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Update Actions with Lychee and GitHub Token (#12592)

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* 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
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utils/loggers/comet/__init__.py
+125 -118
View File
@@ -17,7 +17,7 @@ try:
# Project Configuration
config = comet_ml.config.get_config()
COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
except ImportError:
comet_ml = None
COMET_PROJECT_NAME = None
@@ -31,42 +31,40 @@ from utils.dataloaders import img2label_paths
from utils.general import check_dataset, scale_boxes, xywh2xyxy
from utils.metrics import box_iou
COMET_PREFIX = 'comet://'
COMET_PREFIX = "comet://"
COMET_MODE = os.getenv('COMET_MODE', 'online')
COMET_MODE = os.getenv("COMET_MODE", "online")
# Model Saving Settings
COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
# Dataset Artifact Settings
COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true'
COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true"
# Evaluation Settings
COMET_LOG_CONFUSION_MATRIX = (os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true')
COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true'
COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100))
COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true"
COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true"
COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100))
# Confusion Matrix Settings
CONF_THRES = float(os.getenv('CONF_THRES', 0.001))
IOU_THRES = float(os.getenv('IOU_THRES', 0.6))
CONF_THRES = float(os.getenv("CONF_THRES", 0.001))
IOU_THRES = float(os.getenv("IOU_THRES", 0.6))
# Batch Logging Settings
COMET_LOG_BATCH_METRICS = (os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true')
COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1)
COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1)
COMET_LOG_PER_CLASS_METRICS = (os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true')
COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true"
COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1)
COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1)
COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true"
RANK = int(os.getenv('RANK', -1))
RANK = int(os.getenv("RANK", -1))
to_pil = T.ToPILImage()
class CometLogger:
"""Log metrics, parameters, source code, models and much more
with Comet
"""
"""Log metrics, parameters, source code, models and much more with Comet."""
def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None:
def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None:
self.job_type = job_type
self.opt = opt
self.hyp = hyp
@@ -87,57 +85,58 @@ class CometLogger:
# Default parameters to pass to Experiment objects
self.default_experiment_kwargs = {
'log_code': False,
'log_env_gpu': True,
'log_env_cpu': True,
'project_name': COMET_PROJECT_NAME, }
"log_code": False,
"log_env_gpu": True,
"log_env_cpu": True,
"project_name": COMET_PROJECT_NAME,
}
self.default_experiment_kwargs.update(experiment_kwargs)
self.experiment = self._get_experiment(self.comet_mode, run_id)
self.experiment.set_name(self.opt.name)
self.data_dict = self.check_dataset(self.opt.data)
self.class_names = self.data_dict['names']
self.num_classes = self.data_dict['nc']
self.class_names = self.data_dict["names"]
self.num_classes = self.data_dict["nc"]
self.logged_images_count = 0
self.max_images = COMET_MAX_IMAGE_UPLOADS
if run_id is None:
self.experiment.log_other('Created from', 'YOLOv5')
self.experiment.log_other("Created from", "YOLOv5")
if not isinstance(self.experiment, comet_ml.OfflineExperiment):
workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:]
workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:]
self.experiment.log_other(
'Run Path',
f'{workspace}/{project_name}/{experiment_id}',
"Run Path",
f"{workspace}/{project_name}/{experiment_id}",
)
self.log_parameters(vars(opt))
self.log_parameters(self.opt.hyp)
self.log_asset_data(
self.opt.hyp,
name='hyperparameters.json',
metadata={'type': 'hyp-config-file'},
name="hyperparameters.json",
metadata={"type": "hyp-config-file"},
)
self.log_asset(
f'{self.opt.save_dir}/opt.yaml',
metadata={'type': 'opt-config-file'},
f"{self.opt.save_dir}/opt.yaml",
metadata={"type": "opt-config-file"},
)
self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
if hasattr(self.opt, 'conf_thres'):
if hasattr(self.opt, "conf_thres"):
self.conf_thres = self.opt.conf_thres
else:
self.conf_thres = CONF_THRES
if hasattr(self.opt, 'iou_thres'):
if hasattr(self.opt, "iou_thres"):
self.iou_thres = self.opt.iou_thres
else:
self.iou_thres = IOU_THRES
self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres})
self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres})
self.comet_log_predictions = COMET_LOG_PREDICTIONS
if self.opt.bbox_interval == -1:
self.comet_log_prediction_interval = (1 if self.opt.epochs < 10 else self.opt.epochs // 10)
self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
else:
self.comet_log_prediction_interval = self.opt.bbox_interval
@@ -147,30 +146,35 @@ class CometLogger:
self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
