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-rw-r--r--src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp236
1 files changed, 216 insertions, 20 deletions
diff --git a/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp
index 7345ff5151..c3a6aa1a3c 100644
--- a/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp
+++ b/src/backends/backendsCommon/test/FullyConnectedEndToEndTestImpl.hpp
@@ -103,6 +103,61 @@ armnn::INetworkPtr CreateFullyConnectedNetworkNoTensorInfoConstWeights(const arm
return network;
}
+armnn::INetworkPtr CreateFullyConnectedNetworkNoConnectedWeightsExplicit(const armnn::TensorInfo& inputTensorInfo,
+ const armnn::TensorInfo& outputTensorInfo,
+ const armnn::TensorInfo& biasTensorInfo,
+ armnn::FullyConnectedDescriptor descriptor)
+{
+ armnn::INetworkPtr network(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
+ armnn::IConnectableLayer* biasLayer = network->AddInputLayer(2, "Bias_Input");
+ armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected");
+ armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
+
+ Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);
+ Connect(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2);
+ Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);
+
+ return network;
+}
+
+armnn::INetworkPtr CreateFullyConnectedNetworkNoConnectedWeightsAndBias(const armnn::TensorInfo& inputTensorInfo,
+ const armnn::TensorInfo& outputTensorInfo,
+ armnn::FullyConnectedDescriptor descriptor)
+{
+ armnn::INetworkPtr network(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
+ armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected");
+ armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
+
+ Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);
+ Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);
+
+ return network;
+}
+
+armnn::INetworkPtr CreateFullyConnectedNetworkNoConnectedBiasExplicit(const armnn::TensorInfo& inputTensorInfo,
+ const armnn::TensorInfo& outputTensorInfo,
+ const armnn::TensorInfo& weightsTensorInfo,
+ const armnn::ConstTensor& weightsConstantTensor,
+ armnn::FullyConnectedDescriptor descriptor)
+{
+ armnn::INetworkPtr network(armnn::INetwork::Create());
+
+ armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
+ armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weightsConstantTensor, "Weights");
+ armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected");
+ armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
+
+ Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);
+ Connect(weightsLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);
+ Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);
+
+ return network;
+}
+
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId>& backends)
{
@@ -160,8 +215,7 @@ void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void FullyConnectedWithDynamicOrConstantInputsEndToEnd(const std::vector<armnn::BackendId>& backends,
const bool transposeWeights,
- const bool constantWeightsOrBias,
- const bool tensorInfoSet)
+ const bool constantWeightsOrBias)
{
unsigned int inputWidth = 1;
unsigned int inputHeight = 1;
@@ -230,24 +284,7 @@ void FullyConnectedWithDynamicOrConstantInputsEndToEnd(const std::vector<armnn::
descriptor.m_TransposeWeightMatrix = transposeWeights;
descriptor.m_ConstantWeights = constantWeightsOrBias;
- if(!tensorInfoSet)
- {
- // Tests constant weights and non constant bias.
- ConstTensor weightsConstantTensor(weightsDesc, weights.data());
-
- armnn::INetworkPtr network = CreateFullyConnectedNetworkNoTensorInfoConstWeights(inputTensorInfo,
- outputTensorInfo,
- weightsConstantTensor,
- descriptor);
- CHECK(network);
-
- // Create runtime in which test will run
- IRuntime::CreationOptions options;
- IRuntimePtr runtime(IRuntime::Create(options));
-
- CHECK_THROWS_AS( Optimize(*network, backends, runtime->GetDeviceSpec()), LayerValidationException );
- }
- else if (!constantWeightsOrBias)
+ if (!constantWeightsOrBias)
{
// Tests non constant weights and constant bias.
