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46 lines
1.3 KiB
46 lines
1.3 KiB
2 years ago
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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_scalers(metric_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 metric_dict.items():
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task.get_logger().report_scalar(group, k, v, step)
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def before_train(trainer):
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# TODO: reuse existing task
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task = Task.init(project_name=trainer.args.project if trainer.args.project != 'runs/train' else 'YOLOv5',
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task_name=trainer.args.name if trainer.args.name != 'exp' else 'Training',
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tags=['YOLOv5'],
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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(trainer.args, name='parameters')
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def on_batch_end(trainer):
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train_loss = trainer.tloss
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_log_scalers(trainer.label_loss_items(train_loss), "train", trainer.epoch)
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def on_val_end(trainer):
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metrics = trainer.metrics
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val_losses = trainer.validator.loss
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val_loss_dict = trainer.label_loss_items(val_losses)
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_log_scalers(val_loss_dict, "val", trainer.epoch)
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_log_scalers(metrics, "metrics", trainer.epoch)
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callbacks = {
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"before_train": before_train,
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"on_val_end": on_val_end,
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"on_batch_end": on_batch_end,}
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