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49 lines
1.6 KiB
49 lines
1.6 KiB
2 years ago
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# 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 wandb as wb
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assert hasattr(wb, '__version__')
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except (ImportError, AssertionError):
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wb = None
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def on_pretrain_routine_start(trainer):
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wb.init(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, config=vars(
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trainer.args)) if not wb.run else wb.run
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def on_fit_epoch_end(trainer):
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wb.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['inference'], 3)}
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wb.run.log(model_info, step=trainer.epoch + 1)
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def on_train_epoch_end(trainer):
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wb.run.log(trainer.label_loss_items(trainer.tloss, prefix='train'), step=trainer.epoch + 1)
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wb.run.log(trainer.lr, step=trainer.epoch + 1)
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if trainer.epoch == 1:
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wb.run.log({f.stem: wb.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 = wb.Artifact(type='model', name=f'run_{wb.run.id}_model')
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if trainer.best.exists():
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art.add_file(trainer.best)
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wb.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_fit_epoch_end': on_fit_epoch_end,
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'on_train_end': on_train_end} if wb else {}
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