parameters don't match models

[zhouchengcheng]

when I use the pretrained model download in the eval program, the parameters don't match as follows, could you check it? Thanks a lot:
RuntimeError: Error(s) in loading state_dict for Net:
Missing key(s) in state_dict: "feat0.conv.weight", "feat0.conv.bias", "feat0.act.weight", "feat1.conv.weight", "feat1.conv.bias", "feat1.act.weight", "DBPN.feat1.conv.weight", "DBPN.feat1.conv.bias", "DBPN.feat1.act.weight", "DBPN.up1.up_conv1.deconv.weight", "DBPN.up1.up_conv1.deconv.bias", "DBPN.up1.up_conv1.act.weight", "DBPN.up1.up_conv2.conv.weight", "DBPN.up1.up_conv2.conv.bias", "DBPN.up1.up_conv2.act.weight", "DBPN.up1.up_conv3.deconv.weight", "DBPN.up1.up_conv3.deconv.bias", "DBPN.up1.up_conv3.act.weight", "DBPN.down1.down_conv1.conv.weight", "DBPN.down1.down_conv1.conv.bias", "DBPN.down1.down_conv1.act.weight", "DBPN.down1.down_conv2.deconv.weight", "DBPN.down1.down_conv2.deconv.bias", "DBPN.down1.down_conv2.act.weight", "DBPN.down1.down_conv3.conv.weight", "DBPN.down1.down_conv3.conv.bias", "DBPN.down1.down_conv3.act.weight", "DBPN.up2.up_conv1.deconv.weight", "DBPN.up2.up_conv1.deconv.bias", "DBPN.up2.up_conv1.act.weight", "DBPN.up2.up_conv2.conv.weight", "DBPN.up2.up_conv2.conv.bias", "DBPN.up2.up_conv2.act.weight", "DBPN.up2.up_conv3.deconv.weight", "DBPN.up2.up_conv3.deconv.bias", "DBPN.up2.up_conv3.act.weight", "DBPN.down2.down_conv1.conv.weight", "DBPN.down2.down_conv1.conv.bias", "DBPN.down2.down_conv1.act.weight", "DBPN.down2.down_conv2.deconv.weight", "DBPN.down2.down_conv2.deconv.bias", "DBPN.down2.down_conv2.act.weight", "DBPN.down2.down_conv3.conv.weight", "DBPN.down2.down_conv3.conv.bias", "DBPN.down2.down_conv3.act.weight", "DBPN.up3.up_conv1.deconv.weight", "DBPN.up3.up_conv1.deconv.bias", "DBPN.up3.up_conv1.act.weight", "DBPN.up3.up_conv2.conv.weight", "DBPN.up3.up_conv2.conv.bias", "DBPN.up3.up_conv2.act.weight", "DBPN.up3.up_conv3.deconv.weight", "DBPN.up3.up_conv3.deconv.bias", "DBPN.up3.up_conv3.act.weight", "DBPN.output.conv.weight", "DBPN.output.conv.bias", "res_feat1.0.conv1.weight", "res_feat1.0.conv1.bias", "res_feat1.0.conv2.weight", "res_feat1.0.conv2.bias", "res_feat1.0.act.weight", "res_feat1.1.conv1.weight", "res_feat1.1.conv1.bias", "res_feat1.1.conv2.weight", "res_feat1.1.conv2.bias", "res_feat1.1.act.weight", "res_feat1.2.conv1.weight", "res_feat1.2.conv1.bias", "res_feat1.2.conv2.weight", "res_feat1.2.conv2.bias", "res_feat1.2.act.weight", "res_feat1.3.conv1.weight", "res_feat1.3.conv1.bias", "res_feat1.3.conv2.weight", "res_feat1.3.conv2.bias", "res_feat1.3.act.weight", "res_feat1.4.conv1.weight", "res_feat1.4.conv1.bias", "res_feat1.4.conv2.weight", "res_feat1.4.conv2.bias", "res_feat1.4.act.weight", "res_feat1.5.deconv.weight", "res_feat1.5.deconv.bias", "res_feat1.5.act.weight", "res_feat2.0.conv1.weight", "res_feat2.0.conv1.bias", "res_feat2.0.conv2.weight", "res_feat2.0.conv2.bias", "res_feat2.0.act.weight", "res_feat2.1.conv1.weight", "res_feat2.1.conv1.bias", "res_feat2.1.conv2.weight", "res_feat2.1.conv2.bias", "res_feat2.1.act.weight", "res_feat2.2.conv1.weight", "res_feat2.2.conv1.bias", "res_feat2.2.conv2.weight", "res_feat2.2.conv2.bias", "res_feat2.2.act.weight", "res_feat2.3.conv1.weight", "res_feat2.3.conv1.bias", "res_feat2.3.conv2.weight", "res_feat2.3.conv2.bias", "res_feat2.3.act.weight", "res_feat2.4.conv1.weight", "res_feat2.4.conv1.bias", "res_feat2.4.conv2.weight", "res_feat2.4.conv2.bias", "res_feat2.4.act.weight", "res_feat2.5.conv.weight", "res_feat2.5.conv.bias", "res_feat2.5.act.weight", "res_feat3.0.conv1.weight", "res_feat3.0.conv1.bias", "res_feat3.0.conv2.weight", "res_feat3.0.conv2.bias", "res_feat3.0.act.weight", "res_feat3.1.conv1.weight", "res_feat3.1.conv1.bias", "res_feat3.1.conv2.weight", "res_feat3.1.conv2.bias", "res_feat3.1.act.weight", "res_feat3.2.conv1.weight", "res_feat3.2.conv1.bias", "res_feat3.2.conv2.weight", "res_feat3.2.conv2.bias", "res_feat3.2.act.weight", "res_feat3.3.conv1.weight", "res_feat3.3.conv1.bias", "res_feat3.3.conv2.weight", "res_feat3.3.conv2.bias", "res_feat3.3.act.weight", "res_feat3.4.conv1.weight", "res_feat3.4.conv1.bias", "res_feat3.4.conv2.weight", "res_feat3.4.conv2.bias", "res_feat3.4.act.weight", "res_feat3.5.conv.weight", "res_feat3.5.conv.bias", "res_feat3.5.act.weight", "output.conv.weight", "output.conv.bias".
