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-rw-r--r--src/armnn/test/optimizations/FoldPadTests.cpp27
-rw-r--r--src/armnn/test/optimizations/FuseActivationTests.cpp5
-rw-r--r--src/armnn/test/optimizations/FuseBatchNormTests.cpp106
3 files changed, 59 insertions, 79 deletions
diff --git a/src/armnn/test/optimizations/FoldPadTests.cpp b/src/armnn/test/optimizations/FoldPadTests.cpp
index 9919c6d0e6..027b10377d 100644
--- a/src/armnn/test/optimizations/FoldPadTests.cpp
+++ b/src/armnn/test/optimizations/FoldPadTests.cpp
@@ -47,6 +47,12 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer")
std::vector<float> weightsVector(18);
ConstTensor weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), 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);
@@ -56,6 +62,7 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer")
// Connect up layers - input -> pad -> conv2d -> output
input->GetOutputSlot().Connect(padLayer->GetInputSlot(0));
padLayer->GetOutputSlot().Connect(conv2dLayer->GetInputSlot(0));
+ weightsLayer->GetOutputSlot().Connect(conv2dLayer->GetInputSlot(1));
conv2dLayer->GetOutputSlot().Connect(output->GetInputSlot(0));
auto checkSimpleConv2d = [](const Layer* const layer)->bool {
@@ -69,10 +76,11 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer")
};
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
- &IsLayerOfType<InputLayer>,
- &IsLayerOfType<PadLayer>,
- checkSimpleConv2d,
- &IsLayerOfType<OutputLayer>));
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<PadLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ checkSimpleConv2d,
+ &IsLayerOfType<OutputLayer>));
armnn::Optimizer::Pass(graph, armnn::MakeOptimizations(FoldPadIntoConvolution2d()));
@@ -87,9 +95,10 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer")
};
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
- &IsLayerOfType<InputLayer>,
- checkPadFoldedIntoConv2d,
- &IsLayerOfType<OutputLayer>));
+ &IsLayerOfType<InputLayer>,
+ checkPadFoldedIntoConv2d,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<OutputLayer>));
}
TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer")
@@ -628,12 +637,12 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio
TensorInfo biasInfo({4}, DataType::Float32, 0.0f, 0, true);
ConstTensor bias(biasInfo, biasVector);
Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
-
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
IConnectableLayer* conv2dLayer = network->AddConvolution2dLayer(convDescriptor,
weights,
optionalBias,
"Conv2D");
-
+ ARMNN_NO_DEPRECATE_WARN_END
TensorInfo outputInfo(4, outputShape, DataType::Float32);
conv2dLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
diff --git a/src/armnn/test/optimizations/FuseActivationTests.cpp b/src/armnn/test/optimizations/FuseActivationTests.cpp
index e5f54208f0..0cca86f93b 100644
--- a/src/armnn/test/optimizations/FuseActivationTests.cpp
+++ b/src/armnn/test/optimizations/FuseActivationTests.cpp
@@ -42,7 +42,7 @@ struct Convolution2dTest
{
using LayerType = Convolution2dLayer;
static const bool isElementWise = false;
- static const bool isConstTensorAsInputSupported = false;
