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car-detection-bayes
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align loss to darknet

This commit is contained in:
Glenn Jocher 2018-09-25 03:45:52 +02:00
parent 7416c1842a
commit ff630b1960
1 changed files with 3 additions and 3 deletions
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6
models.py
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@ -139,7 +139,7 @@ class YOLOLayer(nn.Module):
if targets is not None: if targets is not None:
MSELoss = nn.MSELoss(size_average=True) MSELoss = nn.MSELoss(size_average=True)
BCEWithLogitsLoss = nn.BCEWithLogitsLoss(size_average=True) BCEWithLogitsLoss = nn.BCEWithLogitsLoss(size_average=True)
# CrossEntropyLoss = nn.CrossEntropyLoss() CrossEntropyLoss = nn.CrossEntropyLoss()
if requestPrecision: if requestPrecision:
gx = self.grid_x[:, :, :nG, :nG] gx = self.grid_x[:, :, :nG, :nG]
@ -170,8 +170,8 @@ class YOLOLayer(nn.Module):
# lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float()) # lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
lconf = k * BCEWithLogitsLoss(pred_conf, mask.float()) lconf = k * BCEWithLogitsLoss(pred_conf, mask.float())
# lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1)) lcls = k * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float()) # lcls = k * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
else: else:
lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0]) lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])