FangxiangFeng / DM-GAN-MDD

Modality Disentangled Discriminator for Text-to-Image Synthesis

Introduction

This project page provides pytorch code that implements the paper: "Modality Disentangled Discriminator for Text-to-Image Synthesis".

How to use

Python

• Python2.7
• Pytorch1.0+
• 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

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-MDD for bird. Download and save it to models
• DM-GAN-MDD for coco. Download and save it to models
• IS for bird
  • python google_drive.py 0B3y_msrWZaXLMzNMNWhWdW0zVWs eval/IS/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_DMGANMDD.yml --gpu 0
• coco: python main.py --cfg cfg/coco_DMGANMDD.yml --gpu 0

Validation

1. Images generation:
   • go into code/ folder
   • python main.py --cfg cfg/eval_bird_DMGANMDD.yml --gpu 0
   • python main.py --cfg cfg/eval_coco_DMGANMDD.yml --gpu 0
2. Inception score (IS for bird, IS for coco):
   • cd DM-GAN-MDD/eval/IS/bird && CUDA_VISIBLE_DEVICES=0 python inception_score_bird.py --image_folder ../../../models/netG_DMGANMDD_bird
   • cd DM-GAN-MDD/eval/IS/coco && CUDA_VISIBLE_DEVICES=0 python inception_score_coco.py ../../../models/netG_DMGANMDD_coco
3. FID:
   • cd DM-GAN-MDD/eval/FID && python fid_score.py --gpu 0 --path1 bird_val.npz --path2 ../../models/netG_DMGANMDD_bird
   • cd DM-GAN-MDD/eval/FID && python fid_score.py --gpu 0 --path1 coco_val.npz --path2 ../../models/netG_DMGANMDD_coco

Performance

As DM-GAN, we use the Pytorch implementation to measure FID score.

Model	R-precision↑	IS↑	Pytorch FID↓
bird_AttnGAN (paper)	67.82% ± 4.43%	4.36 ± 0.03	23.98
bird_DMGAN (paper)	72.31% ± 0.91%	4.75 ± 0.07	16.09
bird_DMGAN_MDD	79.73% ± 0.68%	4.86 ± 0.06	15.76
coco_AttnGAN (paper)	85.47% ± 3.69%	25.89 ± 0.47	35.49
coco_DMGAN (paper)	88.56% ± 0.28%	30.49 ± 0.57	32.64
coco_DMGAN_MDD	94.37% ± 0.36%	34.46 ± 0.72	24.30

License

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