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ultralytics 8.0.50 AMP check and YOLOv5u YAMLs (#1263)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Troy <wudashuo@vip.qq.com> Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
3861e6c82a
commit
f0d8e4718b
@ -55,7 +55,7 @@ include train, val, and predict.
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| mode | 'train' | YOLO mode, i.e. train, val, predict, or export |
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| resume | False | resume training from last checkpoint or custom checkpoint if passed as resume=path/to/best.pt |
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| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
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| data | null | path to data file, i.e. i.e. coco128.yaml |
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| data | null | path to data file, i.e. coco128.yaml |
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### Training
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@ -69,7 +69,7 @@ task.
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| Key | Value | Description |
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|-----------------|--------|--------------------------------------------------------------------------------|
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| model | null | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
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| data | null | path to data file, i.e. i.e. coco128.yaml |
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| data | null | path to data file, i.e. coco128.yaml |
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| epochs | 100 | number of epochs to train for |
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| patience | 50 | epochs to wait for no observable improvement for early stopping of training |
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| batch | 16 | number of images per batch (-1 for AutoBatch) |
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@ -47,7 +47,7 @@ source can be used as a stream and the model argument required for that source.
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| CSV | | `'sources.csv'` | `str`, `Path` | RTSP, RTMP, HTTP |
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| video | ✓ | `'vid.mp4'` | `str`, `Path` | |
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| directory | ✓ | `'path/'` | `str`, `Path` | |
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| glob | ✓ | `path/*.jpg'` | `str` | Use `*` operator |
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| glob | ✓ | `'path/*.jpg'` | `str` | Use `*` operator |
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| YouTube | ✓ | `'https://youtu.be/Zgi9g1ksQHc'` | `str` | |
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| stream | ✓ | `'rtsp://example.com/media.mp4'` | `str` | RTSP, RTMP, HTTP |
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@ -49,6 +49,8 @@ def test_predict_dir():
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def test_predict_img():
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model = YOLO(MODEL)
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seg_model = YOLO('yolov8n-seg.pt')
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cls_model = YOLO('yolov8n-cls.pt')
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im = cv2.imread(str(SOURCE))
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assert len(model(source=Image.open(SOURCE), save=True, verbose=True)) == 1 # PIL
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assert len(model(source=im, save=True, save_txt=True)) == 1 # ndarray
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@ -64,6 +66,18 @@ def test_predict_img():
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np.zeros((320, 640, 3))] # numpy
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assert len(model(batch)) == len(batch) # multiple sources in a batch
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# Test tensor inference
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im = cv2.imread(str(SOURCE)) # OpenCV
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t = cv2.resize(im, (32, 32))
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t = torch.from_numpy(t.transpose((2, 0, 1)))
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t = torch.stack([t, t, t, t])
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results = model(t)
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assert len(results) == t.shape[0]
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results = seg_model(t)
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assert len(results) == t.shape[0]
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results = cls_model(t)
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assert len(results) == t.shape[0]
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def test_predict_grey_and_4ch():
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model = YOLO(MODEL)
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@ -199,3 +213,6 @@ def test_result():
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res = model(SOURCE)
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res[0].plot()
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print(res[0].path)
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test_predict_img()
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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__version__ = '8.0.49'
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__version__ = '8.0.50'
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from ultralytics.yolo.engine.model import YOLO
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from ultralytics.yolo.utils.checks import check_yolo as checks
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55
ultralytics/models/v5/yolov5l6u.yaml
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55
ultralytics/models/v5/yolov5l6u.yaml
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@ -0,0 +1,55 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 1.0 # model depth multiple
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width_multiple: 1.0 # layer channel multiple
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C3, [768]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 11
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]
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# YOLOv5 v6.0 head
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head:
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[[-1, 1, Conv, [768, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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[-1, 3, C3, [768, False]], # 15
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[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 19
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 23 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 20], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 16], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [768, False]], # 29 (P5/32-large)
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[-1, 1, Conv, [768, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P6
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[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
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[[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6)
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]
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55
ultralytics/models/v5/yolov5m6u.yaml
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55
ultralytics/models/v5/yolov5m6u.yaml
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@ -0,0 +1,55 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.67 # model depth multiple
