diff options
author | Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> | 2019-02-11 12:21:27 +0000 |
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committer | Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> | 2019-02-11 13:33:20 +0000 |
commit | 0085978ac40ecd008195d635cd009a1d4f49fb74 (patch) | |
tree | 560c296e74b94826d6338b7d0d92224ae526a426 /src/armnn/test/ConstTensorLayerVisitor.cpp | |
parent | 3dad5acc5d8eda6fc472b9a255c1d893d4e1f942 (diff) | |
download | armnn-0085978ac40ecd008195d635cd009a1d4f49fb74.tar.gz |
IVGCVSW-2676 Make biases optional in ILayerVisitor for Convolution2D, DepthwiseConvolution2D and FullyConnected
Change-Id: I3048504ff699fdb266488e7c07b7262e5843d4b0
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Diffstat (limited to 'src/armnn/test/ConstTensorLayerVisitor.cpp')
-rw-r--r-- | src/armnn/test/ConstTensorLayerVisitor.cpp | 481 |
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); |