base mixin class for prediction
tefla.core.prediction.PredictSession (weights_from, gpu_memory_fraction=None)
Args
- weights_from: path to the weights file
- gpu_memory_fraction: fraction of gpu memory to use, if not cpu prediction
One crop Predictor, it predict network out put from a single crop of an input image
tefla.core.prediction.OneCropPredictor (model, cnf, weights_from, prediction_iterator)
Args
- model: model definition file
- cnf: prediction configs
- weights_from: location of the model weights file
- prediction_iterator: iterator to access and augment the data for prediction
- gpu_memory_fraction: fraction of gpu memory to use, if not cpu prediction
Quasi transform predictor
tefla.core.prediction.QuasiPredictor (model, cnf, weights_from, prediction_iterator, number_of_transforms)
Args
- model: model definition file
- cnf: prediction configs
- weights_from: location of the model weights file
- prediction_iterator: iterator to access and augment the data for prediction
- number_of_transform: number of determinastic augmentaions to be performed on the input data resulted predictions are averaged over the augmentated transformation prediction outputs
- gpu_memory_fraction: fraction of gpu memory to use, if not cpu prediction
Multiples non Data augmented crops predictor
tefla.core.prediction.CropPredictor (model, cnf, weights_from, prediction_iterator, im_size, crop_size)
Args
- model: model definition file
- cnf: prediction configs
- weights_from: location of the model weights file
- prediction_iterator: iterator to access and augment the data for prediction
- crop_size: crop size for network input
- im_size: original image size
- number_of_crops: total number of crops to extract from the input image
- gpu_memory_fraction: fraction of gpu memory to use, if not cpu prediction
Returns predcitions from multiples models
tefla.core.prediction.EnsemblePredictor (predictors)
Ensembled predictions from multiples models using ensemble type
Args
- predictors: predictor instances
Methods
predict (X, ensemble_type='mean')
Args
- X: 4D tensor, inputs
- ensemble_type: operation to combine models probabilitiesavailable type: ['mean', 'gmean', 'log_mean']