ultralytics 8.2.38 official YOLOv10 support (#13113)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Burhan
GitHub
parent
821e5fa477
commit
ffb46fd7fb
23 changed files with 785 additions and 32 deletions
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.2.37"
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__version__ = "8.2.38"
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import os
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42
ultralytics/cfg/models/v10/yolov10b.yaml
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42
ultralytics/cfg/models/v10/yolov10b.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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b: [0.67, 1.00, 512]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2fCIB, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 1, PSA, [1024]] # 10
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# YOLOv8.0n head
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head:
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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, C2fCIB, [512, True]] # 13
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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, C2f, [256]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
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- [-1, 1, SCDown, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
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42
ultralytics/cfg/models/v10/yolov10l.yaml
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42
ultralytics/cfg/models/v10/yolov10l.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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l: [1.00, 1.00, 512]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2fCIB, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 1, PSA, [1024]] # 10
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# YOLOv8.0n head
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head:
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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, C2fCIB, [512, True]] # 13
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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, C2f, [256]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
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- [-1, 1, SCDown, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
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42
ultralytics/cfg/models/v10/yolov10m.yaml
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42
ultralytics/cfg/models/v10/yolov10m.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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m: [0.67, 0.75, 768]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2fCIB, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 1, PSA, [1024]] # 10
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# YOLOv8.0n head
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head:
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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, C2f, [512]] # 13
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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, C2f, [256]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
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- [-1, 1, SCDown, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
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42
ultralytics/cfg/models/v10/yolov10n.yaml
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42
ultralytics/cfg/models/v10/yolov10n.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [0.33, 0.25, 1024]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2f, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 1, PSA, [1024]] # 10
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# YOLOv8.0n head
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head:
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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, C2f, [512]] # 13
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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, C2f, [256]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
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- [-1, 1, SCDown, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
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42
ultralytics/cfg/models/v10/yolov10s.yaml
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42
ultralytics/cfg/models/v10/yolov10s.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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s: [0.33, 0.50, 1024]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2f, [512, True]]
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- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2fCIB, [1024, True, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 1, PSA, [1024]] # 10
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# YOLOv8.0n head
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head:
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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, C2f, [512]] # 13
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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, C2f, [256]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
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- [-1, 1, SCDown, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
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42
ultralytics/cfg/models/v10/yolov10x.yaml
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42
ultralytics/cfg/models/v10/yolov10x.yaml
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv10 object detection model. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
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# [depth, width, max_channels]
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x: [1.00, 1.25, 512]
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backbone:
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# [from, repeats, module, args]
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- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
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- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
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- [-1, 3, C2f, [128, True]]
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- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
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- [-1, 6, C2f, [256, True]]
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- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
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- [-1, 6, C2fCIB, [512, True]]
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- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
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- [-1, 3, C2fCIB, [1024, True]]
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- [-1, 1, SPPF, [1024, 5]] # 9
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- [-1, 1, PSA, [1024]] # 10
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# YOLOv8.0n head
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head:
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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, C2fCIB, [512, True]] # 13
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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, C2f, [256]] # 16 (P3/8-small)
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- [-1, 1, Conv, [256, 3, 2]]
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- [[-1, 13], 1, Concat, [1]] # cat head P4
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- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
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- [-1, 1, SCDown, [512, 3, 2]]
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- [[-1, 10], 1, Concat, [1]] # cat head P5
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- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
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- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
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@ -920,6 +920,7 @@ class Exporter:
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@try_export
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def export_tflite(self, keras_model, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:")):
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"""YOLOv8 TensorFlow Lite export."""
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# BUG https://github.com/ultralytics/ultralytics/issues/13436
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import tensorflow as tf # noqa
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LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...")
