Parameters in a Complete Model Comparison Result

Index

ID of an operator in a network model.

OpSequence

Sequence in which an operator runs during comparison on some operators. That is, ID of the operator in the network-wide information file specified by the -f parameter. This option is displayed only when -r or -s is configured.

OpType

Operator type. It is used to obtain the operator type when the -f option is specified.

NPUDump

Operator name of the My Output model.

DataType

Data type of operators on the NPU Dump side.

Address

Memory address of the dump tensor, which detects memory faults of an operator. The address can be extracted only for network-wide comparison of dump data files generated during network running on the Ascend AI Processor.

GroundTruth

Operator name of the Ground Truth model.

DataType

Data type of operators on the Ground Truth side.

TensorIndex

Input ID and output ID of the operator that generates the dump data during running on the Ascend AI Processor.

Shape

Shape of the compared tensor.

OverFlow

Overflow/Underflow operator. YES indicates that overflow/underflow occurs on an operator. NO indicates that no overflow/underflow occurs on the operator. NaN indicates that overflow/underflow detection is not performed. This option is displayed when -overflow_detection is set.

CosineSimilarity

Result of the cosine similarity comparison. The value ranges from –1 to 1. A value closer to 1 indicates higher similarity.

MaxAbsoluteError

Result of the maximum absolute error comparison. The value ranges from 0 to infinity. A value closer to 0 indicates higher similarity.

AccumulatedRelativeError

Result of the accumulated relative error comparison. The value ranges from 0 to infinity. A value closer to 0 indicates higher similarity.

RelativeEuclideanDistance

Result of the Euclidean relative distance comparison. The value ranges from 0 to infinity. A value closer to 0 indicates higher similarity.

KullbackLeiblerDivergence

Result of the Kullback-Leibler divergence comparison. The value ranges from 0 to infinity. The smaller the Kullback-Leibler divergence, the closer the approximate distribution is to the true distribution.

StandardDeviation

Result of the standard deviation comparison. The value ranges from 0 to infinity. The smaller the standard deviation is, the smaller the dispersion is, and the closer the value is to the average value. The mean value and standard deviation of the dump data are displayed in the format (mean value;standard deviation). The first set of data is the result of the model running on the Ascend AI Processor, and the second set is the result of the model running on the GPU or CPU.

MeanAbsoluteError

Mean absolute error. The value ranges from 0 to infinity. If values of both MeanAbsoluteError and RootMeanSquareError are close to 0, the measured value is more approximate to the actual value. If the value of MeanAbsoluteError is close to 0, a larger value of RootMeanSquareError indicates that some values are excessively large. A larger value of MeanAbsoluteError and RootMeanSquareError value equal to or approximate to that of MeanAbsoluteError indicate that the overall deviation is more centralized. A larger value of MeanAbsoluteError and RootMeanSquareError value larger than that of MeanAbsoluteError indicate that the overall deviation exists and its distribution is scattered. Other situations do not exist because "RootMeanSquareError ≥ MeanAbsoluteError" is always true.

RootMeanSquareError

Root mean square error. The value ranges from 0 to infinity. If values of both MeanAbsoluteError and RootMeanSquareError are close to 0, the measured value is more approximate to the actual value. If the value of MeanAbsoluteError is close to 0, a larger value of RootMeanSquareError indicates that some values are excessively large. A larger value of MeanAbsoluteError and RootMeanSquareError value equal to or approximate to that of MeanAbsoluteError indicate that the overall deviation is more centralized. A larger value of MeanAbsoluteError and RootMeanSquareError value larger than that of MeanAbsoluteError indicate that the overall deviation exists and its distribution is scattered. Other situations do not exist because "RootMeanSquareError ≥ MeanAbsoluteError" is always true.

MaxRelativeError

Max. relative error. The value ranges from 0 to infinity. A value closer to 0 indicates higher similarity.

MeanRelativeError

Mean relative error. The value ranges from 0 to infinity. A value closer to 0 indicates higher similarity.

CompareFailReason

Cause of the comparison failure.

If the cosine similarity is 1, check whether the input or output shapes of the operator are empty or all 1. If the input or output shapes of the operator are empty or all 1, the input or output of the operator is a scalar. In this case, the following message is displayed: "this tensor is scalar."