tta
The tta config group manages test time augmentation during inference. Note that this feature is still in beta
phase. By default (tta=none) no tta is performed. If activated the model performs multiple predictions on the same
sample via using different augmentations - after the prediction any geometrical distortion is reversed and the produced
predictions are merged. The augmentations are loaded through the
KorniaAugmentationModule that can deal with any (unnested) list of
kornia augmentations.
Note that tta is only active during testing and predicting with models - not during training nor validation.
The following examples give a good overview on the configuration options:
rotate
- mode
- default: mean
sets the mode of the
PredictionMergercurrently only “mean” is supported
- variations
- identity
- default: {name: RandomHorizontalFlip, p: 0.0}
perform no augmentation
- rot90
- default: {name: RandomRotation, p: 1.0, degrees: [ 90, 90 ]}
rotate by 90 degrees
- rot270
- default: {name: RandomRotation, p: 1.0, degrees: [ 270, 270 ]}
rotate by 270 degrees
- hflip
- default: {name: RandomHorizontalFlip, p: 1.0}
flip the image horizontally
crop
- mode
- default: mean
sets the mode of the
PredictionMergercurrently only “mean” is supported
- size
- default: ???
central parameter to specify output size
expects two ints
- variations
- identity
- default: {name: RandomHorizontalFlip, p: 0.0}
perform no augmentation
- crop1
- default: {name: RandomResizedCrop, p: 1.0, size: ${tta.size}}
crop a part of the image
- crop2
- default: {name: RandomResizedCrop, p: 1.0, size: ${tta.size}}
crop a part of the image
- crop3
- default: {name: RandomResizedCrop, p: 1.0, size: ${tta.size}}
crop a part of the image