T2I_CL

This is the official Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning

Requirements

• Linux

• Python ≥ 3.6

• PyTorch ≥ 1.4.0

Prepare Data

Download the preprocessed datasets from AttnGAN

Alternatively, another site is from DM-GAN

Training

• Pretrain DAMSM+CL:

  • 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 0

• Train AttnGAN+CL:

  • For bird dataset: python main.py --cfg cfg/bird_attn2.yml --gpu 0
  • For coco dataset: python main.py --cfg cfg/coco_attn2.yml --gpu 0

• Train DM-GAN+CL:

  • For bird dataset: python main.py --cfg cfg/bird_DMGAN.yml --gpu 0
  • For coco dataset: python main.py --cfg cfg/coco_DMGAN.yml --gpu 0

Pretrained Models

• DAMSM+CL for bird. Download and save it to DAMSMencoders/
• DAMSM+CL for coco. Download and save it to DAMSMencoders/
• AttnGAN+CL for bird. Download and save it to models/
• AttnGAN+CL for coco. Download and save it to models/
• DM-GAN+CL for bird. Download and save it to models/
• DM-GAN+CL for coco. Download and save it to models/

Evaluation

• Sampling and get the R-precision:

  • python main.py --cfg cfg/eval_bird.yml --gpu 0
  • python main.py --cfg cfg/eval_coco.yml --gpu 0

• Inception score:

  • python inception_score_bird.py --image_folder fake_images_bird
  • python inception_score_coco.py fake_images_coco

• FID:

  • python fid_score.py --gpu 0 --batch-size 50 --path1 real_images_bird --path2 fake_images_bird
  • python fid_score.py --gpu 0 --batch-size 50 --path1 real_images_coco --path2 fake_images_coco

Citation

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

@article{ye2021improving,
  title={Improving Text-to-Image Synthesis Using Contrastive Learning},
  author={Ye, Hui and Yang, Xiulong and Takac, Martin and Sunderraman, Rajshekhar and Ji, Shihao},
  journal={The 32nd British Machine Vision Conference (BMVC)},
  year={2021}
}

Acknowledge

Our work is based on the following works:

• AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks [code]
• DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis [code]