Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2019-08-23 17:24:50 +02:00
parent 5f2b551818
commit 2f256ee274
1 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
utils/utils.py
View File

@ -321,8 +321,8 @@ def compute_loss(p, targets, model, arc='default'): # predictions, targets, mod
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
BCE = nn.BCEWithLogitsLoss()
CE = nn.CrossEntropyLoss() # weight=model.class_weights
FBCE = FocalLoss(nn.BCEWithLogitsLoss())
FCE = FocalLoss(nn.CrossEntropyLoss()) # weight=model.class_weights
# Compute losses
bs = p[0].shape[0] # batch size
@ -361,13 +361,13 @@ def compute_loss(p, targets, model, arc='default'): # predictions, targets, mod
t = torch.zeros_like(pi[..., 0], dtype=torch.long) # targets
if nb:
t[b, a, gj, gi] = tcls[i] + 1
lcls += CE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
lcls += FCE(pi[..., 4:].view(-1, model.nc + 1), t.view(-1))
elif arc == 'uBCE': # unified BCE (1 background + 80 classes), hyps 200-30
t = torch.zeros_like(pi[..., 5:]) # targets
if nb:
t[b, a, gj, gi, tcls[i]] = 1.0
lobj += BCE(pi[..., 5:], t)
lobj += FBCE(pi[..., 5:], t)
lbox *= k * h['giou']
lobj *= k * h['obj']