Introduction
This project page provides pytorch code that implements the following TNNLS paper:
Title: "DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation"
Arxiv: https://arxiv.org/abs/2204.07945
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:
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
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
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
DR-GAN for bird. Download and save it to models https://pan.baidu.com/s/1EhCc4Hz16b0MgIq1fV4O4Q PASSWD：XIUP
DR-GAN for coco. Download and save it to models https://pan.baidu.com/s/10bOuC30AlOAX9km4gjGbbA PASSWD：XIUP
#Training:
go into code/ folder
bird: python main.py --cfg cfg/bird_DRGAN.yml --gpu 0
coco: python main.py --cfg cfg/coco_DRGAN.yml --gpu 0
#Validation:
python main.py --cfg cfg/eval_bird.yml --gpu 0
python main.py --cfg cfg/eval_coco.yml --gpu 0
License
This code is released under the MIT License (refer to the LICENSE file for details).