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Release 8.0.5 PR (#279)

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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>
Co-authored-by: Izam Mohammed <106471909+izam-mohammed@users.noreply.github.com>
Co-authored-by: Yue WANG 王跃 <92371174+yuewangg@users.noreply.github.com>
Co-authored-by: Thibaut Lucas <thibautlucas13@gmail.com>
This commit is contained in:
Laughing
2023-01-13 00:09:26 +08:00
committed by GitHub
parent 9552827157
commit c42e44a021
28 changed files with 940 additions and 311 deletions
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10
ultralytics/yolo/v8/segment/val.py
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@ -22,7 +22,7 @@ class SegmentationValidator(DetectionValidator):
def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
super().__init__(dataloader, save_dir, pbar, logger, args)
self.args.task = "segment"
self.metrics = SegmentMetrics(save_dir=self.save_dir, plot=self.args.plots)
self.metrics = SegmentMetrics(save_dir=self.save_dir)
def preprocess(self, batch):
batch = super().preprocess(batch)
@ -31,13 +31,15 @@ class SegmentationValidator(DetectionValidator):
def init_metrics(self, model):
head = model.model[-1] if self.training else model.model.model[-1]
self.is_coco = self.data.get('val', '').endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
val = self.data.get('val', '') # validation path
self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.nc = head.nc
self.nm = head.nm if hasattr(head, "nm") else 32
self.names = model.names
self.metrics.names = self.names
self.metrics.plot = self.args.plots
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
self.plot_masks = []
self.seen = 0
@ -97,7 +99,9 @@ class SegmentationValidator(DetectionValidator):
# Evaluate
if nl:
tbox = ops.xywh2xyxy(bbox) # target boxes
height, width = batch["img"].shape[2:]
tbox = ops.xywh2xyxy(bbox) * torch.tensor(
(width, height, width, height), device=self.device) # target boxes
ops.scale_boxes(batch["img"][si].shape[1:], tbox, shape,
ratio_pad=batch["ratio_pad"][si]) # native-space labels
labelsn = torch.cat((cls, tbox), 1) # native-space labels