yolov5/utils/callbacks.py

#!/usr/bin/env python

class Callbacks:
    """"
    Handles all registered callbacks for YOLOv5 Hooks
    """

    _callbacks = {
        'on_pretrain_routine_start': [],
        'on_pretrain_routine_end': [],

        'on_train_start': [],
        'on_train_epoch_start': [],
        'on_train_batch_start': [],
        'optimizer_step': [],
        'on_before_zero_grad': [],
        'on_train_batch_end': [],
        'on_train_epoch_end': [],

        'on_val_start': [],
        'on_val_batch_start': [],
        'on_val_image_end': [],
        'on_val_batch_end': [],
        'on_val_end': [],

        'on_fit_epoch_end': [],  # fit = train + val
        'on_model_save': [],
        'on_train_end': [],

        'teardown': [],
    }

    def __init__(self):
        return

    def register_action(self, hook, name='', callback=None):
        """
        Register a new action to a callback hook

        Args:
            hook        The callback hook name to register the action to
            name        The name of the action
            callback    The callback to fire
        """
        assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
        assert callable(callback), f"callback '{callback}' is not callable"
        self._callbacks[hook].append({'name': name, 'callback': callback})

    def get_registered_actions(self, hook=None):
        """"
        Returns all the registered actions by callback hook

        Args:
            hook The name of the hook to check, defaults to all
        """
        if hook:
            return self._callbacks[hook]
        else:
            return self._callbacks

    @staticmethod
    def run_callbacks(register, *args, **kwargs):
        """
        Loop through the registered actions and fire all callbacks
        """
        for logger in register:
            # print(f"Running callbacks.{logger['callback'].__name__}()")
            logger['callback'](*args, **kwargs)

    def on_pretrain_routine_start(self, *args, **kwargs):
        """
        Fires all registered callbacks at the start of each pretraining routine
        """
        self.run_callbacks(self._callbacks['on_pretrain_routine_start'], *args, **kwargs)

    def on_pretrain_routine_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of each pretraining routine
        """
        self.run_callbacks(self._callbacks['on_pretrain_routine_end'], *args, **kwargs)

    def on_train_start(self, *args, **kwargs):
        """
        Fires all registered callbacks at the start of each training
        """
        self.run_callbacks(self._callbacks['on_train_start'], *args, **kwargs)

    def on_train_epoch_start(self, *args, **kwargs):
        """
        Fires all registered callbacks at the start of each training epoch
        """
        self.run_callbacks(self._callbacks['on_train_epoch_start'], *args, **kwargs)

    def on_train_batch_start(self, *args, **kwargs):
        """
        Fires all registered callbacks at the start of each training batch
        """
        self.run_callbacks(self._callbacks['on_train_batch_start'], *args, **kwargs)

    def optimizer_step(self, *args, **kwargs):
        """
        Fires all registered callbacks on each optimizer step
        """
        self.run_callbacks(self._callbacks['optimizer_step'], *args, **kwargs)

    def on_before_zero_grad(self, *args, **kwargs):
        """
        Fires all registered callbacks before zero grad
        """
        self.run_callbacks(self._callbacks['on_before_zero_grad'], *args, **kwargs)

    def on_train_batch_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of each training batch
        """
        self.run_callbacks(self._callbacks['on_train_batch_end'], *args, **kwargs)

    def on_train_epoch_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of each training epoch
        """
        self.run_callbacks(self._callbacks['on_train_epoch_end'], *args, **kwargs)

    def on_val_start(self, *args, **kwargs):
        """
        Fires all registered callbacks at the start of the validation
        """
        self.run_callbacks(self._callbacks['on_val_start'], *args, **kwargs)

    def on_val_batch_start(self, *args, **kwargs):
        """
        Fires all registered callbacks at the start of each validation batch
        """
        self.run_callbacks(self._callbacks['on_val_batch_start'], *args, **kwargs)

    def on_val_image_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of each val image
        """
        self.run_callbacks(self._callbacks['on_val_image_end'], *args, **kwargs)

    def on_val_batch_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of each validation batch
        """
        self.run_callbacks(self._callbacks['on_val_batch_end'], *args, **kwargs)

    def on_val_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of the validation
        """
        self.run_callbacks(self._callbacks['on_val_end'], *args, **kwargs)

    def on_fit_epoch_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of each fit (train+val) epoch
        """
        self.run_callbacks(self._callbacks['on_fit_epoch_end'], *args, **kwargs)

    def on_model_save(self, *args, **kwargs):
        """
        Fires all registered callbacks after each model save
        """
        self.run_callbacks(self._callbacks['on_model_save'], *args, **kwargs)

    def on_train_end(self, *args, **kwargs):
        """
        Fires all registered callbacks at the end of training
        """
        self.run_callbacks(self._callbacks['on_train_end'], *args, **kwargs)

    def teardown(self, *args, **kwargs):
        """
        Fires all registered callbacks before teardown
        """
        self.run_callbacks(self._callbacks['teardown'], *args, **kwargs)