Skip to content

vicissitude1999/text2image

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

60 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

text2image

PyTorch implementation of paper paper Generating Images from Captions with Attention by Elman Mansimov, Emilio Parisotto, Jimmy Ba and Ruslan Salakhutdinov; ICLR 2016.

Getting Started

MNIST with Captions

To train MNIST, run

bash tools/train.sh # AlignDRAW
bash tools/train_clip.sh #clip_AlignDRAW

To test MNIST, run

python src/test.py --train_dir --caption_path --dataset --batch_size --model_type

Examples are in tools/test.sh

COCO with Captions

Download the following to text2image/data/

wget http://www.cs.toronto.edu/~emansim/datasets/text2image/train-images-32x32.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/train-images-56x56.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/train-captions.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/train-captions-len.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/train-cap2im.pkl
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/dev-images-32x32.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/dev-images-56x56.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/dev-captions.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/dev-captions-len.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/dev-cap2im.pkl
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/test-images-32x32.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/test-captions.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/test-captions-len.npy
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/test-cap2im.pkl
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/gan.hdf5
wget http://www.cs.toronto.edu/~emansim/datasets/text2image/dictionary.pkl

To train COCO, run

bash tools/train.sh # AlignDRAW
bash tools/train_clip.sh #clip_AlignDRAW

To validate COCO, run

python src/test.py --train_dir --dataset --batch_size --model_type --name

To test COCO on any captions, provide a file of captions like tools/captions_mnist.txt and run

python src/test.py --mode test --train_dir --caption_path --dataset --batch_size --model_type --name

Make sure that all captions in the file have the same length. Some examples are in tools/test.sh

Reference

https://github.com/mansimov/text2image

https://github.com/Natsu6767/Generating-Devanagari-Using-DRAW/blob/master/draw_model.py

About

PyTorch implementation of text2image

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published