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car-detection-bayes
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loss function cleanup

This commit is contained in:
Glenn Jocher 2020-03-16 17:31:37 -07:00
parent f1208f784e
commit 1a12667ce1
1 changed files with 7 additions and 22 deletions
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29
utils/utils.py
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@ -377,21 +377,19 @@ def compute_loss(p, targets, model): # predictions, targets, model
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)
h = model.hyp # hyperparameters h = model.hyp # hyperparameters
arc = model.arc # # (default, uCE, uBCE) detection architectures arc = model.arc # architecture
red = 'mean' # Loss reduction (sum or mean) red = 'mean' # Loss reduction (sum or mean)
# Define criteria # Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]), reduction=red) BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]), reduction=red)
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]), reduction=red) BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]), reduction=red)
BCE = nn.BCEWithLogitsLoss(reduction=red)
CE = nn.CrossEntropyLoss(reduction=red) # weight=model.class_weights
# class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 # class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
cp, cn = smooth_BCE(eps=0.0) cp, cn = smooth_BCE(eps=0.0)
if 'F' in arc: # add focal loss # focal loss
g = h['fl_gamma'] if 'F' in arc:
BCEcls, BCEobj, BCE, CE = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g), FocalLoss(BCE, g), FocalLoss(CE, g) BCEcls, BCEobj = FocalLoss(BCEcls, h['fl_gamma']), FocalLoss(BCEobj, h['fl_gamma'])
# Compute losses # Compute losses
np, ng = 0, 0 # number grid points, targets np, ng = 0, 0 # number grid points, targets
@ -415,8 +413,8 @@ def compute_loss(p, targets, model): # predictions, targets, model
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 - model.gr) + model.gr * giou.detach().type(tobj.dtype) # giou ratio tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().type(tobj.dtype) # giou ratio
if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes) if model.nc > 1: # cls loss (only if multiple classes)
t = torch.zeros_like(ps[:, 5:]) + cn # targets t = torch.full_like(ps[:, 5:], cn) # targets
t[range(nb), tcls[i]] = cp t[range(nb), tcls[i]] = cp
lcls += BCEcls(ps[:, 5:], t) # BCE lcls += BCEcls(ps[:, 5:], t) # BCE
# lcls += CE(ps[:, 5:], tcls[i]) # CE # lcls += CE(ps[:, 5:], tcls[i]) # CE
@ -425,20 +423,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
# with open('targets.txt', 'a') as file: # with open('targets.txt', 'a') as file:
# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
if 'default' in arc: # separate obj and cls lobj += BCEobj(pi[..., 4], tobj) # obj loss
lobj += BCEobj(pi[..., 4], tobj) # obj loss
elif 'BCE' in arc: # unified BCE (80 classes)
t = torch.zeros_like(pi[..., 5:]) # targets
if nb:
t[b, a, gj, gi, tcls[i]] = 1.0
lobj += BCE(pi[..., 5:], t)
elif 'CE' in arc: # unified CE (1 background + 80 classes)
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))
lbox *= h['giou'] lbox *= h['giou']
lobj *= h['obj'] lobj *= h['obj']