* Add cache-on-disk and cache-directory to cache images on disk

* Fix load_image with cache_on_disk

* Add no_cache flag for load_image

* Revert the parts('logging' and a new line) that do not need to be modified

* Add the assertion for shapes of cached images

* Add a suffix string for cached images

* Fix boundary-error of letterbox for load_mosaic

* Add prefix as cache-key of cache-on-disk

* Update cache-function on disk

* Add psutil in requirements.txt

* Update train.py

* Cleanup1

* Cleanup2

* Skip existing npy

* Include re-space

* Export return character fix

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

* Addition refactor `export.py`

* Update export.py

* Refactor `export.py`

* cleanup

* Update check_requirements()

* Update export.py

* 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>

* Check CoreML models.train() mode

* Update export.py

* 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>

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

* Update export.py

* Add file_size() function

* Update export.py

* Update export.py for 2 dry runs

* Update export.py

* 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>

* 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

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

* Update check_file()

* Update hubconf.py

* Update README.md

* option for skip last layer and cuda export support

* added parameter device

* fix import

* cleanup 1

* cleanup 2

* opt-in grid

--grid will export with grid computation, default export will skip grid (same as current)

* default --device cpu

GPU export causes ONNX and CoreML errors.

Co-authored-by: Jan Hajek <jan.hajek@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* add: dynamic onnx export

* delete: test onnx inference

* fix dynamic output axis

* Code reduction

* fix: dynamic output axes, dynamic input naming

* Remove fixed axes

Co-authored-by: Shivam Swanrkar <ss8464@nyu.edu>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

img size checks are warnings rather than errors, so current implementation allows improperly formed model inputs.

* Update ci-testing.yml

* Update yolo.py

* Update ci-testing.yml

* Update export.py

* Update yolo.py

* Add logging setup

* Fix fusing layers message

* Fix logging does not have end

* Add logging

* Change logging to use logger

* Update yolo.py

I tried this in a cloned branch, and everything seems to work fine

* Update yolo.py

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

* expand imports

* optimize

* miss

* fix