+ static const bool isConstTensorAsInputSupported = true;
static TensorShape GetInputShape() { return TensorShape( {1, 4, 4, 3}); } // NHWCin
static TensorShape GetOutputShape() { return TensorShape( {1, 3, 3, 4}); } // NHWCout
@@ -69,8 +69,9 @@ struct Convolution2dTest
TensorInfo weightsInfo(GetWeightsShape(), ArmnnType, scale, offset, true);
ConstTensor weights(weightsInfo, weightsVector);
Optional<ConstTensor> optionalBias;
-
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
return network->AddConvolution2dLayer(descriptor, weights, optionalBias, name);
+ ARMNN_NO_DEPRECATE_WARN_END
}
static std::vector<IConnectableLayer*> AddConstantLayers(INetwork* network,
diff --git a/src/armnn/test/optimizations/FuseBatchNormTests.cpp b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
index b28bb17773..4a94f7889b 100644
--- a/src/armnn/test/optimizations/FuseBatchNormTests.cpp
+++ b/src/armnn/test/optimizations/FuseBatchNormTests.cpp
@@ -24,7 +24,6 @@ class Conv2dTest
public:
using ConvDescriptorType = armnn::Convolution2dDescriptor;
using ConvLayerType = armnn::Convolution2dLayer;
- static const bool isConstTensorAsInputSupported = false;
static IConnectableLayer *AddConvolution(INetwork *network,
const Convolution2dDescriptor &descriptor,
@@ -32,7 +31,9 @@ public:
const Optional<ConstTensor> &biases,
const char *name)
{
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
return network->AddConvolution2dLayer(descriptor, weights, biases, name);
+ ARMNN_NO_DEPRECATE_WARN_END
}
static std::vector<IConnectableLayer*> AddConstantLayers(INetwork *network,
@@ -54,13 +55,12 @@ class DepthwiseConv2dTest
public:
using ConvDescriptorType = armnn::DepthwiseConvolution2dDescriptor;
using ConvLayerType = armnn::DepthwiseConvolution2dLayer;
- static const bool isConstTensorAsInputSupported = true;
- static IConnectableLayer *AddConvolution(INetwork *network,
- const DepthwiseConvolution2dDescriptor &descriptor,
- const ConstTensor &weights,
- const Optional<ConstTensor> &biases,
- const char *name)
+ static IConnectableLayer* AddConvolution(INetwork* network,
+ const DepthwiseConvolution2dDescriptor& descriptor,
+ const ConstTensor& weights,
+ const Optional<ConstTensor>& biases,
+ const char* name)
{
IgnoreUnused(weights);
IgnoreUnused(biases);
@@ -183,19 +183,15 @@ INetworkPtr CreateNetwork(bool depthwise, bool preventFusing)
output2Layer = network->AddOutputLayer(1);
}
- // If ConstTensorAsInputs is supported weights and bias are stored as constant layers.
- if (Conv2dTest::isConstTensorAsInputSupported)
- {
- std::vector<IConnectableLayer*> constantLayers = Conv2dTest::AddConstantLayers(network.get(),
- convolution2dDescriptor,
- weights,
- Optional<ConstTensor>());
+ std::vector<IConnectableLayer*> constantLayers = Conv2dTest::AddConstantLayers(network.get(),
+ convolution2dDescriptor,
+ weights,
+ Optional<ConstTensor>());
- // Connect constant layers to receiverLayer.
- for (unsigned int i = 0; i < constantLayers.size(); ++i)
- {
- constantLayers[i]->GetOutputSlot(0).Connect(convLayer->GetInputSlot(i + 1));
- }
+ // Connect constant layers to receiverLayer.
