diff options
Diffstat (limited to 'src/armnn/test/optimizations/FoldPadTests.cpp')
| -rw-r--r-- | src/armnn/test/optimizations/FoldPadTests.cpp | 61 |
1 files changed, 28 insertions, 33 deletions
diff --git a/src/armnn/test/optimizations/FoldPadTests.cpp b/src/armnn/test/optimizations/FoldPadTests.cpp index 14c211f9bf..4d7defcabe 100644 --- a/src/armnn/test/optimizations/FoldPadTests.cpp +++ b/src/armnn/test/optimizations/FoldPadTests.cpp @@ -25,6 +25,7 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer") TensorInfo inputInfo(4, inputShape, DataType::Float32); TensorInfo paddedInfo(4, paddedShape, DataType::Float32); + TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 1.0f, 0, true); TensorInfo outputInfo(4, outputShape, DataType::Float32); Layer* input = graph.AddLayer<InputLayer>(0, "input"); @@ -45,16 +46,13 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer") convolution2dDescriptor.m_DataLayout = DataLayout::NHWC; std::vector<float> weightsVector(18); - ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), weightsVector); + ConstTensor weights(weightsInfo, weightsVector); ConstantLayer* weightsLayer = graph.AddLayer<ConstantLayer>("Weights"); weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weights); - - TensorInfo weightsInfo = weightsLayer->m_LayerOutput->GetTensorInfo(); weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo); Convolution2dLayer* conv2dLayer = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "conv2d"); - conv2dLayer->m_Weight = std::make_unique<ScopedTensorHandle>(weights); conv2dLayer->GetOutputSlot().SetTensorInfo(outputInfo); Layer* output = graph.AddLayer<OutputLayer>(0, "output"); @@ -75,12 +73,11 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer") (conv2dLayerParams.m_BiasEnabled == false) && (conv2dLayerParams.m_DataLayout == DataLayout::NHWC); }; - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - &IsLayerOfType<PadLayer>, - &IsLayerOfType<ConstantLayer>, - checkSimpleConv2d, - &IsLayerOfType<OutputLayer>)); + CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + &IsLayerOfType<PadLayer>, + checkSimpleConv2d, + &IsLayerOfType<OutputLayer>)); armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FoldPadIntoConvolution2d())); @@ -94,11 +91,10 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer") (conv2dLayerParams.m_BiasEnabled == false) && (conv2dLayerParams.m_DataLayout == DataLayout::NHWC); }; - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - checkPadFoldedIntoConv2d, - &IsLayerOfType<ConstantLayer>, - &IsLayerOfType<OutputLayer>)); + CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + checkPadFoldedIntoConv2d, + &IsLayerOfType<OutputLayer>)); } TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer") @@ -111,6 +107,7 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer") TensorInfo inputInfo(4, inputShape, DataType::Float32); TensorInfo paddedInfo(4, paddedShape, DataType::Float32); + TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 1.0f, 0, true); TensorInfo outputInfo(4, outputShape, DataType::Float32); Layer* input = graph.AddLayer<InputLayer>(0, "input"); @@ -131,15 +128,15 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer") depthwiseConvolution2dDescriptor.m_DataLayout = DataLayout::NHWC; std::vector<float> weightsVector(18); - ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), weightsVector); + ConstTensor weights(weightsInfo, weightsVector); - auto* depthwiseConv2dLayer = graph.AddLayer<DepthwiseConvolution2dLayer>(depthwiseConvolution2dDescriptor, - "depthwiseConv2d"); auto* weightsLayer = graph.AddLayer<ConstantLayer>("weights"); + weightsLayer->GetOutputSlot().SetTensorInfo(weightsInfo); + weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weights); - weightsLayer->GetOutputSlot().SetTensorInfo(weights.GetInfo()); + auto* depthwiseConv2dLayer = graph.AddLayer<DepthwiseConvolution2dLayer>(depthwiseConvolution2dDescriptor, + "depthwiseConv2d"); depthwiseConv2dLayer->GetOutputSlot().SetTensorInfo(outputInfo); - depthwiseConv2dLayer->m_Weight = std::make_shared<ScopedTensorHandle>(weights); Layer* output = graph.AddLayer<OutputLayer>(0, "output"); @@ -160,12 +157,11 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer") (depthwiseConv2dLayerParams.m_DataLayout == DataLayout::NHWC); }; - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - &IsLayerOfType<PadLayer>, - checkSimpleDepthwiseConv2d, - &IsLayerOfType<ConstantLayer>, - &IsLayerOfType<OutputLayer>)); + CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + &IsLayerOfType<PadLayer>, + checkSimpleDepthwiseConv2d, + &IsLayerOfType<OutputLayer>)); armnn::Optimizer::Pass(graph, MakeOptimizations(FoldPadIntoDepthwiseConvolution2d())); @@ -181,11 +177,10 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer") (depthwiseConv2dLayerParams.m_DataLayout == DataLayout::NHWC); }; - CHECK(CheckSequence(graph.cbegin(), graph.cend(), - &IsLayerOfType<InputLayer>, - checkPadFoldedIntoDepthwiseConv2d, - &IsLayerOfType<ConstantLayer>, - &IsLayerOfType<OutputLayer>)); + CHECK(CheckSequence(graph.cbegin(), graph.cend(), &IsLayerOfType<InputLayer>, + &IsLayerOfType<ConstantLayer>, + checkPadFoldedIntoDepthwiseConv2d, + &IsLayerOfType<OutputLayer>)); } TEST_CASE("FoldPadLayerIntoPooling2dLayer") @@ -631,10 +626,10 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42}; - TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true); + TensorInfo weightsInfo(4, weightsShape, DataType::Float32, 1.0f, 0, true); ConstTensor weights(weightsInfo, weightsData); std::vector<float> biasVector = {5, 6, 7, 8}; - TensorInfo biasInfo({4}, DataType::Float32, 0.0f, 0, true); + TensorInfo biasInfo({4}, DataType::Float32, 1.0f, 0, true); ConstTensor bias(biasInfo, biasVector); IConnectableLayer* conv2dLayer = network->AddConvolution2dLayer(convDescriptor, "Conv2D"); |