RuntimeError: Error(s) in loading state_dict for ZouFCNFusionLight:

[yuyusmile]

python=3.6.2
torch=1.2.0
nvidia 2080ti 11G
cuda=10.0
cudnn=7..6.4.38
python demo_prog.py --img_path ./test_images/apple.jpg --canvas_color 'white' --max_m_strokes 500 --max_divide 5 --renderer oilpaintbrush --renderer_checkpoint_dir checkpoints_G_oilpaintbrush
initialize network with normal
loading renderer from pre-trained checkpoint...
Traceback (most recent call last):
File "demo_prog.py", line 113, in
optimize_x(pt)
File "demo_prog.py", line 49, in optimize_x
pt._load_checkpoint()
File "/home/banana/GAN/stylized-neural-painting-main12/painter.py", line 71, in _load_checkpoint
self.net_G.load_state_dict(checkpoint['model_G_state_dict'])
File "/home/banana/.local/lib/python3.6/site-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 ZouFCNFusionLight:
Unexpected key(s) in state_dict: "huangnet.fc4.weight", "huangnet.fc4.bias", "huangnet.conv3.weight", "huangnet.conv3.bias", "huangnet.conv4.weight", "huangnet.conv4.bias", "huangnet.conv5.weight", "huangnet.conv5.bias", "huangnet.conv6.weight", "huangnet.conv6.bias", "dcgan.main.10.weight", "dcgan.main.10.bias", "dcgan.main.10.running_mean", "dcgan.main.10.running_var", "dcgan.main.10.num_batches_tracked", "dcgan.main.12.weight", "dcgan.main.13.weight", "dcgan.main.13.bias", "dcgan.main.13.running_mean", "dcgan.main.13.running_var", "dcgan.main.13.num_batches_tracked", "dcgan.main.15.weight".
size mismatch for huangnet.conv1.weight: copying a param with shape torch.Size([32, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 8, 3, 3]).
size mismatch for huangnet.conv1.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for huangnet.conv2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([12, 64, 3, 3]).
size mismatch for huangnet.conv2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([12]).
size mismatch for dcgan.main.3.weight: copying a param with shape torch.Size([512, 512, 4, 4]) from checkpoint, the shape in current model is torch.Size([512, 256, 4, 4]).
size mismatch for dcgan.main.4.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for dcgan.main.4.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for dcgan.main.4.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for dcgan.main.4.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for dcgan.main.6.weight: copying a param with shape torch.Size([512, 256, 4, 4]) from checkpoint, the shape in current model is torch.Size([256, 128, 4, 4]).
size mismatch for dcgan.main.7.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for dcgan.main.7.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for dcgan.main.7.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for dcgan.main.7.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for dcgan.main.9.weight: copying a param with shape torch.Size([256, 128, 4, 4]) from checkpoint, the shape in current model is torch.Size([128, 6, 4, 4]).

[new-cainiao]

I also encountered the same question. Have you solved it?

[jiupinjia]

@new-cainiao @yuyusmile Can you follow the README instruction and add --net_G zou-fusion-net? Thanks for your feedback. Please tell me whether it solves your problem.