First Commit: Converted files for usage with pre-train DAMSM model. Notes:

1. Manually edit the config.py file to correspond with the YAML you wish to use.
2. If you have a 0-index tensor, remove the [0] corresponding to train/eval.

Original Source: https://github.com/taoxugit/AttnGAN

Pytorch implementation for reproducing AttnGAN results in the paper AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks by Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, Xiaodong He. (This work was performed when Tao was an intern with Microsoft Research).

python 3.6+

Pytorch 1.0+

In addition, please add the project folder to PYTHONPATH and pip install the following packages:

• python-dateutil
• easydict
• pandas
• torchfile
• nltk
• scikit-image

Data

1. Download our preprocessed metadata for birds coco and save them to data/
2. Download the birds image data. Extract them to data/birds/
3. Download coco dataset and extract the images to data/coco/

Training

• Pre-train DAMSM models:

  • For bird dataset: python pretrain_DAMSM.py --cfg cfg/DAMSM/bird.yml --gpu 0
  • For coco dataset: python pretrain_DAMSM.py --cfg cfg/DAMSM/coco.yml --gpu 1

• Train AttnGAN models:

  • For bird dataset: python main.py --cfg cfg/bird_attn2.yml --gpu 2
  • For coco dataset: python main.py --cfg cfg/coco_attn2.yml --gpu 3

• *.yml files are example configuration files for training/evaluation our models.

Pretrained Model

• DAMSM for bird. Download and save it to DAMSMencoders/

• DAMSM for coco. Download and save it to DAMSMencoders/

• AttnGAN for bird. Download and save it to models/

• AttnGAN for coco. Download and save it to models/

• AttnDCGAN for bird. Download and save it to models/

  • This is an variant of AttnGAN which applies the propsoed attention mechanisms to DCGAN framework.

Sampling

• Run python main.py --cfg cfg/eval_bird.yml --gpu 1 to generate examples from captions in files listed in "./data/birds/example_filenames.txt". Results are saved to DAMSMencoders/.
• Change the eval_*.yml files to generate images from other pre-trained models.
• Input your own sentence in "./data/birds/example_captions.txt" if you wannt to generate images from customized sentences.

Validation

• To generate images for all captions in the validation dataset, change B_VALIDATION to True in the eval_*.yml. and then run python main.py --cfg cfg/eval_bird.yml --gpu 1
• We compute inception score for models trained on birds using StackGAN-inception-model.
• We compute inception score for models trained on coco using improved-gan/inception_score.

Examples generated by AttnGAN [Blog]

bird example	coco example

Evaluation code embedded into a callable containerized API is included in the eval\ folder.

If you find AttnGAN useful in your research, please consider citing:

@article{Tao18attngan,
  author    = {Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, Xiaodong He},
  title     = {AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks},
  Year = {2018},
  booktitle = {{CVPR}}
}

Reference

• StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks [code]
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [code]