optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)optimizer_config = dict(grad_clip=None) 使用梯度剪辑来稳定训练 optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 其中,_delete_=True将用新键替换backbone字段中的所有旧键 2. 学习率配...
其中,optimizer_config、momentum_config和lr_config上文已经涉及了,这里不再介绍,介绍剩下的三个的作用。 Checkpoint config来源于MMCV中的CheckpointHook,用于控制模型参数的本地保存,可以在配置文件中添加如下字段来设置,其中常用的参数有三个,interval表示保存频率(多少轮进行一次保存),save_optimizer表示是否保存优化器...
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) # 优化参数,lr为学习率,momentum为动量因子,weight_decay为权重衰减因子 optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # 梯度均衡参数 # learning policy lr_config = dict( policy='step', # 优化策...
例如,在configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py配置文件中,可以找到以下代码段: optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict( policy='step', warmup='linear', warmu...
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)optimizer_config = dict(grad_clip=None) # 是一种防止梯度爆炸的策略 # lr 参数 lr_config = dict( policy='step', # lr decay的方式,其余的还有consine cyclic(不知道缩写是否一样) ...
optimizer_config = dict(grad_clip=None) 1. 2. 使用梯度剪辑来稳定训练 optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 1. 2. 其中,_delete_=True将用新键替换backbone字段中的所有旧键 2. 学习率配置 ...
runner.register_training_hooks(cfg.lr_config,optimizer_config,cfg.checkpoint_config,cfg.log_config,cfg.get('momentum_config',None))#5.如果需要 val,则还需要注册 EvalHook runner.register_hook(eval_hook(val_dataloader,**eval_cfg))#6.注册用户自定义 hook ...
optimizer=dict(type='SGD',lr=0.08,momentum=0.9,weight_decay=0.0001)optimizer_config=dict(_delete_=True,grad_clip=dict(max_norm=35,norm_type=2))lr_config=dict(policy='step',warmup='linear',warmup_iters=26000,warmup_ratio=1.0/64,step=[8,11]) ...
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) lr_config = dict(policy='step', warmup='linear', warmup_iters=500, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12) ``` ##模型权重文件 除了模型配置文件,mmdetection还生成了训练得到的模型权重文件...
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict(policy='step', step=[3]) # actual epoch = 3 * 3 = 9 ...