Unexpected key(s) in state_dict: "module.model.motion_feat.conv.weight", "module.model.motion_feat.conv.bias", "module.model.motion.0.conv1.weight", "module.model.motion.0.conv1.bias", "module.model.motion.0.conv2.weight", "module.model.motion.0.conv2.bias", "module.model.motion.1.conv1.weight", "module.model.motion.1.conv1.bias", "module.model.motion.1.conv2.weight", "module.model.motion.1.conv2.bias", "module.model.motion.2.conv.weight", "module.model.motion.2.conv.bias", "module.model.t_net_lr.0.conv1.weight", "module.model.t_net_lr.0.conv1.bias", "module.model.t_net_lr.0.conv2.weight", "module.model.t_net_lr.0.conv2.bias", "module.model.t_net_lr.1.conv1.weight", "module.model.t_net_lr.1.conv1.bias", "module.model.t_net_lr.1.conv2.weight", "module.model.t_net_lr.1.conv2.bias", "module.model.t_net_lr.2.conv.weight", "module.model.t_net_lr.2.conv.bias", "module.model.t_net_hr.0.conv.weight", "module.model.t_net_hr.0.conv.bias", "module.model.t_net_hr.1.l1_1.conv1.weight", "module.model.t_net_hr.1.l1_1.conv1.bias", "module.model.t_net_hr.1.l1_1.conv2.weight", "module.model.t_net_hr.1.l1_1.conv2.bias", "module.model.t_net_hr.1.l1_2.conv1.weight", "module.model.t_net_hr.1.l1_2.conv1.bias", "module.model.t_net_hr.1.l1_2.conv2.weight", "module.model.t_net_hr.1.l1_2.conv2.bias", "module.model.t_net_hr.1.l1_3.conv1.weight", "module.model.t_net_hr.1.l1_3.conv1.bias", "module.model.t_net_hr.1.l1_3.conv2.weight", "module.model.t_net_hr.1.l1_3.conv2.bias", "module.model.t_net_hr.1.l1_4.conv1.weight", "module.model.t_net_hr.1.l1_4.conv1.bias", "module.model.t_net_hr.1.l1_4.conv2.weight", "module.model.t_net_hr.1.l1_4.conv2.bias", "module.model.t_net_hr.1.l1_5.conv1.weight", "module.model.t_net_hr.1.l1_5.conv1.bias", "module.model.t_net_hr.1.l1_5.conv2.weight", "module.model.t_net_hr.1.l1_5.conv2.bias", "module.model.t_net_hr.1.l2_1.conv1.weight", "module.model.t_net_hr.1.l2_1.conv1.bias", "module.model.t_net_hr.1.l2_1.conv2.weight", "module.model.t_net_hr.1.l2_1.conv2.bias", "module.model.t_net_hr.1.l2_2.conv1.weight", "module.model.t_net_hr.1.l2_2.conv1.bias", "module.model.t_net_hr.1.l2_2.conv2.weight", "module.model.t_net_hr.1.l2_2.conv2.bias", "module.model.t_net_hr.1.l2_3.conv1.weight", "module.model.t_net_hr.1.l2_3.conv1.bias", "module.model.t_net_hr.1.l2_3.conv2.weight", "module.model.t_net_hr.1.l2_3.conv2.bias", "module.model.t_net_hr.1.l2_4.conv1.weight", "module.model.t_net_hr.1.l2_4.conv1.bias", "module.model.t_net_hr.1.l2_4.conv2.weight", "module.model.t_net_hr.1.l2_4.conv2.bias", "module.model.t_net_hr.1.l3_1.conv1.weight", "module.model.t_net_hr.1.l3_1.conv1.bias", "module.model.t_net_hr.1.l3_1.conv2.weight", "module.model.t_net_hr.1.l3_1.conv2.bias", "module.model.t_net_hr.1.l3_2.conv1.weight", "module.model.t_net_hr.1.l3_2.conv1.bias", "module.model.t_net_hr.1.l3_2.conv2.weight", "module.model.t_net_hr.1.l3_2.conv2.bias", "module.model.t_net_hr.1.l3_3.conv1.weight", "module.model.t_net_hr.1.l3_3.conv1.bias", "module.model.t_net_hr.1.l3_3.conv2.weight", "module.model.t_net_hr.1.l3_3.conv2.bias", "module.model.t_net_hr.1.down1.conv.weight", "module.model.t_net_hr.1.down1.conv.bias", "module.model.t_net_hr.1.down2.conv.weight", "module.model.t_net_hr.1.down2.conv.bias", "module.model.t_net_hr.1.up1.deconv.