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Update .pre-commit-config.yaml (#2019)

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* Update .pre-commit-config.yaml

* Update __init__.py

* Update .pre-commit-config.yaml

* Precommit updates
This commit is contained in:
Glenn Jocher
2023-02-17 21:52:12 +01:00
committed by GitHub
parent a0a4012739
commit 527ce02916
39 changed files with 383 additions and 386 deletions
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utils/loggers/comet/__init__.py
+96 -96
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 (ModuleNotFoundError, ImportError):
comet_ml = None
COMET_PROJECT_NAME = None
@@ -31,32 +31,32 @@ 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()
@@ -66,7 +66,7 @@ class CometLogger:
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
@@ -87,52 +87,52 @@ 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.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:
@@ -147,22 +147,22 @@ 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,})
'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,
@@ -182,11 +182,11 @@ 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
@@ -210,12 +210,12 @@ 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
@@ -232,12 +232,12 @@ class CometLogger:
data_config = yaml.safe_load(f)
if data_config['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)
@@ -253,8 +253,8 @@ 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)
@@ -263,22 +263,22 @@ 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]},})
'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]},})
'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
@@ -305,35 +305,35 @@ 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):
image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
try:
artifact.add(image_file, logical_path=image_logical_path, metadata={"split": split})
artifact.add(label_file, logical_path=label_logical_path, metadata={"split": split})
artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split})
artifact.add(label_file, logical_path=label_logical_path, 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(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
@@ -352,13 +352,13 @@ 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 type(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 type(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"
@@ -366,13 +366,13 @@ class CometLogger:
return 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
@@ -413,11 +413,11 @@ 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)
@@ -481,7 +481,7 @@ class CometLogger:
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(
@@ -491,7 +491,7 @@ class CometLogger:
epoch=epoch,
column_label='Actual Category',
row_label='Predicted Category',
file_name=f"confusion-matrix-epoch-{epoch}.json",
file_name=f'confusion-matrix-epoch-{epoch}.json',
)
def on_fit_epoch_end(self, result, epoch):
utils/loggers/comet/comet_utils.py
+21 -21
View File
@@ -11,28 +11,28 @@ import yaml
logger = logging.getLogger(__name__)
COMET_PREFIX = "comet://"
COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt")
COMET_PREFIX = 'comet://'
COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt')
def download_model_checkpoint(opt, experiment):
model_dir = f"{opt.project}/{experiment.name}"
model_dir = f'{opt.project}/{experiment.name}'
os.makedirs(model_dir, exist_ok=True)
model_name = COMET_MODEL_NAME
model_asset_list = experiment.get_model_asset_list(model_name)
if len(model_asset_list) == 0:
logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}")
logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}')
return
model_asset_list = sorted(
model_asset_list,
key=lambda x: x["step"],
key=lambda x: x['step'],
reverse=True,
)
logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list}
logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list}
resource_url = urlparse(opt.weights)
checkpoint_filename = resource_url.query
@@ -44,22 +44,22 @@ def download_model_checkpoint(opt, experiment):
checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
if asset_id is None:
logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment")
logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment')
return
try:
logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}")
logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}')
asset_filename = checkpoint_filename
model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
model_download_path = f"{model_dir}/{asset_filename}"
with open(model_download_path, "wb") as f:
model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
model_download_path = f'{model_dir}/{asset_filename}'
with open(model_download_path, 'wb') as f:
f.write(model_binary)
opt.weights = model_download_path
except Exception as e:
logger.warning("COMET WARNING: Unable to download checkpoint from Comet")
logger.warning('COMET WARNING: Unable to download checkpoint from Comet')
logger.exception(e)
@@ -75,9 +75,9 @@ def set_opt_parameters(opt, experiment):
resume_string = opt.resume
for asset in asset_list:
if asset["fileName"] == "opt.yaml":
asset_id = asset["assetId"]
asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
if asset['fileName'] == 'opt.yaml':
asset_id = asset['assetId']
asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
opt_dict = yaml.safe_load(asset_binary)
for key, value in opt_dict.items():
setattr(opt, key, value)
@@ -85,11 +85,11 @@ def set_opt_parameters(opt, experiment):
# Save hyperparameters to YAML file
# Necessary to pass checks in training script
save_dir = f"{opt.project}/{experiment.name}"
save_dir = f'{opt.project}/{experiment.name}'
os.makedirs(save_dir, exist_ok=True)
hyp_yaml_path = f"{save_dir}/hyp.yaml"
with open(hyp_yaml_path, "w") as f:
hyp_yaml_path = f'{save_dir}/hyp.yaml'
with open(hyp_yaml_path, 'w') as f:
yaml.dump(opt.hyp, f)
opt.hyp = hyp_yaml_path
@@ -113,7 +113,7 @@ def check_comet_weights(opt):
if opt.weights.startswith(COMET_PREFIX):
api = comet_ml.API()
resource = urlparse(opt.weights)
experiment_path = f"{resource.netloc}{resource.path}"
experiment_path = f'{resource.netloc}{resource.path}'
experiment = api.get(experiment_path)
download_model_checkpoint(opt, experiment)
return True
@@ -140,7 +140,7 @@ def check_comet_resume(opt):
if opt.resume.startswith(COMET_PREFIX):
api = comet_ml.API()
resource = urlparse(opt.resume)
experiment_path = f"{resource.netloc}{resource.path}"
experiment_path = f'{resource.netloc}{resource.path}'
experiment = api.get(experiment_path)
set_opt_parameters(opt, experiment)
download_model_checkpoint(opt, experiment)
utils/loggers/comet/hpo.py
+16 -16
View File
@@ -21,7 +21,7 @@ from utils.torch_utils import select_device
# 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')
def get_args(known=False):
@@ -68,30 +68,30 @@ def get_args(known=False):
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
# Comet Arguments
parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.")
parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.")
parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.")
parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.")
parser.add_argument("--comet_optimizer_workers",
parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.')
parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.')
parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.")
parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.')
parser.add_argument('--comet_optimizer_workers',
type=int,
default=1,
help="Comet: Number of Parallel Workers to use with the Comet Optimizer.")
help='Comet: Number of Parallel Workers to use with the Comet Optimizer.')
return parser.parse_known_args()[0] if known else parser.parse_args()
def run(parameters, opt):
hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]}
hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']}
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
opt.batch_size = parameters.get("batch_size")
opt.epochs = parameters.get("epochs")
opt.batch_size = parameters.get('batch_size')
opt.epochs = parameters.get('epochs')
device = select_device(opt.device, batch_size=opt.batch_size)
train(hyp_dict, opt, device, callbacks=Callbacks())
if __name__ == "__main__":
if __name__ == '__main__':
opt = get_args(known=True)
opt.weights = str(opt.weights)
@@ -99,7 +99,7 @@ if __name__ == "__main__":
opt.data = str(opt.data)
opt.project = str(opt.project)
optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
optimizer_id = os.getenv('COMET_OPTIMIZER_ID')
if optimizer_id is None:
with open(opt.comet_optimizer_config) as f:
optimizer_config = json.load(f)
@@ -110,9 +110,9 @@ if __name__ == "__main__":
opt.comet_optimizer_id = optimizer.id
status = optimizer.status()
opt.comet_optimizer_objective = status["spec"]["objective"]
opt.comet_optimizer_metric = status["spec"]["metric"]
opt.comet_optimizer_objective = status['spec']['objective']
opt.comet_optimizer_metric = status['spec']['metric']
logger.info("COMET INFO: Starting Hyperparameter Sweep")
logger.info('COMET INFO: Starting Hyperparameter Sweep')
for parameter in optimizer.get_parameters():
run(parameter["parameters"], opt)
run(parameter['parameters'], opt)