75 lines
2.3 KiB
Markdown
75 lines
2.3 KiB
Markdown
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# Installation
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The code was tested on Ubuntu 16.04, with [Anaconda](https://www.anaconda.com/download) Python 3.6 and [PyTorch]((http://pytorch.org/)) v0.4.1. NVIDIA GPUs are needed for both training and testing.
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After install Anaconda:
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0. [Optional but recommended] create a new conda environment.
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~~~
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conda create --name CenterNet python=3.6
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~~~
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And activate the environment.
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~~~
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conda activate CenterNet
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~~~
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1. Install pytorch0.4.1:
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~~~
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conda install pytorch=0.4.1 torchvision -c pytorch
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~~~
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And disable cudnn batch normalization(Due to [this issue](https://github.com/xingyizhou/pytorch-pose-hg-3d/issues/16)).
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~~~
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# PYTORCH=/path/to/pytorch # usually ~/anaconda3/envs/CenterNet/lib/python3.6/site-packages/
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# for pytorch v0.4.0
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sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
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# for pytorch v0.4.1
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sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
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~~~
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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.
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2. Install [COCOAPI](https://github.com/cocodataset/cocoapi):
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~~~
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# COCOAPI=/path/to/clone/cocoapi
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git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
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cd $COCOAPI/PythonAPI
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make
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python setup.py install --user
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~~~
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3. Clone this repo:
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~~~
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CenterNet_ROOT=/path/to/clone/CenterNet
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git clone --recursive https://github.com/xingyizhou/CenterNet $CenterNet_ROOT
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~~~
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4. Install the requirements
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~~~
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pip install -r requirements.txt
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~~~
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5. Compile deformable convolutional (from [DCNv2](https://github.com/CharlesShang/DCNv2/tree/pytorch_0.4)).
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~~~
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cd $CenterNet_ROOT/src/lib/models/networks/DCNv2
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./make.sh
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~~~
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6. [Optional] Compile NMS if your want to use multi-scale testing or test ExtremeNet.
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~~~
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cd $CenterNet_ROOT/src/lib/external
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make
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~~~
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7. Download pertained models for [detection]() or [pose estimation]() and move them to `$CenterNet_ROOT/models/`. More models can be found in [Model zoo](MODEL_ZOO.md).
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