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width_multiple: 0.75 # layer channel multiple
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C3, [768]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 11
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]
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# YOLOv5 v6.0 head
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head:
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[[-1, 1, Conv, [768, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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[-1, 3, C3, [768, False]], # 15
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[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 19
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 23 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 20], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 16], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [768, False]], # 29 (P5/32-large)
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[-1, 1, Conv, [768, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P6
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[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
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[[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6)
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]
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55
ultralytics/models/v5/yolov5n6u.yaml
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55
ultralytics/models/v5/yolov5n6u.yaml
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@ -0,0 +1,55 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.25 # layer channel multiple
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C3, [768]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 11
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]
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# YOLOv5 v6.0 head
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head:
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[[-1, 1, Conv, [768, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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[-1, 3, C3, [768, False]], # 15
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[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 19
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 23 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 20], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 16], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [768, False]], # 29 (P5/32-large)
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[-1, 1, Conv, [768, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P6
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[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
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[[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6)
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]
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55
ultralytics/models/v5/yolov5s6u.yaml
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55
ultralytics/models/v5/yolov5s6u.yaml
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.50 # layer channel multiple
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C3, [768]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 11
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]
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# YOLOv5 v6.0 head
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head:
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[[-1, 1, Conv, [768, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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[-1, 3, C3, [768, False]], # 15
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[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 19
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 23 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 20], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 16], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [768, False]], # 29 (P5/32-large)
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[-1, 1, Conv, [768, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P6
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[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
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[[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6)
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]
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@ -5,7 +5,6 @@ nc: 80 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.50 # layer channel multiple
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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55
ultralytics/models/v5/yolov5x6u.yaml
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55
ultralytics/models/v5/yolov5x6u.yaml
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# Ultralytics YOLO 🚀, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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depth_multiple: 1.33 # model depth multiple
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width_multiple: 1.25 # layer channel multiple
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C3, [768]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 11
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]
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# YOLOv5 v6.0 head
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head:
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[[-1, 1, Conv, [768, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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[-1, 3, C3, [768, False]], # 15
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[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 19
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 23 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 20], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 16], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [768, False]], # 29 (P5/32-large)
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[-1, 1, Conv, [768, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P6
|
||||
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
|
||||
|
||||
[[23, 26, 29, 32], 1, Detect, [nc]], # Detect(P3, P4, P5, P6)
|
||||
]
|
||||
@ -27,6 +27,10 @@ def check_class_names(names):
|
||||
if isinstance(names, dict):
|
||||
if not all(isinstance(k, int) for k in names.keys()): # convert string keys to int, i.e. '0' to 0
|
||||
names = {int(k): v for k, v in names.items()}
|
||||
n = len(names)
|
||||
if max(names.keys()) >= n:
|
||||
raise KeyError(f'{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices '
|
||||
f'{min(names.keys())}-{max(names.keys())} defined in your dataset YAML.')
|
||||
if isinstance(names[0], str) and names[0].startswith('n0'): # imagenet class codes, i.e. 'n01440764'
|
||||
map = yaml_load(ROOT / 'datasets/ImageNet.yaml')['map'] # human-readable names
|
||||
names = {k: map[v] for k, v in names.items()}
|
||||
@ -35,12 +39,14 @@ def check_class_names(names):
|
||||
|
||||
class AutoBackend(nn.Module):
|
||||
|
||||
def _apply_default_class_names(self, data):
|
||||
with contextlib.suppress(Exception):
|
||||
return yaml_load(check_yaml(data))['names']
|
||||
return {i: f'class{i}' for i in range(999)} # return default if above errors
|
||||
|
||||
def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
|
||||
def __init__(self,
|
||||
weights='yolov8n.pt',
|
||||
device=torch.device('cpu'),
|
||||
dnn=False,
|
||||
data=None,
|
||||
fp16=False,
|
||||
fuse=True,
|
||||
verbose=True):
|
||||
"""
|
||||
MultiBackend class for python inference on various platforms using Ultralytics YOLO.
