mirror of
https://github.com/TencentARC/GFPGAN.git
synced 2026-04-25 23:31:29 +00:00
Has colorization been removed or only disabled? #42
Reference in New Issue
Block a user
Delete Branch "%!s()"
Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?
Originally created by @z3Nsk1Fh5a on GitHub (Aug 8, 2021).
Generative facial prior is genius. Wonderful research
Was colorization removed or just disabled by feature? I was unable to locate color jitter or grey settings.
@xinntao commented on GitHub (Aug 8, 2021):
train_gfpgan_v1.ymlcontains colorization settings.@z3Nsk1Fh5a commented on GitHub (Aug 8, 2021):
I am not experienced so I made what attempt I could. I changed references of the current file with "GFPGANv1.pth"; while I am unknowledgeable with training and am only trying to output images both color and B&W to test the accuracy.
Connecting to github-releases.githubusercontent.com (github-releases.githubusercontent.com)|185.199.108.154|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 615378983 (587M) [application/octet-stream]
Saving to: ‘experiments/pretrained_models/GFPGANv1.pth’
GFPGANv1.pth 100%[===================>] 586.87M 24.3MB/s in 26s
2021-08-08 09:40:07 (22.1 MB/s) - ‘experiments/pretrained_models/GFPGANv1.pth’ saved [615378983/615378983]
Then Inference fails
Traceback (most recent call last):
File "inference_gfpgan_full.py", line 128, in
gfpgan.load_state_dict(torch.load(args.model_path, map_location=lambda storage, loc: storage)['params_ema'])
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1407, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for GFPGANv1Clean:
Missing key(s) in state_dict: "conv_body_first.weight", "conv_body_first.bias", "conv_body_down.0.conv1.weight", "conv_body_down.0.conv1.bias", "conv_body_down.0.conv2.weight", "conv_body_down.0.conv2.bias", "conv_body_down.0.skip.weight", "conv_body_down.1.conv1.weight", "conv_body_down.1.conv1.bias", "conv_body_down.1.conv2.weight", "conv_body_down.1.conv2.bias", "conv_body_down.1.skip.weight", "conv_body_down.2.conv1.weight", "conv_body_down.2.conv1.bias", "conv_body_down.2.conv2.weight", "conv_body_down.2.conv2.bias", "conv_body_down.2.skip.weight", "conv_body_down.3.conv1.weight", "conv_body_down.3.conv1.bias", "conv_body_down.3.conv2.weight", "conv_body_down.3.conv2.bias", "conv_body_down.3.skip.weight", "conv_body_down.4.conv1.weight", "conv_body_down.4.conv1.bias", "conv_body_down.4.conv2.weight", "conv_body_down.4.conv2.bias", "conv_body_down.4.skip.weight", "conv_body_down.5.conv1.weight", "conv_body_down.5.conv1.bias", "conv_body_down.5.conv2.weight", "conv_body_down.5.conv2.bias", "conv_body_down.5.skip.weight", "conv_body_down.6.conv1.weight", "conv_body_down.6.conv1.bias", "conv_body_down.6.conv2.weight", "conv_body_down.6.conv2.bias", "conv_body_down.6.skip.weight", "final_conv.weight", "final_conv.bias", "conv_body_up.0.conv1.weight", "conv_body_up.0.conv1.bias", "conv_body_up.0.conv2.bias", "conv_body_up.1.conv1.weight", "conv_body_up.1.conv1.bias", "conv_body_up.1.conv2.bias", "conv_body_up.2.conv1.weight", "conv_body_up.2.conv1.bias", "conv_body_up.2.conv2.bias", "conv_body_up.3.conv1.weight", "conv_body_up.3.conv1.bias", "conv_body_up.3.conv2.bias", "conv_body_up.4.conv1.weight", "conv_body_up.4.conv1.bias", "conv_body_up.4.conv2.bias", "conv_body_up.5.conv1.weight", "conv_body_up.5.conv1.bias", "conv_body_up.5.conv2.bias", "conv_body_up.6.conv1.weight", "conv_body_up.6.conv1.bias", "conv_body_up.6.conv2.bias", "stylegan_decoder.style_mlp.9.weight", "stylegan_decoder.style_mlp.9.bias", "stylegan_decoder.style_mlp.11.weight", "stylegan_decoder.style_mlp.11.bias", "stylegan_decoder.style_mlp.13.weight", "stylegan_decoder.style_mlp.13.bias", "stylegan_decoder.style_mlp.15.weight", "stylegan_decoder.style_mlp.15.bias", "stylegan_decoder.style_conv1.bias", "stylegan_decoder.style_convs.0.bias", "stylegan_decoder.style_convs.1.bias", "stylegan_decoder.style_convs.2.bias", "stylegan_decoder.style_convs.3.bias", "stylegan_decoder.style_convs.4.bias", "stylegan_decoder.style_convs.5.bias", "stylegan_decoder.style_convs.6.bias", "stylegan_decoder.style_convs.7.bias", "stylegan_decoder.style_convs.8.bias", "stylegan_decoder.style_convs.9.bias", "stylegan_decoder.style_convs.10.bias", "stylegan_decoder.style_convs.11.bias", "stylegan_decoder.style_convs.12.bias", "stylegan_decoder.style_convs.13.bias".
