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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 wandb
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assert hasattr(wandb, '__version__')
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except (ImportError, AssertionError):
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wandb = None
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def on_pretrain_routine_start(trainer):
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wandb.init(project=trainer.args.project if trainer.args.project != 'runs/train' else 'YOLOv8',
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name=trainer.args.name,
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config=dict(trainer.args)) if not wandb.run else wandb.run
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def on_val_end(trainer):
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wandb.run.log(trainer.metrics, step=trainer.epoch + 1)
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if trainer.epoch == 0:
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model_info = {
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"model/parameters": get_num_params(trainer.model),
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"model/GFLOPs": round(get_flops(trainer.model), 3),
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"model/speed(ms)": round(trainer.validator.speed[1], 3)}
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wandb.run.log(model_info, step=trainer.epoch + 1)
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def on_train_epoch_end(trainer):
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wandb.run.log(trainer.label_loss_items(trainer.tloss, prefix="train"), step=trainer.epoch + 1)
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if trainer.epoch == 1:
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wandb.run.log({f.stem: wandb.Image(str(f))
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for f in trainer.save_dir.glob('train_batch*.jpg')},
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step=trainer.epoch + 1)
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def on_train_end(trainer):
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art = wandb.Artifact(type="model", name=f"run_{wandb.run.id}_model")
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if trainer.best.exists():
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art.add_file(trainer.best)
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wandb.run.log_artifact(art)
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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_val_end": on_val_end,
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"on_train_end": on_train_end} if wandb else {}
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