* add fairmot deploy

* separate main_jde

* update copyright & add PrintBenchmark

* refine lap

* update PicoDet Architecture and config

* fix naive-lite-hrnet backbone

* add WiderNaive-18 model

* add general yolov3 for deepsort

* refine format

* fix no target infer

* fix readme and conflict

* [dev] add TOOD

* fix link and notes

* replace ConvNormLayer

* add hardnet85 fairmot

* fix hardnet85

* add centernet hardnet fpn, fix config

* update modelzoo

* update modelzoo readme

* remove comments

* add hardnet num_layers assert

* fix hardnet85 config

* add pedestrian vehicle cfgs

* fix metric category

* fix mot metric

* fix mot metric

* update pedestrian and vehicle modelzoo

* update headtracking modelzoo

* add tools for data processing

* add data pre-process code

* fix tools code format

* add hrnet18 fairmot

* add lite hrnet_w18

* add hrnet dlafpn

* update hrnet18_dlafpn modelzoo

* hrnet18dlafpn modelzoo

* add comments

* set neck not none, update modelzoo

* add small scale config

* add model zoo

* remove metric type

* add keypoint top-down cpp infer; total:5ms,pre:0.7ms,infer:3.8ms,post:0.01ms

* add dark_pose support

Revert "add keypoint top-down cpp infer; total:5ms,pre:0.7ms,infer:3.8ms,post:0.01ms (#4058)" (#4216)

This reverts commit 08619b4e.

* Update PicoDet adn GFl

* add faster rcnn with swin_transformer tiny

* fix use_gpu in deploy doc,  test=document_fix

* fix deploy doc, test=document_fix

* updata document, test=document_fix

* use 8 card for training as default, test=document_fix

* add LiteHRNet backbone and config .YML

* test lite18-network param

acc is same with ori-model 1. fix default darkpose=ON, 2. += is not inplace

add new keypoint model Lite-HRNet

* add new keypoint model Lite-HRNet

* 1. Add description of network type; 2. use channel_shuffle in ops.py

* use normal to init conv2d

* add network type description

* add frame_rate for mot infer video

* add doc of frame_rate for mot video infer, test=document_fix

* add --save_prediction_only support for TopDown KeyPoint Metric

* add a use case for save_prediction_only