self.experiment.log_others({
'comet_mode': COMET_MODE,
'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS,
'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
'comet_model_name': COMET_MODEL_NAME, })
self.experiment.log_others(
{
"comet_mode": COMET_MODE,
"comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS,
"comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS,
"comet_log_batch_metrics": COMET_LOG_BATCH_METRICS,
"comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX,
"comet_model_name": COMET_MODEL_NAME,
}
)
# Check if running the Experiment with the Comet Optimizer
if hasattr(self.opt, 'comet_optimizer_id'):
self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id)
self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective)
self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric)
self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp))
if hasattr(self.opt, "comet_optimizer_id"):
self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id)
self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective)
self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric)
self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp))
def _get_experiment(self, mode, experiment_id=None):
if mode == 'offline':
if mode == "offline":
if experiment_id is not None:
return comet_ml.ExistingOfflineExperiment(
previous_experiment=experiment_id,
**self.default_experiment_kwargs,
)
return comet_ml.OfflineExperiment(**self.default_experiment_kwargs, )
return comet_ml.OfflineExperiment(
**self.default_experiment_kwargs,
)
else:
try:
@@ -183,11 +187,13 @@ class CometLogger:
return comet_ml.Experiment(**self.default_experiment_kwargs)
except ValueError:
logger.warning('COMET WARNING: '
'Comet credentials have not been set. '
'Comet will default to offline logging. '
'Please set your credentials to enable online logging.')
return self._get_experiment('offline', experiment_id)
logger.warning(
"COMET WARNING: "
"Comet credentials have not been set. "
"Comet will default to offline logging. "
"Please set your credentials to enable online logging."
)
return self._get_experiment("offline", experiment_id)
return
@@ -211,12 +217,13 @@ class CometLogger:
return
model_metadata = {
'fitness_score': fitness_score[-1],
'epochs_trained': epoch + 1,
'save_period': opt.save_period,
'total_epochs': opt.epochs, }
"fitness_score": fitness_score[-1],
"epochs_trained": epoch + 1,
"save_period": opt.save_period,
"total_epochs": opt.epochs,
}
model_files = glob.glob(f'{path}/*.pt')
model_files = glob.glob(f"{path}/*.pt")
for model_path in model_files:
name = Path(model_path).name
@@ -232,14 +239,14 @@ class CometLogger:
with open(data_file) as f:
data_config = yaml.safe_load(f)
path = data_config.get('path')
path = data_config.get("path")
if path and path.startswith(COMET_PREFIX):
path = data_config['path'].replace(COMET_PREFIX, '')
path = data_config["path"].replace(COMET_PREFIX, "")
data_dict = self.download_dataset_artifact(path)
return data_dict
self.log_asset(self.opt.data, metadata={'type': 'data-config-file'})
self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
return check_dataset(data_file)
@@ -255,8 +262,8 @@ class CometLogger:
filtered_detections = detections[mask]
filtered_labels = labelsn[mask]
image_id = path.split('/')[-1].split('.')[0]
image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}'
image_id = path.split("/")[-1].split(".")[0]
image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}"
if image_name not in self.logged_image_names:
native_scale_image = PIL.Image.open(path)
self.log_image(native_scale_image, name=image_name)
@@ -264,23 +271,21 @@ class CometLogger:
metadata = []
for cls, *xyxy in filtered_labels.tolist():
metadata.append({
'label': f'{self.class_names[int(cls)]}-gt',
'score': 100,
'box': {
'x': xyxy[0],
'y': xyxy[1],
'x2': xyxy[2],
'y2': xyxy[3]}, })
metadata.append(
{
"label": f"{self.class_names[int(cls)]}-gt",
"score": 100,
"box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
}
)
for *xyxy, conf, cls in filtered_detections.tolist():
metadata.append({
'label': f'{self.class_names[int(cls)]}',
'score': conf * 100,
'box': {
'x': xyxy[0],
'y': xyxy[1],
'x2': xyxy[2],
'y2': xyxy[3]}, })
metadata.append(
{
"label": f"{self.class_names[int(cls)]}",
"score": conf * 100,
"box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
}
)
self.metadata_dict[image_name] = metadata
self.logged_images_count += 1
@@ -307,7 +312,7 @@ class CometLogger:
return predn, labelsn
def add_assets_to_artifact(self, artifact, path, asset_path, split):
img_paths = sorted(glob.glob(f'{asset_path}/*'))
img_paths = sorted(glob.glob(f"{asset_path}/*"))
label_paths = img2label_paths(img_paths)
for image_file, label_file in zip(img_paths, label_paths):
@@ -317,33 +322,33 @@ class CometLogger:
artifact.add(
image_file,
logical_path=image_logical_path,
metadata={'split': split},
metadata={"split": split},
)
artifact.add(
label_file,
logical_path=label_logical_path,
metadata={'split': split},
metadata={"split": split},
)
except ValueError as e:
logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
logger.error(f'COMET ERROR: {e}')
logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.")
