* Update datasets.py to read every 4th frame of streams

* Update datasets.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

@KalenMike

* Update `feature_visualization()`

Only plot for data with height, width > 1

* cleanup

* Cleanup

* Add feature map visualization

Add a feature_visualization function to visualize the mid feature map of the model.

* Update yolo.py

* remove boolean from forward and reorder if statement

* remove print from forward

* General cleanup

* Indent

* Update plots.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* gradient accumulation during warmup in train.py

Context:
`accumulate` is the number of batches/gradients accumulated before calling the next optimizer.step().
During warmup, it is ramped up from 1 to the final value nbs / batch_size. 
Although I have not seen this in other libraries, I like the idea. During warmup, as grads are large, too large steps are more of on issue than gradient noise due to small steps.

The bug:
The condition to perform the opt step is wrong
> if ni % accumulate == 0:
This produces irregular step sizes if `accumulate` is not constant. It becomes relevant when batch_size is small and `accumulate` changes many times during warmup.

This demo also shows the proposed solution, to use a ">=" condition instead:
https://colab.research.google.com/drive/1MA2z2eCXYB_BC5UZqgXueqL_y1Tz_XVq?usp=sharing

Further, I propose not to restrict the number of warmup iterations to >= 1000. If the user changes hyp['warmup_epochs'], this causes unexpected behavior. Also, it makes evolution unstable if this parameter was to be optimized.

* replace last_opt_step tracking by do_step(ni)

* add docstrings

* move down nw

* Update train.py

* revert math import move
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* link to the competition repaired

* Update README.md
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

For seed=0 on single-GPU.

* merge master

* Update get_coco.sh

Co-authored-by: U-LAPTOP-5N89P8V7\banhu <ban.huang@foxmail.com>

* update clip_coords for numpy

* uncomment

* cleanup

* Add autosplits

* fix

* cleanup

* Update tables API and windows path fix

* update dataset check

* Edit Comments for numpy2torch tensor process

Edit Comments for numpy2torch tensor process

* add xyxy2xywhn

add xyxy2xywhn

* add xyxy2xywhn

* formatting

* pass arguments

pass arguments

* edit comment for xyxy2xywhn()

edit comment for xyxy2xywhn()

* cleanup datasets.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Add optional dataset.yaml `path` attribute

@KalenMike

* pass locals to python scripts

* handle lists

* update coco128.yaml

* Capitalize first letter

* add test key

* finalize GlobalWheat2020.yaml

* finalize objects365.yaml

* finalize SKU-110K.yaml

* finalize SKU-110K.yaml

* finalize VisDrone.yaml

* NoneType fix

* update download comment

* voc to VOC

* update

* update VOC.yaml

* update VOC.yaml

* remove dashes

* delete get_voc.sh

* force coco and coco128 to ../datasets

* Capitalize Argoverse_HD.yaml

* Capitalize Objects365.yaml

* update Argoverse_HD.yaml

* coco segments fix

* VOC single-thread

* update Argoverse_HD.yaml

* update data_dict in test handling

* create root

edit comment

Fix for https://github.com/ultralytics/yolov5/issues/3741

@KalenMike

* Fix `img2label_paths()` order

* fix, 1

* Add yolov5/ to path

* rename functions to run()

* cleanup

* rename fix

* CI fix

* cleanup find models/export.py

* Allow config cahnge

* Allow val change in wandb config

* Don't resume transfer learning runs

* Add entity in log dataset

* Update train.py explicit arguments

* Update train.py

* Add run method

* Eliminate `total_batch_size` variable

* cleanup

* Update train.py

* Update DDP for `torch.distributed.run`

* Add LOCAL_RANK

* remove opt.local_rank

* backend="gloo|nccl"

* print

* print

* debug

* debug

* os.getenv

* gloo

* gloo

* gloo

* cleanup

* fix getenv

* cleanup

* cleanup destroy

* try nccl

* return opt

* add --local_rank

* add timeout

* add init_method

* gloo

* move destroy

* move destroy

* move print(opt) under if RANK

* destroy only RANK 0

* move destroy inside train()

* restore destroy outside train()

* update print(opt)

* cleanup

* nccl

* gloo with 60 second timeout

* update namespace printing