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 2, 2 }; - - std::vector<float> input0Values = { 2, 3, 4, 5}; - std::vector<float> input1Values = { 10 }; - std::vector<float> expectedOutputValues = { -8, -7, -6, -5 }; - - ElementwiseBinaryTest<float>(tflite::BuiltinOperator_SUB, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void SubUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 2, 2 }; - - std::vector<uint8_t> input0Values = { 10, 12, 14, 16 }; - std::vector<uint8_t> input1Values = { 2 }; - std::vector<uint8_t> expectedOutputValues = { 8, 10, 12, 14 }; - - ElementwiseBinaryTest<uint8_t>(tflite::BuiltinOperator_SUB, - tflite::ActivationFunctionType_NONE, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, 1.0f, 0); -} - -TEST_SUITE("ElementwiseBinary_GpuAccTests") -{ - -TEST_CASE ("ADD_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AddFP32Test(backends); -} - -TEST_CASE ("ADD_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AddBroadcastTest(backends); -} - -TEST_CASE ("ADD_Activation_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AddActivationTest(backends); -} - -TEST_CASE ("ADD_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AddUint8Test(backends); -} - -TEST_CASE ("DIV_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DivFP32Test(backends); -} - -TEST_CASE ("DIV_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DivBroadcastTest(backends); -} - -TEST_CASE ("FLOORDIV_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - FloorDivFP32Test(backends); -} - -TEST_CASE ("MAX_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxFP32Test(backends); -} - -TEST_CASE ("MAX_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxBroadcastTest(backends); -} - -TEST_CASE ("MAX_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxUint8Test(backends); -} - -TEST_CASE ("MIN_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MinFP32Test(backends); -} - -TEST_CASE ("MIN_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MinBroadcastTest(backends); -} - -TEST_CASE ("MIN_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MinUint8Test(backends); -} - -TEST_CASE ("MUL_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MulFP32Test(backends); -} - -TEST_CASE ("MUL_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MulBroadcastTest(backends); -} - -TEST_CASE ("MUL_Activation_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MulActivationTest(backends); -} - -TEST_CASE ("MUL_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MulUint8Test(backends); -} - -TEST_CASE ("SUB_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - SubFP32Test(backends); -} - -TEST_CASE ("SUB_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - SubBroadcastTest(backends); -} - -TEST_CASE ("SUB_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - SubUint8Test(backends); -} - -} //TEST_SUITE("ElementwiseBinary_GpuAccTests") - - - -TEST_SUITE("ElementwiseBinary_CpuAccTests") -{ - -TEST_CASE ("ADD_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AddFP32Test(backends); -} - -TEST_CASE ("ADD_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AddBroadcastTest(backends); -} - -TEST_CASE ("ADD_Activation_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AddActivationTest(backends); -} - -TEST_CASE ("ADD_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AddUint8Test(backends); -} - -TEST_CASE ("DIV_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DivFP32Test(backends); -} - -TEST_CASE ("DIV_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DivBroadcastTest(backends); -} - -TEST_CASE ("FLOORDIV_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - FloorDivFP32Test(backends); -} - -TEST_CASE ("MAX_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxFP32Test(backends); -} - -TEST_CASE ("MAX_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxBroadcastTest(backends); -} - -TEST_CASE ("MAX_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxUint8Test(backends); -} - -TEST_CASE ("MIN_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MinFP32Test(backends); -} - -TEST_CASE ("MIN_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MinBroadcastTest(backends); -} - -TEST_CASE ("MIN_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MinUint8Test(backends); -} - -TEST_CASE ("MUL_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MulFP32Test(backends); -} - -TEST_CASE ("MUL_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MulBroadcastTest(backends); -} - -TEST_CASE ("MUL_Actiation_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MulActivationTest(backends); -} - -TEST_CASE ("MUL_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MulUint8Test(backends); -} - -TEST_CASE ("SUB_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - SubFP32Test(backends); -} - -TEST_CASE ("SUB_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - SubBroadcastTest(backends); -} - -TEST_CASE ("SUB_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - SubUint8Test(backends); -} - -} // TEST_SUITE("ElementwiseBinary_CpuAccTests") - - -TEST_SUITE("ElementwiseBinary_CpuRefTests") -{ - -TEST_CASE ("ADD_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AddFP32Test(backends); -} - -TEST_CASE ("ADD_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AddBroadcastTest(backends); -} - -TEST_CASE ("ADD_Constant_Input_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AddConstInputTest(backends); -} - -TEST_CASE ("ADD_Activation_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AddActivationTest(backends); -} - -TEST_CASE ("ADD_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AddUint8Test(backends); -} - -TEST_CASE ("DIV_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DivFP32Test(backends); -} - -TEST_CASE ("DIV_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DivBroadcastTest(backends); -} - -TEST_CASE ("FLOORDIV_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FloorDivFP32Test(backends); -} - -TEST_CASE ("DIV_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DivUint8Test(backends); -} - -TEST_CASE ("MAX_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxFP32Test(backends); -} - -TEST_CASE ("MAX_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxBroadcastTest(backends); -} - -TEST_CASE ("MAX_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxUint8Test(backends); -} - -TEST_CASE ("MIN_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MinFP32Test(backends); -} - -TEST_CASE ("MIN_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MinBroadcastTest(backends); -} - -TEST_CASE ("MIN_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MinUint8Test(backends); -} - -TEST_CASE ("MUL_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MulFP32Test(backends); -} - -TEST_CASE ("MUL_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MulBroadcastTest(backends); -} - -TEST_CASE ("MUL_Actiation_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MulActivationTest(backends); -} - -TEST_CASE ("MUL_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MulUint8Test(backends); -} - -TEST_CASE ("SUB_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - SubFP32Test(backends); -} - -TEST_CASE ("SUB_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - SubBroadcastTest(backends); -} - -TEST_CASE ("SUB_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - SubUint8Test(backends); -} - -} // TEST_SUITE("ElementwiseBinary_CpuRefTests") - -} // namespace armnnDelegate diff --git a/delegate/src/test/ElementwiseBinaryTestHelper.hpp b/delegate/src/test/ElementwiseBinaryTestHelper.hpp deleted file mode 100644 index 09a715e7f1..0000000000 --- a/delegate/src/test/ElementwiseBinaryTestHelper.hpp +++ /dev/null @@ -1,243 +0,0 @@ -// -// Copyright © 2020, 2023 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 -{ - -template <typename T> -std::vector<char> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode, - tflite::ActivationFunctionType activationType, - tflite::TensorType tensorType, - const std::vector <int32_t>& input0TensorShape, - const std::vector <int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape, - std::vector<T>& input1Values, - bool constantInput = false, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - if (constantInput) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()), - sizeof(T) * input1Values.size()))); - } - else - { - buffers.push_back(CreateBuffer(flatBufferBuilder)); - } - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), - input1TensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("input_1"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; - flatbuffers::Offset<void> operatorBuiltinOptions = 0; - switch (binaryOperatorCode) - { - case BuiltinOperator_ADD: - { - operatorBuiltinOptionsType = BuiltinOptions_AddOptions; - operatorBuiltinOptions = CreateAddOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_DIV: - { - operatorBuiltinOptionsType = BuiltinOptions_DivOptions; - operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_MAXIMUM: - { - operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; - operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_MINIMUM: - { - operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions; - operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_MUL: - { - operatorBuiltinOptionsType = BuiltinOptions_MulOptions; - operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_SUB: - { - operatorBuiltinOptionsType = BuiltinOptions_SubOptions; - operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union(); - break; - } - case BuiltinOperator_FLOOR_DIV: - { - operatorBuiltinOptionsType = tflite::BuiltinOptions_FloorDivOptions; - operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union(); - break; - } - default: - break; - } - const std::vector<int32_t> operatorInputs{0, 1}; - const std::vector<int32_t> operatorOutputs{2}; - flatbuffers::Offset <Operator> elementwiseBinaryOperator = - 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}; - 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(&elementwiseBinaryOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Binary Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, binaryOperatorCode); - - 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 ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode, - tflite::ActivationFunctionType activationType, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& outputShape, - std::vector<T>& input0Values, - std::vector<T>& input1Values, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0, - bool constantInput = false) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode, - activationType, - tensorType, - input0Shape, - input1Shape, - outputShape, - input1Values, - constantInput, - quantScale, - quantOffset); - - 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, input0Values); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); - if (!constantInput) - { - armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, input1Values); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, input1Values); - } - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - outputShape, - expectedOutputValues); - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/ElementwiseUnaryTest.cpp b/delegate/src/test/ElementwiseUnaryTest.cpp deleted file mode 100644 index 4d48d6e2ed..0000000000 --- a/delegate/src/test/ElementwiseUnaryTest.cpp +++ /dev/null @@ -1,420 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ElementwiseUnaryTestHelper.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 -{ - -TEST_SUITE("ElementwiseUnary_GpuAccTests") -{ - -TEST_CASE ("Abs_Float32_GpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - // Set input data - std::vector<float> inputValues - { - -0.1f, -0.2f, -0.3f, - 0.1f, 0.2f, 0.3f - }; - // Calculate output data - std::vector<float> expectedOutputValues(inputValues.size()); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - expectedOutputValues[i] = std::abs(inputValues[i]); - } - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_ABS, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Exp_Float32_GpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - // Set input data - std::vector<float> inputValues - { - 5.0f, 4.0f, - 3.0f, 2.0f, - 1.0f, 1.1f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 148.413159102577f, 54.598150033144f, - 20.085536923188f, 7.389056098931f, - 2.718281828459f, 3.004166023946f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_EXP, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Log_Float32_GpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - // Set input data - std::vector<float> inputValues - { - 1.0f, 1.0f, 2.0f, - 3.0f, 4.0f, 2.71828f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.f, 0.f, 0.69314718056f, - 1.09861228867f, 1.38629436112f, 0.99999932734f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_LOG, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Neg_Float32_GpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - // Set input data - std::vector<float> inputValues - { - 1.f, 0.f, 3.f, - 25.f, 64.f, 100.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - -1.f, 0.f, -3.f, - -25.f, -64.f, -100.f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_NEG, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Rsqrt_Float32_GpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - // Set input data - std::vector<float> inputValues - { - 1.f, 4.f, 16.f, - 25.f, 64.f, 100.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 1.f, 0.5f, 0.25f, - 0.2f, 0.125f, 0.1f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_RSQRT, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Sin_Float32_GpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - // Set input data - std::vector<float> inputValues - { - 0.0f, 1.0f, 16.0f, - 0.5f, 36.0f, -1.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.0f, 0.8414709848f, -0.28790331666f, - 0.4794255386f, -0.99177885344f, -0.8414709848f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_SIN, backends, inputValues, expectedOutputValues); -} -} // TEST_SUITE("ElementwiseUnary_GpuAccTests") - - - -TEST_SUITE("ElementwiseUnary_CpuAccTests") -{ - -TEST_CASE ("Abs_Float32_CpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - // Set input data - std::vector<float> inputValues - { - -0.1f, -0.2f, -0.3f, - 0.1f, 0.2f, 0.3f - }; - // Calculate output data - std::vector<float> expectedOutputValues(inputValues.size()); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - expectedOutputValues[i] = std::abs(inputValues[i]); - } - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_ABS, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Exp_Float32_CpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - // Set input data - std::vector<float> inputValues - { - 5.0f, 4.0f, - 3.0f, 2.0f, - 1.0f, 1.1f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 148.413159102577f, 54.598150033144f, - 20.085536923188f, 7.389056098931f, - 2.718281828459f, 3.004166023946f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_EXP, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Log_Float32_CpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - // Set input data - std::vector<float> inputValues - { - 1.0f, 1.0f, 2.0f, - 3.0f, 4.0f, 2.71828f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.f, 0.f, 0.69314718056f, - 1.09861228867f, 1.38629436112f, 0.99999932734f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_LOG, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Neg_Float32_CpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - // Set input data - std::vector<float> inputValues - { - 1.f, 0.f, 3.f, - 25.f, 64.f, 100.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - -1.f, 0.f, -3.f, - -25.f, -64.f, -100.f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_NEG, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Rsqrt_Float32_CpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - // Set input data - std::vector<float> inputValues - { - 1.f, 4.f, 16.f, - 25.f, 64.f, 100.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 1.f, 0.5f, 0.25f, - 0.2f, 0.125f, 0.1f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_RSQRT, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Sin_Float32_CpuAcc_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - // Set input data - std::vector<float> inputValues - { - 0.0f, 1.0f, 16.0f, - 0.5f, 36.0f, -1.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.0f, 0.8414709848f, -0.28790331666f, - 0.4794255386f, -0.99177885344f, -0.8414709848f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_SIN, backends, inputValues, expectedOutputValues); -} -} // TEST_SUITE("ElementwiseUnary_CpuAccTests") - -TEST_SUITE("ElementwiseUnary_CpuRefTests") -{ - -TEST_CASE ("Abs_Float32_CpuRef_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - -0.1f, -0.2f, -0.3f, - 0.1f, 0.2f, 0.3f - }; - // Calculate output data - std::vector<float> expectedOutputValues(inputValues.size()); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - expectedOutputValues[i] = std::abs(inputValues[i]); - } - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_ABS, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Exp_Float32_CpuRef_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - 5.0f, 4.0f, - 3.0f, 2.0f, - 1.0f, 1.1f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 148.413159102577f, 54.598150033144f, - 20.085536923188f, 7.389056098931f, - 2.718281828459f, 3.004166023946f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_EXP, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Log_Float32_CpuRef_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - 1.0f, 1.0f, 2.0f, - 3.0f, 4.0f, 2.71828f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.f, 0.f, 0.69314718056f, - 1.09861228867f, 1.38629436112f, 0.99999932734f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_LOG, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Neg_Float32_CpuRef_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - 1.f, 0.f, 3.f, - 25.f, 64.f, 100.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - -1.f, 0.f, -3.f, - -25.f, -64.f, -100.f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_NEG, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Rsqrt_Float32_CpuRef_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - 1.f, 4.f, 16.f, - 25.f, 64.f, 100.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 1.f, 0.5f, 0.25f, - 0.2f, 0.125f, 0.1f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_RSQRT, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Sqrt_Float32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - 9.0f, 4.25f, 81.9f, - 0.1f, 0.9f, 169.0f - }; - // Calculate output data - std::vector<float> expectedOutputValues(inputValues.size()); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - expectedOutputValues[i] = std::sqrt(inputValues[i]); - } - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_SQRT, backends, inputValues, expectedOutputValues); -} - -TEST_CASE ("Sin_Float32_CpuRef_Test") -{ - // Create the ArmNN Delegate - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - // Set input data - std::vector<float> inputValues - { - 0.0f, 1.0f, 16.0f, - 0.5f, 36.0f, -1.f - }; - // Set output data - std::vector<float> expectedOutputValues - { - 0.0f, 0.8414709848f, -0.28790331666f, - 0.4794255386f, -0.99177885344f, -0.8414709848f - }; - - ElementwiseUnaryFP32Test(tflite::BuiltinOperator_SIN, backends, inputValues, expectedOutputValues); -} -} // TEST_SUITE("ElementwiseUnary_CpuRefTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ElementwiseUnaryTestHelper.hpp b/delegate/src/test/ElementwiseUnaryTestHelper.hpp deleted file mode 100644 index 230d0fcca5..0000000000 --- a/delegate/src/test/ElementwiseUnaryTestHelper.hpp +++ /dev/null @@ -1,189 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateElementwiseUnaryTfLiteModel(tflite::BuiltinOperator unaryOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& tensorShape) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), - tensorType); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), tensorShape.size()), - tensorType); - - // create operator - const std::vector<int> operatorInputs{0}; - const std::vector<int> operatorOutputs{1}; - flatbuffers::Offset <Operator> unaryOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); - - 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(&unaryOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Unary Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, unaryOperatorCode); - - 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()); -} - -void ElementwiseUnaryFP32Test(tflite::BuiltinOperator unaryOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<float>& inputValues, - std::vector<float>& expectedOutputValues) -{ - using namespace tflite; - std::vector<int32_t> inputShape { { 3, 1, 2} }; - std::vector<char> modelBuffer = CreateElementwiseUnaryTfLiteModel(unaryOperatorCode, - ::tflite::TensorType_FLOAT32, - inputShape); - - 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(armnnDelegateInterpreter, 0, inputValues); - armnnDelegate::FillInput(tfLiteInterpreter, 0, inputValues); - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, inputShape, expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); - tfLiteInterpreter.reset(nullptr); -} - -void ElementwiseUnaryBoolTest(tflite::BuiltinOperator unaryOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<bool>& inputValues, - std::vector<bool>& expectedOutputValues) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateElementwiseUnaryTfLiteModel(unaryOperatorCode, - ::tflite::TensorType_BOOL, - inputShape); - - 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(armnnDelegateInterpreter, 0, inputValues); - armnnDelegate::FillInput(tfLiteInterpreter, 0, inputValues); - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function - // directly instead. This is because Boolean types get converted to a bit representation in a vector. - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<bool>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<bool>(armnnDelegateOutputId); - - armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); - - armnnDelegateInterpreter.reset(nullptr); - tfLiteInterpreter.reset(nullptr); -} - -} // anonymous namespace - - - - diff --git a/delegate/src/test/FillTest.cpp b/delegate/src/test/FillTest.cpp deleted file mode 100644 index 50f7f53d56..0000000000 --- a/delegate/src/test/FillTest.cpp +++ /dev/null @@ -1,221 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "FillTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void Fill2dTest(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator fillOperatorCode = tflite::BuiltinOperator_FILL, - float fill = 2.0f ) -{ - std::vector<int32_t> inputShape { 2 }; - std::vector<int32_t> tensorShape { 2, 2 }; - std::vector<float> expectedOutputValues = { fill, fill, - fill, fill }; - - FillTest<float>(fillOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - tensorShape, - expectedOutputValues, - fill); -} - -void Fill3dTest(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator fillOperatorCode = tflite::BuiltinOperator_FILL, - float fill = 5.0f ) -{ - std::vector<int32_t> inputShape { 3 }; - std::vector<int32_t> tensorShape { 3, 3, 3 }; - std::vector<float> expectedOutputValues = { fill, fill, fill, - fill, fill, fill, - fill, fill, fill, - - fill, fill, fill, - fill, fill, fill, - fill, fill, fill, - - fill, fill, fill, - fill, fill, fill, - fill, fill, fill }; - - FillTest<float>(fillOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - tensorShape, - expectedOutputValues, - fill); -} - -void Fill4dTest(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator fillOperatorCode = tflite::BuiltinOperator_FILL, - float fill = 3.0f ) -{ - std::vector<int32_t> inputShape { 4 }; - std::vector<int32_t> tensorShape { 2, 2, 4, 4 }; - std::vector<float> expectedOutputValues = { fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill, - fill, fill, fill, fill }; - - FillTest<float>(fillOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - tensorShape, - expectedOutputValues, - fill); -} - -void FillInt32Test(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator fillOperatorCode = tflite::BuiltinOperator_FILL, - int32_t fill = 2 ) -{ - std::vector<int32_t> inputShape { 2 }; - std::vector<int32_t> tensorShape { 2, 2 }; - std::vector<int32_t> expectedOutputValues = { fill, fill, - fill, fill }; - - FillTest<int32_t>(fillOperatorCode, - ::tflite::TensorType_INT32, - backends, - inputShape, - tensorShape, - expectedOutputValues, - fill); -} - -TEST_SUITE("Fill_CpuRefTests") -{ - -TEST_CASE ("Fill2d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Fill2dTest(backends); -} - -TEST_CASE ("Fill3d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Fill3dTest(backends); -} - -TEST_CASE ("Fill3d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Fill3dTest(backends); -} - -TEST_CASE ("Fill4d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Fill4dTest(backends); -} - -TEST_CASE ("FillInt32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FillInt32Test(backends); -} - -} - -TEST_SUITE("Fill_CpuAccTests") -{ - -TEST_CASE ("Fill2d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Fill2dTest(backends); -} - -TEST_CASE ("Fill3d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Fill3dTest(backends); -} - -TEST_CASE ("Fill3d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Fill3dTest(backends); -} - -TEST_CASE ("Fill4d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Fill4dTest(backends); -} - -TEST_CASE ("FillInt32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - FillInt32Test(backends); -} - -} - -TEST_SUITE("Fill_GpuAccTests") -{ - -TEST_CASE ("Fill2d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Fill2dTest(backends); -} - -TEST_CASE ("Fill3d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Fill3dTest(backends); -} - -TEST_CASE ("Fill3d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Fill3dTest(backends); -} - -TEST_CASE ("Fill4d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Fill4dTest(backends); -} - -TEST_CASE ("FillInt32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - FillInt32Test(backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/FillTestHelper.hpp b/delegate/src/test/FillTestHelper.hpp deleted file mode 100644 index 8479b72730..0000000000 --- a/delegate/src/test/FillTestHelper.hpp +++ /dev/null @@ -1,159 +0,0 @@ -// -// Copyright © 2021, 2023 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 -{ - -template <typename T> -std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputShape, - const std::vector <int32_t>& tensorShape, - const std::vector<T> fillValue) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(tensorShape.data()), - sizeof(int32_t) * tensorShape.size()))); - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(fillValue.data()), - sizeof(T) * fillValue.size()))); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), - inputShape.size()), - tflite::TensorType_INT32, - 1, - flatBufferBuilder.CreateString("dims")); - - std::vector<int32_t> fillShape = {}; - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(fillShape.data(), - fillShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("value")); - - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output")); - - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FillOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateFillOptions(flatBufferBuilder).Union(); - - // create operator - const std::vector<int> operatorInputs{ {0, 1} }; - const std::vector<int> operatorOutputs{ 2 }; - flatbuffers::Offset <Operator> fillOperator = - 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 }; - 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(&fillOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Fill Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - fillOperatorCode); - - 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 FillTest(tflite::BuiltinOperator fillOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - std::vector<int32_t >& inputShape, - std::vector<int32_t >& tensorShape, - std::vector<T>& expectedOutputValues, - T fillValue) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateFillTfLiteModel<T>(fillOperatorCode, - tensorType, - inputShape, - tensorShape, - {fillValue}); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - - 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); - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, tensorShape, expectedOutputValues); -} - -} // anonymous namespace diff --git a/delegate/src/test/FullyConnectedTest.cpp b/delegate/src/test/FullyConnectedTest.cpp deleted file mode 100644 index 3ef5cedbd7..0000000000 --- a/delegate/src/test/FullyConnectedTest.cpp +++ /dev/null @@ -1,178 +0,0 @@ -// -// Copyright © 2020-2021,2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "FullyConnectedTestHelper.hpp" - -namespace -{ - -void FullyConnectedFp32Test(std::vector<armnn::BackendId>& backends, bool constantWeights = true) -{ - std::vector<int32_t> inputTensorShape { 1, 4, 1, 1 }; - std::vector<int32_t> weightsTensorShape { 1, 4 }; - std::vector<int32_t> biasTensorShape { 1 }; - std::vector<int32_t> outputTensorShape { 1, 1 }; - - std::vector<float> inputValues = { 10, 20, 30, 40 }; - std::vector<float> weightsData = { 2, 3, 4, 5 }; - - std::vector<float> expectedOutputValues = { (400 + 10) }; - - // bias is set std::vector<float> biasData = { 10 } in the model - FullyConnectedTest<float>(backends, - ::tflite::TensorType_FLOAT32, - tflite::ActivationFunctionType_NONE, - inputTensorShape, - weightsTensorShape, - biasTensorShape, - outputTensorShape, - inputValues, - expectedOutputValues, - weightsData, - constantWeights); -} - -void FullyConnectedActivationTest(std::vector<armnn::BackendId>& backends, bool constantWeights = true) -{ - std::vector<int32_t> inputTensorShape { 1, 4, 1, 1 }; - std::vector<int32_t> weightsTensorShape { 1, 4 }; - std::vector<int32_t> biasTensorShape { 1 }; - std::vector<int32_t> outputTensorShape { 1, 1 }; - - std::vector<float> inputValues = { -10, 20, 30, 40 }; - std::vector<float> weightsData = { 2, 3, 4, -5 }; - - std::vector<float> expectedOutputValues = { 0 }; - - // bias is set std::vector<float> biasData = { 10 } in the model - FullyConnectedTest<float>(backends, - ::tflite::TensorType_FLOAT32, - tflite::ActivationFunctionType_RELU, - inputTensorShape, - weightsTensorShape, - biasTensorShape, - outputTensorShape, - inputValues, - expectedOutputValues, - weightsData, - constantWeights); -} - -void FullyConnectedInt8Test(std::vector<armnn::BackendId>& backends, bool constantWeights = true) -{ - std::vector<int32_t> inputTensorShape { 1, 4, 2, 1 }; - std::vector<int32_t> weightsTensorShape { 1, 4 }; - std::vector<int32_t> biasTensorShape { 1 }; - std::vector<int32_t> outputTensorShape { 2, 1 }; - - std::vector<int8_t> inputValues = { 1, 2, 3, 4, 5, 10, 15, 20 }; - std::vector<int8_t> weightsData = { 2, 3, 4, 5 }; - - std::vector<int8_t> expectedOutputValues = { 25, 105 }; // (40 + 10) / 2, (200 + 10) / 2 - - // bias is set std::vector<int32_t> biasData = { 10 } in the model - // input and weights quantization scale 1.0f and offset 0 in the model - // output quantization scale 2.0f and offset 0 in the model - FullyConnectedTest<int8_t>(backends, - ::tflite::TensorType_INT8, - tflite::ActivationFunctionType_NONE, - inputTensorShape, - weightsTensorShape, - biasTensorShape, - outputTensorShape, - inputValues, - expectedOutputValues, - weightsData, - constantWeights); -} - -TEST_SUITE("FullyConnected_GpuAccTests") -{ - -TEST_CASE ("FullyConnected_FP32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - FullyConnectedFp32Test(backends); -} - -TEST_CASE ("FullyConnected_Int8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - FullyConnectedInt8Test(backends); -} - -TEST_CASE ("FullyConnected_Activation_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - FullyConnectedActivationTest(backends); -} - -} // End of TEST_SUITE("FullyConnected_GpuAccTests") - -TEST_SUITE("FullyConnected_CpuAccTests") -{ - -TEST_CASE ("FullyConnected_FP32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - FullyConnectedFp32Test(backends); -} - -TEST_CASE ("FullyConnected_Int8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - FullyConnectedInt8Test(backends); -} - -TEST_CASE ("FullyConnected_Activation_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - FullyConnectedActivationTest(backends); -} - -} // End of TEST_SUITE("FullyConnected_CpuAccTests") - -TEST_SUITE("FullyConnected_CpuRefTests") -{ - -TEST_CASE ("FullyConnected_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FullyConnectedFp32Test(backends); -} - -TEST_CASE ("FullyConnected_Int8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FullyConnectedInt8Test(backends); -} - -TEST_CASE ("FullyConnected_Activation_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FullyConnectedActivationTest(backends); -} - -TEST_CASE ("FullyConnected_Weights_As_Inputs_FP32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FullyConnectedFp32Test(backends, false); -} - -TEST_CASE ("FullyConnected_Weights_As_Inputs_Int8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FullyConnectedInt8Test(backends, false); -} - -TEST_CASE ("FullyConnected_Weights_As_Inputs_Activation_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - FullyConnectedActivationTest(backends, false); -} - -} // End of TEST_SUITE("FullyConnected_CpuRefTests") - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/FullyConnectedTestHelper.hpp b/delegate/src/test/FullyConnectedTestHelper.hpp deleted file mode 100644 index a3f009a863..0000000000 --- a/delegate/src/test/FullyConnectedTestHelper.hpp +++ /dev/null @@ -1,255 +0,0 @@ -// -// Copyright © 2020, 2023 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 -{ - -template <typename T> -std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, - tflite::ActivationFunctionType activationType, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& weightsTensorShape, - const std::vector <int32_t>& biasTensorShape, - std::vector <int32_t>& outputTensorShape, - std::vector <T>& weightsData, - bool constantWeights = true, - float quantScale = 1.0f, - int quantOffset = 0, - float outputQuantScale = 2.0f, - int outputQuantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; - buffers[0] = CreateBuffer(flatBufferBuilder); - buffers[1] = CreateBuffer(flatBufferBuilder); - - auto biasTensorType = ::tflite::TensorType_FLOAT32; - if (tensorType == ::tflite::TensorType_INT8) - { - biasTensorType = ::tflite::TensorType_INT32; - } - if (constantWeights) - { - buffers[2] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(weightsData.data()), - sizeof(T) * weightsData.size())); - - if (tensorType == ::tflite::TensorType_INT8) - { - std::vector<int32_t> biasData = { 10 }; - buffers[3] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), - sizeof(int32_t) * biasData.size())); - - } - else - { - std::vector<float> biasData = { 10 }; - buffers[3] = CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), - sizeof(float) * biasData.size())); - } - } - else - { - buffers[2] = CreateBuffer(flatBufferBuilder); - buffers[3] = CreateBuffer(flatBufferBuilder); - } - buffers[4] = CreateBuffer(flatBufferBuilder); - - 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 })); - - std::array<flatbuffers::Offset<Tensor>, 4> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(), - weightsTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("weights"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), - biasTensorShape.size()), - biasTensorType, - 