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zero-target training bug fix (#609)

This commit is contained in:
Glenn Jocher 2020-08-05 13:35:31 -07:00
parent d0d3dd1040
commit 7eaf225d55
1 changed files with 5 additions and 3 deletions
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8
utils/general.py
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@ -496,8 +496,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
s = 3 / np # output count scaling s = 3 / np # output count scaling
lbox *= h['giou'] * s lbox *= h['giou'] * s
lobj *= h['obj'] * s * (1.4 if np == 4 else 1.) lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
if model.nc > 1: lcls *= h['cls'] * s
lcls *= h['cls'] * s
bs = tobj.shape[0] # batch size bs = tobj.shape[0] # batch size
loss = lbox + lobj + lcls loss = lbox + lobj + lcls
@ -524,7 +523,7 @@ def build_targets(p, targets, model):
gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors # Match targets to anchors
t, offsets = targets * gain, 0 t = targets * gain
if nt: if nt:
# Matches # Matches
r = t[:, :, 4:6] / anchors[:, None] # wh ratio r = t[:, :, 4:6] / anchors[:, None] # wh ratio
@ -540,6 +539,9 @@ def build_targets(p, targets, model):
j = torch.stack((torch.ones_like(j), j, k, l, m)) j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j] t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define # Define
b, c = t[:, :2].long().T # image, class b, c = t[:, :2].long().T # image, class