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225 lines
5.6 KiB
Python
225 lines
5.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# Copyright (c) Alibaba, Inc. and its affiliates.
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from modelscope.utils.constant import TrainerStages
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from modelscope.utils.import_utils import is_method_overridden
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from .priority import Priority
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class Hook:
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"""
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The Hook base class of any modelscope trainer. You can build your own hook inherited from this class.
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"""
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stages = (TrainerStages.before_run, TrainerStages.before_train_epoch,
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TrainerStages.before_train_iter, TrainerStages.after_train_iter,
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TrainerStages.after_train_epoch, TrainerStages.before_val_epoch,
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TrainerStages.before_val_iter, TrainerStages.after_val_iter,
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TrainerStages.after_val_epoch, TrainerStages.after_run)
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PRIORITY = Priority.NORMAL
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def before_run(self, trainer):
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"""
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Will be called before any loop begins.
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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pass
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def after_run(self, trainer):
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"""
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Will be called after all loops end.
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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pass
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def before_epoch(self, trainer):
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"""
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Will be called before every epoch begins.
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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pass
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def after_epoch(self, trainer):
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"""
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Will be called after every epoch ends.
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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pass
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def before_iter(self, trainer):
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"""
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Will be called before every loop begins.
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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pass
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def after_iter(self, trainer):
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"""
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Will be called after every loop ends.
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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pass
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def before_train_epoch(self, trainer):
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"""
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Will be called before every train epoch begins. Default call ``self.before_epoch``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.before_epoch(trainer)
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def before_val_epoch(self, trainer):
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"""
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Will be called before every validation epoch begins. Default call ``self.before_epoch``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.before_epoch(trainer)
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def after_train_epoch(self, trainer):
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"""
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Will be called after every train epoch ends. Default call ``self.after_epoch``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.after_epoch(trainer)
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def after_val_epoch(self, trainer):
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"""
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Will be called after every validation epoch ends. Default call ``self.after_epoch``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.after_epoch(trainer)
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def before_train_iter(self, trainer):
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"""
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Will be called before every train loop begins. Default call ``self.before_iter``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.before_iter(trainer)
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def before_val_iter(self, trainer):
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"""
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Will be called before every validation loop begins. Default call ``self.before_iter``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.before_iter(trainer)
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def after_train_iter(self, trainer):
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"""
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Will be called after every train loop ends. Default call ``self.after_iter``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.after_iter(trainer)
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def after_val_iter(self, trainer):
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"""
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Will be called after every validation loop ends. Default call ``self.after_iter``
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Args:
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trainer: The trainer instance.
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Returns: None
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"""
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self.after_iter(trainer)
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def every_n_epochs(self, trainer, n):
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"""
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Whether to reach every ``n`` epochs
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Returns: bool
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"""
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return (trainer.epoch + 1) % n == 0 if n > 0 else False
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def every_n_inner_iters(self, runner, n):
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"""
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Whether to reach every ``n`` iterations at every epoch
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Returns: bool
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"""
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return (runner.inner_iter + 1) % n == 0 if n > 0 else False
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def every_n_iters(self, trainer, n):
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"""
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Whether to reach every ``n`` iterations
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Returns: bool
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"""
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return (trainer.iter + 1) % n == 0 if n > 0 else False
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def end_of_epoch(self, trainer):
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"""
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Whether to reach the end of every epoch
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Returns: bool
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"""
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return trainer.inner_iter + 1 == trainer.iters_per_epoch
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def is_last_epoch(self, trainer):
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"""
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Whether to reach the last epoch
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Returns: bool
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"""
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return trainer.epoch + 1 == trainer.max_epochs
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def is_last_iter(self, trainer):
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"""
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Whether to reach the last iteration in the entire training process
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Returns: bool
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"""
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return trainer.iter + 1 == trainer.max_iters
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def get_triggered_stages(self):
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trigger_stages = set()
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for stage in Hook.stages:
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if is_method_overridden(stage, Hook, self):
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trigger_stages.add(stage)
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return [stage for stage in Hook.stages if stage in trigger_stages]
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def state_dict(self):
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return {}
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def load_state_dict(self, state_dict):
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pass
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