3, - flatBufferBuilder.CreateString("bias"), - quantizationParameters); - - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 4, - flatBufferBuilder.CreateString("output"), - outputQuantizationParameters); - - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FullyConnectedOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateFullyConnectedOptions(flatBufferBuilder, - activationType, - FullyConnectedOptionsWeightsFormat_DEFAULT, false).Union(); - - const std::vector<int> operatorInputs{0, 1, 2}; - const std::vector<int> operatorOutputs{3}; - flatbuffers::Offset <Operator> fullyConnectedOperator = - 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(&fullyConnectedOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: FullyConnected Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_FULLY_CONNECTED); - - 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 FullyConnectedTest(std::vector<armnn::BackendId>& backends, - tflite::TensorType tensorType, - tflite::ActivationFunctionType activationType, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& weightsTensorShape, - const std::vector <int32_t>& biasTensorShape, - std::vector <int32_t>& outputTensorShape, - std::vector <T>& inputValues, - std::vector <T>& expectedOutputValues, - std::vector <T>& weightsData, - bool constantWeights = true, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - - std::vector<char> modelBuffer = CreateFullyConnectedTfLiteModel(tensorType, - activationType, - inputTensorShape, - weightsTensorShape, - biasTensorShape, - outputTensorShape, - weightsData, - constantWeights, - quantScale, - quantOffset); - 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); - - if (!constantWeights) - { - armnnDelegate::FillInput<T>(tfLiteInterpreter, 1, weightsData); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 1, weightsData); - - if (tensorType == ::tflite::TensorType_INT8) - { - std::vector <int32_t> biasData = {10}; - armnnDelegate::FillInput<int32_t>(tfLiteInterpreter, 2, biasData); - armnnDelegate::FillInput<int32_t>(armnnDelegateInterpreter, 2, biasData); - } - else - { - std::vector<float> biasData = {10}; - armnnDelegate::FillInput<float>(tfLiteInterpreter, 2, biasData); - armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 2, biasData); - } - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - outputTensorShape, - expectedOutputValues); - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/GatherNdTest.cpp b/delegate/src/test/GatherNdTest.cpp deleted file mode 100644 index 2b4fd4207e..0000000000 --- a/delegate/src/test/GatherNdTest.cpp +++ /dev/null @@ -1,113 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "GatherNdTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -// GATHER_ND Operator -void GatherNdUint8Test(std::vector<armnn::BackendId>& backends) -{ - - std::vector<int32_t> paramsShape{ 5, 2 }; - std::vector<int32_t> indicesShape{ 3, 1 }; - std::vector<int32_t> expectedOutputShape{ 3, 2 }; - - std::vector<uint8_t> paramsValues{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; - std::vector<int32_t> indicesValues{ 1, 0, 4 }; - std::vector<uint8_t> expectedOutputValues{ 3, 4, 1, 2, 9, 10 }; - - GatherNdTest<uint8_t>(::tflite::TensorType_UINT8, - backends, - paramsShape, - indicesShape, - expectedOutputShape, - paramsValues, - indicesValues, - expectedOutputValues); -} - -void GatherNdFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> paramsShape{ 5, 2 }; - std::vector<int32_t> indicesShape{ 3, 1 }; - std::vector<int32_t> expectedOutputShape{ 3, 2 }; - - std::vector<float> paramsValues{ 1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.10f }; - std::vector<int32_t> indicesValues{ 1, 0, 4 }; - std::vector<float> expectedOutputValues{ 3.3f, 4.4f, 1.1f, 2.2f, 9.9f, 10.10f }; - - GatherNdTest<float>(::tflite::TensorType_FLOAT32, - backends, - paramsShape, - indicesShape, - expectedOutputShape, - paramsValues, - indicesValues, - expectedOutputValues); -} - -// GATHER_ND Test Suite -TEST_SUITE("GATHER_ND_CpuRefTests") -{ - -TEST_CASE ("GATHER_ND_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - GatherNdUint8Test(backends); -} - -TEST_CASE ("GATHER_ND_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - GatherNdFp32Test(backends); -} - -} - -TEST_SUITE("GATHER_ND_CpuAccTests") -{ - -TEST_CASE ("GATHER_ND_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - GatherNdUint8Test(backends); -} - -TEST_CASE ("GATHER_ND_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - GatherNdFp32Test(backends); -} - -} - -TEST_SUITE("GATHER_ND_GpuAccTests") -{ - -TEST_CASE ("GATHER_ND_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - GatherNdUint8Test(backends); -} - -TEST_CASE ("GATHER_ND_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - GatherNdFp32Test(backends); -} - -} -// End of GATHER_ND Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/GatherNdTestHelper.hpp b/delegate/src/test/GatherNdTestHelper.hpp deleted file mode 100644 index c2cf9ffe9d..0000000000 --- a/delegate/src/test/GatherNdTestHelper.hpp +++ /dev/null @@ -1,181 +0,0 @@ -// -// Copyright © 2022-2023 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> CreateGatherNdTfLiteModel(tflite::TensorType tensorType, - std::vector<int32_t>& paramsShape, - std::vector<int32_t>& indicesShape, - const std::vector<int32_t>& expectedOutputShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(paramsShape.data(), - paramsShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("params"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(), - indicesShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("indices"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(), - expectedOutputShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_GatherNdOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateGatherNdOptions(flatBufferBuilder).Union(); - - const std::vector<int> operatorInputs{{0, 1}}; - const std::vector<int> operatorOutputs{2}; - 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}; - 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: GATHER_ND Operator Model"); - flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - BuiltinOperator_GATHER_ND); - - 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 GatherNdTest(tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& paramsShape, - std::vector<int32_t>& indicesShape, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& paramsValues, - std::vector<int32_t>& indicesValues, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateGatherNdTfLiteModel(tensorType, - paramsShape, - indicesShape, - expectedOutputShape, - 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, paramsValues); - armnnDelegate::FillInput<T>(armnnDelegate, 0, paramsValues); - armnnDelegate::FillInput<int32_t>(tfLiteDelegate, 1, indicesValues); - armnnDelegate::FillInput<int32_t>(armnnDelegate, 1, indicesValues); - - // Run EnqueWorkload - CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); - CHECK(armnnDelegate->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - expectedOutputShape, - expectedOutputValues, - 0); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/GatherTest.cpp b/delegate/src/test/GatherTest.cpp deleted file mode 100644 index 6dd015173c..0000000000 --- a/delegate/src/test/GatherTest.cpp +++ /dev/null @@ -1,117 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "GatherTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -// GATHER Operator -void GatherUint8Test(std::vector<armnn::BackendId>& backends) -{ - - std::vector<int32_t> paramsShape{8}; - std::vector<int32_t> indicesShape{3}; - std::vector<int32_t> expectedOutputShape{3}; - - int32_t axis = 0; - std::vector<uint8_t> paramsValues{1, 2, 3, 4, 5, 6, 7, 8}; - std::vector<int32_t> indicesValues{7, 6, 5}; - std::vector<uint8_t> expectedOutputValues{8, 7, 6}; - - GatherTest<uint8_t>(::tflite::TensorType_UINT8, - backends, - paramsShape, - indicesShape, - expectedOutputShape, - axis, - paramsValues, - indicesValues, - expectedOutputValues); -} - -void GatherFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> paramsShape{8}; - std::vector<int32_t> indicesShape{3}; - std::vector<int32_t> expectedOutputShape{3}; - - int32_t axis = 0; - std::vector<float> paramsValues{1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f}; - std::vector<int32_t> indicesValues{7, 6, 5}; - std::vector<float> expectedOutputValues{8.8f, 7.7f, 6.6f}; - - GatherTest<float>(::tflite::TensorType_FLOAT32, - backends, - paramsShape, - indicesShape, - expectedOutputShape, - axis, - paramsValues, - indicesValues, - expectedOutputValues); -} - -// GATHER Test Suite -TEST_SUITE("GATHER_CpuRefTests") -{ - -TEST_CASE ("GATHER_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - GatherUint8Test(backends); -} - -TEST_CASE ("GATHER_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - GatherFp32Test(backends); -} - -} - -TEST_SUITE("GATHER_CpuAccTests") -{ - -TEST_CASE ("GATHER_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - GatherUint8Test(backends); -} - -TEST_CASE ("GATHER_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - GatherFp32Test(backends); -} - -} - -TEST_SUITE("GATHER_GpuAccTests") -{ - -TEST_CASE ("GATHER_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - GatherUint8Test(backends); -} - -TEST_CASE ("GATHER_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - GatherFp32Test(backends); -} - -} -// End of GATHER Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/GatherTestHelper.hpp b/delegate/src/test/GatherTestHelper.hpp deleted file mode 100644 index 4763e06c73..0000000000 --- a/delegate/src/test/GatherTestHelper.hpp +++ /dev/null @@ -1,184 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateGatherTfLiteModel(tflite::TensorType tensorType, - std::vector<int32_t>& paramsShape, - std::vector<int32_t>& indicesShape, - const std::vector<int32_t>& expectedOutputShape, - int32_t axis, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(paramsShape.data(), - paramsShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("params"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(), - indicesShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("indices"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(), - expectedOutputShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_GatherOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateGatherOptions(flatBufferBuilder).Union(); - - const std::vector<int> operatorInputs{{0, 1}}; - const std::vector<int> operatorOutputs{2}; - 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}; - 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: GATHER Operator Model"); - flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - BuiltinOperator_GATHER); - - 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 GatherTest(tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& paramsShape, - std::vector<int32_t>& indicesShape, - std::vector<int32_t>& expectedOutputShape, - int32_t axis, - std::vector<T>& paramsValues, - std::vector<int32_t>& indicesValues, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateGatherTfLiteModel(tensorType, - paramsShape, - indicesShape, - expectedOutputShape, - axis, - 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, paramsValues); - armnnDelegate::FillInput<T>(armnnDelegate, 0, paramsValues); - armnnDelegate::FillInput<int32_t>(tfLiteDelegate, 1, indicesValues); - armnnDelegate::FillInput<int32_t>(armnnDelegate, 1, indicesValues); - - // Run EnqueWorkload - CHECK(tfLiteDelegate->Invoke() == kTfLiteOk); - CHECK(armnnDelegate->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - expectedOutputShape, - expectedOutputValues, - 0); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/LogicalTest.cpp b/delegate/src/test/LogicalTest.cpp deleted file mode 100644 index 9fa2d3dde0..0000000000 --- a/delegate/src/test/LogicalTest.cpp +++ /dev/null @@ -1,226 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ElementwiseUnaryTestHelper.hpp" -#include "LogicalTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void LogicalBinaryAndBoolTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2 }; - - // Set input and output values - std::vector<bool> input0Values { 0, 0, 1, 1 }; - std::vector<bool> input1Values { 0, 1, 0, 1 }; - std::vector<bool> expectedOutputValues { 0, 0, 0, 1 }; - - LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_AND, - ::tflite::TensorType_BOOL, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LogicalBinaryAndBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2 }; - - std::vector<bool> input0Values { 0, 1, 0, 1 }; - std::vector<bool> input1Values { 1 }; - std::vector<bool> expectedOutputValues { 0, 1, 0, 1 }; - - LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_AND, - ::tflite::TensorType_BOOL, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LogicalBinaryOrBoolTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 2, 2 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2 }; - - std::vector<bool> input0Values { 0, 0, 1, 1 }; - std::vector<bool> input1Values { 0, 1, 0, 1 }; - std::vector<bool> expectedOutputValues { 0, 1, 1, 1 }; - - LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_OR, - ::tflite::TensorType_BOOL, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -void LogicalBinaryOrBroadcastTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> input0Shape { 1, 2, 2 }; - std::vector<int32_t> input1Shape { 1, 1, 1 }; - std::vector<int32_t> expectedOutputShape { 1, 2, 2 }; - - std::vector<bool> input0Values { 0, 1, 0, 1 }; - std::vector<bool> input1Values { 1 }; - std::vector<bool> expectedOutputValues { 1, 1, 1, 1 }; - - LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_OR, - ::tflite::TensorType_BOOL, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues); -} - -// LogicalNot operator uses ElementwiseUnary unary layer and descriptor but is still classed as logical operator. -void LogicalNotBoolTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 1, 2, 2 }; - - std::vector<bool> inputValues { 0, 1, 0, 1 }; - std::vector<bool> expectedOutputValues { 1, 0, 1, 0 }; - - ElementwiseUnaryBoolTest(tflite::BuiltinOperator_LOGICAL_NOT, - backends, - inputShape, - inputValues, - expectedOutputValues); -} - -TEST_SUITE("LogicalBinaryTests_GpuAccTests") -{ - -TEST_CASE ("LogicalBinary_AND_Bool_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LogicalBinaryAndBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_AND_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LogicalBinaryAndBroadcastTest(backends); -} - -TEST_CASE ("Logical_NOT_Bool_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LogicalNotBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_OR_Bool_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LogicalBinaryOrBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_OR_Broadcast_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LogicalBinaryOrBroadcastTest(backends); -} - -} - - -TEST_SUITE("LogicalBinaryTests_CpuAccTests") -{ - -TEST_CASE ("LogicalBinary_AND_Bool_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LogicalBinaryAndBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_AND_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LogicalBinaryAndBroadcastTest(backends); -} - -TEST_CASE ("Logical_NOT_Bool_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LogicalNotBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_OR_Bool_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LogicalBinaryOrBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_OR_Broadcast_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LogicalBinaryOrBroadcastTest(backends); -} - -} - - -TEST_SUITE("LogicalBinaryTests_CpuRefTests") -{ - -TEST_CASE ("LogicalBinary_AND_Bool_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LogicalBinaryAndBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_AND_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LogicalBinaryAndBroadcastTest(backends); -} - -TEST_CASE ("Logical_NOT_Bool_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LogicalNotBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_OR_Bool_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LogicalBinaryOrBoolTest(backends); -} - -TEST_CASE ("LogicalBinary_OR_Broadcast_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LogicalBinaryOrBroadcastTest(backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/LogicalTestHelper.hpp b/delegate/src/test/LogicalTestHelper.hpp deleted file mode 100644 index 2a1ff2b996..0000000000 --- a/delegate/src/test/LogicalTestHelper.hpp +++ /dev/null @@ -1,201 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logicalOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& input0TensorShape, - const std::vector <int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), - input1TensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("input_1"), - quantizationParameters); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; - flatbuffers::Offset<void> operatorBuiltinOptions = 0; - switch (logicalOperatorCode) - { - case BuiltinOperator_LOGICAL_AND: - { - operatorBuiltinOptionsType = BuiltinOptions_LogicalAndOptions; - operatorBuiltinOptions = CreateLogicalAndOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_LOGICAL_OR: - { - operatorBuiltinOptionsType = BuiltinOptions_LogicalOrOptions; - operatorBuiltinOptions = CreateLogicalOrOptions(flatBufferBuilder).Union(); - break; - } - default: - break; - } - const std::vector<int32_t> operatorInputs{ {0, 1} }; - const std::vector<int32_t> operatorOutputs{ 2 }; - flatbuffers::Offset <Operator> logicalBinaryOperator = - 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 }; - 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(&logicalBinaryOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Logical Binary Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, logicalOperatorCode); - - 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 LogicalBinaryTest(tflite::BuiltinOperator logicalOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& input0Values, - std::vector<T>& input1Values, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode, - tensorType, - input0Shape, - input1Shape, - expectedOutputShape, - quantScale, - quantOffset); - - 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 for the armnn interpreter - armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values); - armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values); - - // Set input data for the tflite interpreter - armnnDelegate::FillInput(tfLiteInterpreter, 0, input0Values); - armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values); - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function - // directly. This is because Boolean types get converted to a bit representation in a vector. - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - - armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); - - armnnDelegateInterpreter.reset(nullptr); - tfLiteInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/LstmTest.cpp b/delegate/src/test/LstmTest.cpp deleted file mode 100644 index 1fa9f0c8bf..0000000000 --- a/delegate/src/test/LstmTest.cpp +++ /dev/null @@ -1,189 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "LstmTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void LstmTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 2; - int32_t inputSize = 2; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - std::vector<int32_t> inputShape {batchSize , inputSize}; - std::vector<int32_t> cellStateInTensorInfo {batchSize , numUnits}; - std::vector<int32_t> outputStateInTensorInfo {batchSize , outputSize}; - - std::vector<int32_t> scratchBufferTensorInfo {batchSize, numUnits * 4}; - std::vector<int32_t> cellStateOutTensorInfo {batchSize, numUnits}; - std::vector<int32_t> outputStateOutTensorInfo {batchSize, outputSize}; - std::vector<int32_t> outputTensorInfo {batchSize, outputSize}; - - std::vector<int32_t> tensorInfo4 {numUnits}; - std::vector<int32_t> tensorInfo8 {numUnits, 2}; - std::vector<int32_t> tensorInfo16 {numUnits, 4}; - - //tensorInfo8, - bool hasInputToInputWeights = true; - std::vector<float> inputToInputWeights {-0.45018822f, -0.02338299f, -0.0870589f, - -0.34550029f, 0.04266912f, -0.15680569f, - -0.34856534f, 0.43890524f}; - - std::vector<float> inputToForgetWeights {0.09701663f, 0.20334584f, -0.50592935f, - -0.31343272f, -0.40032279f, 0.44781327f, - 0.01387155f, -0.35593212f}; - - std::vector<float> inputToCellWeights {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, - -0.20583314f, 0.44344562f, 0.22077113f, - -0.29909778f}; - - std::vector<float> inputToOutputWeights {-0.25065863f, -0.28290087f, 0.04613829f, - 0.40525138f, 0.44272184f, 0.03897077f, - -0.1556896f, 0.19487578f}; - - //tensorInfo16, - bool hasRecurrentToInputWeights = true; - std::vector<float> recurrentToInputWeights {-0.0063535f, -0.2042388f, 0.31454784f, - -0.35746509f, 0.28902304f, 0.08183324f, - -0.16555229f, 0.02286911f, -0.13566875f, - 0.03034258f, 0.48091322f, -0.12528998f, - 0.24077177f, -0.51332325f, -0.33502164f, - 0.10629296f}; - - std::vector<float> recurrentToForgetWeights {-0.48684245f, -0.06655136f, 0.42224967f, - 0.2112639f, 0.27654213f, 0.20864892f, - -0.07646349f, 0.45877004f, 0.00141793f, - -0.14609534f, 0.36447752f, 0.09196436f, - 0.28053468f, 0.01560611f, -0.20127171f, - -0.01140004f}; - - std::vector<float> recurrentToCellWeights {-0.3407414f, 0.24443203f, -0.2078532f, - 0.26320225f, 0.05695659f, -0.00123841f, - -0.4744786f, -0.35869038f, -0.06418842f, - -0.13502428f, -0.501764f, 0.22830659f, - -0.46367589f, 0.26016325f, -0.03894562f, - -0.16368064f}; - - std::vector<float> recurrentToOutputWeights {0.43385774f, -0.17194885f, 0.2718237f, - 0.09215671f, 0.24107647f, -0.39835793f, - 0.18212086f, 0.01301402f, 0.48572797f, - -0.50656658f, 0.20047462f, -0.20607421f, - -0.51818722f, -0.15390486f, 0.0468148f, - 0.39922136f}; - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<float> cellToInputWeights {}; - bool hasCellToForgetWeights = false; - std::vector<float> cellToForgetWeights {}; - bool hasCellToOutputWeights = false; - std::vector<float> cellToOutputWeights {}; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias {0., 0., 0., 0.}; - std::vector<float> forgetGateBias {1., 1., 1., 1.}; - std::vector<float> cellBias {0., 0., 0., 0.}; - std::vector<float> outputGateBias {0., 0., 0., 0.}; - - bool hasProjectionWeights = false; - std::vector<float> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues {2., 3., 3., 4.}; - std::vector<float> expectedOutputValues {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, - -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 0.f; - float clippingThresProj = 0.f; - - LstmTestImpl<float>(backends, - ::tflite::TensorType_FLOAT32, - batchSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj); -} - -TEST_SUITE("LstmTest_CpuRefTests") -{ - -TEST_CASE ("LstmTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - LstmTest(backends); -} - -} //End of TEST_SUITE("Convolution2dTest_CpuRef") - -TEST_SUITE("LstmTest_CpuAccTests") -{ - -TEST_CASE ("LstmTest_CpuAcc_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - LstmTest(backends); -} - -} //End of TEST_SUITE("Convolution2dTest_CpuAcc") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/LstmTestHelper.hpp b/delegate/src/test/LstmTestHelper.hpp deleted file mode 100644 index 082d5dea91..0000000000 --- a/delegate/src/test/LstmTestHelper.hpp +++ /dev/null @@ -1,691 +0,0 @@ -// -// Copyright © 2021, 2023 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 <tensorflow/lite/c/common.h> - -#include <doctest/doctest.h> - -namespace -{ - -template <typename T> -std::vector<char> CreateLstmTfLiteModel(tflite::TensorType tensorType, - int32_t batchSize, - int32_t inputSize, - int32_t outputSize, - int32_t numUnits, - bool hasInputToInputWeights, - const std::vector<T>& inputToInputWeights, - const std::vector<T>& inputToForgetWeights, - const std::vector<T>& inputToCellWeights, - const std::vector<T>& inputToOutputWeights, - bool hasRecurrentToInputWeights, - const std::vector<T>& recurrentToInputWeights, - const std::vector<T>& recurrentToForgetWeights, - const std::vector<T>& recurrentToCellWeights, - const std::vector<T>& recurrentToOutputWeights, - bool hasCellToInputWeights, - const std::vector<T>& cellToInputWeights, - bool hasCellToForgetWeights, - const std::vector<T>& cellToForgetWeights, - bool hasCellToOutputWeights, - const std::vector<T>& cellToOutputWeights, - bool hasInputGateBias, - const std::vector<T>& inputGateBias, - const std::vector<T>& forgetGateBias, - const std::vector<T>& cellBias, - const std::vector<T>& outputGateBias, - bool hasProjectionWeights, - const std::vector<T>& projectionWeights, - bool hasProjectionBias, - const std::vector<T>& projectionBias, - bool hasInputLayerNormWeights, - const std::vector<T>& inputLayerNormWeights, - bool hasForgetLayerNormWeights, - const std::vector<T>& forgetLayerNormWeights, - bool hasCellLayerNormWeights, - const std::vector<T>& cellLayerNormWeights, - bool hasOutputLayerNormWeights, - const std::vector<T>& outputLayerNormWeights, - tflite::ActivationFunctionType activationFunction, - float clippingThresCell, - float clippingThresProj, - float quantScale = 1.0f, - int quantOffset = 0, - float outputQuantScale = 2.0f, - int outputQuantOffset = 0) -{ - - std::vector <int32_t> tensorInfo0 {}; - std::vector <int32_t> tensorInfo4 {numUnits}; - std::vector <int32_t> tensorInfo8 {numUnits, static_cast<int32_t>(2)}; - std::vector <int32_t> tensorInfo16 {numUnits, static_cast<int32_t>(4)}; - - std::vector<int32_t> inputShape {batchSize , inputSize}; - std::vector<int32_t> outputShape {batchSize , outputSize}; - - std::vector<int32_t> outputStateInDimensions{batchSize, outputSize}; - std::vector<int32_t> cellStateInDimensions{batchSize, numUnits}; - - std::vector<int> operatorInputs; - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - std::vector<flatbuffers::Offset<Tensor>> tensors; - - 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 })); - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), - inputShape.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("input_0"), - quantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - if (hasInputToInputWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToInputWeights.data()), - sizeof(T) * inputToInputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(), - tensorInfo8.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToInputWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToForgetWeights.data()), - sizeof(T) * inputToForgetWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(), - tensorInfo8.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToForgetWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToCellWeights.data()), - sizeof(T) * inputToCellWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(), - tensorInfo8.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToCellWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(inputToOutputWeights.data()), - sizeof(T) * inputToOutputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo8.data(), - tensorInfo8.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToOutputWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - if (hasRecurrentToInputWeights) - { - buffers.push_back(CreateBuffer( - flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(recurrentToInputWeights.data()), - sizeof(T) * recurrentToInputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(), - tensorInfo16.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToInputWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToForgetWeights.data()), - sizeof(T) * recurrentToForgetWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(), - tensorInfo16.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToForgetWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToCellWeights.data()), - sizeof(T) * recurrentToCellWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(), - tensorInfo16.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToCellWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(recurrentToOutputWeights.data()), - sizeof(T) * recurrentToOutputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo16.data(), - tensorInfo16.size()), - tensorType, - buffers.size() - 1 , - flatBufferBuilder.CreateString("recurrentToOutputWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - if (hasCellToInputWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToInputWeights.data()), - sizeof(T) * cellToInputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellToInputWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasCellToForgetWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToForgetWeights.data()), - sizeof(T) * cellToForgetWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellToForgetWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasCellToOutputWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellToOutputWeights.data()), - sizeof(T) * cellToOutputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellToOutputWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasInputGateBias) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputGateBias.data()), - sizeof(T) * inputGateBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputGateBias"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(forgetGateBias.data()), - sizeof(T) * forgetGateBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("forgetGateBias"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellBias.data()), - sizeof(T) * cellBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellBias"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(outputGateBias.data()), - sizeof(T) * outputGateBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputGateBias"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - - if (hasProjectionWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionWeights.data()), - sizeof(T) * projectionWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputGateBias"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasProjectionBias) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(projectionBias.data()), - sizeof(T) * projectionBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("projectionBias"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(), - outputStateInDimensions.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputStateInInfo"), - outputQuantizationParameters, - true)); - operatorInputs.push_back(buffers.size() - 1); - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), - cellStateInDimensions.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellStateInInfo"), - outputQuantizationParameters, - true)); - operatorInputs.push_back(buffers.size() - 1); - - if (hasInputLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(inputLayerNormWeights.data()), - sizeof(T) * inputLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputLayerNormWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasForgetLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(forgetLayerNormWeights.data()), - sizeof(T) * forgetLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("forgetLayerNormWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasCellLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t *>(cellLayerNormWeights.data()), - sizeof(T) * cellLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellLayerNormWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasOutputLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t *>(outputLayerNormWeights.data()), - sizeof(T) * outputLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfo4.data(), - tensorInfo4.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputLayerNormWeights"), - outputQuantizationParameters)); - operatorInputs.push_back(buffers.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - int outputBufferId = buffers.size(); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), - outputShape.size()), - tensorType, - outputBufferId, - flatBufferBuilder.CreateString("output"), - outputQuantizationParameters)); - std::vector<int> operatorOutputs; - operatorOutputs.push_back(buffers.size() - 1); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_LSTMOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateLSTMOptions(flatBufferBuilder, - activationFunction, - clippingThresCell, - clippingThresProj).Union(); - - flatbuffers::Offset <Operator> lstmOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, operatorBuiltinOptions); - - flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - flatBufferBuilder.CreateVector(&lstmOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: LSTM Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_LSTM); - - 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 LstmTestImpl(std::vector<armnn::BackendId>& backends, - tflite::TensorType tensorType, - int32_t batchSize, - int32_t inputSize, - int32_t outputSize, - int32_t numUnits, - bool hasInputToInputWeights, - const std::vector<T>& inputToInputWeights, - const std::vector<T>& inputToForgetWeights, - const std::vector<T>& inputToCellWeights, - const std::vector<T>& inputToOutputWeights, - bool hasRecurrentToInputWeights, - const std::vector<T>& recurrentToInputWeights, - const std::vector<T>& recurrentToForgetWeights, - const std::vector<T>& recurrentToCellWeights, - const std::vector<T>& recurrentToOutputWeights, - bool hasCellToInputWeights, - const std::vector<T>& cellToInputWeights, - bool hasCellToForgetWeights, - const std::vector<T>& cellToForgetWeights, - bool hasCellToOutputWeights, - const std::vector<T>& cellToOutputWeights, - bool hasInputGateBias, - const std::vector<T>& inputGateBias, - const std::vector<T>& forgetGateBias, - const std::vector<T>& cellBias, - const std::vector<T>& outputGateBias, - bool hasProjectionWeights, - const std::vector<T>& projectionWeights, - bool hasProjectionBias, - const std::vector<T>& projectionBias, - bool hasInputLayerNormWeights, - const std::vector<T>& inputLayerNormWeights, - bool hasForgetLayerNormWeights, - const std::vector<T>& forgetLayerNormWeights, - bool hasCellLayerNormWeights, - const std::vector<T>& cellLayerNormWeights, - bool hasOutputLayerNormWeights, - const std::vector<T>& outputLayerNormWeights, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - tflite::ActivationFunctionType activationFunction, - float clippingThresCell, - float clippingThresProj) -{ - using namespace tflite; - - std::vector<char> modelBuffer = CreateLstmTfLiteModel(tensorType, - batchSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - activationFunction, - clippingThresCell, - clippingThresProj); - - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[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 EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelagateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/MirrorPadTest.cpp b/delegate/src/test/MirrorPadTest.cpp deleted file mode 100644 index ca66181a30..0000000000 --- a/delegate/src/test/MirrorPadTest.cpp +++ /dev/null @@ -1,341 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "PadTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void MirrorPadSymmetric2dTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 3, 3 }; - std::vector<int32_t> outputShape { 7, 7 }; - std::vector<int32_t> paddingShape { 2, 2 }; - - std::vector<float> inputValues = - { - 1.0f, 2.0f, 3.0f, - 4.0f, 5.0f, 6.0f, - 7.0f, 8.0f, 9.0f - }; - - std::vector<float> expectedOutputValues = - { - 5.0f, 4.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, - 2.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 2.0f, - 2.