ASR

This repository contains part of the source code to implement Analytics-aware Super-Resolution (ASR) in Enabling Edge-Cloud Video Analytics for Robotic Applications (Runespoor, INFOCOM '21) and a previous workshop paper (CloudSeg, HotCloud '19). This repository is for archiving and provided as-is. The final model weights are provided for evaluation, and you can use the code as a reference to develop related projects. You might find many development-environment-related hardcoded settings. It might be inconvient that this legacy code does not have a proper configuration input method.

We implement ASR based on a super-resolution model CARN in PyTorch. The semantic segmentation model used here is SwiftNet. The basic idea is to call the CV model (SwiftNet here) to train the super-resolution model to make it anaytics-aware and have better reconstruction performance for the CV task. Please check those two repositories to better understand the code.

Dataset

Please follow the instructions of CARN and SwiftNet to prepare the dataset (Cityscapes). The dataset directory should have a similar structure like:

dataset
└── Cityscapes
    ├── Cityscapes_train_HR
    ├── Cityscapes_train_LR_bicubic
    ├── Cityscapes_valid_HR
    ├── Cityscapes_valid_LR_bicubic (these data directories train the base SR)
    └── gtFine (this is the label of the semantic segmentation task)

swiftnet/datasets/Cityscapes requires symbolic links to the Cityscapes data, including gtFine and img.

Pretrained Models

The pretrained base SR model and the ASR model are in the best directory. Please follow the instruction of SwiftNet to download its pretrained weights.

Code Structure

carn/train.py is the entry to the training code. carn/solver_vanilla_training.py and carn/finetune_solver.py are loss functions used in different training stages (base and analytics-aware). Need to hand config in carn/train.py.

Commands

An example command to train ASR:

$ python carn/train.py --patch_size 64 \
                       --batch_size 64 \
                       --max_steps 600000 \
                       --decay 400000 \
                       --model carn \
                       --ckpt_name carn \
                       --ckpt_dir checkpoint/carn \
                       --scale 4 \
                       --num_gpu 4 \
                       --print_interval 100 \
                       --loss_fn CrossEntropyLoss (add this argument only in analytics-aware training with finetune_solver.py)

Evaluation:

python carn/sample.py --model carn \
                      --test_data_dir dataset/Cityscapes \
                      --scale 4 \
                      --ckpt_path ./best/carn_4_vanilla.pth \
                      --sample_dir sample

Citation

@inproceedings{wang2021enabling,
  title={Enabling Edge-Cloud Video Analytics for Robotic Applications},
  author={Wang, Yiding and Wang, Weiyan and Liu, Duowen and Jin, Xin and Jiang, Junchen and Chen, Kai},
  booktitle={IEEE INFOCOM 2021-IEEE Conference on Computer Communications},
  year={2021},
  organization={IEEE}
}