logger.error(f"COMET ERROR: {e}")
continue
return artifact
def upload_dataset_artifact(self):
dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset')
path = str((ROOT / Path(self.data_dict['path'])).resolve())
dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset")
path = str((ROOT / Path(self.data_dict["path"])).resolve())
metadata = self.data_dict.copy()
for key in ['train', 'val', 'test']:
for key in ["train", "val", "test"]:
split_path = metadata.get(key)
if split_path is not None:
metadata[key] = split_path.replace(path, '')
metadata[key] = split_path.replace(path, "")
artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata)
artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata)
for key in metadata.keys():
if key in ['train', 'val', 'test']:
if key in ["train", "val", "test"]:
if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
continue
@@ -362,26 +367,27 @@ class CometLogger:
metadata = logged_artifact.metadata
data_dict = metadata.copy()
data_dict['path'] = artifact_save_dir
data_dict["path"] = artifact_save_dir
metadata_names = metadata.get('names')
metadata_names = metadata.get("names")
if isinstance(metadata_names, dict):
data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()}
data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()}
elif isinstance(metadata_names, list):
data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
else:
raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
return self.update_data_paths(data_dict)
def update_data_paths(self, data_dict):
path = data_dict.get('path', '')
path = data_dict.get("path", "")
for split in ['train', 'val', 'test']:
for split in ["train", "val", "test"]:
if data_dict.get(split):
split_path = data_dict.get(split)
data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [
f'{path}/{x}' for x in split_path])
data_dict[split] = (
f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path]
)
return data_dict
@@ -422,11 +428,11 @@ class CometLogger:
def on_train_end(self, files, save_dir, last, best, epoch, results):
if self.comet_log_predictions:
curr_epoch = self.experiment.curr_epoch
self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch)
self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch)
for f in files:
self.log_asset(f, metadata={'epoch': epoch})
self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch})
self.log_asset(f, metadata={"epoch": epoch})
self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch})
if not self.opt.evolve:
model_path = str(best if best.exists() else last)
@@ -440,9 +446,9 @@ class CometLogger:
)
# Check if running Experiment with Comet Optimizer
if hasattr(self.opt, 'comet_optimizer_id'):
if hasattr(self.opt, "comet_optimizer_id"):
metric = results.get(self.opt.comet_optimizer_metric)
self.experiment.log_other('optimizer_metric_value', metric)
self.experiment.log_other("optimizer_metric_value", metric)
self.finish_run()
@@ -477,21 +483,22 @@ class CometLogger:
class_name = self.class_names[c]
self.experiment.log_metrics(
{
'mAP@.5': ap50[i],
'mAP@.5:.95': ap[i],
'precision': p[i],
'recall': r[i],
'f1': f1[i],
'true_positives': tp[i],
'false_positives': fp[i],
'support': nt[c], },
"mAP@.5": ap50[i],
"mAP@.5:.95": ap[i],
"precision": p[i],
"recall": r[i],
"f1": f1[i],
"true_positives": tp[i],
"false_positives": fp[i],
"support": nt[c],
},
prefix=class_name,
)
if self.comet_log_confusion_matrix:
epoch = self.experiment.curr_epoch
class_names = list(self.class_names.values())
class_names.append('background')
class_names.append("background")
num_classes = len(class_names)
self.experiment.log_confusion_matrix(
@@ -499,9 +506,9 @@ class CometLogger:
max_categories=num_classes,
labels=class_names,
epoch=epoch,
column_label='Actual Category',
row_label='Predicted Category',
file_name=f'confusion-matrix-epoch-{epoch}.json',
column_label="Actual Category",
row_label="Predicted Category",
file_name=f"confusion-matrix-epoch-{epoch}.json",
)
def on_fit_epoch_end(self, result, epoch):