Unexpected key(s) in state_dict: "conv_body_first.0.weight", "conv_body_first.1.bias", "conv_body_down.0.conv1.0.weight", "conv_body_down.0.conv1.1.bias", "conv_body_down.0.conv2.1.weight", "conv_body_down.0.conv2.2.bias", "conv_body_down.0.skip.1.weight", "conv_body_down.1.conv1.0.weight", "conv_body_down.1.conv1.1.bias", "conv_body_down.1.conv2.1.weight", "conv_body_down.1.conv2.2.bias", "conv_body_down.1.skip.1.weight", "conv_body_down.2.conv1.0.weight", "conv_body_down.2.conv1.1.bias", "conv_body_down.2.conv2.1.weight", "conv_body_down.2.conv2.2.bias", "conv_body_down.2.skip.1.weight", "conv_body_down.3.conv1.0.weight", "conv_body_down.3.conv1.1.bias", "conv_body_down.3.conv2.1.weight", "conv_body_down.3.conv2.2.bias", "conv_body_down.3.skip.1.weight", "conv_body_down.4.conv1.0.weight", "conv_body_down.4.conv1.1.bias", "conv_body_down.4.conv2.1.weight", "conv_body_down.4.conv2.2.bias", "conv_body_down.4.skip.1.weight", "conv_body_down.5.conv1.0.weight", "conv_body_down.5.conv1.1.bias", "conv_body_down.5.conv2.1.weight", "conv_body_down.5.conv2.2.bias", "conv_body_down.5.skip.1.weight", "conv_body_down.6.conv1.0.weight", "conv_body_down.6.conv1.1.bias", "conv_body_down.6.conv2.1.weight", "conv_body_down.6.conv2.2.bias", "conv_body_down.6.skip.1.weight", "final_conv.0.weight", "final_conv.1.bias", "conv_body_up.0.conv1.0.weight", "conv_body_up.0.conv1.1.bias", "conv_body_up.0.conv2.activation.bias", "conv_body_up.1.conv1.0.weight", "conv_body_up.1.conv1.1.bias", "conv_body_up.1.conv2.activation.bias", "conv_body_up.2.conv1.0.weight", "conv_body_up.2.conv1.1.bias", "conv_body_up.2.conv2.activation.bias", "conv_body_up.3.conv1.0.weight", "conv_body_up.3.conv1.1.bias", "conv_body_up.3.conv2.activation.bias", "conv_body_up.4.conv1.0.weight", "conv_body_up.4.conv1.1.bias", "conv_body_up.4.conv2.activation.bias", "conv_body_up.5.conv1.0.weight", "conv_body_up.5.conv1.1.bias", "conv_body_up.5.conv2.activation.bias", "conv_body_up.6.conv1.0.weight", "conv_body_up.6.conv1.1.bias", "conv_body_up.6.conv2.activation.bias", "stylegan_decoder.style_mlp.2.weight", "stylegan_decoder.style_mlp.2.bias", "stylegan_decoder.style_mlp.4.weight", "stylegan_decoder.style_mlp.4.bias", "stylegan_decoder.style_mlp.6.weight", "stylegan_decoder.style_mlp.6.bias", "stylegan_decoder.style_mlp.8.weight", "stylegan_decoder.style_mlp.8.bias", "stylegan_decoder.style_conv1.activate.bias", "stylegan_decoder.style_convs.0.activate.bias", "stylegan_decoder.style_convs.1.activate.bias", "stylegan_decoder.style_convs.2.activate.bias", "stylegan_decoder.style_convs.3.activate.bias", "stylegan_decoder.style_convs.4.activate.bias", "stylegan_decoder.style_convs.5.activate.bias", "stylegan_decoder.style_convs.6.activate.bias", "stylegan_decoder.style_convs.7.activate.bias", "stylegan_decoder.style_convs.8.activate.bias", "stylegan_decoder.style_convs.9.activate.bias", "stylegan_decoder.style_convs.10.activate.bias", "stylegan_decoder.style_convs.11.activate.bias", "stylegan_decoder.style_convs.12.activate.bias", "stylegan_decoder.style_convs.13.activate.bias".
size mismatch for conv_body_up.3.conv2.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for conv_body_up.3.skip.weight: copying a param with shape torch.Size([128, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
size mismatch for conv_body_up.4.conv2.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
size mismatch for conv_body_up.4.skip.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
size mismatch for conv_body_up.5.conv2.weight: copying a param with shape torch.Size([32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 128, 3, 3]).
size mismatch for conv_body_up.5.skip.weight: copying a param with shape torch.Size([32, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
size mismatch for conv_body_up.6.conv2.weight: copying a param with shape torch.Size([16, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 64, 3, 3]).
size mismatch for conv_body_up.6.skip.weight: copying a param with shape torch.Size([16, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 64, 1, 1]).
size mismatch for toRGB.3.weight: copying a param with shape torch.Size([3, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 256, 1, 1]).
size mismatch for toRGB.4.weight: copying a param with shape torch.Size([3, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 128, 1, 1]).
size mismatch for toRGB.5.weight: copying a param with shape torch.Size([3, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 64, 1, 1]).
size mismatch for toRGB.6.weight: copying a param with shape torch.Size([3, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 32, 1, 1]).
size mismatch for stylegan_decoder.style_convs.6.modulated_conv.weight: copying a param with shape torch.Size([1, 256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 512, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.weight: copying a param with shape torch.Size([1, 256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 512, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.weight: copying a param with shape torch.Size([1, 128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 256, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.weight: copying a param with shape torch.Size([1, 128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 256, 256, 3, 3]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.weight: copying a param with shape torch.Size([1, 64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 128, 256, 3, 3]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.weight: copying a param with shape torch.Size([1, 64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 128, 128, 3, 3]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.weight: copying a param with shape torch.Size([1, 32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 128, 3, 3]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.weight: copying a param with shape torch.Size([1, 32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 64, 3, 3]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.modulation.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([64, 512]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.modulation.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 512, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 256, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 128, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 64, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.modulation.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([64, 512]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.modulation.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.3.0.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_scale.3.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_scale.3.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_scale.3.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_scale.4.0.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_scale.4.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_scale.4.2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_scale.4.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_scale.5.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_scale.5.0.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.5.2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_scale.5.2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.6.0.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_scale.6.0.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_scale.6.2.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_scale.6.2.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_shift.3.0.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_shift.3.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_shift.3.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_shift.3.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_shift.4.0.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_shift.4.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_shift.4.2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_shift.4.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_shift.5.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_shift.5.0.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_shift.5.2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_shift.5.2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_shift.6.0.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_shift.6.0.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_shift.6.2.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_shift.6.2.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
ls: cannot access 'results/cmp': No such file or directory
@xinntao commented on GitHub (Aug 8, 2021):
If you want to use the original model with colorization, please see: https://github.com/TencentARC/GFPGAN/blob/master/PaperModel.md
@z3Nsk1Fh5a commented on GitHub (Aug 8, 2021):
I chose option 2 on both and added the code to the first cell.
File "", line 12
BASICSR_EXT=True pip install basicsr -vvv
^
SyntaxError: invalid syntax
@z3Nsk1Fh5a commented on GitHub (Aug 8, 2021):
Inference:
File "", line 3
BASICSR_JIT=True python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results --arch original --channel 1
^
SyntaxError: invalid syntax
@woctezuma commented on GitHub (Aug 8, 2021):
Please create other "Github issues" for other issues.
As for the colorization, reply was above: there are 2 models now, one with and one without colorization.
cf. https://github.com/TencentARC/GFPGAN/releases/tag/v0.2.0
@z3Nsk1Fh5a commented on GitHub (Aug 8, 2021):
Apologies, those following issues correlate and result from an inexperienced attempt to follow the coders instructions. Total lack of Python experience or knowledge to copy/paste in the correct areas knowingly.
