* Allow to manual selection of device for torchhub models

* single line device

nested torch.device(torch.device(device)) ok

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

* Scope all hubconf.py imports for torch.hub.list()

* Update hubconf.py

* Add verbose and update print to logging

* Fix positonal param

* Revert auto formatting changes

* Update hubconf.py

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

* PyTorch Hub cv2 .save() .show() bug fix

cv2.rectangle() was failing on non-contiguous np array inputs. This checks for contiguous arrays and applies is necessary:
```python
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
```

* Update plots.py

```python
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
```

* Update hubconf.py

Expand CI tests to OpenCV image.

* torch.jit.trace(model, img, strict=False)

* Update check_file()

* Update hubconf.py

* Update README.md

* Speed profiling improvements

* Update torch_utils.py

deepcopy() required to avoid adding elements to model.

* Update torch_utils.py

* YOLOv5 PyTorch Hub models >> check_requirements()

Update YOLOv5 PyTorch Hub requirements.txt path to cache path.

* Update hubconf.py

* Update hubconf.py with check_requirements()

Dependency checks have been missing from YOLOv5 PyTorch Hub model loading, causing errors in some cases when users are attempting to import hub models in unsupported environments. This should examine the YOLOv5 requirements.txt file and pip install any missing or version-conflict packages encountered. 

This is highly experimental (!), please let us know if this creates problems in your custom workflows.

* Update hubconf.py

* PyTorch Hub models default to CUDA:0 if available

* device as string bug fix

* Add autoShape() speed profiling

* Update common.py

* Create README.md

* Update hubconf.py

* cleanuip

* EMA bug fix 2

* update

* Update requirements.txt

* Update ci-testing.yml

* Update hubconf.py

* Add autoshape parameter

* Remove autoshape call in ReadMe

* Update hubconf.py

* file/URI inputs and autoshape check passthrough

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

* Simplify autoshape() post-process

* cleanup

* cleanup

* PyTorch Hub and autoShape update

* comment x for imgs

* reduce comment

* Use pathlib instead of low-level module

* Use pathlib instead of low-level module

* Update detect.py

* Update test.py

* reformat

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

* Increment train, test, detect runs/

* Update ci-testing.yml

* inference/images to data/images

* move images

* runs/exp to runs/train/exp

* update 'results saved to %s' str

* torch 1.7.0 compatibility updates

* add inference verification

* expand imports

* optimize

* miss

* fix