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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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139 changed files with 6870 additions and 5125 deletions
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42
ultralytics/models/rtdetr/val.py
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@ -7,7 +7,7 @@ from ultralytics.data.augment import Compose, Format, v8_transforms
from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.utils import colorstr, ops
__all__ = 'RTDETRValidator', # tuple or list
__all__ = ("RTDETRValidator",) # tuple or list
class RTDETRDataset(YOLODataset):
@ -37,13 +37,16 @@ class RTDETRDataset(YOLODataset):
# transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), auto=False, scaleFill=True)])
transforms = Compose([])
transforms.append(
Format(bbox_format='xywh',
normalize=True,
return_mask=self.use_segments,
return_keypoint=self.use_keypoints,
batch_idx=True,
mask_ratio=hyp.mask_ratio,
mask_overlap=hyp.overlap_mask))
Format(
bbox_format="xywh",
normalize=True,
return_mask=self.use_segments,
return_keypoint=self.use_keypoints,
batch_idx=True,
mask_ratio=hyp.mask_ratio,
mask_overlap=hyp.overlap_mask,
)
)
return transforms
@ -68,7 +71,7 @@ class RTDETRValidator(DetectionValidator):
For further details on the attributes and methods, refer to the parent DetectionValidator class.
"""
def build_dataset(self, img_path, mode='val', batch=None):
def build_dataset(self, img_path, mode="val", batch=None):
"""
Build an RTDETR Dataset.
@ -85,8 +88,9 @@ class RTDETRValidator(DetectionValidator):
hyp=self.args,
rect=False, # no rect
cache=self.args.cache or None,
prefix=colorstr(f'{mode}: '),
data=self.data)
prefix=colorstr(f"{mode}: "),
data=self.data,
)
def postprocess(self, preds):
"""Apply Non-maximum suppression to prediction outputs."""
@ -108,12 +112,12 @@ class RTDETRValidator(DetectionValidator):
def _prepare_batch(self, si, batch):
"""Prepares a batch for training or inference by applying transformations."""
idx = batch['batch_idx'] == si
cls = batch['cls'][idx].squeeze(-1)
bbox = batch['bboxes'][idx]
ori_shape = batch['ori_shape'][si]
imgsz = batch['img'].shape[2:]
ratio_pad = batch['ratio_pad'][si]
idx = batch["batch_idx"] == si
cls = batch["cls"][idx].squeeze(-1)
bbox = batch["bboxes"][idx]
ori_shape = batch["ori_shape"][si]
imgsz = batch["img"].shape[2:]
ratio_pad = batch["ratio_pad"][si]
if len(cls):
bbox = ops.xywh2xyxy(bbox) # target boxes
bbox[..., [0, 2]] *= ori_shape[1] # native-space pred
@ -124,6 +128,6 @@ class RTDETRValidator(DetectionValidator):
def _prepare_pred(self, pred, pbatch):
"""Prepares and returns a batch with transformed bounding boxes and class labels."""
predn = pred.clone()
predn[..., [0, 2]] *= pbatch['ori_shape'][1] / self.args.imgsz # native-space pred
predn[..., [1, 3]] *= pbatch['ori_shape'][0] / self.args.imgsz # native-space pred
predn[..., [0, 2]] *= pbatch["ori_shape"][1] / self.args.imgsz # native-space pred
predn[..., [1, 3]] *= pbatch["ori_shape"][0] / self.args.imgsz # native-space pred
return predn.float()