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zaiweizhang / H3DNet

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RuntimeError: Error(s) in loading state_dict for DataParallel: #21

Closed giangdip2410 closed 3 years ago

giangdip2410 commented 3 years ago

Hi Zhang,

I train H3D on 1 GPU, everything is okay. But when I move to train on multi GPUs (4 GPUs), a issue happens . I tried the solution load layer as order as the guide https://github.com/bearpaw/pytorch-classification/issues/27 but the issue still happens. Do you have any idea about this issue ? Thank you very much.

net.load_state_dict(new_state_dict['module.model_state_dict']) File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 845, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for DataParallel: Missing key(s) in state_dict: "module.backbone_net1.sa1.mlp_module.layer0.conv.weight", "module.backbone_net1.sa1.mlp_module.layer0.bn.bn.weight", "module.backbone_net1.sa1.mlp_module.layer0.bn.bn.bias", "module.backbone_net1.sa1.mlp_module.layer0.bn.bn.running_mean", "module.backbone_net1.sa1.mlp_module.layer0.bn.bn.running_var", "module.backbone_net1.sa1.mlp_module.layer1.conv.weight", "module.backbone_net1.sa1.mlp_module.layer1.bn.bn.weight", "module.backbone_net1.sa1.mlp_module.layer1.bn.bn.bias", "module.backbone_net1.sa1.mlp_module.layer1.bn.bn.running_mean", "module.backbone_net1.sa1.mlp_module.layer1.bn.bn.running_var", "module.backbone_net1.sa1.mlp_module.layer2.conv.weight", "module.backbone_net1.sa1.mlp_module.layer2.bn.bn.weight", "module.backbone_net1.sa1.mlp_module.layer2.bn.bn.bias", "module.backbone_net1.sa1.mlp_module.layer2.bn.bn.running_mean", "module.backbone_net1.sa1.mlp_module.layer2.bn.bn.running_var", "module.backbone_net1.sa2.mlp_module.layer0.conv.weight", "module.backbone_net1.sa2.mlp_module.layer0.bn.bn.weight", "module.backbone_net1.sa2.mlp_module.layer0.bn.bn.bias", "module.backbone_net1.sa2.mlp_module.layer0.bn.bn.running_mean", "module.backbone_net1.sa2.mlp_module.layer0.bn.bn.running_var", "module.backbone_net1.sa2.mlp_module.layer1.conv.weight", "module.backbone_net1.sa2.mlp_module.layer1.bn.bn.weight", "module.backbone_net1.sa2.mlp_module.layer1.bn.bn.bias", "module.backbone_net1.sa2.mlp_module.layer1.bn.bn.running_mean", "module.backbone_net1.sa2.mlp_module.layer1.bn.bn.running_var", "module.backbone_net1.sa2.mlp_module.layer2.conv.weight", "module.backbone_net1.sa2.mlp_module.layer2.bn.bn.weight", 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"module.pnet_final.match_line_center.mlp_module.layer1.bn.bn.running_var", "module.pnet_final.match_line_center.mlp_module.layer2.conv.weight", "module.pnet_final.match_line_center.mlp_module.layer2.bn.bn.weight", "module.pnet_final.match_line_center.mlp_module.layer2.bn.bn.bias", "module.pnet_final.match_line_center.mlp_module.layer2.bn.bn.running_mean", "module.pnet_final.match_line_center.mlp_module.layer2.bn.bn.running_var", "module.pnet_final.conv1.weight", "module.pnet_final.conv1.bias", "module.pnet_final.conv2.weight", "module.pnet_final.conv2.bias", "module.pnet_final.conv3.weight", "module.pnet_final.conv3.bias", "module.pnet_final.bn1.weight", "module.pnet_final.bn1.bias", "module.pnet_final.bn1.running_mean", "module.pnet_final.bn1.running_var", "module.pnet_final.bn2.weight", "module.pnet_final.bn2.bias", "module.pnet_final.bn2.running_mean", "module.pnet_final.bn2.running_var", "module.pnet_final.conv_match1.weight", "module.pnet_final.conv_match1.bias", "module.pnet_final.conv_match2.weight", "module.pnet_final.conv_match2.bias", "module.pnet_final.bn_match1.weight", "module.pnet_final.bn_match1.bias", "module.pnet_final.bn_match1.running_mean", "module.pnet_final.bn_match1.running_var", "module.pnet_final.conv_match_sem1.weight", "module.pnet_final.conv_match_sem1.bias", "module.pnet_final.conv_match_sem2.weight", "module.pnet_final.conv_match_sem2.bias", "module.pnet_final.bn_match_sem1.weight", "module.pnet_final.bn_match_sem1.bias", "module.pnet_final.bn_match_sem1.running_mean", "module.pnet_final.bn_match_sem1.running_var", "module.pnet_final.conv_surface1.weight", "module.pnet_final.conv_surface1.bias", "module.pnet_final.conv_surface2.weight", "module.pnet_final.conv_surface2.bias", "module.pnet_final.bn_surface1.weight", "module.pnet_final.bn_surface1.bias", "module.pnet_final.bn_surface1.running_mean", "module.pnet_final.bn_surface1.running_var", "module.pnet_final.bn_surface2.weight", "module.pnet_final.bn_surface2.bias", "module.pnet_final.bn_surface2.running_mean", "module.pnet_final.bn_surface2.running_var", "module.pnet_final.conv_line1.weight", "module.pnet_final.conv_line1.bias", "module.pnet_final.conv_line2.weight", "module.pnet_final.conv_line2.bias", "module.pnet_final.bn_line1.weight", "module.pnet_final.bn_line1.bias", "module.pnet_final.bn_line1.running_mean", "module.pnet_final.bn_line1.running_var", "module.pnet_final.bn_line2.weight", "module.pnet_final.bn_line2.bias", "module.pnet_final.bn_line2.running_mean", "module.pnet_final.bn_line2.running_var", "module.pnet_final.conv_refine1.weight", "module.pnet_final.conv_refine1.bias", "module.pnet_final.conv_refine2.weight", "module.pnet_final.conv_refine2.bias", "module.pnet_final.conv_refine3.weight", "module.pnet_final.conv_refine3.bias", "module.pnet_final.conv_refine4.weight", "module.pnet_final.conv_refine4.bias", "module.pnet_final.bn_refine1.weight", "module.pnet_final.bn_refine1.bias", "module.pnet_final.bn_refine1.running_mean", "module.pnet_final.bn_refine1.running_var", "module.pnet_final.bn_refine2.weight", "module.pnet_final.bn_refine2.bias", "module.pnet_final.bn_refine2.running_mean", "module.pnet_final.bn_refine2.running_var", "module.pnet_final.bn_refine3.weight", "module.pnet_final.bn_refine3.bias", "module.pnet_final.bn_refine3.running_mean", "module.pnet_final.bn_refine3.running_var". 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zaiweizhang commented 3 years ago

Yes. I know the reason. When you are trying to use multiple GPUs, the network parameter will be named as "module." + the actual name. So if you want to load the pre-trained weight, you will have to update the names of the pretrained model. Please look at this code for reference.

giangdip2410 commented 3 years ago

yeah, thank you so much @zaiweizhang . It is exactly what I need :)