Problem with channel_pruning and other models

Hello @ProGamerGov

Thanks for this great repo. VGG and NIN models work like a charm, but using Voltax3 from u/vic8760, I encountered problems with channel_pruning and nyud-fcn32s-color-heavy. With channel_pruning, it returns me this log:

NIN Architecture Detected
Traceback (most recent call last):
  File "neural_style.py", line 409, in <module>
    main()
  File "neural_style.py", line 56, in main
    cnn, layerList = loadCaffemodel(params.model_file, params.pooling, params.gpu)  
  File "/home/GitHub/style_transfer/pytorch_style/CaffeLoader.py", line 136, in loadCaffemodel
    cnn.load_state_dict(torch.load(model_file))
  File "/home/anaconda3/envs/style/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 NIN:
	Missing key(s) in state_dict: "features.4.weight", "features.4.bias", "features.9.weight", "features.9.bias", "features.11.weight", "features.11.bias", "features.16.weight", "features.16.bias", "features.18.weight", "features.18.bias", "features.22.weight", "features.22.bias". 
	Unexpected key(s) in state_dict: "classifier.0.weight", "classifier.0.bias", "classifier.3.weight", "classifier.3.bias", "features.5.weight", "features.5.bias", "features.10.weight", "features.10.bias", "features.12.weight", "features.12.bias", "features.17.weight", "features.17.bias", "features.19.weight", "features.19.bias", "features.21.weight", "features.21.bias", "features.28.weight", "features.28.bias". 
	size mismatch for features.0.weight: copying a param with shape torch.Size([24, 3, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 3, 11, 11]).
	size mismatch for features.0.bias: copying a param with shape torch.Size([24]) from checkpoint, the shape in current model is torch.Size([96]).
	size mismatch for features.2.weight: copying a param with shape torch.Size([22, 24, 3, 3]) from checkpoint, the shape in current model is torch.Size([96, 96, 1, 1]).
	size mismatch for features.2.bias: copying a param with shape torch.Size([22]) from checkpoint, the shape in current model is torch.Size([96]).
	size mismatch for features.7.weight: copying a param with shape torch.Size([51, 41, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 96, 5, 5]).
	size mismatch for features.7.bias: copying a param with shape torch.Size([51]) from checkpoint, the shape in current model is torch.Size([256]).
	size mismatch for features.14.weight: copying a param with shape torch.Size([111, 89, 3, 3]) from checkpoint, the shape in current model is torch.Size([384, 256, 3, 3]).
	size mismatch for features.14.bias: copying a param with shape torch.Size([111]) from checkpoint, the shape in current model is torch.Size([384]).
	size mismatch for features.24.weight: copying a param with shape torch.Size([512, 228, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 1, 1]).
	size mismatch for features.24.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).
	size mismatch for features.26.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1000, 1024, 1, 1]).
	size mismatch for features.26.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1000]).

Like model saved hasn’t the expected structure. And with nyud-fcn32s-color-heavy, the message isn’t clear to me:

Traceback (most recent call last):
  File "neural_style.py", line 409, in <module>
    main()
  File "neural_style.py", line 56, in main
    cnn, layerList = loadCaffemodel(params.model_file, params.pooling, params.gpu)  
  File "/home/GitHub/style_transfer/pytorch_style/CaffeLoader.py", line 135, in loadCaffemodel
    cnn, layerList = modelSelector(str(model_file).lower(), pooling)
  File "/home/GitHub/style_transfer/pytorch_style/CaffeLoader.py", line 119, in modelSelector
    raise ValueError("Model architecture not recognized.")
ValueError: Model architecture not recognized.

In the wiki it’s said that these models work. I try different style and content layers names. Maybe I am doing something wrong?

Thanks in advance

[JaledMC]

I’m suscribed to your repo, and it always amazes me your dedication. Keep this fantastic work!

[ProGamerGov]

@JaledMC I finally figured out how to implement -normalize_gradients and I’ve added it to the master branch!

https://github.com/ProGamerGov/neural-style-pt/commit/cbcd023326a3487a2d75270ed1f3b3ddb4b72407