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Display Val images per class (#12645)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Adamcode 2024-05-29 23:02:41 +08:00 • committed by GitHub
parent b95b583237
commit 7cd871dbd0
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4 changed files with 13 additions and 8 deletions
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14
ultralytics/models/yolo/detect/val.py
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@ -32,6 +32,7 @@ class DetectionValidator(BaseValidator):
"""Initialize detection model with necessary variables and settings."""
super().__init__(dataloader, save_dir, pbar, args, _callbacks)
self.nt_per_class = None
self.nt_per_image = None
self.is_coco = False
self.is_lvis = False
self.class_map = None
@ -77,7 +78,7 @@ class DetectionValidator(BaseValidator):
self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf)
self.seen = 0
self.jdict = []
self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[])
self.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
def get_desc(self):
"""Return a formatted string summarizing class metrics of YOLO model."""
@ -130,6 +131,7 @@ class DetectionValidator(BaseValidator):
cls, bbox = pbatch.pop("cls"), pbatch.pop("bbox")
nl = len(cls)
stat["target_cls"] = cls
stat["target_img"] = cls.unique()
if npr == 0:
if nl:
for k in self.stats.keys():
@ -168,11 +170,11 @@ class DetectionValidator(BaseValidator):
def get_stats(self):
"""Returns metrics statistics and results dictionary."""
stats = {k: torch.cat(v, 0).cpu().numpy() for k, v in self.stats.items()} # to numpy
self.nt_per_class = np.bincount(stats["target_cls"].astype(int), minlength=self.nc)
self.nt_per_image = np.bincount(stats["target_img"].astype(int), minlength=self.nc)
stats.pop("target_img", None)
if len(stats) and stats["tp"].any():
self.metrics.process(**stats)
self.nt_per_class = np.bincount(
stats["target_cls"].astype(int), minlength=self.nc
) # number of targets per class
return self.metrics.results_dict
def print_results(self):
@ -185,7 +187,9 @@ class DetectionValidator(BaseValidator):
# Print results per class
if self.args.verbose and not self.training and self.nc > 1 and len(self.stats):
for i, c in enumerate(self.metrics.ap_class_index):
LOGGER.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
LOGGER.info(
pf % (self.names[c], self.nt_per_image[c], self.nt_per_class[c], *self.metrics.class_result(i))
)
if self.args.plots:
for normalize in True, False: