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Diffstat (limited to 'src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp')
-rw-r--r--src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp126
1 files changed, 61 insertions, 65 deletions
diff --git a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp
index abfb621c93..b7b514573c 100644
--- a/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp
+++ b/src/backends/backendsCommon/test/LayerReleaseConstantDataTest.cpp
@@ -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
//
@@ -12,8 +12,6 @@
#include <doctest/doctest.h>
-#include <utility>
-
using namespace armnn;
using namespace std;
@@ -84,29 +82,15 @@ TEST_CASE("ReleaseConvolution2dLayerConstantDataTest")
layerDesc.m_StrideY = 4;
layerDesc.m_BiasEnabled = true;
- Convolution2dLayer* const layer = graph.AddLayer<Convolution2dLayer>(layerDesc, "layer");
-
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(TensorInfo({ 2, 3, 5, 3 },
- armnn::DataType::Float32));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>
- (TensorInfo({ 2 }, GetBiasDataType(armnn::DataType::Float32)));
-
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
-
- ConstantLayer* weightsLayer = graph.AddLayer<ConstantLayer>("Weights");
- ConstantLayer* biasLayer = graph.AddLayer<ConstantLayer>("Bias");
+ auto* const convolutionLayer = graph.AddLayer<Convolution2dLayer>(layerDesc, "convolution");
+ auto* const weightsLayer = graph.AddLayer<ConstantLayer>("weights");
+ auto* const biasLayer = graph.AddLayer<ConstantLayer>("bias");
- weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(TensorInfo({ 2, 3, 5, 3 },
- armnn::DataType::Float32));
+ TensorInfo weightsInfo = TensorInfo({ 2, 3, 5, 3 }, armnn::DataType::Float32, 1.0, 0.0, true);
+ TensorInfo biasInfo = TensorInfo({ 2 }, GetBiasDataType(armnn::DataType::Float32), 1.0, 0.0, true);
- biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(
- TensorInfo({2}, GetBiasDataType(armnn::DataType::Float32)));
-
- TensorInfo weightsInfo = weightsLayer->m_LayerOutput->GetTensorInfo();
- weightsInfo.SetConstant();
- TensorInfo biasInfo = biasLayer->m_LayerOutput->GetTensorInfo();
- biasInfo.SetConstant();
+ weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weightsInfo);
+ biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biasInfo);
weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
@@ -116,21 +100,21 @@ TEST_CASE("ReleaseConvolution2dLayerConstantDataTest")
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
- Connect(input, layer, TensorInfo({ 2, 3, 8, 16 }, armnn::DataType::Float32));
- weightsLayer->GetOutputSlot().Connect(layer->GetInputSlot(1));
- biasLayer->GetOutputSlot().Connect(layer->GetInputSlot(2));
- Connect(layer, output, TensorInfo({ 2, 2, 2, 10 }, armnn::DataType::Float32));
+ Connect(input, convolutionLayer, TensorInfo({ 2, 3, 8, 16 }, armnn::DataType::Float32));
+ weightsLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(1));
+ biasLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(2));
+ Connect(convolutionLayer, output, TensorInfo({ 2, 2, 2, 10 }, armnn::DataType::Float32));
// check the constants that they are not NULL
CHECK(weightsLayer->m_LayerOutput != nullptr);
CHECK(biasLayer->m_LayerOutput != nullptr);
- // free up the constants..
- layer->ReleaseConstantData();
+ // free up the constants.
+ convolutionLayer->ReleaseConstantData();
- // check the constants that they are NULL now
- CHECK(weightsLayer->m_LayerOutput == nullptr);
- CHECK(biasLayer->m_LayerOutput == nullptr);
+ // check the constants that they are still not NULL
+ CHECK(weightsLayer->m_LayerOutput != nullptr);
+ CHECK(biasLayer->m_LayerOutput != nullptr);
}
TEST_CASE("ReleaseDepthwiseConvolution2dLayerConstantDataTest")
@@ -147,33 +131,39 @@ TEST_CASE("ReleaseDepthwiseConvolution2dLayerConstantDataTest")
layerDesc.m_StrideY = 4;
layerDesc.m_BiasEnabled = true;
- DepthwiseConvolution2dLayer* const layer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "layer");
+ auto* const convolutionLayer = graph.AddLayer<DepthwiseConvolution2dLayer>(layerDesc, "convolution");
+ auto* const weightsLayer = graph.AddLayer<ConstantLayer>("weights");
+ auto* const biasLayer = graph.AddLayer<ConstantLayer>("bias");
+
+ TensorInfo weightsInfo = TensorInfo({ 3, 3, 5, 3 }, armnn::DataType::Float32, 1.0, 0.0, true);
+ TensorInfo biasInfo = TensorInfo({ 9 }, GetBiasDataType(armnn::DataType::Float32), 1.0, 0.0, true);
+
+ weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weightsInfo);
+ biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biasInfo);
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(
- TensorInfo({3, 3, 5, 3}, DataType::Float32));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(
- TensorInfo({9}, DataType::Float32));
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
// create extra layers
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
- Connect(input, layer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
- Connect(layer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32));
+ Connect(input, convolutionLayer, TensorInfo({2, 3, 8, 16}, armnn::DataType::Float32));
+ weightsLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(1));
+ biasLayer->GetOutputSlot().Connect(convolutionLayer->GetInputSlot(2));
+ Connect(convolutionLayer, output, TensorInfo({2, 9, 2, 10}, armnn::DataType::Float32));
// check the constants that they are not NULL
- CHECK(layer->m_Weight != nullptr);
- CHECK(layer->m_Bias != nullptr);
+ CHECK(weightsLayer->m_LayerOutput != nullptr);
+ CHECK(biasLayer->m_LayerOutput != nullptr);
- // free up the constants..