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@ -22,18 +22,22 @@ from .block import (
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C2,
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C3,
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C3TR,
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CIB,
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DFL,
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ELAN1,
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PSA,
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SPP,
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SPPELAN,
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SPPF,
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AConv,
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ADown,
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Attention,
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BNContrastiveHead,
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Bottleneck,
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BottleneckCSP,
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C2f,
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C2fAttn,
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C2fCIB,
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C3Ghost,
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C3x,
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CBFuse,
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@ -46,7 +50,9 @@ from .block import (
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Proto,
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RepC3,
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RepNCSPELAN4,
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RepVGGDW,
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ResNetLayer,
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SCDown,
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)
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from .conv import (
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CBAM,
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@ -63,7 +69,7 @@ from .conv import (
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RepConv,
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SpatialAttention,
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)
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from .head import OBB, Classify, Detect, Pose, RTDETRDecoder, Segment, WorldDetect
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from .head import OBB, Classify, Detect, Pose, RTDETRDecoder, Segment, WorldDetect, v10Detect
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from .transformer import (
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AIFI,
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MLP,
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@ -137,4 +143,10 @@ __all__ = (
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"CBLinear",
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"AConv",
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"ELAN1",
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"RepVGGDW",
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"CIB",
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"C2fCIB",
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"Attention",
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"PSA",
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"SCDown",
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)
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@ -5,6 +5,8 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from ultralytics.utils.torch_utils import fuse_conv_and_bn
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from .conv import Conv, DWConv, GhostConv, LightConv, RepConv, autopad
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from .transformer import TransformerBlock
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@ -39,6 +41,12 @@ __all__ = (
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"CBFuse",
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"CBLinear",
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"Silence",
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"RepVGGDW",
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"CIB",
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"C2fCIB",
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"Attention",
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"PSA",
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"SCDown",
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)
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@ -699,3 +707,251 @@ class CBFuse(nn.Module):
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target_size = xs[-1].shape[2:]
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res = [F.interpolate(x[self.idx[i]], size=target_size, mode="nearest") for i, x in enumerate(xs[:-1])]
|
||||
return torch.sum(torch.stack(res + xs[-1:]), dim=0)
|
||||
|
||||
|
||||
class RepVGGDW(torch.nn.Module):
|
||||
"""RepVGGDW is a class that represents a depth wise separable convolutional block in RepVGG architecture."""
|
||||
|
||||
def __init__(self, ed) -> None:
|
||||
super().__init__()
|
||||
self.conv = Conv(ed, ed, 7, 1, 3, g=ed, act=False)
|
||||
self.conv1 = Conv(ed, ed, 3, 1, 1, g=ed, act=False)
|
||||
self.dim = ed
|
||||
self.act = nn.SiLU()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Performs a forward pass of the RepVGGDW block.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Output tensor after applying the depth wise separable convolution.
|
||||
"""
|
||||
return self.act(self.conv(x) + self.conv1(x))
|
||||
|
||||
def forward_fuse(self, x):
|
||||
"""
|
||||
Performs a forward pass of the RepVGGDW block without fusing the convolutions.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Output tensor after applying the depth wise separable convolution.
|
||||
"""
|
||||
return self.act(self.conv(x))
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse(self):
|
||||
"""
|
||||
Fuses the convolutional layers in the RepVGGDW block.
|
||||
|
||||
This method fuses the convolutional layers and updates the weights and biases accordingly.
|
||||
"""
|
||||
conv = fuse_conv_and_bn(self.conv.conv, self.conv.bn)
|
||||
conv1 = fuse_conv_and_bn(self.conv1.conv, self.conv1.bn)
|
||||
|
||||
conv_w = conv.weight
|
||||
conv_b = conv.bias
|
||||
conv1_w = conv1.weight
|
||||
conv1_b = conv1.bias
|
||||
|
||||
conv1_w = torch.nn.functional.pad(conv1_w, [2, 2, 2, 2])
|
||||
|
||||
final_conv_w = conv_w + conv1_w
|
||||
final_conv_b = conv_b + conv1_b
|
||||
|
||||
conv.weight.data.copy_(final_conv_w)
|
||||
conv.bias.data.copy_(final_conv_b)
|
||||
|
||||
self.conv = conv
|
||||
del self.conv1
|
||||
|
||||
|
||||
class CIB(nn.Module):
|
||||
"""
|
||||
Conditional Identity Block (CIB) module.
|
||||
|
||||
Args:
|
||||
c1 (int): Number of input channels.
|
||||
c2 (int): Number of output channels.
|
||||
shortcut (bool, optional): Whether to add a shortcut connection. Defaults to True.
|
||||
e (float, optional): Scaling factor for the hidden channels. Defaults to 0.5.
|
||||
lk (bool, optional): Whether to use RepVGGDW for the third convolutional layer. Defaults to False.
|
||||
"""
|
||||
|
||||
def __init__(self, c1, c2, shortcut=True, e=0.5, lk=False):
|
||||
"""Initializes the custom model with optional shortcut, scaling factor, and RepVGGDW layer."""
