一開始沒有安裝anaconda，直接用的python,安包也是各種pip,遇到了非常多的bug,改到懷疑人生，強烈建議先安裝anaconda,建立python3.6的虛擬環境，再進行網絡的安裝編譯運行。
首先要檢查自己的環境，環境不對的話，后期會出現奇奇怪怪的問題。程序所需環境為：cuda9.0 , torch0.4.1,cudnn7.0,我是在Ubuntu16.04中運行的。
1.創建python3.6的環境，並激活環境，可以在終端輸入python，查看自己的版本。
2.安裝torch，或者輸入python，import torch，print（torch__version__）可以查看torch的版本，沒有的話用conda進行安裝。
運行nvcc -V命令，查看自己的cuda版本是否為9.0.
cuda9.0linux安裝可以參考https://blog.csdn.net/qq_25241325/article/details/90753830
如果官網安裝太慢的話，可以加清華源的鏡像，去掉-c pytorch 進行安裝。
3.把官網上的代碼git下來，按照CenterNet官網里的INSTALL,進行requirements.txt的安裝和COCOAPI的安裝（注意：先安裝requirements.txt,因為COCOAPI需要里面的CPython）。
API需要make,按照官網操作即可
4.編譯DCNv2，這一步如果出現一大串的問題，基本都是cuda路徑沒設置對的原因。
5.編譯成功后，即可運行demo.py,會出現以下報錯：
解決方法：
還會出現錯誤：
解決方法：（要提前mkdir output）
最后就可以成功運行demo.py啦！！！放個效果圖，識別的還是可以的
新手一枚，之前因為環境的原因，改了許多奇怪的bug,這里記錄一下，希望能夠幫到小伙伴們。
————————————————————————————————————————————————————————————————————————————————————————————————————————————————
CenterNet官網里的INSTALL文檔：
# Installation
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.
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](https://github.com/xingyizhou/pytorch-pose-hg-3d/issues/16)).
~~~
# 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](https://github.com/cocodataset/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](https://github.com/CharlesShang/DCNv2/tree/pytorch_0.4)).
~~~
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](MODEL_ZOO.md).
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
參考博客：https://blog.csdn.net/m0_37798080/article/details/101196884#demopy_52
https://blog.csdn.net/mary_0830/article/details/103333639#comments_12208483
https://blog.csdn.net/weixin_38715903/article/details/98039181