Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-03-05 10:20:08 -08:00
parent b8b89a3132
commit 1dc1761f45
2 changed files with 2 additions and 2 deletions
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2
train.py
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@ -211,7 +211,7 @@ def train():
print('Starting training for %g epochs...' % epochs) print('Starting training for %g epochs...' % epochs)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train() model.train()
model.hyp['gr'] = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2 # GIoU <-> 1.0 loss ratio model.gr = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2 # GIoU <-> 1.0 loss ratio
# Prebias # Prebias
if prebias: if prebias:

2
utils/utils.py
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@ -401,7 +401,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
pbox = torch.cat((pxy, pwh), 1) # predicted box pbox = torch.cat((pxy, pwh), 1) # predicted box
giou = 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 += (1.0 - giou).sum() if red == 'sum' else (1.0 - 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 - h['gr']) + h['gr'] * giou.detach().clamp(0).type(tobj.dtype) # giou ratio tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype) # giou ratio
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