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 2.0f, - 5.0f, 4.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f, - 8.0f, 7.0f, 7.0f, 8.0f, 9.0f, 9.0f, 8.0f, - 8.0f, 7.0f, 7.0f, 8.0f, 9.0f, 9.0f, 8.0f, - 5.0f, 4.0f, 4.0f, 5.0f, 6.0f, 6.0f, 5.0f - }; - - std::vector<int32_t> paddingDim = { 2, 2, 2, 2 }; - - PadTest<float>(tflite::BuiltinOperator_MIRROR_PAD, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - 0, // Padding value - Not used in these tests. - 1.0f, // Scale - 0, // Offset - tflite::MirrorPadMode_SYMMETRIC); -} - -void MirrorPadReflect2dTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 3, 3 }; - std::vector<int32_t> outputShape { 7, 7 }; - std::vector<int32_t> paddingShape { 2, 2 }; - - std::vector<float> inputValues = - { - 1.0f, 2.0f, 3.0f, - 4.0f, 5.0f, 6.0f, - 7.0f, 8.0f, 9.0f - }; - - std::vector<float> expectedOutputValues = - { - 9.0f, 8.0f, 7.0f, 8.0f, 9.0f, 8.0f, 7.0f, - 6.0f, 5.0f, 4.0f, 5.0f, 6.0f, 5.0f, 4.0f, - 3.0f, 2.0f, 1.0f, 2.0f, 3.0f, 2.0f, 1.0f, - 6.0f, 5.0f, 4.0f, 5.0f, 6.0f, 5.0f, 4.0f, - 9.0f, 8.0f, 7.0f, 8.0f, 9.0f, 8.0f, 7.0f, - 6.0f, 5.0f, 4.0f, 5.0f, 6.0f, 5.0f, 4.0f, - 3.0f, 2.0f, 1.0f, 2.0f, 3.0f, 2.0f, 1.0f - }; - - std::vector<int32_t> paddingDim = { 2, 2, 2, 2 }; - - PadTest<float>(tflite::BuiltinOperator_MIRROR_PAD, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - 0, // Padding value - Not used in these tests. - 1.0f, // Scale - 0, // Offset - tflite::MirrorPadMode_REFLECT); -} - -void MirrorPadSymmetric3dTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2 }; - std::vector<int32_t> outputShape { 4, 4, 4 }; - std::vector<int32_t> paddingShape { 3, 2 }; - - std::vector<float> inputValues = - { - // Channel 0, Height (2) x Width (2) - 1.0f, 2.0f, - 3.0f, 4.0f, - - // Channel 1, Height (2) x Width (2) - 5.0f, 6.0f, - 7.0f, 8.0f - }; - - std::vector<float> expectedOutputValues = - { - 1.0f, 1.0f, 2.0f, 2.0f, - 1.0f, 1.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 4.0f, 4.0f, - 3.0f, 3.0f, 4.0f, 4.0f, - - 1.0f, 1.0f, 2.0f, 2.0f, - 1.0f, 1.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 4.0f, 4.0f, - 3.0f, 3.0f, 4.0f, 4.0f, - - 5.0f, 5.0f, 6.0f, 6.0f, - 5.0f, 5.0f, 6.0f, 6.0f, - 7.0f, 7.0f, 8.0f, 8.0f, - 7.0f, 7.0f, 8.0f, 8.0f, - - 5.0f, 5.0f, 6.0f, 6.0f, - 5.0f, 5.0f, 6.0f, 6.0f, - 7.0f, 7.0f, 8.0f, 8.0f, - 7.0f, 7.0f, 8.0f, 8.0f - }; - - std::vector<int32_t> paddingDim = { 1, 1, 1, 1, 1, 1 }; - - PadTest<float>(tflite::BuiltinOperator_MIRROR_PAD, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - 0, // Padding value - Not used in these tests. - 1.0f, // Scale - 0, // Offset - tflite::MirrorPadMode_SYMMETRIC); -} - -void MirrorPadReflect3dTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2 }; - std::vector<int32_t> outputShape { 4, 4, 4 }; - std::vector<int32_t> paddingShape { 3, 2 }; - - std::vector<float> inputValues = - { - // Channel 0, Height (2) x Width (2) - 1.0f, 2.0f, - 3.0f, 4.0f, - - // Channel 1, Height (2) x Width (2) - 5.0f, 6.0f, - 7.0f, 8.0f - }; - - std::vector<float> expectedOutputValues = - { - 8.0f, 7.0f, 8.0f, 7.0f, - 6.0f, 5.0f, 6.0f, 5.0f, - 8.0f, 7.0f, 8.0f, 7.0f, - 6.0f, 5.0f, 6.0f, 5.0f, - - 4.0f, 3.0f, 4.0f, 3.0f, - 2.0f, 1.0f, 2.0f, 1.0f, - 4.0f, 3.0f, 4.0f, 3.0f, - 2.0f, 1.0f, 2.0f, 1.0f, - - 8.0f, 7.0f, 8.0f, 7.0f, - 6.0f, 5.0f, 6.0f, 5.0f, - 8.0f, 7.0f, 8.0f, 7.0f, - 6.0f, 5.0f, 6.0f, 5.0f, - - 4.0f, 3.0f, 4.0f, 3.0f, - 2.0f, 1.0f, 2.0f, 1.0f, - 4.0f, 3.0f, 4.0f, 3.0f, - 2.0f, 1.0f, 2.0f, 1.0f - }; - - std::vector<int32_t> paddingDim = { 1, 1, 1, 1, 1, 1 }; - - PadTest<float>(tflite::BuiltinOperator_MIRROR_PAD, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - 0, // Padding value - Not used in these tests. - 1.0f, // Scale - 0, // Offset - tflite::MirrorPadMode_REFLECT); -} - -void MirrorPadSymmetricUint8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 3, 3 }; - std::vector<int32_t> outputShape { 5, 7 }; - std::vector<int32_t> paddingShape { 2, 2 }; - - std::vector<uint8_t> inputValues = - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9 - }; - - std::vector<uint8_t> expectedOutputValues = - { - 2, 1, 1, 2, 3, 3, 2, - 2, 1, 1, 2, 3, 3, 2, - 5, 4, 4, 5, 6, 6, 5, - 8, 7, 7, 8, 9, 9, 8, - 8, 7, 7, 8, 9, 9, 8, - }; - - std::vector<int32_t> paddingDim = { 1, 1, 2, 2 }; - - PadTest<uint8_t>(tflite::BuiltinOperator_MIRROR_PAD, - ::tflite::TensorType_UINT8, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - 0, // Padding value - Not used in these tests. - 1.0f, // Scale - 1, // Offset - tflite::MirrorPadMode_SYMMETRIC); -} - -void MirrorPadReflectInt8Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 3, 3 }; - std::vector<int32_t> outputShape { 7, 5 }; - std::vector<int32_t> paddingShape { 2, 2 }; - - std::vector<int8_t> inputValues = - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9 - }; - - std::vector<int8_t> expectedOutputValues = - { - 8, 7, 8, 9, 8, - 5, 4, 5, 6, 5, - 2, 1, 2, 3, 2, - 5, 4, 5, 6, 5, - 8, 7, 8, 9, 8, - 5, 4, 5, 6, 5, - 2, 1, 2, 3, 2 - }; - - std::vector<int32_t> paddingDim = { 2, 2, 1, 1 }; - - PadTest<int8_t>(tflite::BuiltinOperator_MIRROR_PAD, - ::tflite::TensorType_INT8, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - 0, // Padding value - Not used in these tests. - 1.0f, // Scale - 1, // Offset - tflite::MirrorPadMode_REFLECT); -} - -TEST_SUITE("MirrorPad_CpuRefTests") -{ - -TEST_CASE ("MirrorPadSymmetric2d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MirrorPadSymmetric2dTest(backends); -} - -TEST_CASE ("MirrorPadReflect2d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MirrorPadReflect2dTest(backends); -} - -TEST_CASE ("MirrorPadSymmetric3d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MirrorPadSymmetric3dTest(backends); -} - -TEST_CASE ("MirrorPadReflect3d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MirrorPadReflect3dTest(backends); -} - -TEST_CASE ("MirrorPadSymmetricUint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MirrorPadSymmetricUint8Test(backends); -} - -TEST_CASE ("MirrorPadSymmetricInt8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MirrorPadReflectInt8Test(backends); -} - -} // TEST_SUITE("MirrorPad_CpuRefTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/NeonDelegateTests_NDK_Issue.cpp b/delegate/src/test/NeonDelegateTests_NDK_Issue.cpp deleted file mode 100644 index a437a08a49..0000000000 --- a/delegate/src/test/NeonDelegateTests_NDK_Issue.cpp +++ /dev/null @@ -1,63 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "NormalizationTestHelper.hpp" -#include "SoftmaxTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ -// There's a known Android NDK bug which causes this subset of Neon Tests to -// fail. We'll exclude these tests in if we're doing -// a debug build and NDK is less than r21. -// The exclusion takes place in test/CMakeLists.txt -// https://github.com/android/ndk/issues/1135 - -TEST_SUITE ("Softmax_CpuAccTests") -{ - -TEST_CASE ("Softmax_Standard_Beta_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - std::vector<float> expectedOutput = {0.00994190481, 0.0445565246, 0.0734612942, 0.329230666, 0.542809606, - 0.710742831, 0.158588171, 0.0961885825, 0.0214625746, 0.0130177103}; - SoftmaxTestCase(tflite::BuiltinOperator_SOFTMAX, backends, 1, expectedOutput); -} - -TEST_CASE ("Softmax_Different_Beta_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - std::vector<float> expectedOutput = { - 0.0946234912, 0.148399189, 0.172415257, 0.270400971, 0.314161092, - 0.352414012, 0.224709094, 0.193408906, 0.123322964, 0.106145054}; - SoftmaxTestCase(tflite::BuiltinOperator_SOFTMAX, backends, 0.3, expectedOutput); -} - -TEST_CASE ("Log_Softmax_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - std::vector<float> expectedOutput = - {-4.61099672, -3.11099672, -2.61099672, -1.11099672, -0.610996664, - -0.341444582, -1.84144461, -2.34144449, -3.84144449, -4.34144449}; - SoftmaxTestCase(tflite::BuiltinOperator_LOG_SOFTMAX, backends, 0, expectedOutput); -} -} // TEST_SUITE ("Softmax_CpuAccTests") - -TEST_SUITE("L2Normalization_CpuAccTests") -{ - -TEST_CASE ("L2NormalizationFp32Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - L2NormalizationTest(backends); -} -} // TEST_SUITE("L2NormalizationFp32Test_CpuAcc_Test") -}
\ No newline at end of file diff --git a/delegate/src/test/NormalizationTest.cpp b/delegate/src/test/NormalizationTest.cpp deleted file mode 100644 index e33dcf056e..0000000000 --- a/delegate/src/test/NormalizationTest.cpp +++ /dev/null @@ -1,72 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "NormalizationTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -TEST_SUITE("L2Normalization_CpuRefTests") -{ - -TEST_CASE ("L2NormalizationFp32Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - L2NormalizationTest(backends); -} - -} // TEST_SUITE("L2Normalization_CpuRefTests") - -TEST_SUITE("L2Normalization_GpuAccTests") -{ - -TEST_CASE ("L2NormalizationFp32Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - L2NormalizationTest(backends); -} - -} // TEST_SUITE("L2Normalization_GpuAccTests") - -TEST_SUITE("LocalResponseNormalization_CpuRefTests") -{ - -TEST_CASE ("LocalResponseNormalizationTest_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - LocalResponseNormalizationTest(backends, 3, 1.f, 1.f, 1.f); -} - -} // TEST_SUITE("LocalResponseNormalization_CpuRefTests") - -TEST_SUITE("LocalResponseNormalization_CpuAccTests") -{ - -TEST_CASE ("LocalResponseNormalizationTest_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - LocalResponseNormalizationTest(backends, 3, 1.f, 1.f, 1.f); -} - -} // TEST_SUITE("LocalResponseNormalization_CpuAccTests") - -TEST_SUITE("LocalResponseNormalization_GpuAccTests") -{ - -TEST_CASE ("LocalResponseNormalizationTest_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - LocalResponseNormalizationTest(backends, 3, 1.f, 1.f, 1.f); -} - -} // TEST_SUITE("LocalResponseNormalization_GpuAccTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/NormalizationTestHelper.hpp b/delegate/src/test/NormalizationTestHelper.hpp deleted file mode 100644 index 510b578c02..0000000000 --- a/delegate/src/test/NormalizationTestHelper.hpp +++ /dev/null @@ -1,263 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& outputTensorShape, - int32_t radius, - float bias, - float alpha, - float beta, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - auto inputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor }; - - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - std::vector<int32_t> operatorInputs = { 0 }; - std::vector<int> subgraphInputs = { 0 }; - - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_L2NormOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateL2NormOptions(flatBufferBuilder, - tflite::ActivationFunctionType_NONE).Union(); - - if (normalizationOperatorCode == tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION) - { - operatorBuiltinOptionsType = BuiltinOptions_LocalResponseNormalizationOptions; - operatorBuiltinOptions = - CreateLocalResponseNormalizationOptions(flatBufferBuilder, radius, bias, alpha, beta).Union(); - } - - // create operator - const std::vector<int32_t> operatorOutputs{ 1 }; - flatbuffers::Offset <Operator> normalizationOperator = - 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> 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(&normalizationOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Normalization Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - normalizationOperatorCode); - - 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 NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - const std::vector<int32_t>& inputShape, - std::vector<int32_t>& outputShape, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - int32_t radius = 0, - float bias = 0.f, - float alpha = 0.f, - float beta = 0.f, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateNormalizationTfLiteModel(normalizationOperatorCode, - tensorType, - inputShape, - outputShape, - radius, - bias, - alpha, - beta, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - - 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 - armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); -} - -void L2NormalizationTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 1, 1, 10 }; - std::vector<int32_t> outputShape { 1, 1, 1, 10 }; - - std::vector<float> inputValues - { - 1.0f, - 2.0f, - 3.0f, - 4.0f, - 5.0f, - 6.0f, - 7.0f, - 8.0f, - 9.0f, - 10.0f - }; - - const float approxInvL2Norm = 0.050964719f; - std::vector<float> expectedOutputValues - { - 1.0f * approxInvL2Norm, - 2.0f * approxInvL2Norm, - 3.0f * approxInvL2Norm, - 4.0f * approxInvL2Norm, - 5.0f * approxInvL2Norm, - 6.0f * approxInvL2Norm, - 7.0f * approxInvL2Norm, - 8.0f * approxInvL2Norm, - 9.0f * approxInvL2Norm, - 10.0f * approxInvL2Norm - }; - - NormalizationTest<float>(tflite::BuiltinOperator_L2_NORMALIZATION, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void LocalResponseNormalizationTest(std::vector<armnn::BackendId>& backends, - int32_t radius, - float bias, - float alpha, - float beta) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2, 1 }; - std::vector<int32_t> outputShape { 2, 2, 2, 1 }; - - std::vector<float> inputValues - { - 1.0f, 2.0f, - 3.0f, 4.0f, - 5.0f, 6.0f, - 7.0f, 8.0f - }; - - std::vector<float> expectedOutputValues - { - 0.5f, 0.400000006f, 0.300000012f, 0.235294119f, - 0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f - }; - - NormalizationTest<float>(tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - radius, - bias, - alpha, - beta); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/PackTest.cpp b/delegate/src/test/PackTest.cpp deleted file mode 100644 index aea903bcd0..0000000000 --- a/delegate/src/test/PackTest.cpp +++ /dev/null @@ -1,516 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "PackTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -template <typename T> -void PackFp32Axis0Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 2, 3, 2, 3 }; - - std::vector<std::vector<T>> inputValues; - inputValues.push_back( - { - 1, 2, 3, - 4, 5, 6, - - 7, 8, 9, - 10, 11, 12, - - 13, 14, 15, - 16, 17, 18 - }); - - inputValues.push_back( - { - 19, 20, 21, - 22, 23, 24, - - 25, 26, 27, - 28, 29, 30, - - 31, 32, 33, - 34, 35, 36 - }); - - std::vector<T> expectedOutputValues = - { - 1, 2, 3, - 4, 5, 6, - - 7, 8, 9, - 10, 11, 12, - - 13, 14, 15, - 16, 17, 18, - - - 19, 20, 21, - 22, 23, 24, - - 25, 26, 27, - 28, 29, 30, - - 31, 32, 33, - 34, 35, 36 - }; - - PackTest<T>(tflite::BuiltinOperator_PACK, - tensorType, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 0); -} - -template <typename T> -void PackFp32Axis1Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 3, 2, 2, 3 }; - - std::vector<std::vector<T>> inputValues; - inputValues.push_back( - { - 1, 2, 3, - 4, 5, 6, - - 7, 8, 9, - 10, 11, 12, - - 13, 14, 15, - 16, 17, 18 - }); - - inputValues.push_back( - { - 19, 20, 21, - 22, 23, 24, - - 25, 26, 27, - 28, 29, 30, - - 31, 32, 33, - 34, 35, 36 - }); - - std::vector<T> expectedOutputValues = - { - 1, 2, 3, - 4, 5, 6, - - 19, 20, 21, - 22, 23, 24, - - - 7, 8, 9, - 10, 11, 12, - - 25, 26, 27, - 28, 29, 30, - - - 13, 14, 15, - 16, 17, 18, - - 31, 32, 33, - 34, 35, 36 - }; - - PackTest<T>(tflite::BuiltinOperator_PACK, - tensorType, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 1); -} - -template <typename T> -void PackFp32Axis2Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 3, 2, 2, 3 }; - - std::vector<std::vector<T>> inputValues; - inputValues.push_back( - { - 1, 2, 3, - 4, 5, 6, - - 7, 8, 9, - 10, 11, 12, - - 13, 14, 15, - 16, 17, 18 - }); - - inputValues.push_back( - { - 19, 20, 21, - 22, 23, 24, - - 25, 26, 27, - 28, 29, 30, - - 31, 32, 33, - 34, 35, 36 - }); - - std::vector<float> expectedOutputValues = - { - 1, 2, 3, - 19, 20, 21, - - 4, 5, 6, - 22, 23, 24, - - 7, 8, 9, - 25, 26, 27, - - 10, 11, 12, - 28, 29, 30, - - 13, 14, 15, - 31, 32, 33, - - 16, 17, 18, - 34, 35, 36 - }; - - PackTest<T>(tflite::BuiltinOperator_PACK, - tensorType, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 2); -} - -template <typename T> -void PackFp32Axis3Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3 }; - std::vector<int32_t> expectedOutputShape { 3, 2, 3, 2 }; - - std::vector<std::vector<T>> inputValues; - inputValues.push_back( - { - 1, 2, 3, - 4, 5, 6, - - 7, 8, 9, - 10, 11, 12, - - 13, 14, 15, - 16, 17, 18 - }); - - inputValues.push_back( - { - 19, 20, 21, - 22, 23, 24, - - 25, 26, 27, - 28, 29, 30, - - 31, 32, 33, - 34, 35, 36 - }); - - std::vector<T> expectedOutputValues = - { - 1, 19, - 2, 20, - 3, 21, - - 4, 22, - 5, 23, - 6, 24, - - - 7, 25, - 8, 26, - 9, 27, - - 10, 28, - 11, 29, - 12, 30, - - - 13, 31, - 14, 32, - 15, 33, - - 16, 34, - 17, 35, - 18, 36 - }; - - PackTest<T>(tflite::BuiltinOperator_PACK, - tflite::TensorType_FLOAT32, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 3); -} - -template <typename T> -void PackFp32Inputs3Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 3 }; - std::vector<int32_t> expectedOutputShape { 3, 3, 3 }; - - std::vector<std::vector<T>> inputValues; - inputValues.push_back( - { - 1, 2, 3, - 4, 5, 6, - 7, 8, 9 - }); - - inputValues.push_back( - { - 10, 11, 12, - 13, 14, 15, - 16, 17, 18 - }); - - inputValues.push_back( - { - 19, 20, 21, - 22, 23, 24, - 25, 26, 27 - }); - - std::vector<T> expectedOutputValues = - { - 1, 2, 3, - 10, 11, 12, - 19, 20, 21, - - 4, 5, 6, - 13, 14, 15, - 22, 23, 24, - - 7, 8, 9, - 16, 17, 18, - 25, 26, 27 - }; - - PackTest<T>(tflite::BuiltinOperator_PACK, - tensorType, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 1); -} - -TEST_SUITE("Pack_CpuAccTests") -{ - -// Fp32 -TEST_CASE ("Pack_Fp32_Axis0_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Axis0Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis1_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Axis1Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis2_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Axis2Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis3_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Axis3Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Inputs3_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Inputs3Test<float>(tflite::TensorType_FLOAT32, backends); -} - -// Uint8 -TEST_CASE ("Pack_Uint8_Axis0_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Axis0Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -TEST_CASE ("Pack_Uint8_Inputs3_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Inputs3Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -// Uint8 -TEST_CASE ("Pack_Int8_Axis0_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Axis0Test<int8_t>(tflite::TensorType_INT8, backends); -} - -TEST_CASE ("Pack_Int8_Inputs3_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - PackFp32Inputs3Test<int8_t>(tflite::TensorType_INT8, backends); -} - -} - -TEST_SUITE("Pack_GpuAccTests") -{ - -// Fp32 -TEST_CASE ("Pack_Fp32_Axis0_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Axis0Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis1_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Axis1Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis2_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Axis2Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis3_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Axis3Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Inputs3_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Inputs3Test<float>(tflite::TensorType_FLOAT32, backends); -} - -// Uint8 -TEST_CASE ("Pack_Uint8_Axis0_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Axis0Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -TEST_CASE ("Pack_Uint8_Inputs3_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Inputs3Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -// Int8 -TEST_CASE ("Pack_Int8_Axis0_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Axis0Test<int8_t>(tflite::TensorType_INT8, backends); -} - -TEST_CASE ("Pack_Int8_Inputs3_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - PackFp32Inputs3Test<int8_t>(tflite::TensorType_INT8, backends); -} - -} - -TEST_SUITE("Pack_CpuRefTests") -{ - -// Fp32 -TEST_CASE ("Pack_Fp32_Axis0_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Axis0Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis1_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Axis1Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis2_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Axis2Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Axis3_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Axis3Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Pack_Fp32_Inputs3_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Inputs3Test<float>(tflite::TensorType_FLOAT32, backends); -} - -// Uint8 -TEST_CASE ("Pack_Uint8_Axis0_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Axis0Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -TEST_CASE ("Pack_Uint8_Inputs3_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Inputs3Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -// Int8 -TEST_CASE ("Pack_Int8_Axis0_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Axis0Test<int8_t>(tflite::TensorType_INT8, backends); -} - -TEST_CASE ("Pack_Int8_Inputs3_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - PackFp32Inputs3Test<int8_t>(tflite::TensorType_INT8, backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/PackTestHelper.hpp b/delegate/src/test/PackTestHelper.hpp deleted file mode 100644 index a9e2ee17bc..0000000000 --- a/delegate/src/test/PackTestHelper.hpp +++ /dev/null @@ -1,186 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreatePackTfLiteModel(tflite::BuiltinOperator packOperatorCode, - tflite::TensorType tensorType, - std::vector<int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - const int32_t inputTensorNum, - unsigned int axis = 0, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::vector<int32_t> operatorInputs{}; - const std::vector<int32_t> operatorOutputs{inputTensorNum}; - std::vector<int> subgraphInputs{}; - const std::vector<int> subgraphOutputs{inputTensorNum}; - - std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1); - for (int i = 0; i < inputTensorNum; ++i) - { - tensors[i] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input" + std::to_string(i)), - quantizationParameters); - - // Add number of inputs to vector. - operatorInputs.push_back(i); - subgraphInputs.push_back(i); - } - - // Create output tensor - tensors[inputTensorNum] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_PackOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreatePackOptions(flatBufferBuilder, inputTensorNum, axis).Union(); - - flatbuffers::Offset <Operator> packOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, - operatorBuiltinOptions); - - 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(&packOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Pack Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, packOperatorCode); - - 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 PackTest(tflite::BuiltinOperator packOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<int32_t>& expectedOutputShape, - std::vector<std::vector<T>>& inputValues, - std::vector<T>& expectedOutputValues, - unsigned int axis = 0, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreatePackTfLiteModel(packOperatorCode, - tensorType, - inputShape, - expectedOutputShape, - inputValues.size(), - axis, - quantScale, - quantOffset); - - 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 for all input tensors. - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - // Get single input tensor and assign to interpreters. - auto inputTensorValues = inputValues[i]; - armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues); - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - expectedOutputShape, - expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/PadTest.cpp b/delegate/src/test/PadTest.cpp deleted file mode 100644 index 4721b685cc..0000000000 --- a/delegate/src/test/PadTest.cpp +++ /dev/null @@ -1,606 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "PadTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void Pad2dTest(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD, - float pad = 0.0f) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2 }; - std::vector<int32_t> outputShape { 3, 5, 6 }; - std::vector<int32_t> paddingShape { 3, 2 }; - - std::vector<float> inputValues = { 0.0f, 4.0f, - 2.0f, -5.0f, - 6.0f, 1.0f, - 5.0f, -2.0f }; - - std::vector<float> expectedOutputValues = { pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, 0.0f, 4.0f, pad, pad, - pad, pad, 2.0f, -5.0f, pad, pad, - pad, pad, pad, pad, pad, pad, - - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, 6.0f, 1.0f, pad, pad, - pad, pad, 5.0f, -2.0f, pad, pad, - pad, pad, pad, pad, pad, pad, - - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad }; - - std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 }; - - PadTest<float>(padOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - pad); -} - -void Pad3dTest(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD, - float pad = 0.0f) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2 }; - std::vector<int32_t> outputShape { 3, 5, 6 }; - std::vector<int32_t> paddingShape { 3, 2 }; - - std::vector<float> inputValues = { 0.0f, 4.0f, - 2.0f, 5.0f, - 6.0f, 1.0f, - 5.0f, 2.0f }; - - std::vector<float> expectedOutputValues = { pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, 0.0f, 4.0f, pad, pad, - pad, pad, 2.0f, 5.0f, pad, pad, - pad, pad, pad, pad, pad, pad, - - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, 6.0f, 1.0f, pad, pad, - pad, pad, 5.0f, 2.0f, pad, pad, - pad, pad, pad, pad, pad, pad, - - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad, - pad, pad, pad, pad, pad, pad }; - - std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 }; - - PadTest<float>(padOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - pad); -} - -void Pad4dTest(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD, - float pad = 0.0f) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 3, 2 }; - std::vector<int32_t> outputShape { 4, 5, 7, 4 }; - std::vector<int32_t> paddingShape { 4, 2 }; - - std::vector<float> inputValues = { 0.0f, 1.0f, - 2.0f, 3.0f, - 4.0f, 5.0f, - - 6.0f, 7.0f, - 8.0f, 9.0f, - 10.0f, 11.0f, - - 12.0f, 13.0f, - 14.0f, 15.0f, - 16.0f, 17.0f, - - 18.0f, 19.0f, - 20.0f, 21.0f, - 22.0f, 23.0f }; - - std::vector<float> expectedOutputValues = { pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, 0.0f, 1.0f, pad, - pad, 2.0f, 3.0f, pad, - pad, 4.0f, 5.0f, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, 6.0f, 7.0f, pad, - pad, 8.0f, 9.0f, pad, - pad, 10.0f, 11.0f, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, 12.0f, 13.0f, pad, - pad, 14.0f, 15.0f, pad, - pad, 16.0f, 17.0f, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, 18.0f, 19.0f, pad, - pad, 20.0f, 21.0f, pad, - pad, 22.0f, 23.0f, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad, - pad, pad, pad, pad }; - - std::vector<int32_t> paddingDim = { 1, 1, 2, 1, 3, 1, 1, 1 }; - - PadTest<float>(padOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - pad); -} - -void PadInt8Test(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD, - int8_t paddingValue = 0, - int8_t p = 3, - float quantizationScale = -2.0f, - int32_t quantizationOffset = 3) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2 }; - std::vector<int32_t> outputShape { 3, 5, 6 }; - std::vector<int32_t> paddingShape { 3, 2 }; - - std::vector<int8_t> inputValues = { 0, 4, - 2, -5, - 6, 1, - 5, -2 }; - - std::vector<int8_t> expectedOutputValues = { p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, 0, 4, p, p, - p, p, 2, -5, p, p, - p, p, p, p, p, p, - - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, 6, 1, p, p, - p, p, 5, -2, p, p, - p, p, p, p, p, p, - - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, p, p, p, p }; - - std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 }; - - PadTest<int8_t>(padOperatorCode, - ::tflite::TensorType_INT8, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - paddingValue, - quantizationScale, - quantizationOffset); -} - -void PadUint8Test(std::vector<armnn::BackendId>& backends, - tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD, - uint8_t paddingValue = 0, - uint8_t p = 3, - float quantizationScale = -2.0f, - int32_t quantizationOffset = 3) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 2, 2 }; - std::vector<int32_t> outputShape { 3, 5, 6 }; - std::vector<int32_t> paddingShape { 3, 2 }; - - std::vector<uint8_t> inputValues = { 0, 4, - 2, 5, - 6, 1, - 5, 2 }; - - std::vector<uint8_t> expectedOutputValues = { p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, 0, 4, p, p, - p, p, 2, 5, p, p, - p, p, p, p, p, p, - - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, 6, 1, p, p, - p, p, 5, 2, p, p, - p, p, p, p, p, p, - - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, p, p, p, p, - p, p, p, p, p, p }; - - std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 }; - - PadTest<uint8_t>(padOperatorCode, - ::tflite::TensorType_UINT8, - backends, - inputShape, - paddingShape, - outputShape, - inputValues, - paddingDim, - expectedOutputValues, - paddingValue, - quantizationScale, - quantizationOffset); -} - -TEST_SUITE("Pad_CpuRefTests") -{ - -TEST_CASE ("Pad2d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Pad2dTest(backends); -} - -TEST_CASE ("Pad3d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Pad3dTest(backends); -} - -TEST_CASE ("Pad4d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Pad4dTest(backends); -} - -TEST_CASE ("Pad_Int8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PadInt8Test(backends); -} - -TEST_CASE ("Pad_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PadUint8Test(backends); -} - -TEST_CASE ("PadV22d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Pad2dTest(backends, tflite::BuiltinOperator_PADV2, -2.5); -} - -TEST_CASE ("PadV23d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Pad3dTest(backends, tflite::BuiltinOperator_PADV2, 2.0); -} - -TEST_CASE ("PadV24d_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - Pad4dTest(backends, tflite::BuiltinOperator_PADV2, -1.33); -} - -TEST_CASE ("PadV2_Int8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PadInt8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1); -} - -TEST_CASE ("PadV2_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PadUint8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1); -} - -} // TEST_SUITE("Pad_CpuRefTests") - -TEST_SUITE("Pad_CpuAccTests") -{ - -TEST_CASE ("Pad2d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Pad2dTest(backends); -} - -TEST_CASE ("Pad3d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Pad3dTest(backends); -} - -TEST_CASE ("Pad4d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Pad4dTest(backends); -} - -TEST_CASE ("Pad_Int8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PadInt8Test(backends); -} - -TEST_CASE ("Pad_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PadUint8Test(backends); -} - -TEST_CASE ("PadV22d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Pad2dTest(backends, tflite::BuiltinOperator_PADV2, -2.5); -} - -TEST_CASE ("PadV23d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Pad3dTest(backends, tflite::BuiltinOperator_PADV2, 2.0); -} - -TEST_CASE ("PadV24d_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - Pad4dTest(backends, tflite::BuiltinOperator_PADV2, -1.33); -} - -TEST_CASE ("PadV2_Int8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PadInt8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1); -} - -TEST_CASE ("PadV2_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PadUint8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1); -} - -} // TEST_SUITE("Pad_CpuAccTests") - -TEST_SUITE("Pad_GpuAccTests") -{ - -TEST_CASE ("Pad2d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Pad2dTest(backends); -} - -TEST_CASE ("Pad3d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Pad3dTest(backends); -} - -TEST_CASE ("Pad4d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Pad4dTest(backends); -} - -TEST_CASE ("Pad_Int8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PadInt8Test(backends); -} - -TEST_CASE ("Pad_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PadUint8Test(backends); -} - -TEST_CASE ("PadV22d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Pad2dTest(backends, tflite::BuiltinOperator_PADV2, -2.5); -} - -TEST_CASE ("PadV23d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Pad3dTest(backends, tflite::BuiltinOperator_PADV2, 2.0); -} - -TEST_CASE ("PadV24d_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - Pad4dTest(backends, tflite::BuiltinOperator_PADV2, -1.33); -} - -TEST_CASE ("PadV2_Int8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PadInt8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1); -} - -TEST_CASE ("PadV2_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PadUint8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1); -} - -} // TEST_SUITE("Pad_GpuAccTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/PadTestHelper.hpp b/delegate/src/test/PadTestHelper.hpp deleted file mode 100644 index e96bc4bfe3..0000000000 --- a/delegate/src/test/PadTestHelper.hpp +++ /dev/null @@ -1,224 +0,0 @@ -// -// Copyright © 2020, 2023 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 -{ - -template <typename T> -std::vector<char> CreatePadTfLiteModel( - tflite::BuiltinOperator padOperatorCode, - tflite::TensorType tensorType, - tflite::MirrorPadMode paddingMode, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& paddingTensorShape, - const std::vector<int32_t>& outputTensorShape, - const std::vector<int32_t>& paddingDim, - const std::vector<T> paddingValue, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - auto inputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 0, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - auto paddingTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(paddingTensorShape.data(), - paddingTensorShape.size()), - tflite::TensorType_INT32, - 1, - flatBufferBuilder.CreateString("padding")); - - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor}; - - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()), - sizeof(int32_t) * paddingDim.size()))); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - std::vector<int32_t> operatorInputs; - std::vector<int> subgraphInputs; - - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_PadOptions; - flatbuffers::Offset<void> operatorBuiltinOptions; - - if (padOperatorCode == tflite::BuiltinOperator_PAD) - { - operatorInputs = {{ 0, 1 }}; - subgraphInputs = {{ 0, 1 }}; - operatorBuiltinOptions = CreatePadOptions(flatBufferBuilder).Union(); - } - else if(padOperatorCode == tflite::BuiltinOperator_MIRROR_PAD) - { - operatorInputs = {{ 0, 1 }}; - subgraphInputs = {{ 0, 1 }}; - - operatorBuiltinOptionsType = BuiltinOptions_MirrorPadOptions; - operatorBuiltinOptions = CreateMirrorPadOptions(flatBufferBuilder, paddingMode).Union(); - } - else if (padOperatorCode == tflite::BuiltinOperator_PADV2) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingValue.data()), - sizeof(T)))); - - const std::vector<int32_t> shape = { 1 }; - auto padValueTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(shape.data(), - shape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("paddingValue"), - quantizationParameters); - - tensors.push_back(padValueTensor); - - operatorInputs = {{ 0, 1, 3 }}; - subgraphInputs = {{ 0, 1, 3 }}; - - operatorBuiltinOptionsType = BuiltinOptions_PadV2Options; - operatorBuiltinOptions = CreatePadV2Options(flatBufferBuilder).Union(); - } - - // create operator - const std::vector<int32_t> operatorOutputs{ 2 }; - flatbuffers::Offset <Operator> paddingOperator = - 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> 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(&paddingOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Pad Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - padOperatorCode); - - 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 PadTest(tflite::BuiltinOperator padOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - const std::vector<int32_t>& inputShape, - const std::vector<int32_t>& paddingShape, - std::vector<int32_t>& outputShape, - std::vector<T>& inputValues, - std::vector<int32_t>& paddingDim, - std::vector<T>& expectedOutputValues, - T paddingValue, - float quantScale = 1.0f, - int quantOffset = 0, - tflite::MirrorPadMode paddingMode = tflite::MirrorPadMode_SYMMETRIC) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreatePadTfLiteModel<T>(padOperatorCode, - tensorType, - paddingMode, - inputShape, - paddingShape, - outputShape, - paddingDim, - {paddingValue}, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - - 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); - - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); -} - -} // anonymous namespace diff --git a/delegate/src/test/Pooling2dTest.cpp b/delegate/src/test/Pooling2dTest.cpp deleted file mode 100644 index fd52aee70d..0000000000 --- a/delegate/src/test/Pooling2dTest.cpp +++ /dev/null @@ -1,1275 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "Pooling2dTestHelper.