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = nn.Sequential(
|
||||
Conv(c1, c1, 3, g=c1),
|
||||
Conv(c1, 2 * c_, 1),
|
||||
Conv(2 * c_, 2 * c_, 3, g=2 * c_) if not lk else RepVGGDW(2 * c_),
|
||||
Conv(2 * c_, c2, 1),
|
||||
Conv(c2, c2, 3, g=c2),
|
||||
)
|
||||
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass of the CIB module.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Output tensor.
|
||||
"""
|
||||
return x + self.cv1(x) if self.add else self.cv1(x)
|
||||
|
||||
|
||||
class C2fCIB(C2f):
|
||||
"""
|
||||
C2fCIB class represents a convolutional block with C2f and CIB modules.
|
||||
|
||||
Args:
|
||||
c1 (int): Number of input channels.
|
||||
c2 (int): Number of output channels.
|
||||
n (int, optional): Number of CIB modules to stack. Defaults to 1.
|
||||
shortcut (bool, optional): Whether to use shortcut connection. Defaults to False.
|
||||
lk (bool, optional): Whether to use local key connection. Defaults to False.
|
||||
g (int, optional): Number of groups for grouped convolution. Defaults to 1.
|
||||
e (float, optional): Expansion ratio for CIB modules. Defaults to 0.5.
|
||||
"""
|
||||
|
||||
def __init__(self, c1, c2, n=1, shortcut=False, lk=False, g=1, e=0.5):
|
||||
"""Initializes the module with specified parameters for channel, shortcut, local key, groups, and expansion."""
|
||||
super().__init__(c1, c2, n, shortcut, g, e)
|
||||
self.m = nn.ModuleList(CIB(self.c, self.c, shortcut, e=1.0, lk=lk) for _ in range(n))
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
"""
|
||||
Attention module that performs self-attention on the input tensor.
|
||||
|
||||
Args:
|
||||
dim (int): The input tensor dimension.
|
||||
num_heads (int): The number of attention heads.
|
||||
attn_ratio (float): The ratio of the attention key dimension to the head dimension.
|
||||
|
||||
Attributes:
|
||||
num_heads (int): The number of attention heads.
|
||||
head_dim (int): The dimension of each attention head.
|
||||
key_dim (int): The dimension of the attention key.
|
||||
scale (float): The scaling factor for the attention scores.
|
||||
qkv (Conv): Convolutional layer for computing the query, key, and value.
|
||||
proj (Conv): Convolutional layer for projecting the attended values.
|
||||
pe (Conv): Convolutional layer for positional encoding.
|
||||
"""
|
||||
|
||||
def __init__(self, dim, num_heads=8, attn_ratio=0.5):
|
||||
"""Initializes multi-head attention module with query, key, and value convolutions and positional encoding."""
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.key_dim = int(self.head_dim * attn_ratio)
|
||||
self.scale = self.key_dim**-0.5
|
||||
nh_kd = nh_kd = self.key_dim * num_heads
|
||||
h = dim + nh_kd * 2
|
||||
self.qkv = Conv(dim, h, 1, act=False)
|
||||
self.proj = Conv(dim, dim, 1, act=False)
|
||||
self.pe = Conv(dim, dim, 3, 1, g=dim, act=False)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass of the Attention module.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): The input tensor.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The output tensor after self-attention.
|
||||
"""
|
||||
B, C, H, W = x.shape
|
||||
N = H * W
|
||||
qkv = self.qkv(x)
|
||||
q, k, v = qkv.view(B, self.num_heads, self.key_dim * 2 + self.head_dim, N).split(
|
||||
[self.key_dim, self.key_dim, self.head_dim], dim=2
|
||||
)
|
||||
|
||||
attn = (q.transpose(-2, -1) @ k) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
x = (v @ attn.transpose(-2, -1)).view(B, C, H, W) + self.pe(v.reshape(B, C, H, W))
|
||||
x = self.proj(x)
|
||||
return x
|
||||
|
||||
|
||||
class PSA(nn.Module):
|
||||
"""
|
||||
Position-wise Spatial Attention module.
|
||||
|
||||
Args:
|
||||
c1 (int): Number of input channels.
|
||||
c2 (int): Number of output channels.
|
||||
e (float): Expansion factor for the intermediate channels. Default is 0.5.
|
||||
|
||||
Attributes:
|
||||
c (int): Number of intermediate channels.
|
||||
cv1 (Conv): 1x1 convolution layer to reduce the number of input channels to 2*c.
|
||||
cv2 (Conv): 1x1 convolution layer to reduce the number of output channels to c.
|
||||
attn (Attention): Attention module for spatial attention.
|
||||
ffn (nn.Sequential): Feed-forward network module.
|
||||
"""
|
||||
|
||||
def __init__(self, c1, c2, e=0.5):
|
||||
"""Initializes convolution layers, attention module, and feed-forward network with channel reduction."""
|
||||
super().__init__()
|
||||
assert c1 == c2
|
||||
self.c = int(c1 * e)
|
||||
self.cv1 = Conv(c1, 2 * self.c, 1, 1)
|
||||
self.cv2 = Conv(2 * self.c, c1, 1)
|
||||
|
||||
self.attn = Attention(self.c, attn_ratio=0.5, num_heads=self.c // 64)
|
||||
self.ffn = nn.Sequential(Conv(self.c, self.c * 2, 1), Conv(self.c * 2, self.c, 1, act=False))
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass of the PSA module.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Output tensor.
