diff options
Diffstat (limited to 'src/armnn/test/OptimizerTests.cpp')
| -rw-r--r-- | src/armnn/test/OptimizerTests.cpp | 35 |
1 files changed, 15 insertions, 20 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 |