ColossalAI/colossalai/legacy/trainer/hooks/_base_hook.py

83 lines
2.6 KiB
Python

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from abc import ABC
from torch import Tensor
class BaseHook(ABC):
"""This class allows users to add desired actions in specific time points
during training or evaluation.
:param priority: Priority in the printing, hooks with small priority will be printed in front
:type priority: int
"""
def __init__(self, priority: int) -> None:
self.priority = priority
def after_hook_is_attached(self, trainer):
"""Actions after hooks are attached to trainer."""
def before_train(self, trainer):
"""Actions before training."""
def after_train(self, trainer):
"""Actions after training."""
def before_train_iter(self, trainer):
"""Actions before running a training iteration."""
def after_train_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
"""Actions after running a training iteration.
Args:
trainer (:class:`Trainer`): Trainer which is using this hook.
output (:class:`torch.Tensor`): Output of the model.
label (:class:`torch.Tensor`): Labels of the input data.
loss (:class:`torch.Tensor`): Loss between the output and input data.
"""
def before_train_epoch(self, trainer):
"""Actions before starting a training epoch."""
def after_train_epoch(self, trainer):
"""Actions after finishing a training epoch."""
def before_test(self, trainer):
"""Actions before evaluation."""
def after_test(self, trainer):
"""Actions after evaluation."""
def before_test_epoch(self, trainer):
"""Actions before starting a testing epoch."""
def after_test_epoch(self, trainer):
"""Actions after finishing a testing epoch."""
def before_test_iter(self, trainer):
"""Actions before running a testing iteration."""
def after_test_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
"""Actions after running a testing iteration.
Args:
trainer (:class:`Trainer`): Trainer which is using this hook
output (:class:`torch.Tensor`): Output of the model
label (:class:`torch.Tensor`): Labels of the input data
loss (:class:`torch.Tensor`): Loss between the output and input data
"""
def init_runner_states(self, trainer, key, val):
"""Initializes trainer's state.
Args:
trainer (:class:`Trainer`): Trainer which is using this hook
key: Key of state to be reset
val: Value of state to be reset
"""
if key not in trainer.states:
trainer.states[key] = val