@xinntao commented on GitHub (Aug 9, 2021):
@Ghee36 If you want to use the original model, please see the instructions: https://github.com/TencentARC/GFPGAN/blob/master/PaperModel.md for installation and inference.
As for the
SyntaxError: invalid syntax, it seems that it has some unsupported (invisible) characters.@z3Nsk1Fh5a commented on GitHub (Aug 10, 2021):
I mirrored the changes from the original model to the current notebook.
Clone GFPGAN and enter the GFPGAN folder
%cd /content
!rm -rf GFPGAN
!git clone https://github.com/TencentARC/GFPGAN.git
%cd GFPGAN
Set up the environment
Install basicsr - https://github.com/xinntao/BasicSR
Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient.
!BASICSR_EXT=True pip install basicsr
Install facexlib - https://github.com/xinntao/facexlib
We use face detection and face restoration helper in the facexlib package
!pip install facexlib
Install other depencencies
!pip install -r requirements.txt
!python setup.py develop
!pip install realesrgan # used for enhancing the background (non-face) regions
Download the pre-trained model
!wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
Works good no problems
Now we use the GFPGAN to restore the above low-quality images
We use Real-ESRGAN for enhancing the background (non-face) regions
!rm -rf results
!python inference_gfpgan.py --upscale 2 --test_path inputs/upload --save_root results --model_path experiments/pretrained_models/GFPGANv1.pth --bg_upsampler realesrgan
!ls results/cmp
This provides errors despite no longer referencing GFPGANv1Clean(is this for color?)
Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth" to /usr/local/lib/python3.7/dist-packages/realesrgan/weights/RealESRGAN_x2plus.pth
100% 64.0M/64.0M [00:01<00:00, 66.7MB/s]
Downloading: "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" to /usr/local/lib/python3.7/dist-packages/facexlib/weights/detection_Resnet50_Final.pth
100% 104M/104M [00:01<00:00, 65.1MB/s]
Traceback (most recent call last):
File "inference_gfpgan.py", line 98, in
main()
File "inference_gfpgan.py", line 57, in main
bg_upsampler=bg_upsampler)
File "/content/GFPGAN/gfpgan/utils.py", line 65, in init
self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1407, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for GFPGANv1Clean:
Missing key(s) in state_dict: "conv_body_first.weight", "conv_body_first.bias", "conv_body_down.0.conv1.weight", "conv_body_down.0.conv1.bias", "conv_body_down.0.conv2.weight", "conv_body_down.0.conv2.bias", "conv_body_down.0.skip.weight", "conv_body_down.1.conv1.weight", "conv_body_down.1.conv1.bias", "conv_body_down.1.conv2.weight", "conv_body_down.1.conv2.bias", "conv_body_down.1.skip.weight", "conv_body_down.2.conv1.weight", "conv_body_down.2.conv1.bias", "conv_body_down.2.conv2.weight", "conv_body_down.2.conv2.bias", "conv_body_down.2.skip.weight", "conv_body_down.3.conv1.weight", "conv_body_down.3.conv1.bias", "conv_body_down.3.conv2.weight", "conv_body_down.3.conv2.bias", "conv_body_down.3.skip.weight", "conv_body_down.4.conv1.weight", "conv_body_down.4.conv1.bias", "conv_body_down.4.conv2.weight", "conv_body_down.4.conv2.bias", "conv_body_down.4.skip.weight", "conv_body_down.5.conv1.weight", "conv_body_down.5.conv1.bias", "conv_body_down.5.conv2.weight", "conv_body_down.5.conv2.bias", "conv_body_down.5.skip.weight", "conv_body_down.6.conv1.weight", "conv_body_down.6.conv1.bias", "conv_body_down.6.conv2.weight", "conv_body_down.6.conv2.bias", "conv_body_down.6.skip.weight", "final_conv.weight", "final_conv.bias", "conv_body_up.0.conv1.weight", "conv_body_up.0.conv1.bias", "conv_body_up.0.conv2.bias", "conv_body_up.1.conv1.weight", "conv_body_up.1.conv1.bias", "conv_body_up.1.conv2.bias", "conv_body_up.2.conv1.weight", "conv_body_up.2.conv1.bias", "conv_body_up.2.conv2.bias", "conv_body_up.3.conv1.weight", "conv_body_up.3.conv1.bias", "conv_body_up.3.conv2.bias", "conv_body_up.4.conv1.weight", "conv_body_up.4.conv1.bias", "conv_body_up.4.conv2.bias", "conv_body_up.5.conv1.weight", "conv_body_up.5.conv1.bias", "conv_body_up.5.conv2.bias", "conv_body_up.6.conv1.weight", "conv_body_up.6.conv1.bias", "conv_body_up.6.conv2.bias", "stylegan_decoder.style_mlp.9.weight", "stylegan_decoder.style_mlp.9.bias", "stylegan_decoder.style_mlp.11.weight", "stylegan_decoder.style_mlp.11.bias", "stylegan_decoder.style_mlp.13.weight", "stylegan_decoder.style_mlp.13.bias", "stylegan_decoder.style_mlp.15.weight", "stylegan_decoder.style_mlp.15.bias", "stylegan_decoder.style_conv1.bias", "stylegan_decoder.style_convs.0.bias", "stylegan_decoder.style_convs.1.bias", "stylegan_decoder.style_convs.2.bias", "stylegan_decoder.style_convs.3.bias", "stylegan_decoder.style_convs.4.bias", "stylegan_decoder.style_convs.5.bias", "stylegan_decoder.style_convs.6.bias", "stylegan_decoder.style_convs.7.bias", "stylegan_decoder.style_convs.8.bias", "stylegan_decoder.style_convs.9.bias", "stylegan_decoder.style_convs.10.bias", "stylegan_decoder.style_convs.11.bias", "stylegan_decoder.style_convs.12.bias", "stylegan_decoder.style_convs.13.bias".
