diff options
Diffstat (limited to 'src/armnn/layers/TransposeConvolution2dLayer.cpp')
-rw-r--r-- | src/armnn/layers/TransposeConvolution2dLayer.cpp | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/src/armnn/layers/TransposeConvolution2dLayer.cpp b/src/armnn/layers/TransposeConvolution2dLayer.cpp index dca77b4c09..05941f7d78 100644 --- a/src/armnn/layers/TransposeConvolution2dLayer.cpp +++ b/src/armnn/layers/TransposeConvolution2dLayer.cpp @@ -26,14 +26,14 @@ TransposeConvolution2dLayer::TransposeConvolution2dLayer(const TransposeConvolut std::unique_ptr<IWorkload> TransposeConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const { - BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null."); + ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null."); TransposeConvolution2dQueueDescriptor descriptor; descriptor.m_Weight = m_Weight.get(); if (m_Param.m_BiasEnabled) { - BOOST_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null."); + ARMNN_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null."); descriptor.m_Bias = m_Bias.get(); } @@ -57,11 +57,11 @@ TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) co std::vector<TensorShape> TransposeConvolution2dLayer::InferOutputShapes( const std::vector<TensorShape>& inputShapes) const { - BOOST_ASSERT(inputShapes.size() == 2); + ARMNN_ASSERT(inputShapes.size() == 2); const TensorShape& inputShape = inputShapes[0]; const TensorShape& kernelShape = inputShapes[1]; - BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input"); + ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input"); DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout); @@ -82,8 +82,8 @@ std::vector<TensorShape> TransposeConvolution2dLayer::InferOutputShapes( unsigned int kernelElements = kernelShape[0] * kernelShape[dataLayoutIndex.GetChannelsIndex()]; unsigned int inputElements = batches * inputShape[dataLayoutIndex.GetChannelsIndex()]; - BOOST_ASSERT_MSG(inputElements != 0, "Invalid number of input elements"); - BOOST_ASSERT_MSG(kernelElements % inputElements == 0, "Invalid number of elements"); + ARMNN_ASSERT_MSG(inputElements != 0, "Invalid number of input elements"); + ARMNN_ASSERT_MSG(kernelElements % inputElements == 0, "Invalid number of elements"); unsigned int channels = kernelElements / inputElements; @@ -98,13 +98,13 @@ void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs() { VerifyLayerConnections(1, CHECK_LOCATION()); - BOOST_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null."); + ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null."); auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), m_Weight->GetTensorInfo().GetShape() }); - BOOST_ASSERT(inferredShapes.size() == 1); + ARMNN_ASSERT(inferredShapes.size() == 1); ConditionalThrowIfNotEqual<LayerValidationException>( "TransposeConvolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.", |