diff options
author | Mike Kelly <mike.kelly@arm.com> | 2022-11-25 13:55:24 +0000 |
---|---|---|
committer | mike.kelly <mike.kelly@arm.com> | 2022-12-12 15:58:21 +0000 |
commit | ec67a0f08e0f96a5aebf3cac65331c67f6649f5e (patch) | |
tree | 94146a1f43c74d89d83fd5da54688ae0fc19cf85 /src/armnn/test | |
parent | 5383767a7a759c867235ab66bd71f88281e3bd06 (diff) | |
download | armnn-ec67a0f08e0f96a5aebf3cac65331c67f6649f5e.tar.gz |
IVGCVSW-7209 Remove deprecated code due to be removed in 23.02
* Removed weights and bias from Convolution, DepthwiseConv & FullyConnected
layers
* Removed the weight and bias ConstTensorHandles from the QueueDescriptors
* Updated Workloads to take tensors from WorkloadInfo rather than the
QueueDescriptors
* Removed unused RedirectMembersToConstantInputs optimization and tests.
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I9ffcdc4a1c0dff725539dd69fc435b700bd98a56
Diffstat (limited to 'src/armnn/test')
6 files changed, 63 insertions, 137 deletions
diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp index b78863dddc..f83900404b 100644 --- a/src/armnn/test/OptimizerTests.cpp +++ b/src/armnn/test/OptimizerTests.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2017 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -441,16 +441,15 @@ void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape, Layer* input = graph.AddLayer<InputLayer>(0, "input"); input->GetOutputSlot().SetTensorInfo(inputInfo); - ConstantLayer* weightsLayer = nullptr; - weightsLayer = graph.AddLayer<ConstantLayer>("Weights"); + ConstantLayer* weightsLayer = graph.AddLayer<ConstantLayer>("Weights"); weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weights); weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsLayer->m_LayerOutput->GetTensorInfo()); Convolution2dLayer* layer = graph.AddLayer<Convolution2dLayer>(desc, "conv2d"); - layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); layer->GetOutputSlot().SetTensorInfo(outputInfo); Layer* output = graph.AddLayer<OutputLayer>(0, "output"); + input->GetOutputSlot().Connect(layer->GetInputSlot(0)); layer->GetOutputSlot().Connect(output->GetInputSlot(0)); weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1)); @@ -908,11 +907,10 @@ TEST_CASE("OptimizeForExclusiveConnectionsFuseTest") { std::vector<float> biasVector = { 11 }; ConstTensor bias(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), biasVector); - biasLayer =graph.AddLayer<ConstantLayer>("Bias"); + biasLayer = graph.AddLayer<ConstantLayer>("Bias"); biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(bias); biasLayer->GetOutputSlot(0).SetTensorInfo(biasLayer->m_LayerOutput->GetTensorInfo()); biasLayer->GetOutputSlot(0).Connect(conv->GetInputSlot(2)); - conv->m_Bias = biasLayer->m_LayerOutput; } // Connect layers @@ -921,9 +919,6 @@ TEST_CASE("OptimizeForExclusiveConnectionsFuseTest") conv->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0)); batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - // Temporary workaround to ensure the descriptor weights are populated - conv->m_Weight = weightsLayer->m_LayerOutput; - if (convolution2dDescriptor.m_BiasEnabled) { CHECK(6 == graph.GetNumLayers()); @@ -983,22 +978,22 @@ TEST_CASE("OptimizeForExclusiveConnectionsWithoutFuseTest") batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0)); conv->GetOutputSlot(0).Connect(output2->GetInputSlot(0)); - CHECK(5 == graph.GetNumLayers()); + CHECK((5 == graph.GetNumLayers())); CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::BatchNormalizationLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::Convolution2dLayer>, + &IsLayerOfType<armnn::BatchNormalizationLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); // Optimize graph armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FuseBatchNormIntoConvolution2DFloat32())); CHECK(5 == graph.GetNumLayers()); CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::Convolution2dLayer>, - &IsLayerOfType<armnn::BatchNormalizationLayer>, - &IsLayerOfType<armnn::OutputLayer>, - &IsLayerOfType<armnn::OutputLayer>)); + &IsLayerOfType<armnn::InputLayer>, + &IsLayerOfType<armnn::Convolution2dLayer>, + &IsLayerOfType<armnn::BatchNormalizationLayer>, + &IsLayerOfType<armnn::OutputLayer>, + &IsLayerOfType<armnn::OutputLayer>)); } } // Optimizer TestSuite diff --git