|
||||
|
||||
@ -51,6 +57,7 @@ class AutoBackend(nn.Module):
|
||||
data (str), (Path): Additional data.yaml file for class names, optional
|
||||
fp16 (bool): If True, use half precision. Default: False
|
||||
fuse (bool): Whether to fuse the model or not. Default: True
|
||||
verbose (bool): Whether to run in verbose mode or not. Default: True
|
||||
|
||||
Supported formats and their naming conventions:
|
||||
| Format | Suffix |
|
||||
@ -83,7 +90,7 @@ class AutoBackend(nn.Module):
|
||||
# NOTE: special case: in-memory pytorch model
|
||||
if nn_module:
|
||||
model = weights.to(device)
|
||||
model = model.fuse() if fuse else model
|
||||
model = model.fuse(verbose=verbose) if fuse else model
|
||||
names = model.module.names if hasattr(model, 'module') else model.names # get class names
|
||||
stride = max(int(model.stride.max()), 32) # model stride
|
||||
model.half() if fp16 else model.float()
|
||||
@ -410,6 +417,12 @@ class AutoBackend(nn.Module):
|
||||
for _ in range(2 if self.jit else 1): #
|
||||
self.forward(im) # warmup
|
||||
|
||||
@staticmethod
|
||||
def _apply_default_class_names(data):
|
||||
with contextlib.suppress(Exception):
|
||||
return yaml_load(check_yaml(data))['names']
|
||||
return {i: f'class{i}' for i in range(999)} # return default if above errors
|
||||
|
||||
@staticmethod
|
||||
def _model_type(p='path/to/model.pt'):
|
||||
"""
|
||||
|
||||
@ -8,9 +8,7 @@ import thop
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
|
||||
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
|
||||
GhostBottleneck, GhostConv, Segment)
|
||||
from ultralytics.nn.modules import * # noqa: F403
|
||||
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
|
||||
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
|
||||
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
|
||||
@ -87,7 +85,7 @@ class BaseModel(nn.Module):
|
||||
if c:
|
||||
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
||||
|
||||
def fuse(self):
|
||||
def fuse(self, verbose=True):
|
||||
"""
|
||||
Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
|
||||
computation efficiency.
|
||||
@ -105,7 +103,7 @@ class BaseModel(nn.Module):
|
||||
m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.forward_fuse # update forward
|
||||
self.info()
|
||||
self.info(verbose=verbose)
|
||||
|
||||
return self
|
||||
|
||||
@ -130,7 +128,7 @@ class BaseModel(nn.Module):
|
||||
verbose (bool): if True, prints out the model information. Defaults to False
|
||||
imgsz (int): the size of the image that the model will be trained on. Defaults to 640
|
||||
"""
|
||||
model_info(self, verbose, imgsz)
|
||||
model_info(self, verbose=verbose, imgsz=imgsz)
|
||||
|
||||
def _apply(self, fn):
|
||||
"""
|
||||
@ -437,7 +435,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
|
||||
ch = [ch]
|
||||
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
|
||||
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m] # get module
|
||||
for j, a in enumerate(args):
|
||||
# TODO: re-implement with eval() removal if possible
|
||||
# args[j] = (locals()[a] if a in locals() else ast.literal_eval(a)) if isinstance(a, str) else a
|
||||
|
||||
@ -61,8 +61,10 @@ CFG_BOOL_KEYS = ('save', 'exist_ok', 'pretrained', 'verbose', 'deterministic', '
|
||||
'v5loader')
|
||||
|
||||
# Define valid tasks and modes
|
||||
TASKS = 'detect', 'segment', 'classify'
|
||||
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
|
||||
TASKS = 'detect', 'segment', 'classify'
|
||||
TASK2DATA = {'detect': 'coco128.yaml', 'segment': 'coco128-seg.yaml', 'classify': 'imagenet100'}
|
||||
TASK2MODEL = {'detect': 'yolov8n.pt', 'segment': 'yolov8n-seg.pt', 'classify': 'yolov8n-cls.pt'}
|
||||
|
||||
|
||||
def cfg2dict(cfg):
|
||||
@ -274,8 +276,11 @@ def entrypoint(debug=''):
|
||||
|
||||
# Task
|
||||
task = overrides.pop('task', None)
|
||||
if task and task not in TASKS:
|
||||
raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
|
||||
if task:
|
||||
if task not in TASKS:
|
||||
raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
|
||||
if 'model' not in overrides:
|
||||
overrides['model'] = TASK2MODEL[task]
|
||||
|
||||
# Model
|
||||
model = overrides.pop('model', DEFAULT_CFG.model)
|
||||
@ -287,9 +292,10 @@ def entrypoint(debug=''):
|
||||
model = YOLO(model, task=task)
|
||||
|
||||
# Task Update
|
||||
if task and task != model.task:
|
||||
LOGGER.warning(f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
|
||||
f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model.")
