检查ConfigDict类是否应该有train_cfg这个属性: train_cfg属性通常用于存储训练相关的配置信息,如训练策略、超参数等。 在MMDetection等项目中,train_cfg是一个常见的配置属性。因此,如果你的代码是基于这些项目,那么ConfigDict类应该包含train_cfg属性。 查找为何在实例中缺失该属性的原因: 可能是配置文件(如YAML文件...
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1) val_cfg = dict() test_cfg = dict() # NOTE: `auto_scale_lr` is for automatically scaling LR, # based on the actual training batch size. auto_scale_lr = dict(base_batch_size=256) 最后是'../_base_/default_runtime...
env_cfg = dict( # 是否开启 cudnn benchmark cudnn_benchmark=False, # 设置多进程参数 mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), # 设置分布式参数 dist_cfg=dict(backend='nccl'), ) # 设置可视化工具 vis_backends = [dict(type='LocalVisBackend')] # 使用磁盘(HDD)后端 v...
I still have a question about the old error 'ConfigDict' object has no attribute 'train_cfg'. I modified my model = build_detector( cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) to model = build_detector( cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get...
train_cfg=dict(by_epoch=True,max_epochs=220)runner=Runner(model,work_dir='runs/gan/',train_dataloader=train_dataloader,train_cfg=train_cfg,optim_wrapper=opt_wrapper_dict)runner.train() 到这里,我们就完成了一个 GAN 的训练,通过下面的代码可以查看刚才训练的 GAN 生成的结果。
def train(net, data_loader, loss_dict, optimizer, scheduler,logger, epoch, metric_dict, dataset): # 设置成训练模式 net.train() # 创建进度条 progress_bar = dist_tqdm(train_loader) # 遍历数据加载器 for b_idx, data_label in enumerate(progress_bar): global_step = epoch * len(data_...
(Dict, optional): Model configuration.weights (str, optional): Path to pre-trained model weights.verbose (bool): Verbose logging if True.Returns:(RTDETRDetectionModel): Initialized model."""model=RTDETRDetectionModel(cfg,nc=self.data["nc"],verbose=verboseandRANK==-1)ifweights:model.load(...
(cfg or hyp.get('anchors')) and not resume else [] # exclude keys # 将预训练模型中的所有参数保存下来,赋值给csd csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 # 判断预训练参数和新创建的模型参数有多少是相同的 # 筛选字典中的键值对,把exclude删除 csd = ...
model=Model(opt.cfg, ch=3, nc=nc).to(device) # 在./models/yolo.py, class Model(nn.Module)#把model改造成不含有anchor部分的FP32模型,然后重新加载exclude = ['anchor']ifopt.cfgorhyp.get('anchors')else[] state_dict=ckpt['model'].float().state_dict()#to FP32state_dict = intersect_d...
class CLIP(BaseModel): def __init__(self, vision_backbone: dict, projection: dict, text_backbone: dict, tokenizer: dict, vocab_size: int, transformer_width: int, proj_dim: int, context_length: int = 77, data_preprocessor: Optional[dict] = None, init_cfg: Optional[dict] = None): ...