* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
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* Cleanup
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* Fixes
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* Simplify
* space to prefix
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* Improve mAP0.5-0.95
Two changes provided
1. Added limit on the maximum number of detections for each image likewise pycocotools
2. Rework process_batch function
Changes #2 solved issue #4251
I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function.
These changes solve this problem.
Currently during validation yolov5x.pt model the following results were obtained:
from yolov5 validation
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s]
all 5000 36335 0.743 0.626 0.682 0.506
from pycocotools
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685
These results are very close, although not completely pass the competition issue #2258.
I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution.
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* Remove line to retain pycocotools results
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* FROM nvcr.io/nvidia/pytorch:22.04-py3
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* Add redundant weights download mirrors
This PR seeks to improve access worldwide to YOLOv5 weights. Universal access to AI for all is our core value, and we are against any censorship or restriction efforts.
I've uploaded the official YOLOv5 v6.1 weights to a primary backup bucket on GCP and to secondary backup on Google Drive at https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl. Autodownload with try the first two locations (GitHuB release assets and GCP bucket), and point users to the Google Drive folder if the first two fail.
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* support nomedia
* support nomedia for validation
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* Update train.py
* Revert no plot evolve
evolve plots do not contain any images
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contains no media
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* Added a way to skip dependency auto-installation.
Setting the environment variable `YOLOv5_AUTOINSTALL=False` will
skip installing any missing dependencies as if the user had passed
`install=False` to `check_requirements`.
* Cleanup
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* Update `git_describe()`
Add .git path check to avoid `fatal: not a git repository (or any of the parent directories): .git` printout
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* Print dataset scan only `if RANK in (-1, 0)`
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* Simplify callbacks.py return
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* Copy wandb param dict before training to avoid overwrites.
Copy the hyperparameter dict retrieved from wandb configuration before passing it to `train()`. Training overwrites parameters in the dictionary (eg scaling obj/box/cls gains), which causes the values reported in wandb to not match the input values. This is confusing as it makes it hard to reproduce a run, and also throws off wandb's Bayesian sweep algorithm.
* Cleanup
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* fix: disable usage of root logger
`logging.basicConfig` configures Python's root logger. This prohibits
fine control of logging, overwrites logging configuration done outside
the package, and is not best practice. Instead, the used logger is now
configured directly, and the root logger is untouched.
Example:
If yolov5 is used as part of another project with some sophisticated
logging, the internal `logging.basicConfig` call overwrites all the
external configuration.
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* Update general.py
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* Comment kaggle
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* [pre-commit.ci] pre-commit suggestions
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* precommit: yapf
* align isort
* fix
# Conflicts:
# utils/plots.py
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* PyTorch Hub tuple fix
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* PyTorch Hub tuple fix3
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* 🎉🆕 now can read Chinese image path.
use "cv2.imdecode(np.fromfile(f, np.uint8), cv2.IMREAD_COLOR)" instead of "cv2.imread(f)" for Chinese image path.
* Update datasets.py
* Update __init__.py
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Stems not working correctly for YOLOv5l with current .rstrip() implementation. After fix:
```
YOLOv5l summary: 468 layers, 46563709 parameters, 46563709 gradients, 109.3 GFLOPs
```
* Update loss.py
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* fix: add default font as fallback
Add default font as fallback if the downloading of the Arial.ttf font
fails for some reason, e.g. no access to public internet.
* Update plots.py
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* Update AutoAnchor
* Update AutoAnchor
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* Update plots for Chinese fonts
* make is_chinese() non-str safe
* Add global FONT
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* Update general.py
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* New flag 'stop_training' in util.callbacks.Callbacks class to prematurely stop training from callback handler
* Removed most of the new checks, leaving only the one after calling 'on_train_batch_end'
* Cleanup
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* log best result in summary
* comment added
* add space for `flake8`
* log `best/epoch`
* fix `dimension` for epoch
ValueError: all the input arrays must have same number of dimensions
* log `best/` in `utils.logger.__init__`
* fix pre-commit
1. missing whitespace around operator
2. over-indented
Consider that the default value is CIOU，adjust the order of judgment could reduce the number of judgments.
And “elif CIoU:” didn't need 'if'.
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* Check TensorRT>=8.0.0 version
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* Making `ar_thr` available as a hyperparameter
* Disabling ar_thr as hyperparameter and computing from the dataset instead
* Fixing bug in ar_thr computation
* Fix `ar_thr` to 100
* Find out a bug. When set batch_size = -1 to use the autobatch.
reproduce:
* Fix type conflict
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
When OpenCV retrieving image fail, original code would modify source images **inplace**, which may result in plotting bounding boxes on a black image. That is, before inference, source image `im0s[i]` is OK, but after inference before `Process predictions`, `im0s[i]` may have been changed.
* Remove NCOLS from tqdm
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* update
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* Add feature map to save npy files
Add feature map to save npy files,export npy files with 32 feature maps per layer.
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* prune unused imports
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Addresses issues #5374, #5395, #5611
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* Fix to resource warning allocation; utilize file.open within a context manager
* rename fh to f
in keeping with naming convention
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* Created using Colaboratory
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* Fix `save_one_box()`
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Resolves https://github.com/ultralytics/yolov5/pull/5341#issuecomment-961774729
Tests:
```python
import glob
import re
from pathlib import Path
def increment_path(path, exist_ok=False, sep='', mkdir=False):
# Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc.
path = Path(path) # os-agnostic
if path.exists() and not exist_ok:
path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
dirs = glob.glob(f"{path}{sep}*") # similar paths
matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
i = [int(m.groups()[0]) for m in matches if m] # indices
n = max(i) + 1 if i else 2 # increment number
path = Path(f"{path}{sep}{n}{suffix}") # increment path
if mkdir:
path.mkdir(parents=True, exist_ok=True) # make directory
return path
print(increment_path('runs'))
print(increment_path('export.py'))
print(increment_path('abc.def.dir'))
print(increment_path('abc.def.file'))
```
* precommit: isort
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* Meshgrid `indexing='ij'` for PyTorch 1.10
Will not merge currently as breaks backwards compatibility.
* Meshgrid `indexing='ij'` for PyTorch 1.10
Will not merge currently as breaks backwards compatibility.
* Add check_version hard argument
* Update comment
* take EXIF orientation tags into account when fixing corrupt images
* fit 120 char
* sort imports
* Update local exif_transpose comment
We have a local inplace version that is faster than the official as the image is not copied. AutoShape() uses this for Hub models, but here it is not important as the datasets.py usage is infrequent (AutoShape() it is applied every image).
* Update datasets.py
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* take image files with uppercase extensions into account in autosplit
* case fix
* Refactor implementation
Removes additional variable (capital variable names are also only for global variables), and uses the same methodology as implemented earlier in datasets.py L409.
* Remove redundant rglob characters
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Autofix duplicate labels
PR changes duplicate label handling from report error and ignore image-label pair to report warning and autofix image-label pair.
This should fix this common issue for users and allow everyone to get started and get a model trained faster and easier than before.
* sign fix
* Cleanup
* Increment cache version
* all to any fix
* Add train class filter feature to datasets.py
Allows for training on a subset of total classes if `include_class` list is defined on datasets.py L448:
```python
include_class = [] # filter labels to include only these classes (optional)
```
* segments fix
* added callbacks
* added back callback to main
* added save_dir to callback output
* merged in upstream
* removed ghost code
* fixed parsing error for google temp links
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`check_file()` is now limited to searching opt-in directories: /data, /models, /utils. This prevents large non-project directories like /.git and /venv from being searched, which may cause `check_file()` to slow significantly.
Glenn Jocher
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