From fc9d5e7d1e0c1a4d7fed4ebc363832e03c3e2543 Mon Sep 17 00:00:00 2001 From: Matthew Sloyan Date: Thu, 8 Dec 2022 13:38:23 +0000 Subject: IVGCVSW-7204 Add TransposeConv2d support to TOSA Reference Backend Signed-off-by: Matthew Sloyan Change-Id: I9bfd597afd41468f304edfbe5d7141378ce60d4f --- .../TransposeConvolution2dEndToEndTestImpl.hpp | 70 ++++++++- src/backends/tosaCommon/TosaMappings.cpp | 5 + .../tosaCommon/operatorMappings/CMakeLists.txt | 2 + .../operatorMappings/TosaCommonOperators.hpp | 3 +- .../operatorMappings/TosaOperatorUtils.hpp | 17 +++ .../operatorMappings/TransposeConv2dOperator.cpp | 161 +++++++++++++++++++++ .../operatorMappings/TransposeConv2dOperator.hpp | 20 +++ .../tosaCommon/test/OneToOneMappingTests.cpp | 102 +++++++++++++ src/backends/tosaCommon/test/TosaTestUtils.hpp | 21 ++- src/backends/tosaReference/TosaRefLayerSupport.cpp | 13 ++ .../tosaReference/test/TosaRefEndToEndTests.cpp | 14 ++ .../test/TosaRefLayerSupportTests.cpp | 51 ++++++- 12 files changed, 468 insertions(+), 11 deletions(-) create mode 100644 src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp create mode 100644 src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp diff --git a/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp index e12813ad91..47d6c28c2e 100644 --- a/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp +++ b/src/backends/backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp @@ -1,5 +1,5 @@ // -// Copyright © 2017 Arm Ltd. All rights reserved. +// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #pragma once @@ -147,3 +147,71 @@ void TransposeConvolution2dEndToEnd(const std::vector& backend { { 0, qExpectedOutputData } }, backends); } + +template +void SimpleTransposeConvolution2dEndToEnd(const std::vector& backends, + armnn::DataLayout dataLayout) +{ + using namespace armnn; + using T = ResolveType; + + const float qScale = IsQuantizedType() ? 0.25f : 1.0f; + const int32_t qOffset = IsQuantizedType() ? 50 : 0; + + TensorInfo inputInfo({1, 2, 2, 1}, ArmnnType, qScale, qOffset, true); + TensorInfo outputInfo({1, 3, 3, 1}, ArmnnType, qScale, qOffset); + TensorInfo weightsInfo({1, 2, 2, 1}, ArmnnType, qScale, qOffset, true); + TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true); + + std::vector inputData = + { + 1, 2, 3, 4 + }; + + std::vector weightsData = + { + 0, 1, 2, 4 + }; + std::vector biasesData = { 0.f }; + + std::vector expectedOutputData = + { + 0, 1, 2, + 2, 11, 12, + 6, 20, 16 + }; + + TransposeConvolution2dDescriptor descriptor; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 0; + descriptor.m_PadBottom = 0; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 1; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = dataLayout; + descriptor.m_OutputShapeEnabled = true; + descriptor.m_OutputShape = { 1, 3, 3, 1 }; + + // quantize data + std::vector qInputData = armnnUtils::QuantizedVector(inputData, qScale, qOffset); + std::vector qWeightsData = armnnUtils::QuantizedVector(weightsData, qScale, qOffset); + std::vector qExpectedOutputData = armnnUtils::QuantizedVector(expectedOutputData, qScale, qOffset); + + using BT = ResolveType; + std::vector qBiasesData = armnnUtils::QuantizedVector(biasesData, qScale * qScale, 0); + + ConstTensor weights(weightsInfo, qWeightsData); + ConstTensor biases(biasesInfo, qBiasesData); + + INetworkPtr network = CreateTransposeConvolution2dNetwork(descriptor, + inputInfo, + outputInfo, + weights, + Optional(biases)); + + EndToEndLayerTestImpl(std::move(network), + { { 0, qInputData } }, + { { 0, qExpectedOutputData } }, + backends); +} diff --git a/src/backends/tosaCommon/TosaMappings.cpp b/src/backends/tosaCommon/TosaMappings.cpp index 15629ffab0..7ecf7266ac 100644 --- a/src/backends/tosaCommon/TosaMappings.cpp +++ b/src/backends/tosaCommon/TosaMappings.cpp @@ -67,6 +67,11 @@ TosaSerializationBasicBlock* GetTosaMapping(const Layer* layer, auto sliceDesc = PolymorphicDowncast(&descriptor); return ConvertSliceToTosaOperator(layer, inputs, outputs, sliceDesc); } + case LayerType::TransposeConvolution2d: + { + auto transposeConv2dDesc = PolymorphicDowncast(&descriptor); + return ConvertTransposeConv2dToTosaOperator(layer, inputs, outputs, transposeConv2dDesc); + } default: { return CreateEmptyTosaSerializationBasicBlock(); diff --git a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt index cb1d68e625..90c1a4f958 100644 --- a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt +++ b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt @@ -19,6 +19,8 @@ list(APPEND armnnTosaBackendOperators_sources SliceOperator.hpp SliceOperator.cpp TosaOperatorUtils.hpp + TransposeConv2dOperator.hpp + TransposeConv2dOperator.cpp ) add_library(armnnTosaBackendOperators OBJECT ${armnnTosaBackendOperators_sources}) diff --git a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp index a3597f0461..1a9d6be9c0 100644 --- a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp +++ b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp @@ -11,4 +11,5 @@ #include "AvgPool2DIgnoreValueOperator.hpp" #include "Pooling2DOperator.hpp" #include "ReshapeOperator.hpp" -#include "SliceOperator.hpp" \ No newline at end of file +#include "SliceOperator.hpp" +#include "TransposeConv2dOperator.hpp" \ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp index 288966badd..be2f53e413 100644 --- a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp +++ b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp @@ -75,6 +75,23 @@ inline std::string GenerateUniqueName(const Layer& layer, uint32_t layerSlot) } } +// Function that generates unique output name using the layer type, input slot and layer guid. +inline std::string GenerateUniqueOutputName(const Layer& layer, uint32_t layerSlot) +{ + Layer& connectedLayer = layer.GetOutputSlot().GetConnection(0)->GetOwningLayer(); + + // Get the layer connected to the output slot, if output use that layer and id, + // otherwise use current layer and id. + if(connectedLayer.GetType() == LayerType::Output) + { + return GenerateUniqueName(connectedLayer, layerSlot); + } + else + { + return GenerateUniqueName(layer, layerSlot); + } +} + // Function to return unique int as a string to ensure uniqueness between all input, output and block names. static int uniqueTosaMappingID = 0; inline std::string GetUniqueTosaMappingID() diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp new file mode 100644 index 0000000000..a0d58e2fa8 --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp @@ -0,0 +1,161 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "TransposeConv2dOperator.hpp" + +#include "layers/TransposeConvolution2dLayer.hpp" + +TosaSerializationBasicBlock* ConvertTransposeConv2dToTosaOperator(const Layer* layer, + const std::vector& inputs, + const std::vector& outputs, + const TransposeConvolution2dDescriptor* descriptor) +{ + std::string input0Name = std::string("input0_"); + std::string input1Name = std::string("constant_") + GetUniqueTosaMappingID(); + std::string input2Name = std::string("constant_") + GetUniqueTosaMappingID(); + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_TRANSPOSE_CONV2D_block_") + GetUniqueTosaMappingID(); + + // If a layer is present then the block will be used for execution, so input and output names need to be determined + // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. + if(layer != nullptr) + { + Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + input0Name = GenerateUniqueName(connectedInputLayer, 0); + + outputName = GenerateUniqueOutputName(*layer, 0); + } + + std::vector tensors; + std::vector operators; + + // Setup