YOLOv5 segmentation model support (#9052)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Resolve precommit min_wh

* Resolve precommit utils/segment/plots

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fix

* Add train to CI

* fix precommit

* fix precommit CI

* fix precommit pycocotools

* fix val float issues

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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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>

.github/workflows/ci-testing.yml

@@ -15,6 +15,7 @@ jobs:
   Benchmarks:
     runs-on: ${{ matrix.os }}
     strategy:
+      fail-fast: false
       matrix:
         os: [ ubuntu-latest ]
         python-version: [ '3.9' ]  # requires python<=3.9
@@ -37,9 +38,12 @@ jobs:
           python --version
           pip --version
           pip list
-      - name: Run benchmarks
+      - name: Benchmark DetectionModel
         run: |
-          python utils/benchmarks.py --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29
+          python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29
+      - name: Benchmark SegmentationModel
+        run: |
+          python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320
 
   Tests:
     timeout-minutes: 60
@@ -126,6 +130,20 @@ jobs:
               model(im)  # warmup, build grids for trace
               torch.jit.trace(model, [im])
           EOF
+      - name: Test segmentation
+        shell: bash  # for Windows compatibility
+        run: |
+          m=${{ matrix.model }}-seg  # official weights
+          b=runs/train-seg/exp/weights/best  # best.pt checkpoint
+          python segment/train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu  # train
+          python segment/train.py --imgsz 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device cpu  # train
+          for d in cpu; do  # devices
+            for w in $m $b; do  # weights
+              python segment/val.py --imgsz 64 --batch 32 --weights $w.pt --device $d  # val
+              python segment/predict.py --imgsz 64 --weights $w.pt --device $d  # predict
+              python export.py --weights $w.pt --img 64 --include torchscript --device $d  # export
+            done
+          done
       - name: Test classification
         shell: bash  # for Windows compatibility
         run: |