Dense Graph Propagation

The code for the paper Rethinking Knowledge Graph Propagation for Zero-Shot Learning.

Citation

@inproceedings{kampffmeyer2019rethinking,
  title={Rethinking knowledge graph propagation for zero-shot learning},
  author={Kampffmeyer, Michael and Chen, Yinbo and Liang, Xiaodan and Wang, Hao and Zhang, Yujia and Xing, Eric P},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={11487--11496},
  year={2019}
}

Requirements

python 3
pytorch 0.4.0
nltk

Instructions

Materials Preparation

There is a folder materials/, which contains some meta data and programs already.

Glove Word Embedding

Download: http://nlp.stanford.edu/data/glove.6B.zip
Unzip it, find and put glove.6B.300d.txt to materials/.

Graphs

cd materials/
Run python make_induced_graph.py, get imagenet-induced-graph.json
Run python make_dense_graph.py, get imagenet-dense-graph.json
Run python make_dense_grouped_graph.py, get imagenet-dense-grouped-graph.json

Pretrained ResNet50

Download: https://download.pytorch.org/models/resnet50-19c8e357.pth
Rename and put it as materials/resnet50-raw.pth
cd materials/, run python process_resnet.py, get fc-weights.json and resnet50-base.pth

ImageNet and AwA2

Download ImageNet and AwA2, create the softlinks (command ln -s): materials/datasets/imagenet and materials/datasets/awa2, to the root directory of the dataset.

An ImageNet root directory should contain image folders, each folder with the wordnet id of the class.

An AwA2 root directory should contain the folder JPEGImages.

Training

Make a directory save/ for saving models.

In most programs, use --gpu to specify the devices to run the code (default: use gpu 0).

Train Graph Networks

SGCN: Run python train_gcn_basic.py, get results in save/gcn-basic
DGP: Run python train_gcn_dense_att.py, get results in save/gcn-dense-att

In the results folder:

*.pth is the state dict of Graph Networks model
*.pred is the prediction file, which can be loaded by torch.load(). It is a python dict, having two keys: wnids - the wordnet ids of the predicted classes, pred - the predicted fc weights

Finetune ResNet

Run python train_resnet_fit.py with the args:

--pred: the .pred file for finetuning
--train-dir: the directory contains 1K imagenet training classes, each class with a folder named by its wordnet id
--save-path: the folder you want to save the result, e.g. save/resnet-fit-xxx

(In the paper's setting, --train-dir is the folder composed of 1K classes from fall2011.tar, with the missing class "teddy bear" from ILSVRC2012.)

Testing

ImageNet

Run python evaluate_imagenet.py with the args:

--cnn: path to resnet50 weights, e.g. materials/resnet50-base.pth or save/resnet-fit-xxx/x.pth
--pred: the .pred file for testing
--test-set: load test set in materials/imagenet-testsets.json, choices: [2-hops, 3-hops, all]
(optional) --keep-ratio for the ratio of testing data, --consider-trains to include training classes' classifiers, --test-train for testing with train classes images only.

AwA2

Run python evaluate_awa2.py with the args:

--cnn: path to resnet50 weights, e.g. materials/resnet50-base.pth or save/resnet-fit-xxx/x.pth
--pred: the .pred file for testing
(optional) --consider-trains to include training classes' classifiers