Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-11 11:56:46 +02:00
parent 3373006d0e
commit b005a17eff
2 changed files with 6 additions and 6 deletions
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2
train.py
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@ -15,7 +15,7 @@ from utils.utils import *
hyp = {'giou': 1.666, # giou loss gain hyp = {'giou': 1.666, # giou loss gain
'xy': 4.062, # xy loss gain 'xy': 4.062, # xy loss gain
'wh': 0.1845, # wh loss gain 'wh': 0.1845, # wh loss gain
'cls': 1.0, # cls loss gain 'cls': 42.6, # cls loss gain
'cls_pw': 3.34, # cls BCELoss positive_weight 'cls_pw': 3.34, # cls BCELoss positive_weight
'obj': 12.61, # obj loss gain 'obj': 12.61, # obj loss gain
'obj_pw': 8.338, # obj BCELoss positive_weight 'obj_pw': 8.338, # obj BCELoss positive_weight

10
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
@ -304,10 +304,10 @@ 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(len(b)), 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: