Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2019-12-27 11:51:27 -08:00
parent 043a0e457c
commit 59de209ab2
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
utils/utils.py
View File

@ -391,15 +391,15 @@ def compute_loss(p, targets, model): # predictions, targets, model
if nb: # number of targets if nb: # number of targets
ng += nb ng += nb
ps = pi[b, a, gj, gi] # prediction subset corresponding to targets ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
tobj[b, a, gj, gi] = 1.0 # obj
# ps[:, 2:4] = torch.sigmoid(ps[:, 2:4]) # wh power loss (uncomment) # ps[:, 2:4] = torch.sigmoid(ps[:, 2:4]) # wh power loss (uncomment)
# GIoU # GIoU
pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy) pxy = torch.sigmoid(ps[:, 0:2]) # pxy = pxy * s - (s - 1) / 2, s = 1.5 (scale_xy)
pwh = torch.exp(ps[:, 2:4]).clamp(max=1E3) * anchor_vec[i] pwh = torch.exp(ps[:, 2:4]).clamp(max=1E3) * anchor_vec[i]
pbox = torch.cat((pxy, pwh), 1) # predicted box pbox = torch.cat((pxy, pwh), 1) # predicted box
giou = 1.0 - bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
lbox += giou.sum() if red == 'sum' else giou.mean() # giou loss lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean() # giou loss
tobj[b, a, gj, gi] = 1.0 # giou.type(tobj.dtype) # obj
if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes) if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes)
t = torch.zeros_like(ps[:, 5:]) # targets t = torch.zeros_like(ps[:, 5:]) # targets