|
||||
"""
|
||||
a, b = self.cv1(x).split((self.c, self.c), dim=1)
|
||||
b = b + self.attn(b)
|
||||
b = b + self.ffn(b)
|
||||
return self.cv2(torch.cat((a, b), 1))
|
||||
|
||||
|
||||
class SCDown(nn.Module):
|
||||
def __init__(self, c1, c2, k, s):
|
||||
"""
|
||||
Spatial Channel Downsample (SCDown) module.
|
||||
|
||||
Args:
|
||||
c1 (int): Number of input channels.
|
||||
c2 (int): Number of output channels.
|
||||
k (int): Kernel size for the convolutional layer.
|
||||
s (int): Stride for the convolutional layer.
|
||||
"""
|
||||
super().__init__()
|
||||
self.cv1 = Conv(c1, c2, 1, 1)
|
||||
self.cv2 = Conv(c2, c2, k=k, s=s, g=c2, act=False)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Forward pass of the SCDown module.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): Output tensor after applying the SCDown module.
|
||||
"""
|
||||
return self.cv2(self.cv1(x))
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
"""Model head modules."""
|
||||
|
||||
import copy
|
||||
import math
|
||||
|
||||
import torch
|
||||
|
|
@ -14,7 +15,7 @@ from .conv import Conv
|
|||
from .transformer import MLP, DeformableTransformerDecoder, DeformableTransformerDecoderLayer
|
||||
from .utils import bias_init_with_prob, linear_init
|
||||
|
||||
__all__ = "Detect", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder"
|
||||
__all__ = "Detect", "Segment", "Pose", "Classify", "OBB", "RTDETRDecoder", "v10Detect"
|
||||
|
||||
|
||||
class Detect(nn.Module):
|
||||
|
|
@ -22,6 +23,8 @@ class Detect(nn.Module):
|
|||
|
||||
dynamic = False # force grid reconstruction
|
||||
export = False # export mode
|
||||
end2end = False # end2end
|
||||
max_det = 300 # max_det
|
||||
shape = None
|
||||
anchors = torch.empty(0) # init
|
||||
strides = torch.empty(0) # init
|
||||
|
|
@ -41,13 +44,48 @@ class Detect(nn.Module):
|
|||
self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch)
|
||||
self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity()
|
||||
|
||||
if self.end2end:
|
||||
self.one2one_cv2 = copy.deepcopy(self.cv2)
|
||||
self.one2one_cv3 = copy.deepcopy(self.cv3)
|
||||
|
||||
def forward(self, x):
|
||||
"""Concatenates and returns predicted bounding boxes and class probabilities."""
|
||||
if self.end2end:
|
||||
return self.forward_end2end(x)
|
||||
|
||||
for i in range(self.nl):
|
||||
x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
|
||||
if self.training: # Training path
|
||||
return x
|
||||
y = self._inference(x)
|
||||
return y if self.export else (y, x)
|
||||
|
||||
def forward_end2end(self, x):
|
||||
"""
|
||||
Performs forward pass of the v10Detect module.
|
||||
|
||||
Args:
|
||||
x (tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
(dict, tensor): If not in training mode, returns a dictionary containing the outputs of both one2many and one2one detections.
|
||||
If in training mode, returns a dictionary containing the outputs of one2many and one2one detections separately.
|
||||
"""
|
||||
x_detach = [xi.detach() for xi in x]
|
||||
one2one = [
|
||||
torch.cat((self.one2one_cv2[i](x_detach[i]), self.one2one_cv3[i](x_detach[i])), 1) for i in range(self.nl)
|
||||
]
|
||||
for i in range(self.nl):
|
||||
x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)
|
||||
if self.training: # Training path
|
||||
return {"one2many": x, "one2one": one2one}
|
||||
|
||||
y = self._inference(one2one)
|
||||
y = self.postprocess(y.permute(0, 2, 1), self.max_det, self.nc)
|
||||
return y if self.export else (y, {"one2many": x, "one2one": one2one})
|
||||
|
||||
def _inference(self, x):
|
||||
"""Decode predicted bounding boxes and class probabilities based on multiple-level feature maps."""
|
||||
# Inference path
|
||||
shape = x[0].shape # BCHW
|
||||
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
|
||||
|
|
@ -73,7 +111,7 @@ class Detect(nn.Module):
|
|||
dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
|
||||
|
||||
y = torch.cat((dbox, cls.sigmoid()), 1)
|
||||
return y if self.export else (y, x)
|
||||
return y
|
||||
|
||||
def bias_init(self):
|
||||
"""Initialize Detect() biases, WARNING: requires stride availability."""
