Callbacks

paz.optimization.callbacks.DrawInferences(save_path, images, pipeline, topic='image', verbose=1)

Saves an image with its corresponding inferences

Arguments

save_path: String. Path in which the images will be saved.
images: List of numpy arrays of shape.
pipeline: Function that takes as input an element of ''images'' and outputs a ''Dict'' with inferences.
topic: Key to the ''inferences'' dictionary containing as value the drawn inferences.
verbose: Integer. If is bigger than 1 messages would be displayed.

paz.optimization.callbacks.LearningRateScheduler(learning_rate, gamma_decay, scheduled_epochs, verbose=1)

Callback for reducing learning rate at specific epochs.

Arguments

learning_rate: float. Indicates the starting learning rate.
gamma_decay: float. In an scheduled epoch the learning rate is multiplied by this factor.
scheduled_epochs: List of integers. Indicates in which epochs the learning rate will be multiplied by the gamma decay factor.
verbose: Integer. If is bigger than 1 messages would be displayed.

paz.optimization.callbacks.EvaluateMAP(data_manager, detector, period, save_path, iou_thresh=0.5)

Evaluates mean average precision (MAP) of an object detector.

Arguments

data_manager: Data manager and loader class. See ''paz.datasets'' for examples.
detector: Tensorflow-Keras model.
period: Int. Indicates how often the evaluation is performed.
save_path: Str.
iou_thresh: Float.