ConstTensor biasConstantTensor(biasesDesc, biasValues.data());
@@ -293,4 +330,163 @@ void FullyConnectedWithDynamicOrConstantInputsEndToEnd(const std::vector<armnn::
}
}
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+void FullyConnectedErrorChecking(const std::vector<armnn::BackendId>& backends,
+ const bool explicitCheck,
+ const bool biasEnabled,
+ const bool connectedWeights,
+ const bool connectedBias,
+ const bool tensorInfoSet)
+{
+ unsigned int inputWidth = 1;
+ unsigned int inputHeight = 1;
+ unsigned int inputChannels = 5;
+ unsigned int inputNum = 2;
+
+ unsigned int outputChannels = 3;
+ unsigned int outputNum = 2;
+
+ unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
+ unsigned int outputShape[] = { outputNum, outputChannels };
+ unsigned int weightsShape[] = { inputChannels, outputChannels };
+
+ unsigned int biasShape[] = { outputChannels };
+
+ armnn::TensorInfo inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
+ armnn::TensorInfo outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32);
+ armnn::TensorInfo weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32);
+ armnn::TensorInfo biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32);
+
+ std::vector<float> weights =
+ {
+ .5f, 2.f, .5f,
+ .5f, 2.f, 1.f,
+ .5f, 2.f, 2.f,
+ .5f, 2.f, 3.f,
+ .5f, 2.f, 4.f
+ };
+
+ FullyConnectedDescriptor descriptor;
+ descriptor.m_BiasEnabled = biasEnabled;
+
+ if(explicitCheck)
+ {
+ if(!biasEnabled)
+ {
+ try
+ {
+ CreateFullyConnectedNetworkNoConnectedWeightsExplicit(inputTensorInfo,
+ outputTensorInfo,
+ biasesDesc,
+ descriptor);
+ FAIL("LayerValidationException should have been thrown");
+ }
+ catch (const LayerValidationException& exc)
+ {
+ CHECK(strcmp(exc.what(), "Tried to connect bias to FullyConnected layer when bias is not enabled: "
+ "Failed to connect to input slot 2 on FullyConnected layer "
+ "\"Fully_Connected\" as the slot does not exist or is unavailable") == 0);
+ }
+ }
+ else if (!connectedWeights)
+ {
+ armnn::INetworkPtr network = CreateFullyConnectedNetworkNoConnectedWeightsExplicit(inputTensorInfo,
+ outputTensorInfo,
+ biasesDesc,
+ descriptor);
+ CHECK(network);
+
+ // Create runtime in which test will run
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+
+ try
+ {
+ Optimize(*network, backends, runtime->GetDeviceSpec());
+ FAIL("LayerValidationException should have been thrown");
+ }
+ catch (const LayerValidationException& exc)
+ {
+ CHECK(strcmp(exc.what(), "FullyConnected layer weights not set: Input slot(s) 1 not connected "
+ "to an output slot on FullyConnected layer \"Fully_Connected\"") == 0);
+ }
+ }
+ else if (!connectedBias)
+ {
+ // Tests with constant weights.
+ ConstTensor weightsConstantTensor(weightsDesc, weights.data());
+
+ armnn::INetworkPtr network = CreateFullyConnectedNetworkNoConnectedBiasExplicit(inputTensorInfo,
+ outputTensorInfo,
+ weightsDesc,
+ weightsConstantTensor,
+ descriptor);
+ CHECK(network);
+
+ // Create runtime in which test will run
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+
+ try
+ {
+ Optimize(*network, backends, runtime->GetDeviceSpec());
+ FAIL("LayerValidationException should have been thrown");
+ }
+ catch (const LayerValidationException& exc)
+ {
+ CHECK(strcmp(exc.what(), "FullyConnected layer bias not set: Input slot(s) 2 not connected "
+ "to an output slot on FullyConnected layer \"Fully_Connected\"") == 0);
+ }
+ }
+ }
+ else if(!connectedWeights && !connectedBias)
+ {
+ armnn::INetworkPtr network = CreateFullyConnectedNetworkNoConnectedWeightsAndBias(inputTensorInfo,
+ outputTensorInfo,
+ descriptor);
+ CHECK(network);
+
+ // Create runtime in which test will run
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+
+ try
+ {
+ Optimize(*network, backends, runtime->GetDeviceSpec());
+ FAIL("LayerValidationException should have been thrown");
+ }
+ catch (const LayerValidationException& exc)
+ {
+ CHECK(strcmp(exc.what(), "FullyConnected layer weights and bias not set: Input slot(s) 1 & 2 not "
+ "connected to an output slot on FullyConnected layer \"Fully_Connected\"") == 0);
+ }
+
+ }
+ else if(!tensorInfoSet)
+ {
+ // Tests with constant weights.
+ ConstTensor weightsConstantTensor(weightsDesc, weights.data());
+
+ armnn::INetworkPtr network = CreateFullyConnectedNetworkNoTensorInfoConstWeights(inputTensorInfo,
+ outputTensorInfo,
+ weightsConstantTensor,
+ descriptor);
+ CHECK(network);
+
+ // Create runtime in which test will run
+ IRuntime::CreationOptions options;
+ IRuntimePtr runtime(IRuntime::Create(options));
+
+ try
+ {
+ Optimize(*network, backends, runtime->GetDeviceSpec());
+ FAIL("LayerValidationException should have been thrown");
+ }
+ catch (const LayerValidationException& exc)
+ {
+ CHECK(strcmp(exc.what(), "Output slot TensorInfo not set on Constant layer \"Weights\"") == 0);
+ }
+ }
+}
+
} // anonymous namespace
generated by cgit v1.2.1 (git 2.23.0) at 2024-07-03 18:56:51 +0000