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-rw-r--r--src/armnn/test/ConstTensorLayerVisitor.cpp481
1 files changed, 240 insertions, 241 deletions
diff --git a/src/armnn/test/ConstTensorLayerVisitor.cpp b/src/armnn/test/ConstTensorLayerVisitor.cpp
index 6ab2ea89a2..5b77ddeb97 100644
--- a/src/armnn/test/ConstTensorLayerVisitor.cpp
+++ b/src/armnn/test/ConstTensorLayerVisitor.cpp
@@ -122,11 +122,11 @@ BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
- TestConvolution2dLayerVisitor visitor(descriptor, weights);
+ TestConvolution2dLayerVisitor visitor(descriptor, weights, EmptyOptional());
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddConvolution2dLayer(descriptor, weights);
layer->Accept(visitor);
@@ -146,11 +146,11 @@ BOOST_AUTO_TEST_CASE(CheckNamedConvolution2dLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
- TestConvolution2dLayerVisitor visitor(descriptor, weights, layerName);
+ TestConvolution2dLayerVisitor visitor(descriptor, weights, EmptyOptional(), layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddConvolution2dLayer(descriptor, weights, layerName);
layer->Accept(visitor);
@@ -170,16 +170,15 @@ BOOST_AUTO_TEST_CASE(CheckConvolution2dLayerWithBiases)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> biasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> biasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor biases(TensorInfo(4, biasDimensions.data(), armnn::DataType::Float32), biasData);
+ ConstTensor biases(TensorInfo(4, biasDimensions.data(), DataType::Float32), biasData);
+ TestConvolution2dLayerVisitor visitor(descriptor, weights, Optional<ConstTensor>(biases));
- TestConvolution2dWithBiasLayerVisitor visitor(descriptor, weights, biases);
-
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddConvolution2dLayer(descriptor, weights, biases);
layer->Accept(visitor);
@@ -200,15 +199,15 @@ BOOST_AUTO_TEST_CASE(CheckNamedConvolution2dLayerWithBiases)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> biasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> biasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor biases(TensorInfo(4, biasDimensions.data(), armnn::DataType::Float32), biasData);
+ ConstTensor biases(TensorInfo(4, biasDimensions.data(), DataType::Float32), biasData);
- TestConvolution2dWithBiasLayerVisitor visitor(descriptor, weights, biases, layerName);
+ TestConvolution2dLayerVisitor visitor(descriptor, weights, Optional<ConstTensor>(biases), layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddConvolution2dLayer(descriptor, weights, biases, layerName);
layer->Accept(visitor);
@@ -227,11 +226,11 @@ BOOST_AUTO_TEST_CASE(CheckDepthwiseConvolution2dLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
- TestDepthwiseConvolution2dLayerVisitor visitor(descriptor, weights);
+ TestDepthwiseConvolution2dLayerVisitor visitor(descriptor, weights, EmptyOptional());
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddDepthwiseConvolution2dLayer(descriptor, weights);
layer->Accept(visitor);
@@ -251,11 +250,11 @@ BOOST_AUTO_TEST_CASE(CheckNamedDepthwiseConvolution2dLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
- TestDepthwiseConvolution2dLayerVisitor visitor(descriptor, weights, layerName);
+ TestDepthwiseConvolution2dLayerVisitor visitor(descriptor, weights, EmptyOptional(), layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddDepthwiseConvolution2dLayer(descriptor, weights, layerName);
layer->Accept(visitor);
@@ -275,15 +274,15 @@ BOOST_AUTO_TEST_CASE(CheckDepthwiseConvolution2dLayerWithBiases)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> biasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> biasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor biases(TensorInfo(4, biasDimensions.data(), armnn::DataType::Float32), biasData);
+ ConstTensor biases(TensorInfo(4, biasDimensions.data(), DataType::Float32), biasData);
- TestDepthwiseConvolution2dWithBiasLayerVisitor visitor(descriptor, weights, biases);
+ TestDepthwiseConvolution2dLayerVisitor visitor(descriptor, weights, Optional<ConstTensor>(biases));
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddDepthwiseConvolution2dLayer(descriptor, weights, biases);
layer->Accept(visitor);
@@ -304,15 +303,15 @@ BOOST_AUTO_TEST_CASE(CheckNamedDepthwiseConvolution2dLayerWithBiases)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> biasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> biasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor biases(TensorInfo(4, biasDimensions.data(), armnn::DataType::Float32), biasData);
+ ConstTensor biases(TensorInfo(4, biasDimensions.data(), DataType::Float32), biasData);
- TestDepthwiseConvolution2dWithBiasLayerVisitor visitor(descriptor, weights, biases, layerName);