input tensor + // Only add tensor if connected layer is an input layer. + // As intermediate or constant tensors will be created separately. + // There also can't be duplicate tensors. + if(input0Name.find("input0_") != std::string::npos) + { + std::vector inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); + DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {})); + } + + // Setup weights tensor, constant data will get copied during SetConstantTensorData + operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input1Name})); + + // During validation the TensorInfo can be retrieved from the inputs. + // During execution, it is only available through the layer so use m_Weight. + if(layer == nullptr) + { + std::vector inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); + DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {})); + } + else + { + auto transposeConv2dLayer = PolymorphicDowncast(layer); + + std::vector inputShape1 = GetTosaTensorShape( + transposeConv2dLayer->m_Weight->GetTensorInfo().GetShape()); + DType inputDType1 = ArmNNToDType(transposeConv2dLayer->m_Weight->GetTensorInfo().GetDataType()); + + std::vector uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Weight); + tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, uint8Data)); + } + + // Setup bias operator and tensor, constant data will get copied during SetConstantTensorData + operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input2Name})); + + // During validation the TensorInfo can be retrieved from the inputs. + // During execution, it is only available through the layer so use m_Bias. + if(layer == nullptr && descriptor->m_BiasEnabled) + { + std::vector inputShape2 = GetTosaTensorShape(inputs[2]->GetShape()); + DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, {})); + } + else if(descriptor->m_BiasEnabled) + { + auto transposeConv2dLayer = PolymorphicDowncast(layer); + + std::vector inputShape2 = GetTosaTensorShape( + transposeConv2dLayer->m_Bias->GetTensorInfo().GetShape()); + DType inputDType2 = ArmNNToDType(transposeConv2dLayer->m_Bias->GetTensorInfo().GetDataType()); + + std::vector uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Bias); + tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, uint8Data)); + } + else + { + // If bias is disabled, create a constant bias tensor of 0's as three inputs are required. + // The size of the bias must match the channels dimension, so get the correct index. + unsigned int index = (descriptor->m_DataLayout == DataLayout::NHWC) ? + outputs[0]->GetShape()[3] : outputs[0]->GetShape()[1]; + + std::vector uint8Data; + std::vector data(outputs[0]->GetShape()[index], 0.0f); + + TosaSerializationHandler::ConvertF32toU8(data, uint8Data); + + tensors.push_back(new TosaSerializationTensor(input2Name, + {static_cast(outputs[0]->GetShape()[index])}, + DType_FP32, + uint8Data)); + } + + // Setup Output Tensor + std::vector outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); + DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); + + // Set up TRANSPOSE_CONV2D operator + // The TOSA Reference Model pads the output shape, so it is added to output shape. + // In Arm NN we pad the input shape, so it is taken away. + // To offset this the negative padding value can be used. + std::vector pad = {-static_cast(descriptor->m_PadTop), + -static_cast(descriptor->m_PadBottom), + -static_cast(descriptor->m_PadLeft), + -static_cast(descriptor->m_PadRight)}; + std::vector stride = {static_cast(descriptor->m_StrideY), + static_cast(descriptor->m_StrideX)}; + + std::vector outputShape; + // If available use shape in descriptor otherwise use output shape. + if (descriptor->m_OutputShape.size() == 4) + { + for (uint32_t i = 0; i < descriptor->m_OutputShape.size(); ++i) + { + outputShape.push_back(static_cast(descriptor->m_OutputShape[i])); + } + } + else + { + for (uint32_t i = 0; i < outputs[0]->GetNumDimensions(); ++i) + { + outputShape.push_back(static_cast(outputs[0]->GetShape()[i])); + } + } + + TosaTransposeConvAttribute attribute(pad, stride, outputShape, 0, 0, ArmNNToDType(inputs[0]->GetDataType())); + + auto* op = new TosaSerializationOperator(Op_TRANSPOSE_CONV2D, + Attribute_TransposeConvAttribute, + &attribute, + {input0Name, input1Name, input2Name}, + {outputName}); + operators.push_back(op); + + // operatorInputNames/operatorOutputNames ends up being the same as + // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings + return new TosaSerializationBasicBlock(blockName, // name + operators, // operators + tensors, // tensors + {input0Name, input1Name, input2Name}, // inputs + {outputName}); // outputs +} \ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp new file mode 100644 index 0000000000..eb911a1195 --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp @@ -0,0 +1,20 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TosaOperatorUtils.hpp" + +#include + +#include + +using namespace armnn; +using namespace tosa; + +TosaSerializationBasicBlock* ConvertTransposeConv2dToTosaOperator(const Layer* layer, + const std::vector& inputs, + const std::vector& outputs, + const TransposeConvolution2dDescriptor* descriptor); diff --git a/src/backends/tosaCommon/test/OneToOneMappingTests.cpp b/src/backends/tosaCommon/test/OneToOneMappingTests.cpp index 0d19a328d6..2b0c1e55c7 100644 --- a/src/backends/tosaCommon/test/OneToOneMappingTests.cpp +++ b/src/backends/tosaCommon/test/OneToOneMappingTests.cpp @@ -438,6 +438,108 @@ TEST_CASE("GetTosaMappingFromLayer_SliceLayer") } +TEST_CASE("GetTosaMapping_TransposeConv2dLayer") +{ + const TensorInfo inputInfo ({ 1, 7, 7, 1 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 9, 9, 1 }, DataType::Float32); + const TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true); + const TensorInfo biasesInfo ({ 1 }, DataType::Float32, 0.0f, 0, true); + + TransposeConvolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 1; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = DataLayout::NHWC; + + TosaSerializationBasicBlock* basicBlock = GetTosaMapping(nullptr, + LayerType::TransposeConvolution2d, + {&inputInfo, &weightsInfo, &biasesInfo}, + {&outputInfo}, + descriptor); + + CHECK(basicBlock->GetInputs().size() == 3); + CHECK(basicBlock->GetOutputs().size() == 1); + CHECK(basicBlock->GetOperators().size() == 3); + CHECK(basicBlock->GetTensors().size() == 4); + + CHECK(basicBlock->GetInputs()[0].find("input0_") != std::string::npos); + CHECK(basicBlock->GetInputs()[1].find("constant_") != std::string::npos); + CHECK(basicBlock->GetInputs()[2].find("constant_") != std::string::npos); + CHECK(basicBlock->GetOutputs()[0].find("output0_") != std::string::npos); + + VerifyTosaAttribute(descriptor, + basicBlock->GetOperators().at(2)->GetAttribute(), + {}, + {}, + LayerType::TransposeConvolution2d); +} + +TEST_CASE("GetTosaMappingFromLayer_TransposeConv2dLayer") +{ + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + const TensorInfo inputInfo ({ 1, 7, 7, 1 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 9, 9, 1 }, DataType::Float32); + const TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true); + const TensorInfo biasesInfo ({ 1 }, DataType::Float32, 0.0f, 0, true); + + std::vector weightsData = GenerateRandomData(weightsInfo.GetNumElements()); + ConstTensor weights(weightsInfo, weightsData); + + std::vector biasesData = GenerateRandomData(biasesInfo.GetNumElements()); + ConstTensor biases(biasesInfo, biasesData); + + TransposeConvolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 1; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = DataLayout::NHWC; + + IConnectableLayer* const