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) warmup='linear', # 初始的学习率增...
config_file='configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'checkpoint_file='checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'device='cuda:0'# 初始化检测器 model=init_detector(config_file,checkpoint_file,device=device)# 推理演示图像 img='img/demo.jpg'result=inference_d...
test_mode=True)) # optimizer 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)) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3,...
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_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) 1. 2. 动量调度 MMDetection支持依据学习率动态调整动量使得训练收敛加快,示例如下。 lr_config = dict( policy='cyclic', target_ratio=(10, 1e-4),
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_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 ...
https://github.com/open-mmlab/mmdetection/blob/master/tools/convert_datasets/pascal_voc.py)。 然后,你可以简单地使用CustomDataset。自定义优化 在mmdet/core/optimizer/copy_of_sgd.py中定义了定制优化器CopyOfSGD的示例。 更一般地,可以如下定义定制的优化器。在mmdet/core/optimizer/my_optimizer.py中：from...
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...
(interval=1, metric='bbox') # 优化器配置 optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) # optimizer hook配置 optimizer_config = dict(grad_clip=None) # 学习率配置 lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, ...