Implement independent Callbacks class

[kaylode]

For example:

Source: tensorflow - keras

class CustomCallback(keras.callbacks.Callback):
    def on_train_begin(self, logs=None):
        keys = list(logs.keys())
        print("Starting training; got log keys: {}".format(keys))

    def on_train_end(self, logs=None):
        keys = list(logs.keys())
        print("Stop training; got log keys: {}".format(keys))

    def on_epoch_begin(self, epoch, logs=None):
        keys = list(logs.keys())
        print("Start epoch {} of training; got log keys: {}".format(epoch, keys))

    def on_epoch_end(self, epoch, logs=None):
        keys = list(logs.keys())
        print("End epoch {} of training; got log keys: {}".format(epoch, keys))

• Example usage:

trainer = ClassificationTrainer(..., callbacks = List[Callbacks])

[kaylode]

https://github.com/keras-team/keras/blob/v2.8.0/keras/callbacks.py#L650-L653

[kaylode]

https://github.com/ultralytics/yolov5/blob/e6e36aac109794999f1dafab244b9ec4887a33d1/utils/callbacks.py#L7