+ TestDepthwiseConvolution2dLayerVisitor visitor(descriptor, weights, Optional<ConstTensor>(biases), layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddDepthwiseConvolution2dLayer(descriptor, weights, biases, layerName);
layer->Accept(visitor);
@@ -325,11 +324,11 @@ BOOST_AUTO_TEST_CASE(CheckFullyConnectedLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
- TestFullyConnectedLayerVistor visitor(descriptor, weights);
+ TestFullyConnectedLayerVistor visitor(descriptor, weights, EmptyOptional());
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddFullyConnectedLayer(descriptor, weights);
layer->Accept(visitor);
@@ -343,11 +342,11 @@ BOOST_AUTO_TEST_CASE(CheckNamedFullyConnectedLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
- TestFullyConnectedLayerVistor visitor(descriptor, weights, layerName);
+ TestFullyConnectedLayerVistor visitor(descriptor, weights, EmptyOptional(), layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddFullyConnectedLayer(descriptor, weights, layerName);
layer->Accept(visitor);
@@ -361,15 +360,15 @@ BOOST_AUTO_TEST_CASE(CheckFullyConnectedLayerWithBiases)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> biasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> biasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor biases(TensorInfo(4, biasDimensions.data(), armnn::DataType::Float32), biasData);
+ ConstTensor biases(TensorInfo(4, biasDimensions.data(), DataType::Float32), biasData);
- TestFullyConnectedLayerWithBiasesVisitor visitor(descriptor, weights, biases);
+ TestFullyConnectedLayerVistor visitor(descriptor, weights, Optional<ConstTensor>(biases));
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddFullyConnectedLayer(descriptor, weights, biases);
layer->Accept(visitor);
@@ -384,15 +383,15 @@ BOOST_AUTO_TEST_CASE(CheckNamedFullyConnectedLayerWithBiases)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor weights(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor weights(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> biasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> biasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor biases(TensorInfo(4, biasDimensions.data(), armnn::DataType::Float32), biasData);
+ ConstTensor biases(TensorInfo(4, biasDimensions.data(), DataType::Float32), biasData);
- TestFullyConnectedLayerWithBiasesVisitor visitor(descriptor, weights, biases, layerName);
+ TestFullyConnectedLayerVistor visitor(descriptor, weights, Optional<ConstTensor>(biases), layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddFullyConnectedLayer(descriptor, weights, biases, layerName);
layer->Accept(visitor);
@@ -406,23 +405,23 @@ BOOST_AUTO_TEST_CASE(CheckBatchNormalizationLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor mean(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor mean(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> varianceData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> varianceDimensions = {1, 1, 3, 3};
- armnn::ConstTensor variance(TensorInfo(4, varianceDimensions.data(), armnn::DataType::Float32), varianceData);
+ ConstTensor variance(TensorInfo(4, varianceDimensions.data(), DataType::Float32), varianceData);
std::vector<float> betaData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> betaDimensions = {1, 1, 3, 3};
- armnn::ConstTensor beta(TensorInfo(4, betaDimensions.data(), armnn::DataType::Float32), betaData);
+ ConstTensor beta(TensorInfo(4, betaDimensions.data(), DataType::Float32), betaData);
std::vector<float> gammaData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> gammaDimensions = {1, 1, 3, 3};
- armnn::ConstTensor gamma(TensorInfo(4, gammaDimensions.data(), armnn::DataType::Float32), gammaData);
+ ConstTensor gamma(TensorInfo(4, gammaDimensions.data(), DataType::Float32), gammaData);
TestBatchNormalizationLayerVisitor visitor(descriptor, mean, variance, beta, gamma);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma);
layer->Accept(visitor);
@@ -437,23 +436,23 @@ BOOST_AUTO_TEST_CASE(CheckNamedBatchNormalizationLayer)
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor mean(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor mean(TensorInfo(4, dimensions.data(), DataType::Float32), data);
std::vector<float> varianceData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> varianceDimensions = {1, 1, 3, 3};
- armnn::ConstTensor variance(TensorInfo(4, varianceDimensions.data(), armnn::DataType::Float32), varianceData);
+ ConstTensor variance(TensorInfo(4, varianceDimensions.data(), DataType::Float32), varianceData);
std::vector<float> betaData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> betaDimensions = {1, 1, 3, 3};
- armnn::ConstTensor beta(TensorInfo(4, betaDimensions.data(), armnn::DataType::Float32), betaData);
+ ConstTensor beta(TensorInfo(4, betaDimensions.data(), DataType::Float32), betaData);
