`process_batch()` as numpy arrays (#8254)
Avoid potential issues with deterministic ops. [ ] - verify for identical mAP to master
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d6051382f1
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4
val.py
4
val.py
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@ -77,7 +77,7 @@ def process_batch(detections, labels, iouv):
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Returns:
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Returns:
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correct (Array[N, 10]), for 10 IoU levels
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correct (Array[N, 10]), for 10 IoU levels
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"""
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"""
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correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
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correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
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iou = box_iou(labels[:, 1:], detections[:, :4])
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iou = box_iou(labels[:, 1:], detections[:, :4])
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correct_class = labels[:, 0:1] == detections[:, 5]
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correct_class = labels[:, 0:1] == detections[:, 5]
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for i in range(len(iouv)):
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for i in range(len(iouv)):
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@ -90,7 +90,7 @@ def process_batch(detections, labels, iouv):
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# matches = matches[matches[:, 2].argsort()[::-1]]
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# matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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correct[matches[:, 1].astype(int), i] = True
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correct[matches[:, 1].astype(int), i] = True
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return correct
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return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
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@torch.no_grad()
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@torch.no_grad()
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