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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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59
ultralytics/utils/callbacks/dvc.py
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@ -4,9 +4,10 @@ from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, checks
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['dvc'] is True # verify integration is enabled
assert SETTINGS["dvc"] is True # verify integration is enabled
import dvclive
assert checks.check_version('dvclive', '2.11.0', verbose=True)
assert checks.check_version("dvclive", "2.11.0", verbose=True)
import os
import re
@ -24,24 +25,24 @@ except (ImportError, AssertionError, TypeError):
dvclive = None
def _log_images(path, prefix=''):
def _log_images(path, prefix=""):
"""Logs images at specified path with an optional prefix using DVCLive."""
if live:
name = path.name
# Group images by batch to enable sliders in UI
if m := re.search(r'_batch(\d+)', name):
if m := re.search(r"_batch(\d+)", name):
ni = m[1]
new_stem = re.sub(r'_batch(\d+)', '_batch', path.stem)
new_stem = re.sub(r"_batch(\d+)", "_batch", path.stem)
name = (Path(new_stem) / ni).with_suffix(path.suffix)
live.log_image(os.path.join(prefix, name), path)
def _log_plots(plots, prefix=''):
def _log_plots(plots, prefix=""):
"""Logs plot images for training progress if they have not been previously processed."""
for name, params in plots.items():
timestamp = params['timestamp']
timestamp = params["timestamp"]
if _processed_plots.get(name) != timestamp:
_log_images(name, prefix)
_processed_plots[name] = timestamp
@ -53,15 +54,15 @@ def _log_confusion_matrix(validator):
preds = []
matrix = validator.confusion_matrix.matrix
names = list(validator.names.values())
if validator.confusion_matrix.task == 'detect':
names += ['background']
if validator.confusion_matrix.task == "detect":
names += ["background"]
for ti, pred in enumerate(matrix.T.astype(int)):
for pi, num in enumerate(pred):
targets.extend([names[ti]] * num)
preds.extend([names[pi]] * num)
live.log_sklearn_plot('confusion_matrix', targets, preds, name='cf.json', normalized=True)
live.log_sklearn_plot("confusion_matrix", targets, preds, name="cf.json", normalized=True)
def on_pretrain_routine_start(trainer):
@ -71,12 +72,12 @@ def on_pretrain_routine_start(trainer):
live = dvclive.Live(save_dvc_exp=True, cache_images=True)
LOGGER.info("DVCLive is detected and auto logging is enabled (run 'yolo settings dvc=False' to disable).")
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ DVCLive installed but not initialized correctly, not logging this run. {e}')
LOGGER.warning(f"WARNING ⚠️ DVCLive installed but not initialized correctly, not logging this run. {e}")
def on_pretrain_routine_end(trainer):
"""Logs plots related to the training process at the end of the pretraining routine."""
_log_plots(trainer.plots, 'train')
_log_plots(trainer.plots, "train")
def on_train_start(trainer):
@ -95,17 +96,18 @@ def on_fit_epoch_end(trainer):
"""Logs training metrics and model info, and advances to next step on the end of each fit epoch."""
global _training_epoch
if live and _training_epoch:
all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix="train"), **trainer.metrics, **trainer.lr}
for metric, value in all_metrics.items():
live.log_metric(metric, value)
if trainer.epoch == 0:
from ultralytics.utils.torch_utils import model_info_for_loggers
for metric, value in model_info_for_loggers(trainer).items():
live.log_metric(metric, value, plot=False)
_log_plots(trainer.plots, 'train')
_log_plots(trainer.validator.plots, 'val')
_log_plots(trainer.plots, "train")
_log_plots(trainer.validator.plots, "val")
live.next_step()
_training_epoch = False
@ -115,24 +117,29 @@ def on_train_end(trainer):
"""Logs the best metrics, plots, and confusion matrix at the end of training if DVCLive is active."""
if live:
# At the end log the best metrics. It runs validator on the best model internally.
all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix="train"), **trainer.metrics, **trainer.lr}
for metric, value in all_metrics.items():
live.log_metric(metric, value, plot=False)
_log_plots(trainer.plots, 'val')
_log_plots(trainer.validator.plots, 'val')
_log_plots(trainer.plots, "val")
_log_plots(trainer.validator.plots, "val")
_log_confusion_matrix(trainer.validator)
if trainer.best.exists():
live.log_artifact(trainer.best, copy=True, type='model')
live.log_artifact(trainer.best, copy=True, type="model")
live.end()
callbacks = {
'on_pretrain_routine_start': on_pretrain_routine_start,
'on_pretrain_routine_end': on_pretrain_routine_end,
'on_train_start': on_train_start,
'on_train_epoch_start': on_train_epoch_start,
'on_fit_epoch_end': on_fit_epoch_end,
'on_train_end': on_train_end} if dvclive else {}
callbacks = (
{
"on_pretrain_routine_start": on_pretrain_routine_start,
"on_pretrain_routine_end": on_pretrain_routine_end,
"on_train_start": on_train_start,
"on_train_epoch_start": on_train_epoch_start,
"on_fit_epoch_end": on_fit_epoch_end,
"on_train_end": on_train_end,
}
if dvclive
else {}
)