xclele/yolov10
1
0
Fork 0
mirror of https://github.com/THU-MIG/yolov10.git synced 2025-05-23 21:44:22 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Model enhancements 2 (#109)

Browse Source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia 2022-12-28 21:01:51 +05:30 committed by GitHub
parent 8406b49b49
commit 0303ced8ab
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 14 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
ultralytics/yolo/engine/model.py
View File

@ -112,8 +112,9 @@ class YOLO:
source (str): Accepts all source types accepted by yolo source (str): Accepts all source types accepted by yolo
**kwargs : Any other args accepted by the predictors. Too see all args check 'configuration' section in the docs **kwargs : Any other args accepted by the predictors. Too see all args check 'configuration' section in the docs
""" """
kwargs.update(self.overrides) overrides = self.overrides.copy()
predictor = self.PredictorClass(overrides=kwargs) overrides.update(kwargs)
predictor = self.PredictorClass(overrides=overrides)
# check size type # check size type
sz = predictor.args.imgsz sz = predictor.args.imgsz
@ -136,8 +137,9 @@ class YOLO:
if not self.model: if not self.model:
raise Exception("model not initialized!") raise Exception("model not initialized!")
kwargs.update(self.overrides) overrides = self.overrides.copy()
args = get_config(config=DEFAULT_CONFIG, overrides=kwargs) overrides.update(kwargs)
args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
args.data = data or args.data args.data = data or args.data
args.task = self.task args.task = self.task

16
ultralytics/yolo/engine/predictor.py
View File

@ -46,7 +46,9 @@ class BasePredictor:
def __init__(self, config=DEFAULT_CONFIG, overrides={}): def __init__(self, config=DEFAULT_CONFIG, overrides={}):
self.args = get_config(config, overrides) self.args = get_config(config, overrides)
self.save_dir = increment_path(Path(self.args.project) / self.args.name, exist_ok=self.args.exist_ok) project = overrides.get("project") or self.args.task
name = overrides.get("name") or self.args.mode
self.save_dir = increment_path(Path("runs") / project / name, exist_ok=self.args.exist_ok)
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True) (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
self.done_setup = False self.done_setup = False
@ -84,18 +86,6 @@ class BasePredictor:
if is_url and is_file: if is_url and is_file:
source = check_file(source) # download source = check_file(source) # download
# data
if self.data:
try:
if self.data.endswith(".yaml"):
self.data = check_dataset_yaml(self.data)
else:
self.data = check_dataset(self.data)
except AssertionError as e:
LOGGER.info(f"Error ocurred: {e}")
finally:
LOGGER.info("Predictor will continue without reading the dataset")
# model # model
device = select_device(self.args.device) device = select_device(self.args.device)
model = model or self.args.model model = model or self.args.model

7
ultralytics/yolo/engine/validator.py
View File

@ -31,8 +31,11 @@ class BaseValidator:
self.training = True self.training = True
self.speed = None self.speed = None
self.jdict = None self.jdict = None
self.save_dir = save_dir if save_dir is not None else \
increment_path(Path(self.args.project) / self.args.name, exist_ok=self.args.exist_ok) project = self.args.project if self.args.project != "runs/train" else self.args.task
name = self.args.name if self.args.name != "exp" else self.args.mode
self.save_dir = increment_path(Path("runs") / project / name, exist_ok=self.args.exist_ok)
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
@smart_inference_mode() @smart_inference_mode()
def __call__(self, trainer=None, model=None): def __call__(self, trainer=None, model=None):