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ultralytics 8.0.209 fix P6 model validation (#6261)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Jie Li <32835610+jedi007@users.noreply.github.com>
Co-authored-by: Haruto Watahiki <43723360+WATA-Haru@users.noreply.github.com>
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Glenn Jocher 2023-11-14 00:54:30 +01:00 • committed by GitHub
parent 87233ea17c
commit 9a5891444e
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48 changed files with 95 additions and 92 deletions
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2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.208'
__version__ = '8.0.209'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM

3
ultralytics/engine/validator.py
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@ -77,7 +77,7 @@ class BaseValidator:
self.args = get_cfg(overrides=args)
self.dataloader = dataloader
self.pbar = pbar
self.model = None
self.stride = None
self.data = None
self.device = None
self.batch_i = None
@ -146,6 +146,7 @@ class BaseValidator:
self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading
if not pt:
self.args.rect = False
self.stride = model.stride # used in get_dataloader() for padding
self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch)
model.eval()

4
ultralytics/models/yolo/detect/val.py
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@ -12,7 +12,6 @@ from ultralytics.utils import LOGGER, ops
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou
from ultralytics.utils.plotting import output_to_target, plot_images
from ultralytics.utils.torch_utils import de_parallel
class DetectionValidator(BaseValidator):
@ -191,8 +190,7 @@ class DetectionValidator(BaseValidator):
mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
"""
gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=gs)
return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, stride=self.stride)
def get_dataloader(self, dataset_path, batch_size):
"""Construct and return dataloader."""