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57 lines
1.8 KiB
57 lines
1.8 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
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try:
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import clearml
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from clearml import Task
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assert hasattr(clearml, '__version__')
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except (ImportError, AssertionError):
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clearml = None
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def _log_images(imgs_dict, group="", step=0):
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task = Task.current_task()
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if task:
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for k, v in imgs_dict.items():
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task.get_logger().report_image(group, k, step, v)
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def on_pretrain_routine_start(trainer):
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# TODO: reuse existing task
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task = Task.init(project_name=trainer.args.project or "YOLOv8",
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task_name=trainer.args.name,
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tags=['YOLOv8'],
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output_uri=True,
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reuse_last_task_id=False,
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auto_connect_frameworks={'pytorch': False})
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task.connect(dict(trainer.args), name='General')
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def on_train_epoch_end(trainer):
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if trainer.epoch == 1:
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_log_images({f.stem: str(f) for f in trainer.save_dir.glob('train_batch*.jpg')}, "Mosaic", trainer.epoch)
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def on_fit_epoch_end(trainer):
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if trainer.epoch == 0:
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model_info = {
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"Parameters": get_num_params(trainer.model),
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"GFLOPs": round(get_flops(trainer.model), 3),
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"Inference speed (ms/img)": round(trainer.validator.speed[1], 3)}
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Task.current_task().connect(model_info, name='Model')
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def on_train_end(trainer):
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Task.current_task().update_output_model(model_path=str(trainer.best),
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model_name=trainer.args.name,
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auto_delete_file=False)
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callbacks = {
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"on_pretrain_routine_start": on_pretrain_routine_start,
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"on_train_epoch_end": on_train_epoch_end,
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"on_fit_epoch_end": on_fit_epoch_end,
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"on_train_end": on_train_end} if clearml else {}
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