Unexpected key(s) in state_dict: "conv_body_first.0.weight", "conv_body_first.1.bias", "conv_body_down.0.conv1.0.weight", "conv_body_down.0.conv1.1.bias", "conv_body_down.0.conv2.1.weight", "conv_body_down.0.conv2.2.bias", "conv_body_down.0.skip.1.weight", "conv_body_down.1.conv1.0.weight", "conv_body_down.1.conv1.1.bias", "conv_body_down.1.conv2.1.weight", "conv_body_down.1.conv2.2.bias", "conv_body_down.1.skip.1.weight", "conv_body_down.2.conv1.0.weight", "conv_body_down.2.conv1.1.bias", "conv_body_down.2.conv2.1.weight", "conv_body_down.2.conv2.2.bias", "conv_body_down.2.skip.1.weight", "conv_body_down.3.conv1.0.weight", "conv_body_down.3.conv1.1.bias", "conv_body_down.3.conv2.1.weight", "conv_body_down.3.conv2.2.bias", "conv_body_down.3.skip.1.weight", "conv_body_down.4.conv1.0.weight", "conv_body_down.4.conv1.1.bias", "conv_body_down.4.conv2.1.weight", "conv_body_down.4.conv2.2.bias", "conv_body_down.4.skip.1.weight", "conv_body_down.5.conv1.0.weight", "conv_body_down.5.conv1.1.bias", "conv_body_down.5.conv2.1.weight", "conv_body_down.5.conv2.2.bias", "conv_body_down.5.skip.1.weight", "conv_body_down.6.conv1.0.weight", "conv_body_down.6.conv1.1.bias", "conv_body_down.6.conv2.1.weight", "conv_body_down.6.conv2.2.bias", "conv_body_down.6.skip.1.weight", "final_conv.0.weight", "final_conv.1.bias", "conv_body_up.0.conv1.0.weight", "conv_body_up.0.conv1.1.bias", "conv_body_up.0.conv2.activation.bias", "conv_body_up.1.conv1.0.weight", "conv_body_up.1.conv1.1.bias", "conv_body_up.1.conv2.activation.bias", "conv_body_up.2.conv1.0.weight", "conv_body_up.2.conv1.1.bias", "conv_body_up.2.conv2.activation.bias", "conv_body_up.3.conv1.0.weight", "conv_body_up.3.conv1.1.bias", "conv_body_up.3.conv2.activation.bias", "conv_body_up.4.conv1.0.weight", "conv_body_up.4.conv1.1.bias", "conv_body_up.4.conv2.activation.bias", "conv_body_up.5.conv1.0.weight", "conv_body_up.5.conv1.1.bias", "conv_body_up.5.conv2.activation.bias", "conv_body_up.6.conv1.0.weight", "conv_body_up.6.conv1.1.bias", "conv_body_up.6.conv2.activation.bias", "stylegan_decoder.style_mlp.2.weight", "stylegan_decoder.style_mlp.2.bias", "stylegan_decoder.style_mlp.4.weight", "stylegan_decoder.style_mlp.4.bias", "stylegan_decoder.style_mlp.6.weight", "stylegan_decoder.style_mlp.6.bias", "stylegan_decoder.style_mlp.8.weight", "stylegan_decoder.style_mlp.8.bias", "stylegan_decoder.style_conv1.activate.bias", "stylegan_decoder.style_convs.0.activate.bias", "stylegan_decoder.style_convs.1.activate.bias", "stylegan_decoder.style_convs.2.activate.bias", "stylegan_decoder.style_convs.3.activate.bias", "stylegan_decoder.style_convs.4.activate.bias", "stylegan_decoder.style_convs.5.activate.bias", "stylegan_decoder.style_convs.6.activate.bias", "stylegan_decoder.style_convs.7.activate.bias", "stylegan_decoder.style_convs.8.activate.bias", "stylegan_decoder.style_convs.9.activate.bias", "stylegan_decoder.style_convs.10.activate.bias", "stylegan_decoder.style_convs.11.activate.bias", "stylegan_decoder.style_convs.12.activate.bias", "stylegan_decoder.style_convs.13.activate.bias".
size mismatch for conv_body_up.3.conv2.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for conv_body_up.3.skip.weight: copying a param with shape torch.Size([128, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
size mismatch for conv_body_up.4.conv2.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
size mismatch for conv_body_up.4.skip.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
size mismatch for conv_body_up.5.conv2.weight: copying a param with shape torch.Size([32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 128, 3, 3]).
size mismatch for conv_body_up.5.skip.weight: copying a param with shape torch.Size([32, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
size mismatch for conv_body_up.6.conv2.weight: copying a param with shape torch.Size([16, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 64, 3, 3]).
size mismatch for conv_body_up.6.skip.weight: copying a param with shape torch.Size([16, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 64, 1, 1]).
size mismatch for toRGB.3.weight: copying a param with shape torch.Size([3, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 256, 1, 1]).
size mismatch for toRGB.4.weight: copying a param with shape torch.Size([3, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 128, 1, 1]).
