- Python 3 required (for python build)
- Install PyTorch and dependencies from http://pytorch.org
- Install required modules (numpy scipy pandas sklearn matplotlib statistics pyyaml).
pip3 install -r requirement.txt
root ├── code │ ├── baseline │ ├── checkpoints │ ├── dataloader │ └── ... │ └── data ├── ECM ├── LabEating └── MD2K
- Clone this repo:
git clone https://github.com/HAbitsLab/SensorSynthesis.git
cd SensorSynthesis
- Install PyTorch and 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
- For pip users, please type the command
pip install -r requirements.txt
. - For Conda users, we provide a installation script
./scripts/conda_deps.sh
. Alternatively, you can create a new Conda environment usingconda env create -f environment.yml
. - For Docker users, we provide the pre-built Docker image and Dockerfile. Please refer to our Docker page.
- For pip users, please type the command
In folder preprocess
:
- Stanford ECM dataset:
python3 run_pwcnet_ecm.py
- MD2K Sense2StopSync dataset: use
convert2mp4.sh
, thenpython3 run_pwcnet_MD2K.py
I3D Feature Extraction is used:
In folder
utils/feature_extraction
, runpython3 run_i3d_ecm_gpu.py
andpython3 run_i3d_MD2K_gpu.py
.
Shibo Zhang (shibozhang2015@u.northwestern.edu)
@inproceedings{dummmy,
}