weight", "module.model.t_net_hr.1.up1.deconv.bias", "module.model.t_net_hr.1.up2.deconv.weight", "module.model.t_net_hr.1.up2.deconv.bias", "module.model.t_net_hr.1.final.conv.weight", "module.model.t_net_hr.1.final.conv.bias", "module.model.t_net_hr.2.conv.weight", "module.model.t_net_hr.2.conv.bias", "module.model.interp_block.0.conv1.weight", "module.model.interp_block.0.conv1.bias", "module.model.interp_block.0.conv2.weight", "module.model.interp_block.0.conv2.bias", "module.model.interp_block.1.conv1.weight", "module.model.interp_block.1.conv1.bias", "module.model.interp_block.1.conv2.weight", "module.model.interp_block.1.conv2.bias", "module.model.interp_block.2.conv1.weight", "module.model.interp_block.2.conv1.bias", "module.model.interp_block.2.conv2.weight", "module.model.interp_block.2.conv2.bias", "module.model.interp_block.3.conv1.weight", "module.model.interp_block.3.conv1.bias", "module.model.interp_block.3.conv2.weight", "module.model.interp_block.3.conv2.bias", "module.model.interp_block.4.conv1.weight", "module.model.interp_block.4.conv1.bias", "module.model.interp_block.4.conv2.weight", "module.model.interp_block.4.conv2.bias", "module.model.interp_block.5.deconv.weight", "module.model.interp_block.5.deconv.bias", "module.model.motion_up_f.0.conv1.weight", "module.model.motion_up_f.0.conv1.bias", "module.model.motion_up_f.0.conv2.weight", "module.model.motion_up_f.0.conv2.bias", "module.model.motion_up_f.1.conv1.weight", "module.model.motion_up_f.1.conv1.bias", "module.model.motion_up_f.1.conv2.weight", "module.model.motion_up_f.1.conv2.bias", "module.model.motion_up_f.2.conv1.weight", "module.model.motion_up_f.2.conv1.bias", "module.model.motion_up_f.2.conv2.weight", "module.model.motion_up_f.2.conv2.bias", "module.model.motion_up_f.3.conv1.weight", "module.model.motion_up_f.3.conv1.bias", "module.model.motion_up_f.3.conv2.weight", "module.model.motion_up_f.3.conv2.bias", "module.model.motion_up_f.4.conv1.weight", "module.model.motion_up_f.4.conv1.bias", "module.model.motion_up_f.4.conv2.weight", "module.model.motion_up_f.4.conv2.bias", "module.model.motion_up_f.5.deconv.weight", "module.model.motion_up_f.5.deconv.bias", "module.model.motion_up_b.0.conv1.weight", "module.model.motion_up_b.0.conv1.bias", "module.model.motion_up_b.0.conv2.weight", "module.model.motion_up_b.0.conv2.bias", "module.model.motion_up_b.1.conv1.weight", "module.model.motion_up_b.1.conv1.bias", "module.model.motion_up_b.1.conv2.weight", "module.model.motion_up_b.1.conv2.bias", "module.model.motion_up_b.2.conv1.weight", "module.model.motion_up_b.2.conv1.bias", "module.model.motion_up_b.2.conv2.weight", "module.model.motion_up_b.2.conv2.bias", "module.model.motion_up_b.3.conv1.weight", "module.model.motion_up_b.3.conv1.bias", "module.model.motion_up_b.3.conv2.weight", "module.model.motion_up_b.3.conv2.bias", "module.model.motion_up_b.4.conv1.weight", "module.model.motion_up_b.4.conv1.bias", "module.model.motion_up_b.4.conv2.weight", "module.model.motion_up_b.4.conv2.bias", "module.model.motion_up_b.5.deconv.weight", "module.model.motion_up_b.5.deconv.bias", "module.model.motion_down.0.conv1.weight", "module.model.motion_down.0.conv1.bias", "module.model.motion_down.0.conv2.weight", "module.model.motion_down.0.conv2.bias", "module.model.motion_down.1.conv1.weight", "module.model.motion_down.1.conv1.bias", "module.model.motion_down.1.conv2.weight", "module.model.motion_down.1.conv2.bias", "module.model.motion_down.2.conv.weight", "module.model.motion_down.2.conv.bias", "module.model.reconstruction_l.conv.weight", "module.model.reconstruction_l.conv.bias", "module.model.reconstruction_h.conv.weight", "module.model.reconstruction_h.conv.bias", "module.model.RBPN.feat0.conv.weight", "module.model.RBPN.feat0.conv.bias", "module.model.RBPN.feat0.act.weight