Ankle/yolov5_birdproject
1
0 Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

Update Actions with Lychee and GitHub Token (#12592)

Browse Source 
* Add Ultralytics Actions

* Auto-format by Ultralytics actions

* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Paula DerrengerUltralyticsAssistantGlenn Jocher
 authored and GitHub committed 2024-01-08 01:29:14 +01:00
1 parent f56355d321
commit ad97dc6a6d
47 files changed
+4217 -3462
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

utils/loggers/wandb/wandb_utils.py
+62 -47
View File
@@ -15,34 +15,35 @@ FILE = Path(__file__).resolve()
ROOT = FILE.parents[3] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
RANK = int(os.getenv('RANK', -1))
DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \
f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
RANK = int(os.getenv("RANK", -1))
DEPRECATION_WARNING = (
f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. "
f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
)
try:
import wandb
assert hasattr(wandb, '__version__') # verify package import not local dir
assert hasattr(wandb, "__version__") # verify package import not local dir
LOGGER.warning(DEPRECATION_WARNING)
except (ImportError, AssertionError):
wandb = None
class WandbLogger():
"""Log training runs, datasets, models, and predictions to Weights & Biases.
class WandbLogger:
"""
Log training runs, datasets, models, and predictions to Weights & Biases.
This logger sends information to W&B at wandb.ai. By default, this information
includes hyperparameters, system configuration and metrics, model metrics,
and basic data metrics and analyses.
This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system
configuration and metrics, model metrics, and basic data metrics and analyses.
By providing additional command line arguments to train.py, datasets,
models and predictions can also be logged.
By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.
For more on how this logger is used, see the Weights & Biases documentation:
https://docs.wandb.com/guides/integrations/yolov5
"""
def __init__(self, opt, run_id=None, job_type='Training'):
def __init__(self, opt, run_id=None, job_type="Training"):
"""
- Initialize WandbLogger instance
- Upload dataset if opt.upload_dataset is True
@@ -53,7 +54,7 @@ class WandbLogger():
run_id (str) -- Run ID of W&B run to be resumed
job_type (str) -- To set the job_type for this run
"""
"""
# Pre-training routine --
self.job_type = job_type
self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
@@ -64,17 +65,23 @@ class WandbLogger():
self.max_imgs_to_log = 16
self.data_dict = None
if self.wandb:
self.wandb_run = wandb.init(config=opt,
resume='allow',
project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
entity=opt.entity,
name=opt.name if opt.name != 'exp' else None,
job_type=job_type,
id=run_id,
allow_val_change=True) if not wandb.run else wandb.run
self.wandb_run = (
wandb.init(
config=opt,
resume="allow",
project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem,
entity=opt.entity,
name=opt.name if opt.name != "exp" else None,
job_type=job_type,
id=run_id,
allow_val_change=True,
)
if not wandb.run
else wandb.run
)
if self.wandb_run:
if self.job_type == 'Training':
if self.job_type == "Training":
if isinstance(opt.data, dict):
# This means another dataset manager has already processed the dataset info (e.g. ClearML)
# and they will have stored the already processed dict in opt.data
@@ -97,11 +104,17 @@ class WandbLogger():
if isinstance(opt.resume, str):
model_dir, _ = self.download_model_artifact(opt)
if model_dir:
self.weights = Path(model_dir) / 'last.pt'
self.weights = Path(model_dir) / "last.pt"
config = self.wandb_run.config
opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
config.hyp, config.imgsz
opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = (
str(self.weights),
config.save_period,
config.batch_size,
config.bbox_interval,
config.epochs,
config.hyp,
config.imgsz,
)
if opt.bbox_interval == -1:
self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
@@ -110,7 +123,7 @@ class WandbLogger():
def log_model(self, path, opt, epoch, fitness_score, best_model=False):
"""
Log the model checkpoint as W&B artifact
Log the model checkpoint as W&B artifact.
arguments:
path (Path) -- Path of directory containing the checkpoints
@@ -119,26 +132,30 @@ class WandbLogger():
fitness_score (float) -- fitness score for current epoch
best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
"""
model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
type='model',
metadata={
'original_url': str(path),
'epochs_trained': epoch + 1,
'save period': opt.save_period,
'project': opt.project,
'total_epochs': opt.epochs,
'fitness_score': fitness_score})
model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
wandb.log_artifact(model_artifact,
aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
LOGGER.info(f'Saving model artifact on epoch {epoch + 1}')
model_artifact = wandb.Artifact(
"run_" + wandb.run.id + "_model",
type="model",
metadata={
"original_url": str(path),
"epochs_trained": epoch + 1,
"save period": opt.save_period,
"project": opt.project,
"total_epochs": opt.epochs,
"fitness_score": fitness_score,
},
)
model_artifact.add_file(str(path / "last.pt"), name="last.pt")
wandb.log_artifact(
model_artifact, aliases=["latest", "last", "epoch " + str(self.current_epoch), "best" if best_model else ""]
)
LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
def val_one_image(self, pred, predn, path, names, im):
pass
def log(self, log_dict):
"""
save the metrics to the logging dictionary
Save the metrics to the logging dictionary.
arguments:
log_dict (Dict) -- metrics/media to be logged in current step
@@ -149,7 +166,7 @@ class WandbLogger():
def end_epoch(self):
"""
commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
arguments:
best_result (boolean): Boolean representing if the result of this evaluation is best or not
@@ -160,16 +177,14 @@ class WandbLogger():
wandb.log(self.log_dict)
except BaseException as e:
LOGGER.info(
f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}'
f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
)
self.wandb_run.finish()
self.wandb_run = None
self.log_dict = {}
def finish_run(self):
"""
Log metrics if any and finish the current W&B run
"""
"""Log metrics if any and finish the current W&B run."""
if self.wandb_run:
if self.log_dict:
with all_logging_disabled():
@@ -180,7 +195,7 @@ class WandbLogger():
@contextmanager
def all_logging_disabled(highest_level=logging.CRITICAL):
""" source - https://gist.github.com/simon-weber/7853144
"""source - https://gist.github.com/simon-weber/7853144
A context manager that will prevent any logging messages triggered during the body from being processed.
:param highest_level: the maximum logging level in use.
This would only need to be changed if a custom level greater than CRITICAL is defined.