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 MaxPool2dFP32PaddingValidTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 1, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 12.0f, 7.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 2, - 2, - 2, - 2); -} - -void MaxPool2dInt8PaddingValidTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 1, 2, 1 }; - - std::vector<int8_t > inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { 12, 7 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 1); -} - -void MaxPool2dFP32PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 12.0f, 7.0f, 3.0f, -1.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2); -} - -void MaxPool2dInt8PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<int8_t> inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { 12, 7, 3, -1 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 1); -} - -void MaxPool2dFP32ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<float> inputValues = { -5.0f, -8.0f, -10.0f, 7.0f, - -8.0f, -12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 0.0f, 0.0f, 7.0f, 3.0f, 0.0f, 2.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU); -} - -void MaxPool2dInt8ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<int8_t> inputValues = { -5, -8, -10, 7, - -8, -12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { 1, 1, 7, 3, 1, 2 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU, - 2.0f, - 1); -} - -void MaxPool2dFP32Relu6Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, -8.0f, -10.0f, 7.0f, - -8.0f, -12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 0.0f, 0.0f, 3.0f, 0.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 1, - 1, - ::tflite::ActivationFunctionType_RELU6); -} - -void MaxPool2dInt8Relu6Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<int8_t> inputValues = { -5, -8, -10, 7, - -8, -12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { 1, 1, 3, 1 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 1, - 1, - ::tflite::ActivationFunctionType_RELU6, - 2.0f, - 1); -} - -void MaxPool2dUint8PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<uint8_t> inputValues = { 5, 8, 10, 7, - 8, 12, 15, 2, - 3, 4, 1, 11 }; - - std::vector<uint8_t> expectedOutputValues = { 12, 15, 4, 11 }; - - Pooling2dTest<uint8_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 1); -} - -void MaxPool2dUint8ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<uint8_t> inputValues = { 12, 8, 10, 15, - 8, 5, 7, 2, - 3, 4, 1, 11 }; - - std::vector<uint8_t> expectedOutputValues = { 12, 10, 15, 8, 7, 11 }; - - Pooling2dTest<uint8_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU, - 2.0f, - 1); -} - -void MaxPool2dInt16PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<int16_t> inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int16_t> expectedOutputValues = { 12, 7, 3, -1 }; - - Pooling2dTest<int16_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 0); -} - -void MaxPool2dInt16ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<int16_t> inputValues = { -5, -8, -10, 7, - -8, -12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int16_t> expectedOutputValues = { 0, 0, 7, 3, 0, 2 }; - - Pooling2dTest<int16_t>(tflite::BuiltinOperator_MAX_POOL_2D, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU, - 2.0f, - 0); -} - -void AveragePool2dFP32PaddingValidTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 1, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 5.75f, -4.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 2, - 2, - 2, - 2); -} - -void AveragePool2dInt8PaddingValidTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 1, 2, 1 }; - - std::vector<int8_t > inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { 6, -4 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 1); -} - -void AveragePool2dFP32PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 5.75f, -4.0f, -0.5f, -6.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2); -} - -void AveragePool2dInt8PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<int8_t > inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { 6, -4, -1, -6 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 1); -} - -void AveragePool2dFP32ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - -8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, 11.0f }; - - std::vector<float> expectedOutputValues = { 1.75f, 0.0f, 0.0f, 0.75f, 0.0f, 0.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU); -} - -void AveragePool2dInt8ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<int8_t> inputValues = { -5, 8, -10, 7, - -8, 12, -15, 2, - 3, -4, -1, 11 }; - - std::vector<int8_t> expectedOutputValues = { 2, 1, 1, 1, 1, 1 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU, - 2.5f, - 1); -} - -void AveragePool2dFP32Relu6Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - -8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, 11.0f }; - - std::vector<float> expectedOutputValues = { 0.0f, 0.0f, 3.0f, 0.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 1, - 1, - ::tflite::ActivationFunctionType_RELU6); -} - -void AveragePool2dInt8Relu6Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<int8_t> inputValues = { -5, 8, -10, 7, - -8, 12, -15, 2, - 3, -4, -1, 11 }; - - std::vector<int8_t> expectedOutputValues = { 1, 1, 3, 1 }; - - Pooling2dTest<int8_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 1, - 1, - ::tflite::ActivationFunctionType_RELU6, - 2.5f, - 1); -} - -void AveragePool2dUint8PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<uint8_t> inputValues = { 5, 8, 10, 7, - 8, 12, 15, 2, - 3, 4, 1, 11 }; - - std::vector<uint8_t> expectedOutputValues = { 8, 9, 4, 6 }; - - Pooling2dTest<uint8_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 1); -} - -void AveragePool2dUint8ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<uint8_t> inputValues = { 12, 8, 10, 15, - 8, 5, 7, 2, - 3, 4, 1, 11 }; - - std::vector<uint8_t> expectedOutputValues = { 8, 8, 9, 5, 4, 5 }; - - Pooling2dTest<uint8_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU, - 2.0f, - 1); -} - -void AveragePool2dInt16PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<int16_t > inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int16_t> expectedOutputValues = { 6, -4, -1, -6 }; - - Pooling2dTest<int16_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2, - tflite::ActivationFunctionType_NONE, - 2.5f, - 0); -} - -void AveragePool2dInt16ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<int16_t> inputValues = { -5, 8, -10, 7, - -8, 12, -15, 2, - 3, -4, -1, 11 }; - - std::vector<int16_t> expectedOutputValues = { 2, 0, 0, 1, 0, 0 }; - - Pooling2dTest<int16_t>(tflite::BuiltinOperator_AVERAGE_POOL_2D, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU, - 2.5f, - 0); -} - -void L2Pool2dFP32PaddingValidTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 1, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 8.616844f, 9.721111f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_L2_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 2, - 2, - 2, - 2); -} - -void L2Pool2dFP32PaddingSameTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { 8.616844f, 9.721111f, 3.535534f, 7.81025f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_L2_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 2, - 2); -} - -void L2Pool2dFP32ReluTest(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - -8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, 11.0f }; - - std::vector<float> expectedOutputValues = { 8.616844f, 11.543396f, 9.721111f, 7.632169f, 9.8234415f, 9.367497f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_L2_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_VALID, - 1, - 1, - 2, - 2, - ::tflite::ActivationFunctionType_RELU); -} - -void L2Pool2dFP32Relu6Test(std::vector<armnn::BackendId>& backends) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 2, 2, 1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - -8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, 11.0f }; - - std::vector<float> expectedOutputValues = { 5.0f, 6.0f, 3.0f, 1.0f }; - - Pooling2dTest<float>(tflite::BuiltinOperator_L2_POOL_2D, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - ::tflite::Padding_SAME, - 2, - 2, - 1, - 1, - ::tflite::ActivationFunctionType_RELU6); -} - -TEST_SUITE("Pooling2d_GpuAccTests") -{ - -TEST_CASE ("MaxPooling2d_FP32_PaddingValid_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_PaddingValid_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dInt8PaddingValidTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dInt8PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dFP32ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dInt8ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_Relu6_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dFP32Relu6Test(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_Relu6_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dInt8Relu6Test(backends); -} - -TEST_CASE ("MaxPooling2d_Uint8_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dUint8PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Uint8_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - MaxPool2dUint8ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_PaddingValid_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_PaddingValid_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dInt8PaddingValidTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dInt8PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dFP32ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_Relu6_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dFP32Relu6Test(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dInt8ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_Relu6_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dInt8Relu6Test(backends); -} - -TEST_CASE ("AveragePooling2d_Uint8_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dUint8PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Uint8_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - AveragePool2dUint8ReluTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_PaddingValid_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - L2Pool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_PaddingSame_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - L2Pool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_Relu_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - L2Pool2dFP32ReluTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_Relu6_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - L2Pool2dFP32Relu6Test(backends); -} - -} // TEST_SUITE("Pooling2d_GpuAccTests") - -TEST_SUITE("Pooling2d_CpuAccTests") -{ - -TEST_CASE ("MaxPooling2d_FP32_PaddingValid_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_PaddingValid_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dInt8PaddingValidTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dInt8PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dFP32ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dInt8ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_Relu6_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dFP32Relu6Test(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_Relu6_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dInt8Relu6Test(backends); -} - -TEST_CASE ("MaxPooling2d_Uint8_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dUint8PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Uint8_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - MaxPool2dUint8ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_PaddingValid_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_PaddingValid_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dInt8PaddingValidTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dInt8PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dFP32ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_Relu6_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dFP32Relu6Test(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dInt8ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_Relu6_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dInt8Relu6Test(backends); -} - -TEST_CASE ("AveragePooling2d_Uint8_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dUint8PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Uint8_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - AveragePool2dUint8ReluTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_PaddingValid_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - L2Pool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_PaddingSame_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - L2Pool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_Relu_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - L2Pool2dFP32ReluTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_Relu6_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - L2Pool2dFP32Relu6Test(backends); -} - -} // TEST_SUITE("Pooling2d_CpuAccTests") - -TEST_SUITE("Pooling2d_CpuRefTests") -{ - -TEST_CASE ("MaxPooling2d_FP32_PaddingValid_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_PaddingValid_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dInt8PaddingValidTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dInt8PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dFP32ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dInt8ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_FP32_Relu6_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dFP32Relu6Test(backends); -} - -TEST_CASE ("MaxPooling2d_Int8_Relu6_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dInt8Relu6Test(backends); -} - -TEST_CASE ("MaxPooling2d_Uint8_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dUint8PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Uint8_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dUint8ReluTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int16_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dInt16PaddingSameTest(backends); -} - -TEST_CASE ("MaxPooling2d_Int16_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - MaxPool2dInt16ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_PaddingValid_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_PaddingValid_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dInt8PaddingValidTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dInt8PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dFP32ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_FP32_Relu6_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dFP32Relu6Test(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dInt8ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int8_Relu6_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dInt8Relu6Test(backends); -} - -TEST_CASE ("AveragePooling2d_Uint8_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dUint8PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Uint8_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dUint8ReluTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int16_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dInt16PaddingSameTest(backends); -} - -TEST_CASE ("AveragePooling2d_Int16_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - AveragePool2dInt16ReluTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_PaddingValid_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - L2Pool2dFP32PaddingValidTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_PaddingSame_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - L2Pool2dFP32PaddingSameTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_Relu_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - L2Pool2dFP32ReluTest(backends); -} - -TEST_CASE ("L2Pooling2d_FP32_Relu6_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - L2Pool2dFP32Relu6Test(backends); -} - -} // TEST_SUITE("Pooling2d_CpuRefTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/Pooling2dTestHelper.hpp b/delegate/src/test/Pooling2dTestHelper.hpp deleted file mode 100644 index c7457dbb22..0000000000 --- a/delegate/src/test/Pooling2dTestHelper.hpp +++ /dev/null @@ -1,196 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreatePooling2dTfLiteModel( - tflite::BuiltinOperator poolingOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - tflite::Padding padding = tflite::Padding_SAME, - int32_t strideWidth = 0, - int32_t strideHeight = 0, - int32_t filterWidth = 0, - int32_t filterHeight = 0, - tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - flatbuffers::Offset<tflite::Buffer> buffers[3] = {CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder)}; - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - flatbuffers::Offset<Tensor> tensors[2] { - CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters), - - CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters) - }; - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_Pool2DOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreatePool2DOptions(flatBufferBuilder, - padding, - strideWidth, - strideHeight, - filterWidth, - filterHeight, - fusedActivation).Union(); - - const std::vector<int32_t> operatorInputs{0}; - const std::vector<int32_t> operatorOutputs{1}; - flatbuffers::Offset <Operator> poolingOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs), - operatorBuiltinOptionsType, - operatorBuiltinOptions); - - const int subgraphInputs[1] = {0}; - const int subgraphOutputs[1] = {1}; - flatbuffers::Offset <SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors, 2), - flatBufferBuilder.CreateVector<int32_t>(subgraphInputs, 1), - flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs, 1), - flatBufferBuilder.CreateVector(&poolingOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Pooling2d Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, poolingOperatorCode); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers, 3)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void Pooling2dTest(tflite::BuiltinOperator poolingOperatorCode, - 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, - tflite::Padding padding = tflite::Padding_SAME, - int32_t strideWidth = 0, - int32_t strideHeight = 0, - int32_t filterWidth = 0, - int32_t filterHeight = 0, - tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreatePooling2dTfLiteModel(poolingOperatorCode, - tensorType, - inputShape, - outputShape, - padding, - strideWidth, - strideHeight, - filterWidth, - filterHeight, - fusedActivation, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - // 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 - 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); -} - -} // anonymous namespace - - - - diff --git a/delegate/src/test/Pooling3dTest.cpp b/delegate/src/test/Pooling3dTest.cpp deleted file mode 100644 index c0a186210e..0000000000 --- a/delegate/src/test/Pooling3dTest.cpp +++ /dev/null @@ -1,431 +0,0 @@ -// -// 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 deleted file mode 100644 index 47e00f7b7f..0000000000 --- a/delegate/src/test/Pooling3dTestHelper.hpp +++ /dev/null @@ -1,298 +0,0 @@ -// -// Copyright © 2022-2023 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - - 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/delegate/src/test/PreluTest.cpp b/delegate/src/test/PreluTest.cpp deleted file mode 100644 index d9e08d20ca..0000000000 --- a/delegate/src/test/PreluTest.cpp +++ /dev/null @@ -1,134 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "PreluTestHelper.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 PreluFloatSimpleTest(std::vector <armnn::BackendId>& backends, bool isAlphaConst, bool isDynamicOutput = false) { - std::vector<int32_t> inputShape { 1, 2, 3 }; - std::vector<int32_t> alphaShape { 1 }; - std::vector<int32_t> outputShape { 1, 2, 3 }; - - if (isDynamicOutput) - { - outputShape.clear(); - } - - std::vector<float> inputData = { -14.f, 2.f, 0.f, 1.f, -5.f, 14.f }; - std::vector<float> alphaData = { 0.5f }; - std::vector<float> expectedOutput = { -7.f, 2.f, 0.f, 1.f, -2.5f, 14.f }; - - PreluTest(tflite::BuiltinOperator_PRELU, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - alphaShape, - outputShape, - inputData, - alphaData, - expectedOutput, - isAlphaConst); -} - -TEST_SUITE("Prelu_CpuRefTests") -{ - -TEST_CASE ("PreluFp32SimpleConstTest_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PreluFloatSimpleTest(backends, true); -} - -TEST_CASE ("PreluFp32SimpleTest_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PreluFloatSimpleTest(backends, false); -} - -TEST_CASE ("PreluFp32SimpleConstDynamicTest_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PreluFloatSimpleTest(backends, true, true); -} - -TEST_CASE ("PreluFp32SimpleDynamicTest_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - PreluFloatSimpleTest(backends, false, true); -} - -} // TEST_SUITE("Prelu_CpuRefTests") - -TEST_SUITE("Prelu_CpuAccTests") -{ - -TEST_CASE ("PreluFp32SimpleConstTest_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PreluFloatSimpleTest(backends, true); -} - -TEST_CASE ("PreluFp32SimpleTest_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PreluFloatSimpleTest(backends, false); -} - -TEST_CASE ("PreluFp32SimpleConstDynamicTest_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PreluFloatSimpleTest(backends, true, true); -} - -TEST_CASE ("PreluFp32SimpleDynamicTest_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - PreluFloatSimpleTest(backends, false, true); -} - -} // TEST_SUITE("Prelu_CpuAccTests") - -TEST_SUITE("Prelu_GpuAccTests") -{ - -TEST_CASE ("PreluFp32SimpleConstTest_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PreluFloatSimpleTest(backends, true); -} - -TEST_CASE ("PreluFp32SimpleTest_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PreluFloatSimpleTest(backends, false); -} - -TEST_CASE ("PreluFp32SimpleConstDynamicTest_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PreluFloatSimpleTest(backends, true, true); -} - -TEST_CASE ("PreluFp32SimpleDynamicTest_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - PreluFloatSimpleTest(backends, false, true); -} - -} // TEST_SUITE("Prelu_GpuAccTests") - -}
\ No newline at end of file diff --git a/delegate/src/test/PreluTestHelper.hpp b/delegate/src/test/PreluTestHelper.hpp deleted file mode 100644 index b50c37763f..0000000000 --- a/delegate/src/test/PreluTestHelper.hpp +++ /dev/null @@ -1,195 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputShape, - const std::vector<int32_t>& alphaShape, - const std::vector<int32_t>& outputShape, - std::vector<float>& alphaData, - bool alphaIsConstant) -{ - 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 *>(alphaData.data()), sizeof(float) * alphaData.size()))); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ 1.0f }), - flatBufferBuilder.CreateVector<int64_t>({ 0 })); - - auto inputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), - inputShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - auto alphaTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(alphaShape.data(), - alphaShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("alpha"), - quantizationParameters); - - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), - outputShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, alphaTensor, outputTensor }; - - const std::vector<int> operatorInputs{0, 1}; - const std::vector<int> operatorOutputs{2}; - flatbuffers::Offset <Operator> preluOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); - - std::vector<int> subgraphInputs{0}; - if (!alphaIsConstant) - { - subgraphInputs.push_back(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(&preluOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Prelu Operator Model"); - flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, preluOperatorCode); - - 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 PreluTest(tflite::BuiltinOperator preluOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - const std::vector<int32_t>& inputShape, - const std::vector<int32_t>& alphaShape, - std::vector<int32_t>& outputShape, - std::vector<float>& inputData, - std::vector<float>& alphaData, - std::vector<float>& expectedOutput, - bool alphaIsConstant) -{ - using namespace tflite; - - std::vector<char> modelBuffer = CreatePreluTfLiteModel(preluOperatorCode, - tensorType, - inputShape, - alphaShape, - outputShape, - alphaData, - alphaIsConstant); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - - CHECK(tfLiteModel != nullptr); - - 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<float>(tfLiteInterpreter, 0, inputData); - armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 0, inputData); - - // Set alpha data if not constant - if (!alphaIsConstant) { - armnnDelegate::FillInput<float>(tfLiteInterpreter, 1, alphaData); - armnnDelegate::FillInput<float>(armnnDelegateInterpreter, 1, alphaData); - } - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - - auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId); - - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); - - for (size_t i = 0; i < expectedOutput.size(); i++) - { - CHECK(expectedOutput[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteDelegateOutputData[i] == expectedOutput[i]); - CHECK(tfLiteDelegateOutputData[i] == armnnDelegateOutputData[i]); - } -} -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/QuantizationTest.cpp b/delegate/src/test/QuantizationTest.cpp deleted file mode 100644 index fbc2903d38..0000000000 --- a/delegate/src/test/QuantizationTest.cpp +++ /dev/null @@ -1,455 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "QuantizationTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -// Dequantize operator test functions. -void DequantizeUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - // Set input and output data - std::vector<uint8_t> inputValues - { - 0, 1, 2, 3, // Lower bounds - 252, 253, 254, 255 // Upper bounds - }; - std::vector<float> expectedOutputValues - { - 0.f, 1.f, 2.f, 3.f, - 252.f, 253.f, 254.f, 255.f - }; - - QuantizationTest<uint8_t, float>(tflite::BuiltinOperator_DEQUANTIZE, - ::tflite::TensorType_UINT8, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void DequantizeInt8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<int8_t> inputValues - { - -1, 0, 1, 2, - -128, -127, 126, 127 - }; - std::vector<float> expectedOutputValues - { - -1.f, 0.f, 1.f, 2.f, - -128.f, -127.f, 126.f, 127.f - }; - - QuantizationTest<int8_t , float>(tflite::BuiltinOperator_DEQUANTIZE, - ::tflite::TensorType_INT8, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void DequantizeInt16Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 5 }; - std::vector<int32_t> outputShape { 2, 5 }; - - std::vector<int16_t> inputValues - { - -1, 0, 1, 2, - -32768, -16384, 16384, 32767 - }; - std::vector<float> expectedOutputValues - { - -1.f, 0.f, 1.f, 2.f, - -32768.f, -16384.f, 16384.f, 32767.f - }; - - QuantizationTest<int16_t, float>(tflite::BuiltinOperator_DEQUANTIZE, - ::tflite::TensorType_INT16, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -// Quantize operator test functions. -void QuantizeFloat32Uint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - // Set input and output data - std::vector<float> inputValues - { - -1.f, 0.f, 1.f, 2.f, // Lower bounds - 252.f, 253.f, 255.f, 256.f // Upper bounds - }; - std::vector<uint8_t> expectedOutputValues - { - 0, 0, 1, 2, - 252, 253, 255, 255 - }; - - QuantizationTest<float, uint8_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_FLOAT32, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void QuantizeFloat32Int8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<float> inputValues - { - -1.f, 0.f, 1.f, 2.f, - -128.5f, -127.f, 126.f, 127.5f - }; - std::vector<int8_t> expectedOutputValues - { - -1, 0, 1, 2, - -128, -127, 126, 127 - }; - - QuantizationTest<float, int8_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_FLOAT32, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void QuantizeFloat32Int16Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<float> inputValues - { - -1.f, 0.f, 1.f, 2.f, - -32768.5f, -16384.f, 16384.f, 32767.5f - }; - std::vector<int16_t> expectedOutputValues - { - -1, 0, 1, 2, - -32768, -16384, 16384, 32767 - }; - - QuantizationTest<float, int16_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_FLOAT32, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void QuantizeInt16Int16Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<int16_t> inputValues - { - -1, 0, 1, 2, - -32768, -16384, 16384, 32767 - }; - std::vector<int16_t> expectedOutputValues - { - -1, 0, 1, 2, - -32768, -16384, 16384, 32767 - }; - - QuantizationTest<int16_t, int16_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_INT16, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void QuantizeInt16Int8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<int16_t> inputValues - { - -1, 0, 1, 2, - -32768, -16384, 16384, 32767 - }; - std::vector<int8_t> expectedOutputValues - { - -1, 0, 1, 2, - -128, -128, 127, 127 - }; - - QuantizationTest<int16_t, int8_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_INT16, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void QuantizeInt8Uint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<int8_t> inputValues - { - -1, 0, 1, 2, - -128, -127, 126, 127 - }; - std::vector<uint8_t> expectedOutputValues - { - 0, 0, 1, 2, - 0, 0, 126, 127 - }; - - QuantizationTest<int8_t, uint8_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_INT8, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -void QuantizeUint8Int8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 2, 4 }; - std::vector<int32_t> outputShape { 2, 4 }; - - std::vector<uint8_t> inputValues - { - 0, 1, 2, 3, - 126, 127, 254, 255 - }; - std::vector<int8_t> expectedOutputValues - { - 0, 1, 2, 3, - 126, 127, 127, 127 - }; - - QuantizationTest<uint8_t, int8_t>(tflite::BuiltinOperator_QUANTIZE, - ::tflite::TensorType_UINT8, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues); -} - -TEST_SUITE("CpuRef_QuantizationTests") -{ - -TEST_CASE ("DEQUANTIZE_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DequantizeUint8Test(backends); -} - - -TEST_CASE ("DEQUANTIZE_INT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DequantizeInt8Test(backends); -} - - -TEST_CASE ("DEQUANTIZE_INT16_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DequantizeInt16Test(backends); -} - - -TEST_CASE ("QUANTIZE_FLOAT32_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeFloat32Uint8Test(backends); -} - - -TEST_CASE ("QUANTIZE_FLOAT32_INT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeFloat32Int8Test(backends); -} - - -TEST_CASE ("QUANTIZE_FLOAT32_INT16_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeFloat32Int16Test(backends); -} - - -TEST_CASE ("QUANTIZE_INT16_INT16_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeInt16Int16Test(backends); -} - - -TEST_CASE ("QUANTIZE_INT16_INT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeInt16Int8Test(backends); -} - - - -TEST_CASE ("QUANTIZE_INT8_UINT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeInt8Uint8Test(backends); -} - - -TEST_CASE ("QUANTIZE_UINT8_INT8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - QuantizeUint8Int8Test(backends); -} - -} - -TEST_SUITE("CpuAcc_QuantizationTests") -{ - -// Dequantize Operator Tests -TEST_CASE ("DEQUANTIZE_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DequantizeUint8Test(backends); -} - -TEST_CASE ("DEQUANTIZE_INT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DequantizeInt8Test(backends); -} - -TEST_CASE ("DEQUANTIZE_INT16_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DequantizeInt16Test(backends); -} - -// Quantize Operator Tests -TEST_CASE ("QUANTIZE_FLOAT32_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - QuantizeFloat32Uint8Test(backends); -} - -TEST_CASE ("QUANTIZE_FLOAT32_INT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - QuantizeFloat32Int8Test(backends); -} - -TEST_CASE ("QUANTIZE_INT8_UINT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - QuantizeInt8Uint8Test(backends); -} - -TEST_CASE ("QUANTIZE_UINT8_INT8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - QuantizeUint8Int8Test(backends); -} - -} - -TEST_SUITE("GpuAcc_QuantizationTests") -{ - -// Dequantize Operator Tests -TEST_CASE ("DEQUANTIZE_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DequantizeUint8Test(backends); -} - -TEST_CASE ("DEQUANTIZE_INT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DequantizeInt8Test(backends); -} - -TEST_CASE ("DEQUANTIZE_INT16_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DequantizeInt16Test(backends); -} - -// Quantize Operator Tests -TEST_CASE ("QUANTIZE_FLOAT32_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - QuantizeFloat32Uint8Test(backends); -} - -TEST_CASE ("QUANTIZE_FLOAT32_INT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - QuantizeFloat32Int8Test(backends); -} - -TEST_CASE ("QUANTIZE_INT8_UINT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - QuantizeInt8Uint8Test(backends); -} - -TEST_CASE ("QUANTIZE_UINT8_INT8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - QuantizeUint8Int8Test(backends); -} - -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/QuantizationTestHelper.hpp b/delegate/src/test/QuantizationTestHelper.hpp deleted file mode 100644 index a8b102271a..0000000000 --- a/delegate/src/test/QuantizationTestHelper.hpp +++ /dev/null @@ -1,200 +0,0 @@ -// -// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> CreateQuantizationTfLiteModel(tflite::BuiltinOperator quantizationOperatorCode, - tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset }), - QuantizationDetails_CustomQuantization); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - inputTensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - outputTensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; - flatbuffers::Offset<void> operatorBuiltinOptions = 0; - switch (quantizationOperatorCode) - { - case BuiltinOperator_QUANTIZE: - { - operatorBuiltinOptionsType = BuiltinOptions_QuantizeOptions; - operatorBuiltinOptions = CreateQuantizeOptions(flatBufferBuilder).Union(); - break; - } - case BuiltinOperator_DEQUANTIZE: - { - operatorBuiltinOptionsType = BuiltinOptions_DequantizeOptions; - operatorBuiltinOptions = CreateDequantizeOptions(flatBufferBuilder).Union(); - break; - } - default: - break; - } - - const std::vector<int32_t> operatorInputs{0}; - const std::vector<int32_t> operatorOutputs{1}; - flatbuffers::Offset <Operator> quantizationOperator = - 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}; - 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(&quantizationOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Quantization Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, quantizationOperatorCode); - - 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 InputT, typename OutputT> -void QuantizationTest(tflite::BuiltinOperator quantizeOperatorCode, - tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<int32_t>& outputShape, - std::vector<InputT>& inputValues, - std::vector<OutputT>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateQuantizationTfLiteModel(quantizeOperatorCode, - inputTensorType, - outputTensorType, - inputShape, - outputShape, - quantScale, - quantOffset); - - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<InputT>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<InputT>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<OutputT>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<OutputT>(armnnDelegateOutputId); - - for (size_t i = 0; i < expectedOutputValues.size(); i++) - { - CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]); - } -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/RedefineTestHelper.hpp b/delegate/src/test/RedefineTestHelper.hpp deleted file mode 100644 index 7f811d56dd..0000000000 --- a/delegate/src/test/RedefineTestHelper.hpp +++ /dev/null @@ -1,202 +0,0 @@ -// -// Copyright © 2020, 2023 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> CreateRedefineTfLiteModel( - tflite::BuiltinOperator redefineOperatorCode, - tflite::TensorType tensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& outputTensorShape, - const std::vector<int32_t>& targetShape, - bool useOption = true, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - auto inputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - std::vector<flatbuffers::Offset<Tensor>> tensors; - std::vector<int32_t> operatorInputs; - std::vector<int> subgraphInputs; - flatbuffers::Offset<void> operatorBuiltinOptions; - - if (useOption) - { - buffers.push_back(CreateBuffer(flatBufferBuilder)); - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - tensors = { inputTensor, outputTensor}; - operatorInputs = {0}; - subgraphInputs = {0}; - operatorBuiltinOptions = CreateReshapeOptions( - flatBufferBuilder, - flatBufferBuilder.CreateVector(targetShape.data(), targetShape.size())).Union(); - } - else - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(targetShape.data()), - sizeof(int32_t) * targetShape.size()))); - int32_t size = static_cast<int32_t>(targetShape.size()); - auto shapeTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>( { size } ), - tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("shape")); - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - auto outputTensor = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - tensors = { inputTensor, outputTensor, shapeTensor }; - operatorInputs = {0, 2}; - subgraphInputs = {0, 2}; - operatorBuiltinOptions = CreateReshapeOptions(flatBufferBuilder).Union(); - } - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ReshapeOptions; - - const std::vector<int32_t> operatorOutputs{1}; - flatbuffers::Offset <Operator> redefineOperator = - 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> 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(&redefineOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Reshape Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - redefineOperatorCode); - - 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 RedefineTest(tflite::BuiltinOperator redefineOperatorCode, - tflite::TensorType tensorType, - const std::vector<armnn::BackendId>& backends, - const std::vector<int32_t>& inputShape, - std::vector<int32_t>& outputShape, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - std::vector<int32_t>& targetShape, - bool useOption = true, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateRedefineTfLiteModel(redefineOperatorCode, - tensorType, - inputShape, - outputShape, - targetShape, - useOption, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - // 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); - - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/ReduceTest.cpp b/delegate/src/test/ReduceTest.cpp deleted file mode 100644 index 9c11c8736c..0000000000 --- a/delegate/src/test/ReduceTest.cpp +++ /dev/null @@ -1,423 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ReduceTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void ReduceUint8KeepDimsTest(tflite::BuiltinOperator reduceOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<uint8_t>& expectedOutputValues) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 3 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0Values { 1, 2, 3, - 4, 3, 1 }; // Inputs - std::vector<int32_t> input1Values { 2 }; // Axis - - ReduceTest<uint8_t>(reduceOperatorCode, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - true); -} - -void ReduceUint8Test(tflite::BuiltinOperator reduceOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<uint8_t>& expectedOutputValues) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 3 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 3 }; - - std::vector<uint8_t> input0Values { 1, 2, 3, - 4, 3, 1 }; // Inputs - std::vector<int32_t> input1Values { 2 }; // Axis - - ReduceTest<uint8_t>(reduceOperatorCode, - ::tflite::TensorType_UINT8, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - false); -} - -void ReduceFp32KeepDimsTest(tflite::BuiltinOperator reduceOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<float>& expectedOutputValues) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 3 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 1, 3 }; - - std::vector<float> input0Values { 1001.0f, 11.0f, 1003.0f, - 10.0f, 1002.0f, 12.0f }; // Inputs - std::vector<int32_t> input1Values { 2 }; // Axis - - ReduceTest<float>(reduceOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - true); -} - -void ReduceFp32Test(tflite::BuiltinOperator reduceOperatorCode, - std::vector<armnn::BackendId>& backends, - std::vector<float>& expectedOutputValues) -{ - std::vector<int32_t> input0Shape { 1, 1, 2, 3 }; - std::vector<int32_t> input1Shape { 1 }; - std::vector<int32_t> expectedOutputShape { 1, 1, 3 }; - - std::vector<float> input0Values { 1001.0f, 11.0f, 1003.0f, - 10.0f, 1002.0f, 12.0f }; // Inputs - std::vector<int32_t> input1Values { 2 }; // Axis - - ReduceTest<float>(reduceOperatorCode, - ::tflite::TensorType_FLOAT32, - backends, - input0Shape, - input1Shape, - expectedOutputShape, - input0Values, - input1Values, - expectedOutputValues, - false); -} - -// REDUCE_MAX Tests -TEST_SUITE("ReduceMax_CpuRefTests") -{ - -TEST_CASE ("ReduceMax_Uint8_KeepDims_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<uint8_t> expectedOutputValues { 4, 3, 3 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<uint8_t> expectedOutputValues { 4, 3, 3 }; - ReduceUint8Test(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Fp32_KeepDims_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<float> expectedOutputValues { 1001.0f, 1002.0f, 1003.0f }; - ReduceFp32KeepDimsTest(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<float> expectedOutputValues { 1001.0f, 1002.0f, 1003.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -} // End of ReduceMax_CpuRefTests - -TEST_SUITE("ReduceMax_CpuAccTests") -{ - -TEST_CASE ("ReduceMax_Uint8_KeepDims_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<uint8_t> expectedOutputValues { 4, 3, 3 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<uint8_t> expectedOutputValues { 4, 3, 3 }; - ReduceUint8Test(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - - -TEST_CASE ("ReduceMax_Fp32_KeepDims_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<float> expectedOutputValues { 1001.0f, 1002.0f, 1003.0f }; - ReduceFp32KeepDimsTest(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<float> expectedOutputValues { 1001.0f, 1002.0f, 1003.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -} // End of ReduceMax_CpuAccTests - -TEST_SUITE("ReduceMax_GpuAccTests") -{ - -TEST_CASE ("ReduceMax_Uint8_KeepDims_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<uint8_t> expectedOutputValues { 4, 3, 3 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<uint8_t> expectedOutputValues { 4, 3, 3 }; - ReduceUint8Test(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - - -TEST_CASE ("ReduceMax_Fp32_KeepDims_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<float> expectedOutputValues { 1001.0f, 1002.0f, 1003.0f }; - ReduceFp32KeepDimsTest(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -TEST_CASE ("ReduceMax_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<float> expectedOutputValues { 1001.0f, 1002.0f, 1003.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_MAX, - backends, - expectedOutputValues); -} - -} // End of ReduceMax_GpuAccTests - -// REDUCE_MIN Tests -TEST_SUITE("ReduceMin_CpuRefTests") -{ - -TEST_CASE ("ReduceMin_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<float> expectedOutputValues { 10.0f, 11.0f, 12.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_MIN, - backends, - expectedOutputValues); -} - -} // End of ReduceMin_CpuRefTests - -TEST_SUITE("ReduceMin_CpuAccTests") -{ - -TEST_CASE ("ReduceMin_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<float> expectedOutputValues { 10.0f, 11.0f, 12.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_MIN, - backends, - expectedOutputValues); -} - -} // End of ReduceMin_CpuAccTests - -TEST_SUITE("ReduceMin_GpuAccTests") -{ - -TEST_CASE ("ReduceMin_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<float> expectedOutputValues { 10.0f, 11.0f, 12.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_MIN, - backends, - expectedOutputValues); -} - -} // End of ReduceMin_GpuAccTests - -// SUM Tests -TEST_SUITE("Sum_CpuRefTests") -{ - -TEST_CASE ("Sum_Uint8_KeepDims_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<uint8_t> expectedOutputValues { 5, 5, 4 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_SUM, - backends, - expectedOutputValues); -} - -TEST_CASE ("Sum_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<float> expectedOutputValues { 1011.0f, 1013.0f, 1015.0f }; - ReduceFp32Test(tflite::BuiltinOperator_SUM, - backends, - expectedOutputValues); -} - -} // End of Sum_CpuRefTests - -TEST_SUITE("Sum_CpuAccTests") -{ - -TEST_CASE ("Sum_Uint8_KeepDims_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<uint8_t> expectedOutputValues { 5, 5, 4 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_SUM, - backends, - expectedOutputValues); -} - -TEST_CASE ("Sum_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<float> expectedOutputValues { 1011.0f, 1013.0f, 1015.0f }; - ReduceFp32Test(tflite::BuiltinOperator_SUM, - backends, - expectedOutputValues); -} - -} // End of Sum_CpuAccTests - -TEST_SUITE("Sum_GpuAccTests") -{ - -TEST_CASE ("Sum_Uint8_KeepDims_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<uint8_t> expectedOutputValues { 5, 5, 4 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_SUM, - backends, - expectedOutputValues); -} - -TEST_CASE ("Sum_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<float> expectedOutputValues { 1011.0f, 1013.0f, 1015.0f }; - ReduceFp32Test(tflite::BuiltinOperator_SUM, - backends, - expectedOutputValues); -} - -} // End of Sum_GpuAccTests - -// PROD Tests -TEST_SUITE("Prod_CpuRefTests") -{ - -TEST_CASE ("Prod_Uint8_KeepDims_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<uint8_t> expectedOutputValues { 4, 6, 3 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_REDUCE_PROD, - backends, - expectedOutputValues); -} - -TEST_CASE ("Prod_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - std::vector<float> expectedOutputValues { 10010.0f, 11022.0f, 12036.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_PROD, - backends, - expectedOutputValues); -} - -} // End of Prod_CpuRefTests - -TEST_SUITE("Prod_CpuAccTests") -{ - -TEST_CASE ("Prod_Uint8_KeepDims_CpuAcc_Test" ) -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<uint8_t> expectedOutputValues { 4, 6, 3 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_REDUCE_PROD, - backends, - expectedOutputValues); -} - -TEST_CASE ("Prod_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - std::vector<float> expectedOutputValues { 10010.0f, 11022.0f, 12036.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_PROD, - backends, - expectedOutputValues); -} - -} // End of Prod_CpuAccTests - -TEST_SUITE("Prod_GpuAccTests") -{ - -TEST_CASE ("Prod_Uint8_KeepDims_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<uint8_t> expectedOutputValues { 4, 6, 3 }; - ReduceUint8KeepDimsTest(tflite::BuiltinOperator_REDUCE_PROD, - backends, - expectedOutputValues); -} - -TEST_CASE ("Prod_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - std::vector<float> expectedOutputValues { 10010.0f, 11022.0f, 12036.0f }; - ReduceFp32Test(tflite::BuiltinOperator_REDUCE_PROD, - backends, - expectedOutputValues); -} - -} // End of Prod_GpuAccTests - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ReduceTestHelper.hpp b/delegate/src/test/ReduceTestHelper.hpp deleted file mode 100644 index f500736080..0000000000 --- a/delegate/src/test/ReduceTestHelper.hpp +++ /dev/null @@ -1,228 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperatorCode, - tflite::TensorType tensorType, - std::vector<int32_t>& input0TensorShape, - std::vector<int32_t>& input1TensorShape, - const std::vector <int32_t>& outputTensorShape, - std::vector<int32_t>& axisData, - const bool keepDims, - float quantScale = 1.0f, - int quantOffset = 0, - bool kTfLiteNoQuantizationForQuantized = false) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - flatbuffers::Offset<tflite::Buffer> buffers[4] = { - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), - sizeof(int32_t) * axisData.size())), - CreateBuffer(flatBufferBuilder) - }; - - flatbuffers::Offset<tflite::QuantizationParameters> quantizationParametersAxis - = CreateQuantizationParameters(flatBufferBuilder); - - flatbuffers::Offset<tflite::QuantizationParameters> quantizationParameters; - - if (kTfLiteNoQuantizationForQuantized) - { - if ((quantScale == 1 || quantScale == 0) && quantOffset == 0) - { - // Creates quantization parameter with quantization.type = kTfLiteNoQuantization - quantizationParameters = CreateQuantizationParameters(flatBufferBuilder); - } - else - { - // Creates quantization parameter with quantization.type != kTfLiteNoQuantization - quantizationParameters = CreateQuantizationParameters( - flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - } - } - else - { - quantizationParameters = CreateQuantizationParameters( - flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - } - - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(), - input1TensorShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("axis"), - quantizationParametersAxis); - - // Create output tensor - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 3, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - // Create operator. Reduce operations MIN, MAX, SUM, MEAN, PROD uses ReducerOptions. - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union(); - - const std::vector<int> operatorInputs{ {0, 1} }; - const std::vector<int> operatorOutputs{ 2 }; - flatbuffers::Offset <Operator> reduceOperator = - 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 }; - 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(&reduceOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Reduce Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, reduceOperatorCode); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers, 4)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void ReduceTest(tflite::BuiltinOperator reduceOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& input0Shape, - std::vector<int32_t>& input1Shape, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& input0Values, - std::vector<int32_t>& input1Values, - std::vector<T>& expectedOutputValues, - const bool keepDims, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBufferArmNN = CreateReduceTfLiteModel(reduceOperatorCode, - tensorType, - input0Shape, - input1Shape, - expectedOutputShape, - input1Values, - keepDims, - quantScale, - quantOffset, - false); - std::vector<char> modelBufferTFLite = CreateReduceTfLiteModel(reduceOperatorCode, - tensorType, - input0Shape, - input1Shape, - expectedOutputShape, - input1Values, - keepDims, - quantScale, - quantOffset, - true); - - const Model* tfLiteModelArmNN = GetModel(modelBufferArmNN.data()); - const Model* tfLiteModelTFLite = GetModel(modelBufferTFLite.data()); - - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - CHECK(InterpreterBuilder(tfLiteModelArmNN, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - CHECK(InterpreterBuilder(tfLiteModelTFLite, ::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, input0Values); - armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, input0Values); - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - expectedOutputShape, - expectedOutputValues); - - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/ReshapeTest.cpp b/delegate/src/test/ReshapeTest.cpp deleted file mode 100644 index 11449e29b8..0000000000 --- a/delegate/src/test/ReshapeTest.cpp +++ /dev/null @@ -1,517 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RedefineTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -#include <half/half.hpp> - -using Half = half_float::half; - -namespace armnnDelegate -{ - -void ReshapeSimpleTest(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 3, 2, 2 }; - std::vector<int32_t> targetShape { 1, 3, 2, 2 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - RedefineTest<float>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption); -} - -using namespace half_float::literal; - -void ReshapeSimpleFloat16Test(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 3, 2, 2 }; - std::vector<int32_t> targetShape { 1, 3, 2, 2 }; - - std::vector<Half> inputValues = { 5._h, -8._h, -10._h, 7._h, - 8._h, 12._h, -15._h, 2._h, - 3._h, -4._h, -1._h, -11._h }; - - std::vector<Half> expectedOutputValues = { 5._h, -8._h, -10._h, 7._h, - 8._h, 12._h, -15._h, 2._h, - 3._h, -4._h, -1._h, -11._h }; - - RedefineTest<Half>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_FLOAT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption); -} - -void ReshapeReduceDimTest(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 1, 4, 3 }; - std::vector<int32_t> targetShape { 1, 4, 3 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - RedefineTest<float>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption); -} - -void ReshapeFlattenTest(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 6, 2 }; - std::vector<int32_t> targetShape { -1, 2 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - RedefineTest<float>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption); -} - -void ReshapeFlattenAllTest(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 12 }; - std::vector<int32_t> targetShape { -1 }; - - std::vector<float> inputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - std::vector<float> expectedOutputValues = { -5.0f, 8.0f, -10.0f, 7.0f, - 8.0f, 12.0f, -15.0f, 2.0f, - 3.0f, -4.0f, -1.0f, -11.0f }; - - RedefineTest<float>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption); -} - -void ReshapeInt8Test(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 6, 2 }; - std::vector<int32_t> targetShape { -1, 2 }; - - std::vector<int8_t> inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int8_t> expectedOutputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - RedefineTest<int8_t>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_INT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption, - 2.5f, - 1); -} - -void ReshapeUint8Test(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 6, 2 }; - std::vector<int32_t> targetShape { -1, 2 }; - - std::vector<uint8_t> inputValues = { 5, 8, 10, 7, - 8, 12, 15, 2, - 3, 4, 1, 11 }; - - std::vector<uint8_t> expectedOutputValues = { 5, 8, 10, 7, - 8, 12, 15, 2, - 3, 4, 1, 11 }; - - RedefineTest<uint8_t>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption, - 2.5f, - 1); -} - -void ReshapeInt16Test(std::vector<armnn::BackendId>& backends, bool useOption = true) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 3, 4, 1 }; - std::vector<int32_t> outputShape { 6, 2 }; - std::vector<int32_t> targetShape { -1, 2 }; - - std::vector<int16_t> inputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - std::vector<int16_t> expectedOutputValues = { -5, 8, -10, 7, - 8, 12, -15, 2, - 3, -4, -1, -11 }; - - RedefineTest<int16_t>(tflite::BuiltinOperator_RESHAPE, - ::tflite::TensorType_INT16, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - targetShape, - useOption, - 2.5f, - 0); -} - -TEST_SUITE("Reshape_GpuAccTests") -{ - -TEST_CASE ("Reshape_Simple_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeSimpleTest(backends); -} - -TEST_CASE ("Reshape_ReduceDimension_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeReduceDimTest(backends); -} - -TEST_CASE ("Reshape_Flatten_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeFlattenTest(backends); -} - -TEST_CASE ("Reshape_FlattenAll_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeFlattenAllTest(backends); -} - -TEST_CASE ("Reshape_Int8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeInt8Test(backends); -} - -TEST_CASE ("Reshape_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeUint8Test(backends); -} - -TEST_CASE ("Reshape_Float16_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeSimpleFloat16Test(backends); -} - -TEST_CASE ("Reshape_Simple_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeSimpleTest(backends, false); -} - -TEST_CASE ("Reshape_ReduceDimension_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeReduceDimTest(backends, false); -} - -TEST_CASE ("Reshape_Flatten_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeFlattenTest(backends, false); -} - -TEST_CASE ("Reshape_FlattenAll_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeFlattenAllTest(backends, false); -} - -TEST_CASE ("Reshape_Int8_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeInt8Test(backends, false); -} - -TEST_CASE ("Reshape_Uint8_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeUint8Test(backends, false); -} - -TEST_CASE ("Reshape_Float16_ShapeTensor_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - ReshapeSimpleFloat16Test(backends, false); -} - -} // TEST_SUITE("Reshape_GpuAccTests") - -TEST_SUITE("Reshape_CpuAccTests") -{ - -TEST_CASE ("Reshape_Simple_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeSimpleTest(backends); -} - -TEST_CASE ("Reshape_ReduceDimension_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeReduceDimTest(backends); -} - -TEST_CASE ("Reshape_Flatten_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeFlattenTest(backends); -} - -TEST_CASE ("Reshape_FlattenAll_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeFlattenAllTest(backends); -} - -TEST_CASE ("Reshape_Int8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeInt8Test(backends); -} - -TEST_CASE ("Reshape_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeUint8Test(backends); -} - -TEST_CASE ("Reshape_Float16_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeSimpleFloat16Test(backends); -} - -TEST_CASE ("Reshape_Simple_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeSimpleTest(backends, false); -} - -TEST_CASE ("Reshape_ReduceDimension_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeReduceDimTest(backends, false); -} - -TEST_CASE ("Reshape_Flatten_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeFlattenTest(backends, false); -} - -TEST_CASE ("Reshape_FlattenAll_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeFlattenAllTest(backends, false); -} - -TEST_CASE ("Reshape_Int8_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeInt8Test(backends, false); -} - -TEST_CASE ("Reshape_Uint8_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeUint8Test(backends, false); -} - -TEST_CASE ("Reshape_Float16_ShapeTensor_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - ReshapeSimpleFloat16Test(backends, false); -} - -} // TEST_SUITE("Reshape_CpuAccTests") - -TEST_SUITE("Reshape_CpuRefTests") -{ - -TEST_CASE ("Reshape_Simple_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeSimpleTest(backends); -} - -TEST_CASE ("Reshape_ReduceDimension_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeReduceDimTest(backends); -} - -TEST_CASE ("Reshape_Flatten_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeFlattenTest(backends); -} - -TEST_CASE ("Reshape_FlattenAll_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeFlattenAllTest(backends); -} - -TEST_CASE ("Reshape_Int8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeInt8Test(backends); -} - -TEST_CASE ("Reshape_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeUint8Test(backends); -} - -TEST_CASE ("Reshape_Int16_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeInt16Test(backends); -} - -TEST_CASE ("Reshape_Float16_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeSimpleFloat16Test(backends); -} - -TEST_CASE ("Reshape_Simple_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeSimpleTest(backends, false); -} - -TEST_CASE ("Reshape_ReduceDimension_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeReduceDimTest(backends, false); -} - -TEST_CASE ("Reshape_Flatten_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeFlattenTest(backends, false); -} - -TEST_CASE ("Reshape_FlattenAll_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeFlattenAllTest(backends, false); -} - -TEST_CASE ("Reshape_Int8_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeInt8Test(backends, false); -} - -TEST_CASE ("Reshape_Uint8_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeUint8Test(backends, false); -} - -TEST_CASE ("Reshape_Int16_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeInt16Test(backends, false); -} - -TEST_CASE ("Reshape_Float16_ShapeTensor_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ReshapeSimpleFloat16Test(backends, false); -} - -} // TEST_SUITE("Reshape_CpuRefTests") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ResizeTest.cpp b/delegate/src/test/ResizeTest.cpp deleted file mode 100644 index 394ad6c7ae..0000000000 --- a/delegate/src/test/ResizeTest.cpp +++ /dev/null @@ -1,134 +0,0 @@ -// -// 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 deleted file mode 100644 index 6937a4ba43..0000000000 --- a/delegate/src/test/ResizeTestHelper.hpp +++ /dev/null @@ -1,194 +0,0 @@ -// -// Copyright © 2020, 2023 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(sizeTensorData.data()), - sizeof(int32_t) * sizeTensorData.size()))); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - 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")); - - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), - sizeTensorShape.size()), - TensorType_INT32, - 2, - flatBufferBuilder.CreateString("size_input_tensor")); - - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - inputTensorType, - 3, - 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/RoundTest.cpp b/delegate/src/test/RoundTest.cpp deleted file mode 100644 index 9d323f3700..0000000000 --- a/delegate/src/test/RoundTest.cpp +++ /dev/null @@ -1,72 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RoundTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void FloorFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape {1, 3, 2, 3}; - std::vector<int32_t> outputShape {1, 3, 2, 3}; - - std::vector<float> inputValues { -37.5f, -15.2f, -8.76f, -2.0f, -1.5f, -1.3f, -0.5f, -0.4f, 0.0f, - 1.0f, 0.4f, 0.5f, 1.3f, 1.5f, 2.0f, 8.76f, 15.2f, 37.5f }; - - std::vector<float> expectedOutputValues { -38.0f, -16.0f, -9.0f, -2.0f, -2.0f, -2.0f, -1.0f, -1.0f, 0.0f, - 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 2.0f, 8.0f, 15.0f, 37.0f }; - - RoundTest<float>(tflite::BuiltinOperator_FLOOR, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - inputValues, - expectedOutputValues); -} - -// FLOOR Test Suite -TEST_SUITE("FLOOR_CpuRefTests") -{ - -TEST_CASE ("FLOOR_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - FloorFp32Test(backends); -} - -} - -TEST_SUITE("FLOOR_CpuAccTests") -{ - -TEST_CASE ("FLOOR_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - FloorFp32Test(backends); -} - -} - -TEST_SUITE("FLOOR_GpuAccTests") -{ - -TEST_CASE ("FLOOR_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - FloorFp32Test(backends); -} - -} -// End of FLOOR Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/RoundTestHelper.hpp b/delegate/src/test/RoundTestHelper.hpp deleted file mode 100644 index 6638607dcf..0000000000 --- a/delegate/src/test/RoundTestHelper.hpp +++ /dev/null @@ -1,163 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateRoundTfLiteModel(tflite::BuiltinOperator roundOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& tensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - const std::vector<int32_t> operatorInputs({0}); - const std::vector<int32_t> operatorOutputs({1}); - - flatbuffers::Offset<Operator> roundOperator; - flatbuffers::Offset<flatbuffers::String> modelDescription; - flatbuffers::Offset<OperatorCode> operatorCode; - - switch (roundOperatorCode) - { - case tflite::BuiltinOperator_FLOOR: - default: - roundOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); - modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Floor Operator Model"); - operatorCode = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_FLOOR); - break; - } - const std::vector<int32_t> subgraphInputs({0}); - const std::vector<int32_t> 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(&roundOperator, 1)); - - 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 RoundTest(tflite::BuiltinOperator roundOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& shape, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateRoundTfLiteModel(roundOperatorCode, - tensorType, - shape, - 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 - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - shape, - expectedOutputValues, - 0); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} - -} // anonymous namespace diff --git a/delegate/src/test/ShapeTest.cpp b/delegate/src/test/ShapeTest.cpp deleted file mode 100644 index b49910adf6..0000000000 --- a/delegate/src/test/ShapeTest.cpp +++ /dev/null @@ -1,45 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "ShapeTestHelper.hpp" - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void ShapeSimpleTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape{ 1, 3, 2, 3 }; - - std::vector<int32_t> inputValues{ 1, 1, 1, 1, 1, 1, 1, 1, - 1, 1, 1, 1, 1, 1, 1, 1, }; - - std::vector<int32_t> expectedOutputShape{ 4 }; - std::vector<int32_t> expectedOutputValues{ 1, 3, 2, 3 }; - - ShapeTest<int32_t, int32_t>(::tflite::TensorType_INT32, - ::tflite::TensorType_INT32, - backends, - inputShape, - inputValues, - expectedOutputValues, - expectedOutputShape); -} - -// SHAPE Test Suite -TEST_SUITE("SHAPE_CpuRefTests") -{ - -TEST_CASE("SHAPE_Simple_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - ShapeSimpleTest(backends); -} - -} -// End of SHAPE Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/ShapeTestHelper.hpp b/delegate/src/test/ShapeTestHelper.hpp deleted file mode 100644 index 9b3d574e23..0000000000 --- a/delegate/src/test/ShapeTestHelper.hpp +++ /dev/null @@ -1,173 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateShapeTfLiteModel(tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& outputTensorShape, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - inputTensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - outputTensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - const std::vector<int32_t> operatorInputs({ 0 }); - const std::vector<int32_t> operatorOutputs({ 1 }); - - flatbuffers::Offset<Operator> shapeOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), - operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), - operatorOutputs.size()), - BuiltinOptions_ShapeOptions, - CreateShapeOptions(flatBufferBuilder, outputTensorType).Union()); - - flatbuffers::Offset<flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: SHAPE Operator Model"); - - flatbuffers::Offset<OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_SHAPE); - - const std::vector<int32_t> subgraphInputs({ 0 }); - const std::vector<int32_t> 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(&shapeOperator, 1)); - - 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 K> -void ShapeTest(tflite::TensorType inputTensorType, - tflite::TensorType outputTensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<T>& inputValues, - std::vector<K>& expectedOutputValues, - std::vector<int32_t>& expectedOutputShape, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateShapeTfLiteModel(inputTensorType, - outputTensorType, - inputShape, - expectedOutputShape, - 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 - armnnDelegate::CompareOutputData<K>(tfLiteDelegate, - armnnDelegate, - expectedOutputShape, - expectedOutputValues, - 0); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} - -} // anonymous namespace diff --git a/delegate/src/test/SliceTest.cpp b/delegate/src/test/SliceTest.cpp deleted file mode 100644 index 1d7133f1fd..0000000000 --- a/delegate/src/test/SliceTest.cpp +++ /dev/null @@ -1,81 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "SliceTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void SliceFixtureSimpleTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3 }; - std::vector<int32_t> outputShape { 2, 1, 3 }; - std::vector<int32_t> beginShape { 3 }; - std::vector<int32_t> sizeShape { 3 }; - - std::vector<int32_t> beginData { 1, 0, 0 }; - std::vector<int32_t> sizeData { 2, 1, 3 }; - std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, - 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f }; - std::vector<float> outputData { 3.0f, 3.0f, 3.0f, - 5.0f, 5.0f, 5.0f }; - - SliceTestImpl<float>( - backends, - inputData, - outputData, - beginData, - sizeData, - inputShape, - beginShape, - sizeShape, - outputShape); -} - -TEST_SUITE("Slice_CpuRefTests") -{ - -TEST_CASE ("Slice_Simple_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SliceFixtureSimpleTest(backends); -} - -} // Slice_CpuRefTests TestSuite - - - -TEST_SUITE("Slice_CpuAccTests") -{ - -TEST_CASE ("Slice_Simple_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SliceFixtureSimpleTest(backends); -} - -} // Slice_CpuAccTests TestSuite - - - -TEST_SUITE("StridedSlice_GpuAccTests") -{ - -TEST_CASE ("Slice_Simple_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SliceFixtureSimpleTest(backends); -} - -} // Slice_GpuAccTests TestSuite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/SliceTestHelper.hpp b/delegate/src/test/SliceTestHelper.hpp deleted file mode 100644 index 94c076b4f7..0000000000 --- a/delegate/src/test/SliceTestHelper.hpp +++ /dev/null @@ -1,183 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "TestUtils.hpp" - -#include <armnn_delegate.hpp> -#include <armnn/DescriptorsFwd.