This is the official project page for the NoScope project.
Please read the blog post and paper for more details!
This repository contains the code for the optimization step in the paper. The inference code is here.
You will need the following installed:
- python-setuptools python-tk
- CUDA, CUDNN
- tensorflow-gpu, OpenCV 3.2 with FFmpeg bindings
- g++ 5.4 or later
Your machine will need at least:
- AVX2 capabilities
- 300+GB of memory
- 500+GB of space
- A GPU (this has only been tested with NVIDIA K80 and P100)
To set up the inference engine, do the following:
git clone https://github.com/stanford-futuredata/tensorflow-noscope.git
cd tensorflow-noscope
git checkout speedhax
git submodule init
git submodule update
./configure
cd tensorflow
bazel build -c opt --copt=-mavx2 --config=cuda noscope
The build will fail. To fix this, update the BUILD file to point towards your OpenCV install and add this directory to your PATH environmental variable. Please encourage the Tensorflow developers to support non-bazel building and linking. Due to a quirk in bazel, it may occasionally "forget" that tensorflow-noscope was built. If this happens, rebuild.
Once you have inference engine set up, the example/ subfolder contains the script to reproduce
Figure 5d in the paper.
First, download the coral-reef video and labels:
wget https://storage.googleapis.com/noscope-data/csvs-yolo/coral-reef-long.csv
wget https://storage.googleapis.com/noscope-data/videos/coral-reef-long.mp4
You will need to update the code and data paths in run.sh. Additionally, you will need to update
noscope_motherdog.py to point to the YOLO configuration files and weights.
The datasets that are currently available are coral-reef-long and jackson-town-square.
The mp4 video files are available at https://storage.googleapis.com/noscope-data/videos/VIDEO_NAME.mp4
The CSVs with ground truth labels are available at
https://storage.googleapis.com/noscope-data/csvs-yolo/VIDEO_NAME.csv