diff options
| -rw-r--r-- | src/backends/reference/workloads/RefBatchNormalizationWorkload.cpp | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/src/backends/reference/workloads/RefBatchNormalizationWorkload.cpp b/src/backends/reference/workloads/RefBatchNormalizationWorkload.cpp index b43b104459..54e7d0d38b 100644 --- a/src/backends/reference/workloads/RefBatchNormalizationWorkload.cpp +++ b/src/backends/reference/workloads/RefBatchNormalizationWorkload.cpp @@ -26,20 +26,20 @@ void RefBatchNormalizationWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefBatchNormalizationWorkload_Execute"); - std::unique_ptr<Decoder<float>> meanDecoder = MakeDecoder<float>(GetTensorInfo(m_Mean.get()), - m_Mean.get()->Map(true)); - std::unique_ptr<Decoder<float>> varianceDecoder = MakeDecoder<float>(GetTensorInfo(m_Variance.get()), - m_Variance.get()->Map(true)); - std::unique_ptr<Decoder<float>> gammaDecoder = MakeDecoder<float>(GetTensorInfo(m_Gamma.get()), - m_Gamma.get()->Map(true)); - std::unique_ptr<Decoder<float>> betaDecoder = MakeDecoder<float>(GetTensorInfo(m_Beta.get()), - m_Beta.get()->Map(true)); - std::unique_ptr<Decoder<float>> inputDecoder = MakeDecoder<float>(GetTensorInfo(m_Data.m_Inputs[0]), - m_Data.m_Inputs[0]->Map()); - std::unique_ptr<Encoder<float>> outputEncoder = MakeEncoder<float>(GetTensorInfo(m_Data.m_Outputs[0]), - m_Data.m_Outputs[0]->Map()); + std::unique_ptr<Decoder<float>> meanDecoder = MakeDecoder<float>(m_Mean->GetTensorInfo(), + m_Mean->Map(true)); + std::unique_ptr<Decoder<float>> varianceDecoder = MakeDecoder<float>(m_Variance->GetTensorInfo(), + m_Variance->Map(true)); + std::unique_ptr<Decoder<float>> gammaDecoder = MakeDecoder<float>(m_Gamma->GetTensorInfo(), + m_Gamma->Map(true)); + std::unique_ptr<Decoder<float>> betaDecoder = MakeDecoder<float>(m_Beta->GetTensorInfo(), + m_Beta->Map(true)); + std::unique_ptr<Decoder<float>> inputDecoder = MakeDecoder<float>(GetTensorInfo(m_Data.m_Inputs[0]), + m_Data.m_Inputs[0]->Map()); + std::unique_ptr<Encoder<float>> outputEncoder = MakeEncoder<float>(GetTensorInfo(m_Data.m_Outputs[0]), + m_Data.m_Outputs[0]->Map()); BatchNormImpl(m_Data, *meanDecoder, *varianceDecoder, *betaDecoder, *gammaDecoder, *inputDecoder, *outputEncoder); } -} //namespace armnn +} // namespace armnn |