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DEPARA

Getting Started

These instructions below will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Install the following:

- Python >= 3.6
- pytorch >= 1.0.1
- torchvision >= 0.2.2

Then, install python packages:

pip install -r requirements.txt

Datasets

Here we utilize syn2real closed-set classification task (train/validation/test), for more infomation about syn2real dataset please refer to Syn2Real: A New Benchmark for Synthetic-to-Real Visual Domain Adaptation.

You can download the datasets in your project $DIR with:

cd $DIR/data
wget http://csr.bu.edu/ftp/visda17/clf/train.tar
tar xvf train.tar

wget http://csr.bu.edu/ftp/visda17/clf/validation.tar
tar xvf validation.tar

We view part train as source domain(synthetic) and part validation as target domain(real), and we split each domain into train/validation/test in a ratio of 7:2:1.

python split_dataset.py --part synthetic --data-dir $DIR/data --split-path syn2real-data
python split_dataser.py --part mscoco --data-dir $DIR/data --split-path syn2real-data

After splitting, the datasets should be arranged in the following format:

|- syn2real-data
|   |---synthetic
|   |   |---train
|   |   |   |---0001
|   |   |   |   |---src_1_02691156_1b0b1d2cb9f9d5c0575bd26acccafabd__10_10_150.png
|   |   |   |---...
|   |   |   |---0012
|   |   |---val
|   |   |   |---0001
|   |   |   |---...
|   |   |   |---0012
|   |   |---test
|   |   |   |---src_1_02691156_1b0b1d2cb9f9d5c0575bd26acccafabd__112_123_150.png
|   |   |   |---...
|   |---mscoco
|   |   |---train
|   |   |   |---0001
|   |   |   |   |---aeroplane_1363018.jpg
|   |   |   |---...
|   |   |   |---0012
|   |   |---val
|   |   |   |---0001
|   |   |   |---...
|   |   |   |---0012
|   |   |---test
|   |   |   |---aeroplane_1363101.jpg
|   |   |   |---...

Task Transferability

We test Task Transferability on Taskonomy Models. For pre-trained models, testing images, please follow the instruction of TransferabilityfromAttributionMaps. Make sure to utilize those codes to generate each task's features and attribution maps. We also provide ready-made features and attributions affinity in GoogleDrive. Move attributions affinity to $result_save.

Generate edges of the graph:

cd tools
python spearman_edge.py --feature-dir $FEATURE --save-dir result_save --dataset taskonomy 
python spearman_edge.py --feature-dir $FEATURE --save-dir result_save --dataset coco
python spearman_edge.py --feature-dir $FEATURE --save-dir result_save --dataset indoor

Visualization

Plot Precision&Recall curve:

cd $DIR/prcurve
python plot.py --dataset taskonomy --save-dir result_save

e.g.

Plot Task Similarity Tree of DEPARA:

matlab -nosplash -nodesktop task_similarity_tree.m

e.g.

Layer Transferability

Dataset Shrinkage

We randomly select 1/10 of the whole dataset's train images to form a shrinking dataset:

cd $DIR/syn2real-data
python shrink.py --data-dir syn2real-data --save-dir syn2real-data-tenth --shrink-ratio 10

Also, 1/100:

python shrink.py --data-dir syn2real-data --save-dir syn2real-data-hundredth --shrink-ratio 100

Training

We first train some baselines in each domain. Baseline(Domain Synthetic):

cd $DIR/tools
python vgg_syn2real.py --data-dir syn2real-data --task-name synthetic --log-dir $LOG --model-save-dir $SAVE

Baseline(Domain Real) with complete/0.1/0.01 datasets:

python vgg_syn2real.py --data-dir syn2real-data --task-name mscoco --log-dir $LOG --model-save-dir $SAVE 
python vgg_syn2real.py --data-dir syn2real-data-tenth --task-name mscoco --log-dir $LOG --model-save-dir $SAVE 
python vgg_syn2real.py --data-dir syn2real-data-hundredth --task-name mscoco --log-dir $LOG --model-save-dir $SAVE

You can skip execute baseline(real) which will not be used in the next steps, but baseline(synthetic) is essential.

Finetuning(transfer)

Finetune with 1/10(or 1/100) target data and pre-trained models trained on the synthetic domain:

python vgg_layer_transfer.py --data-dir syn2real-data-tenth --source synthetic
python vgg_layer_transfer.py --data-dir syn2real-data-hundredth --source synthetic

Finetune with 1/10(or 1/100) target data and ImageNet pre-trained models:

python vgg_layer_transfer.py --data-dir syn2real-data-tenth --source Imagenet
python vgg_layer_transfer.py --data-dir syn2real-data-hundredth --source Imagenet

Test Performance of DEPARA for Layer Selection

Extract DEPARA from finetuned models(last layer and other conv-layers) with randomly selected 200 images from part val, you can also test on other images:

python vgg_extract.py --target --imlist imlist_200.txt
python vgg_extract.py --imlist imlist_200.txt

Test performance w.r.t. each layer:

python layer_select.py

Todo

Test more network architectures and tasks, on which we test performance of DEPARA.

Develop advanced metrics and attribution methods for establishing DEPARA.

Acknowledgement

We thank the TransferabilityfromAttributionMaps and pytorch-cnn-visualizations for supporting the base codes of this repo.

We thank the syn2real dataset for conducting experiments upon it.

Citation

Contact

If you have any question, please feel free to contact

Jie Song, sjie@zju.edu.cn;

Yixin Chen, chenyix@zju.edu.cn.