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ultralytics 8.0.18 new python callbacks and minor fixes (#580)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jeroen Rombouts <36196499+jarombouts@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ayush Chaurasia 2023-01-23 23:01:29 +05:30 • committed by GitHub
parent e9ab157330
commit 936414c615
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24 changed files with 136 additions and 106 deletions
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12
ultralytics/yolo/engine/validator.py
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@ -9,8 +9,8 @@ from tqdm import tqdm
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.utils import DEFAULT_CFG_PATH, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, emojis
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.ops import Profile
@ -70,7 +70,7 @@ class BaseValidator:
if self.args.conf is None:
self.args.conf = 0.001 # default conf=0.001
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
self.callbacks = defaultdict(list, {k: v for k, v in callbacks.default_callbacks.items()}) # add callbacks
@smart_inference_mode()
def __call__(self, trainer=None, model=None):
@ -109,9 +109,11 @@ class BaseValidator:
f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
if isinstance(self.args.data, str) and self.args.data.endswith(".yaml"):
self.data = check_dataset_yaml(self.args.data)
self.data = check_det_dataset(self.args.data)
elif self.args.task == 'classify':
self.data = check_cls_dataset(self.args.data)
else:
self.data = check_dataset(self.args.data)
raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
if self.device.type == 'cpu':
self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading