This is the implementation for the paper Server-Driven Video Streaming for Deep Learning Inference.
Please check Kuntai Du's home page for more DDS-related resources.
To run our code, please make sure that conda is installed. Then, under dds repo, run
conda env create -f conda_environment_configuration.yml
to install dds environment. Note that this installation assumes that you have GPU resources on your machine. If not, please edit tensorflow-gpu=1.14 to tensorflow=1.14 in conda_environment_configuration.yml.
Now run
conda activate dds
to activate dds environment, and
cd workspace
and run
wget people.cs.uchicago.edu/~kuntai/frozen_inference_graph.pb
to download the object detection model (FasterRCNN-ResNet101).
Under DDSrepo/workspace, run
python entrance.py
to run DDS!
Under DDSrepo/workspace, run
python examine.py trafficcam_1 results stats
you should see something like
trafficcam_1_dds_0.8_0.8_36_26_0.0_twosides_batch_15_0.5_0.3_0.01 3474KB 0.901
trafficcam_1_mpeg_0.8_26 8652KB 0.904
trafficcam_1_mpeg_0.8_36 2369KB 0.876
The number might vary by platform.
If you are considering building your projects based on our codebase, here are some details.
Implementation means we run DDS under real network environment through HTTP post. To do so, in DDSrepo, run
FLASK_APP=backend/backend.py flask run --port=5001
and copy the frozen_inference_graph.pb to DDSrepo to help the server find the model.
Then use another terminal, cd to DDSrepo/workspace, and edit the mode to implementation and edit the hname to ip:5001 (ip should be 127.0.0.1 if you run the server locally) to run DDS on implementation mode. You can also run other methods in implementation mode by changing the default value of mode to implementation.
Inside workspace folder, we use a configuration file configuration.yml to control the configuration for both the client and the server. This file will be only loaded once inside the whole python entrance.py process. You can add new keys and values in this file. We even support caching, parameter sweeping, and some fancy functionalities in this file. Please read the comments inside this file to utilize it.
We search some keywords through youtube in the anonymous mode of Chrome. The top-ranked search results, corresponding URLS are listed below. We filter out some of these videos.
After downloading these videos, we get 7 traffic camera videos and 9 dash camera videos.
We obtain drone videos through a public dataset called VisDrone. We only use 13 videos in this dataset (there are too many videos inside...).
We use clips from TBBT and Friends.