+ for (unsigned int i = 0; i < constantLayers.size(); ++i)
+ {
+ constantLayers[i]->GetOutputSlot(0).Connect(convLayer->GetInputSlot(i + 1));
}
// Set layer information
@@ -241,26 +237,14 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b
(layer->GetNameStr() == "fused-batchNorm-into-convolution");
};
- if (Conv2dTest::isConstTensorAsInputSupported)
- {
- CHECK(5 == graphFused.GetNumLayers());
- CHECK(CheckSequence(graphFused.cbegin(),
- graphFused.cend(),
- &IsLayerOfType<InputLayer>,
- &IsLayerOfType<ConstantLayer>,
- &IsLayerOfType<ConstantLayer>,
- checkFusedConv2d,
- &IsLayerOfType<OutputLayer>));
- }
- else
- {
- CHECK(3 == graphFused.GetNumLayers());
- CHECK(CheckSequence(graphFused.cbegin(),
- graphFused.cend(),
- &IsLayerOfType<InputLayer>,
- checkFusedConv2d,
- &IsLayerOfType<OutputLayer>));
- }
+ CHECK(5 == graphFused.GetNumLayers());
+ CHECK(CheckSequence(graphFused.cbegin(),
+ graphFused.cend(),
+ &IsLayerOfType<InputLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ &IsLayerOfType<ConstantLayer>,
+ checkFusedConv2d,
+ &IsLayerOfType<OutputLayer>));
// Load network into runtime
NetworkId networkIdentifier;
@@ -278,10 +262,10 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b
TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
inputTensorInfo.SetConstant(true);
- InputTensors inputTensorsFused {
+ InputTensors inputTensorsFused {
{0, ConstTensor(inputTensorInfo, inputDataFused.data())}};
OutputTensors outputTensorsFused{
- {0, Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputDataFused.data())}};
+ {0, Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputDataFused.data())}};
// Execute network
run->EnqueueWorkload(networkIdentifier, inputTensorsFused, outputTensorsFused);
@@ -294,33 +278,19 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b
IRuntimePtr runNotFused = IRuntime::Create(IRuntime::CreationOptions()); // default options
// Optimise ArmNN network
- IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, {backendId}, runNotFused->GetDeviceSpec());
+ IOptimizedNetworkPtr optNetNotFused = Optimize(*networkNotFused, { backendId }, runNotFused->GetDeviceSpec());
Graph& graphNotFused = GetGraphForTesting(optNetNotFused.get());
- if (Conv2dTest::isConstTensorAsInputSupported)
- {
- CHECK(6 == graphNotFused.GetNumLayers());
- CHECK(CheckSequence(graphNotFused.cbegin(),
- graphNotFused.cend(),
- &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<armnn::ConstantLayer>,
- &IsLayerOfType<ConvLayerType>,
- &IsLayerOfType<armnn::BatchNormalizationLayer>,
- &IsLayerOfType<armnn::OutputLayer>,
- &IsLayerOfType<armnn::OutputLayer>));
- }
- else
- {
- CHECK(5 == graphNotFused.GetNumLayers());
- CHECK(CheckSequence(graphNotFused.cbegin(),
- graphNotFused.cend(),
- &IsLayerOfType<armnn::InputLayer>,
- &IsLayerOfType<ConvLayerType>,
- &IsLayerOfType<armnn::BatchNormalizationLayer>,
- &IsLayerOfType<armnn::OutputLayer>,
- &IsLayerOfType<armnn::OutputLayer>));
- }
+ CHECK(6 == graphNotFused.GetNumLayers());
+ CHECK(CheckSequence(graphNotFused.cbegin(),
+ graphNotFused.cend(),
+ &IsLayerOfType<armnn::InputLayer>,
+ &IsLayerOfType<armnn::ConstantLayer>,
+ &IsLayerOfType<ConvLayerType>,
+ &IsLayerOfType<armnn::BatchNormalizationLayer>,
+ &IsLayerOfType<armnn::OutputLayer>,
+ &IsLayerOfType<armnn::OutputLayer>));
// Load network into runtime
NetworkId networkIdentifierNotFused;
@@ -341,10 +311,10 @@ void FuseBatchNormIntoConvTest(bool depthwise, float tolerance, armnn::Compute b
TensorInfo inputTensorInfo2 = runNotFused->GetInputTensorInfo(networkIdentifierNotFused, 0);
inputTensorInfo2.SetConstant(true);
InputTensors inputTensorsNotFused{
- {0, ConstTensor(inputTensorInfo2, inputDataNotFused.data())}};
+ { 0, ConstTensor(inputTensorInfo2, inputDataNotFused.data()) } };
OutputTensors outputTensorsNotFused{
- {0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data())},
- {1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data())}};
+ { 0, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 0), outputDataNotFused.data()) },
+ { 1, Tensor(runNotFused->GetOutputTensorInfo(networkIdentifierNotFused, 1), outputData2NotFused.data()) } };
// Execute network
runNotFused->EnqueueWorkload(networkIdentifierNotFused, inputTensorsNotFused, outputTensorsNotFused);
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-01 00:09:38 +0000