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CPGAN

Pytorch implementation for reproducing CPGAN results in the paper CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-image Synthesis by Jiadong Liang, Wenjie Pei, Feng Lu.

Getting Started

  • Create anaconda virtual environment
conda create -n CPGAN python=2.7
conda activate CPGAN
conda install pytorch=1.0.1 torchvision==0.2.1 cudatoolkit=10.0.130
  • PIP Install
pip install python-dateutil easydict pandas torchfile nltk scikit-image h5py pyyaml
  • Clone this repo:
https://github.com/dongdongdong666/CPGAN.git
  • Download train2014-text.zip from here and unzip it to data/coco/text/.

  • Unzip val2014-text.zip in data/coco/text/.

  • Download memory features for each word from here and put the features in memory/.

  • Download Inception Enocder , Generator , Text Encoder and put these models in models/.

Test

Sampling

  • Set B_VALIDATION:False in "/code/cfg/eval_coco.yml".
  • Run python eval.py --cfg cfg/eval_coco.yml --gpu 0 to generate examples from captions listed in "data/coco/example_captions.txt". Results are saved to "outputs/Inference_Images/example_captions".

Validation

  • Set B_VALIDATION:True in "/code/cfg/eval_coco.yml".
  • Run python eval.py --cfg cfg/eval_coco.yml --gpu 0 to generate examples for all captions in the validation dataset. Results are saved to "outputs/Inference_Images/single".
  • Compute inception score for the model trained on coco.
python caculate_IS.py --dir ../outputs/Inference_Images/single/
  • Compute R precison for the model trained on coco.
python caculate_R_precison.py --cfg cfg/coco.yml --gpu 0 --valid_dir ../outputs/Inference_Images/single/

Train

TBA

Examples generated by CPGAN

  • Qualitative comparison between different modules of our model for ablation study, the results of AttnGAN are also provided for reference.

  • Qualitative comparison between our CPGAN with other classical models for text-to-image synthesis.

Performance

Note that after cleaning and refactoring the code of the paper, the results are slightly different. We use the Pytorch implementation to measure inception score and R precision.

Model R-precision↑ IS↑
Reed (-) 7.88 ± 0.07
StackGAN (-) 8.45 ± 0.03
Infer (-) 11.46 ± 0.09
SD-GAN (-) 35.69 ± 0.50
MirrorGAN (-) 26.47 ± 0.41
SEGAN (-) 27.86 ± 0.31
DMGAN 88.56% 30.49 ± 0.57
AttnGAN 82.98% 25.89 ± 0.47
objGAN 91.05% 30.29 ± 0.33
CPGAN 93.59% 52.73 ± 0.61

Citing CPGAN

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

@misc{liang2019cpgan,
    title={CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis},
    author={Jiadong Liang and Wenjie Pei and Feng Lu},
    year={2019},
    eprint={1912.08562},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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The method of text-to-image

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