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ultralytics 8.0.96 TAL speed and memory improvements (#2484)

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Signed-off-by: Evangelos Petrongonas <e.petrongonas@hellenicdrones.com>
Co-authored-by: Evangelos Petrongonas <24351757+vpetrog@users.noreply.github.com>
Co-authored-by: JF Chen <k-2feng@hotmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-05-08 23:41:27 +02:00
committed by GitHub
parent e21428ca4e
commit 6ee3a9a74b
13 changed files with 163 additions and 53 deletions
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16
ultralytics/yolo/utils/callbacks/clearml.py
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@ -84,9 +84,15 @@ def on_pretrain_routine_start(trainer):
def on_train_epoch_end(trainer):
"""Logs debug samples for the first epoch of YOLO training."""
if trainer.epoch == 1 and Task.current_task():
_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
task = Task.current_task()
if task:
"""Logs debug samples for the first epoch of YOLO training."""
if trainer.epoch == 1:
_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
"""Report the current training progress."""
for k, v in trainer.validator.metrics.results_dict.items():
task.get_logger().report_scalar('train', k, v, iteration=trainer.epoch)
def on_fit_epoch_end(trainer):
@ -119,7 +125,9 @@ def on_train_end(trainer):
task = Task.current_task()
if task:
# Log final results, CM matrix + PR plots
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [
'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
*(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
for f in files:
_log_plot(title=f.stem, plot_path=f)

4
ultralytics/yolo/utils/callbacks/neptune.py
View File

@ -87,7 +87,9 @@ def on_train_end(trainer):
"""Callback function called at end of training."""
if run:
# Log final results, CM matrix + PR plots
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [
'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
*(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
for f in files:
_log_plot(title=f.stem, plot_path=f)