a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp index 34e5f6d3b6..118907e703 100644 --- a/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp +++ b/src/armnn/test/optimizations/ConvertConstantsFloatToHalfTests.cpp @@ -1,12 +1,12 @@ // -// Copyright © 2017 Arm Ltd. All rights reserved. +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include <TestUtils.hpp> -#include <Optimizer.hpp> #include <Half.hpp> +#include <Optimizer.hpp> #include <doctest/doctest.h> @@ -25,33 +25,38 @@ TEST_CASE("ConvertConstantsFloatToHalfTest") // Create const tensor from fp32 data unsigned int dims[] = { 4, 1, 1, 1 }; std::vector<float> floatWeights{ 1.0f, 2.0f, 3.0f, 4.0f }; - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true), floatWeights); + armnn::TensorInfo weightsInfo = armnn::TensorInfo(4, dims, armnn::DataType::Float32, 0.0f, 0, true); + armnn::ConstTensor weights(weightsInfo, floatWeights); // Create simple test network auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); input->GetOutputSlot().SetTensorInfo(info); auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); - fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); fc->GetOutputSlot().SetTensorInfo(info); + auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("weights"); + weightsLayer->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weights); + weightsLayer->GetOutputSlot().SetTensorInfo(weightsInfo); + auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); // Connect up the layers input->GetOutputSlot().Connect(fc->GetInputSlot(0)); + weightsLayer->GetOutputSlot().Connect(fc->GetInputSlot(1)); fc->GetOutputSlot().Connect(output->GetInputSlot(0)); // Check tensor data type before conversion - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); + CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32); // Run the optimizer armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf())); // Check tensor data type after conversion - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16); + CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16); // Check whether data matches expected fp16 data - const Half* data = fc->m_Weight->GetConstTensor<Half>(); + const Half* data = weightsLayer->m_LayerOutput->GetConstTensor<Half>(); CHECK(data[0] == Half(1.0f)); CHECK(data[1] == Half(2.0f)); CHECK(data[2] == Half(3.0f)); @@ -100,12 +105,14 @@ TEST_CASE("ConvertConstantsFloatToHalfTest_constant") fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); // Check tensor data type before conversion + CHECK(5 == graph.GetNumLayers()); CHECK(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32); // Run the optimizer armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsFloatToHalf())); // Check tensor data type after conversion + CHECK(5 == graph.GetNumLayers()); CHECK(weights->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16); // Check whether weights data matches expected fp16 data diff --git a/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp b/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp index 4c453cc799..778d7b0814 100644 --- a/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp +++ b/src/armnn/test/optimizations/ConvertConstantsHalfToFloatTests.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2017 Arm Ltd. All rights reserved. +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -25,33 +25,38 @@ TEST_CASE("ConvertConstantsHalfToFloatTest") std::vector<uint16_t> halfWeights(4); armnnUtils::FloatingPointConverter::ConvertFloat32To16(convWeightsData.data(), convWeightsData.size(), halfWeights.data()); - armnn::ConstTensor weights(armnn::TensorInfo(4, dims, armnn::DataType::Float16, 0.0f, 0, true), halfWeights); + armnn::TensorInfo weightInfo = armnn::TensorInfo(4, dims, armnn::DataType::Float16, 0.0f, 0, true); + armnn::ConstTensor weights(weightInfo, halfWeights); //Create the simple test network auto input = graph.AddLayer<armnn::InputLayer>(0, "input"); input->GetOutputSlot().SetTensorInfo(info); auto fc = graph.AddLayer<armnn::FullyConnectedLayer>(armnn::FullyConnectedDescriptor(), "fc"); - fc->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weights); fc->GetOutputSlot().SetTensorInfo(info); + auto weightsLayer = graph.AddLayer<armnn::ConstantLayer>("weights"); + weightsLayer->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weights); + weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo); + auto