在NVIDIA Jetson Xavier NX上安裝llvm和numba，以及在Jetxon NX上跑通CenterTrack的目標跟蹤模型
持續創作，加速成長！這是我參與「掘金日新計劃 · 6 月更文挑戰」的第18天，點選檢視活動詳情
1 我的安裝環境如下：
1、檢視Jetson的相關版本資訊
jetson_release -v
```python [email protected]hihui-desktop:~$ jetson_release -v - NVIDIA Jetson Xavier NX (Developer Kit Version) * Jetpack UNKNOWN [L4T 32.4.4] * NV Power Mode: MODE_15W_6CORE - Type: 2 * jetson_stats.service: active - Board info: * Type: Xavier NX (Developer Kit Version) * SOC Family: tegra194 - ID:25 * Module: P3668 - Board: P3509-000 * Code Name: jakku * CUDA GPU architecture (ARCH_BIN): 7.2 * Serial Number: 1422420034718 - Libraries: * CUDA: 10.2.89 * cuDNN: 8.0.0.180 * TensorRT: 7.1.3.0 * Visionworks: 1.6.0.501 * OpenCV: 4.1.1 compiled CUDA: NO * VPI: 0.4.4 * Vulkan: 1.2.70 - jetson-stats: * Version 3.0.1 * Works on Python 3.6.9 [email protected]:~$
```
2、也可以使用jtop命令檢視
3、torch的相關版本（這個是在Jetson NX裝置上的）
- torch 1.6.0
- torchvision 0.7.0a0+78ed10c
4、我在伺服器測試的CenterTrack環境如下：
- cuda 10.2
- cudnn 7.6.5
- torch 1.4.0
- torchvision 0.5.0
2 安裝llvmlite
2.1 安裝llvm
在安裝llvmlite之前要先安裝llvm
LLVM官網：http://releases.llvm.org/
下面是LLVM的安裝流程：
1、建議安裝9.0.0或10.0.0版本的Pre-Built Binaries版本：選擇AArch64-Linux版本（arm架構）下載即可，
-
llvm9.0.0下載：
wget http://releases.llvm.org/9.0.0/clang+llvm-9.0.0-aarch64-linux-gnu.tar.xz -
llvm10.0.0下載
wget http://github.com/llvm/llvm-project/releases/download/llvmorg-10.0.0/clang+llvm-10.0.0-aarch64-linux-gnu.tar.xz
2、解壓壓縮包
tar xJvf clang+llvm-10.0.0-aarch64-linux-gnu.tar.xz
3、修改環境變數
vim ~/.bashrc
4、把如下的環境變數PATH新增到~/.bashrc檔案中
export PATH=$PATH:/home/nano/clang+llvm-10.0.0-aarch64-linux-gnu/bin
因為是編譯好的庫包，因此直接新增到環境變數中就可以使用！
5、環境變數生效
source ~/.bashrc
6、測試安裝的llvm是否安裝成功
clang++ -v# 返回如下資訊表示安裝成功
```python [email protected]:~$ clang++ -v clang version 10.0.0 (http://git.linaro.org/toolchain/jenkins-scripts.git 271be43175ac464ec52b009ab70d952167512aef) Target: aarch64-unknown-linux-gnu Thread model: posix InstalledDir: /home/zhihui/shl/clang+llvm-10.0.0-aarch64-linux-gnu/bin Found candidate GCC installation: /usr/lib/gcc/aarch64-linux-gnu/7 Found candidate GCC installation: /usr/lib/gcc/aarch64-linux-gnu/7.5.0 Found candidate GCC installation: /usr/lib/gcc/aarch64-linux-gnu/8 Selected GCC installation: /usr/lib/gcc/aarch64-linux-gnu/7.5.0 Candidate multilib: .;@m64 Selected multilib: .;@m64 Found CUDA installation: /usr/local/cuda-10.2, version 10.1 [email protected]:~$
```
2.2 安裝llvmlite
安裝llvlite之氣，首先要檢視一下你要安裝的llvmlite的版本和llvm版本，因為他們之間有著對應關係，如下是llvmlite提在pypi上提供的對應關係：
2.2.1 使用pip安裝llvmlite
1、使用pip即可安裝llvmlite
pip install llvmlite==0.34.0
如果你安裝報錯，繼續往下看！
2.2.2 pip安裝llvmlite報錯：/usr/bin/ld: cannot find -ltinfo
如下是我在用pip安裝llvmlite的時候報錯資訊如下：
