check_suffix(weights, '.pt') # check weights pretrained = weights.endswith('.pt') if pretrained: with torch_distributed_zero_first(LOCAL_RANK): weights = attempt_download(weights) # download if not found locally ckpt = torch.load(weights, map_location=device) # load checkpoint model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect model.load_state_dict(csd, strict=False) # load LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report else: model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create

这段代码看起来是用来加载预训练模型的，它首先检查模型文件的后缀名是否为'.pt'，如果是，则尝试从本地下载该文件，如果未找到，则从互联网下载。然后，它加载该预训练模型的状态字典，并将其转换为FP32格式。接下来，它将该状态字典与所创建模型的状态字典进行交集操作，并将结果加载到该模型中。最后，它打印出从预训练模型中转移的状态字典中成功加载的项数。