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@ -27,11 +27,13 @@ def _log_debug_samples(files, title='Debug Samples'):
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files (List(PosixPath)) a list of file paths in PosixPath format
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title (str) A title that groups together images with the same values
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"""
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task = Task.current_task()
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if task:
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for f in files:
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if f.exists():
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it = re.search(r'_batch(\d+)', f.name)
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iteration = int(it.groups()[0]) if it else 0
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Task.current_task().get_logger().report_image(title=title,
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task.get_logger().report_image(title=title,
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series=f.name.replace(it.group(), ''),
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local_path=str(f),
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iteration=iteration)
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@ -54,11 +56,9 @@ def _log_plot(title, plot_path):
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def on_pretrain_routine_start(trainer):
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# TODO: reuse existing task
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try:
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if Task.current_task():
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task = Task.current_task()
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if task:
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# Make sure the automatic pytorch and matplotlib bindings are disabled!
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# We are logging these plots and model files manually in the integration
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PatchPyTorchModelIO.update_current_task(None)
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@ -80,13 +80,15 @@ def on_pretrain_routine_start(trainer):
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def on_train_epoch_end(trainer):
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if trainer.epoch == 1:
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if trainer.epoch == 1 and Task.current_task():
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_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
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def on_fit_epoch_end(trainer):
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task = Task.current_task()
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if task:
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# You should have access to the validation bboxes under jdict
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Task.current_task().get_logger().report_scalar(title='Epoch Time',
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task.get_logger().report_scalar(title='Epoch Time',
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series='Epoch Time',
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value=trainer.epoch_time,
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iteration=trainer.epoch)
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@ -96,15 +98,18 @@ def on_fit_epoch_end(trainer):
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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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for k, v in model_info.items():
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Task.current_task().get_logger().report_single_value(k, v)
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task.get_logger().report_single_value(k, v)
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def on_val_end(validator):
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if Task.current_task():
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# Log val_labels and val_pred
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_log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')
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def on_train_end(trainer):
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task = Task.current_task()
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if task:
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# Log final results, CM matrix + PR plots
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files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
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files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
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@ -112,11 +117,9 @@ def on_train_end(trainer):
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_log_plot(title=f.stem, plot_path=f)
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# Report final metrics
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for k, v in trainer.validator.metrics.results_dict.items():
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Task.current_task().get_logger().report_single_value(k, v)
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task.get_logger().report_single_value(k, v)
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# Log the final model
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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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task.update_output_model(model_path=str(trainer.best), model_name=trainer.args.name, auto_delete_file=False)
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callbacks = {
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