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>

models/common.py

@@ -375,7 +375,6 @@ class DetectMultiBackend(nn.Module):
             if batch_dim.is_static:
                 batch_size = batch_dim.get_length()
             executable_network = ie.compile_model(network, device_name="CPU")  # device_name="MYRIAD" for Intel NCS2
-            output_layer = next(iter(executable_network.outputs))
             stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -491,7 +490,7 @@ class DetectMultiBackend(nn.Module):
             y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
         elif self.xml:  # OpenVINO
             im = im.cpu().numpy()  # FP32
-            y = self.executable_network([im])[self.output_layer]
+            y = list(self.executable_network([im]).values())
         elif self.engine:  # TensorRT
             if self.dynamic and im.shape != self.bindings['images'].shape:
                 i_in, i_out = (self.model.get_binding_index(x) for x in ('images', 'output'))
@@ -786,8 +785,21 @@ class Detections:
         return ''
 
 
+class Proto(nn.Module):
+    # YOLOv5 mask Proto module for segmentation models
+    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
+        super().__init__()
+        self.cv1 = Conv(c1, c_, k=3)
+        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
+        self.cv2 = Conv(c_, c_, k=3)
+        self.cv3 = Conv(c_, c2)
+
+    def forward(self, x):
+        return self.cv3(self.cv2(self.upsample(self.cv1(x))))
+
+
 class Classify(nn.Module):
-    # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
         super().__init__()
         c_ = 1280  # efficientnet_b0 size