SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning

Haoran You, Baopu Li, Zhanyi Sun, Xu Ouyang, Yingyan Lin

Accepted by ECCV 2022

Environment

Require Python3, CUDA>=10.1, and torch>=1.4, all dependencies are as follows:

pip3 install torch==1.4.0 torchvision==0.5.0 opencv-python tqdm tensorboard lmdb pyyaml packaging Pillow==6.2.2 matplotlib yacs pyarrow==0.17.1
pip3 install cityscapesscripts  # for Cityscapes segmentation
pip3 install mmcv-full # for Segmentation data loader
pip3 install pycocotools shapely==1.6.4 Cython pandas pyyaml json_tricks scikit-image  # for COCO keypoint estimation

or conda env create -f environment.yml

Setup

Training script in general

python3 -m torch.distributed.launch --nproc_per_node=${NPROC_PER_NODE} --nnodes=${N_NODES} \
        --node_rank=${NODE_RANK} --master_addr=${MASTER_ADDR} --master_port=${MASTER_PORT} \
        --use_env train.py app:configs/YOUR_TASK.yml

Supported tasks:

• cls_imagenet
• seg_cityscapes
• seg_ade20k
• keypoint_coco

Examples for training SuperTickets on ImageNet, COCO keypoint, Cityscapes, and ADE20K

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env train.py app:configs/cls_imagenet_g_0.9.yml

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env train.py app:configs/keypoint_coco_g_0.9.yml

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env train.py app:configs/seg_cityscapes_g_0.8.yml

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env train.py app:configs/seg_ade20k_g_0.9.yml

Examples for testing the trained models

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env validate.py app:configs/cls_imagenet_g_0.9.yml \
--resume 'output/cls_imagenet_g_0.9/20220202-001816/pruned_checkpoint.pt' \
--test_only True

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env validate.py app:configs/keypoint_coco_g_0.9.yml \
--resume 'output/keypoint_coco_g_0.9/20220211-160107/pruned_checkpoint.pt' \
--test_only True

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env validate.py app:configs/seg_cityscapes_g_0.8.yml \
--resume 'output/seg_cityscapes_g_0.7/20220108-175942/pruned_checkpoint.pt' \
--test_only True

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env validate.py app:configs/seg_ade20k_g_0.9.yml \
--resume 'output/seg_ade20k_g_0.9/20220119-223955/pruned_checkpoint.pt' \
--test_only True

Example for transferring from ImageNet to COCO keypoint

(only transfer the feature extractor while keep the classification layer unchanged)

python3 -m torch.distributed.launch \
--nproc_per_node=8 \
--nnodes=1 \
--node_rank=0 \
--master_port=1234 \
--use_env transfer_img.py app:configs/transfer/cls_imagenet_to_keypoint_coco_g_0.9.yml \
--pretrained "output/cls_imagenet_g_0.9/20220209-130559/retrain_checkpoint.pt" \
--cur_model 'output/keypoint_coco_g_0.9/20220211-160107/pruned_checkpoint.pt'

Datasets

1. ImageNet

   • Prepare ImageNet data following pytorch example.
   • Optional: Generate lmdb dataset by utils/lmdb_dataset.py. If not, please overwrite dataset:imagenet1k_lmdb in yaml to dataset:imagenet1k.
   • The directory structure of $DATA_ROOT should look like this:

   ${DATA_ROOT}
   ├── imagenet
   └── imagenet_lmdb
   

   • Link the data:

   ln -s YOUR_LMDB_DIR data/imagenet_lmdb

2. Cityscapes

   • Download data from Cityscapes.
   • unzip gtFine_trainvaltest.zip leftImg8bit_trainvaltest.zip
   • Link the data:

   ln -s YOUR_DATA_DIR data/cityscapes

   • preprocess the data:

   python3 tools/convert_cityscapes.py data/cityscapes --nproc 8

3. ADE20K

   • Download data from ADE20K.
   • unzip ADEChallengeData2016.zip
   • Link the data:

   ln -s YOUR_DATA_DIR data/ade20k

4. COCO keypoints

   • Download data from COCO.
   • build tools

   git clone https://github.com/cocodataset/cocoapi.git
   cd cocoapi/PythonAPI
   python3 setup.py build_ext --inplace
   python3 setup.py build_ext install
   make  # for nms

   • Unzip and Link the data:

   ln -s YOUR_DATA_DIR data/coco

   • We also provide person detection result of COCO val2017 and test-dev2017 to reproduce our multi-person pose estimation results. Please download from OneDrive or GoogleDrive.
   • Download and extract them under data/coco/person_detection_results, and make them look like this:

   ${POSE_ROOT}
   |-- data
   `-- |-- coco
       `-- |-- annotations
           |   |-- person_keypoints_train2017.json
           |   `-- person_keypoints_val2017.json
           |-- person_detection_results
           |   |-- COCO_val2017_detections_AP_H_56_person.json
           |   |-- COCO_test-dev2017_detections_AP_H_609_person.json
           `-- images
               |-- train2017
               |   |-- 000000000009.jpg
               |   |-- 000000000025.jpg
               |   |-- 000000000030.jpg
               |   |-- ...
               `-- val2017
                   |-- 000000000139.jpg
                   |-- 000000000285.jpg
                   |-- 000000000632.jpg
                   |-- ...
   

Miscellaneous

1. Plot keypoint detection results.

   python3 tools/plot_coco.py --prediction output/results/keypoints_val2017_results_0.json --save-path output/vis/

2. About YAML config

• The codebase is a general ImageNet training framework using yaml config with several extension under apps dir, based on PyTorch.
  • YAML config with additional features
    • ${ENV} in yaml config.
    • _include for hierachy config.
    • _default key for overwriting.
    • xxx.yyy.zzz for partial overwriting.
  • --{{opt}} {{new_val}} for command line overwriting.

3. If you find our work useful in your research please consider citing our paper:

   @inproceedings{SuperTickets,
       title={SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning},
       author={Haoran You, Baopu Li, Zhanyi Sun, Xu Ouyang, Yingyan Lin},
       booktitle={European Conference on Computer Vision (ECCV)},
       year={2022},
   }
   

Acknowledgement

• Code is inspired by HR-NAS
• Work was done during Haoran's internship at Baidu Research at USA