```python [email protected]:~/shl/clang+llvm-9.0.0-aarch64-linux-gnu/bin$ pip install llvmlite==0.34.0 Defaulting to user installation because normal site-packages is not writeable Looking in indexes: http://pypi.tuna.tsinghua.edu.cn/simple Collecting llvmlite==0.34.0 Using cached http://pypi.tuna.tsinghua.edu.cn/packages/0b/96/07bfa93a103fb9e3e9ae7f9f7c6687ae714aee66b6f3000da3fad71e0aa2/llvmlite-0.34.0.tar.gz (107 kB) Building wheels for collected packages: llvmlite Building wheel for llvmlite (setup.py) ... error ERROR: Command errored out with exit status 1: command: /usr/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-x97lw89z/llvmlite/setup.py'"'"'; file='"'"'/tmp/pip-install-x97lw89z/llvmlite/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-d39gbtll cwd: /tmp/pip-install-x97lw89z/llvmlite/ Complete output (23 lines): running bdist_wheel /usr/bin/python3 /tmp/pip-install-x97lw89z/llvmlite/ffi/build.py
...... lLLVMAArch64Info -lLLVMSupport -lLLVMDemangle -lz -lrt -ldl -ltinfo -lpthread -lm /usr/bin/ld: cannot find -ltinfo collect2: error: ld returned 1 exit status Makefile.linux:20: recipe for target 'libllvmlite.so' failed make: *** [libllvmlite.so] Error 1 9.0.0
SVML not detected
Traceback (most recent call last):
File "/tmp/pip-install-e3p8ncuv/llvmlite/ffi/build.py", line 191, in <module>
main()
File "/tmp/pip-install-e3p8ncuv/llvmlite/ffi/build.py", line 181, in main
main_posix('linux', '.so')
File "/tmp/pip-install-e3p8ncuv/llvmlite/ffi/build.py", line 173, in main_posix
subprocess.check_call(['make', '-f', makefile])
File "/usr/lib/python3.6/subprocess.py", line 311, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['make', '-f', 'Makefile.linux']' returned non-zero exit status 2.
error: command '/usr/bin/python3' failed with exit status 1
----------------------------------------
ERROR: Command errored out with exit status 1: /usr/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-e3p8ncuv/llvmlite/setup.py'"'"'; file='"'"'/tmp/pip-install-e3p8ncuv/llvmlite/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record /tmp/pip-record-dlz3z5md/install-record.txt --single-version-externally-managed --user --prefix= --compile --install-headers /home/zhihui/.local/include/python3.6m/llvmlite Check the logs for full command output. [email protected]:~/shl/clang+llvm-9.0.0-aarch64-linux-gnu/bin$ ^Ct
```
我們來先看一下錯誤問題，從下圖可以看到錯誤主要要兩條：
- 1）
/usr/bin/ld: cannot find -ltinfo - 2）
Makefile.linux:20: recipe for target 'libllvmlite.so' failed
我一開始就是先按照第二條錯誤找解決方法，但是幾乎沒有什麼有用資訊，有些和我的情況也不符合，最終也沒有解決問題！
後面我就開始按照第一條錯誤找解決方法，發現是由於缺少了庫檔案tinfo（關於檢視缺少的是什麼檔案參考這篇部落格）
1、查詢有沒有相關的tinfo庫檔案
python
[email protected]:~/shl/clang+llvm-9.0.0-aarch64-linux-gnu/bin$ apt-cache search libtinfo
libtinfo-dev - developer's library for the low-level terminfo library
libtinfo5 - shared low-level terminfo library for terminal handling
libtinfo5-dbg - debugging/profiling library for the low-level terminfo library
2、查詢到tinfo的庫檔案libtinfo-dev，然後安裝
sudo apt-get install libtinfo-dev
python
[email protected]:~/shl/clang+llvm-9.0.0-aarch64-linux-gnu/bin$ sudo apt-get install libtinfo-dev
