Merge `develop` branch into `master` (#3518)
* 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>
pull/3524/head
Glenn Jocher
GitHub
parent
3f03acb3db
commit
f3c3d2ce5d
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@ -2,12 +2,10 @@ name: CI CPU testing
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on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows
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push:
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branches: [ master ]
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branches: [ master, develop ]
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pull_request:
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# The branches below must be a subset of the branches above
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branches: [ master ]
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schedule:
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- cron: '0 0 * * *' # Runs at 00:00 UTC every day
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branches: [ master, develop ]
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jobs:
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cpu-tests:
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@ -10,8 +10,26 @@ jobs:
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- uses: actions/stale@v3
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with:
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repo-token: ${{ secrets.GITHUB_TOKEN }}
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stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
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stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
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stale-issue-message: |
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👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
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Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources:
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- **Wiki** – https://github.com/ultralytics/yolov5/wiki
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- **Tutorials** – https://github.com/ultralytics/yolov5#tutorials
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- **Docs** – https://docs.ultralytics.com
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Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
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- **Ultralytics HUB** – https://ultralytics.com/pricing
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- **Vision API** – https://ultralytics.com/yolov5
|
||||
- **About Us** – https://ultralytics.com/about
|
||||
- **Join Our Team** – https://ultralytics.com/work
|
||||
- **Contact Us** – https://ultralytics.com/contact
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Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
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Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!
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stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
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days-before-stale: 30
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days-before-close: 5
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exempt-issue-labels: 'documentation,tutorial'
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26
README.md
26
README.md
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@ -1,5 +1,5 @@
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<a align="left" href="https://apps.apple.com/app/id1452689527" target="_blank">
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<img width="800" src="https://user-images.githubusercontent.com/26833433/98699617-a1595a00-2377-11eb-8145-fc674eb9b1a7.jpg"></a>
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<img width="850" src="https://user-images.githubusercontent.com/26833433/121094150-72607500-c7ee-11eb-9f39-1d9e4ce89a9e.jpg"></a>
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<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
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@ -30,19 +30,19 @@ This repository represents Ultralytics open-source research into future object d
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[assets]: https://github.com/ultralytics/yolov5/releases
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Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPS<br><sup>640 (B)
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--- |--- |--- |--- |--- |--- |---|--- |---
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[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0
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[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3
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[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4
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[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8
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|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B)
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|--- |--- |--- |--- |--- |--- |---|--- |---
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|[YOLOv5s][assets] |640 |36.7 |36.7 |55.4 |**2.0** | |7.3 |17.0
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|[YOLOv5m][assets] |640 |44.5 |44.5 |63.1 |2.7 | |21.4 |51.3
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|[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4
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|[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8
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| | | | | | || |
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[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4
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[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4
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[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7
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[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9
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|[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4
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|[YOLOv5m6][assets] |1280 |50.5 |50.5 |68.7 |8.4 | |35.9 |52.4
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|[YOLOv5l6][assets] |1280 |53.4 |53.4 |71.1 |12.3 | |77.2 |117.7
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|[YOLOv5x6][assets] |1280 |**54.4** |**54.4** |**72.0** |22.4 | |141.8 |222.9
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| | | | | | || |
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[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-
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|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8 | |- |-
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<details>
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<summary>Table Notes (click to expand)</summary>
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@ -112,7 +112,7 @@ Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, devi
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YOLOv5 v4.0-96-g83dc1b4 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)
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Fusing layers...
