* Improve mAP0.5-0.95

Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function

Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.

Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00,  2.33it/s]
                 all       5000      36335      0.743      0.626      0.682      0.506
from pycocotools
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.505
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.685

These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.

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* Remove line to retain pycocotools results

* Update val.py

* Update val.py

* Remove to device op

* Higher precision int conversion

* Update val.py

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

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* precommit: yapf

* align isort

* fix

# Conflicts:
#	utils/plots.py

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Fix attempt for https://github.com/ultralytics/yolov5/issues/6626

* legend-labels Adjust legend labels for classes without instances.

* #5158 Re-indexed series names: only classes containing data.

* #5158 Re-indexed series names: only classes containing data.

* Cleanup

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

This PR brings alignment in AP computation practices with Detectron2 and MMDetection. 

Problem first noted by @yusiyoh in https://github.com/ultralytics/yolov5/issues/4546