inputLayer = net->AddInputLayer(0); + IConnectableLayer* const convLayer = + net->AddTransposeConvolution2dLayer(descriptor, + weights, + Optional(biases), + "transposeConvolution2d"); + IConnectableLayer* const outputLayer = net->AddOutputLayer(0); + + inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); + + inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); + convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); + + TosaSerializationBasicBlock* basicBlock = GetTosaMappingFromLayer(PolymorphicDowncast(convLayer)); + + CHECK(basicBlock->GetInputs().size() == 3); + CHECK(basicBlock->GetOutputs().size() == 1); + CHECK(basicBlock->GetOperators().size() == 3); + CHECK(basicBlock->GetTensors().size() == 4); + + CHECK(basicBlock->GetInputs()[0].find("input0_") != std::string::npos); + CHECK(basicBlock->GetInputs()[1].find("constant_") != std::string::npos); + CHECK(basicBlock->GetInputs()[2].find("constant_") != std::string::npos); + CHECK(basicBlock->GetOutputs()[0].find("output0_") != std::string::npos); + + VerifyTosaAttribute(descriptor, + basicBlock->GetOperators().at(2)->GetAttribute(), + {}, + {}, + LayerType::TransposeConvolution2d); +} + TEST_CASE("GetTosaMapping_Unimplemented") { TosaSerializationBasicBlock* basicBlock = diff --git a/src/backends/tosaCommon/test/TosaTestUtils.hpp b/src/backends/tosaCommon/test/TosaTestUtils.hpp index 93b9e7d36f..140cb83983 100644 --- a/src/backends/tosaCommon/test/TosaTestUtils.hpp +++ b/src/backends/tosaCommon/test/TosaTestUtils.hpp @@ -144,6 +144,20 @@ inline void VerifyTosaAttribute(const BaseDescriptor& descriptor, break; } + case LayerType::TransposeConvolution2d: + { + auto transposeConv2dDesc = PolymorphicDowncast(&descriptor); + std::vector outPad = {-static_cast(transposeConv2dDesc->m_PadTop), + -static_cast(transposeConv2dDesc->m_PadBottom), + -static_cast(transposeConv2dDesc->m_PadLeft), + -static_cast(transposeConv2dDesc->m_PadRight)}; + std::vector stride = {static_cast(transposeConv2dDesc->m_StrideY), + static_cast(transposeConv2dDesc->m_StrideX)}; + TosaTransposeConvAttribute transposeConvAttribute(attribute); + CHECK(outPad == transposeConvAttribute.out_pad()); + CHECK(stride == transposeConvAttribute.stride()); + break; + } default: break; } @@ -167,12 +181,7 @@ inline void AssertTosaOneToOneMappingBasicBlock(TosaSerializationBasicBlock* bas // The number of tensors in the block can be different if there are constant layers, as they are created separately. if(type == LayerType::Convolution2d) { - numInputTensors = 2; - auto conv2dDesc = PolymorphicDowncast(&descriptor); - if(conv2dDesc->m_BiasEnabled) - { - numInputTensors = 3; - } + numInputTensors = PolymorphicDowncast(&descriptor)->m_BiasEnabled ? 3 : 2; } std::string blockStr = operatorString + "_block_"; diff --git a/src/backends/tosaReference/TosaRefLayerSupport.cpp b/src/backends/tosaReference/TosaRefLayerSupport.cpp index 928a19c232..e5427ebc93 100644 --- a/src/backends/tosaReference/TosaRefLayerSupport.cpp +++ b/src/backends/tosaReference/TosaRefLayerSupport.cpp @@ -66,6 +66,19 @@ bool TosaRefLayerSupport::IsLayerSupported(const LayerType& type, inputInfos.push_back(&infos[0]); outputInfos.push_back(&infos[1]); break; + case LayerType::TransposeConvolution2d: + { + inputInfos.push_back(&infos[0]); // input + outputInfos.push_back(&infos[1]); // output + inputInfos.push_back(&infos[2]); // weights + + auto conv2dDesc = PolymorphicDowncast(&descriptor); + if(conv2dDesc->m_BiasEnabled) + { + inputInfos.push_back(&infos[3]); // bias + } + break; + } default: break; } diff --git a/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp b/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp index 2f1231013a..00c0386b51 100644 --- a/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp +++ b/src/backends/tosaReference/test/TosaRefEndToEndTests.cpp @@ -10,6 +10,7 @@ #include "backendsCommon/test/Pooling2dEndToEndTestImpl.hpp" #include "backendsCommon/test/ReshapeEndToEndTestImpl.hpp" #include "backendsCommon/test/SliceEndToEndTestImpl.hpp" +#include "backendsCommon/test/TransposeConvolution2dEndToEndTestImpl.hpp" #include @@ -108,4 +109,17 @@ TEST_CASE("TosaRefSliceEndtoEndTestFloat16") SliceEndToEndFloat16(tosaDefaultBackends); } +// TransposeConvolution2d +TEST_CASE("TosaRefTransposeConvolution2dEndToEndFloatNhwcTest") +{ + TransposeConvolution2dEndToEnd( + tosaDefaultBackends, armnn::DataLayout::NHWC); +} + +TEST_CASE("TosaRefSimpleTransposeConvolution2dEndToEndFloatNhwcTest") +{ + SimpleTransposeConvolution2dEndToEnd( + tosaDefaultBackends, armnn::DataLayout::NHWC); +} + } \ No newline at end of file diff --git a/src/backends/tosaReference/test/TosaRefLayerSupportTests.cpp b/src/backends/tosaReference/test/TosaRefLayerSupportTests.cpp index 0d0cd6eefc..3c3abc2af3 100644 --- a/src/backends/tosaReference/test/TosaRefLayerSupportTests.cpp +++ b/src/backends/tosaReference/test/TosaRefLayerSupportTests.cpp @@ -105,7 +105,7 @@ TEST_CASE("IsLayerSupportedTosaReferenceConv2d") TosaRefLayerSupport supportChecker; std::string reasonIfNotSupported; - auto supported = supportChecker.IsLayerSupported(armnn::LayerType::Convolution2d, + auto supported = supportChecker.IsLayerSupported(LayerType::Convolution2d, {inputInfo, outputInfo, weightsInfo, biasesInfo}, desc, EmptyOptional(), @@ -128,7 +128,7 @@ TEST_CASE("IsLayerSupportedTosaReferenceConv2dUnsupported") TosaRefLayerSupport supportChecker; std::string reasonIfNotSupported; - auto supported = supportChecker.IsLayerSupported(armnn::LayerType::Convolution2d, + auto supported = supportChecker.IsLayerSupported(LayerType::Convolution2d, {inputInfo, outputInfo, weightsInfo, biasesInfo}, desc, EmptyOptional(), @@ -150,7 +150,7 @@ TEST_CASE("IsLayerSupportedTosaReferenceMaxPooling2d") desc.m_PoolWidth = 1; desc.m_StrideX = 1; desc.m_StrideY = 1; - desc.m_PoolType = armnn::PoolingAlgorithm::Max; + desc.m_PoolType = PoolingAlgorithm::Max; TosaRefLayerSupport supportChecker; std::string reasonIfNotSupported; @@ -324,4 +324,49 @@ TEST_CASE("IsLayerSupportedTosaReferenceSliceUnsupported") CHECK(!supported); } +TEST_CASE("IsLayerSupportedTosaReferenceTransposeConv2d") +{ + TensorInfo inputInfo ({ 1, 3, 3, 1 }, DataType::Float32); + TensorInfo outputInfo({ 1, 5, 5, 1 }, DataType::Float32); + TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32); + TensorInfo biasesInfo ({ 1 }, DataType::Float32); + + TransposeConvolution2dDescriptor desc; + desc.m_StrideX = 1; + desc.m_StrideY = 1; + desc.m_BiasEnabled = true; + + TosaRefLayerSupport supportChecker; + std::string reasonIfNotSupported; + auto supported = supportChecker.IsLayerSupported(LayerType::TransposeConvolution2d, + {inputInfo, outputInfo, weightsInfo, biasesInfo}, + desc, + EmptyOptional(), + EmptyOptional(), + reasonIfNotSupported); + CHECK(supported); +} + +TEST_CASE("IsLayerSupportedTosaReferenceTransposeConv2dUnsupported") +{ + // If inputs and weights are Fp32, output must match. + TensorInfo inputInfo ({ 1, 3, 3, 1 }, DataType::Float32); + TensorInfo outputInfo({ 1, 5, 5, 1 }, DataType::Float32); + TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true); + TensorInfo biasesInfo ({ 1 }, DataType::Float32, 0.0f, 0, true); + + TransposeConvolution2dDescriptor desc; + desc.m_BiasEnabled = true; + + TosaRefLayerSupport supportChecker; + std::string reasonIfNotSupported; + auto supported = supportChecker.IsLayerSupported(LayerType::TransposeConvolution2d, + {inputInfo, outputInfo, weightsInfo, biasesInfo}, + desc, + EmptyOptional(), + EmptyOptional(), + reasonIfNotSupported); + CHECK(!supported); +} + } -- cgit v1.2.1