* Simplify autoshape() post-process

* cleanup

* cleanup

* Create codeql-analysis.yml

* Update ci-testing.yml

* Pycocotools best.pt after COCO train

* cleanup

* Update matplotlib tight_layout=True

* udpate

* udpate

* update

* png to ps

* update

* update

fix bugs in plot_images

* Daemon thread plotting

* remove process_batch

* plot after print

* FROM pytorch/pytorch:latest

* FROM nvcr.io/nvidia/pytorch:20.10-py3

* Caching bug fix #1508

* np.zeros((0,5)) x2

* Update loss.py

implement the quality focal loss which is a more general case of focal loss
more detail in https://arxiv.org/abs/2006.04388 

In the obj loss (or the case cls loss with label smooth), the targets is no long barely be 0 or 1 (can be 0.7), in this case, the normal focal loss is not work accurately
quality focal loss in behave the same as focal loss when the target is equal to 0 or 1, and work accurately when targets in (0, 1)

example:

targets:
tensor([[0.6225, 0.0000, 0.0000],
        [0.9000, 0.0000, 0.0000],
        [1.0000, 0.0000, 0.0000]])
___________________________
pred_prob:
tensor([[0.6225, 0.2689, 0.1192],
        [0.7773, 0.5000, 0.2227],
        [0.8176, 0.8808, 0.1978]])
____________________________
focal_loss
tensor([[0.0937, 0.0328, 0.0039],
        [0.0166, 0.1838, 0.0199],
        [0.0039, 1.3186, 0.0145]])
______________
qfocal_loss
tensor([[7.5373e-08, 3.2768e-02, 3.9179e-03],
        [4.8601e-03, 1.8380e-01, 1.9857e-02],
        [3.9233e-03, 1.3186e+00, 1.4545e-02]])
 
we can see that targets[0][0] = 0.6255 is almost the same as pred_prob[0][0] = 0.6225, 
the targets[1][0] = 0.9 is greater then pred_prob[1][0] = 0.7773 by 0.1227
however, the focal loss[0][0] = 0.0937 larger then focal loss[1][0] = 0.0166 (which against the purpose of focal loss)

for the quality focal loss , it implement the case of targets not equal to 0 or 1

* Update loss.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>