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add yolo v10 and modify pipeline

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This commit is contained in:
王庆刚
2025-03-28 13:19:54 +08:00
parent 183299c06b
commit 798c596acc
471 changed files with 19109 additions and 7342 deletions
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94
ultralytics/utils/callbacks/clearml.py
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@ -4,19 +4,19 @@ from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['clearml'] is True # verify integration is enabled
assert SETTINGS["clearml"] is True # verify integration is enabled
import clearml
from clearml import Task
from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
from clearml.binding.matplotlib_bind import PatchedMatplotlib
assert hasattr(clearml, '__version__') # verify package is not directory
assert hasattr(clearml, "__version__") # verify package is not directory
except (ImportError, AssertionError):
clearml = None
def _log_debug_samples(files, title='Debug Samples') -> None:
def _log_debug_samples(files, title="Debug Samples") -> None:
"""
Log files (images) as debug samples in the ClearML task.
@ -29,12 +29,11 @@ def _log_debug_samples(files, title='Debug Samples') -> None:
if task := Task.current_task():
for f in files:
if f.exists():
it = re.search(r'_batch(\d+)', f.name)
it = re.search(r"_batch(\d+)", f.name)
iteration = int(it.groups()[0]) if it else 0
task.get_logger().report_image(title=title,
series=f.name.replace(it.group(), ''),
local_path=str(f),
iteration=iteration)
task.get_logger().report_image(
title=title, series=f.name.replace(it.group(), ""), local_path=str(f), iteration=iteration
)
def _log_plot(title, plot_path) -> None:
@ -50,13 +49,12 @@ def _log_plot(title, plot_path) -> None:
img = mpimg.imread(plot_path)
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[]) # no ticks
ax.imshow(img)
Task.current_task().get_logger().report_matplotlib_figure(title=title,
series='',
figure=fig,
report_interactive=False)
Task.current_task().get_logger().report_matplotlib_figure(
title=title, series="", figure=fig, report_interactive=False
)
def on_pretrain_routine_start(trainer):
@ -68,19 +66,21 @@ def on_pretrain_routine_start(trainer):
PatchPyTorchModelIO.update_current_task(None)
PatchedMatplotlib.update_current_task(None)
else:
task = Task.init(project_name=trainer.args.project or 'YOLOv8',
task_name=trainer.args.name,
tags=['YOLOv8'],
output_uri=True,
reuse_last_task_id=False,
auto_connect_frameworks={
'pytorch': False,
'matplotlib': False})
LOGGER.warning('ClearML Initialized a new task. If you want to run remotely, '
'please add clearml-init and connect your arguments before initializing YOLO.')
task.connect(vars(trainer.args), name='General')
task = Task.init(
project_name=trainer.args.project or "YOLOv8",
task_name=trainer.args.name,
tags=["YOLOv8"],
output_uri=True,
reuse_last_task_id=False,
auto_connect_frameworks={"pytorch": False, "matplotlib": False},
)
LOGGER.warning(
"ClearML Initialized a new task. If you want to run remotely, "
"please add clearml-init and connect your arguments before initializing YOLO."
)
task.connect(vars(trainer.args), name="General")
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ ClearML installed but not initialized correctly, not logging this run. {e}')
LOGGER.warning(f"WARNING ⚠️ ClearML installed but not initialized correctly, not logging this run. {e}")
def on_train_epoch_end(trainer):
@ -88,22 +88,26 @@ def on_train_epoch_end(trainer):
if task := Task.current_task():
# Log debug samples
if trainer.epoch == 1:
_log_debug_samples(sorted(trainer.save_dir.glob('train_batch*.jpg')), 'Mosaic')
_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)
for k, v in trainer.label_loss_items(trainer.tloss, prefix="train").items():
task.get_logger().report_scalar("train", k, v, iteration=trainer.epoch)
for k, v in trainer.lr.items():
task.get_logger().report_scalar("lr", k, v, iteration=trainer.epoch)
def on_fit_epoch_end(trainer):
"""Reports model information to logger at the end of an epoch."""
if task := Task.current_task():
# You should have access to the validation bboxes under jdict
task.get_logger().report_scalar(title='Epoch Time',
series='Epoch Time',
value=trainer.epoch_time,
iteration=trainer.epoch)
task.get_logger().report_scalar(
title="Epoch Time", series="Epoch Time", value=trainer.epoch_time, iteration=trainer.epoch
)
for k, v in trainer.metrics.items():
task.get_logger().report_scalar("val", k, v, iteration=trainer.epoch)
if trainer.epoch == 0:
from ultralytics.utils.torch_utils import model_info_for_loggers
for k, v in model_info_for_loggers(trainer).items():
task.get_logger().report_single_value(k, v)
@ -112,7 +116,7 @@ def on_val_end(validator):
"""Logs validation results including labels and predictions."""
if Task.current_task():
# Log val_labels and val_pred
_log_debug_samples(sorted(validator.save_dir.glob('val*.jpg')), 'Validation')
_log_debug_samples(sorted(validator.save_dir.glob("val*.jpg")), "Validation")
def on_train_end(trainer):
@ -120,8 +124,11 @@ def on_train_end(trainer):
if task := Task.current_task():
# Log final results, CM matrix + PR plots
files = [
'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
*(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
"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)
@ -132,9 +139,14 @@ def on_train_end(trainer):
task.update_output_model(model_path=str(trainer.best), model_name=trainer.args.name, auto_delete_file=False)
callbacks = {
'on_pretrain_routine_start': on_pretrain_routine_start,
'on_train_epoch_end': on_train_epoch_end,
'on_fit_epoch_end': on_fit_epoch_end,
'on_val_end': on_val_end,
'on_train_end': on_train_end} if clearml else {}
callbacks = (
{
"on_pretrain_routine_start": on_pretrain_routine_start,
"on_train_epoch_end": on_train_epoch_end,
"on_fit_epoch_end": on_fit_epoch_end,
"on_val_end": on_val_end,
"on_train_end": on_train_end,
}
if clearml
else {}
)