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> CreateSliceTfLiteModel(tflite::TensorType tensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& beginTensorData, - const std::vector<int32_t>& sizeTensorData, - const std::vector<int32_t>& beginTensorShape, - const std::vector<int32_t>& sizeTensorShape, - const std::vector<int32_t>& outputTensorShape) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - flatbuffers::Offset<tflite::Buffer> buffers[5] = { - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), - sizeof(int32_t) * beginTensorData.size())), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(sizeTensorData.data()), - sizeof(int32_t) * sizeTensorData.size())), - CreateBuffer(flatBufferBuilder) - }; - - std::array<flatbuffers::Offset<Tensor>, 4> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input")); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(), - beginTensorShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("begin_tensor")); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(), - sizeTensorShape.size()), - ::tflite::TensorType_INT32, - 3, - flatBufferBuilder.CreateString("size_tensor")); - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 4, - flatBufferBuilder.CreateString("output")); - - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SliceOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateSliceOptions(flatBufferBuilder).Union(); - - const std::vector<int> operatorInputs{ 0, 1, 2 }; - const std::vector<int> operatorOutputs{ 3 }; - flatbuffers::Offset <Operator> sliceOperator = - 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(&sliceOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Slice Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - BuiltinOperator_SLICE); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers, 5)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void SliceTestImpl(std::vector<armnn::BackendId>& backends, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - std::vector<int32_t>& beginTensorData, - std::vector<int32_t>& sizeTensorData, - std::vector<int32_t>& inputTensorShape, - std::vector<int32_t>& beginTensorShape, - std::vector<int32_t>& sizeTensorShape, - std::vector<int32_t>& outputTensorShape) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateSliceTfLiteModel( - ::tflite::TensorType_FLOAT32, - inputTensorShape, - beginTensorData, - sizeTensorData, - beginTensorShape, - sizeTensorShape, - outputTensorShape); - - auto 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 - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - outputTensorShape, - expectedOutputValues); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} // End of Slice Test - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/SoftmaxTest.cpp b/delegate/src/test/SoftmaxTest.cpp deleted file mode 100644 index 3339c09918..0000000000 --- a/delegate/src/test/SoftmaxTest.cpp +++ /dev/null @@ -1,77 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "SoftmaxTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ -TEST_SUITE ("Softmax_GpuAccTests") -{ - -TEST_CASE ("Softmax_Standard_Beta_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - std::vector<float> expectedOutput = {0.00994190481, 0.0445565246, 0.0734612942, 0.329230666, 0.542809606, - 0.710742831, 0.158588171, 0.0961885825, 0.0214625746, 0.0130177103}; - SoftmaxTestCase(tflite::BuiltinOperator_SOFTMAX, backends, 1, expectedOutput); -} - -TEST_CASE ("Softmax_Different_Beta_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - std::vector<float> expectedOutput = {0.0946234912, 0.148399189, 0.172415257, 0.270400971, 0.314161092, 0.352414012, - 0.224709094, 0.193408906, 0.123322964, 0.106145054}; - SoftmaxTestCase(tflite::BuiltinOperator_SOFTMAX, backends, 0.3, expectedOutput); - -} - -TEST_CASE ("Log_Softmax_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - std::vector<float> expectedOutput = - {-4.61099672, -3.11099672, -2.61099672, -1.11099672, -0.610996664, - -0.341444582, -1.84144461, -2.34144449, -3.84144449, -4.34144449}; - SoftmaxTestCase(tflite::BuiltinOperator_LOG_SOFTMAX, backends, 0, expectedOutput); -} -} // TEST_SUITE ("Softmax_GpuAccTests") - -TEST_SUITE ("Softmax_CpuRefTests") -{ - -TEST_CASE ("Softmax_Standard_Beta_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<float> expectedOutput = { - 0.00994190481, 0.0445565246, 0.0734612942, 0.329230666, 0.542809606, - 0.710742831, 0.158588171, 0.0961885825, 0.0214625746, 0.0130177103}; - SoftmaxTestCase(tflite::BuiltinOperator_SOFTMAX, backends, 1, expectedOutput); -} - -TEST_CASE ("Softmax_Different_Beta_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<float> expectedOutput = { - 0.0946234912, 0.148399189, 0.172415257, 0.270400971, 0.314161092, - 0.352414012, 0.224709094, 0.193408906, 0.123322964, 0.106145054}; - SoftmaxTestCase(tflite::BuiltinOperator_SOFTMAX, backends, 0.3, expectedOutput); -} - -TEST_CASE ("Log_Softmax_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - std::vector<float> expectedOutput = - {-4.61099672, -3.11099672, -2.61099672, -1.11099672, -0.610996664, - -0.341444582, -1.84144461, -2.34144449, -3.84144449, -4.34144449}; - SoftmaxTestCase(tflite::BuiltinOperator_LOG_SOFTMAX, backends, 0, expectedOutput); -} -} // TEST_SUITE ("Softmax_CpuRefTests") -} // namespace armnnDelegate diff --git a/delegate/src/test/SoftmaxTestHelper.hpp b/delegate/src/test/SoftmaxTestHelper.hpp deleted file mode 100644 index f3367f9d24..0000000000 --- a/delegate/src/test/SoftmaxTestHelper.hpp +++ /dev/null @@ -1,194 +0,0 @@ -// -// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <armnn_delegate.hpp> -#include <armnnUtils/FloatingPointComparison.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> CreateSoftmaxTfLiteModel(tflite::BuiltinOperator softmaxOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& tensorShape, - float beta) -{ - 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)); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 1); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), - tensorShape.size()), - tensorType, - 2); - - const std::vector<int32_t> operatorInputs({0}); - const std::vector<int32_t> operatorOutputs({1}); - - flatbuffers::Offset<Operator> softmaxOperator; - flatbuffers::Offset<flatbuffers::String> modelDescription; - flatbuffers::Offset<OperatorCode> operatorCode; - - switch (softmaxOperatorCode) - { - case tflite::BuiltinOperator_SOFTMAX: - softmaxOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_SoftmaxOptions, - CreateSoftmaxOptions(flatBufferBuilder, beta).Union()); - modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: Softmax Operator Model"); - operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_SOFTMAX); - break; - case tflite::BuiltinOperator_LOG_SOFTMAX: - softmaxOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_LogSoftmaxOptions, - CreateLogSoftmaxOptions(flatBufferBuilder).Union()); - flatBufferBuilder.CreateString("ArmnnDelegate: Log-Softmax Operator Model"); - operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_LOG_SOFTMAX); - break; - default: - break; - } - const std::vector<int32_t> subgraphInputs({0}); - const std::vector<int32_t> 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(&softmaxOperator, 1)); - 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()); -} - -void SoftmaxTest(tflite::BuiltinOperator softmaxOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& shape, - std::vector<float>& inputValues, - std::vector<float>& expectedOutputValues, - float beta = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateSoftmaxTfLiteModel(softmaxOperatorCode, - tensorType, - shape, - beta); - - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteInterpreterInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteInterpreterInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); - - for (size_t i = 0; i < inputValues.size(); ++i) - { - CHECK(armnnUtils::within_percentage_tolerance(expectedOutputValues[i], armnnDelegateOutputData[i], 0.1)); - CHECK(armnnUtils::within_percentage_tolerance(tfLiteInterpreterOutputData[i], - armnnDelegateOutputData[i], 0.1)); - } -} - - -/// Convenience function to run softmax and log-softmax test cases -/// \param operatorCode tflite::BuiltinOperator_SOFTMAX or tflite::BuiltinOperator_LOG_SOFTMAX -/// \param backends armnn backends to target -/// \param beta multiplicative parameter to the softmax function -/// \param expectedOutput to be checked against transformed input -void SoftmaxTestCase(tflite::BuiltinOperator operatorCode, - std::vector<armnn::BackendId> backends, float beta, std::vector<float> expectedOutput) { - std::vector<float> input = { - 1.0, 2.5, 3.0, 4.5, 5.0, - -1.0, -2.5, -3.0, -4.5, -5.0}; - std::vector<int32_t> shape = {2, 5}; - - SoftmaxTest(operatorCode, - tflite::TensorType_FLOAT32, - backends, - shape, - input, - expectedOutput, - beta); -} - -} // anonymous namespace diff --git a/delegate/src/test/SpaceDepthTest.cpp b/delegate/src/test/SpaceDepthTest.cpp deleted file mode 100644 index f80e749b87..0000000000 --- a/delegate/src/test/SpaceDepthTest.cpp +++ /dev/null @@ -1,207 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "SpaceDepthTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void DepthToSpaceFp32Test(std::vector<armnn::BackendId>& backends, int blockSize) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 4 }; - std::vector<int32_t> outputShape { 1, 4, 4, 1 }; - - std::vector<float> inputValues = { 1.f, 2.f, 3.f, 4.f, - 5.f, 6.f, 7.f, 8.f, - 9.f, 10.f, 11.f, 12.f, - 13.f, 14.f, 15.f, 16.f }; - - std::vector<float> expectedOutputValues = { 1.f, 2.f, 5.f, 6.f, - 3.f, 4.f, 7.f, 8.f, - 9.f, 10.f, 13.f, 14.f, - 11.f, 12.f, 15.f, 16.f }; - - SpaceDepthTest<float>(tflite::BuiltinOperator_DEPTH_TO_SPACE, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - blockSize); -} - -void DepthToSpaceUint8Test(std::vector<armnn::BackendId>& backends, int blockSize) -{ - // Set input data - std::vector<int32_t> inputShape { 2, 1, 1, 4 }; - std::vector<int32_t> outputShape { 2, 2, 2, 1 }; - - std::vector<uint8_t> inputValues = { 1, 2, 3, 4, - 5, 6, 7, 8 }; - - std::vector<uint8_t> expectedOutputValues = { 1, 2, 3, 4, - 5, 6, 7, 8 }; - - SpaceDepthTest<uint8_t>(tflite::BuiltinOperator_DEPTH_TO_SPACE, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - blockSize); -} - -void SpaceToDepthFp32Test(std::vector<armnn::BackendId>& backends, int blockSize) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 2 }; - std::vector<int32_t> outputShape { 1, 1, 1, 8 }; - - std::vector<float> inputValues = { 1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f }; - std::vector<float> expectedOutputValues = { 1.4f, 2.3f, 3.2f, 4.1f, 5.4f, 6.3f, 7.2f, 8.1f }; - - SpaceDepthTest<float>(tflite::BuiltinOperator_SPACE_TO_DEPTH, - ::tflite::TensorType_FLOAT32, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - blockSize); -} - -void SpaceToDepthUint8Test(std::vector<armnn::BackendId>& backends, int blockSize) -{ - // Set input data - std::vector<int32_t> inputShape { 1, 2, 2, 1 }; - std::vector<int32_t> outputShape { 1, 1, 1, 4 }; - - std::vector<uint8_t> inputValues = { 1, 2, 3, 2 }; - std::vector<uint8_t> expectedOutputValues = { 1, 2, 3, 2 }; - - SpaceDepthTest<uint8_t>(tflite::BuiltinOperator_SPACE_TO_DEPTH, - ::tflite::TensorType_UINT8, - backends, - inputShape, - outputShape, - inputValues, - expectedOutputValues, - blockSize); -} - -TEST_SUITE("DepthToSpace_CpuRefTests") -{ - -TEST_CASE ("DepthToSpaceFp32Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DepthToSpaceFp32Test(backends, 2); -} - -TEST_CASE ("DepthToSpaceUint8Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - DepthToSpaceUint8Test(backends, 2); -} - -} // TEST_SUITE("DepthToSpace_CpuRefTests") - - -TEST_SUITE("DepthToSpace_CpuAccTests") -{ - -TEST_CASE ("DepthToSpaceFp32Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DepthToSpaceFp32Test(backends, 2); -} - -TEST_CASE ("DepthToSpaceUint8Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - DepthToSpaceUint8Test(backends, 2); -} - -} // TEST_SUITE("DepthToSpace_CpuAccTests") - -TEST_SUITE("DepthToSpace_GpuAccTests") -{ - -TEST_CASE ("DepthToSpaceFp32Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DepthToSpaceFp32Test(backends, 2); -} - -TEST_CASE ("DepthToSpaceUint8Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - DepthToSpaceUint8Test(backends, 2); -} - -} // TEST_SUITE("DepthToSpace_GpuAccTests") - -TEST_SUITE("SpaceToDepth_CpuRefTests") -{ - -TEST_CASE ("SpaceToDepthFp32Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - SpaceToDepthFp32Test(backends, 2); -} - -TEST_CASE ("SpaceToDepthUint8Test_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - SpaceToDepthUint8Test(backends, 2); -} - -} // TEST_SUITE("SpaceToDepth_CpuRefTests") - -TEST_SUITE("SpaceToDepth_CpuAccTests") -{ - -TEST_CASE ("SpaceToDepthFp32Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - SpaceToDepthFp32Test(backends, 2); -} - -TEST_CASE ("SpaceToDepthUint8Test_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc }; - SpaceToDepthUint8Test(backends, 2); -} - -} // TEST_SUITE("SpaceToDepth_CpuAccTests") - -TEST_SUITE("SpaceToDepth_GpuAccTests") -{ - -TEST_CASE ("SpaceToDepthFp32Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - SpaceToDepthFp32Test(backends, 2); -} - -TEST_CASE ("SpaceToDepthUint8Test_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc }; - SpaceToDepthUint8Test(backends, 2); -} - -} // TEST_SUITE("SpaceToDepth_GpuAccTests") - -} // namespace armnnDelegate diff --git a/delegate/src/test/SpaceDepthTestHelper.hpp b/delegate/src/test/SpaceDepthTestHelper.hpp deleted file mode 100644 index 737e199ef7..0000000000 --- a/delegate/src/test/SpaceDepthTestHelper.hpp +++ /dev/null @@ -1,168 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateSpaceDepthTfLiteModel(tflite::BuiltinOperator spaceDepthOperatorCode, - tflite::TensorType tensorType, - const std::vector <int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - int32_t blockSize) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ 1.0f }), - flatBufferBuilder.CreateVector<int64_t>({ 0 })); - - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - std::array<flatbuffers::Offset<Tensor>, 2> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 2, - flatBufferBuilder.CreateString("output"), - quantizationParameters); - - const std::vector<int32_t> operatorInputs({0}); - const std::vector<int32_t> operatorOutputs({1}); - - flatbuffers::Offset<Operator> spaceDepthOperator; - flatbuffers::Offset<flatbuffers::String> modelDescription; - flatbuffers::Offset<OperatorCode> operatorCode; - - switch (spaceDepthOperatorCode) - { - case tflite::BuiltinOperator_SPACE_TO_DEPTH: - spaceDepthOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_SpaceToDepthOptions, - CreateSpaceToDepthOptions(flatBufferBuilder, blockSize).Union()); - modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: SPACE_TO_DEPTH Operator Model"); - operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_SPACE_TO_DEPTH); - break; - case tflite::BuiltinOperator_DEPTH_TO_SPACE: - spaceDepthOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_DepthToSpaceOptions, - CreateDepthToSpaceOptions(flatBufferBuilder, blockSize).Union()); - flatBufferBuilder.CreateString("ArmnnDelegate: DEPTH_TO_SPACE Operator Model"); - operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_DEPTH_TO_SPACE); - break; - default: - break; - } - const std::vector<int32_t> subgraphInputs({0}); - const std::vector<int32_t> 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(&spaceDepthOperator, 1)); - 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 SpaceDepthTest(tflite::BuiltinOperator spaceDepthOperatorCode, - 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, - int32_t blockSize = 2) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateSpaceDepthTfLiteModel(spaceDepthOperatorCode, - tensorType, - inputShape, - outputShape, - blockSize); - - 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 EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); -} - -} // anonymous namespace diff --git a/delegate/src/test/SplitTest.cpp b/delegate/src/test/SplitTest.cpp deleted file mode 100644 index 5940516583..0000000000 --- a/delegate/src/test/SplitTest.cpp +++ /dev/null @@ -1,262 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "SplitTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -// SPLIT Operator -void SplitUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> axisShape { 1 }; - std::vector<int32_t> inputShape { 2, 2, 2, 2} ; - std::vector<int32_t> outputShape0 { 2, 2, 2, 1 }; - std::vector<int32_t> outputShape1 { 2, 2, 2, 1 }; - std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; - - std::vector<int32_t> axisData { 3 }; // Axis - std::vector<uint8_t> inputValues { 1, 2, 3, 4, 5, 6, 7, 8, - 9, 10, 11, 12, 13, 14, 15, 16 }; // Input - - - std::vector<uint8_t> expectedOutputValues0 { 1, 3, 5, 7, 9, 11, 13, 15 }; - std::vector<uint8_t> expectedOutputValues1 { 2, 4, 6, 8, 10, 12, 14, 16 }; - std::vector<std::vector<uint8_t>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; - - int32_t numSplits = 2; - - SplitTest<uint8_t>(::tflite::TensorType_UINT8, - backends, - axisShape, - inputShape, - outputShapes, - axisData, - inputValues, - expectedOutputValues, - numSplits); -} - -void SplitFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> axisShape { 1 }; - std::vector<int32_t> inputShape { 2, 2, 2, 2 }; - std::vector<int32_t> outputShape0 { 2, 1, 2, 2 }; - std::vector<int32_t> outputShape1 { 2, 1, 2, 2 }; - std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; - - std::vector<int32_t> axisData { 1 }; // Axis - std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, - 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f }; // Input - - - std::vector<float> expectedOutputValues0 { 1.0f, 2.0f, 3.0f, 4.0f, 9.0f, 10.0f, 11.0f, 12.0f }; - std::vector<float> expectedOutputValues1 { 5.0f, 6.0f, 7.0f, 8.0f, 13.0f, 14.0f, 15.0f, 16.0f }; - std::vector<std::vector<float>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; - - int32_t numSplits = 2; - - SplitTest<float>(::tflite::TensorType_FLOAT32, - backends, - axisShape, - inputShape, - outputShapes, - axisData, - inputValues, - expectedOutputValues, - numSplits); -} - -// SPLIT Test Suite -TEST_SUITE("SPLIT_CpuRefTests") -{ - -TEST_CASE ("SPLIT_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SplitUint8Test(backends); -} - -TEST_CASE ("SPLIT_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SplitFp32Test(backends); -} - -} - -TEST_SUITE("SPLIT_CpuAccTests") -{ - -TEST_CASE ("SPLIT_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SplitUint8Test(backends); -} - -TEST_CASE ("SPLIT_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SplitFp32Test(backends); -} - -} - -TEST_SUITE("SPLIT_GpuAccTests") -{ - -TEST_CASE ("SPLIT_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SplitUint8Test(backends); -} - -TEST_CASE ("SPLIT_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SplitFp32Test(backends); -} - -} -// End of SPLIT Test Suite - -// SPLIT_V Operator -void SplitVUint8Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> axisShape { 1 }; - std::vector<int32_t> inputShape { 2, 4, 2, 2 }; - std::vector<int32_t> splitsShape { 2 }; - std::vector<int32_t> outputShape0 { 2, 3, 2, 2 }; - std::vector<int32_t> outputShape1 { 2, 1, 2, 2 }; - std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; - - std::vector<int32_t> axisData { 1 }; // Axis - std::vector<int32_t> splitsData { 3, 1 }; // Splits - std::vector<uint8_t> inputValues { 1, 2, 3, 4, 5, 6, 7, 8, - 9, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, - 25, 26, 27, 28, 29, 30, 31, 32 }; // Input - - - std::vector<uint8_t> expectedOutputValues0 { 1, 2, 3, 4, 5, 6, 7, 8, - 9, 10, 11, 12, 17, 18, 19, 20, - 21, 22, 23, 24, 25, 26, 27, 28 }; - std::vector<uint8_t> expectedOutputValues1 { 13, 14, 15, 16, 29, 30, 31, 32 }; - std::vector<std::vector<uint8_t>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; - - int32_t numSplits = 2; - - SplitVTest<uint8_t>(::tflite::TensorType_UINT8, - backends, - inputShape, - splitsShape, - axisShape, - outputShapes, - inputValues, - splitsData, - axisData, - expectedOutputValues, - numSplits); -} - -void SplitVFp32Test(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> axisShape { 1 }; - std::vector<int32_t> inputShape { 2, 4, 2, 2 }; - std::vector<int32_t> splitsShape { 2 }; - std::vector<int32_t> outputShape0 { 2, 3, 2, 2 }; - std::vector<int32_t> outputShape1 { 2, 1, 2, 2 }; - std::vector<std::vector<int32_t>> outputShapes{ outputShape0, outputShape1 }; - - std::vector<int32_t> axisData { 1 }; // Axis - std::vector<int32_t> splitsData { 3, 1 }; // Splits - std::vector<float> inputValues { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, - 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, - 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, - 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f }; // Input - - - std::vector<float> expectedOutputValues0 { 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, - 9.0f, 10.0f, 11.0f, 12.0f, 17.0f, 18.0f, 19.0f, 20.0f, - 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f }; - std::vector<float> expectedOutputValues1 { 13.0f, 14.0f, 15.0f, 16.0f, 29.0f, 30.0f, 31.0f, 32.0f }; - std::vector<std::vector<float>> expectedOutputValues{ expectedOutputValues0, expectedOutputValues1 }; - - int32_t numSplits = 2; - - SplitVTest<float>(::tflite::TensorType_FLOAT32, - backends, - inputShape, - splitsShape, - axisShape, - outputShapes, - inputValues, - splitsData, - axisData, - expectedOutputValues, - numSplits); -} - -// SPLIT_V Test Suite -TEST_SUITE("SPLIT_V_CpuRefTests") -{ - -TEST_CASE ("SPLIT_V_Uint8_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SplitVUint8Test(backends); -} - -TEST_CASE ("SPLIT_V_Fp32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - SplitVFp32Test(backends); -} - -} - -TEST_SUITE("SPLIT_V_CpuAccTests") -{ - -TEST_CASE ("SPLIT_V_Uint8_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SplitVUint8Test(backends); -} - -TEST_CASE ("SPLIT_V_Fp32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - SplitVFp32Test(backends); -} - -} - -TEST_SUITE("SPLIT_V_GpuAccTests") -{ - -TEST_CASE ("SPLIT_V_Uint8_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SplitVUint8Test(backends); -} - -TEST_CASE ("SPLIT_V_Fp32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - SplitVFp32Test(backends); -} - -} -// End of SPLIT_V Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/SplitTestHelper.hpp b/delegate/src/test/SplitTestHelper.hpp deleted file mode 100644 index 3c5f50ffac..0000000000 --- a/delegate/src/test/SplitTestHelper.hpp +++ /dev/null @@ -1,370 +0,0 @@ -// -// Copyright © 2020, 2023 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::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*>(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, - 2, - flatBufferBuilder.CreateString("axis"), - quantizationParameters); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - // Create output tensor - for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) - { - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors[i + 2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), - outputTensorShapes[i].size()), - tensorType, - (i+3), - 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)); - - 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 diff --git a/delegate/src/test/StridedSliceTest.cpp b/delegate/src/test/StridedSliceTest.cpp deleted file mode 100644 index 43aea8a449..0000000000 --- a/delegate/src/test/StridedSliceTest.cpp +++ /dev/null @@ -1,241 +0,0 @@ -// -// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "StridedSliceTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void StridedSlice4DTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - std::vector<int32_t> beginShape { 4 }; - std::vector<int32_t> endShape { 4 }; - std::vector<int32_t> strideShape { 4 }; - - std::vector<int32_t> beginData { 1, 0, 0, 0 }; - std::vector<int32_t> endData { 2, 2, 3, 1 }; - std::vector<int32_t> strideData { 1, 1, 1, 1 }; - std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, - 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f }; - std::vector<float> outputData { 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f }; - - StridedSliceTestImpl<float>( - backends, - inputData, - outputData, - beginData, - endData, - strideData, - inputShape, - beginShape, - endShape, - strideShape, - outputShape - ); -} - -void StridedSlice4DReverseTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3, 1 }; - std::vector<int32_t> outputShape { 1, 2, 3, 1 }; - std::vector<int32_t> beginShape { 4 }; - std::vector<int32_t> endShape { 4 }; - std::vector<int32_t> strideShape { 4 }; - - std::vector<int32_t> beginData { 1, -1, 0, 0 }; - std::vector<int32_t> endData { 2, -3, 3, 1 }; - std::vector<int32_t> strideData { 1, -1, 1, 1 }; - std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, - 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f }; - std::vector<float> outputData { 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f }; - - StridedSliceTestImpl<float>( - backends, - inputData, - outputData, - beginData, - endData, - strideData, - inputShape, - beginShape, - endShape, - strideShape, - outputShape - ); -} - -void StridedSliceSimpleStrideTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3, 1 }; - std::vector<int32_t> outputShape { 2, 1, 2, 1 }; - std::vector<int32_t> beginShape { 4 }; - std::vector<int32_t> endShape { 4 }; - std::vector<int32_t> strideShape { 4 }; - - std::vector<int32_t> beginData { 0, 0, 0, 0 }; - std::vector<int32_t> endData { 3, 2, 3, 1 }; - std::vector<int32_t> strideData { 2, 2, 2, 1 }; - std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, - 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f }; - std::vector<float> outputData { 1.0f, 1.0f, - 5.0f, 5.0f }; - - StridedSliceTestImpl<float>( - backends, - inputData, - outputData, - beginData, - endData, - strideData, - inputShape, - beginShape, - endShape, - strideShape, - outputShape - ); -} - -void StridedSliceSimpleRangeMaskTest(std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 3, 2, 3, 1 }; - std::vector<int32_t> outputShape { 3, 2, 3, 1 }; - std::vector<int32_t> beginShape { 4 }; - std::vector<int32_t> endShape { 4 }; - std::vector<int32_t> strideShape { 4 }; - - std::vector<int32_t> beginData { 1, 1, 1, 1 }; - std::vector<int32_t> endData { 1, 1, 1, 1 }; - std::vector<int32_t> strideData { 1, 1, 1, 1 }; - - int beginMask = -1; - int endMask = -1; - - std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, - 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f }; - std::vector<float> outputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, - 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f, - 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f }; - - StridedSliceTestImpl<float>( - backends, - inputData, - outputData, - beginData, - endData, - strideData, - inputShape, - beginShape, - endShape, - strideShape, - outputShape, - beginMask, - endMask - ); -} - -TEST_SUITE("StridedSlice_CpuRefTests") -{ - -TEST_CASE ("StridedSlice_4D_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - StridedSlice4DTest(backends); -} - -TEST_CASE ("StridedSlice_4D_Reverse_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - StridedSlice4DReverseTest(backends); -} - -TEST_CASE ("StridedSlice_SimpleStride_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - StridedSliceSimpleStrideTest(backends); -} - -TEST_CASE ("StridedSlice_SimpleRange_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; - StridedSliceSimpleRangeMaskTest(backends); -} - -} // StridedSlice_CpuRefTests TestSuite - - - -TEST_SUITE("StridedSlice_CpuAccTests") -{ - -TEST_CASE ("StridedSlice_4D_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - StridedSlice4DTest(backends); -} - -TEST_CASE ("StridedSlice_4D_Reverse_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - StridedSlice4DReverseTest(backends); -} - -TEST_CASE ("StridedSlice_SimpleStride_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - StridedSliceSimpleStrideTest(backends); -} - -TEST_CASE ("StridedSlice_SimpleRange_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - StridedSliceSimpleRangeMaskTest(backends); -} - -} // StridedSlice_CpuAccTests TestSuite - - - -TEST_SUITE("StridedSlice_GpuAccTests") -{ - -TEST_CASE ("StridedSlice_4D_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - StridedSlice4DTest(backends); -} - -TEST_CASE ("StridedSlice_4D_Reverse_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - StridedSlice4DReverseTest(backends); -} - -TEST_CASE ("StridedSlice_SimpleStride_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - StridedSliceSimpleStrideTest(backends); -} - -TEST_CASE ("StridedSlice_SimpleRange_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - StridedSliceSimpleRangeMaskTest(backends); -} - -} // StridedSlice_GpuAccTests TestSuite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/StridedSliceTestHelper.hpp b/delegate/src/test/StridedSliceTestHelper.hpp deleted file mode 100644 index ef944d7e7a..0000000000 --- a/delegate/src/test/StridedSliceTestHelper.hpp +++ /dev/null @@ -1,221 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "TestUtils.hpp" - -#include <armnn_delegate.hpp> -#include <armnn/DescriptorsFwd.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> CreateStridedSliceTfLiteModel(tflite::TensorType tensorType, - const std::vector<int32_t>& inputTensorShape, - const std::vector<int32_t>& beginTensorData, - const std::vector<int32_t>& endTensorData, - const std::vector<int32_t>& strideTensorData, - const std::vector<int32_t>& beginTensorShape, - const std::vector<int32_t>& endTensorShape, - const std::vector<int32_t>& strideTensorShape, - const std::vector<int32_t>& outputTensorShape, - const int32_t beginMask, - const int32_t endMask, - const int32_t ellipsisMask, - const int32_t newAxisMask, - const int32_t ShrinkAxisMask, - const armnn::DataLayout& dataLayout) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - - flatbuffers::Offset<tflite::Buffer> buffers[6] = { - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()), - sizeof(int32_t) * beginTensorData.size())), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()), - sizeof(int32_t) * endTensorData.size())), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()), - sizeof(int32_t) * strideTensorData.size())), - CreateBuffer(flatBufferBuilder) - }; - - std::array<flatbuffers::Offset<Tensor>, 5> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input")); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(), - beginTensorShape.size()), - ::tflite::TensorType_INT32, - 2, - flatBufferBuilder.CreateString("begin_tensor")); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(), - endTensorShape.size()), - ::tflite::TensorType_INT32, - 3, - flatBufferBuilder.CreateString("end_tensor")); - tensors[3] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(), - strideTensorShape.size()), - ::tflite::TensorType_INT32, - 4, - flatBufferBuilder.CreateString("stride_tensor")); - tensors[4] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - 5, - flatBufferBuilder.CreateString("output")); - - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_StridedSliceOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = CreateStridedSliceOptions(flatBufferBuilder, - beginMask, - endMask, - ellipsisMask, - newAxisMask, - ShrinkAxisMask).Union(); - - const std::vector<int> operatorInputs{ 0, 1, 2, 3 }; - const std::vector<int> operatorOutputs{ 4 }; - flatbuffers::Offset <Operator> sliceOperator = - 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, 3 }; - const std::vector<int> subgraphOutputs{ 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(&sliceOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: StridedSlice Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - BuiltinOperator_STRIDED_SLICE); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers, 6)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void StridedSliceTestImpl(std::vector<armnn::BackendId>& backends, - std::vector<T>& inputValues, - std::vector<T>& expectedOutputValues, - std::vector<int32_t>& beginTensorData, - std::vector<int32_t>& endTensorData, - std::vector<int32_t>& strideTensorData, - std::vector<int32_t>& inputTensorShape, - std::vector<int32_t>& beginTensorShape, - std::vector<int32_t>& endTensorShape, - std::vector<int32_t>& strideTensorShape, - std::vector<int32_t>& outputTensorShape, - const int32_t beginMask = 0, - const int32_t endMask = 0, - const int32_t ellipsisMask = 0, - const int32_t newAxisMask = 0, - const int32_t ShrinkAxisMask = 0, - const armnn::DataLayout& dataLayout = armnn::DataLayout::NHWC) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateStridedSliceTfLiteModel( - ::tflite::TensorType_FLOAT32, - inputTensorShape, - beginTensorData, - endTensorData, - strideTensorData, - beginTensorShape, - endTensorShape, - strideTensorShape, - outputTensorShape, - beginMask, - endMask, - ellipsisMask, - newAxisMask, - ShrinkAxisMask, - dataLayout); - - auto 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 - armnnDelegate::CompareOutputData<T>(tfLiteDelegate, - armnnDelegate, - outputTensorShape, - expectedOutputValues); - - tfLiteDelegate.reset(nullptr); - armnnDelegate.reset(nullptr); -} // End of StridedSlice Test - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/TestUtils.cpp b/delegate/src/test/TestUtils.cpp deleted file mode 100644 index 9dce4461da..0000000000 --- a/delegate/src/test/TestUtils.cpp +++ /dev/null @@ -1,152 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "TestUtils.hpp" - -namespace armnnDelegate -{ - -void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize) -{ - auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));}; - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(compareBool(tensor1[i], tensor2[i])); - } -} - -void CompareData(std::vector<bool>& tensor1, bool tensor2[], size_t tensorSize) -{ - auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));}; - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(compareBool(tensor1[i], tensor2[i])); - } -} - -void CompareData(float tensor1[], float tensor2[], size_t tensorSize) -{ - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(tensor1[i] == doctest::Approx( tensor2[i] )); - } -} - -void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance) -{ - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= - std::abs(tensor1[i]*percentTolerance/100)); - } -} - -void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize) -{ - uint8_t tolerance = 1; - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); - } -} - -void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize) -{ - int16_t tolerance = 1; - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); - } -} - -void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize) -{ - int32_t tolerance = 1; - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); - } -} - -void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize) -{ - int8_t tolerance = 1; - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance); - } -} - -void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize) -{ - for (size_t i = 0; i < tensorSize; i++) - { - CHECK(tensor1[i] == doctest::Approx( tensor2[i] )); - } -} - -void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize) -{ - uint16_t tolerance = 1; - for (size_t i = 0; i < tensorSize; i++) - { - uint16_t tensor1Data = tensor1[i].data; - uint16_t tensor2Data = tensor2[i].data; - CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance); - } -} - -void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize) { - uint16_t tolerance = 1; - for (size_t i = 0; i < tensorSize; i++) - { - uint16_t tensor1Data = tensor1[i].data; - uint16_t tensor2Data = half_float::detail::float2half<std::round_indeterminate, float>(tensor2[i]); - CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance); - } -} - -template <> -void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter, - std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter, - std::vector<int32_t>& expectedOutputShape, - std::vector<Half>& expectedOutputValues, - unsigned int outputIndex) -{ - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex]; - auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId); - auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex]; - auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId); - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<TfLiteFloat16>(armnnDelegateOutputId); - - CHECK(expectedOutputShape.size() == tfLiteDelegateOutputTensor->dims->size); - CHECK(expectedOutputShape.size() == armnnDelegateOutputTensor->dims->size); - - for (size_t i = 0; i < expectedOutputShape.size(); i++) - { - CHECK(armnnDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]); - CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]); - CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]); - } - - armnnDelegate::CompareData(armnnDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); -} - -template <> -void FillInput<Half>(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<Half>& inputValues) -{ - auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex]; - auto tfLiteDelageInputData = interpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i].data = half_float::detail::float2half<std::round_indeterminate, float>(inputValues[i]); - - } -} - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/TestUtils.hpp b/delegate/src/test/TestUtils.hpp deleted file mode 100644 index 5d4a0ed7d4..0000000000 --- a/delegate/src/test/TestUtils.hpp +++ /dev/null @@ -1,101 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include <tensorflow/lite/c/common.h> -#include <tensorflow/lite/interpreter.h> - -#include <doctest/doctest.h> - -#include <half/half.hpp> - -using Half = half_float::half; - -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]; - } -} - -template <> -void FillInput(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<Half>& inputValues); - -/// Can be used to compare bool data coming from a tflite interpreter -/// Boolean types get converted to a bit representation in a vector. vector.data() returns a void pointer -/// instead of a pointer to bool. Therefore a special function to compare to vector of bool is required -void CompareData(std::vector<bool>& tensor1, bool tensor2[], size_t tensorSize); -void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize); - -/// Can be used to compare float data coming from a tflite interpreter with a tolerance of limit_of_float*100 -void CompareData(float tensor1[], float tensor2[], size_t tensorSize); - -/// Can be used to compare float data coming from a tflite interpreter with a given percentage tolerance -void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance); - -/// Can be used to compare int8_t data coming from a tflite interpreter with a tolerance of 1 -void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize); - -/// Can be used to compare uint8_t data coming from a tflite interpreter with a tolerance of 1 -void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize); - -/// Can be used to compare int16_t data coming from a tflite interpreter with a tolerance of 1 -void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize); - -/// Can be used to compare int32_t data coming from a tflite interpreter with a tolerance of 1 -void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize); - -/// Can be used to compare Half (Float16) data with a tolerance of limit_of_float*100 -void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize); - -/// Can be used to compare TfLiteFloat16 data coming from a tflite interpreter -void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize); - -/// Can be used to compare Half (Float16) data and TfLiteFloat16 data coming from a tflite interpreter -void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize); - -/// Can be used to compare the output tensor shape and values -/// from armnnDelegateInterpreter and tfLiteInterpreter. -/// Example usage can be found in ControlTestHelper.hpp -template <typename T> -void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter, - std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& expectedOutputValues, - unsigned int outputIndex = 0) -{ - auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex]; - auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId); - auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex]; - auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId); - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId); - - CHECK(expectedOutputShape.size() == tfLiteDelegateOutputTensor->dims->size); - CHECK(expectedOutputShape.size() == armnnDelegateOutputTensor->dims->size); - - for (size_t i = 0; i < expectedOutputShape.size(); i++) - { - CHECK(expectedOutputShape[i] == armnnDelegateOutputTensor->dims->data[i]); - CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]); - CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]); - } - - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData , expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelegateOutputData , expectedOutputValues.data(), expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelegateOutputData , armnnDelegateOutputData , expectedOutputValues.size()); -} - -} // namespace armnnDelegate diff --git a/delegate/src/test/TransposeTest.cpp b/delegate/src/test/TransposeTest.cpp deleted file mode 100644 index 67751e325a..0000000000 --- a/delegate/src/test/TransposeTest.cpp +++ /dev/null @@ -1,46 +0,0 @@ -// -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "TransposeTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <doctest/doctest.h> -#include <flatbuffers/flatbuffers.h> - -namespace armnnDelegate -{ - -TEST_SUITE ("Transpose_GpuAccTests") -{ - -TEST_CASE ("Transpose_Float32_GpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; - TransposeFP32Test(backends); -} - -} - -TEST_SUITE ("Transpose_CpuAccTests") -{ - -TEST_CASE ("Transpose_Float32_CpuAcc_Test") -{ - std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; - TransposeFP32Test(backends); -} - -} - -TEST_SUITE ("Transpose_CpuRefTests") -{ -TEST_CASE ("Transpose_Float32_CpuRef_Test") -{ - std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef }; - TransposeFP32Test(backends); -} -} -} // namespace armnnDelegate diff --git a/delegate/src/test/TransposeTestHelper.hpp b/delegate/src/test/TransposeTestHelper.hpp deleted file mode 100644 index 4479c486cb..0000000000 --- a/delegate/src/test/TransposeTestHelper.hpp +++ /dev/null @@ -1,177 +0,0 @@ -// -// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#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> CreateTransposeTfLiteModel(tflite::TensorType tensorType, - const std::vector <int32_t>& input0TensorShape, - const std::vector <int32_t>& inputPermVecShape, - const std::vector <int32_t>& outputTensorShape, - const std::vector<int32_t>& inputPermVec) -{ - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - flatbuffers::Offset<tflite::Buffer> buffers[4]{ - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder), - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputPermVec.data()), - sizeof(int32_t) * inputPermVec.size())), - CreateBuffer(flatBufferBuilder) - }; - std::array<flatbuffers::Offset<Tensor>, 3> tensors; - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(), - input0TensorShape.size()), - tensorType, 1); - tensors[1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputPermVecShape.data(), - inputPermVecShape.size()), - tflite::TensorType_INT32, 2, - flatBufferBuilder.CreateString("permutation_vector")); - tensors[2] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType,3); - const std::vector<int32_t> operatorInputs{0, 1}; - const std::vector<int32_t> operatorOutputs{2}; - flatbuffers::Offset <Operator> transposeOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - BuiltinOptions_TransposeOptions, - CreateTransposeOptions(flatBufferBuilder).Union()); - 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(&transposeOperator, 1)); - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Transpose Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_TRANSPOSE); - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers, 4)); - flatBufferBuilder.Finish(flatbufferModel); - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -void TransposeFP32Test(std::vector<armnn::BackendId>& backends) -{ - using namespace tflite; - - // set test input data - std::vector<int32_t> input0Shape {4, 2, 3}; - std::vector<int32_t> inputPermVecShape {3}; - std::vector<int32_t> outputShape {2, 3, 4}; - - std::vector<float> input0Values = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, - 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}; - std::vector<int32_t> inputPermVec = {2, 0, 1}; - std::vector<float> expectedOutputValues = {0, 3, 6, 9, 12, 15, 18, 21, 1, 4, 7, 10, - 13, 16, 19, 22, 2, 5, 8, 11, 14, 17, 20, 23}; - - // create model - std::vector<char> modelBuffer = CreateTransposeTfLiteModel(::tflite::TensorType_FLOAT32, - input0Shape, - inputPermVecShape, - outputShape, - inputPermVec); - - 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 for tflite - auto tfLiteInterpreterInput0Id = tfLiteInterpreter->inputs()[0]; - auto tfLiteInterpreterInput0Data = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterInput0Id); - for (unsigned int i = 0; i < input0Values.size(); ++i) - { - tfLiteInterpreterInput0Data[i] = input0Values[i]; - } - - auto tfLiteInterpreterInput1Id = tfLiteInterpreter->inputs()[1]; - auto tfLiteInterpreterInput1Data = tfLiteInterpreter->typed_tensor<int32_t>(tfLiteInterpreterInput1Id); - for (unsigned int i = 0; i < inputPermVec.size(); ++i) - { - tfLiteInterpreterInput1Data[i] = inputPermVec[i]; - } - - //Set input data for armnn delegate - auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInput0Id); - for (unsigned int i = 0; i < input0Values.size(); ++i) - { - armnnDelegateInput0Data[i] = input0Values[i]; - } - - auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1]; - auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<int32_t>(armnnDelegateInput1Id); - for (unsigned int i = 0; i < inputPermVec.size(); ++i) - { - armnnDelegateInput1Data[i] = inputPermVec[i]; - } - - // Run EnqueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - // Compare output data - auto tfLiteInterpreterOutputId = tfLiteInterpreter->outputs()[0]; - auto tfLiteInterpreterOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteInterpreterOutputId); - auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0]; - auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId); - for (size_t i = 0; i < expectedOutputValues.size(); ++i) - { - CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]); - CHECK(tfLiteInterpreterOutputData[i] == expectedOutputValues[i]); - CHECK(tfLiteInterpreterOutputData[i] == armnnDelegateOutputData[i]); - } - - armnnDelegateInterpreter.reset(nullptr); -} -} diff --git a/delegate/src/test/UnidirectionalSequenceLstmTest.cpp b/delegate/src/test/UnidirectionalSequenceLstmTest.cpp deleted file mode 100644 index 4bee715788..0000000000 --- a/delegate/src/test/UnidirectionalSequenceLstmTest.cpp +++ /dev/null @@ -1,1464 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "UnidirectionalSequenceLstmTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -void UnidirectionalSequenceLstmTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - //tensorInfo12, - bool hasInputToInputWeights = true; - std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, - -0.117484632f, 0.3298470976f, -0.1179017122f, - 0.214305695f, 0.42135173085f, 0.003878414626f, - -0.348303917f, -0.1881275477f, 0.0343011027f }; - - std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, - -0.3810434485f, 0.268383264f, -0.009807467424f, - -0.3522925403f, -0.24275735512f, -0.28344226125f, - 0.13512269116f, -0.4932442977f, -0.10039821991f }; - - std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, - 0.386399507f, -0.259465157985f, -0.16545993089f, - -0.4230232555f, 0.341664791103f, -0.18127849691f, - -0.2277662414f, -0.55275535589f, 0.34184026718f }; - - std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, - 0.53969591851f, 0.23393625035f, -0.27140527306f, - 0.50009280443f, 0.07511717046f, 0.3998299249f, - -0.51717478049f, 0.1889653282f, -0.367323637f }; - - //tensorInfo16, - bool hasRecurrentToInputWeights = true; - std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, - -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, - 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, - 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f }; - - std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, - -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, - -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, - -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; - - std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, - -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, - 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, - 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; - - std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, - -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, - 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, - -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<float> cellToInputWeights; - bool hasCellToForgetWeights = false; - std::vector<float> cellToForgetWeights; - bool hasCellToOutputWeights = false; - std::vector<float> cellToOutputWeights; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = {0., 0., 0., 0.}; - std::vector<float> forgetGateBias = {1., 1., 1., 1.}; - std::vector<float> cellBias = {0., 0., 0., 0.}; - std::vector<float> outputGateBias = {0., 0., 0., 0.}; - - bool hasProjectionWeights = false; - std::vector<float> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., - 3., 2., 1., 2., 3., 4., - 5., 4., 3., 2., 1., 2. }; - std::vector<float> expectedOutputValues = { -0.0714901f, -0.162117f, -0.175168f, -0.0232934f, - -0.168107f, -0.414129f, -0.549875f, -0.00803579f, - -0.0668735f, 0.204078f, -0.42765f, -0.0312321f, - -0.120003f, -0.0941918f, -0.456391f, -0.0287019f, - -0.0342921f, 0.20824f, -0.656989f, -0.00415265f, - -0.10493f, 0.14211f, -0.583478f, -0.0329754f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<float>(backends, - ::tflite::TensorType_FLOAT32, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor); -} - -void UnidirectionalSequenceLstmTimeMajorTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - std::vector<int32_t> inputShape = {timeSize, batchSize, inputSize}; - std::vector<int32_t> cellStateInTensorInfo = {batchSize, numUnits}; - std::vector<int32_t> outputStateInTensorInfo = {batchSize, outputSize}; - - std::vector<int32_t> outputTensorInfo = {timeSize, batchSize, outputSize}; - - //tensorInfo12 - bool hasInputToInputWeights = true; - std::vector<float> inputToInputWeights = { 0.27277296781539917f, 0.3813590407371521f, -0.394489049911499f, - 0.2782636880874634f, -0.3793870210647583f, -0.018918335437774658f, - 0.2724653482437134f, -0.19314253330230713f, -0.2947450876235962f, - -0.30253493785858154f, 0.4241350293159485f, -0.22560018301010132f }; - - std::vector<float> inputToForgetWeights = { -0.2667974531650543f, -0.05505800247192383f, -0.20932340621948242f, - -0.14345619082450867f, 0.09666192531585693f, -0.2604355812072754f, - -0.2681812047958374f, -0.3314584493637085f, 0.4485899806022644f, - -0.23467743396759033f, 0.5072842240333557f, -0.4192768931388855f }; - - std::vector<float> inputToCellWeights = { -0.15782442688941956f, -0.027530014514923096f, 0.4789854884147644f, - 0.23227906227111816f, 0.28259342908859253f, -0.030095696449279785f, - 0.10071521997451782f, -0.08535495400428772f, 0.18563997745513916f, - -0.3049069046974182f, -0.478048175573349f, 0.025234103202819824f }; - - std::vector<float> inputToOutputWeights = { -0.04584759473800659f, -0.2716066539287567f, 0.012970447540283203f, - -0.4729190170764923f, -0.37422770261764526f, 0.49352723360061646f, - 0.3163864016532898f, -0.436781644821167f, -0.33074596524238586f, - -0.32885751128196716f, -0.40959352254867554f, -0.2124689817428589f }; - - //tensorInfo16 - bool hasRecurrentToInputWeights = true; - std::vector<float> recurrentToInputWeights = { 0.23788475990f, -0.24948765337f, 0.50044941902f, 0.14431896805f, - -0.115940228137f, -0.717082679f, -0.17208620906f, 0.17850610617f, - -0.16702319684f, -0.11384502053f, -0.309785276245f, -0.3316611672f, - 0.52380162477f, -0.06839632987f, -0.391478359627f, -0.10756178963f }; - - std::vector<float> recurrentToForgetWeights = { 0.11383482068f, 0.1676601767f, -0.08550968004f, 0.03399394089f, - 0.08042152225f, -0.2133381964f, 0.05182432704f, 0.38161808255f, - -0.5018365979f, -0.08043262364f, 0.07894329014f, -0.07547105155f, - 0.12047368288f, 0.2986997961f, 0.0485043078f, -0.13372567296f }; - - std::vector<float> recurrentToCellWeights = { 0.0433832928545f, 0.07587072294f, -0.120520234107f, 0.604576051f, - -0.434353142986f, 0.009314475068f, 0.005085289478f, 0.08488202038f, - -0.00025437487886f, 0.15245915082f, -0.1936587542f, 0.004754020f, - -0.1582719236f, 0.3307867646f, 0.0236605107784f, 0.307716339826f }; - - std::vector<float> recurrentToOutputWeights = { -0.079031050201f, 0.041414566286f, -0.583727357285f, 0.1025384515f, - -0.172372072937f, 0.09214124082f, 0.178184121827f, -0.2439443916f, - 0.104485116899f, 0.2600405514f, 0.064414866268f, 0.24141204357f, - 0.281875759363f, -0.14234502664f, 0.15126448862f, -0.24421440064f }; - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<float> cellToInputWeights; - bool hasCellToForgetWeights = false; - std::vector<float> cellToForgetWeights; - bool hasCellToOutputWeights = false; - std::vector<float> cellToOutputWeights; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = {0., 0., 0., 0.}; - std::vector<float> forgetGateBias = {1., 1., 1., 1.}; - std::vector<float> cellBias = {0., 0., 0., 0.}; - std::vector<float> outputGateBias = {0., 0., 0., 0.}; - - bool hasProjectionWeights = false; - std::vector<float> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., - 3., 2., 1., 2., 3., 4., - 5., 4., 3., 2., 1., 2. }; - std::vector<float> expectedOutputValues = { 0.135658f, 0.124673f, 0.021209f, -0.0530204f, - 0.106138f, 0.0404792f, 0.0151644f, -0.00675166f, - -0.0128514f, 0.0644884f, 0.0709072f, -0.0454045f, - 0.162886f, 0.166494f, 0.0277046f, -0.0369807f, - 0.111716f, 0.043119f, 0.0762981f, -0.0122854f, - 0.104397f, 0.2144f, 0.119192f, -0.0839058f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = true; - - UnidirectionalSequenceLstmTestImpl<float>(backends, - ::tflite::TensorType_FLOAT32, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor); -} - -void UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 2; - int32_t timeSize = 3; - int32_t inputSize = 4; - int32_t outputSize = 5; - int32_t numUnits = 6; - - std::vector<int32_t> inputShape = {batchSize, timeSize, inputSize}; - std::vector<int32_t> cellStateInTensorInfo = {batchSize, numUnits}; - std::vector<int32_t> outputStateInTensorInfo = {batchSize, outputSize}; - - std::vector<int32_t> outputTensorInfo = {batchSize, timeSize, outputSize}; - - //tensorInfoInputSize, - bool hasInputToInputWeights = true; - std::vector<float> inputToInputWeights = { 0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, - -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, - -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, - -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, - -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, - -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f }; - - std::vector<float> inputToForgetWeights = { -0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, - 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, - 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, - -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, - -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, - 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f}; - - std::vector<float> inputToCellWeights = { -0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, - -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, - -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, - -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, - -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, - 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f }; - - std::vector<float> inputToOutputWeights = { -0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, - -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, - -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, - 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, - 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, - -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f }; - - //tensorInfoOutputSize, - bool hasRecurrentToInputWeights = true; - std::vector<float> recurrentToInputWeights = { -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, - -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, - -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, - -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, - 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, - 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, - -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, - 0.14283475f, -0.07390571f }; - - std::vector<float> recurrentToForgetWeights = { -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, - 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, - -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, - 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, - 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, - -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, - -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, - 0.061878487f, -0.04729229f }; - - std::vector<float> recurrentToCellWeights = { -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, - 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, - 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, - -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, - 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, - 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, - -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, - -0.019443132f, -0.030755889f }; - - std::vector<float> recurrentToOutputWeights = { 0.025825322f, -0.05813119f, 0.09495884f, - -0.045984812f,-0.01255415f, -0.0026479573f, - -0.08196161f, -0.054914974f, -0.0046604523f, - -0.029587349f, -0.044576716f, -0.07480124f, - -0.082868785f, 0.023254942f, 0.027502948f, - -0.0039728214f, -0.08683098f, -0.08116779f, - -0.014675607f, -0.037924774f, -0.023314456f, - -0.007401714f, -0.09255757f, 0.029460307f, - -0.08829125f, -0.005139627f, -0.08989442f, - -0.0555066f, 0.13596267f, 0.025062224f }; - // tensorInfoNumUnits - bool hasCellToInputWeights = true; - std::vector<float> cellToInputWeights = { 0.040369894f, 0.030746894f, 0.24704495f, - 0.018586371f, -0.037586458f, -0.15312155f }; - bool hasCellToForgetWeights = true; - std::vector<float> cellToForgetWeights = { -0.01998659f, -0.15568835f, -0.24248174f, - -0.012770197f, 0.041331276f, -0.072311886f }; - bool hasCellToOutputWeights = true; - std::vector<float> cellToOutputWeights = { 0.08286371f, -0.08261836f, -0.51210177f, - 0.002913762f, 0.17764764f, -0.5495371f }; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, - 0.10380666f, 0.053110216f, -0.06928846f }; - std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.03032477f, - 0.23027696f, 0.11098921f, 0.08989442f }; - std::vector<float> cellBias = { -0.024379363f, 0.0055531194f, 0.23377132f, - 0.033463873f, -0.1483596f, 0.029460307f }; - std::vector<float> outputGateBias = { 0.046159424f, -0.0012809046f, 0.03563469f, - 0.12648113f, 0.027195795f, 0.35373217f }; - - bool hasProjectionWeights = true; - std::vector<float> projectionWeights = { -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, - 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, - -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, - -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, - 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, - 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f }; - - bool hasProjectionBias = true; - std::vector<float> projectionBias(outputSize, 0.f); - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., - 3., 2., 1., 2., 3., 4., - 5., 4., 3., 2., 1., 2., - 1., 2., 3., 4., 5., 4.}; - std::vector<float> expectedOutputValues = { -0.0135612f, -0.0263441f, 0.0314008f, -0.00883455f, 0.00763052f, - -0.00126877f, -0.0292959f, 0.0449957f, -0.00976195f, -0.00492338f, - -0.0175702f, -0.0431753f, 0.0597117f, -0.0169154f, 0.0142087f, - 0.00472515f, -0.0196355f, 0.0342524f, -0.00407936f, -0.0253189f, - -0.00512944f, -0.0293754f, 0.0512771f, -0.0151874f, -0.0246433f, - -0.00744986f, -0.0345103f, 0.0450666f, -0.00944991f, 0.0126895f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<float>(backends, - ::tflite::TensorType_FLOAT32, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor); -} - -void UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - //tensorInfo12 - bool hasInputToInputWeights = false; - std::vector<float> inputToInputWeights{}; - - std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, - -0.3810434485f, 0.268383264f, -0.009807467424f, - -0.3522925403f, -0.24275735512f, -0.28344226125f, - 0.13512269116f, -0.4932442977f, -0.10039821991f }; - - std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, - 0.386399507f, -0.259465157985f, -0.16545993089f, - -0.4230232555f, 0.341664791103f, -0.18127849691f, - -0.2277662414f, -0.55275535589f, 0.34184026718f }; - - std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, - 0.53969591851f, 0.23393625035f, -0.27140527306f, - 0.50009280443f, 0.07511717046f, 0.3998299249f, - -0.51717478049f, 0.1889653282f, -0.367323637f }; - - //tensorInfo16 - bool hasRecurrentToInputWeights = false; - std::vector<float> recurrentToInputWeights{}; - - std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, - -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, - -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, - -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f }; - - std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, - -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, - 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, - 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f }; - - std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, - -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, - 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, - -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f }; - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<float> cellToInputWeights; - bool hasCellToForgetWeights = true; - std::vector<float> cellToForgetWeights = {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; - bool hasCellToOutputWeights = true; - std::vector<float> cellToOutputWeights = {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; - - bool hasInputGateBias = false; - std::vector<float> inputGateBias; - std::vector<float> forgetGateBias = {1., 1., 1., 1.}; - std::vector<float> cellBias = {0., 0., 0., 0.}; - std::vector<float> outputGateBias = {0., 0., 0., 0.}; - - bool hasProjectionWeights = false; - std::vector<float> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., - 3., 2., 1., 2., 3., 4., - 5., 4., 3., 2., 1., 2. }; - std::vector<float> expectedOutputValues = { -0.0129257f, -0.070531f, -0.153508f, -0.0392391f, - -0.0300169f, -0.195717f, -0.528679f, -0.0818106f, - -0.0332748f, 0.155429f, -0.353966f, -0.0801505f, - -0.032312f, -0.0407911f, -0.435053f, -0.0932317f, - -0.0108233f, 0.165584f, -0.640424f, -0.0447535f, - -0.031675f, 0.125987f, -0.526695f, -0.110093f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<float>(backends, - ::tflite::TensorType_FLOAT32, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor); -} - -void UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNormTest( - std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - int32_t numUnits = 5; - - //tensorInfo15 - bool hasInputToInputWeights = true; - std::vector<float> inputToInputWeights = { -0.49536117f, -0.0556083915f, -0.102400711f, - -0.117484632f, 0.3298470976f, -0.1179017122f, - 0.214305695f, 0.42135173085f, 0.003878414626f, - -0.348303917f, -0.1881275477f, 0.0343011027f, - -0.38837709614f, -0.05636804124f, 0.4259087456f}; - - std::vector<float> inputToForgetWeights = { 0.2415594226f, 0.15400093799f, 0.4566498398f, - -0.3810434485f, 0.268383264f, -0.009807467424f, - -0.3522925403f, -0.24275735512f, -0.28344226125f, - 0.13512269116f, -0.4932442977f, -0.10039821991f, - 0.2726137042f, 0.09216640889f, -0.06551410215f}; - - std::vector<float> inputToCellWeights = { -0.2504855627f, 0.184490025045f, -0.2480507493f, - 0.386399507f, -0.259465157985f, -0.16545993089f, - -0.4230232555f, 0.341664791103f, -0.18127849691f, - -0.2277662414f, -0.55275535589f, 0.34184026718f, - 0.3954237699f, -0.19407111404f, 0.30412107706f}; - - std::vector<float> inputToOutputWeights = { 0.2303854227f, 0.5218806862f, -0.4865379333f, - 0.53969591851f, 0.23393625035f, -0.27140527306f, - 0.50009280443f, 0.07511717046f, 0.3998299249f, - -0.51717478049f, 0.1889653282f, -0.367323637f, - -0.12584099173f, -0.12319286912f, 0.2407919466f}; - - //tensorInfo20 - bool hasRecurrentToInputWeights = true; - std::vector<float> recurrentToInputWeights = { -0.128009796112f, 0.1995525098f, -0.07745539397f, 0.1558421701f, - -0.265254765766f, -0.38837709614f, -0.05636804124f, 0.4259087456f, - 0.17628988623f, 0.3877420127f, 0.53300309181f, -0.0959980934f, - 0.00302857416f, 0.3266998827f, -0.142509296562f, -0.04433270756f, - 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f }; - - std::vector<float> recurrentToForgetWeights = { -0.09499983487f, -0.08814888417f, -0.04834804721f, 0.1516668247f, - -0.3967529535f, -0.06463699788f, 0.4952811002f, 0.003274492938f, - -0.0968840941f, 0.17928104102f, 0.0031281141592f, -0.3387276584f, - -0.3587934076f, 0.06705895066f, 0.22463923692f, 0.1961955726f, - 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f }; - - std::vector<float> recurrentToCellWeights = { -0.21938985582f, -0.3023648226f, -0.1170005202f, -0.3509177422f, - -0.4286288613f, 0.2726137042f, 0.09216640889f, -0.06551410215f, - 0.20453298098f, 0.2393476665f, 0.11846517771f, 0.2630801796f, - 0.3954237699f, -0.19407111404f, 0.30412107706f, -0.27342408554f, - 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f }; - - std::vector<float> recurrentToOutputWeights = { -0.32921677827f, 0.32624614238f, -0.1388191282f, -0.17879831790f, - -0.15185534954f, -0.16918526583f, -0.10087361183f, -0.5436913968f, - 0.016758225858f, 0.30454617738f, -0.41493862867f, -0.005565764375f, - -0.12584099173f, -0.12319286912f, 0.2407919466f, -0.08879069983f, - 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f }; - // tensorInfo5 - bool hasCellToInputWeights = true; - std::vector<float> cellToInputWeights = { 0.05f, 0.1f, 0.25f, 0.15f, -0.02f }; - bool hasCellToForgetWeights = true; - std::vector<float> cellToForgetWeights = { -0.02f, -0.15f, -0.25f, -0.03f, 0.15f }; - bool hasCellToOutputWeights = true; - std::vector<float> cellToOutputWeights = { 0.1f, -0.1f, -0.5f, 0.05f, 0.01f }; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; - std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; - std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; - std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; - - bool hasProjectionWeights = true; - std::vector<float> projectionWeights = { -0.1f, 0.2f, 0.01f, -0.2f, - 0.1f, 0.5f, 0.3f, 0.08f, - 0.07f, 0.2f, -0.4f, 0.2f, - 0.5f, -0.4f, 0.3f, -0.2f, - 0.3f, 0.08f, -0.07f, 0.2f}; //{outputSize, numUnits} - bool hasProjectionBias = true; - std::vector<float> projectionBias(outputSize, 0.f);; - - bool hasInputLayerNormWeights = true; - std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, 0.3f, 0.5f, 0.8f }; - bool hasForgetLayerNormWeights = true; - std::vector<float> forgetLayerNormWeights = { 0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; - bool hasCellLayerNormWeights = true; - std::vector<float> cellLayerNormWeights = { 0.7f, 0.2f, 0.3f, 0.8f, 0.5f }; - bool hasOutputLayerNormWeights = true; - std::vector<float> outputLayerNormWeights = { 0.6f, 0.2f, 0.2f, 0.5f, 0.1f }; - - std::vector<float> inputValues = { 1., 2., 3., 4., 5., 4., - 3., 2., 1., 2., 3., 4., - 5., 4., 3., 2., 1., 2. }; - std::vector<float> expectedOutputValues = { 0.0642256f, 0.0343966f, 0.184122f, 0.114717f, - 0.11458f, 0.0407109f, 0.300327f, 0.174301f, - 0.0864761f, 0.0362912f, 0.178635f, 0.115689f, - 0.108008f, 0.0386623f, 0.273471f, 0.167115f, - 0.0859545f, 0.0331481f, 0.186051f, 0.11888f, - 0.106649f, 0.0276847f, 0.229863f, 0.166958f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<float>(backends, - ::tflite::TensorType_FLOAT32, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor); -} - -void UnidirectionalSequenceLstmInt8Test(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - //tensorInfo12 - bool hasInputToInputWeights = true; - std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; - - std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; - - std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; - - std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; - - //tensorInfo16 - bool hasRecurrentToInputWeights = true; - std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; - - std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; - - std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; - - std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; - - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<int8_t> cellToInputWeights; - bool hasCellToForgetWeights = false; - std::vector<int8_t> cellToForgetWeights; - bool hasCellToOutputWeights = false; - std::vector<int8_t> cellToOutputWeights; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = { 0., 0., 0., 0. }; - std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; - std::vector<float> cellBias = { 0., 0., 0., 0. }; - std::vector<float> outputGateBias = { 0., 0., 0., 0. }; - - bool hasProjectionWeights = false; - std::vector<int8_t> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, - 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, - 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; - - std::vector<float> expectedOutputValues = { -0.0142517f, -0.0198845f, -0.0120569f, -0.0116868f, - -0.0350714f, -0.0343202f, -0.047504f, -0.0569789f, - -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f, - -0.0294759f, -0.0129935f, -0.0444175f, -0.0444354f, - -0.0280855f, 0.00545101f, -0.051422f, -0.0463838f, - -0.0310702f, 0.00915739f, -0.0625207f, -0.0482648f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<int8_t>(backends, - ::tflite::TensorType_INT8, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - 0.1f); -} - -void UnidirectionalSequenceLstmInt8TimeMajorTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - //tensorInfo12 - bool hasInputToInputWeights = true; - std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; - - std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; - - std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; - - std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; - - //tensorInfo16 - bool hasRecurrentToInputWeights = true; - std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; - - std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; - - std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; - - std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; - - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<int8_t> cellToInputWeights; - bool hasCellToForgetWeights = false; - std::vector<int8_t> cellToForgetWeights; - bool hasCellToOutputWeights = false; - std::vector<int8_t> cellToOutputWeights; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = { 0., 0., 0., 0. }; - std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; - std::vector<float> cellBias = { 0., 0., 0., 0. }; - std::vector<float> outputGateBias = { 0., 0., 0., 0. }; - - bool hasProjectionWeights = false; - std::vector<int8_t> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, - 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, - 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; - - std::vector<float> expectedOutputValues = { -0.0142517f, -0.0198845f, -0.0120122f, -0.0116868f, - -0.0261295f, -0.0188487f, -0.0345463f, -0.049733f, - -0.0146346f, 0.0106663f, -0.0247238f, -0.0319502f, - -0.0291863f, -0.0369402f, -0.0354071f, -0.0296529f, - -0.0419539f, -0.00617731f, -0.0814796f, -0.0804005f, - -0.0244737f, 0.0119905f, -0.0457527f, -0.0331862f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = true; - - UnidirectionalSequenceLstmTestImpl<int8_t>(backends, - ::tflite::TensorType_INT8, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - 0.1); -} - -void UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - int32_t numUnits = 4; - - bool hasInputToInputWeights = true; - std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3 }; - - std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; - - std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; - - std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; - - //tensorInfo16 - bool hasRecurrentToInputWeights = true; - std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -1, -1 }; - - std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; - - std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; - - std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; - - // tensorInfo4 - bool hasCellToInputWeights = true; - std::vector<int8_t> cellToInputWeights = { 5, 10, 25, 15 }; - bool hasCellToForgetWeights = true; - std::vector<int8_t> cellToForgetWeights = { -5, 15, 25, 3 }; - bool hasCellToOutputWeights = true; - std::vector<int8_t> cellToOutputWeights = { 10, -10, -5, 50 }; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = { 0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f}; - std::vector<float> forgetGateBias = { 0.035185695f, -0.042891346f, -0.3032477f, 0.23027696f}; - std::vector<float> cellBias = { -0.124379363f, 0.55531194f, 0.23377132f, 0.033463873f }; - std::vector<float> outputGateBias = { 0.046159424f, -0.12809046f, 0.03563469f, 0.12648113f }; - - bool hasProjectionWeights = true; - std::vector<int8_t> projectionWeights = { -25, 51, 3, -5, 25, 127, 77, 20, 18, 51, -10, 51, -25, 88, 77, -13 }; - bool hasProjectionBias = true; - std::vector<float> projectionBias(outputSize, 0.f); - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, - 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, - 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; - - std::vector<float> expectedOutputValues = { 0.612103f, 1.56788f, 0.31966f, 1.42956f, - 0.909718f, 3.07916f, -0.560586f, 3.8907f, - 0.753671f, 1.77485f, 0.365122f, 1.60077f, - 0.812644f, 2.79092f, -0.605396f, 3.61742f, - 0.791857f, 1.64353f, 0.316588f, 1.55192f, - 0.807265f, 2.47012f, -0.539598f, 3.25654f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<int8_t>(backends, - ::tflite::TensorType_INT8, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - 0.1f); -} - -void UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - // cellSize and outputSize have the same size when there is no projection. - int32_t numUnits = outputSize; - - //tensorInfo12, - bool hasInputToInputWeights = false; - std::vector<int8_t> inputToInputWeights; - - std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1 }; - - std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3 }; - - std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4 }; - - //tensorInfo16, - bool hasRecurrentToInputWeights = false; - std::vector<int8_t> recurrentToInputWeights; - std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, 5, -1, 1, 3, -2, -1 }; - - std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, 1, 2, 3, -2, 3, -3 }; - - std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, -4, -1, -1, -1, 2, -1 }; - - // tensorInfo4 - bool hasCellToInputWeights = false; - std::vector<int8_t> cellToInputWeights; - bool hasCellToForgetWeights = true; - std::vector<int8_t> cellToForgetWeights = { 47, -52, -24, 31 }; - bool hasCellToOutputWeights = true; - std::vector<int8_t> cellToOutputWeights = { -17, 82, 85, -77 }; - - bool hasInputGateBias = false; - std::vector<float> inputGateBias; - std::vector<float> forgetGateBias = { 1., 1., 1., 1. }; - std::vector<float> cellBias = { 0., 0., 0., 0. }; - std::vector<float> outputGateBias = { 0., 0., 0., 0. }; - - bool hasProjectionWeights = false; - std::vector<int8_t> projectionWeights; - bool hasProjectionBias = false; - std::vector<float> projectionBias; - - bool hasInputLayerNormWeights = false; - std::vector<float> inputLayerNormWeights; - bool hasForgetLayerNormWeights = false; - std::vector<float> forgetLayerNormWeights; - bool hasCellLayerNormWeights = false; - std::vector<float> cellLayerNormWeights; - bool hasOutputLayerNormWeights = false; - std::vector<float> outputLayerNormWeights; - - std::vector<float> inputValues = { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.4f, - 0.3f, 0.2f, 0.1f, 0.2f, 0.3f, 0.4f, - 0.5f, 0.4f, 0.3f, 0.2f, 0.1f, 0.2f }; - - std::vector<float> expectedOutputValues = { -0.0072104f, -0.00991171f, -0.00650478f, -0.00713055f, - -0.0191782f, -0.0161269f, -0.0233683f, -0.054299f, - -0.00783725f, 0.00635271f, -0.0126718f, -0.022613f, - -0.0161351f, -0.00775868f, -0.021054f, -0.0339778f, - -0.0146392f, 0.00330261f, -0.0258733f, -0.0407797f, - -0.0174297f, 0.0050105f, -0.0266275f, -0.0362564f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<int8_t>(backends, - ::tflite::TensorType_INT8, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - 0.1); -} - -void UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest( - std::vector<armnn::BackendId>& backends) -{ - int32_t batchSize = 3; - int32_t timeSize = 2; - int32_t inputSize = 3; - int32_t outputSize = 4; - int32_t numUnits = 5; - - bool hasInputToInputWeights = true; - std::vector<int8_t> inputToInputWeights = { -4, -1, -1, -2, 3, -2, 2, 4, 1, -4, -2, 3, 2, 2, -4 }; - - std::vector<int8_t> inputToForgetWeights = { 2, 1, 4, -4, 3, -1, -3, -2, -3, 1, -4, -1, -3, -2, -4 }; - - std::vector<int8_t> inputToCellWeights = { -2, 1, -2, 4, -3, -2, -4, 3, -2, -2, -6, 3, 2, 5, -4 }; - - std::vector<int8_t> inputToOutputWeights = { 2, 5, -4, 5, 2, -3, 5, 7, 3, -5, 1, -4, -4, -1, -1 }; - - bool hasRecurrentToInputWeights = true; - std::vector<int8_t> recurrentToInputWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, - 5, -1, 1, 3, -1, -1, -1, 4, 2, 3 }; - - std::vector<int8_t> recurrentToForgetWeights = { -1, 1, -1, 1, -3, -4, -1, 4, 2, 3, - 5, -1, 1, 3, -2, -1, -1, 2, 2, 1 }; - - std::vector<int8_t> recurrentToCellWeights = { -2, -3, -1, -3, -4, 2, 1, -1, 2, 2, - 1, 2, 3, -2, 3, -3, -1, -5, 1, 3 }; - - std::vector<int8_t> recurrentToOutputWeights = { -3, 3, -1, -2, -2, -2, -1, -5, 1, 3, - -4, -1, -1, -1, 2, -1, 5, 1, -3, -4 }; - - // tensorInfo5 - bool hasCellToInputWeights = true; - std::vector<int8_t> cellToInputWeights = { 5, 3, 8, -5, 2 }; - bool hasCellToForgetWeights = true; - std::vector<int8_t> cellToForgetWeights = { -2, -7, 5, -3, 4 }; - bool hasCellToOutputWeights = true; - std::vector<int8_t> cellToOutputWeights = { 9, -10 , -5, 5, 1 }; - - bool hasInputGateBias = true; - std::vector<float> inputGateBias = { 0.03f, 0.15f, 0.22f, 0.38f, 0.05f }; - std::vector<float> forgetGateBias = { 0.1f, -0.3f, -0.2f, 0.1f, 0.4f }; - std::vector<float> cellBias = { -0.05f, 0.72f, 0.25f, 0.08f, 0.1f }; - std::vector<float> outputGateBias = { 0.05f, -0.01f, 0.2f, 0.1f, -0.2f }; - - bool hasProjectionWeights = true; - std::vector<int8_t> projectionWeights = { -1, 2, 1, -2, 1, 5, 3, 8, 7, 2, - -4, 2, 5, -4, 3, -2, 3, 8, -7, 2 }; - bool hasProjectionBias = true; - std::vector<float> projectionBias(outputSize, 0.f); - - bool hasInputLayerNormWeights = true; - std::vector<float> inputLayerNormWeights = { 0.1f, 0.2f, -0.3f, -0.1f, 0.5f }; - bool hasForgetLayerNormWeights = true; - std::vector<float> forgetLayerNormWeights = { -0.1f, 0.2f, 0.3f, 0.5f, 0.2f }; - bool hasCellLayerNormWeights = true; - std::vector<float> cellLayerNormWeights = { 0.5f, 0.2f, 0.3f, 0.4f, -0.5f }; - bool hasOutputLayerNormWeights = true; - std::vector<float> outputLayerNormWeights = { 0.6f, -0.2f, -0.2f, 0.5f, 0.1f }; - - std::vector<float> inputValues = { 1., 8., 3., 4., 5., 4., - 3., 2., 1., 2., 3., 4., - 5., 4., 3., 2., 1., 2. }; - - std::vector<float> expectedOutputValues = { 0.0471276f, 0.0168155f, 0.0789885f, 0.16550f, - 0.0643133f, -0.0400722f, 0.100593f, 0.197722f, - 0.0465562f, -0.0600682f, 0.0622087f, 0.115053f, - 0.056287f, -0.0566218f, 0.0856832f, 0.148484f, - 0.0457859f, -0.0588112f, 0.0623636f, 0.114333f, - 0.0509271f, -0.0754262f, 0.058600f, 0.0801288f }; - - tflite::ActivationFunctionType activationFunction = tflite::ActivationFunctionType_TANH; - float clippingThresCell = 10.f; - float clippingThresProj = 0.f; - bool isTimeMajor = false; - - UnidirectionalSequenceLstmTestImpl<int8_t>(backends, - ::tflite::TensorType_INT8, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - inputValues, - expectedOutputValues, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - 0.1); -} - -TEST_SUITE("UnidirectionalSequenceLstmTest_CpuRefTests") -{ - -TEST_CASE ("UnidirectionalSequenceLstmTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmTimeMajorTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmTimeMajorTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmWithCifgWithPeepholeNoProjectionTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNormTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmNoCifgWithPeepholeWithProjectionWithLayerNormTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmInt8Test_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmInt8Test(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmTimeInt8TimeMajorTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmInt8TimeMajorTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmInt8WithCifgWithPeepholeNoProjectionTest(backends); -} - -TEST_CASE ("UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest_CpuRef_Test") -{ - std::vector <armnn::BackendId> backends = {armnn::Compute::CpuRef}; - UnidirectionalSequenceLstmInt8NoCifgWithPeepholeWithProjectionWithLayerNormTest(backends); -} - -} //End of TEST_SUITE("UnidirectionalSequenceLstmTest_CpuRef") - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp b/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp deleted file mode 100644 index 10555aca1a..0000000000 --- a/delegate/src/test/UnidirectionalSequenceLstmTestHelper.hpp +++ /dev/null @@ -1,742 +0,0 @@ -// -// Copyright © 2021, 2023 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 <tensorflow/lite/c/common.h> - -#include <doctest/doctest.h> - -#include <armnn/utility/IgnoreUnused.hpp> -#include <armnn/utility/NumericCast.hpp> -#include <armnn/TypesUtils.hpp> - -#include <armnn/Types.hpp> - -#include <initializer_list> -#include <iterator> -#include <vector> - -namespace -{ - -template<typename T> -std::vector<char> CreateUnidirectionalSequenceLstmTfLiteModel(tflite::TensorType tensorType, - int32_t batchSize, - int32_t timeSize, - int32_t inputSize, - int32_t outputSize, - int32_t numUnits, - bool hasInputToInputWeights, - const std::vector<T>& inputToInputWeights, - const std::vector<T>& inputToForgetWeights, - const std::vector<T>& inputToCellWeights, - const std::vector<T>& inputToOutputWeights, - bool hasRecurrentToInputWeights, - const std::vector<T>& recurrentToInputWeights, - const std::vector<T>& recurrentToForgetWeights, - const std::vector<T>& recurrentToCellWeights, - const std::vector<T>& recurrentToOutputWeights, - bool hasCellToInputWeights, - const std::vector<T>& cellToInputWeights, - bool hasCellToForgetWeights, - const std::vector<T>& cellToForgetWeights, - bool hasCellToOutputWeights, - const std::vector<T>& cellToOutputWeights, - bool hasInputGateBias, - const std::vector<float>& inputGateBias, - const std::vector<float>& forgetGateBias, - const std::vector<float>& cellBias, - const std::vector<float>& outputGateBias, - bool hasProjectionWeights, - const std::vector<T>& projectionWeights, - bool hasProjectionBias, - const std::vector<float>& projectionBias, - bool hasInputLayerNormWeights, - const std::vector<float>& inputLayerNormWeights, - bool hasForgetLayerNormWeights, - const std::vector<float>& forgetLayerNormWeights, - bool hasCellLayerNormWeights, - const std::vector<float>& cellLayerNormWeights, - bool hasOutputLayerNormWeights, - const std::vector<float>& outputLayerNormWeights, - tflite::ActivationFunctionType activationFunction, - float clippingThresCell, - float clippingThresProj, - bool isTimeMajor, - float quantScale, - int quantOffset = 0) -{ - - std::vector<int32_t> tensorInfo0{}; - std::vector<int32_t> tensorInfoNumUnits{numUnits}; - std::vector<int32_t> tensorInfoInputSize{numUnits, inputSize}; - std::vector<int32_t> tensorInfoOutputSize{numUnits, outputSize}; - - std::vector<int32_t> inputShape; - std::vector<int32_t> outputShape; - if (isTimeMajor) - { - inputShape = {timeSize, batchSize, inputSize}; - outputShape = {timeSize, batchSize, outputSize}; - } - else - { - inputShape = {batchSize, timeSize, inputSize}; - outputShape = {batchSize, timeSize, outputSize}; - } - std::vector<int32_t> outputStateInDimensions{batchSize, outputSize}; - std::vector<int32_t> cellStateInDimensions{batchSize, numUnits}; - std::vector<int32_t> projectionWeightDimensions{outputSize, numUnits}; - std::vector<int32_t> projectionBiasDimensions{outputSize}; - - std::vector<int> operatorInputs; - using namespace tflite; - flatbuffers::FlatBufferBuilder flatBufferBuilder; - std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; - std::vector<flatbuffers::Offset<Tensor>> tensors; - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({1.0f}), - flatBufferBuilder.CreateVector<int64_t>({0})); - - auto weightQuantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({quantScale}), - flatBufferBuilder.CreateVector<int64_t>({quantOffset})); - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), - inputShape.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("input_0"))); - operatorInputs.push_back(tensors.size() - 1); - - if (hasInputToInputWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(inputToInputWeights.data()), - sizeof(T) * inputToInputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), - tensorInfoInputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToInputWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(inputToForgetWeights.data()), - sizeof(T) * inputToForgetWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), - tensorInfoInputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToForgetWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(inputToCellWeights.data()), - sizeof(T) * inputToCellWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), - tensorInfoInputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToCellWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(inputToOutputWeights.data()), - sizeof(T) * inputToOutputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoInputSize.data(), - tensorInfoInputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputToOutputWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - - if (hasRecurrentToInputWeights) - { - buffers.push_back(CreateBuffer( - flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(recurrentToInputWeights.data()), - sizeof(T) * recurrentToInputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), - tensorInfoOutputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToInputWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - recurrentToForgetWeights.data()), - sizeof(T) * recurrentToForgetWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), - tensorInfoOutputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToForgetWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - recurrentToCellWeights.data()), - sizeof(T) * recurrentToCellWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), - tensorInfoOutputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToCellWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - recurrentToOutputWeights.data()), - sizeof(T) * recurrentToOutputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoOutputSize.data(), - tensorInfoOutputSize.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("recurrentToOutputWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - - if (hasCellToInputWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - cellToInputWeights.data()), - sizeof(T) * cellToInputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellToInputWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasCellToForgetWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - cellToForgetWeights.data()), - sizeof(T) * cellToForgetWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellToForgetWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasCellToOutputWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - cellToOutputWeights.data()), - sizeof(T) * cellToOutputWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellToOutputWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasInputGateBias) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(inputGateBias.data()), - sizeof(float) * inputGateBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputGateBias"))); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(forgetGateBias.data()), - sizeof(float) * forgetGateBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("forgetGateBias"))); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(cellBias.data()), - sizeof(float) * cellBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellBias"))); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(outputGateBias.data()), - sizeof(float) * outputGateBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputGateBias"))); - operatorInputs.push_back(tensors.size() - 1); - - if (hasProjectionWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(projectionWeights.data()), - sizeof(T) * projectionWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(projectionWeightDimensions.data(), - projectionWeightDimensions.size()), - tensorType, - buffers.size() - 1, - flatBufferBuilder.CreateString("projectionWeights"), - weightQuantizationParameters)); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasProjectionBias) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(projectionBias.data()), - sizeof(float) * projectionBias.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(projectionBiasDimensions.data(), - projectionBiasDimensions.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("projectionBias"))); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputStateInDimensions.data(), - outputStateInDimensions.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputStateInInfo"), - quantizationParameters, - true)); - operatorInputs.push_back(tensors.size() - 1); - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(cellStateInDimensions.data(), - cellStateInDimensions.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellStateInInfo"), - quantizationParameters, - true)); - operatorInputs.push_back(tensors.size() - 1); - - if (hasInputLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(inputLayerNormWeights.data()), - sizeof(float) * inputLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("inputLayerNormWeights"))); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasForgetLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(forgetLayerNormWeights.data()), - sizeof(float) * forgetLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("forgetLayerNormWeights"))); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasCellLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>( - cellLayerNormWeights.data()), - sizeof(float) * cellLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("cellLayerNormWeights"))); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - - if (hasOutputLayerNormWeights) - { - buffers.push_back( - CreateBuffer(flatBufferBuilder, - flatBufferBuilder.CreateVector( - reinterpret_cast<const uint8_t*>(outputLayerNormWeights.data()), - sizeof(float) * outputLayerNormWeights.size()))); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(tensorInfoNumUnits.data(), - tensorInfoNumUnits.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("outputLayerNormWeights"))); - operatorInputs.push_back(tensors.size() - 1); - } - else - { - operatorInputs.push_back(kTfLiteOptionalTensor); - } - buffers.push_back(CreateBuffer(flatBufferBuilder)); - tensors.push_back(CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), - outputShape.size()), - ::tflite::TensorType_FLOAT32, - buffers.size() - 1, - flatBufferBuilder.CreateString("output"))); - std::vector<int> operatorOutputs; - operatorOutputs.push_back(tensors.size() - 1); - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_UnidirectionalSequenceLSTMOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateUnidirectionalSequenceLSTMOptions(flatBufferBuilder, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor).Union(); - - flatbuffers::Offset<Operator> lstmOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), - operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), - operatorOutputs.size()), - operatorBuiltinOptionsType, operatorBuiltinOptions); - - flatbuffers::Offset<SubGraph> subgraph = - CreateSubGraph(flatBufferBuilder, - flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), - operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), - operatorOutputs.size()), - flatBufferBuilder.CreateVector(&lstmOperator, 1)); - - flatbuffers::Offset<flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString( - "ArmnnDelegate: UnidirectionalSequenceLSTM Operator Model"); - flatbuffers::Offset<OperatorCode> operatorCode = - CreateOperatorCode(flatBufferBuilder, - tflite::BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM); - - flatbuffers::Offset<Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template<typename T> -void UnidirectionalSequenceLstmTestImpl(std::vector<armnn::BackendId>& backends, - tflite::TensorType tensorType, - int32_t batchSize, - int32_t timeSize, - int32_t inputSize, - int32_t outputSize, - int32_t numUnits, - bool hasInputToInputWeights, - const std::vector<T>& inputToInputWeights, - const std::vector<T>& inputToForgetWeights, - const std::vector<T>& inputToCellWeights, - const std::vector<T>& inputToOutputWeights, - bool hasRecurrentToInputWeights, - const std::vector<T>& recurrentToInputWeights, - const std::vector<T>& recurrentToForgetWeights, - const std::vector<T>& recurrentToCellWeights, - const std::vector<T>& recurrentToOutputWeights, - bool hasCellToInputWeights, - const std::vector<T>& cellToInputWeights, - bool hasCellToForgetWeights, - const std::vector<T>& cellToForgetWeights, - bool hasCellToOutputWeights, - const std::vector<T>& cellToOutputWeights, - bool hasInputGateBias, - const std::vector<float>& inputGateBias, - const std::vector<float>& forgetGateBias, - const std::vector<float>& cellBias, - const std::vector<float>& outputGateBias, - bool hasProjectionWeights, - const std::vector<T>& projectionWeights, - bool hasProjectionBias, - const std::vector<float>& projectionBias, - bool hasInputLayerNormWeights, - const std::vector<float>& inputLayerNormWeights, - bool hasForgetLayerNormWeights, - const std::vector<float>& forgetLayerNormWeights, - bool hasCellLayerNormWeights, - const std::vector<float>& cellLayerNormWeights, - bool hasOutputLayerNormWeights, - const std::vector<float>& outputLayerNormWeights, - std::vector<float>& inputValues, - std::vector<float>& expectedOutputValues, - tflite::ActivationFunctionType activationFunction, - float clippingThresCell, - float clippingThresProj, - bool isTimeMajor, - float quantScale = 0.1f) -{ - using namespace tflite; - - std::vector<char> modelBuffer = CreateUnidirectionalSequenceLstmTfLiteModel(tensorType, - batchSize, - timeSize, - inputSize, - outputSize, - numUnits, - hasInputToInputWeights, - inputToInputWeights, - inputToForgetWeights, - inputToCellWeights, - inputToOutputWeights, - hasRecurrentToInputWeights, - recurrentToInputWeights, - recurrentToForgetWeights, - recurrentToCellWeights, - recurrentToOutputWeights, - hasCellToInputWeights, - cellToInputWeights, - hasCellToForgetWeights, - cellToForgetWeights, - hasCellToOutputWeights, - cellToOutputWeights, - hasInputGateBias, - inputGateBias, - forgetGateBias, - cellBias, - outputGateBias, - hasProjectionWeights, - projectionWeights, - hasProjectionBias, - projectionBias, - hasInputLayerNormWeights, - inputLayerNormWeights, - hasForgetLayerNormWeights, - forgetLayerNormWeights, - hasCellLayerNormWeights, - cellLayerNormWeights, - hasOutputLayerNormWeights, - outputLayerNormWeights, - activationFunction, - clippingThresCell, - clippingThresProj, - isTimeMajor, - quantScale); - - 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 - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelageInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - - // 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); - - if (tensorType == ::tflite::TensorType_INT8) - { - // Allow 2% tolerance for Quantized weights - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, - expectedOutputValues.size(), 2); - armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, - expectedOutputValues.size(), 2); - armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, - expectedOutputValues.size(), 2); - } - else - { - armnnDelegate::CompareData(expectedOutputValues.data(), armnnDelegateOutputData, - expectedOutputValues.size()); - armnnDelegate::CompareData(expectedOutputValues.data(), tfLiteDelagateOutputData, - expectedOutputValues.size()); - armnnDelegate::CompareData(tfLiteDelagateOutputData, armnnDelegateOutputData, expectedOutputValues.size()); - } -} - -} // anonymous namespace
\ No newline at end of file diff --git a/delegate/src/test/UnpackTest.cpp b/delegate/src/test/UnpackTest.cpp deleted file mode 100644 index c036f649ef..0000000000 --- a/delegate/src/test/UnpackTest.cpp +++ /dev/null @@ -1,179 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "UnpackTestHelper.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <tensorflow/lite/schema/schema_generated.h> - -#include <doctest/doctest.h> - -namespace armnnDelegate -{ - -template <typename T> -void UnpackAxis0Num4Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 4, 1, 6 }; - std::vector<int32_t> expectedOutputShape { 1, 6 }; - - std::vector<T> inputValues { 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24 }; - - std::vector<T> expectedOutputValues0 { 1, 2, 3, 4, 5, 6 }; - std::vector<T> expectedOutputValues1 { 7, 8, 9, 10, 11, 12 }; - std::vector<T> expectedOutputValues2 { 13, 14, 15, 16, 17, 18 }; - std::vector<T> expectedOutputValues3 { 19, 20, 21, 22, 23, 24 }; - - std::vector<std::vector<T>> expectedOutputValues{ expectedOutputValues0, - expectedOutputValues1, - expectedOutputValues2, - expectedOutputValues3 }; - - UnpackTest<T>(tflite::BuiltinOperator_UNPACK, - tensorType, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 0); -} - -template <typename T> -void UnpackAxis2Num6Test(tflite::TensorType tensorType, std::vector<armnn::BackendId>& backends) -{ - std::vector<int32_t> inputShape { 4, 1, 6 }; - std::vector<int32_t> expectedOutputShape { 4, 1 }; - - std::vector<T> inputValues { 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18, - 19, 20, 21, 22, 23, 24 }; - - std::vector<T> expectedOutputValues0 { 1, 7, 13, 19 }; - std::vector<T> expectedOutputValues1 { 2, 8, 14, 20 }; - std::vector<T> expectedOutputValues2 { 3, 9, 15, 21 }; - std::vector<T> expectedOutputValues3 { 4, 10, 16, 22 }; - std::vector<T> expectedOutputValues4 { 5, 11, 17, 23 }; - std::vector<T> expectedOutputValues5 { 6, 12, 18, 24 }; - - std::vector<std::vector<T>> expectedOutputValues{ expectedOutputValues0, - expectedOutputValues1, - expectedOutputValues2, - expectedOutputValues3, - expectedOutputValues4, - expectedOutputValues5 }; - - UnpackTest<T>(tflite::BuiltinOperator_UNPACK, - tensorType, - backends, - inputShape, - expectedOutputShape, - inputValues, - expectedOutputValues, - 2); -} - -TEST_SUITE("Unpack_CpuRefTests") -{ - -// Fp32 -TEST_CASE ("Unpack_Fp32_Axis0_Num4_CpuRef_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; -UnpackAxis0Num4Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Unpack_Fp32_Axis2_Num6_CpuRef_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; -UnpackAxis2Num6Test<float>(tflite::TensorType_FLOAT32, backends); -} - -// Uint8 -TEST_CASE ("Unpack_Uint8_Axis0_Num4_CpuRef_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; -UnpackAxis0Num4Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -TEST_CASE ("Unpack_Uint8_Axis2_Num6_CpuRef_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef}; -UnpackAxis2Num6Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -} // End of Unpack_CpuRefTests - -TEST_SUITE("Unpack_CpuAccTests") -{ - -// Fp32 -TEST_CASE ("Unpack_Fp32_Axis0_Num4_CpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; -UnpackAxis0Num4Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Unpack_Fp32_Axis2_Num6_CpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; -UnpackAxis2Num6Test<float>(tflite::TensorType_FLOAT32, backends); -} - -// Uint8 -TEST_CASE ("Unpack_Uint8_Axis0_Num4_CpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; -UnpackAxis0Num4Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -TEST_CASE ("Unpack_Uint8_Axis2_Num6_CpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc}; -UnpackAxis2Num6Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -} // End of Unpack_CpuAccTests - -TEST_SUITE("Unpack_GpuAccTests") -{ - -// Fp32 -TEST_CASE ("Unpack_Fp32_Axis0_Num4_GpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; -UnpackAxis0Num4Test<float>(tflite::TensorType_FLOAT32, backends); -} - -TEST_CASE ("Unpack_Fp32_Axis2_Num6_GpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; -UnpackAxis2Num6Test<float>(tflite::TensorType_FLOAT32, backends); -} - -// Uint8 -TEST_CASE ("Unpack_Uint8_Axis0_Num4_GpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; -UnpackAxis0Num4Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -TEST_CASE ("Unpack_Uint8_Axis2_Num6_GpuAcc_Test") -{ -std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc}; -UnpackAxis2Num6Test<uint8_t>(tflite::TensorType_UINT8, backends); -} - -} // End of Unpack_GpuAccTests - -// End of Unpack Test Suite - -} // namespace armnnDelegate
\ No newline at end of file diff --git a/delegate/src/test/UnpackTestHelper.hpp b/delegate/src/test/UnpackTestHelper.hpp deleted file mode 100644 index 0e12d72279..0000000000 --- a/delegate/src/test/UnpackTestHelper.hpp +++ /dev/null @@ -1,188 +0,0 @@ -// -// Copyright © 2021, 2023 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> CreateUnpackTfLiteModel(tflite::BuiltinOperator unpackOperatorCode, - tflite::TensorType tensorType, - std::vector<int32_t>& inputTensorShape, - const std::vector <int32_t>& outputTensorShape, - const int32_t outputTensorNum, - unsigned int axis = 0, - 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)); - buffers.push_back(CreateBuffer(flatBufferBuilder)); - - - auto quantizationParameters = - CreateQuantizationParameters(flatBufferBuilder, - 0, - 0, - flatBufferBuilder.CreateVector<float>({ quantScale }), - flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); - - const std::vector<int32_t> operatorInputs{ 0 }; - std::vector<int32_t> operatorOutputs{}; - const std::vector<int> subgraphInputs{ 0 }; - std::vector<int> subgraphOutputs{}; - - std::vector<flatbuffers::Offset<Tensor>> tensors(outputTensorNum + 1); - - // Create input tensor - tensors[0] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), - inputTensorShape.size()), - tensorType, - 1, - flatBufferBuilder.CreateString("input"), - quantizationParameters); - - for (int i = 0; i < outputTensorNum; ++i) - { - tensors[i + 1] = CreateTensor(flatBufferBuilder, - flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), - outputTensorShape.size()), - tensorType, - (i + 2), - flatBufferBuilder.CreateString("output" + std::to_string(i)), - quantizationParameters); - - buffers.push_back(CreateBuffer(flatBufferBuilder)); - operatorOutputs.push_back(i + 1); - subgraphOutputs.push_back(i + 1); - } - - // create operator - tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_UnpackOptions; - flatbuffers::Offset<void> operatorBuiltinOptions = - CreateUnpackOptions(flatBufferBuilder, outputTensorNum, axis).Union(); - - flatbuffers::Offset <Operator> unpackOperator = - CreateOperator(flatBufferBuilder, - 0, - flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), - flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), - operatorBuiltinOptionsType, - operatorBuiltinOptions); - - 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(&unpackOperator, 1)); - - flatbuffers::Offset <flatbuffers::String> modelDescription = - flatBufferBuilder.CreateString("ArmnnDelegate: Unpack Operator Model"); - flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, unpackOperatorCode); - - flatbuffers::Offset <Model> flatbufferModel = - CreateModel(flatBufferBuilder, - TFLITE_SCHEMA_VERSION, - flatBufferBuilder.CreateVector(&operatorCode, 1), - flatBufferBuilder.CreateVector(&subgraph, 1), - modelDescription, - flatBufferBuilder.CreateVector(buffers)); - - flatBufferBuilder.Finish(flatbufferModel); - - return std::vector<char>(flatBufferBuilder.GetBufferPointer(), - flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); -} - -template <typename T> -void UnpackTest(tflite::BuiltinOperator unpackOperatorCode, - tflite::TensorType tensorType, - std::vector<armnn::BackendId>& backends, - std::vector<int32_t>& inputShape, - std::vector<int32_t>& expectedOutputShape, - std::vector<T>& inputValues, - std::vector<std::vector<T>>& expectedOutputValues, - unsigned int axis = 0, - float quantScale = 1.0f, - int quantOffset = 0) -{ - using namespace tflite; - std::vector<char> modelBuffer = CreateUnpackTfLiteModel(unpackOperatorCode, - tensorType, - inputShape, - expectedOutputShape, - expectedOutputValues.size(), - axis, - quantScale, - quantOffset); - - 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 - for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) - { - armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, - armnnDelegateInterpreter, - expectedOutputShape, - expectedOutputValues[i], - i); - } - - armnnDelegateInterpreter.reset(nullptr); -} - -} // anonymous namespace
\ No newline at end of file |