YOLOv5 segmentation model support (#9052)

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

for more information, see https://pre-commit.ci

* Fix duplicate plots.py

* Fix check_font()

* # torch.use_deterministic_algorithms(True)

* update doc detect->predict

* Resolve precommit for segment/train and segment/val

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

for more information, see https://pre-commit.ci

* Resolve precommit for utils/segment

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

for more information, see https://pre-commit.ci

* Resolve precommit min_wh

* Resolve precommit utils/segment/plots

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

for more information, see https://pre-commit.ci

* Resolve precommit utils/segment/general

* Align NMS-seg closer to NMS

* restore deterministic init_seeds code

* remove easydict dependency

* update

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

for more information, see https://pre-commit.ci

* restore output_to_target mask

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

for more information, see https://pre-commit.ci

* update

* cleanup

* Remove unused ImageFont import

* Unified NMS

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

for more information, see https://pre-commit.ci

* 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

for more information, see https://pre-commit.ci

* 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

for more information, see https://pre-commit.ci

* fix masks float float issues

* suppress errors

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

for more information, see https://pre-commit.ci

* fix no-predictions plotting bug

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

for more information, see https://pre-commit.ci

* Add CSV Logger

* fix val len(plot_masks)

* speed up evaluation

* fix process_mask

* fix plots

* update segment/utils build_targets

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

for more information, see https://pre-commit.ci

* optimize utils/segment/general crop()

* optimize utils/segment/general crop() 2

* minor updates

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

for more information, see https://pre-commit.ci

* torch.where revert

* downsample only if different shape

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

for more information, see https://pre-commit.ci

* loss cleanup

* loss cleanup 2

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

for more information, see https://pre-commit.ci

* 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

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

for more information, see https://pre-commit.ci

* precommit fix

* precommit fix

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

for more information, see https://pre-commit.ci

* 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

for more information, see https://pre-commit.ci

* 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

for more information, see https://pre-commit.ci

* 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

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

for more information, see https://pre-commit.ci

* Add support for TF export

* remove debugger trace

* add call

* update

* update

* Merge master

* Merge master

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

for more information, see https://pre-commit.ci

* 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

for more information, see https://pre-commit.ci

* 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

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

for more information, see https://pre-commit.ci

* 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

for more information, see https://pre-commit.ci

* 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

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

for more information, see https://pre-commit.ci

* Fix CI error

* fix predict log

* remove upsample

* update interpolate

* fix validation plot logging

* Annotator.masks() cleanup

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

for more information, see https://pre-commit.ci

* 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/tf.py

@@ -30,7 +30,7 @@ from tensorflow import keras
 from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv,
                            DWConvTranspose2d, Focus, autopad)
 from models.experimental import MixConv2d, attempt_load
-from models.yolo import Detect
+from models.yolo import Detect, Segment
 from utils.activations import SiLU
 from utils.general import LOGGER, make_divisible, print_args
 
@@ -320,6 +320,36 @@ class TFDetect(keras.layers.Layer):
         return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
 
 
+class TFSegment(TFDetect):
+    # YOLOv5 Segment head for segmentation models
+    def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
+        super().__init__(nc, anchors, ch, imgsz, w)
+        self.nm = nm  # number of masks
+        self.npr = npr  # number of protos
+        self.no = 5 + nc + self.nm  # number of outputs per anchor
+        self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]  # output conv
+        self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto)  # protos
+        self.detect = TFDetect.call
+
+    def call(self, x):
+        p = self.proto(x[0])
+        x = self.detect(self, x)
+        return (x, p) if self.training else ((x[0], p),)
+
+
+class TFProto(keras.layers.Layer):
+
+    def __init__(self, c1, c_=256, c2=32, w=None):
+        super().__init__()
+        self.cv1 = TFConv(c1, c_, k=3, w=w.cv1)
+        self.upsample = TFUpsample(None, scale_factor=2, mode='nearest')
+        self.cv2 = TFConv(c_, c_, k=3, w=w.cv2)
+        self.cv3 = TFConv(c_, c2, w=w.cv3)
+
+    def call(self, inputs):
+        return self.cv3(self.cv2(self.upsample(self.cv1(inputs))))
+
+
 class TFUpsample(keras.layers.Layer):
     # TF version of torch.nn.Upsample()
     def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
@@ -377,10 +407,12 @@ def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
             args = [ch[f]]
         elif m is Concat:
             c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
-        elif m is Detect:
+        elif m in [Detect, Segment]:
             args.append([ch[x + 1] for x in f])
             if isinstance(args[1], int):  # number of anchors
                 args[1] = [list(range(args[1] * 2))] * len(f)
+            if m is Segment:
+                args[3] = make_divisible(args[3] * gw, 8)
             args.append(imgsz)
         else:
             c2 = ch[f]