124 lines
5.0 KiB
Python
124 lines
5.0 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@project:
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@File : polyscheduler
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@Author : qiqq
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@create_time : 2022/11/4 20:42
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"""
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from torch.optim.lr_scheduler import _LRScheduler
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import types
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import math
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from torch._six import inf
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from functools import wraps
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import warnings
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import weakref
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from collections import Counter
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from bisect import bisect_right
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from torch.optim import Optimizer
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'''网上复现的多项式学习率，貌似见过的论文都用这个'''
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class LinoPolyScheduler(_LRScheduler):
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r"""
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Args:
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optimizer (Optimizer): Wrapped optimizer.
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total_steps (int): The total number of steps in the cycle. Note that
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if a value is not provided here, then it must be inferred by providing
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a value for epochs and steps_per_epoch.
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Default: None
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epochs (int): The number of epochs to train for. This is used along
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with steps_per_epoch in order to infer the total number of steps in the cycle
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if a value for total_steps is not provided.
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Default: None
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steps_per_epoch (int): The number of steps per epoch to train for. This is
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used along with epochs in order to infer the total number of steps in the
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cycle if a value for total_steps is not provided.
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Default: None
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last_epoch (int): The index of the last batch. This parameter is used when
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resuming a training job. Since `step()` should be invoked after each
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batch instead of after each epoch, this number represents the total
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number of *batches* computed, not the total number of epochs computed.
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When last_epoch=-1, the schedule is started from the beginning.
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Default: -1
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verbose (bool): If ``True``, prints a message to stdout for
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each update. Default: ``False``.
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Example:
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# >>> data_loader = torch.utils.data.DataLoader(...)
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# >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
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# >>> scheduler = torch.optim.lr_scheduler.PolyScheduler(optimizer, min_lr=0.01, steps_per_epoch=None, epochs=10)
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# >>> for epoch in range(10):
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# >>> for batch in data_loader:
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# >>> train_batch(...)
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# >>> scheduler.step()
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.. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
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https://arxiv.org/abs/1708.07120
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"""
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def __init__(self,
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optimizer,
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power=0.9,
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total_steps=None,
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epochs=None,
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steps_per_epoch=None,
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min_lr=0,
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last_epoch=-1,
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verbose=False):
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# Validate optimizer
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if not isinstance(optimizer, Optimizer):
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raise TypeError('{} is not an Optimizer'.format(
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type(optimizer).__name__))
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self.optimizer = optimizer
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# self.by_epoch = by_epoch
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self.epochs = epochs
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self.min_lr = min_lr
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self.power = power
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# check param
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param_dic = {'total_steps': total_steps, 'epochs': epochs, 'steps_per_epoch': steps_per_epoch}
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for k, v in param_dic.items():
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if v is not None:
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if v <= 0 or not isinstance(v, int):
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raise ValueError("Expected positive integer {}, but got {}".format(k, v))
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# Validate total_steps
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if total_steps is not None:
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self.total_steps = total_steps
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elif epochs is not None and steps_per_epoch is None:
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self.total_steps = epochs
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elif epochs is not None and steps_per_epoch is not None:
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self.total_steps = epochs * steps_per_epoch
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else:
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raise ValueError("You must define either total_steps OR epochs OR (epochs AND steps_per_epoch)")
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super(LinoPolyScheduler, self).__init__(optimizer, last_epoch, verbose)
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def _format_param(self, name, optimizer, param):
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"""Return correctly formatted lr/momentum for each param group."""
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if isinstance(param, (list, tuple)):
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if len(param) != len(optimizer.param_groups):
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raise ValueError("expected {} values for {}, got {}".format(
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len(optimizer.param_groups), name, len(param)))
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return param
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else:
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return [param] * len(optimizer.param_groups)
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def get_lr(self):
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if not self._get_lr_called_within_step:
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warnings.warn("To get the last learning rate computed by the scheduler, "
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"please use `get_last_lr()`.", UserWarning)
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step_num = self.last_epoch
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if step_num > self.total_steps:
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raise ValueError("Tried to step {} times. The specified number of total steps is {}"
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.format(step_num + 1, self.total_steps))
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coeff = (1 - step_num / self.total_steps) ** self.power
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return [(base_lr - self.min_lr) * coeff + self.min_lr for base_lr in self.base_lrs]
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