RepVGG代码中sgd_optimizer()的作用是什么？

Question

在RepVGG官方代码的train.py中，没看懂这个函数在干什么：

def sgd_optimizer(model, lr, momentum, weight_decay, use_custwd):
    params = []
    for key, value in model.named_parameters():  # key是网络层名称，value是参数
        if not value.requires_grad:
            continue
        apply_weight_decay = weight_decay
        apply_lr = lr
        if (use_custwd and ('rbr_dense' in key or 'rbr_1x1' in key)) or 'bias' in key or 'bn' in key:
            apply_weight_decay = 0
            print('set weight decay=0 for {}'.format(key))
        if 'bias' in key:
            apply_lr = 2 * lr  # Just a Caffe-style common practice. Made no difference.
        params += [{'params': [value], 'lr': apply_lr, 'weight_decay': apply_weight_decay}]
    optimizer = torch.optim.SGD(params, lr, momentum=momentum)
    return optimizer
```python

```

Answer 1

针对不同的层设置学习率(lr)、momentum(动量)和weight_decay(权重衰减)，然后使用SGD算法对整个网络进行优化。