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

updates

This commit is contained in:
Glenn Jocher 2020-03-01 20:55:20 -08:00
parent e6cda0fea4
commit 7823473d2f
1 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
train.py
View File

@ -97,6 +97,7 @@ def train():
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases) optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
optimizer.param_groups[2]['lr'] *= 2.0 # bias lr
del pg0, pg1, pg2 del pg0, pg1, pg2
start_epoch = 0 start_epoch = 0
@ -140,10 +141,9 @@ def train():
# lf = lambda x: 1 - x / epochs # linear ramp to zero # lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp # lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp # lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# lf = lambda x: 0.5 * (1 + math.cos(x * math.pi / epochs)) # cosine https://arxiv.org/pdf/1812.01187.pdf lf = lambda x: 0.5 * (1 + math.cos(x * math.pi / epochs)) # cosine https://arxiv.org/pdf/1812.01187.pdf
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=range(59, 70, 1), gamma=0.8) # gradual fall to 0.1*lr0 # scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(epochs * x) for x in [0.8, 0.9]], gamma=0.1)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(epochs * x) for x in [0.8, 0.9]], gamma=0.1)
scheduler.last_epoch = start_epoch scheduler.last_epoch = start_epoch
# # Plot lr schedule # # Plot lr schedule
@ -216,10 +216,10 @@ def train():
# Prebias # Prebias
if prebias: if prebias:
if epoch < 3: # prebias ne = 3 # number of prebias epochs
ps = np.interp(epoch, [0, 3], [0.1, hyp['lr0']]), 0.0 # prebias settings (lr=0.1, momentum=0.0) ps = np.interp(epoch, [0, ne], [0.1, hyp['lr0'] * 2]), \
else: # normal training np.interp(epoch, [0, ne], [0.9, hyp['momentum']]) # prebias settings (lr=0.1, momentum=0.9)
ps = hyp['lr0'], hyp['momentum'] # normal training settings if epoch == ne:
print_model_biases(model) print_model_biases(model)
prebias = False prebias = False