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DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis

Introduction

This project page provides pytorch code that implements the following CVPR2019 paper:

Title: "DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis"

Arxiv: https://arxiv.org/abs/1904.01310

How to use

Python

  • Python2.7
  • Pytorch0.4 (conda install pytorch=0.4.1 cuda90 torchvision=0.2.1 -c pytorch)
  • tensorflow (pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.12.0-cp27-none-linux_x86_64.whl)
  • pip install easydict pathlib
  • conda install requests nltk pandas scikit-image pyyaml cudatoolkit=9.0

Data

  1. Download metadata for birds coco and save them to data/

    • python google_drive.py 1O_LtUP9sch09QH3s_EBAgLEctBQ5JBSJ ./data/bird.zip
    • python google_drive.py 1rSnbIGNDGZeHlsUlLdahj0RJ9oo6lgH9 ./data/coco.zip

  2. Download the birds image data. Extract them to data/birds/

    • cd data/birds
    • wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz
    • tar -xvzf CUB_200_2011.tgz

  3. Download coco dataset and extract the images to data/coco/

    • cd data/coco
    • wget http://images.cocodataset.org/zips/train2014.zip
    • wget http://images.cocodataset.org/zips/val2014.zip
    • unzip train2014.zip
    • unzip val2014.zip
    • mv train2014 images
    • cp val2014/* images

Pretrained Models

  • DAMSM for bird. Download and save it to DAMSMencoders/
    • python google_drive.py 1GNUKjVeyWYBJ8hEU-yrfYQpDOkxEyP3V DAMSMencoders/bird.zip
  • DAMSM for coco. Download and save it to DAMSMencoders/
    • python google_drive.py 1zIrXCE9F6yfbEJIbNP5-YrEe2pZcPSGJ DAMSMencoders/coco.zip
  • DM-GAN for bird. Download and save it to models
    • python google_drive.py 1BmDKqIyNY_7XWhXpxa2gm6TYxB2DQHS3 models/bird_DMGAN.pth
  • DM-GAN for coco. Download and save it to models
    • python google_drive.py 1tQ9CJNiLlRLBKSUKHXKYms2tbfzllyO- models/coco_DMGAN.pth
  • IS for bird
    • python google_drive.py 0B3y_msrWZaXLMzNMNWhWdW0zVWs eval/IS/bird/inception_finetuned_models.zip
  • FID for bird
    • python google_drive.py 1747il5vnY2zNkmQ1x_8hySx537ZAJEtj eval/FID/bird_val.npz
  • FID for coco
    • python google_drive.py 10NYi4XU3_bLjPEAg5KQal-l8A_d8lnL5 eval/FID/coco_val.npz

Training

  • go into code/ folder
  • bird: python main.py --cfg cfg/bird_DMGAN.yml --gpu 0
  • coco: python main.py --cfg cfg/coco_DMGAN.yml --gpu 0

Validation

  1. Images generation:
    • go into code/ folder
    • python main.py --cfg cfg/eval_bird.yml --gpu 0
    • python main.py --cfg cfg/eval_coco.yml --gpu 0
  2. Inception score (IS for bird, IS for coco):
    • cd DM-GAN/eval/IS/bird && python inception_score_bird.py --image_folder ../../../models/bird_DMGAN
    • cd DM-GAN/eval/IS/coco && python inception_score_coco.py ../../../models/coco_DMGAN
  3. FID:
    • cd DM-GAN/eval/FID && python fid_score.py --gpu 0 --batch-size 50 --path1 bird_val.npz --path2 ../../models/bird_DMGAN
    • cd DM-GAN/eval/FID && python fid_score.py --gpu 0 --batch-size 50 --path1 coco_val.npz --path2 ../../models/coco_DMGAN

Performance

Note that after cleaning and refactoring the code of the paper, the results are slightly different. We use the Pytorch implementation to measure FID score. However, the official implementation (Tensorflow FID) gives different scores.

Model R-precision↑ IS↑ Pytorch FID↓ TF FID↓
bird_AttnGAN (paper) 67.82% ± 4.43% 4.36 ± 0.03 23.98 14.01
bird_DMGAN (paper) 72.31% ± 0.91% 4.75 ± 0.07 16.09 (-)
bird_DMGAN (pretrained model) 76.58% ± 0.53% 4.71 ± 0.06 15.34 11.91
coco_AttnGAN (paper) 85.47% ± 3.69% 25.89 ± 0.47 35.49 29.53
coco_DMGAN (paper) 88.56% ± 0.28% 30.49 ± 0.57 32.64 (-)
coco_DMGAN (pretrained model) 92.23% ± 0.37% 32.43 ± 0.58 26.55 24.24

License

This code is released under the MIT License (refer to the LICENSE file for details).