|
||||
if task != model.task:
|
||||
if task:
|
||||
LOGGER.warning(f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
|
||||
f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model.")
|
||||
task = model.task
|
||||
|
||||
# Mode
|
||||
@ -299,8 +305,7 @@ def entrypoint(debug=''):
|
||||
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
|
||||
elif mode in ('train', 'val'):
|
||||
if 'data' not in overrides:
|
||||
task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='imagenet100')
|
||||
overrides['data'] = task2data.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
|
||||
overrides['data'] = TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
|
||||
LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
|
||||
elif mode == 'export':
|
||||
if 'format' not in overrides:
|
||||
@ -322,4 +327,4 @@ def copy_default_cfg():
|
||||
|
||||
if __name__ == '__main__':
|
||||
# entrypoint(debug='yolo predict model=yolov8n.pt')
|
||||
entrypoint(debug='')
|
||||
entrypoint(debug='yolo train model=yolov8n-seg.pt')
|
||||
|
||||
@ -6,7 +6,7 @@ mode: train # YOLO mode, i.e. train, val, predict, export
|
||||
|
||||
# Train settings -------------------------------------------------------------------------------------------------------
|
||||
model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml
|
||||
data: # path to data file, i.e. i.e. coco128.yaml
|
||||
data: # path to data file, i.e. coco128.yaml
|
||||
epochs: 100 # number of epochs to train for
|
||||
patience: 50 # epochs to wait for no observable improvement for early stopping of training
|
||||
batch: 16 # number of images per batch (-1 for AutoBatch)
|
||||
|
||||
@ -35,7 +35,8 @@ class BaseDataset(Dataset):
|
||||
batch_size=None,
|
||||
stride=32,
|
||||
pad=0.5,
|
||||
single_cls=False):
|
||||
single_cls=False,
|
||||
classes=None):
|
||||
super().__init__()
|
||||
self.img_path = img_path
|
||||
self.imgsz = imgsz
|
||||
@ -45,8 +46,7 @@ class BaseDataset(Dataset):
|
||||
|
||||
self.im_files = self.get_img_files(self.img_path)
|
||||
self.labels = self.get_labels()
|
||||
if self.single_cls:
|
||||
self.update_labels(include_class=[])
|
||||
self.update_labels(include_class=classes) # single_cls and include_class
|
||||
|
||||
self.ni = len(self.labels)
|
||||
|
||||
@ -96,7 +96,7 @@ class BaseDataset(Dataset):
|
||||
"""include_class, filter labels to include only these classes (optional)"""
|
||||
include_class_array = np.array(include_class).reshape(1, -1)
|
||||
for i in range(len(self.labels)):
|
||||
if include_class:
|
||||
if include_class is not None:
|
||||
cls = self.labels[i]['cls']
|
||||
bboxes = self.labels[i]['bboxes']
|
||||
segments = self.labels[i]['segments']
|
||||
@ -104,7 +104,7 @@ class BaseDataset(Dataset):
|
||||
self.labels[i]['cls'] = cls[j]
|
||||
self.labels[i]['bboxes'] = bboxes[j]
|
||||
if segments:
|
||||
self.labels[i]['segments'] = segments[j]
|
||||
self.labels[i]['segments'] = [segments[si] for si, idx in enumerate(j) if idx]
|
||||
if self.single_cls:
|
||||
self.labels[i]['cls'][:, 0] = 0
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@ from PIL import Image
|
||||
from torch.utils.data import DataLoader, dataloader, distributed
|
||||
|
||||
from ultralytics.yolo.data.dataloaders.stream_loaders import (LOADERS, LoadImages, LoadPilAndNumpy, LoadScreenshots,
|
||||
LoadStreams, SourceTypes, autocast_list)
|
||||
LoadStreams, LoadTensor, SourceTypes, autocast_list)
|
||||
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
|
||||
from ultralytics.yolo.utils.checks import check_file
|
||||
|
||||
@ -82,7 +82,8 @@ def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, ra
|
||||
prefix=colorstr(f'{mode}: '),
|
||||
use_segments=cfg.task == 'segment',
|
||||
use_keypoints=cfg.task == 'keypoint',
|
