Weird results except for examples

[csh589]

Thank you for your great job!
Following the guidance, I correctly installed pytorch and other dependence. Then I ran the notebook and got a fair result for examples as showed originally.

However, when I tried to use other images as input, for example,

I got a total failure like:

I also doubted that wrong cropping causes the weird results, so I did some additional experiments. First, referring to the issue #4 , I corrected the cropping size and tried again, then got similar results Second, I apply the same cropping method to the example images, then the results of cropped examples looked good.
Shortly, I completely get confused by the result, do you have any suggestions for it?

@csh589 were you able to resolve this and what method did you use?

[oawiles]

Sorry I didn't respond: @csh589 did you ensure you were getting the same results when you just run the notebook initially as the stuff that is already there? If you didn't you have to check your pytorch version, else I'm not sure what the cause would be.

[melih-unsal]

Hi when I give the same input as you gave, it gives very bad result. I also give my input then the output is more terrible. Do you know what is the problem?

[csh589]

@Melih1996 seems the same result like #15 , maybe you need to check your pytorch version.

[melih-unsal]

@csh589 Thank you it works for the input that is given however is it normal such a wierd output below?

I also get very slow run when I downgraded pytorch version. Normally I could run the notebook in 4-5 seconds but after I downgraded, I could run in around 10-15 minutes

[oawiles]

I am not sure why it is slower -- I think it wasn't that slow for me. Maybe you didn't compile with cuda? For the results. You need to make sure the faces are cropped similarly to the examples given. The model won't work properly if the crops are too different.

[vade]

Ive not seen any documentation of the code fixes for Python 3 - so ive been able to get what appears to be better results on Ubuntu CUDA with a NV 1070 TI.

Ubuntu 18.04 LTS,
IDIA-SMI 430.14 Driver Version: 430.14 CUDA Version: 10.2
Python 3.7.3
Pytorch 0.4.1

My install was to install via Conda:

conda install pytorch=0.4.1 torchvision cuda92 -c pytorch

I then checked out a branch which has some preliminary python 3 fixes (there is an unmerged PR for these fixes)

See: #16

The last remaining step is to update the alignment for the upscale call, which requires one to update a few calls to nn.Upsample()

Update NoSkipNet_X2Face.py and NoSkipNet_X2Face_pose.py

And change calls to nn.Upsample to read:

upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

I now have output like:

I am curious if this appears like an appropriate fix from the authors. Thank you for sharing this code!

@oawiles what are you using to crop the faces?

[akoepke]

Ive not seen any documentation of the code fixes for Python 3 - so ive been able to get what appears to be better results on Ubuntu CUDA with a NV 1070 TI.

Ubuntu 18.04 LTS,
IDIA-SMI 430.14 Driver Version: 430.14 CUDA Version: 10.2
Python 3.7.3
Pytorch 0.4.1

My install was to install via Conda:

conda install pytorch=0.4.1 torchvision cuda92 -c pytorch

I then checked out a branch which has some preliminary python 3 fixes (there is an unmerged PR for these fixes)

See: #16

The last remaining step is to update the alignment for the upscale call, which requires one to update a few calls to nn.Upsample()

Update NoSkipNet_X2Face.py and NoSkipNet_X2Face_pose.py

And change calls to nn.Upsample to read:

upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

I now have output like:

I am curious if this appears like an appropriate fix from the authors. Thank you for sharing this code!

You can now find code in the branch 'py37_pytorch_0.4.1' to use the demo notebooks with python 3.7 and pytorch 0.4.1. Those allow to reproduce the results without the artefacts that you seem to be getting in the example that you posted.

@akoepke what is being used to crop the faces?
I just tried the new branch torch==0.4.1 and got this result, also noticed that the mouth interior is not generated

[akoepke]

See here. We wouldn't expect the model to be able to generate the mouth interior if the source image does not contain it.

@akoepke ok, I cropped the face to 256x256 using https://github.com/leblancfg/autocrop
autocrop -i ./sourceframe/ -w 256 -H 256
and got this result