这样加载预训练模型对吗？这样加载训练的时候显示没有下载成功。model = DeepLabV3P(num_classes=num_class, backbone=ResNet50_vd(), align_corners=False, pretrained='https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz')

[xiao12301]

Thanks for your issue. To help us solve the issue better, please provide following information：

1. PaddleSeg version: (please specify the branch as well，e.g. PaddleSeg release/2.3)
2. PaddlePaddle version: (e.g. PaddlePaddle 2.1.0)
3. Operation system: (e.g. Linux/Windows/MacOS)
4. Python version: (e.g. Python3.6/7/8)
5. CUDA/cuDNN version: (e.g. CUDA10.2/cuDNN 7.6.5)
6. Additional context: (Add any other context about the problem)

---

欢迎您反馈PaddleSeg使用问题，辛苦您提供以下信息，方便我们快速定位和解决问题：

1. PaddleSeg版本：（请提供版本号和分支信息，如PaddleSeg release/2.3）
2. PaddlePaddle版本：（如PaddlePaddle 2.1.0）
3. 操作系统信息：（如Linux/Windows/MacOS）
4. Python版本号：（如Python3.6/7/8）
5. CUDA/cuDNN版本：（ 如CUDA10.2/cuDNN 7.6.5等）
6. 其他内容: （增加其他与问题相关的内容）

[shiyutang]

The code seems alright, please provide us with the error. It could be the network that cause the download failure.

[xiao12301]

Loading pretrained model from https://paddleseg.bj.bcebos.com/dygraph/backbone/mobilenetv3_large_x1_0_ssld.tar.gz

Connecting to https://paddleseg.bj.bcebos.com/dygraph/backbone/mobilenetv3_large_x1_0_ssld.tar.gz Downloading mobilenetv3_large_x1_0_ssld.tar.gz [==================================================] 100.00% Uncompress mobilenetv3_large_x1_0_ssld.tar.gz [==================================================] 100.00% 2022-07-23 09:44:45 [WARNING] backbone.conv.conv.weight is not in pretrained model 2022-07-23 09:44:45 [WARNING] backbone.conv.bn.weight is not in pretrained model 2022-07-23 09:44:45 [WARNING] backbone.conv.bn.bias is not in pretrained model 2022-07-23 09:44:45 [WARNING] backbone.conv.bn._mean is not in pretrained model 2022-07-23 09:44:45 [WARNING] backbone.conv.bn._variance is not in pretrained model 最后显示的是这样的 There are 0/285 variables loaded into LRASPP.

[shiyutang]

显示结果表明下载成果，但是参数名称不一致导致没有加载进网络。

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed in 7 days if no further activity occurs. Thank you for your contributions.