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
activity-log-manager archdetect-deb bamfdaemon bogl-bterm busybox-static compiz-core compiz-plugins-default
cryptsetup-bin debhelper dh-autoreconf dh-strip-nondeterminism dpkg-repack gir1.2-accounts-1.0 gir1.2-gdata-0.0
gir1.2-gst-plugins-base-1.0 gir1.2-gstreamer-1.0 gir1.2-harfbuzz-0.0 gir1.2-rb-3.0 gir1.2-signon-1.0
gir1.2-timezonemap-1.0 gir1.2-totem-1.0 gir1.2-totemplparser-1.0 gir1.2-xkl-1.0 gnome-calculator gnome-system-monitor
grub-common gtk3-nocsd icu-devtools kde-window-manager kpackagetool5 kwayland-data kwin-common kwin-x11
libarchive-cpio-perl libatkmm-1.6-1v5 libboost-python1.65.1 libcairomm-1.0-1v5 libcolumbus1-common libcolumbus1v5
libcompizconfig0 libdebian-installer4 libdecoration0 libdmapsharing-3.0-2 libegl1-mesa-dev libeigen3-dev
libfile-stripnondeterminism-perl libgeonames-common libgeonames0 libgles2-mesa-dev libglibmm-2.4-1v5 libgpod-common
libgpod4 libgraphite2-dev libgtk3-nocsd0 libgtkmm-3.0-1v5 libharfbuzz-gobject0 libicu-le-hb0 libiculx60
libkdecorations2-5v5 libkdecorations2private5v5 libkf5activities5 libkf5declarative-data libkf5declarative5
libkf5globalaccelprivate5 libkf5idletime5 libkf5kcmutils-data libkf5kcmutils5 libkf5package-data libkf5package5
libkf5plasma5 libkf5quickaddons5 libkf5waylandclient5 libkf5waylandserver5 libkscreenlocker5 libkwin4-effect-builtins1
libkwineffects11 libkwinglutils11 libkwinxrenderutils11 libmail-sendmail-perl libnm-gtk0 liborc-0.4-dev
liborc-0.4-dev-bin libpanel-applet3 libpangomm-1.4-1v5 libpcre16-3 libpcre3-dev libpcre32-3 libpcrecpp0v5 libpinyin-data
libpinyin13 libqt5designer5 libqt5help5 libqt5positioning5 libqt5sensors5 libqt5sql5 libqt5test5 libqt5webchannel5
libqt5webkit5 libsgutils2-2 libsignon-glib1 libsys-hostname-long-perl libtimezonemap-data libtimezonemap1
libunity-control-center1 libunity-core-6.0-9 libunity-misc4 libwayland-bin libwayland-dev libxcb-composite0
libxcb-cursor0 libxcb-damage0 libxrandr-dev libxrender-dev libzeitgeist-1.0-1 os-prober pkg-config po-debconf
qml-module-org-kde-kquickcontrolsaddons rdate session-shortcuts tasksel tasksel-data unity-asset-pool unity-greeter
unity-lens-applications unity-lens-files unity-lens-music unity-lens-video unity-schemas unity-scope-video-remote
unity-scopes-master-default unity-scopes-runner x11proto-randr-dev
Use 'sudo apt autoremove' to remove them.
The following NEW packages will be installed:
libtinfo-dev
0 upgraded, 1 newly installed, 0 to remove and 5 not upgraded.
Need to get 74.2 kB of archives.
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Fetched 74.2 kB in 2s (45.2 kB/s)
Selecting previously unselected package libtinfo-dev:arm64.
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Unpacking libtinfo-dev:arm64 (6.1-1ubuntu1.18.04) ...
Setting up libtinfo-dev:arm64 (6.1-1ubuntu1.18.04) ...
