testloss_to_file,
trainAcc_to_file,
testAcc_to_file,
Parameters,
model_name,
train_mode,
dataset,
plot=False)
######### error-2
lr_stage2 = 1e-4 #max(1e-5,10*optimizer.param_groups[-1]['lr'])
print('STAGE2')
# optimizer=torch.optim.Adam([{'params': model.features.parameters()},{'params': model.compressed_features.parameters()},
# {'params': model.frozen_features.parameters(), 'lr': lr_stage2*1},
# {'params': model.classifier.parameters(), 'lr': lr_stage2*1}], lr=lr_stage2,betas=(0.9, 0.999), eps=1e-08,weight_decay=0.0005)
optimizer = torch.optim.Adam(model.parameters(),
lr=lr_stage2,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0.0005)
scheduler = ReduceLROnPlateau(optimizer,
'max',
verbose=True,
patience=5,
eps=1e-9)
trainAcc_to_file, testAcc_to_file, trainloss_to_file, testloss_to_file, Parameters = Train_stage2(
model,
optimizer,
Trainloader,
Testloader,
epochs=None,
model = torch.nn.DataParallel(model) #make model DataParrallel
if (ensembleModel is not None):
ensembleModel=torch.nn.DataParallel(ensembleModel)
print('model='+model_name)
print('big model='+big_model_name)
lr_stage1=1e-4
if 'fitnet'==train_mode.lower():
optimizer=torch.optim.Adam([{'params': model.features.parameters()},{'params': model.compressed_features.parameters(), 'lr': lr_stage1*1},
{'params': model.frozen_features.parameters(), 'lr': lr_stage1*1},
{'params': model.regressor.parameters(), 'lr': lr_stage1*1},
{'params': model.classifier.parameters(), 'lr': lr_stage1*1}], lr=lr_stage1,
betas=(0.9, 0.999), eps=1e-08,weight_decay=0.0005)
else:
optimizer=torch.optim.Adam(model.parameters(), lr=lr_stage1,
betas=(0.9, 0.999), eps=1e-08,weight_decay=0.0005)
scheduler=ReduceLROnPlateau(optimizer, 'min',verbose=True,patience=5,eps=1e-8,threshold=1e-20)
print ('STAGE1')
trainloss_to_file,testloss_to_file,Parameters=Train_stage1(model,optimizer,Trainloader,Testloader,epochs=None,Train_mode=train_mode,
Model_name=model_name,
Dataset=dataset,
scheduler=scheduler,
big_model_name=big_model_name,
ensembleModel=ensembleModel)
#########################################################
#####EVAL
trainAcc_to_file,testAcc_to_file,_,_,_=Train_stage2(model,optimizer,Trainloader,Testloader, epochs=0,
Train_mode=train_mode,