- layer->ReleaseConstantData();
+ // free up the constants.
+ convolutionLayer->ReleaseConstantData();
- // check the constants that they are NULL now
- CHECK(layer->m_Weight == nullptr);
- CHECK(layer->m_Bias == nullptr);
+ // check the constants that they are still not NULL
+ CHECK(weightsLayer->m_LayerOutput != nullptr);
+ CHECK(biasLayer->m_LayerOutput != nullptr);
}
TEST_CASE("ReleaseFullyConnectedLayerConstantDataTest")
@@ -185,36 +175,42 @@ TEST_CASE("ReleaseFullyConnectedLayerConstantDataTest")
layerDesc.m_BiasEnabled = true;
layerDesc.m_TransposeWeightMatrix = true;
- FullyConnectedLayer* const layer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
+ auto* const fullyConnectedLayer = graph.AddLayer<FullyConnectedLayer>(layerDesc, "layer");
+ auto* const weightsLayer = graph.AddLayer<ConstantLayer>("weights");
+ auto* const biasLayer = graph.AddLayer<ConstantLayer>("bias");
float inputsQScale = 1.0f;
float outputQScale = 2.0f;
- layer->m_Weight = std::make_unique<ScopedTensorHandle>(
- TensorInfo({7, 20}, DataType::QAsymmU8, inputsQScale, 0));
- layer->m_Bias = std::make_unique<ScopedTensorHandle>(
- TensorInfo({7}, GetBiasDataType(DataType::QAsymmU8), inputsQScale));
- layer->m_Weight->Allocate();
- layer->m_Bias->Allocate();
+ TensorInfo weightsInfo = TensorInfo({ 7, 20 }, DataType::QAsymmU8, inputsQScale, 0.0, true);
+ TensorInfo biasInfo = TensorInfo({ 7 }, GetBiasDataType(DataType::QAsymmU8), inputsQScale, 0.0, true);
+
+ weightsLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(weightsInfo);
+ biasLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(biasInfo);
+
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo);
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
// create extra layers
Layer* const input = graph.AddLayer<InputLayer>(0, "input");
Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
// connect up
- Connect(input, layer, TensorInfo({3, 1, 4, 5}, DataType::QAsymmU8, inputsQScale));
- Connect(layer, output, TensorInfo({3, 7}, DataType::QAsymmU8, outputQScale));
+ Connect(input, fullyConnectedLayer, TensorInfo({ 3, 1, 4, 5 }, DataType::QAsymmU8, inputsQScale));
+ weightsLayer->GetOutputSlot().Connect(fullyConnectedLayer->GetInputSlot(1));
+ biasLayer->GetOutputSlot().Connect(fullyConnectedLayer->GetInputSlot(2));
+ Connect(fullyConnectedLayer, output, TensorInfo({ 3, 7 }, DataType::QAsymmU8, outputQScale));
// check the constants that they are not NULL
- CHECK(layer->m_Weight != nullptr);
- CHECK(layer->m_Bias != nullptr);
+ CHECK(weightsLayer->m_LayerOutput != nullptr);
+ CHECK(biasLayer->m_LayerOutput != nullptr);
- // free up the constants..
- layer->ReleaseConstantData();
+ // free up the constants.
+ fullyConnectedLayer->ReleaseConstantData();
- // check the constants that they are NULL now
- CHECK(layer->m_Weight == nullptr);
- CHECK(layer->m_Bias == nullptr);
+ // check the constants that they are still not NULL
+ CHECK(weightsLayer->m_LayerOutput != nullptr);
+ CHECK(biasLayer->m_LayerOutput != nullptr);
}
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-08 07:46:54 +0000