RTDETR更换Lion优化器

论文：https://arxiv.org/abs/2302.06675
代码：https://github.com/google/automl/blob/master/lion/lion_pytorch.py

简介：
Lion优化器是一种基于梯度的优化算法，旨在提高梯度下降法在深度学习中的优化效果。Lion优化器具有以下几个特点：

• 自适应学习率：Lion优化器能够自动调整学习率，根据每个参数的梯度情况来自适应地更新学习率。这使得模型能够更快地收敛，并且不易陷入局部最优点。

• 动量加速：Lion优化器引入了动量概念，通过积累历史梯度的一部分来加速梯度更新。这样可以增加参数更新的稳定性，避免陷入震荡或振荡状态。

• 参数分布均衡：Lion优化器通过分析模型参数的梯度分布情况，对梯度进行动态调整，以实现参数分布的均衡。这有助于避免某些参数过于稀疏或过于密集的问题，提高模型的泛化能力。

与AdamW 和各种自适应优化器需要同时保存一阶和二阶矩相比，Lion 只需要动量，将额外的内存占用减半；
由于 Lion 的简单性，Lion 在我们的实验中具有更快的运行时间（step/s），通常比 AdamW 和 Adafactor 提速 2-15%；

优化器代码：

# Copyright 2023 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""PyTorch implementation of the Lion optimizer."""
import torch
from torch.optim.optimizer import Optimizer


class Lion(Optimizer):
  r"""Implements Lion algorithm."""

  def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
    """Initialize the hyperparameters.

    Args:
      params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
      lr (float, optional): learning rate (default: 1e-4)
      betas (Tuple[float, float], optional): coefficients used for computing
        running averages of gradient and its square (default: (0.9, 0.99))
      weight_decay (float, optional): weight decay coefficient (default: 0)
    """

    if not 0.0 <= lr:
      raise ValueError('Invalid learning rate: {}'.format(lr))
    if not 0.0 <= betas[0] < 1.0:
      raise ValueError('Invalid beta parameter at index 0: {}'.format(betas[0]))
    if not 0.0 <= betas[1] < 1.0:
      raise ValueError('Invalid beta parameter at index 1: {}'.format(betas[1]))
    defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
    super().__init__(params, defaults)

  @torch.no_grad()
  def step(self, closure=None):
    """Performs a single optimization step.

    Args:
      closure (callable, optional): A closure that reevaluates the model
        and returns the loss.

    Returns:
      the loss.
    """
    loss = None
    if closure is not None:
      with torch.enable_grad():
        loss = closure()

    for group in self.param_groups:
      for p in group['params']:
        if p.grad is None:
          continue

        # Perform stepweight decay
        p.data.mul_(1 - group['lr'] * group['weight_decay'])

        grad = p.grad
        state = self.state[p]
        # State initialization
        if len(state) == 0:
          # Exponential moving average of gradient values
          state['exp_avg'] = torch.zeros_like(p)

        exp_avg = state['exp_avg']
        beta1, beta2 = group['betas']

        # Weight update
        update = exp_avg * beta1 + grad * (1 - beta1)

        p.add_(update.sign_(), alpha=-group['lr'])

        # Decay the momentum running average coefficient
        exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)

    return loss

将上述代码复制粘贴在ultralytics/engine下创建lion_pytorch.py文件。

在ultralytics/engine/trainer.py中导入Lion

from ultralytics.engine.lion_pytorch import Lion

然后在def build_optimizer(self）函数中加入下列代码

        elif name == 'Lion':
            optimizer = Lion(g[2])

之后就可以在训练时使用Lion优化器了

    results = model.train(data="ultralytics/cfg/datasets/coco.yaml", epochs=500, batch=16, workers=8,
                          resume=False,
                          close_mosaic=10, name='cfg', patience=500, pretrained=False, cos_lr=True,optimizer ='Lion',
                          device=1)  # 训练模型