​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

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Deep Learning PAMA
Overview

PAMA

​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

Requirements

  • python 3.6
  • pytorch 1.2.0+
  • PIL, numpy, matplotlib

Checkpoints

Please download the pre-trained checkpoints at google drive and put them in ./checkpoints.

Here we also provide some other pre-trained results with different loss weights:

Type Loss Download
high consistency w/o color loss PAMA_consistency.pth
high style 2x style loss PAMA_style.pth
high content 2x content loss PAMA_content.pth

The checkpionts will be uploaded recently.

Training

The training set consists of two parts, the content images from COCO2014 and style images from Wikiart.

python main.py train --lr 1e-4 --content_folder ./COCO2014 --style_folder ./Wikiart

Testing

To test the code, you need to specify the path of the content image and the style image.

python main.py eval --content ./content/1.jpg --style ./style/1.jpg

If you want to do a batch operation for all pictures under the folder at one time, please execute the following code.

python main.py eval --run_folder True --content ./content/ --style ./style/

Results Presentation

​ The results prove the quality of PAMA from three dimensions: Regional Consistency, Content Proservation, Style Quality.

Regional Consistency

39-13-content

8-35-content

Content preservation

18-4-content

27-8-consistency

Style Quality

4-29-style

13-32-style

Other Results

other

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Comments
  • run eval but got gray image

    run eval but got gray image

    python main.py eval the content and sample images are the example on website https://huggingface.co/spaces/akhaliq/PAMA and the weights is download from google drive named <original_PAMA.zip> The program does not report an error but I got a gray image

    image

    And I found that the website provided could not return results, and each time it ended with error image

    opened by WuChuYi 1
  • Question about Content/Style Loss

    Question about Content/Style Loss

    Hi @luoxuan-cs, I am interested in your research. So I have some questions:

    1. Why content loss is different from style loss. Why don't use self-similarity loss for style loss?
    2. In Eq. 8, can I use mean and std instead of mean and covariance?

    Hope to hear from you soon.

    opened by sonnguyen129 0
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