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car-detection-bayes
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xy and wh losses respectively merged

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Glenn Jocher 2019-02-19 20:03:42 +01:00
parent 15bba5a345
commit 3eb49be263
1 changed files with 3 additions and 5 deletions
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utils/utils.py
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@ -269,19 +269,17 @@ def build_targets(target, anchor_wh, nA, nC, nG):
if iou_best < 0.10: if iou_best < 0.10:
continue continue
tc, gx, gy, gw, gh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG tc, gx, gy, gwh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3:5] * nG
# Coordinates # Coordinates
txy[b, a, gj, gi, 0] = gx - gi.float() txy[b, a, gj, gi, 0] = gx - gi.float()
txy[b, a, gj, gi, 1] = gy - gj.float() txy[b, a, gj, gi, 1] = gy - gj.float()
# Width and height (yolo method) # Width and height (yolo method)
twh[b, a, gj, gi, 0] = torch.log(gw / anchor_wh[a, 0]) twh[b, a, gj, gi] = torch.log(gwh / anchor_wh[a])
twh[b, a, gj, gi, 1] = torch.log(gh / anchor_wh[a, 1])
# Width and height (power method) # Width and height (power method)
# twh[b, a, gj, gi, 0] = torch.sqrt(gw / anchor_wh[a, 0]) / 2 # twh[b, a, gj, gi] = torch.sqrt(gwh / anchor_wh[a]) / 2
# twh[b, a, gj, gi, 1] = torch.sqrt(gh / anchor_wh[a, 1]) / 2
# One-hot encoding of label # One-hot encoding of label
tcls[b, a, gj, gi, tc] = 1 tcls[b, a, gj, gi, tc] = 1