INSTALL.md

Installation

The code was tested on Ubuntu 16.04, with Anaconda Python 3.6 and PyTorch v0.4.1. NVIDIA GPUs are needed for both training and testing. After install Anaconda:

0. [Optional but recommended] create a new conda environment.

   conda create --name CenterNet python=3.6
   

   And activate the environment.

   conda activate CenterNet
   

1. Install pytorch0.4.1:

   conda install pytorch=0.4.1 torchvision -c pytorch
   

   And disable cudnn batch normalization(Due to this issue).

   # PYTORCH=/path/to/pytorch # usually ~/anaconda3/envs/CenterNet/lib/python3.6/site-packages/
   # for pytorch v0.4.0
   sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
   # for pytorch v0.4.1
   sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
   

   For other pytorch version, you can manually open torch/nn/functional.py and find the line with torch.batch_norm and replace the torch.backends.cudnn.enabled with False. We observed slight worse training results without doing so.

2. Install COCOAPI:

   # COCOAPI=/path/to/clone/cocoapi
   git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
   cd $COCOAPI/PythonAPI
   make
   python setup.py install --user
   

3. Clone this repo:

   CenterNet_ROOT=/path/to/clone/CenterNet
   git clone https://github.com/xingyizhou/CenterNet $CenterNet_ROOT
   

4. Install the requirements

   pip install -r requirements.txt
   

5. Compile deformable convolutional (from DCNv2).

   cd $CenterNet_ROOT/src/lib/models/networks/DCNv2
   ./make.sh
   

6. [Optional, only required if you are using extremenet or multi-scale testing] Compile NMS if your want to use multi-scale testing or test ExtremeNet.

   cd $CenterNet_ROOT/src/lib/external
   make
   

7. Download pertained models for detection or pose estimation and move them to $CenterNet_ROOT/models/. More models can be found in Model zoo.