Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-03-04 13:20:08 -08:00
parent 6ab753a9e7
commit 981b452b1d
2 changed files with 4 additions and 3 deletions
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3
train.py
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@ -211,6 +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.hyps['gr'] = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2 # GIoU <-> 1.0 ratio
# Prebias # Prebias
if prebias: if prebias:
@ -271,7 +272,7 @@ def train():
pred = model(imgs) pred = model(imgs)
# Compute loss # Compute loss
loss, loss_items = compute_loss(pred, targets, model, not prebias) loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss): if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items) print('WARNING: non-finite loss, ending training ', loss_items)
return results return results

4
utils/utils.py
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@ -363,7 +363,7 @@ class FocalLoss(nn.Module):
return loss return loss
def compute_loss(p, targets, model, giou_flag=True): # predictions, targets, model def compute_loss(p, targets, model): # predictions, targets, model
ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
lcls, lbox, lobj = ft([0]), ft([0]), ft([0]) lcls, lbox, lobj = ft([0]), ft([0]), ft([0])
tcls, tbox, indices, anchor_vec = build_targets(model, targets) tcls, tbox, indices, anchor_vec = build_targets(model, targets)
@ -401,7 +401,7 @@ def compute_loss(p, targets, model, giou_flag=True): # predictions, targets, mo
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] = giou.detach().clamp(0).type(tobj.dtype) if giou_flag else 1.0 tobj[b, a, gj, gi] = (1.0 - h['gr']) + h['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