* 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>

* 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

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

* update ci-testing.yml (#3322)

* update ci-testing.yml

* update greetings.yml

* bring back os matrix

* update ci-testing.yml (#3322)

* update ci-testing.yml

* update greetings.yml

* bring back os matrix

* Enable direct `--weights URL` definition (#3373)

* Enable direct `--weights URL` definition

@KalenMike this PR will enable direct --weights URL definition. Example use case:
```
python train.py --weights https://storage.googleapis.com/bucket/dir/model.pt
```

* cleanup

* bug fixes

* weights = attempt_download(weights)

* Update experimental.py

* Update hubconf.py

* return bug fix

* comment mirror

* min_bytes

* Update tutorial.ipynb (#3368)

add Open in Kaggle badge

* `cv2.imread(img, -1)` for IMREAD_UNCHANGED (#3379)

* Update datasets.py

* comment

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

* COCO evolution fix (#3388)

* COCO evolution fix

* cleanup

* update print

* print fix

* Create `is_pip()` function (#3391)

Returns `True` if file is part of pip package. Useful for contextual behavior modification.

```python
def is_pip():
    # Is file in a pip package?
    return 'site-packages' in Path(__file__).absolute().parts
```

* Revert "`cv2.imread(img, -1)` for IMREAD_UNCHANGED (#3379)" (#3395)

This reverts commit 21a9607e00.

* Update FLOPs description (#3422)

* Update README.md

* Changing FLOPS to FLOPs.

Co-authored-by: BuildTools <unconfigured@null.spigotmc.org>

* Parse URL authentication (#3424)

* Parse URL authentication

* urllib.parse.unquote()

* improved error handling

* improved error handling

* remove %3F

* update check_file()

* Add FLOPs title to table (#3453)

* Suppress jit trace warning + graph once (#3454)

* Suppress jit trace warning + graph once

Suppress harmless jit trace warning on TensorBoard add_graph call. Also fix multiple add_graph() calls bug, now only on batch 0.

* Update train.py

* Update MixUp augmentation `alpha=beta=32.0` (#3455)

Per VOC empirical results https://github.com/ultralytics/yolov5/issues/3380#issuecomment-853001307 by @developer0hye

* Add `timeout()` class (#3460)

* Add `timeout()` class

* rearrange order

* Faster HSV augmentation (#3462)

remove datatype conversion process that can be skipped

* Add `check_git_status()` 5 second timeout (#3464)

* Add check_git_status() 5 second timeout

This should prevent the SSH Git bug that we were discussing @KalenMike

* cleanup

* replace timeout with check_output built-in timeout

* Improved `check_requirements()` offline-handling (#3466)

Improve robustness of `check_requirements()` function to offline environments (do not attempt pip installs when offline).

* Add `output_names` argument for ONNX export with dynamic axes (#3456)

* Add output names & dynamic axes for onnx export

Add output_names and dynamic_axes names for all outputs in torch.onnx.export. The first four outputs of the model will have names output0, output1, output2, output3

* use first output only + cleanup

Co-authored-by: Samridha Shrestha <samridha.shrestha@g42.ai>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Revert FP16 `test.py` and `detect.py` inference to FP32 default (#3423)

* fixed inference bug ,while use half precision

* replace --use-half with --half

* replace space and PEP8 in detect.py

* PEP8 detect.py

* update --half help comment

* Update test.py

* revert space

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

* Add additional links/resources to stale.yml message (#3467)

* Update stale.yml

* cleanup

* Update stale.yml

* reformat

* Update stale.yml HUB URL (#3468)

* Stale `github.actor` bug fix (#3483)

* Explicit `model.eval()` call `if opt.train=False` (#3475)

* call model.eval() when opt.train is False

call model.eval() when opt.train is False

* single-line if statement

* cleanup

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

* check_requirements() exclude `opencv-python` (#3495)

Fix for 3rd party or contrib versions of installed OpenCV as in https://github.com/ultralytics/yolov5/issues/3494.

* Earlier `assert` for cpu and half option (#3508)

* early assert for cpu and half option

early assert for cpu and half option

* Modified comment

Modified comment

* Update tutorial.ipynb (#3510)

* Reduce test.py results spacing (#3511)

* Update README.md (#3512)

* Update README.md

Minor modifications

* 850 width

* Update greetings.yml

revert greeting change as PRs will now merge to master.

Co-authored-by: Piotr Skalski <SkalskiP@users.noreply.github.com>
Co-authored-by: SkalskiP <piotr.skalski92@gmail.com>
Co-authored-by: Peretz Cohen <pizzaz93@users.noreply.github.com>
Co-authored-by: tudoulei <34886368+tudoulei@users.noreply.github.com>
Co-authored-by: chocosaj <chocosaj@users.noreply.github.com>
Co-authored-by: BuildTools <unconfigured@null.spigotmc.org>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Sam_S <SamSamhuns@users.noreply.github.com>
Co-authored-by: Samridha Shrestha <samridha.shrestha@g42.ai>
Co-authored-by: edificewang <609552430@qq.com>

* Enable direct `--weights URL` definition

@KalenMike this PR will enable direct --weights URL definition. Example use case:
```
python train.py --weights https://storage.googleapis.com/bucket/dir/model.pt
```

* cleanup

* bug fixes

* weights = attempt_download(weights)

* Update experimental.py

* Update hubconf.py

* return bug fix

* comment mirror

* min_bytes

This fix should allow for visualizing YOLOv5 model graphs correctly in Tensorboard by uncommenting line 335 in train.py:
```python
                    if tb_writer:
                        tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), [])  # add model graph
```

The problem was that the detect() layer checks the input size to adapt the grid if required, and tracing does not seem to like this shape check (even if the shape is fine and no grid recomputation is required). The following will warn:
0cae7576a9/train.py (L335)

Solution is below. This is a YOLOv5s model displayed in TensorBoard. You can see the Detect() layer merging the 3 layers into a single output for example, and everything appears to work and visualize correctly.
```python
tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), [])
```
<img width="893" alt="Screenshot 2021-04-11 at 01 10 09" src="https://user-images.githubusercontent.com/26833433/114286928-349bd600-9a63-11eb-941f-7139ee6cd602.png">

Revert unintentional change to test batch sizes caused by PR https://github.com/ultralytics/yolov5/pull/2125

* Update tensorboard>=2.4.1 

Update tensorboard version to attempt to address https://github.com/ultralytics/yolov5/issues/2573 (tensorboard logging fail in Docker image).

* cleanup