3、在安裝llvmlite就沒有問題了
pip install llvmlite==0.33.0
```python [email protected]:~/shl/clang+llvm-9.0.0-aarch64-linux-gnu/bin$ pip install llvmlite==0.33.0 Defaulting to user installation because normal site-packages is not writeable Looking in indexes: http://pypi.tuna.tsinghua.edu.cn/simple Collecting llvmlite==0.33.0 Using cached http://pypi.tuna.tsinghua.edu.cn/packages/71/4e/b1086722f4fa0b52cf8c0d4b2c985fb3f95d2f1be1b010259497b2464c1d/llvmlite-0.33.0.tar.gz (104 kB) Building wheels for collected packages: llvmlite Building wheel for llvmlite (setup.py) ... done Created wheel for llvmlite: filename=llvmlite-0.33.0-cp36-cp36m-linux_aarch64.whl size=18340330 sha256=1311e2f739409b98058c31c6224c1d6cc1b84029c24adee46fefbc10c71309fd Stored in directory: /home/zhihui/.cache/pip/wheels/e3/75/d7/0c4099140cbaac772fc57aed71df74e650660d1f206e6ffbc7 Successfully built llvmlite Installing collected packages: llvmlite Successfully installed llvmlite-0.33.0 [email protected]:~/shl/clang+llvm-9.0.0-aarch64-linux-gnu/bin$
```
3 開始安裝numba
numba可以基於llvm動態生成優化程式碼，提高python的執行效率，使用很方便，只需要給python程式碼加上修飾器就好了。由於numba依賴llvm，因此需要事先安裝好llvm。
3.1 使用pip安裝numba
直接使用pip安裝numba就可以
pip install numba
如果你安裝過程中報錯，請繼續看下文！
3.2 安裝numba報錯：error "TBB version is too old, 2019 update 5,
然後開始安裝numba的時候又報錯誤：#error "TBB version is too old, 2019 update 5, i.e. TBB_INTERFACE_VERSION >= 11005 required"，意思就是說TBB的版本太低，但是誰能夠告訴我TBB是個什麼東西？？？，具體報錯資訊如下：
```python numba/np/ufunc/tbbpool.cpp:30:2: error: #error "TBB version is too old, 2019 update 5, i.e. TBB_INTERFACE_VERSION >= 11005 required"
error "TBB version is too old, 2019 update 5, i.e. TBB_INTERFACE_VERSION >= 11005 required"
```
後面經過查詢，TTB是Threading Building Blocks的縮寫，就是關於執行緒的一個模組，而我們安裝的numba是依賴這個模組的，因此需要先把這個TTB版本過低的問題解決！（參考）
安裝比較新的TTB版本：
python
git clone http://github.com/wjakob/tbb.git
cd tbb/build
cmake ..
make -j
sudo make install
也有說用apt的方式安裝tbb的我不知道是不是同一個東西，也不知道行不行，沒有嘗試，如果要試自己嘗試：
sudo apt install libtbb-dev
在這裡可以檢視TBB的版本更細迭代的資訊：
安裝編譯好之後，再安裝numba，可以成功安裝numba，奈斯！！！
```python
[email protected]:~/shl/tbb/build$ pip install numba
Defaulting to user installation because normal site-packages is not writeable
Looking in indexes: http://pypi.tuna.tsinghua.edu.cn/simple
Collecting numba
Downloading http://pypi.tuna.tsinghua.edu.cn/packages/5e/81/6fd1dd064bcf71a79da109e8966a39e2da61d68bf0bd1e0839fa997f8c41/numba-0.51.2.tar.gz (2.1 MB)
|████████████████████████████████| 2.1 MB 708 kB/s
Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /home/zhihui/.local/lib/python3.6/site-packages (from numba) (0.34.0)
Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from numba) (1.16.1)
Requirement already satisfied: setuptools in /home/zhihui/.local/lib/python3.6/site-packages (from numba) (50.3.2)
Building wheels for collected packages: numba
Building wheel for numba (setup.py) ... done
Created wheel for numba: filename=numba-0.51.2-cp36-cp36m-linux_aarch64.whl size=2975288 sha256=494f4df1ba36fe4eb209827bead47b0910e35702eba0c39e5d83c8d0eb046e2b
Stored in directory: /home/zhihui/.cache/pip/wheels/cc/7a/97/f45c63a61c0d48218e1f0091cac2adf05d5ff6ec5803cbc6ac
Successfully built numba
Installing collected packages: numba
Successfully installed numba-0.51.2
[email protected]:~/shl/tbb/build$
```
4 Jetson NX安裝sklearn一直停止等待
安裝sklearn的時候，會一直停滯在Building wheel for scikit-learn (PEP 517) ... |的位置等待！不知道是什麼原因！