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Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
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Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs
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image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.010s)
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image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.011s)
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Results saved to runs/detect/exp2
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@ -28,7 +28,7 @@ def detect(opt):
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# Initialize
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set_logging()
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device = select_device(opt.device)
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half = device.type != 'cpu' # half precision only supported on CUDA
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half = opt.half and device.type != 'cpu' # half precision only supported on CUDA
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# Load model
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model = attempt_load(weights, map_location=device) # load FP32 model
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@ -172,6 +172,7 @@ if __name__ == '__main__':
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parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
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parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
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parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
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parser.add_argument('--half', type=bool, default=False, help='use FP16 half-precision inference')
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opt = parser.parse_args()
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print(opt)
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check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))
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@ -42,8 +42,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
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cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
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model = Model(cfg, channels, classes) # create model
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if pretrained:
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attempt_download(fname) # download if not found locally
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ckpt = torch.load(fname, map_location=torch.device('cpu')) # load
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ckpt = torch.load(attempt_download(fname), map_location=torch.device('cpu')) # load
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msd = model.state_dict() # model state_dict
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csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
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csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter
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@ -116,8 +116,7 @@ def attempt_load(weights, map_location=None, inplace=True):
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# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
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model = Ensemble()
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for w in weights if isinstance(weights, list) else [weights]:
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attempt_download(w)
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ckpt = torch.load(w, map_location=map_location) # load
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ckpt = torch.load(attempt_download(w), map_location=map_location) # load
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model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model
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# Compatibility updates
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@ -44,22 +44,19 @@ if __name__ == '__main__':
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# Load PyTorch model
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device = select_device(opt.device)
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assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
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model = attempt_load(opt.weights, map_location=device) # load FP32 model
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labels = model.names
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# Checks
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# Input
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gs = int(max(model.stride)) # grid size (max stride)
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opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
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assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0'
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# Input
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img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
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# Update model
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if opt.half:
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img, model = img.half(), model.half() # to FP16
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if opt.train:
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model.train() # training mode (no grid construction in Detect layer)
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model.train() if opt.train else model.eval() # training mode = no Detect() layer grid construction
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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@ -96,11 +93,14 @@ if __name__ == '__main__':
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print(f'{prefix} starting export with onnx {onnx.__version__}...')
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f = opt.weights.replace('.pt', '.onnx') # filename
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torch.onnx.export(model, img, f, verbose=False, opset_version=opt.opset_version, input_names=['images'],
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torch.onnx.export(model, img, f, verbose=False, opset_version=opt.opset_version,
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training=torch.onnx.TrainingMode.TRAINING if opt.train else torch.onnx.TrainingMode.EVAL,
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do_constant_folding=not opt.train,
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dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640)
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'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None)
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input_names=['images'],
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output_names=['output'],
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dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
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'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
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} if opt.dynamic else None)
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# Checks
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model_onnx = onnx.load(f) # load onnx model
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@ -21,7 +21,7 @@ from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, s
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select_device, copy_attr
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try:
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import thop # for FLOPS computation
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import thop # for FLOPs computation
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except ImportError:
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thop = None
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@ -140,13 +140,13 @@ class Model(nn.Module):
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x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
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if profile:
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o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS
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o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
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t = time_synchronized()
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for _ in range(10):
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_ = m(x)
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dt.append((time_synchronized() - t) * 100)