std::vector<float> gammaData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> gammaDimensions = {1, 1, 3, 3};
- armnn::ConstTensor gamma(TensorInfo(4, gammaDimensions.data(), armnn::DataType::Float32), gammaData);
+ ConstTensor gamma(TensorInfo(4, gammaDimensions.data(), DataType::Float32), gammaData);
TestBatchNormalizationLayerVisitor visitor(descriptor, mean, variance, beta, gamma, layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddBatchNormalizationLayer(
descriptor, mean, variance, beta, gamma, layerName);
@@ -464,11 +463,11 @@ BOOST_AUTO_TEST_CASE(CheckConstLayer)
{
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor input(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor input(TensorInfo(4, dimensions.data(), DataType::Float32), data);
TestConstantLayerVisitor visitor(input);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddConstantLayer(input);
layer->Accept(visitor);
@@ -479,11 +478,11 @@ BOOST_AUTO_TEST_CASE(CheckNamedConstLayer)
const char* layerName = "ConstantLayer";
std::vector<float> data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> dimensions = {1, 1, 3, 3};
- armnn::ConstTensor input(TensorInfo(4, dimensions.data(), armnn::DataType::Float32), data);
+ ConstTensor input(TensorInfo(4, dimensions.data(), DataType::Float32), data);
TestConstantLayerVisitor visitor(input, layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddConstantLayer(input, layerName);
layer->Accept(visitor);
@@ -499,48 +498,48 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerBasic)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -555,7 +554,7 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerBasic)
TestLstmLayerVisitor visitor(descriptor, params);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params);
layer->Accept(visitor);
@@ -572,48 +571,48 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerBasic)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -628,7 +627,7 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerBasic)
TestLstmLayerVisitor visitor(descriptor, params, layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params, layerName);
layer->Accept(visitor);
@@ -644,68 +643,68 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerCifgDisabled)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
std::vector<float> inputToInputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToInputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToInputWeights(
- TensorInfo(4, inputToInputWeightsDimensions.data(), armnn::DataType::Float32), inputToInputWeightsData);
+ ConstTensor inputToInputWeights(
+ TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::Float32), inputToInputWeightsData);
std::vector<float> recurrentToInputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToInputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToInputWeights(TensorInfo(
- 4, recurrentToInputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToInputWeightsData);
+ ConstTensor recurrentToInputWeights(TensorInfo(
+ 4, recurrentToInputWeightsDimensions.data(), DataType::Float32), recurrentToInputWeightsData);
std::vector<float> cellToInputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellToInputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellToInputWeights(
- TensorInfo(4, cellToInputWeightsDimensions.data(), armnn::DataType::Float32), cellToInputWeightsData);
+ ConstTensor cellToInputWeights(
+ TensorInfo(4, cellToInputWeightsDimensions.data(), DataType::Float32), cellToInputWeightsData);
std::vector<float> inputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputGateBias(
- TensorInfo(4, inputGateBiasDimensions.data(), armnn::DataType::Float32), inputGateBiasData);
+ ConstTensor inputGateBias(
+ TensorInfo(4, inputGateBiasDimensions.data(), DataType::Float32), inputGateBiasData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -725,7 +724,7 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerCifgDisabled)
TestLstmLayerVisitor visitor(descriptor, params);
- armnn::Network net;
+ Network net;
IConnectableLayer* const layer = net.AddLstmLayer(descriptor, params);
layer->Accept(visitor);
@@ -742,68 +741,68 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerCifgDisabled)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
std::vector<float> inputToInputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToInputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToInputWeights(
- TensorInfo(4, inputToInputWeightsDimensions.data(), armnn::DataType::Float32), inputToInputWeightsData);
+ ConstTensor inputToInputWeights(
+ TensorInfo(4, inputToInputWeightsDimensions.data(), DataType::Float32), inputToInputWeightsData);