||||
names=names)
|
||||
names=names,
|
||||
classes=cfg.classes)
|
||||
|
||||
batch = min(batch, len(dataset))
|
||||
nd = torch.cuda.device_count() # number of CUDA devices
|
||||
@ -133,7 +134,7 @@ def build_classification_dataloader(path,
|
||||
|
||||
|
||||
def check_source(source):
|
||||
webcam, screenshot, from_img, in_memory = False, False, False, False
|
||||
webcam, screenshot, from_img, in_memory, tensor = False, False, False, False, False
|
||||
if isinstance(source, (str, int, Path)): # int for local usb camera
|
||||
source = str(source)
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
@ -149,22 +150,25 @@ def check_source(source):
|
||||
from_img = True
|
||||
elif isinstance(source, (Image.Image, np.ndarray)):
|
||||
from_img = True
|
||||
elif isinstance(source, torch.Tensor):
|
||||
tensor = True
|
||||
else:
|
||||
raise TypeError('Unsupported image type. See docs for supported types https://docs.ultralytics.com/predict')
|
||||
|
||||
return source, webcam, screenshot, from_img, in_memory
|
||||
return source, webcam, screenshot, from_img, in_memory, tensor
|
||||
|
||||
|
||||
def load_inference_source(source=None, transforms=None, imgsz=640, vid_stride=1, stride=32, auto=True):
|
||||
"""
|
||||
TODO: docs
|
||||
"""
|
||||
# source
|
||||
source, webcam, screenshot, from_img, in_memory = check_source(source)
|
||||
source_type = source.source_type if in_memory else SourceTypes(webcam, screenshot, from_img)
|
||||
source, webcam, screenshot, from_img, in_memory, tensor = check_source(source)
|
||||
source_type = source.source_type if in_memory else SourceTypes(webcam, screenshot, from_img, tensor)
|
||||
|
||||
# Dataloader
|
||||
if in_memory:
|
||||
if tensor:
|
||||
dataset = LoadTensor(source)
|
||||
elif in_memory:
|
||||
dataset = source
|
||||
elif webcam:
|
||||
dataset = LoadStreams(source,
|
||||
|
||||
@ -26,6 +26,7 @@ class SourceTypes:
|
||||
webcam: bool = False
|
||||
screenshot: bool = False
|
||||
from_img: bool = False
|
||||
tensor: bool = False
|
||||
|
||||
|
||||
class LoadStreams:
|
||||
@ -329,6 +330,23 @@ class LoadPilAndNumpy:
|
||||
return self
|
||||
|
||||
|
||||
class LoadTensor:
|
||||
|
||||
def __init__(self, imgs) -> None:
|
||||
self.im0 = imgs
|
||||
self.bs = imgs.shape[0]
|
||||
|
||||
def __iter__(self):
|
||||
self.count = 0
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.count == 1:
|
||||
raise StopIteration
|
||||
self.count += 1
|
||||
return None, self.im0, self.im0, None, '' # self.paths, im, self.im0, None, ''
|
||||
|
||||
|
||||
def autocast_list(source):
|
||||
"""
|
||||
Merges a list of source of different types into a list of numpy arrays or PIL images
|
||||
|
||||
@ -539,7 +539,7 @@ class LoadImagesAndLabels(Dataset):
|
||||
j = (label[:, 0:1] == include_class_array).any(1)
|
||||
self.labels[i] = label[j]
|
||||
if segment:
|
||||
self.segments[i] = segment[j]
|
||||
self.segments[i] = [segment[si] for si, idx in enumerate(j) if idx]
|
||||
if single_cls: # single-class training, merge all classes into 0
|
||||
self.labels[i][:, 0] = 0
|
||||
|
||||
|
||||
@ -57,12 +57,14 @@ class YOLODataset(BaseDataset):
|
||||
single_cls=False,
|
||||
use_segments=False,
|
||||
use_keypoints=False,
|
||||
names=None):
|
||||
names=None,
|
||||
classes=None):
|
||||
self.use_segments = use_segments
|
||||
self.use_keypoints = use_keypoints
|
||||
self.names = names
|
||||
assert not (self.use_segments and self.use_keypoints), 'Can not use both segments and keypoints.'
|
||||
super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
|
||||
super().__init__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls,
|
||||
classes)
|
||||
|
||||
def cache_labels(self, path=Path('./labels.cache')):
|
||||
"""Cache dataset labels, check images and read shapes.