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if m == self.model[0]:
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logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s} {'module'}")
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logger.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
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logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
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x = m(x) # run
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@ -27,4 +27,4 @@ pandas
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# extras --------------------------------------
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# Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
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pycocotools>=2.0 # COCO mAP
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thop # FLOPS computation
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thop # FLOPs computation
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6
test.py
6
test.py
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@ -95,7 +95,7 @@ def test(data,
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confusion_matrix = ConfusionMatrix(nc=nc)
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names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
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coco91class = coco80_to_coco91_class()
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s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
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s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
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p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
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loss = torch.zeros(3, device=device)
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jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
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@ -228,7 +228,7 @@ def test(data,
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nt = torch.zeros(1)
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# Print results
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pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format
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pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
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print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
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# Print results per class
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@ -306,6 +306,7 @@ if __name__ == '__main__':
|
|||
parser.add_argument('--project', default='runs/test', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--half', type=bool, default=False, help='use FP16 half-precision inference')
|
||||
opt = parser.parse_args()
|
||||
opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.data = check_file(opt.data) # check file
|
||||
|
|
@ -326,6 +327,7 @@ if __name__ == '__main__':
|
|||
save_txt=opt.save_txt | opt.save_hybrid,
|
||||
save_hybrid=opt.save_hybrid,
|
||||
save_conf=opt.save_conf,
|
||||
half_precision=opt.half,
|
||||
opt=opt
|
||||
)
|
||||
|
||||
|
|
|
|||
70
train.py
70
train.py
|
|
@ -4,6 +4,7 @@ import math
|
|||
import os
|
||||
import random
|
||||
import time
|
||||
import warnings
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
|
|
@ -62,7 +63,6 @@ def train(hyp, opt, device, tb_writer=None):
|
|||
init_seeds(2 + rank)
|
||||
with open(opt.data) as f:
|
||||
data_dict = yaml.safe_load(f) # data dict
|
||||
is_coco = opt.data.endswith('coco.yaml')
|
||||
|
||||
# Logging- Doing this before checking the dataset. Might update data_dict
|
||||
loggers = {'wandb': None} # loggers dict
|
||||
|
|
@ -78,12 +78,13 @@ def train(hyp, opt, device, tb_writer=None):
|
|||
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
|
||||
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
|
||||
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
|
||||
is_coco = opt.data.endswith('coco.yaml') and nc == 80 # COCO dataset
|
||||
|
||||
# Model
|
||||
pretrained = weights.endswith('.pt')
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(rank):
|
||||
attempt_download(weights) # download if not found locally
|
||||
weights = attempt_download(weights) # download if not found locally
|
||||
ckpt = torch.load(weights, map_location=device) # load checkpoint
|
||||
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
|
||||
|
|
@ -323,18 +324,19 @@ def train(hyp, opt, device, tb_writer=None):
|
|||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
||||
s = ('%10s' * 2 + '%10.4g' * 6) % (
|
||||
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||||
pbar.set_description(s)
|
||||
|
||||
# Plot
|
||||
if plots and ni < 3:
|
||||
f = save_dir / f'train_batch{ni}.jpg' # filename
|
||||
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
|
||||
if tb_writer:
|
||||
tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs, strict=False), []) # model graph
|
||||
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
|
||||
if tb_writer and ni == 0:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress jit trace warning
|
||||
tb_writer.add_graph(torch.jit.trace(de_parallel(model), imgs, strict=False), []) # graph
|
||||
elif plots and ni == 10 and wandb_logger.wandb:
|
||||
wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
|
||||
wandb_logger.log({'Mosaics': [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
|
||||
save_dir.glob('train*.jpg') if x.exists()]})
|
||||
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
|
|
@ -358,6 +360,7 @@ def train(hyp, opt, device, tb_writer=None):
|
|||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco and final_epoch,
|
||||
verbose=nc < 50 and final_epoch,
|
||||
plots=plots and final_epoch,
|
||||
wandb_logger=wandb_logger,
|
||||
|
|
@ -409,41 +412,38 @@ def train(hyp, opt, device, tb_writer=None):
|
|||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training
|
||||
if rank in [-1, 0]:
|
||||
# Plots
|
||||
logger.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
|
||||
if plots:
|
||||
plot_results(save_dir=save_dir) # save as results.png
|
||||
if wandb_logger.wandb:
|
||||
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
|
||||
wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
|
||||
if (save_dir / f).exists()]})
|
||||
# Test best.pt
|
||||
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
||||
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
|
||||
for m in [last, best] if best.exists() else [last]: # speed, mAP tests
|
||||
results, _, _ = test.test(opt.data,
|
||||
batch_size=batch_size * 2,
|
||||
imgsz=imgsz_test,
|
||||
conf_thres=0.001,
|
||||
iou_thres=0.7,
|
||||
model=attempt_load(m, device).half(),
|
||||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
save_dir=save_dir,
|
||||
save_json=True,
|
||||
plots=False,
|
||||
is_coco=is_coco)
|
||||
|
||||
# Strip optimizers
|
||||
final = best if best.exists() else last # final model
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if opt.bucket:
|
||||
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
|
||||
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
|
||||
wandb_logger.wandb.log_artifact(str(final), type='model',
|
||||
name='run_' + wandb_logger.wandb_run.id + '_model',
|
||||
aliases=['latest', 'best', 'stripped'])
|
||||
if not opt.evolve:
|
||||
if is_coco: # COCO dataset
|
||||
for m in [last, best] if best.exists() else [last]: # speed, mAP tests
|
||||
results, _, _ = test.test(opt.data,
|
||||
batch_size=batch_size * 2,
|
||||
imgsz=imgsz_test,
|
||||
conf_thres=0.001,
|
||||
iou_thres=0.7,
|
||||
model=attempt_load(m, device).half(),
|
||||
single_cls=opt.single_cls,
|
||||
dataloader=testloader,
|
||||
save_dir=save_dir,
|
||||
save_json=True,
|
||||
plots=False,
|
||||
is_coco=is_coco)
|
||||
|
||||
# Strip optimizers
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if wandb_logger.wandb: # Log the stripped model
|
||||
wandb_logger.wandb.log_artifact(str(best if best.exists() else last), type='model',
|
||||
name='run_' + wandb_logger.wandb_run.id + '_model',
|
||||
aliases=['latest', 'best', 'stripped'])
|
||||
wandb_logger.finish_run()
|
||||
else:
|
||||
dist.destroy_process_group()
|
||||
|
|
|
|||
|
|
@ -517,7 +517,8 @@
|
|||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
|
||||
"<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>",
|
||||
"<a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -529,7 +530,7 @@
|
|||
"<img src=\"https://user-images.githubusercontent.com/26833433/98702494-b71c4e80-237a-11eb-87ed-17fcd6b3f066.jpg\">\n",
|
||||
"\n",
|
||||
"This is the **official YOLOv5 🚀 notebook** authored by **Ultralytics**, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n",
|
||||
"For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com. Thank you!"
