mirror of https://github.com/alibaba/EasyCV.git
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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from mmcv.runner.hooks import HOOKS
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from mmcv.runner.hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
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annealing_cos)
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@HOOKS.register_module(force=True)
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class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
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"""YOLOX learning rate scheme.
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There are two main differences between YOLOXLrUpdaterHook
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and CosineAnnealingLrUpdaterHook.
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1. When the current running epoch is greater than
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`max_epoch-last_epoch`, a fixed learning rate will be used
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2. The exp warmup scheme is different with LrUpdaterHook in MMCV
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Args:
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num_last_epochs (int): The number of epochs with a fixed learning rate
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before the end of the training.
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"""
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def __init__(self, num_last_epochs, **kwargs):
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self.num_last_epochs = num_last_epochs
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super(YOLOXLrUpdaterHook, self).__init__(**kwargs)
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def get_warmup_lr(self, cur_iters):
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def _get_warmup_lr(cur_iters, regular_lr):
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# exp warmup scheme
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k = self.warmup_ratio * pow(
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(cur_iters + 1) / float(self.warmup_iters), 2)
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warmup_lr = [_lr * k for _lr in regular_lr]
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return warmup_lr
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if isinstance(self.base_lr, dict):
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lr_groups = {}
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for key, base_lr in self.base_lr.items():
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lr_groups[key] = _get_warmup_lr(cur_iters, base_lr)
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return lr_groups
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else:
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return _get_warmup_lr(cur_iters, self.base_lr)
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def get_lr(self, runner, base_lr):
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last_iter = len(runner.data_loader) * self.num_last_epochs
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if self.by_epoch:
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progress = runner.epoch
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max_progress = runner.max_epochs
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else:
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progress = runner.iter
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max_progress = runner.max_iters
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progress += 1
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if self.min_lr_ratio is not None:
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target_lr = base_lr * self.min_lr_ratio
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else:
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target_lr = self.min_lr
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if progress >= max_progress - last_iter:
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# fixed learning rate
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return target_lr
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else:
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assert max_progress > (self.warmup_iters + last_iter
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), 'Please increase the train epoch'
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return annealing_cos(
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base_lr, target_lr, (progress - self.warmup_iters) /
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(max_progress - self.warmup_iters - last_iter))
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