INSTALL.md

Installation

The code was tested on Ubuntu 16.04, with Anaconda Python 3.6, PyTorch v0.4.1, Numpy v1.17.2 and Pillow v6.2.0. 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.