YOLOv5 segmentation model support (#9052)

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* Fix duplicate plots.py

* Fix check_font()

* # torch.use_deterministic_algorithms(True)

* update doc detect->predict

* Resolve precommit for segment/train and segment/val

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* Resolve precommit for utils/segment

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* Resolve precommit min_wh

* Resolve precommit utils/segment/plots

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* Resolve precommit utils/segment/general

* Align NMS-seg closer to NMS

* restore deterministic init_seeds code

* remove easydict dependency

* update

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* restore output_to_target mask

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* update

* cleanup

* Remove unused ImageFont import

* Unified NMS

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* DetectMultiBackend compatibility

* segment/predict.py update

* update plot colors

* fix bbox shifted

* sort bbox by confidence

* enable overlap by default

* Merge detect/segment output_to_target() function

* Start segmentation CI

* fix plots

* Update ci-testing.yml

* fix training whitespace

* optimize process mask functions (can we merge both?)

* Update predict/detect

* Update plot_images

* Update plot_images_and_masks

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* fix

* Add train to CI

* fix precommit

* fix precommit CI

* fix precommit pycocotools

* fix val float issues

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* fix masks float float issues

* suppress errors

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* fix no-predictions plotting bug

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* Add CSV Logger

* fix val len(plot_masks)

* speed up evaluation

* fix process_mask

* fix plots

* update segment/utils build_targets

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* optimize utils/segment/general crop()

* optimize utils/segment/general crop() 2

* minor updates

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* torch.where revert

* downsample only if different shape

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* loss cleanup

* loss cleanup 2

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* loss cleanup 3

* update project names

* Rename -seg yamls from _underscore to -dash

* prepare for yolov5n-seg.pt

* precommit space fix

* add coco128-seg.yaml

* update coco128-seg comments

* cleanup val.py

* Major val.py cleanup

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* precommit fix

* precommit fix

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* optional pycocotools

* remove CI pip install pycocotools (auto-installed now)

* seg yaml fix

* optimize mask_iou() and masks_iou()

* threaded fix

* Major train.py update

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* Major segments/val/process_batch() update

* yolov5/val updates from segment

* process_batch numpy/tensor fix

* opt-in to pycocotools with --save-json

* threaded pycocotools ops for 2x speed increase

* Avoid permute contiguous if possible

* Add max_det=300 argument to both val.py and segment/val.py

* fix onnx_dynamic

* speed up pycocotools ops

* faster process_mask(upsample=True) for predict

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* eliminate permutations for process_mask(upsample=True)

* eliminate permute-contiguous in crop(), use native dimension order

* cleanup comment

* Add Proto() module

* fix class count

* fix anchor order

* broadcast mask_gti in loss for speed

* Cleanup seg loss

* faster indexing

* faster indexing fix

* faster indexing fix2

* revert faster indexing

* fix validation plotting

* Loss cleanup and mxyxy simplification

* Loss cleanup and mxyxy simplification 2

* revert validation plotting

* replace missing tanh

* Eliminate last permutation

* delete unneeded .float()

* Remove MaskIOULoss and crop(if HWC)

* Final v6.3 SegmentationModel architecture updates

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* Add support for TF export

* remove debugger trace

* add call

* update

* update

* Merge master

* Merge master

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* Update dataloaders.py

* Restore CI

* Update dataloaders.py

* Fix TF/TFLite export for segmentation model

* Merge master

* Cleanup predict.py mask plotting

* cleanup scale_masks()

* rename scale_masks to scale_image

* cleanup/optimize plot_masks

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* Add Annotator.masks()

* Annotator.masks() fix

* Update plots.py

* Annotator mask optimization

* Rename crop() to crop_mask()

* Do not crop in predict.py

* crop always

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* Merge master

* Add vid-stride from master PR

* Update seg model outputs

* Update seg model outputs

* Add segmentation benchmarks

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* Add segmentation benchmarks

* Add segmentation benchmarks

* Add segmentation benchmarks

* Fix DetectMultiBackend for OpenVINO

* update Annotator.masks

* fix val plot

* revert val plot

* clean up

* revert pil

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* Fix CI error

* fix predict log

* remove upsample

* update interpolate

* fix validation plot logging

* Annotator.masks() cleanup

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* Remove segmentation_model definition

* Restore 0.99999 decimals

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Jiacong Fang <zldrobit@126.com>

utils/metrics.py

@@ -28,7 +28,7 @@ def smooth(y, f=0.05):
     return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
 
 
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16):
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=""):
     """ Compute the average precision, given the recall and precision curves.
     Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
     # Arguments
@@ -83,10 +83,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
     names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
     names = dict(enumerate(names))  # to dict
     if plot:
-        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
-        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
-        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
-        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
+        plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
+        plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
+        plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
+        plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
 
     i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
     p, r, f1 = p[:, i], r[:, i], f1[:, i]