output = graph.AddLayer<armnn::OutputLayer>(1, "output"); //Connect up the layers input->GetOutputSlot().Connect(fc->GetInputSlot(0)); + weightsLayer->GetOutputSlot().Connect(fc->GetInputSlot(1)); fc->GetOutputSlot().Connect(output->GetInputSlot(0)); //Test the tensor info is correct. - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float16); + CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float16); // Run the optimizer armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(ConvertConstantsHalfToFloat())); //Test the tensor info is correct. - CHECK(fc->m_Weight->GetTensorInfo().GetDataType() == armnn::DataType::Float32); + CHECK(weightsLayer->m_LayerOutput->GetTensorInfo().GetDataType() == armnn::DataType::Float32); // Now test the data matches float32 data - const float* data = fc->m_Weight->GetConstTensor<float>(); + const float* data = weightsLayer->m_LayerOutput->GetConstTensor<float>(); CHECK(1.0f == data[0]); CHECK(2.0f == data[1]); CHECK(3.0f == data[2]); diff --git a/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp b/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp index bc8839948b..0a4a4fafde 100644 --- a/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp +++ b/src/armnn/test/optimizations/Fp32NetworkToFp16ConverterTests.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2017 Arm Ltd. All rights reserved. +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -33,14 +33,21 @@ TEST_CASE("Fp32NetworkToFp16OptimizationTest") floor->GetOutputSlot().Connect(output->GetInputSlot(0)); CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::FloorLayer>, &IsLayerOfType<armnn::OutputLayer>)); + &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::OutputLayer>)); // Run the optimizer armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(Fp32NetworkToFp16Converter())); CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<armnn::InputLayer>, - &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, &IsLayerOfType<armnn::FloorLayer>, - &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, &IsLayerOfType<armnn::OutputLayer>)); + &IsLayerOfType<armnn::ConvertFp32ToFp16Layer>, + &IsLayerOfType<armnn::FloorLayer>, + &IsLayerOfType<armnn::ConvertFp16ToFp32Layer>, + &IsLayerOfType<armnn::OutputLayer>)); + + CHECK(floor->GetDataType() == armnn::DataType::Float16); + CHECK(floor->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo().GetDataType() == armnn::DataType::Float16); + CHECK(floor->GetOutputSlot(0).GetTensorInfo().GetDataType() == armnn::DataType::Float16); } }
\ No newline at end of file diff --git a/src/armnn/test/optimizations/FuseBatchNormTests.cpp b/src/armnn/test/optimizations/FuseBatchNormTests.cpp index 54cbbce89f..5cbd17fb6a 100644 --- a/src/armnn/test/optimizations/FuseBatchNormTests.cpp +++ b/src/armnn/test/optimizations/FuseBatchNormTests.cpp @@ -1,5 +1,5 @@ // -// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // @@ -27,13 +27,8 @@ public: static IConnectableLayer *AddConvolution(INetwork *network, const Convolution2dDescriptor &descriptor, - const ConstTensor &weights, - const Optional<ConstTensor> &biases, const char *name) { - IgnoreUnused(weights); - IgnoreUnused(biases); - return network->AddConvolution2dLayer(descriptor, name); } @@ -65,12 +60,8 @@ public: static IConnectableLayer* AddConvolution(INetwork* network, const DepthwiseConvolution2dDescriptor& descriptor, - const ConstTensor& weights, - const Optional<ConstTensor>& biases, const char* name) { - IgnoreUnused(weights); - IgnoreUnused(biases); return network->AddDepthwiseConvolution2dLayer(descriptor, name); } @@ -171,8 +162,6 @@ INetworkPtr CreateNetwork(bool depthwise, bool preventFusing) IConnectableLayer* convLayer = Conv2dTest::AddConvolution(network.get(), convolution2dDescriptor, - weights, - Optional<ConstTensor>(), "convolution"); IConnectableLayer* batchNormLayer = network->AddBatchNormalizationLayer(batchNormDescriptor, @@ -243,13 +232,21 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b return IsLayerOfType<ConvLayerType>(layer) && (layer->GetNameStr() == "fused-batchNorm-into-convolution"); }; - + auto checkConstant = [ ](const armnn::Layer* const layer) -> bool + { + const ConstantLayer* constLayer = PolymorphicDowncast<const ConstantLayer*>(layer); + auto tensor = ConstTensor(constLayer->m_LayerOutput->GetTensorInfo(), + constLayer->m_LayerOutput->Map(true)); + const auto* buffer = static_cast<const T*>(tensor.GetMemoryArea()); + std::vector<T> vector(buffer, buffer + tensor.GetNumElements()); + return IsLayerOfType<ConstantLayer>(layer); + }; CHECK(5 == graphFused.GetNumLayers()); CHECK(CheckSequence(graphFused.cbegin(), graphFused.cend(), &IsLayerOfType<InputLayer>, - &IsLayerOfType<ConstantLayer>, - &IsLayerOfType<ConstantLayer>, + checkConstant, + checkConstant, checkFusedConv2d, &IsLayerOfType<OutputLayer>)); diff --git a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp b/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp deleted file mode 100644 index b3f9ed8780..0000000000 --- a/src/armnn/test/optimizations/RedirectMembersToConstantInputsTests.cpp +++ /dev/null @@ -1,85 +0,0 @@ -// -// Copyright © 2021 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include <TestUtils.hpp> - -#include <Optimizer.hpp> - -#include <doctest/doctest.h> - -TEST_SUITE("Optimizer") -{ -using namespace armnn::optimizations; - -TEST_CASE("RedirectMembersToConstantInputsFullyConnectedTest") -{ - armnn::Graph graph; - - const armnn::TensorInfo inputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo outputInfo ({ 1, 2, 2, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 4 }, armnn::DataType::Float32, 0.0f, 0, true); - const armnn::TensorInfo biasesInfo ({ 2 }, armnn::DataType::Float32, 0.0f, 0, true); - - // Check if isConstant is enabled for weights and biases tensor info. - CHECK(weightsInfo.IsConstant()); - CHECK(biasesInfo.IsConstant()); - - armnn::FullyConnectedDescriptor desc; - desc.m_BiasEnabled = true; - desc.m_ConstantWeights = false; - - // Create the simple test network with Weights and Biases as inputs to a FullyConnected layer. - auto input = graph.AddLayer<armnn::InputLayer>(0, "Input"); - auto weights = graph.AddLayer<armnn::ConstantLayer>("Weights"); - auto biases = graph.AddLayer<armnn::ConstantLayer>("Biases"); - auto fcLayer = graph.AddLayer<armnn::FullyConnectedLayer>(desc, "FullyConnected"); - auto output = graph.AddLayer<armnn::OutputLayer>(1, "Output"); - - float expectedWeightsData[] = { 1.0f, 1.0f, 1.0f, 1.0f }; - float expectedBiasesData[] = { 2.0f, 2.0f }; - - // Set the m_LayerOutput for the optimizer to point to. - armnn::ConstTensor weightsTensor(weightsInfo, &expectedWeightsData); - armnn::ConstTensor biasesTensor(biasesInfo, &expectedBiasesData); - weights->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(weightsTensor); - biases->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(biasesTensor); - - input->GetOutputSlot().SetTensorInfo(inputInfo); - weights->GetOutputSlot().SetTensorInfo(weightsInfo); - biases->GetOutputSlot().SetTensorInfo(biasesInfo); - fcLayer->GetOutputSlot().SetTensorInfo(outputInfo); - - // Connect up the layers - input->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(0)); - weights->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(1)); - biases->GetOutputSlot(0).Connect(fcLayer->GetInputSlot(2)); - fcLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0)); - - // Member variables should be null before optimization. - CHECK(fcLayer->m_Weight == nullptr); - CHECK(fcLayer->m_Bias == nullptr); - - // Run the optimizer - armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(RedirectMembersToConstantInputs())); - - // Check if member variables are not null and shape is set correctly. - CHECK(fcLayer->m_Weight != nullptr); - CHECK(fcLayer->m_Bias != nullptr); - CHECK(fcLayer->m_Weight->GetTensorInfo().GetShape() == weightsInfo.GetShape()); - CHECK(fcLayer->m_Bias->GetTensorInfo().GetShape() == biasesInfo.GetShape()); - - // Check whether data matches expected float data - const float* weightsData = fcLayer->m_Weight->GetConstTensor<float>(); - CHECK(weightsData[0] == expectedWeightsData[0]); - CHECK(weightsData[1] == expectedWeightsData[1]); - CHECK(weightsData[2] == expectedWeightsData[2]); - CHECK(weightsData[3] == expectedWeightsData[3]); - - const float* biasesData = fcLayer->m_Bias->GetConstTensor<float>(); - CHECK(biasesData[0] == expectedBiasesData[0]); - CHECK(biasesData[1] == expectedBiasesData[1]); -} - -}
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