std::vector<float> recurrentToInputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToInputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToInputWeights(TensorInfo(
- 4, recurrentToInputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToInputWeightsData);
+ ConstTensor recurrentToInputWeights(TensorInfo(
+ 4, recurrentToInputWeightsDimensions.data(), DataType::Float32), recurrentToInputWeightsData);
std::vector<float> cellToInputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellToInputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellToInputWeights(
- TensorInfo(4, cellToInputWeightsDimensions.data(), armnn::DataType::Float32), cellToInputWeightsData);
+ ConstTensor cellToInputWeights(
+ TensorInfo(4, cellToInputWeightsDimensions.data(), DataType::Float32), cellToInputWeightsData);
std::vector<float> inputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputGateBias(
- TensorInfo(4, inputGateBiasDimensions.data(), armnn::DataType::Float32), inputGateBiasData);
+ ConstTensor inputGateBias(
+ TensorInfo(4, inputGateBiasDimensions.data(), DataType::Float32), inputGateBiasData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -823,7 +822,7 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerCifgDisabled)
TestLstmLayerVisitor visitor(descriptor, params, layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params, layerName);
layer->Accept(visitor);
@@ -841,58 +840,58 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerPeephole)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
std::vector<float> cellToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellToForgetWeights(
- TensorInfo(4, cellToForgetWeightsDimensions.data(), armnn::DataType::Float32), cellToForgetWeightsData);
+ ConstTensor cellToForgetWeights(
+ TensorInfo(4, cellToForgetWeightsDimensions.data(), DataType::Float32), cellToForgetWeightsData);
std::vector<float> cellToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellToOutputWeights(
- TensorInfo(4, cellToOutputWeightsDimensions.data(), armnn::DataType::Float32), cellToOutputWeightsData);
+ ConstTensor cellToOutputWeights(
+ TensorInfo(4, cellToOutputWeightsDimensions.data(), DataType::Float32), cellToOutputWeightsData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -910,7 +909,7 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerPeephole)
TestLstmLayerVisitor visitor(descriptor, params);
- armnn::Network net;
+ Network net;
IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params);
layer->Accept(visitor);
@@ -928,58 +927,58 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerPeephole)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
std::vector<float> cellToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellToForgetWeights(
- TensorInfo(4, cellToForgetWeightsDimensions.data(), armnn::DataType::Float32), cellToForgetWeightsData);
+ ConstTensor cellToForgetWeights(
+ TensorInfo(4, cellToForgetWeightsDimensions.data(), DataType::Float32), cellToForgetWeightsData);
std::vector<float> cellToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellToOutputWeights(
- TensorInfo(4, cellToOutputWeightsDimensions.data(), armnn::DataType::Float32), cellToOutputWeightsData);
+ ConstTensor cellToOutputWeights(
+ TensorInfo(4, cellToOutputWeightsDimensions.data(), DataType::Float32), cellToOutputWeightsData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -997,7 +996,7 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerPeephole)
TestLstmLayerVisitor visitor(descriptor, params, layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params, layerName);
layer->Accept(visitor);
@@ -1015,58 +1014,58 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerProjection)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
std::vector<float> projectionBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> projectionBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor projectionBias(
- TensorInfo(4, projectionBiasDimensions.data(), armnn::DataType::Float32), projectionBiasData);
+ ConstTensor projectionBias(
+ TensorInfo(4, projectionBiasDimensions.data(), DataType::Float32), projectionBiasData);
std::vector<float> projectionWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> projectionWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor projectionWeights(
- TensorInfo(4, projectionWeightsDimensions.data(), armnn::DataType::Float32), projectionWeightsData);
+ ConstTensor projectionWeights(
+ TensorInfo(4, projectionWeightsDimensions.data(), DataType::Float32), projectionWeightsData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -1084,7 +1083,7 @@ BOOST_AUTO_TEST_CASE(CheckLstmLayerProjection)
TestLstmLayerVisitor visitor(descriptor, params);
- armnn::Network net;
+ Network net;
IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params);
layer->Accept(visitor);
@@ -1102,58 +1101,58 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerProjection)