|
||||
|
||||
@ -16,6 +16,7 @@ import numpy as np
|
||||
from PIL import ExifTags, Image, ImageOps
|
||||
from tqdm import tqdm
|
||||
|
||||
from ultralytics.nn.autobackend import check_class_names
|
||||
from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, colorstr, emojis, yaml_load
|
||||
from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
|
||||
from ultralytics.yolo.utils.downloads import download, safe_download, unzip_file
|
||||
@ -211,8 +212,7 @@ def check_det_dataset(dataset, autodownload=True):
|
||||
raise SyntaxError(
|
||||
emojis(f"{dataset} '{k}:' key missing ❌.\n"
|
||||
f"'train', 'val' and 'names' are required in data.yaml files."))
|
||||
if isinstance(data['names'], (list, tuple)): # old array format
|
||||
data['names'] = dict(enumerate(data['names'])) # convert to dict
|
||||
data['names'] = check_class_names(data['names'])
|
||||
data['nc'] = len(data['names'])
|
||||
|
||||
# Resolve paths
|
||||
|
||||
@ -574,7 +574,7 @@ class Exporter:
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
saved_model = Path(str(self.file).replace(self.file.suffix, '_saved_model'))
|
||||
if self.args.int8:
|
||||
f = saved_model / (self.file.stem + 'yolov8n_integer_quant.tflite') # fp32 in/out
|
||||
f = saved_model / (self.file.stem + '_integer_quant.tflite') # fp32 in/out
|
||||
elif self.args.half:
|
||||
f = saved_model / (self.file.stem + '_float16.tflite')
|
||||
else:
|
||||
@ -863,18 +863,6 @@ def export(cfg=DEFAULT_CFG):
|
||||
cfg.model = cfg.model or 'yolov8n.yaml'
|
||||
cfg.format = cfg.format or 'torchscript'
|
||||
|
||||
# exporter = Exporter(cfg)
|
||||
#
|
||||
# model = None
|
||||
# if isinstance(cfg.model, (str, Path)):
|
||||
# if Path(cfg.model).suffix == '.yaml':
|
||||
# model = DetectionModel(cfg.model)
|
||||
# elif Path(cfg.model).suffix == '.pt':
|
||||
# model = attempt_load_weights(cfg.model, fuse=True)
|
||||
# else:
|
||||
# TypeError(f'Unsupported model type {cfg.model}')
|
||||
# exporter(model=model)
|
||||
|
||||
from ultralytics import YOLO
|
||||
model = YOLO(cfg.model)
|
||||
model.export(**vars(cfg))
|
||||
|
||||
@ -203,6 +203,8 @@ class YOLO:
|
||||
if source is None:
|
||||
source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
|
||||
LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
|
||||
is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and \
|
||||
('predict' in sys.argv or 'mode=predict' in sys.argv)
|
||||
|
||||
overrides = self.overrides.copy()
|
||||
overrides['conf'] = 0.25
|
||||
@ -213,10 +215,9 @@ class YOLO:
|
||||
if not self.predictor:
|
||||
self.task = overrides.get('task') or self.task
|
||||
self.predictor = TASK_MAP[self.task][3](overrides=overrides)
|
||||
self.predictor.setup_model(model=self.model)
|
||||
self.predictor.setup_model(model=self.model, verbose=is_cli)
|
||||
else: # only update args if predictor is already setup
|
||||
self.predictor.args = get_cfg(self.predictor.args, overrides)
|
||||
is_cli = sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')
|
||||
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
|
||||
|
||||
def track(self, source=None, stream=False, **kwargs):
|
||||
|
||||
@ -183,6 +183,8 @@ class BasePredictor:
|
||||
'preprocess': self.dt[0].dt * 1E3 / n,
|
||||
'inference': self.dt[1].dt * 1E3 / n,
|
||||
'postprocess': self.dt[2].dt * 1E3 / n}
|
||||
if self.source_type.tensor: # skip write, show and plot operations if input is raw tensor
|
||||
continue
|
||||
p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \
|
||||
else (path, im0s.copy())
|
||||
p = Path(p)
|
||||
@ -218,11 +220,16 @@ class BasePredictor:
|
||||
|
||||
self.run_callbacks('on_predict_end')
|
||||
|
||||
def setup_model(self, model):
|
||||
device = select_device(self.args.device)
|
||||
def setup_model(self, model, verbose=True):
|
||||
device = select_device(self.args.device, verbose=verbose)
|