", "module.model.RBPN.feat1.conv.weight", "module.model.RBPN.feat1.conv.bias", "module.model.RBPN.feat1.act.weight", "module.model.RBPN.DBPN.feat1.conv.weight", "module.model.RBPN.DBPN.feat1.conv.bias", "module.model.RBPN.DBPN.feat1.act.weight", "module.model.RBPN.DBPN.up1.up_conv1.deconv.weight", "module.model.RBPN.DBPN.up1.up_conv1.deconv.bias", "module.model.RBPN.DBPN.up1.up_conv1.act.weight", "module.model.RBPN.DBPN.up1.up_conv2.conv.weight", "module.model.RBPN.DBPN.up1.up_conv2.conv.bias", "module.model.RBPN.DBPN.up1.up_conv2.act.weight", "module.model.RBPN.DBPN.up1.up_conv3.deconv.weight", "module.model.RBPN.DBPN.up1.up_conv3.deconv.bias", "module.model.RBPN.DBPN.up1.up_conv3.act.weight", "module.model.RBPN.DBPN.down1.down_conv1.conv.weight", "module.model.RBPN.DBPN.down1.down_conv1.conv.bias", "module.model.RBPN.DBPN.down1.down_conv1.act.weight", "module.model.RBPN.DBPN.down1.down_conv2.deconv.weight", "module.model.RBPN.DBPN.down1.down_conv2.deconv.bias", "module.model.RBPN.DBPN.down1.down_conv2.act.weight", "module.model.RBPN.DBPN.down1.down_conv3.conv.weight", "module.model.RBPN.DBPN.down1.down_conv3.conv.bias", "module.model.RBPN.DBPN.down1.down_conv3.act.weight", "module.model.RBPN.DBPN.up2.up_conv1.deconv.weight", "module.model.RBPN.DBPN.up2.up_conv1.deconv.bias", "module.model.RBPN.DBPN.up2.up_conv1.act.weight", "module.model.RBPN.DBPN.up2.up_conv2.conv.weight", "module.model.RBPN.DBPN.up2.up_conv2.conv.bias", "module.model.RBPN.DBPN.up2.up_conv2.act.weight", "module.model.RBPN.DBPN.up2.up_conv3.deconv.weight", "module.model.RBPN.DBPN.up2.up_conv3.deconv.bias", "module.model.RBPN.DBPN.up2.up_conv3.act.weight", "module.model.RBPN.DBPN.down2.down_conv1.conv.weight", "module.model.RBPN.DBPN.down2.down_conv1.conv.bias", "module.model.RBPN.DBPN.down2.down_conv1.act.weight", "module.model.RBPN.DBPN.down2.down_conv2.deconv.weight", "module.model.RBPN.DBPN.down2.down_conv2.deconv.bias", "module.model.RBPN.DBPN.down2.down_conv2.act.weight", "module.model.RBPN.DBPN.down2.down_conv3.conv.weight", "module.model.RBPN.DBPN.down2.down_conv3.conv.bias", "module.model.RBPN.DBPN.down2.down_conv3.act.weight", "module.model.RBPN.DBPN.up3.up_conv1.deconv.weight", "module.model.RBPN.DBPN.up3.up_conv1.deconv.bias", "module.model.RBPN.DBPN.up3.up_conv1.act.weight", "module.model.RBPN.DBPN.up3.up_conv2.conv.weight", "module.model.RBPN.DBPN.up3.up_conv2.conv.bias", "module.model.RBPN.DBPN.up3.up_conv2.act.weight", "module.model.RBPN.DBPN.up3.up_conv3.deconv.weight", "module.model.RBPN.DBPN.up3.up_conv3.deconv.bias", "module.model.RBPN.DBPN.up3.up_conv3.act.weight", "module.model.RBPN.DBPN.output.conv.weight", "module.model.RBPN.DBPN.output.conv.bias", "module.model.RBPN.res_feat1.0.conv1.weight", "module.model.RBPN.res_feat1.0.conv1.bias", "module.model.RBPN.res_feat1.0.conv2.weight", "module.model.RBPN.res_feat1.0.conv2.bias", "module.model.RBPN.res_feat1.0.act.weight", "module.model.RBPN.res_feat1.1.conv1.weight", "module.model.RBPN.res_feat1.1.conv1.bias", "module.model.RBPN.res_feat1.1.conv2.weight", "module.model.RBPN.res_feat1.1.conv2.bias", "module.model.RBPN.res_feat1.1.act.weight", "module.model.RBPN.res_feat1.2.conv1.weight", "module.model.RBPN.res_feat1.2.conv1.bias", "module.model.RBPN.res_feat1.2.conv2.weight", "module.model.RBPN.res_feat1.2.conv2.bias", "module.model.RBPN.res_feat1.2.act.weight", "module.model.RBPN.res_feat1.3.conv1.weight", "module.model.RBPN.res_feat1.3.conv1.bias", "module.model.RBPN.res_feat1.3.conv2.weight", "module.model.RBPN.res_feat1.3.conv2.bias", "module.model.RBPN.res_feat1.3.act.weight", "module.model.RBPN.res_feat1.4.conv1.weight", "module.model.RBPN.res_feat1.4.conv1.bias", "module.model.RBPN.res_feat1.4.conv2.weight", 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Process finished with exit code 1

