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if os.path.isfile(CKPT_PATH_L):
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checkpoint = torch.load(CKPT_PATH_L)
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Local_Branch_model.load_state_dict(checkpoint)
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print("=> loaded Local_Branch_model checkpoint")
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if os.path.isfile(CKPT_PATH_F):
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checkpoint = torch.load(CKPT_PATH_F)
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Fusion_Branch_model.load_state_dict(checkpoint)
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print("=> loaded Fusion_Branch_model checkpoint")
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cudnn.benchmark = True
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criterion = nn.BCELoss()
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optimizer_global = optim.Adam(Global_Branch_model.parameters(), lr=LR_G, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
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lr_scheduler_global = lr_scheduler.StepLR(optimizer_global , step_size = 10, gamma = 1)
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optimizer_local = optim.Adam(Local_Branch_model.parameters(), lr=LR_L, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
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lr_scheduler_local = lr_scheduler.StepLR(optimizer_local , step_size = 10, gamma = 1)
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optimizer_fusion = optim.Adam(Fusion_Branch_model.parameters(), lr=LR_F, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
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lr_scheduler_fusion = lr_scheduler.StepLR(optimizer_fusion , step_size = 15, gamma = 0.1)
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print('********************load model succeed!********************')
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print('********************begin training!********************')
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for epoch in range(num_epochs):
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since = time.time()
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print('Epoch {}/{}'.format(epoch , num_epochs - 1))
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print('-' * 10)
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#set the mode of model
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lr_scheduler_global.step() #about lr and gamma
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lr_scheduler_local.step()
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lr_scheduler_fusion.step()
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Global_Branch_model.train() #set model to training mode
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Local_Branch_model.train()
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Fusion_Branch_model.train()
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running_loss = 0.0
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#Iterate over data
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for i, (input, target) in enumerate(train_loader):
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input_var = torch.autograd.Variable(input.cuda())
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target_var = torch.autograd.Variable(target.cuda())
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optimizer_global.zero_grad()
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optimizer_local.zero_grad()
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optimizer_fusion.zero_grad()
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# compute output
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output_global, fm_global, pool_global = Global_Branch_model(input_var)
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patchs_var = Attention_gen_patchs(input,fm_global)
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output_local, _, pool_local = Local_Branch_model(patchs_var)
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#print(fusion_var.shape)
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output_fusion = Fusion_Branch_model(pool_global, pool_local)
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#
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# loss
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loss1 = criterion(output_global, target_var)
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loss2 = criterion(output_local, target_var)
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loss3 = criterion(output_fusion, target_var)
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#
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loss = loss1*0.8 + loss2*0.1 + loss3*0.1
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if (i%500) == 0:
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print('step: {} totalloss: {loss:.3f} loss1: {loss1:.3f} loss2: {loss2:.3f} loss3: {loss3:.3f}'.format(i, loss = loss, loss1 = loss1, loss2 = loss2, loss3 = loss3))
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loss.backward()
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optimizer_global.step()
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optimizer_local.step()
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optimizer_fusion.step()
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#print(loss.data.item())
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running_loss += loss.data.item()
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#break
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'''
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if i == 40:
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print('break')
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break
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'''
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epoch_loss = float(running_loss) / float(i)
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print(' Epoch over Loss: {:.5f}'.format(epoch_loss))
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print('*******testing!*********')
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test(Global_Branch_model, Local_Branch_model, Fusion_Branch_model,test_loader)
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#break
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#save
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if epoch % 1 == 0:
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save_path = save_model_path
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torch.save(Global_Branch_model.state_dict(), save_path+save_model_name+'_Global'+'_epoch_'+str(epoch)+'.pkl')
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print('Global_Branch_model already save!')
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torch.save(Local_Branch_model.state_dict(), save_path+save_model_name+'_Local'+'_epoch_'+str(epoch)+'.pkl')
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print('Local_Branch_model already save!')
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torch.save(Fusion_Branch_model.state_dict(), save_path+save_model_name+'_Fusion'+'_epoch_'+str(epoch)+'.pkl')
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print('Fusion_Branch_model already save!')
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time_elapsed = time.time() - since
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print('Training one epoch complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60 , time_elapsed % 60))
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