||||
model = model or self.args.model
|
||||
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||
self.model = AutoBackend(model, device=device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half)
|
||||
self.model = AutoBackend(model,
|
||||
device=device,
|
||||
dnn=self.args.dnn,
|
||||
data=self.args.data,
|
||||
fp16=self.args.half,
|
||||
verbose=verbose)
|
||||
self.device = device
|
||||
self.model.eval()
|
||||
|
||||
|
||||
@ -25,8 +25,8 @@ from tqdm import tqdm
|
||||
from ultralytics.nn.tasks import attempt_load_one_weight, attempt_load_weights
|
||||
from ultralytics.yolo.cfg import get_cfg
|
||||
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
|
||||
from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, __version__, callbacks,
|
||||
colorstr, emojis, yaml_save)
|
||||
from ultralytics.yolo.utils import (DEFAULT_CFG, LOGGER, ONLINE, RANK, ROOT, SETTINGS, TQDM_BAR_FORMAT, __version__,
|
||||
callbacks, colorstr, emojis, yaml_save)
|
||||
from ultralytics.yolo.utils.autobatch import check_train_batch_size
|
||||
from ultralytics.yolo.utils.checks import check_file, check_imgsz, print_args
|
||||
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
|
||||
@ -111,8 +111,6 @@ class BaseTrainer:
|
||||
print_args(vars(self.args))
|
||||
|
||||
# Device
|
||||
self.amp = self.device.type != 'cpu'
|
||||
self.scaler = amp.GradScaler(enabled=self.amp)
|
||||
if self.device.type == 'cpu':
|
||||
self.args.workers = 0 # faster CPU training as time dominated by inference, not dataloading
|
||||
|
||||
@ -126,7 +124,7 @@ class BaseTrainer:
|
||||
if 'yaml_file' in self.data:
|
||||
self.args.data = self.data['yaml_file'] # for validating 'yolo train data=url.zip' usage
|
||||
except Exception as e:
|
||||
raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
|
||||
raise RuntimeError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
|
||||
|
||||
self.trainset, self.testset = self.get_dataset(self.data)
|
||||
self.ema = None
|
||||
@ -204,6 +202,8 @@ class BaseTrainer:
|
||||
ckpt = self.setup_model()
|
||||
self.model = self.model.to(self.device)
|
||||
self.set_model_attributes()
|
||||
self.amp = check_amp(self.model)
|
||||
self.scaler = amp.GradScaler(enabled=self.amp)
|
||||
if world_size > 1:
|
||||
self.model = DDP(self.model, device_ids=[rank])
|
||||
# Check imgsz
|
||||
@ -597,3 +597,31 @@ class BaseTrainer:
|
||||
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
|
||||
f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias')
|
||||
return optimizer
|
||||
|
||||
|
||||
def check_amp(model):
|
||||
# Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation
|
||||
device = next(model.parameters()).device # get model device
|
||||
if device.type in ('cpu', 'mps'):
|
||||
return False # AMP only used on CUDA devices
|
||||
|
||||
def amp_allclose(m, im):
|
||||
# All close FP32 vs AMP results
|
||||
a = m(im, device=device, verbose=False)[0].boxes.boxes # FP32 inference
|
||||
with torch.cuda.amp.autocast(True):
|
||||
b = m(im, device=device, verbose=False)[0].boxes.boxes # AMP inference
|
||||
return a.shape == b.shape and torch.allclose(a, b.float(), atol=0.1) # close to 10% absolute tolerance
|
||||
|
||||
f = ROOT / 'assets/bus.jpg' # image to check
|
||||
im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if ONLINE else np.ones((640, 640, 3))
|
||||
prefix = colorstr('AMP: ')
|
||||
try:
|
||||
from ultralytics import YOLO
|
||||
LOGGER.info(f'{prefix}running Automatic Mixed Precision (AMP) checks with YOLOv8n...')
|
||||
assert amp_allclose(YOLO('yolov8n.pt'), im)
|
||||
LOGGER.info(f'{prefix}checks passed ✅')
|
||||
return True
|
||||
except AssertionError:
|
||||
LOGGER.warning(f'{prefix}checks failed ❌. Anomalies were detected with AMP on your system that may lead to '
|
||||
f'NaN losses or zero-mAP results, so AMP will be disabled during training.')
