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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-12 12:24:43 +02:00
parent bd9789aa00
commit c77b87489c
1 changed files with 7 additions and 6 deletions
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13
utils/utils.py
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@ -281,7 +281,7 @@ def compute_loss(p, targets, model, giou_loss=True): # predictions, targets, mo
MSE = nn.MSELoss() MSE = nn.MSELoss()
BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']])) BCEcls = nn.BCEWithLogitsLoss(pos_weight=ft([h['cls_pw']]))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']])) BCEobj = nn.BCEWithLogitsLoss(pos_weight=ft([h['obj_pw']]))
CE = nn.CrossEntropyLoss() # (weight=model.class_weights) # CE = nn.CrossEntropyLoss() # (weight=model.class_weights)
# Compute losses # Compute losses
bs = p[0].shape[0] # batch size bs = p[0].shape[0] # batch size
@ -291,7 +291,8 @@ def compute_loss(p, targets, model, giou_loss=True): # predictions, targets, mo
tobj = torch.zeros_like(pi0[..., 0]) # target obj tobj = torch.zeros_like(pi0[..., 0]) # target obj
# Compute losses # Compute losses
if len(b): # number of targets nb = len(b)
if nb: # number of targets
pi = pi0[b, a, gj, gi] # predictions closest to anchors pi = pi0[b, a, gj, gi] # predictions closest to anchors
tobj[b, a, gj, gi] = 1.0 # obj tobj[b, a, gj, gi] = 1.0 # obj
# pi[..., 2:4] = torch.sigmoid(pi[..., 2:4]) # wh power loss (uncomment) # pi[..., 2:4] = torch.sigmoid(pi[..., 2:4]) # wh power loss (uncomment)
@ -304,10 +305,10 @@ def compute_loss(p, targets, model, giou_loss=True): # predictions, targets, mo
lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy loss lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy loss
lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss
# tclsm = torch.zeros_like(pi[..., 5:]) tclsm = torch.zeros_like(pi[..., 5:])
# tclsm[range(len(b)), tcls[i]] = 1.0 tclsm[range(nb), tcls[i]] = 1.0
# lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE) lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE)
lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # cls loss (CE) # lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # cls loss (CE)
# Append targets to text file # Append targets to text file
# with open('targets.txt', 'a') as file: # with open('targets.txt', 'a') as file: