diff options
Diffstat (limited to 'src/backends/backendsCommon/WorkloadData.cpp')
-rw-r--r-- | src/backends/backendsCommon/WorkloadData.cpp | 51 |
1 files changed, 51 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp index c8c4f9aae4..52d14097af 100644 --- a/src/backends/backendsCommon/WorkloadData.cpp +++ b/src/backends/backendsCommon/WorkloadData.cpp @@ -2642,4 +2642,55 @@ void SliceQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const } } +void DepthToSpaceQueueDescriptor::Validate(const WorkloadInfo& workloadInfo) const +{ + const std::string descriptorName{"DepthToSpaceQueueDescriptor"}; + + ValidateNumInputs(workloadInfo, descriptorName, 1); + ValidateNumOutputs(workloadInfo, descriptorName, 1); + + const TensorInfo& inputInfo = workloadInfo.m_InputTensorInfos[0]; + const TensorInfo& outputInfo = workloadInfo.m_OutputTensorInfos[0]; + + ValidateTensorNumDimensions(inputInfo, descriptorName, 4, "input"); + ValidateTensorNumDimensions(outputInfo, descriptorName, 4, "output"); + + std::vector<DataType> supportedTypes = + { + DataType::Float32, + DataType::Float16, + DataType::QuantisedAsymm8, + DataType::QuantisedSymm16 + }; + + ValidateDataTypes(inputInfo, supportedTypes, descriptorName); + ValidateDataTypes(outputInfo, supportedTypes, descriptorName); + + ValidateTensorNumElementsMatch(inputInfo, outputInfo, descriptorName, "input", "output"); + + if (m_Parameters.m_BlockSize == 0) + { + throw InvalidArgumentException(descriptorName + ": Block size cannot be 0."); + } + + DataLayoutIndexed dimensionIndices(m_Parameters.m_DataLayout); + const unsigned int wIndex = dimensionIndices.GetWidthIndex(); + const unsigned int hIndex = dimensionIndices.GetHeightIndex(); + const unsigned int cIndex = dimensionIndices.GetChannelsIndex(); + + const TensorShape& outputShape = outputInfo.GetShape(); + if (outputShape[hIndex] % m_Parameters.m_BlockSize != 0 || outputShape[wIndex] % m_Parameters.m_BlockSize != 0) + { + throw InvalidArgumentException(descriptorName + ": Output width and height shape" + "must be divisible by block size."); + } + + const TensorShape& inputShape = inputInfo.GetShape(); + if (inputShape[cIndex] % (m_Parameters.m_BlockSize * m_Parameters.m_BlockSize) != 0) + { + throw InvalidArgumentException(descriptorName + ": The depth of the input tensor" + "must be divisible by the square of block size." ); + } +} + } // namespace armnn |