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,