|
||||
return False
|
||||
|
||||
@ -236,9 +236,10 @@ def check_suffix(file='yolov8n.pt', suffix='.pt', msg=''):
|
||||
|
||||
def check_yolov5u_filename(file: str, verbose: bool = True):
|
||||
# Replace legacy YOLOv5 filenames with updated YOLOv5u filenames
|
||||
if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
|
||||
if ('yolov3' in file or 'yolov5' in file) and 'u' not in file:
|
||||
original_file = file
|
||||
file = re.sub(r'(.*yolov5([nsmlx]))\.', '\\1u.', file) # i.e. yolov5n.pt -> yolov5nu.pt
|
||||
file = re.sub(r'(.*yolov5([nsmlx])6)\.', '\\1u.', file) # i.e. yolov5n6.pt -> yolov5n6u.pt
|
||||
file = re.sub(r'(.*yolov3(|-tiny|-spp))\.', '\\1u.', file) # i.e. yolov3-spp.pt -> yolov3-sppu.pt
|
||||
if file != original_file and verbose:
|
||||
LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
|
||||
|
||||
@ -162,11 +162,13 @@ def fuse_deconv_and_bn(deconv, bn):
|
||||
return fuseddconv
|
||||
|
||||
|
||||
def model_info(model, verbose=False, imgsz=640):
|
||||
def model_info(model, detailed=False, verbose=True, imgsz=640):
|
||||
# Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
|
||||
if not verbose:
|
||||
return
|
||||
n_p = get_num_params(model)
|
||||
n_g = get_num_gradients(model) # number gradients
|
||||
if verbose:
|
||||
if detailed:
|
||||
LOGGER.info(
|
||||
f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
|
||||
for i, (name, p) in enumerate(model.named_parameters()):
|
||||
|
||||
@ -14,14 +14,13 @@ class ClassificationPredictor(BasePredictor):
|
||||
return Annotator(img, example=str(self.model.names), pil=True)
|
||||
|
||||
def preprocess(self, img):
|
||||
img = (img if isinstance(img, torch.Tensor) else torch.Tensor(img)).to(self.model.device)
|
||||
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
|
||||
return img
|
||||
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
|
||||
return img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
def postprocess(self, preds, img, orig_imgs):
|
||||
results = []
|
||||
for i, pred in enumerate(preds):
|
||||
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
|
||||
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
|
||||
path, _, _, _, _ = self.batch
|
||||
img_path = path[i] if isinstance(path, list) else path
|
||||
results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, probs=pred))
|
||||
|
||||
@ -14,12 +14,12 @@ class DetectionPredictor(BasePredictor):
|
||||
return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
|
||||
|
||||
def preprocess(self, img):
|
||||
img = torch.from_numpy(img).to(self.model.device)
|
||||
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
|
||||
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
|
||||
img /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
return img
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
def postprocess(self, preds, img, orig_imgs):
|
||||
preds = ops.non_max_suppression(preds,
|
||||
self.args.conf,
|
||||
self.args.iou,
|
||||
@ -29,7 +29,7 @@ class DetectionPredictor(BasePredictor):
|
||||
|
||||
results = []
|
||||
for i, pred in enumerate(preds):
|
||||
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
|
||||
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
|
||||
shape = orig_img.shape
|
||||
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
|
||||
path, _, _, _, _ = self.batch
|
||||
|
||||
@ -10,7 +10,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
|
||||
|
||||
class SegmentationPredictor(DetectionPredictor):
|
||||
|
||||
def postprocess(self, preds, img, orig_img):
|
||||
def postprocess(self, preds, img, orig_imgs):
|
||||
# TODO: filter by classes
|
||||
p = ops.non_max_suppression(preds[0],
|
||||
self.args.conf,
|
||||
@ -22,7 +22,7 @@ class SegmentationPredictor(DetectionPredictor):
|
||||
results = []
|
||||
proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
|
||||
for i, pred in enumerate(p):
|
||||
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
|
||||
orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
|
||||
shape = orig_img.shape
|
||||
path, _, _, _, _ = self.batch
|
||||
img_path = path[i] if isinstance(path, list) else path
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user