使用训练过的模型运行demo时发生运行警告

[vic01512]

原来运行demo.py是成功的。训练后对OTB100评估结果如下

|Tracker name| Success | Norm Precision | Precision |

| SiamRPN++ | 0.609 | 0.000 | 0.808 |

训练过后 python demo.py --config ../experiments/siamrpn_r50_l234_dwxcorr/config.yaml --snapshot ../experiments/siamrpn_r50_l234_dwxcorr/snapshot/checkpoint_e20.pth --video ../demo/bag.avi
可以打开窗口并画框，开始追踪发生如下警告
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:53: RuntimeWarning: overflow encountered in exp
delta[2, :] = np.exp(delta[2, :]) * anchor[:, 2]
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:54: RuntimeWarning: overflow encountered in exp
delta[3, :] = np.exp(delta[3, :]) * anchor[:, 3]
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:115: RuntimeWarning: divide by zero encountered in divide
return np.maximum(r, 1. / r)
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:127: RuntimeWarning: invalid value encountered in divide
(pred_bbox[2, :]/pred_bbox[3, :]))
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:126: RuntimeWarning: divide by zero encountered in divide
r_c = change((self.size[0]/self.size[1]) /
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:127: RuntimeWarning: divide by zero encountered in divide
(pred_bbox[2, :]/pred_bbox[3, :]))
C:\Users/---/Desktop/pysot-master\pysot\tracker\siamrpn_tracker.py:53: RuntimeWarning: overflow encountered in multiply
delta[2, :] = np.exp(delta[2, :]) * anchor[:, 2]
.....
运行时窗口内角落上有绿色的一点

使用model_zoo中的siamrpn_r50_l234_dwxcorr model 可以成功运行

使用训练过的模型时，#320按这个改过。

[vic01512]

再次按照#320更改了代码，成功运行了，建议阅读如下的源代码

def load_pretrain(model, pretrained_path): # 加载模型，一般五步
logger.info('load pretrained model from {}'.format(pretrained_path))
# 1.初始化device
device = torch.cuda.current_device() # 返回当前设备索引，直接使用GPU
# 2.加载模型 载入GPU
pretrained_dict = torch.load(pretrained_path,
map_location=lambda storage, loc: storage.cuda(device)) # 进行模型加载
# 3.移除module前缀（一般用于多GPU分布式训练后的模型）
if "state_dict" in pretrained_dict.keys():
pretrained_dict = remove_prefix(pretrained_dict['state_dict'],
'module.')
else:
pretrained_dict = remove_prefix(pretrained_dict, 'module.')
# 4.检查键完整性：即判断当前构建的模型参数与待加载的模型参数是否匹配
try:
check_keys(model, pretrained_dict)
except:
logger.info('[Warning]: using pretrain as features.
Adding "features." as prefix')
new_dict = {}
for k, v in pretrained_dict.items():
k = 'features.' + k
new_dict[k] = v
pretrained_dict = new_dict
check_keys(model, pretrained_dict)
# 5.装载模型参数
model.load_state_dict(pretrained_dict, strict=False)
return model

[sssimpleboy]

好兄弟，这个项目没有验证集，你怎么确定自己训练结果好不好呀？