|
||||
|
|
@ -83,10 +121,47 @@ class Detect(nn.Module):
|
|||
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from
|
||||
a[-1].bias.data[:] = 1.0 # box
|
||||
b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
|
||||
if self.end2end:
|
||||
for a, b, s in zip(m.one2one_cv2, m.one2one_cv3, m.stride): # from
|
||||
a[-1].bias.data[:] = 1.0 # box
|
||||
b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
|
||||
|
||||
def decode_bboxes(self, bboxes, anchors):
|
||||
"""Decode bounding boxes."""
|
||||
return dist2bbox(bboxes, anchors, xywh=True, dim=1)
|
||||
return dist2bbox(bboxes, anchors, xywh=not self.end2end, dim=1)
|
||||
|
||||
@staticmethod
|
||||
def postprocess(preds: torch.Tensor, max_det: int, nc: int = 80):
|
||||
"""
|
||||
Post-processes the predictions obtained from a YOLOv10 model.
|
||||
|
||||
Args:
|
||||
preds (torch.Tensor): The predictions obtained from the model. It should have a shape of (batch_size, num_boxes, 4 + num_classes).
|
||||
max_det (int): The maximum number of detections to keep.
|
||||
nc (int, optional): The number of classes. Defaults to 80.
|
||||
|
||||
Returns:
|
||||
(torch.Tensor): The post-processed predictions with shape (batch_size, max_det, 6),
|
||||
including bounding boxes, scores and cls.
|
||||
"""
|
||||
assert 4 + nc == preds.shape[-1]
|
||||
boxes, scores = preds.split([4, nc], dim=-1)
|
||||
max_scores = scores.amax(dim=-1)
|
||||
max_scores, index = torch.topk(max_scores, min(max_det, max_scores.shape[1]), axis=-1)
|
||||
index = index.unsqueeze(-1)
|
||||
boxes = torch.gather(boxes, dim=1, index=index.repeat(1, 1, boxes.shape[-1]))
|
||||
scores = torch.gather(scores, dim=1, index=index.repeat(1, 1, scores.shape[-1]))
|
||||
|
||||
# NOTE: simplify but result slightly lower mAP
|
||||
# scores, labels = scores.max(dim=-1)
|
||||
# return torch.cat([boxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
|
||||
|
||||
scores, index = torch.topk(scores.flatten(1), max_det, axis=-1)
|
||||
labels = index % nc
|
||||
index = index // nc
|
||||
boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1]))
|
||||
|
||||
return torch.cat([boxes, scores.unsqueeze(-1), labels.unsqueeze(-1).to(boxes.dtype)], dim=-1)
|
||||
|
||||
|
||||
class Segment(Detect):
|
||||
|
|
@ -487,3 +562,39 @@ class RTDETRDecoder(nn.Module):
|
|||
xavier_uniform_(self.query_pos_head.layers[1].weight)
|
||||
for layer in self.input_proj:
|
||||
xavier_uniform_(layer[0].weight)
|
||||
|
||||
|
||||
class v10Detect(Detect):
|
||||
"""
|
||||
v10 Detection head from https://arxiv.org/pdf/2405.14458
|
||||
|
||||
Args:
|
||||
nc (int): Number of classes.
|
||||
ch (tuple): Tuple of channel sizes.
|
||||
|
||||
Attributes:
|
||||
max_det (int): Maximum number of detections.
|
||||
|
||||
Methods:
|
||||
__init__(self, nc=80, ch=()): Initializes the v10Detect object.
|
||||
forward(self, x): Performs forward pass of the v10Detect module.
|
||||
bias_init(self): Initializes biases of the Detect module.
|
||||
|
||||
"""
|
||||
|
||||
end2end = True
|
||||
|
||||
def __init__(self, nc=80, ch=()):
|
||||
"""Initializes the v10Detect object with the specified number of classes and input channels."""
