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73 lines
1.5 KiB
73 lines
1.5 KiB
import torch |
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from ultralytics.yolo import YOLO |
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def test_model_forward(): |
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model = YOLO() |
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model.new("yolov8n-seg.yaml") |
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img = torch.rand(512 * 512 * 3).view(1, 3, 512, 512) |
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model.forward(img) |
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model(img) |
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def test_model_info(): |
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model = YOLO() |
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model.new("yolov8n.yaml") |
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model.info() |
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model.load("balloon-detect.pt") |
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model.info(verbose=True) |
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def test_model_fuse(): |
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model = YOLO() |
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model.new("yolov8n.yaml") |
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model.fuse() |
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model.load("balloon-detect.pt") |
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model.fuse() |
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def test_visualize_preds(): |
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model = YOLO() |
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model.load("balloon-segment.pt") |
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model.predict(source="ultralytics/assets") |
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def test_val(): |
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model = YOLO() |
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model.load("balloon-segment.pt") |
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model.val(data="coco128-seg.yaml", imgsz=32) |
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def test_model_resume(): |
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model = YOLO() |
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model.new("yolov8n-seg.yaml") |
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model.train(epochs=1, imgsz=32, data="coco128-seg.yaml") |
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try: |
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model.resume(task="segment") |
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except AssertionError: |
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print("Successfully caught resume assert!") |
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def test_model_train_pretrained(): |
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model = YOLO() |
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model.load("balloon-detect.pt") |
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model.train(data="coco128.yaml", epochs=1, imgsz=32) |
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model.new("yolov8n.yaml") |
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model.train(data="coco128.yaml", epochs=1, imgsz=32) |
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img = torch.rand(512 * 512 * 3).view(1, 3, 512, 512) |
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model(img) |
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def test(): |
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test_model_forward() |
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test_model_info() |
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test_model_fuse() |
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test_visualize_preds() |
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test_val() |
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test_model_resume() |
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test_model_train_pretrained() |
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if __name__ == "__main__": |
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test()
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