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 ./WikiartTesting
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.jpgIf 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
Content preservation
Style Quality
Other Results
