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Refactor val code into new self.match_predictions() method (#4265)

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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>
This commit is contained in:
Andy
2023-08-09 19:03:46 +02:00
committed by GitHub
parent e9c9b82c42
commit dde89c744c
5 changed files with 32 additions and 56 deletions
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15
ultralytics/models/yolo/detect/val.py
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@ -163,20 +163,7 @@ class DetectionValidator(BaseValidator):
correct (array[N, 10]), for 10 IoU levels
"""
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=detections.device)
return self.match_predictions(detections[:, 5], labels[:, 0], iou)
def build_dataset(self, img_path, mode='val', batch=None):
"""Build YOLO Dataset

15
ultralytics/models/yolo/pose/val.py
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@ -128,20 +128,7 @@ class PoseValidator(DetectionValidator):
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=detections.device)
return self.match_predictions(detections[:, 5], labels[:, 0], iou)
def plot_val_samples(self, batch, ni):
"""Plots and saves validation set samples with predicted bounding boxes and keypoints."""

15
ultralytics/models/yolo/segment/val.py
View File

@ -150,20 +150,7 @@ class SegmentationValidator(DetectionValidator):
else: # boxes
iou = box_iou(labels[:, 1:], detections[:, :4])
correct = np.zeros((detections.shape[0], self.iouv.shape[0])).astype(bool)
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(self.iouv)):
x = torch.where((iou >= self.iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=detections.device)
return self.match_predictions(detections[:, 5], labels[:, 0], iou)
def plot_val_samples(self, batch, ni):
"""Plots validation samples with bounding box labels."""