Assess semantic segmentation model using all samples in a torch DataLoader.
Usage
assessDL(
dl,
model,
multiclass = TRUE,
batchSize,
size,
nCls,
cCodes,
cNames,
usedDS = FALSE,
useCUDA = FALSE,
decimals = 4
)Arguments
- dl
torch DataLoader object.
- model
trained model object.
- multiclass
TRUE or FALSE. If more than two classes are differentiated, use TRUE. If only two classes are differentiated and there are positive and background/negative classes, use FALSE. Default is TRUE. For binary cases, the second class is assumed to be the positive case.
- batchSize
Batch size used in torch DataLoader.
- size
Size of image chips in spatial dimensions (e.g., 128, 256, 512).
- nCls
Number of classes being differentiated.
- cCodes
Class indices as a vector of integer values equal in length to the number of classes.
- cNames
Class names as a vector of character strings with a length equal to the number of classes and in the correct order. Class codes and names are matched by position in the cCodes and cNames vectors. For binary case, this argument is ignored, and the first class is called "Negative" while the second class is called "Positive".
- usedDS
TRUE or FALSE. Whether or not deep supervision was used. Default is FALSE, or it is assumed that deep supervision was not used.
- useCUDA
TRUE or FALSE. Whether or not to use GPU. Default is FALSE, or GPU is not used. We recommend using a CUDA-enabled GPU if one is available since this will speed up computation.
- decimals
Number of decimal places to return for assessment metrics. Default is 4.
Details
This function generates a set of summary assessment metrics based on all samples within a torch data loader. Results are returned as a list object. For multiclass assessment, the class names ($Classes), count of samples per class in the reference data ($referenceCounts), count of samples per class in the predictions ($predictionCounts), confusion matrix ($confusionMatrix), aggregated assessment metrics ($aggMetrics) (OA = overall accuracy, macroF1 = macro-averaged class aggregated F1-score, macroPA = macro-averaged class aggregated producer's accuracy or recall, and macroUA = macro-averaged class aggregated user's accuracy or precision), class-level user's accuracies or precisions ($userAccuracies), class-level producer's accuracies or recalls ($producerAccuracies), and class-level F1-scores ($F1Scores). For a binary case, the $Classes, $referenceCounts, $predictionCounts, and $confusionMatrix objects are also returned; however, the $aggMets object is replaced with $Mets, which stores the following metrics: overall accuracy, recall, precision, specificity, negative predictive value (NPV), and F1-score. For binary cases, the second class is assumed to be the positive case.