# 加载参数, 跳过形状不匹配的。
load_weights(ppyolo, cfg.train_cfg['model_path'])
strs = cfg.train_cfg['model_path'].split('step')
if len(strs) == 2:
iter_id = int(strs[1][:8])
# 冻结，使得需要的显存减少。低显存的卡建议这样配置。
backbone.freeze()
if use_gpu: # 如果有gpu可用，模型（包括了权重weight）存放在gpu显存里
ppyolo = ppyolo.cuda()
ema = None
if cfg.use_ema:
ema = ExponentialMovingAverage(ppyolo, cfg.ema_decay)
ema.register()
# 种类id
_catid2clsid = copy.deepcopy(catid2clsid)
_clsid2catid = copy.deepcopy(clsid2catid)
if num_classes != 80: # 如果不是COCO数据集，而是自定义数据集
_catid2clsid = {}
_clsid2catid = {}
for k in range(num_classes):
_catid2clsid[k] = k
_clsid2catid[k] = k
# 训练集
train_dataset = COCO(cfg.train_path)
train_img_ids = train_dataset.getImgIds()
train_records = data_clean(train_dataset, train_img_ids, _catid2clsid,