|
||||
"For more information please visit https://github.com/ultralytics/yolov5 and https://ultralytics.com. Thank you!"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -610,7 +611,7 @@
|
|||
"YOLOv5 🚀 v5.0-1-g0f395b3 torch 1.8.1+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS\n",
|
||||
"Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs\n",
|
||||
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.008s)\n",
|
||||
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n",
|
||||
"Results saved to runs/detect/exp\n",
|
||||
|
|
@ -733,7 +734,7 @@
|
|||
"100% 168M/168M [00:05<00:00, 32.3MB/s]\n",
|
||||
"\n",
|
||||
"Fusing layers... \n",
|
||||
"Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n",
|
||||
"Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPs\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/val2017' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3102.29it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/val2017.cache\n",
|
||||
" Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:23<00:00, 1.87it/s]\n",
|
||||
|
|
@ -963,7 +964,7 @@
|
|||
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
|
||||
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
|
||||
" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
|
||||
"Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS\n",
|
||||
"Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs\n",
|
||||
"\n",
|
||||
"Transferred 362/362 items from yolov5s.pt\n",
|
||||
"Scaled weight_decay = 0.0005\n",
|
||||
|
|
|
|||
|
|
@ -535,7 +535,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
|
|||
# MixUp https://arxiv.org/pdf/1710.09412.pdf
|
||||
if random.random() < hyp['mixup']:
|
||||
img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
|
||||
r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0
|
||||
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
|
||||
img = (img * r + img2 * (1 - r)).astype(np.uint8)
|
||||
labels = np.concatenate((labels, labels2), 0)
|
||||
|
||||
|
|
@ -655,12 +655,12 @@ def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
|
|||
hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
|
||||
dtype = img.dtype # uint8
|
||||
|
||||
x = np.arange(0, 256, dtype=np.int16)
|
||||
x = np.arange(0, 256, dtype=r.dtype)
|
||||
lut_hue = ((x * r[0]) % 180).astype(dtype)
|
||||
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
|
||||
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
|
||||
|
||||
img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
|
||||
img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
|
||||
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
# YOLOv5 general utils
|
||||
|
||||
import contextlib
|
||||
import glob
|
||||
import logging
|
||||
import math
|
||||
|
|
@ -7,11 +8,13 @@ import os
|
|||
import platform
|
||||
import random
|
||||
import re
|
||||
import subprocess
|
||||
import signal
|
||||
import time
|
||||
import urllib
|
||||
from itertools import repeat
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
from subprocess import check_output
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
|
@ -33,6 +36,26 @@ cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with Py
|
|||
os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads
|
||||
|
||||
|
||||
class timeout(contextlib.ContextDecorator):
|
||||
# Usage: @timeout(seconds) decorator or 'with timeout(seconds):' context manager
|
||||
def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True):
|
||||
self.seconds = int(seconds)
|
||||
self.timeout_message = timeout_msg
|
||||
self.suppress = bool(suppress_timeout_errors)
|
||||
|
||||
def _timeout_handler(self, signum, frame):
|
||||
raise TimeoutError(self.timeout_message)
|
||||
|
||||
def __enter__(self):
|
||||
signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
|
||||
signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
signal.alarm(0) # Cancel SIGALRM if it's scheduled
|
||||
if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
|
||||
return True
|
||||
|
||||
|
||||
def set_logging(rank=-1, verbose=True):
|
||||
logging.basicConfig(
|
||||
format="%(message)s",
|
||||
|
|
@ -53,12 +76,12 @@ def get_latest_run(search_dir='.'):
|
|||
|
||||
|
||||
def is_docker():
