* `Detections()` class `print()` overload

* Update common.py

* fix visualize error

* Revert "fix visualize error"

* add visualise profile

Fix inference from PIL source.

* Feature visualization update

* Save to jpg (faster)

* Save to png

* Update BGR2RGB ops

* speed improvements

* cleanup

* rotating an image according to its exif tag

* Update common.py

* Update datasets.py

* Update datasets.py

faster

* delete extraneous gpg file

* Update common.py

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

* 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>

* Add yolov5/ to path

* rename functions to run()

* cleanup

* rename fix

* CI fix

* cleanup find models/export.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

* Slightly modify CLI execution

This simple change makes it easier to run the primary functions of this
repo (train/detect/test) from within Python. An object which represents
`opt` can be constructed and fed to the `main` function of each of these
modules, rather than having to call the lower level functions directly,
or run the module as a script.

* Update export.py

Add CLI parsing update for more convenient module usage within Python.

Co-authored-by: Lewis Belcher <lb@desupervised.io>

* Refactor models/export.py arguments

* cleanup

* cleanup

* early assert for cpu and half option

early assert for cpu and half option

* Modified comment

Modified comment

* 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>

* 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>

* Update README.md

* Changing FLOPS to FLOPs.

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

* Check CoreML models.train() mode

* Update export.py

* Added max_det parameters in various places

* 120 character line

* PEP8

* 120 character line

* Update inference default to 1000 instances

* Update inference default to 1000 instances

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

* rename class autoShape -> AutoShape

follow other class naming convention

* rename class autoShape -> AutoShape

follow other classes' naming convention

* rename class autoShape -> AutoShape

* Update yolo.py

* Update export.py

* fix export grid

* Update export.py, remove detect export attribute

* rearrange if order

* remove --grid, default inplace=False

* rename exp_dynamic to onnx_dynamic, comment

* replace bs with 1 in anchor_grid[i] index 0

* Update export.py

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

* Added support for fp16 (half) to export.py

* minimize code additions

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

* Added flag to enable/disable all inplace and assignment operations

* Removed shape print statements

* Scope Detect/Model import to avoid circular dependency

* PEP8

* create _descale_pred()

* replace lost space

* replace list with tuple

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

* Add yolov5/ to sys.path() for *.py subdir exec

* Update export.py

* Improved yolo.py profiling

Improved column order and labelling.

* Update yolo.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>

* Add file_size() function

* Update export.py

* Update export.py for 2 dry runs

* Update export.py

* Implement yaml.safe_load()

* yaml.safe_dump()

* Update detect.py

* Update detect.py

* Update greetings.yml

* Update cropping

* cleanup

* Update increment_path()

* Update common.py

* Update detect.py

* Update detect.py

* Update detect.py

* Update common.py

* cleanup

* Update detect.py

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

* Ensure dynamic export works succesfully, onnx simplifier optional

* Update export.py

* add dashes

Co-authored-by: Tim <tim.stokman@hal24k.com>
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.

* ONNX Simplifier

Add ONNX Simplifier to ONNX export pipeline in export.py. Will auto-install onnx-simplifier if onnx is installed but onnx-simplifier is not.

* Update general.py