size mismatch for toRGB.5.weight: copying a param with shape torch.Size([3, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 64, 1, 1]).
size mismatch for toRGB.6.weight: copying a param with shape torch.Size([3, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 32, 1, 1]).
size mismatch for stylegan_decoder.style_convs.6.modulated_conv.weight: copying a param with shape torch.Size([1, 256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 512, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.weight: copying a param with shape torch.Size([1, 256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 512, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.weight: copying a param with shape torch.Size([1, 128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 256, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.weight: copying a param with shape torch.Size([1, 128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 256, 256, 3, 3]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.weight: copying a param with shape torch.Size([1, 64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 128, 256, 3, 3]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.weight: copying a param with shape torch.Size([1, 64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 128, 128, 3, 3]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.weight: copying a param with shape torch.Size([1, 32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 128, 3, 3]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.weight: copying a param with shape torch.Size([1, 32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 64, 3, 3]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.modulation.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([64, 512]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.modulation.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 512, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 256, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 128, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 64, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.modulation.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([64, 512]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.modulation.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.3.0.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_scale.3.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_scale.3.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_scale.3.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_scale.4.0.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_scale.4.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_scale.4.2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_scale.4.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_scale.5.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_scale.5.0.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.5.2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_scale.5.2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.6.0.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_scale.6.0.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_scale.6.2.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_scale.6.2.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_shift.3.0.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_shift.3.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_shift.3.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_shift.3.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_shift.4.0.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_shift.4.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_shift.4.2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_shift.4.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_shift.5.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_shift.5.0.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_shift.5.2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_shift.5.2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_shift.6.0.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_shift.6.0.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_shift.6.2.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_shift.6.2.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
ls: cannot access 'results/cmp': No such file or directory
@xinntao commented on GitHub (Aug 10, 2021):
Use the latest colab:
https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing
For GFPGANv1 model, you should the following cmd:
!python inference_gfpgan.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --save_root results --arch original --channel 1