|
||||
# Is environment a Docker container
|
||||
# Is environment a Docker container?
|
||||
return Path('/workspace').exists() # or Path('/.dockerenv').exists()
|
||||
|
||||
|
||||
def is_colab():
|
||||
# Is environment a Google Colab instance
|
||||
# Is environment a Google Colab instance?
|
||||
try:
|
||||
import google.colab
|
||||
return True
|
||||
|
|
@ -66,6 +89,11 @@ def is_colab():
|
|||
return False
|
||||
|
||||
|
||||
def is_pip():
|
||||
# Is file in a pip package?
|
||||
return 'site-packages' in Path(__file__).absolute().parts
|
||||
|
||||
|
||||
def emojis(str=''):
|
||||
# Return platform-dependent emoji-safe version of string
|
||||
return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
|
||||
|
|
@ -80,13 +108,13 @@ def check_online():
|
|||
# Check internet connectivity
|
||||
import socket
|
||||
try:
|
||||
socket.create_connection(("1.1.1.1", 443), 5) # check host accesability
|
||||
socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
|
||||
return True
|
||||
except OSError:
|
||||
return False
|
||||
|
||||
|
||||
def check_git_status():
|
||||
def check_git_status(err_msg=', for updates see https://github.com/ultralytics/yolov5'):
|
||||
# Recommend 'git pull' if code is out of date
|
||||
print(colorstr('github: '), end='')
|
||||
try:
|
||||
|
|
@ -95,9 +123,9 @@ def check_git_status():
|
|||
assert check_online(), 'skipping check (offline)'
|
||||
|
||||
cmd = 'git fetch && git config --get remote.origin.url'
|
||||
url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url
|
||||
branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
|
||||
n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
|
||||
url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git') # git fetch
|
||||
branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out
|
||||
n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind
|
||||
if n > 0:
|
||||
s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \
|
||||
f"Use 'git pull' to update or 'git clone {url}' to download latest."
|
||||
|
|
@ -105,7 +133,7 @@ def check_git_status():
|
|||
s = f'up to date with {url} ✅'
|
||||
print(emojis(s)) # emoji-safe
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print(f'{e}{err_msg}')
|
||||
|
||||
|
||||
def check_python(minimum='3.7.0', required=True):
|
||||
|
|
@ -135,10 +163,11 @@ def check_requirements(requirements='requirements.txt', exclude=()):
|
|||
try:
|
||||
pkg.require(r)
|
||||
except Exception as e: # DistributionNotFound or VersionConflict if requirements not met
|
||||
n += 1
|
||||
print(f"{prefix} {r} not found and is required by YOLOv5, attempting auto-update...")
|
||||
try:
|
||||
print(subprocess.check_output(f"pip install '{r}'", shell=True).decode())
|
||||
assert check_online(), f"'pip install {r}' skipped (offline)"
|
||||
print(check_output(f"pip install '{r}'", shell=True).decode())
|
||||
n += 1
|
||||
except Exception as e:
|
||||
print(f'{prefix} {e}')
|
||||
|
||||
|
|
@ -178,7 +207,8 @@ def check_file(file):
|
|||
if Path(file).is_file() or file == '': # exists
|
||||
return file
|
||||
elif file.startswith(('http://', 'https://')): # download
|
||||
url, file = file, Path(file).name
|
||||
url, file = file, Path(urllib.parse.unquote(str(file))).name # url, file (decode '%2F' to '/' etc.)
|
||||
file = file.split('?')[0] # parse authentication https://url.com/file.txt?auth...
|
||||
print(f'Downloading {url} to {file}...')
|
||||
torch.hub.download_url_to_file(url, file)
|
||||
assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import os
|
|||
import platform
|
||||
import subprocess
|
||||
import time
|
||||
import urllib
|
||||
from pathlib import Path
|
||||
|
||||
import requests
|
||||
|
|
@ -16,11 +17,39 @@ def gsutil_getsize(url=''):
|
|||
return eval(s.split(' ')[0]) if len(s) else 0 # bytes
|
||||
|
||||
|
||||
def attempt_download(file, repo='ultralytics/yolov5'):
|
||||
def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
|
||||
# Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
|
||||
file = Path(file)
|
||||
assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
|
||||
try: # url1
|
||||
print(f'Downloading {url} to {file}...')
