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68 lines
2.1 KiB
68 lines
2.1 KiB
2 years ago
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import torch
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from ultralytics import YOLO
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from ultralytics.nn.modules import Detect, Segment
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def export_onnx(model, file):
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# YOLOv5 ONNX export
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import onnx
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im = torch.zeros(1, 3, 640, 640)
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model.eval()
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model(im, profile=True)
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for k, m in model.named_modules():
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if isinstance(m, (Detect, Segment)):
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m.export = True
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torch.onnx.export(
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model,
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im,
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file,
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verbose=False,
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opset_version=12,
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do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
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input_names=['images'])
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# Checks
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model_onnx = onnx.load(file) # load onnx model
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onnx.checker.check_model(model_onnx) # check onnx model
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# Metadata
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d = {'stride': int(max(model.stride)), 'names': model.names}
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for k, v in d.items():
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meta = model_onnx.metadata_props.add()
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meta.key, meta.value = k, str(v)
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onnx.save(model_onnx, file)
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if __name__ == "__main__":
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model = YOLO()
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print("yolov8n")
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model.new("yolov8n.yaml")
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print("yolov8n-seg")
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model.new("yolov8n-seg.yaml")
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print("yolov8s")
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model.new("yolov8s.yaml")
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# export_onnx(model.model, "yolov8s.onnx")
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print("yolov8s-seg")
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model.new("yolov8s-seg.yaml")
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# export_onnx(model.model, "yolov8s-seg.onnx")
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print("yolov8m")
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model.new("yolov8m.yaml")
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print("yolov8m-seg")
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model.new("yolov8m-seg.yaml")
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print("yolov8l")
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model.new("yolov8l.yaml")
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print("yolov8l-seg")
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model.new("yolov8l-seg.yaml")
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print("yolov8x")
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model.new("yolov8x.yaml")
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print("yolov8x-seg")
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model.new("yolov8x-seg.yaml")
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# n vs n-seg: 8.9GFLOPs vs 12.8GFLOPs, 3.16M vs 3.6M. ch[0] // 4 (11.9GFLOPs, 3.39M)
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# s vs s-seg: 28.8GFLOPs vs 44.4GFLOPs, 11.1M vs 12.9M. ch[0] // 4 (39.5GFLOPs, 11.7M)
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# m vs m-seg: 79.3GFLOPs vs 113.8GFLOPs, 25.9M vs 29.5M. ch[0] // 4 (103.GFLOPs, 27.1M)
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# l vs l-seg: 165.7GFLOPs vs 226.3GFLOPs, 43.7M vs 49.6M. ch[0] // 4 (207GFLOPs, 45.7M)
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# x vs x-seg: 258.5GFLOPs vs 353.0GFLOPs, 68.3M vs 77.5M. ch[0] // 4 (324GFLOPs, 71.4M)
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