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car-detection-bayes
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align loss to darknet

This commit is contained in:
Glenn Jocher 2018-09-24 03:10:42 +02:00
parent 313a3f6b0c
commit 292af1f2f4
1 changed files with 3 additions and 3 deletions
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6
models.py
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@ -137,8 +137,8 @@ class YOLOLayer(nn.Module):
# Training # Training
if targets is not None: if targets is not None:
MSELoss = nn.MSELoss() MSELoss = nn.MSELoss(size_average=False)
BCEWithLogitsLoss = nn.BCEWithLogitsLoss() BCEWithLogitsLoss = nn.BCEWithLogitsLoss(size_average=False)
# CrossEntropyLoss = nn.CrossEntropyLoss() # CrossEntropyLoss = nn.CrossEntropyLoss()
if requestPrecision: if requestPrecision:
@ -160,7 +160,7 @@ class YOLOLayer(nn.Module):
nT = sum([len(x) for x in targets]) # number of targets nT = sum([len(x) for x in targets]) # number of targets
nM = mask.sum().float() # number of anchors (assigned to targets) nM = mask.sum().float() # number of anchors (assigned to targets)
nB = len(targets) # batch size nB = len(targets) # batch size
k = nM / nB k = 1 / nB
if nM > 0: if nM > 0:
lx = k * MSELoss(x[mask], tx[mask]) lx = k * MSELoss(x[mask], tx[mask])
ly = k * MSELoss(y[mask], ty[mask]) ly = k * MSELoss(y[mask], ty[mask])