[saikatdutta]

Make sure you're using a 'REF' model and sending the model to DataParallel.

[Johnreidsilver]

I also got this problem running in CPU mode

Like saikatdutta mentions we have to add in eval.py before model.load_state_dict
model = torch.nn.DataParallel(model)
because it's inside a CUDA if, so doesn't run in CPU mode

then inside the eval loop we have to edit out the CUDA calls so they run in CPU:
with torch.no_grad():
t_im1 = Variable(input[0])#.cuda(gpus_list[0])
t_im2 = Variable(input[1])#.cuda(gpus_list[0])
t_flow_f = Variable(flow_f).float()#.cuda(gpus_list[0]).float()
t_flow_b = Variable(flow_b).float()#.cuda(gpus_list[0]).float()

[Carlyx]

Make sure you're using a 'REF' model and sending the model to DataParallel.

Hi, your work is amazing!

And I have encountered a similar error.
I download the pre-trained model of RBPN(https://github.com/alterzero/RBPN-PyTorch), but it reports an error: "parameters don't match models" in . Then I follow this (alterzero/RBPN-PyTorch#56) to solve this problem.

But I encounter the same problem again in

STARnet/eval.py

Line 92 in cf7a0bc

model.load_state_dict(torch.load(opt.model, map_location=lambda storage, loc: storage))

.