std::vector<float> inputToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToForgetWeights(
- TensorInfo(4, inputToForgetWeightsDimensions.data(), armnn::DataType::Float32), inputToForgetWeightsData);
+ ConstTensor inputToForgetWeights(
+ TensorInfo(4, inputToForgetWeightsDimensions.data(), DataType::Float32), inputToForgetWeightsData);
std::vector<float> inputToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToCellWeights(
- TensorInfo(4, inputToCellWeightsDimensions.data(), armnn::DataType::Float32), inputToCellWeightsData);
+ ConstTensor inputToCellWeights(
+ TensorInfo(4, inputToCellWeightsDimensions.data(), DataType::Float32), inputToCellWeightsData);
std::vector<float> inputToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> inputToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor inputToOutputWeights(
- TensorInfo(4, inputToOutputWeightsDimensions.data(), armnn::DataType::Float32), inputToOutputWeightsData);
+ ConstTensor inputToOutputWeights(
+ TensorInfo(4, inputToOutputWeightsDimensions.data(), DataType::Float32), inputToOutputWeightsData);
std::vector<float> recurrentToForgetWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToForgetWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToForgetWeights(TensorInfo(
- 4, recurrentToForgetWeightsDimensions.data(), armnn::DataType::Float32), recurrentToForgetWeightsData);
+ ConstTensor recurrentToForgetWeights(TensorInfo(
+ 4, recurrentToForgetWeightsDimensions.data(), DataType::Float32), recurrentToForgetWeightsData);
std::vector<float> recurrentToCellWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToCellWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToCellWeights(TensorInfo(
- 4, recurrentToCellWeightsDimensions.data(), armnn::DataType::Float32), recurrentToCellWeightsData);
+ ConstTensor recurrentToCellWeights(TensorInfo(
+ 4, recurrentToCellWeightsDimensions.data(), DataType::Float32), recurrentToCellWeightsData);
std::vector<float> recurrentToOutputWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> recurrentToOutputWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor recurrentToOutputWeights(TensorInfo(
- 4, recurrentToOutputWeightsDimensions.data(), armnn::DataType::Float32), recurrentToOutputWeightsData);
+ ConstTensor recurrentToOutputWeights(TensorInfo(
+ 4, recurrentToOutputWeightsDimensions.data(), DataType::Float32), recurrentToOutputWeightsData);
std::vector<float> forgetGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> forgetGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor forgetGateBias(TensorInfo(
- 4, forgetGateBiasDimensions.data(), armnn::DataType::Float32), forgetGateBiasData);
+ ConstTensor forgetGateBias(TensorInfo(
+ 4, forgetGateBiasDimensions.data(), DataType::Float32), forgetGateBiasData);
std::vector<float> cellBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> cellBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor cellBias(TensorInfo(
- 4, cellBiasDimensions.data(), armnn::DataType::Float32), cellBiasData);
+ ConstTensor cellBias(TensorInfo(
+ 4, cellBiasDimensions.data(), DataType::Float32), cellBiasData);
std::vector<float> outputGateBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> outputGateBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor outputGateBias(TensorInfo(
- 4, outputGateBiasDimensions.data(), armnn::DataType::Float32), outputGateBiasData);
+ ConstTensor outputGateBias(TensorInfo(
+ 4, outputGateBiasDimensions.data(), DataType::Float32), outputGateBiasData);
std::vector<float> projectionBiasData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> projectionBiasDimensions = {1, 1, 3, 3};
- armnn::ConstTensor projectionBias(
- TensorInfo(4, projectionBiasDimensions.data(), armnn::DataType::Float32), projectionBiasData);
+ ConstTensor projectionBias(
+ TensorInfo(4, projectionBiasDimensions.data(), DataType::Float32), projectionBiasData);
std::vector<float> projectionWeightsData = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
std::vector<unsigned int> projectionWeightsDimensions = {1, 1, 3, 3};
- armnn::ConstTensor projectionWeights(
- TensorInfo(4, projectionWeightsDimensions.data(), armnn::DataType::Float32), projectionWeightsData);
+ ConstTensor projectionWeights(
+ TensorInfo(4, projectionWeightsDimensions.data(), DataType::Float32), projectionWeightsData);
LstmInputParams params;
params.m_InputToForgetWeights = &inputToForgetWeights;
@@ -1171,7 +1170,7 @@ BOOST_AUTO_TEST_CASE(CheckNamedLstmLayerProjection)
TestLstmLayerVisitor visitor(descriptor, params, layerName);
- armnn::Network net;
+ Network net;
IConnectableLayer *const layer = net.AddLstmLayer(descriptor, params, layerName);
layer->Accept(visitor);
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-01 05:35:58 +0000