|
||||
super().__init__(nc, ch)
|
||||
c3 = max(ch[0], min(self.nc, 100)) # channels
|
||||
# Light cls head
|
||||
self.cv3 = nn.ModuleList(
|
||||
nn.Sequential(
|
||||
nn.Sequential(Conv(x, x, 3, g=x), Conv(x, c3, 1)),
|
||||
nn.Sequential(Conv(c3, c3, 3, g=c3), Conv(c3, c3, 1)),
|
||||
nn.Conv2d(c3, self.nc, 1),
|
||||
)
|
||||
for x in ch
|
||||
)
|
||||
self.one2one_cv3 = copy.deepcopy(self.cv3)
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ from ultralytics.nn.modules import (
|
|||
C3TR,
|
||||
ELAN1,
|
||||
OBB,
|
||||
PSA,
|
||||
SPP,
|
||||
SPPELAN,
|
||||
SPPF,
|
||||
|
|
@ -24,6 +25,7 @@ from ultralytics.nn.modules import (
|
|||
BottleneckCSP,
|
||||
C2f,
|
||||
C2fAttn,
|
||||
C2fCIB,
|
||||
C3Ghost,
|
||||
C3x,
|
||||
CBFuse,
|
||||
|
|
@ -46,14 +48,24 @@ from ultralytics.nn.modules import (
|
|||
RepC3,
|
||||
RepConv,
|
||||
RepNCSPELAN4,
|
||||
RepVGGDW,
|
||||
ResNetLayer,
|
||||
RTDETRDecoder,
|
||||
SCDown,
|
||||
Segment,
|
||||
WorldDetect,
|
||||
v10Detect,
|
||||
)
|
||||
from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, emojis, yaml_load
|
||||
from ultralytics.utils.checks import check_requirements, check_suffix, check_yaml
|
||||
from ultralytics.utils.loss import v8ClassificationLoss, v8DetectionLoss, v8OBBLoss, v8PoseLoss, v8SegmentationLoss
|
||||
from ultralytics.utils.loss import (
|
||||
E2EDetectLoss,
|
||||
v8ClassificationLoss,
|
||||
v8DetectionLoss,
|
||||
v8OBBLoss,
|
||||
v8PoseLoss,
|
||||
v8SegmentationLoss,
|
||||
)
|
||||
from ultralytics.utils.plotting import feature_visualization
|
||||
from ultralytics.utils.torch_utils import (
|
||||
fuse_conv_and_bn,
|
||||
|
|
@ -192,6 +204,9 @@ class BaseModel(nn.Module):
|
|||
if isinstance(m, RepConv):
|
||||
m.fuse_convs()
|
||||
m.forward = m.forward_fuse # update forward
|
||||
if isinstance(m, RepVGGDW):
|
||||
m.fuse()
|
||||
m.forward = m.forward_fuse
|
||||
self.info(verbose=verbose)
|
||||
|
||||
return self
|
||||
|
|
@ -294,6 +309,7 @@ class DetectionModel(BaseModel):
|
|||
self.model, self.save = parse_model(deepcopy(self.yaml), ch=ch, verbose=verbose) # model, savelist
|
||||
self.names = {i: f"{i}" for i in range(self.yaml["nc"])} # default names dict
|
||||
self.inplace = self.yaml.get("inplace", True)
|
||||
self.end2end = getattr(self.model[-1], "end2end", False)
|
||||
|
||||
# Build strides
|
||||
m = self.model[-1] # Detect()
|
||||
|
|
@ -303,6 +319,8 @@ class DetectionModel(BaseModel):
|
|||
|
||||
def _forward(x):
|
||||
"""Performs a forward pass through the model, handling different Detect subclass types accordingly."""
|
||||
if self.end2end:
|
||||
return self.forward(x)["one2many"]
|
||||
return self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
|
||||
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
|
|
@ -355,7 +373,7 @@ class DetectionModel(BaseModel):
|
|||
|
||||
def init_criterion(self):
|
||||
"""Initialize the loss criterion for the DetectionModel."""
|
||||
return v8DetectionLoss(self)
|
||||
return E2EDetectLoss(self) if self.end2end else v8DetectionLoss(self)
|
||||
|
||||
|
||||
class OBBModel(DetectionModel):
|
||||
|
|
@ -689,8 +707,8 @@ def temporary_modules(modules={}, attributes={}):
|
|||
|
||||
Example:
|
||||
```python
|
||||
with temporary_modules({'old.module.path': 'new.module.path'}, {'old.module.attribute': 'new.module.attribute'}):
|
||||
import old.module.path # this will now import new.module.path
|
||||
with temporary_modules({'old.module': 'new.module'}, {'old.module.attribute': 'new.module.attribute'}):
|
||||
import old.module # this will now import new.module
|
||||
from old.module import attribute # this will now import new.module.attribute
|
||||
```
|
||||
|
||||
|
|
@ -700,23 +718,19 @@ def temporary_modules(modules={}, attributes={}):
|
|||
applications or libraries. Use this function with caution.
|
||||
"""
|
||||
|
||||
import importlib
|
||||
import sys
|
||||
from importlib import import_module
|
||||
|
||||
try:
|
||||
# Set attributes in sys.modules under their old name
|
||||
for old, new in attributes.items():
|
||||
old_module, old_attr = old.rsplit(".", 1)
|
||||
new_module, new_attr = new.rsplit(".", 1)
|
||||
setattr(
|
||||
importlib.import_module(old_module),
|
||||
old_attr,
|
||||
getattr(importlib.import_module(new_module), new_attr),
|
||||
)
|
||||
setattr(import_module(old_module), old_attr, getattr(import_module(new_module), new_attr))
|
||||
|
||||
# Set modules in sys.modules under their old name
|
||||
for old, new in modules.items():
|
||||
sys.modules[old] = importlib.import_module(new)
|
||||
sys.modules[old] = import_module(new)
|
||||
|
||||
yield
|
||||
finally:
|
||||
|
|
@ -750,9 +764,10 @@ def torch_safe_load(weight):
|
|||
"ultralytics.yolo.data": "ultralytics.data",
|
||||
},
|
||||
attributes={
|
||||
"ultralytics.nn.modules.block.Silence": "torch.nn.Identity",
|
||||
"ultralytics.nn.modules.block.Silence": "torch.nn.Identity", # YOLOv9e
|
||||
"ultralytics.nn.tasks.YOLOv10DetectionModel": "ultralytics.nn.tasks.DetectionModel", # YOLOv10
|
||||
},
|
||||
): # for legacy 8.0 Classify and Pose models
|
||||
):
|
||||
ckpt = torch.load(file, map_location="cpu")
|
||||
|
||||
except ModuleNotFoundError as e: # e.name is missing module name
|
||||
|
|
@ -911,6 +926,9 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
|
|||
DWConvTranspose2d,
|
||||
C3x,
|
||||
RepC3,
|
||||
PSA,
|
||||
SCDown,
|
||||
C2fCIB,
|
||||
}:
|
||||
c1, c2 = ch[f], args[0]
|
||||
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
|
||||
|
|
@ -922,7 +940,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
|
|||
) # num heads
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in {BottleneckCSP, C1, C2, C2f, C2fAttn, C3, C3TR, C3Ghost, C3x, RepC3}:
|
||||
if m in {BottleneckCSP, C1, C2, C2f, C2fAttn, C3, C3TR, C3Ghost, C3x, RepC3, C2fCIB}:
|
||||
args.insert(2, n) # number of repeats
|
||||
n = 1
|
||||
elif m is AIFI:
|
||||
|
|
@ -939,7 +957,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
|
|||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[x] for x in f)
|
||||
elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn}:
|
||||
elif m in {Detect, WorldDetect, Segment, Pose, OBB, ImagePoolingAttn, v10Detect}:
|
||||
args.append([ch[x] for x in f])
|
||||
if m is Segment:
|
||||
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
|
||||
|
|
@ -1024,7 +1042,7 @@ def guess_model_task(model):
|
|||
m = cfg["head"][-1][-2].lower() # output module name
|
||||
if m in {"classify", "classifier", "cls", "fc"}:
|
||||
return "classify"
|
||||
if m == "detect":
|
||||
if "detect" in m:
|
||||
return "detect"
|
||||
if m == "segment":
|
||||
return "segment"
|
||||
|
|
@ -1056,7 +1074,7 @@ def guess_model_task(model):
|
|||
return "pose"
|
||||
elif isinstance(m, OBB):
|
||||
return "obb"
|
||||
elif isinstance(m, (Detect, WorldDetect)):
|
||||
elif isinstance(m, (Detect, WorldDetect, v10Detect)):
|
||||
return "detect"
|
||||
|
||||
# Guess from model filename
|
||||
|
|
|
|||
|
|
@ -81,6 +81,7 @@ def benchmark(
|
|||
device = select_device(device, verbose=False)
|
||||
if isinstance(model, (str, Path)):
|
||||
model = YOLO(model)
|
||||
is_end2end = getattr(model.model.model[-1], "end2end", False)
|
||||
|
||||
y = []
|
||||
t0 = time.time()
|
||||
|
|
@ -96,14 +97,18 @@ def benchmark(
|
|||
assert MACOS or LINUX, "CoreML and TF.js export only supported on macOS and Linux"
|
||||
assert not IS_RASPBERRYPI, "CoreML and TF.js export not supported on Raspberry Pi"
|
||||
assert not IS_JETSON, "CoreML and TF.js export not supported on NVIDIA Jetson"
|
||||
assert not is_end2end, "End-to-end models not supported by CoreML and TF.js yet"
|
||||
if i in {3, 5}: # CoreML and OpenVINO
|
||||
assert not IS_PYTHON_3_12, "CoreML and OpenVINO not supported on Python 3.12"
|
||||
if i in {6, 7, 8, 9, 10}: # All TF formats
|
||||
assert not isinstance(model, YOLOWorld), "YOLOWorldv2 TensorFlow exports not supported by onnx2tf yet"
|
||||