```python [email protected]:~/shl/CenterTrack/src$pip install sklearn Defaulting to user installation because normal site-packages is not writeable Looking in indexes: http://pypi.tuna.tsinghua.edu.cn/simple Collecting scikit-learn Using cached http://pypi.tuna.tsinghua.edu.cn/packages/aa/f6/75297be19f48b7a8c2577753a3a700f98fc4db49d0e5ed3820dd8dee43d4/scikit-learn-0.23.2.tar.gz (7.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing wheel metadata ... done Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.16.1) Collecting joblib>=0.11 Using cached http://pypi.tuna.tsinghua.edu.cn/packages/fc/c9/f58220ac44a1592f79a343caba12f6837f9e0c04c196176a3d66338e1ea8/joblib-0.17.0-py3-none-any.whl (301 kB) Requirement already satisfied: scipy>=0.19.1 in /home/zhihui/.local/lib/python3.6/site-packages (from scikit-learn) (1.4.1) Collecting threadpoolctl>=2.0.0 Using cached http://pypi.tuna.tsinghua.edu.cn/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl (12 kB) Building wheels for collected packages: scikit-learn Building wheel for scikit-learn (PEP 517) ... |
```
此時可以換一種方式安裝，使用apt-get安裝python的sklearn庫包（參考）：
sudo apt-get install python3-sklearn
```python
[email protected]:~/shl/CenterTrack/src$ sudo apt-get install python3-sklearn
[sudo] password for zhihui:
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
activity-log-manager archdetect-deb bamfdaemon bogl-bterm busybox-static compiz-core compiz-plugins-default cryptsetup-bin debhelper
dh-autoreconf dh-strip-nondeterminism dpkg-repack gir1.2-accounts-1.0 gir1.2-gdata-0.0 gir1.2-gst-plugins-base-1.0 gir1.2-gstreamer-1.0
gir1.2-harfbuzz-0.0 gir1.2-rb-3.0 gir1.2-signon-1.0 gir1.2-timezonemap-1.0 gir1.2-totem-1.0 gir1.2-totemplparser-1.0 gir1.2-xkl-1.0
gnome-calculator gnome-system-monitor grub-common gtk3-nocsd icu-devtools kde-window-manager kpackagetool5 kwayland-data kwin-common
kwin-x11 libarchive-cpio-perl libatkmm-1.6-1v5 libboost-python1.65.1 libcairomm-1.0-1v5 libcolumbus1-common libcolumbus1v5 libcompizconfig0
libdebian-installer4 libdecoration0 libdmapsharing-3.0-2 libegl1-mesa-dev libeigen3-dev libfile-stripnondeterminism-perl libgeonames-common
libgeonames0 libgles2-mesa-dev libglibmm-2.4-1v5 libgpod-common libgpod4 libgraphite2-dev libgtk3-nocsd0 libgtkmm-3.0-1v5
libharfbuzz-gobject0 libicu-le-hb0 libiculx60 libkdecorations2-5v5 libkdecorations2private5v5 libkf5activities5 libkf5declarative-data
libkf5declarative5 libkf5globalaccelprivate5 libkf5idletime5 libkf5kcmutils-data libkf5kcmutils5 libkf5package-data libkf5package5
libkf5plasma5 libkf5quickaddons5 libkf5waylandclient5 libkf5waylandserver5 libkscreenlocker5 libkwin4-effect-builtins1 libkwineffects11
libkwinglutils11 libkwinxrenderutils11 libmail-sendmail-perl libnm-gtk0 liborc-0.4-dev liborc-0.4-dev-bin libpanel-applet3
libpangomm-1.4-1v5 libpcre16-3 libpcre3-dev libpcre32-3 libpcrecpp0v5 libpinyin-data libpinyin13 libqt5designer5 libqt5help5
libqt5positioning5 libqt5sensors5 libqt5sql5 libqt5test5 libqt5webchannel5 libqt5webkit5 libsgutils2-2 libsignon-glib1
libsys-hostname-long-perl libtimezonemap-data libtimezonemap1 libunity-control-center1 libunity-core-6.0-9 libunity-misc4 libwayland-bin
libwayland-dev libxcb-composite0 libxcb-cursor0 libxcb-damage0 libxrandr-dev libxrender-dev libzeitgeist-1.0-1 os-prober pkg-config
po-debconf qml-module-org-kde-kquickcontrolsaddons rdate session-shortcuts tasksel tasksel-data unity-asset-pool unity-greeter
unity-lens-applications unity-lens-files unity-lens-music unity-lens-video unity-schemas unity-scope-video-remote
unity-scopes-master-default unity-scopes-runner x11proto-randr-dev
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
python3-attr python3-decorator python3-joblib python3-nose python3-pluggy python3-py python3-pytest python3-scipy python3-sklearn-lib
Suggested packages:
python-attr-doc python-nose-doc subversion python-scipy-doc python3-dap python-sklearn-doc ipython3
The following NEW packages will be installed:
python3-attr python3-decorator python3-joblib python3-nose python3-pluggy python3-py python3-pytest python3-scipy python3-sklearn
python3-sklearn-lib
0 upgraded, 10 newly installed, 0 to remove and 5 not upgraded.