|
||||
torch.hub.download_url_to_file(url, str(file))
|
||||
assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check
|
||||
except Exception as e: # url2
|
||||
file.unlink(missing_ok=True) # remove partial downloads
|
||||
print(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
|
||||
os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -") # curl download, retry and resume on fail
|
||||
finally:
|
||||
if not file.exists() or file.stat().st_size < min_bytes: # check
|
||||
file.unlink(missing_ok=True) # remove partial downloads
|
||||
print(f"ERROR: {assert_msg}\n{error_msg}")
|
||||
print('')
|
||||
|
||||
|
||||
def attempt_download(file, repo='ultralytics/yolov5'): # from utils.google_utils import *; attempt_download()
|
||||
# Attempt file download if does not exist
|
||||
file = Path(str(file).strip().replace("'", ''))
|
||||
|
||||
if not file.exists():
|
||||
# URL specified
|
||||
name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc.
|
||||
if str(file).startswith(('http:/', 'https:/')): # download
|
||||
url = str(file).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
name = name.split('?')[0] # parse authentication https://url.com/file.txt?auth...
|
||||
safe_download(file=name, url=url, min_bytes=1E5)
|
||||
return name
|
||||
|
||||
# GitHub assets
|
||||
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
|
||||
try:
|
||||
response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api
|
||||
|
|
@ -34,27 +63,14 @@ def attempt_download(file, repo='ultralytics/yolov5'):
|
|||
except:
|
||||
tag = 'v5.0' # current release
|
||||
|
||||
name = file.name
|
||||
if name in assets:
|
||||
msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
|
||||
redundant = False # second download option
|
||||
try: # GitHub
|
||||
url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
|
||||
print(f'Downloading {url} to {file}...')
|
||||
torch.hub.download_url_to_file(url, file)
|
||||
assert file.exists() and file.stat().st_size > 1E6 # check
|
||||
except Exception as e: # GCP
|
||||
print(f'Download error: {e}')
|
||||
assert redundant, 'No secondary mirror'
|
||||
url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
|
||||
print(f'Downloading {url} to {file}...')
|
||||
os.system(f"curl -L '{url}' -o '{file}' --retry 3 -C -") # curl download, retry and resume on fail
|
||||
finally:
|
||||
if not file.exists() or file.stat().st_size < 1E6: # check
|
||||
file.unlink(missing_ok=True) # remove partial downloads
|
||||
print(f'ERROR: Download failure: {msg}')
|
||||
print('')
|
||||
return
|
||||
safe_download(file,
|
||||
url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
|
||||
# url2=f'https://storage.googleapis.com/{repo}/ckpt/{name}', # backup url (optional)
|
||||
min_bytes=1E5,
|
||||
error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/')
|
||||
|
||||
return str(file)
|
||||
|
||||
|
||||
def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import torch.nn.functional as F
|
|||
import torchvision
|
||||
|
||||
try:
|
||||
import thop # for FLOPS computation
|
||||
import thop # for FLOPs computation
|
||||
except ImportError:
|
||||
thop = None
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
@ -105,13 +105,13 @@ def profile(x, ops, n=100, device=None):
|
|||
x = x.to(device)
|
||||
x.requires_grad = True
|
||||
print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
|
||||
print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
|
||||
print(f"\n{'Params':>12s}{'GFLOPs':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
|
||||
for m in ops if isinstance(ops, list) else [ops]:
|
||||
m = m.to(device) if hasattr(m, 'to') else m # device
|
||||
m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type
|
||||
dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward
|
||||
try:
|
||||
flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS
|
||||
flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
|
||||
except:
|
||||
flops = 0
|
||||
|
||||
|
|
@ -219,13 +219,13 @@ def model_info(model, verbose=False, img_size=640):
|
|||
print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
|
||||
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
|
||||
|
||||
try: # FLOPS
|
||||
try: # FLOPs
|
||||
from thop import profile
|
||||
stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
|
||||
img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input
|
||||
flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS
|
||||
flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
|
||||
img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float
|
||||
fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS
|
||||
fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPs
|
||||
except (ImportError, Exception):
|
||||
fs = ''
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue