1.Add a backbone: deitiii.
2.Add an optimizer: lamb.
3.Add a sampler: RASampler.
4.Add a lr update hook: CosineAnnealingWarmupByEpochLrUpdaterHook.
5.In easycv/models/classification/classification.py, I remove the default mixup_cfg to keep the classification.py clean.
* add attention layer and more loss function
* add attention layer and various loss functions
* add siou loss
* add tah,various attention layers, and different loss functions
* add asff sim, gsconv
* blade utils fit faster
* blade optimize for yolox static & fp16
* decode output for yolox control by cfg
* add reparameterize_models for export
* e2e trt_nms plugin export support and numeric test
* split preprocess from end2end+blade, speedup from 17ms->7.2ms
Co-authored-by: zouxinyi0625 <zouxinyi.zxy@alibaba-inc.com>
Jiabei-prog
wenmeng zhou
zzoneee
tuofeilun
shouzhou.bx
wuziheng
pengyu.lpy
Cathy0908