Need to get 11.6 MB of archives.
After this operation, 51.9 MB of additional disk space will be used.
Do you want to continue? [Y/n] y
Get:1 http://ports.ubuntu.com/ubuntu-ports bionic/main arm64 python3-attr all 17.4.0-2 [23.8 kB]
Get:2 http://ports.ubuntu.com/ubuntu-ports bionic/universe arm64 python3-decorator all 4.1.2-1 [9,364 B]
Get:3 http://ports.ubuntu.com/ubuntu-ports bionic/universe arm64 python3-joblib all 0.11-1 [109 kB]
Get:4 http://ports.ubuntu.com/ubuntu-ports bionic/universe arm64 python3-nose all 1.3.7-3 [115 kB]
Get:5 http://ports.ubuntu.com/ubuntu-ports bionic/universe arm64 python3-pluggy all 0.6.0-1 [12.3 kB]
Get:6 http://ports.ubuntu.com/ubuntu-ports bionic/universe arm64 python3-py all 1.5.2-1 [65.2 kB]
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[email protected]:~/shl/CenterTrack/src$ python
Python 3.6.9 (default, Oct 8 2020, 12:12:24)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
import sklearn sklearn.version '0.19.1' exit() [email protected]:~/shl/CenterTrack/src$ ```
5 在Jetson NX執行CenterTrack專案
1、執行CenterTrack在Jetson NX上
python demo.py tracking --load_model ../model/coco_tracking.pth --demo ../videos/cars.mp4
```python
[email protected]:~/shl/CenterTrack/src$ python demo.py tracking --load_model ../model/coco_tracking.pth --demo ../videos/cars.mp4
Running tracking
Using tracking threshold for out threshold! 0.3
Fix size testing.
training chunk_sizes: [32]
input h w: 512 512
heads {'hm': 80, 'reg': 2, 'wh': 2, 'tracking': 2}
weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'tracking': 1}
head conv {'hm': [256], 'reg': [256], 'wh': [256], 'tracking': [256]}
Creating model...
Using node type: (
```
2、如果報錯：RuntimeError: Integer division of tensors using div or / is no longer supported, and in a future release div will perform true division as in Python 3. Use true_divide or floor_divide (// in Python) instead.，這是因為在pytorch1.5中的/中tensor除以一個整數的操作，在pytorch1.6中已經不支援了！（[]參考(http://my.oschina.net/u/4389867/blog/4531779)）
把CenterTrack/src/lib/model/utils.py中的77行和81行程式碼修改如下即可：
```python #topk_ys = (topk_inds / width).int().float() topk_ys = torch.floor_divide(topk_inds, width).int().float() topk_xs = (topk_inds % width).int().float()
topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K) #topk_clses = (topk_ind / K).int() topk_clses = torch.floor_divide(topk_ind, K).int()
```
3、成功在Jetson NX上跑通CenterTrack目標跟蹤模型，就是速度有點慢，後面會想辦法把模型轉為TensorRT
```python
[email protected]:~/shl/CenterTrack/src$ python demo.py tracking --load_model ../model/coco_tracking.pth --demo ../videos/cars.mp4
Running tracking
Using tracking threshold for out threshold! 0.3
Fix size testing.
training chunk_sizes: [32]
input h w: 512 512
heads {'hm': 80, 'reg': 2, 'wh': 2, 'tracking': 2}
weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'tracking': 1}
head conv {'hm': [256], 'reg': [256], 'wh': [256], 'tracking': [256]}
Creating model...
Using node type: (
```
參考：http://www.coder.work/article/4985382 # 從原始碼安裝 參考：http://blog.csdn.net/zt1091574181/article/details/108222306 # 從編譯好的檔案 參考：http://blog.csdn.net/qq_45779334/article/details/108640247 參考：http://www.jianshu.com/p/ccaf688f54c0 # 解決安裝過程中的錯誤
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