assert not is_end2end, "End-to-end models not supported by onnx2tf yet"
|
||||
if i in {11}: # Paddle
|
||||
assert not isinstance(model, YOLOWorld), "YOLOWorldv2 Paddle exports not supported yet"
|
||||
assert not is_end2end, "End-to-end models not supported by PaddlePaddle yet"
|
||||
if i in {12}: # NCNN
|
||||
assert not isinstance(model, YOLOWorld), "YOLOWorldv2 NCNN exports not supported yet"
|
||||
assert not is_end2end, "End-to-end models not supported by NCNN yet"
|
||||
if "cpu" in device.type:
|
||||
assert cpu, "inference not supported on CPU"
|
||||
if "cuda" in device.type:
|
||||
|
|
|
|||
|
|
@ -23,6 +23,7 @@ GITHUB_ASSETS_NAMES = (
|
|||
+ [f"yolov8{k}-world.pt" for k in "smlx"]
|
||||
+ [f"yolov8{k}-worldv2.pt" for k in "smlx"]
|
||||
+ [f"yolov9{k}.pt" for k in "ce"]
|
||||
+ [f"yolov10{k}.pt" for k in "nsmblx"]
|
||||
+ [f"yolo_nas_{k}.pt" for k in "sml"]
|
||||
+ [f"sam_{k}.pt" for k in "bl"]
|
||||
+ [f"FastSAM-{k}.pt" for k in "sx"]
|
||||
|
|
|
|||
|
|
@ -148,7 +148,7 @@ class KeypointLoss(nn.Module):
|
|||
class v8DetectionLoss:
|
||||
"""Criterion class for computing training losses."""
|
||||
|
||||
def __init__(self, model): # model must be de-paralleled
|
||||
def __init__(self, model, tal_topk=10): # model must be de-paralleled
|
||||
"""Initializes v8DetectionLoss with the model, defining model-related properties and BCE loss function."""
|
||||
device = next(model.parameters()).device # get model device
|
||||
h = model.args # hyperparameters
|
||||
|
|
@ -164,7 +164,7 @@ class v8DetectionLoss:
|
|||
|
||||
self.use_dfl = m.reg_max > 1
|
||||
|
||||
self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
|
||||
self.assigner = TaskAlignedAssigner(topk=tal_topk, num_classes=self.nc, alpha=0.5, beta=6.0)
|
||||
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
|
||||
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
|
||||
|
||||
|
|
@ -714,3 +714,21 @@ class v8OBBLoss(v8DetectionLoss):
|
|||
b, a, c = pred_dist.shape # batch, anchors, channels
|
||||
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
|
||||
return torch.cat((dist2rbox(pred_dist, pred_angle, anchor_points), pred_angle), dim=-1)
|
||||
|
||||
|
||||
class E2EDetectLoss:
|
||||
"""Criterion class for computing training losses."""
|
||||
|
||||
def __init__(self, model):
|
||||
"""Initialize E2EDetectLoss with one-to-many and one-to-one detection losses using the provided model."""
|
||||
self.one2many = v8DetectionLoss(model, tal_topk=10)
|
||||
self.one2one = v8DetectionLoss(model, tal_topk=1)
|
||||
|
||||
def __call__(self, preds, batch):
|
||||
"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
|
||||
preds = preds[1] if isinstance(preds, tuple) else preds
|
||||
one2many = preds["one2many"]
|
||||
loss_one2many = self.one2many(one2many, batch)
|
||||
one2one = preds["one2one"]
|
||||
loss_one2one = self.one2one(one2one, batch)
|
||||
return loss_one2many[0] + loss_one2one[0], loss_one2many[1] + loss_one2one[1]
|
||||
|
|
|
|||
|
|
@ -64,8 +64,9 @@ def box_iou(box1, box2, eps=1e-7):
|
|||
(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.
|
||||
"""
|
||||
|
||||
# NOTE: Need .float() to get accurate iou values
|
||||
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
||||
(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
|
||||
(a1, a2), (b1, b2) = box1.float().unsqueeze(1).chunk(2, 2), box2.float().unsqueeze(0).chunk(2, 2)
|
||||
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)
|
||||
|
||||
# IoU = inter / (area1 + area2 - inter)
|
||||
|
|
|
|||
|
|
@ -213,6 +213,9 @@ def non_max_suppression(
|
|||
if isinstance(prediction, (list, tuple)): # YOLOv8 model in validation model, output = (inference_out, loss_out)
|
||||
prediction = prediction[0] # select only inference output
|
||||
|
||||
if prediction.shape[-1] == 6: # end-to-end model
|
||||
return [pred[pred[:, 4] > conf_thres] for pred in prediction]
|
||||
|
||||
bs = prediction.shape[0] # batch size
|
||||
nc = nc or (prediction.shape[1] - 4) # number of classes
|
||||
nm = prediction.shape[1] - nc - 4 # number of masks
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue