def main(args): args.cuda = args.use_cuda and torch.cuda.is_available() train_set, validate_set, test_set, train_loader, validate_loader, test_loader = get_data.get_data_atomic( args) #model = models.Atomic(args) model = models.Atomic_edge_only(args) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) #optimizer = torch.optim.SGD(model.parameters(), lr=args.lr) #{'single': 0, 'mutual': 1, 'avert': 2, 'refer': 3, 'follow': 4, 'share': 5} criterion = [ torch.nn.CrossEntropyLoss( weight=torch.Tensor([0.05, 0.05, 0.25, 0.25, 0.25, 0.15])), torch.nn.MSELoss() ] # {'NA': 0, 'single': 1, 'mutual': 2, 'avert': 3, 'refer': 4, 'follow': 5, 'share': 6} scheduler = ReduceLROnPlateau(optimizer, factor=args.lr_decay, patience=1, verbose=True, mode='max') #-------------------------------------------------- # ------------------------ # use multi-gpu if args.cuda and torch.cuda.device_count() > 1: print("Now Using ", len(args.device_ids), " GPUs!") model = torch.nn.DataParallel(model, device_ids=args.device_ids, output_device=args.device_ids[0]).cuda() #model=model.cuda() criterion[0] = criterion[0].cuda() criterion[1] = criterion[1].cuda() elif args.cuda: model = model.cuda() criterion[0] = criterion[0].cuda() criterion[1] = criterion[1].cuda() if args.load_best_checkpoint: loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_acc, avg_epoch_acc, model, optimizer = loaded_checkpoint if args.load_last_checkpoint: loaded_checkpoint = utils.load_last_checkpoint( args, model, optimizer, path=args.resume, version=args.model_load_version) if loaded_checkpoint: args, best_epoch_acc, avg_epoch_acc, model, optimizer = loaded_checkpoint # ------------------------------------------------------------------------------ # Start Training! since = time.time() train_epoch_acc_all = [] val_epoch_acc_all = [] best_acc = 0 avg_epoch_acc = 0 for epoch in range(args.start_epoch, args.epochs): train_epoch_loss, train_epoch_acc = train(train_loader, model, criterion, optimizer, epoch, args) train_epoch_acc_all.append(train_epoch_acc) val_epoch_loss, val_epoch_acc = validate(validate_loader, model, criterion, epoch, args) val_epoch_acc_all.append(val_epoch_acc) print('Epoch {}/{} Training Acc: {:.4f} Validation Acc: {:.4f}'.format( epoch, args.epochs - 1, train_epoch_acc, val_epoch_acc)) print('*' * 15) scheduler.step(val_epoch_acc) is_best = val_epoch_acc > best_acc if is_best: best_acc = val_epoch_acc avg_epoch_acc = np.mean(val_epoch_acc_all) utils.save_checkpoint( { 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_epoch_acc': best_acc, 'avg_epoch_acc': avg_epoch_acc, 'optimizer': optimizer.state_dict(), 'args': args }, is_best=is_best, directory=args.resume, version='epoch_{}'.format(str(epoch))) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best Val Acc: {}, Final Avg Val Acc: {}'.format( best_acc, avg_epoch_acc)) # ---------------------------------------------------------------------------------------------------------- # test loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_acc, avg_epoch_acc, model, optimizer = loaded_checkpoint test_loader.dataset.round_cnt = { 'single': 0, 'mutual': 0, 'avert': 0, 'refer': 0, 'follow': 0, 'share': 0 } test_loss, test_acc, confmat, top2_acc = test(test_loader, model, criterion, args) # save test results if not isdir(args.save_test_res): os.mkdir(args.save_test_res) with open(os.path.join(args.save_test_res, 'raw_test_results.pkl'), 'w') as f: pickle.dump([test_loss, test_acc, confmat, top2_acc], f) print("Test Acc {}".format(test_acc)) print("Top 2 Test Acc {}".format(top2_acc)) # todo: need to change the mode here! get_metric_from_confmat(confmat, 'atomic')
def main(args): args.cuda = args.use_cuda and torch.cuda.is_available() train_set, validate_set, test_set, train_loader, validate_loader, test_loader = get_data.get_data_attmat( args) model = models.AttMat(args) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) criterion = torch.nn.CrossEntropyLoss() scheduler = ReduceLROnPlateau(optimizer, factor=args.lr_decay, patience=1, verbose=True, mode='max') #------------------------ # use multi-gpu if args.cuda and torch.cuda.device_count() > 1: print("Now Using ", len(args.device_ids), " GPUs!") model = torch.nn.DataParallel(model, device_ids=args.device_ids, output_device=args.device_ids[0]).cuda() criterion = criterion.cuda() elif args.cuda: model = model.cuda() criterion = criterion.cuda() if args.load_best_checkpoint: loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint if args.load_last_checkpoint: loaded_checkpoint = utils.load_last_checkpoint( args, model, optimizer, path=args.resume, version=args.model_load_version) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint # ------------------------------------------------------------------------------ # Start Training! since = time.time() train_epoch_acc_all = [] val_epoch_acc_all = [] best_acc = 0 avg_epoch_acc = 0 for epoch in range(args.start_epoch, args.epochs): train_epoch_loss, train_epoch_acc = train(train_loader, model, criterion, optimizer, epoch, args) train_epoch_acc_all.append(train_epoch_acc) val_epoch_loss, val_epoch_acc = validate(validate_loader, model, criterion, epoch, args) val_epoch_acc_all.append(val_epoch_acc) print('Epoch {}/{} Training Acc: {:.4f} Validation Acc: {:.4f}'.format( epoch, args.epochs - 1, train_epoch_acc, val_epoch_acc)) print('*' * 15) scheduler.step(val_epoch_acc) is_best = val_epoch_acc > best_acc if is_best: best_acc = val_epoch_acc avg_epoch_acc = np.mean(val_epoch_acc_all) utils.save_checkpoint( { 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_epoch_acc': best_acc, 'avg_epoch_acc': avg_epoch_acc, 'optimizer': optimizer.state_dict(), 'args': args }, is_best=is_best, directory=args.resume, version='epoch_{}'.format(str(epoch))) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best Val Acc: {}, Final Avg Val Acc: {}'.format( best_acc, avg_epoch_acc)) #---------------------------------------------------------------------------------------------------------- # test loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_acc, avg_epoch_acc, model, optimizer = loaded_checkpoint # test_loss, test_acc, one_acc, zero_acc = test(test_loader, model, criterion, args) print("Test Acc {}, One Acc {}, Zero Acc {}".format( test_acc, one_acc, zero_acc)) # save test results if not isdir(args.save_test_res): os.mkdir(args.save_test_res) with open(os.path.join(args.save_test_res, 'raw_test_results.pkl'), 'w') as f: pickle.dump([test_loss, test_acc, one_acc, zero_acc], f)
def main(args): args.cuda = args.use_cuda and torch.cuda.is_available() train_set, validate_set, test_set, train_loader, validate_loader, test_loader = get_data.get_data_AttMat_msg_lstm( args) model_args = {'roi_feature_size': args.roi_feature_size, 'edge_feature_size': args.roi_feature_size, 'node_feature_size': args.roi_feature_size, 'message_size': args.message_size, 'link_hidden_size': args.link_hidden_size, 'link_hidden_layers': args.link_hidden_layers, 'propagate_layers': args.propagate_layers, 'big_attr_classes': args.big_attr_class_num, 'lstm_hidden_size': args.lstm_hidden_size} model = models.AttMat_msg_lstm(model_args, args) # TODO: check grads and then to set the learning rate for Adam optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) # criterion=torch.nn.CrossEntropyLoss() if args.cuda: model = model.cuda() # criterion=criterion.cuda( ) if args.load_best_checkpoint: loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint if args.load_last_checkpoint: loaded_checkpoint = utils.load_last_checkpoint(args, model, optimizer, path=args.resume, version=args.model_load_version) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint train_error_history = list() train_loss_history = list() val_error_history = list() val_loss_history = list() best_epoch_error = np.inf for epoch in range(args.start_epoch, args.epochs): train_error_rate_cur_epoch, train_loss_cur_epoch = train(train_loader, model, criterion, optimizer, epoch, args) train_error_history.append(train_error_rate_cur_epoch) train_loss_history.append(train_loss_cur_epoch) val_error_rate_cur_epoch, val_loss_cur_epoch = validate(validate_loader, model, criterion, args) val_error_history.append(val_error_rate_cur_epoch) val_loss_history.append(val_loss_cur_epoch) # TODO: why use this schedule for adjusting learning rate, there is no need to decrease lr for Adam every epoch if epoch > 0 and epoch % 1 == 0: args.lr *= args.lr_decay for param_group in optimizer.param_groups: param_group['lr'] = args.lr is_best = val_error_rate_cur_epoch < best_epoch_error best_epoch_error = min(val_error_rate_cur_epoch, best_epoch_error) avg_epoch_error = np.mean(val_error_history) utils.save_checkpoint({ 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_epoch_error': best_epoch_error, 'avg_epoch_error': avg_epoch_error, 'optimizer': optimizer.state_dict(), }, is_best=is_best, directory=args.resume, version='epoch_{}'.format(str(epoch))) print('best_epoch_error: {}, avg_epoch_error: {}'.format(best_epoch_error, avg_epoch_error)) # test # loaded_checkpoint=utils.load_best_checkpoint(args,model,optimizer,path=args.resume) loaded_checkpoint = utils.load_last_checkpoint(args, model, optimizer, path=args.resume, version=args.model_load_version) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint test(test_loader, model, args)
def main(args): args.cuda = args.use_cuda and torch.cuda.is_available() train_set, validate_set, test_set, train_loader, validate_loader, test_loader = get_data.get_data_atomic( args) #model = models.Atomic(args) model = models.Atomic_2branch(args) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) #{'single': 0, 'mutual': 1, 'avert': 2, 'refer': 3, 'follow': 4, 'share': 5} criterion = [ torch.nn.CrossEntropyLoss( weight=torch.Tensor([0.05, 0.05, 0.25, 0.25, 0.25, 0.15])), torch.nn.MSELoss() ] # {'NA': 0, 'single': 1, 'mutual': 2, 'avert': 3, 'refer': 4, 'follow': 5, 'share': 6} scheduler = ReduceLROnPlateau(optimizer, factor=args.lr_decay, patience=1, verbose=True, mode='max') #-------------------------------------------------- # ------------------------ # use multi-gpu if args.cuda and torch.cuda.device_count() > 1: print("Now Using ", len(args.device_ids), " GPUs!") model = torch.nn.DataParallel(model, device_ids=args.device_ids, output_device=args.device_ids[0]).cuda() #model=model.cuda() criterion[0] = criterion[0].cuda() criterion[1] = criterion[1].cuda() elif args.cuda: model = model.cuda() criterion[0] = criterion[0].cuda() criterion[1] = criterion[1].cuda() # ---------------------------------------------------------------------------------------------------------- # test loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_acc, avg_epoch_acc, model, optimizer = loaded_checkpoint test_loader.dataset.round_cnt = { 'single': 0, 'mutual': 0, 'avert': 0, 'refer': 0, 'follow': 0, 'share': 0 } test_loss, test_acc, confmat, top2_acc, correct_rec, error_rec = test( test_loader, model, criterion, args) #fw = open(os.path.join(args.tmp_root, 'correct_list.txt'), 'w') fc_single = open(os.path.join(args.tmp_root, 'correct_single.txt'), 'w') fc_mutual = open(os.path.join(args.tmp_root, 'correct_mutual.txt'), 'w') fc_avert = open(os.path.join(args.tmp_root, 'correct_avert.txt'), 'w') fc_refer = open(os.path.join(args.tmp_root, 'correct_refer.txt'), 'w') fc_follow = open(os.path.join(args.tmp_root, 'correct_follow.txt'), 'w') fc_share = open(os.path.join(args.tmp_root, 'correct_share.txt'), 'w') fe_single = open(os.path.join(args.tmp_root, 'error_single.txt'), 'w') fe_mutual = open(os.path.join(args.tmp_root, 'error_mutual.txt'), 'w') fe_avert = open(os.path.join(args.tmp_root, 'error_avert.txt'), 'w') fe_refer = open(os.path.join(args.tmp_root, 'error_refer.txt'), 'w') fe_follow = open(os.path.join(args.tmp_root, 'error_follow.txt'), 'w') fe_share = open(os.path.join(args.tmp_root, 'error_share.txt'), 'w') for item in correct_rec: if item[1] == '0': for i in range(5): fc_single.write(str(item[0][i].cpu().numpy())) fc_single.write(' ') fc_single.write(str(item[1].cpu().numpy())) fc_single.write(' ') fc_single.write(str(item[2].cpu().numpy())) fc_single.write('\n') elif item[1] == '1': for i in range(5): fc_mutual.write(str(item[0][i].cpu().numpy())) fc_mutual.write(' ') fc_mutual.write(str(item[1].cpu().numpy())) fc_mutual.write(' ') fc_mutual.write(str(item[2].cpu().numpy())) fc_mutual.write('\n') elif item[1] == '2': for i in range(5): fc_avert.write(str(item[0][i].cpu().numpy())) fc_avert.write(' ') fc_avert.write(str(item[1].cpu().numpy())) fc_avert.write(' ') fc_avert.write(str(item[2].cpu().numpy())) fc_avert.write('\n') elif item[1] == '3': for i in range(5): fc_refer.write(str(item[0][i].cpu().numpy())) fc_refer.write(' ') fc_refer.write(str(item[1].cpu().numpy())) fc_refer.write(' ') fc_refer.write(str(item[2].cpu().numpy())) fc_refer.write('\n') elif item[1] == '4': for i in range(5): fc_follow.write(str(item[0][i].cpu().numpy())) fc_follow.write(' ') fc_follow.write(str(item[1].cpu().numpy())) fc_follow.write(' ') fc_follow.write(str(item[2].cpu().numpy())) fc_follow.write('\n') elif item[1] == '5': for i in range(5): fc_share.write(str(item[0][i].cpu().numpy())) fc_share.write(' ') fc_share.write(str(item[1].cpu().numpy())) fc_share.write(' ') fc_share.write(str(item[2].cpu().numpy())) fc_share.write('\n') #fw2 = open(os.path.join(args.tmp_root, 'error_list.txt'), 'w') # for item in error_rec: # for i in range(5): # fw2.write(str(item[0][i].cpu().numpy())) # fw2.write(' ') # fw2.write(str(item[1].cpu().numpy())) # fw2.write(' ') # fw2.write(str(item[2].cpu().numpy())) # fw2.write('\n') for item in error_rec: if item[1] == '0': for i in range(5): fe_single.write(str(item[0][i].cpu().numpy())) fe_single.write(' ') fe_single.write(str(item[1].cpu().numpy())) fe_single.write(' ') fe_single.write(str(item[2].cpu().numpy())) fe_single.write('\n') elif item[1] == '1': for i in range(5): fe_mutual.write(str(item[0][i].cpu().numpy())) fe_mutual.write(' ') fe_mutual.write(str(item[1].cpu().numpy())) fe_mutual.write(' ') fe_mutual.write(str(item[2].cpu().numpy())) fe_mutual.write('\n') elif item[1] == '2': for i in range(5): fe_avert.write(str(item[0][i].cpu().numpy())) fe_avert.write(' ') fe_avert.write(str(item[1].cpu().numpy())) fe_avert.write(' ') fe_avert.write(str(item[2].cpu().numpy())) fe_avert.write('\n') elif item[1] == '3': for i in range(5): fe_refer.write(str(item[0][i].cpu().numpy())) fe_refer.write(' ') fe_refer.write(str(item[1].cpu().numpy())) fe_refer.write(' ') fe_refer.write(str(item[2].cpu().numpy())) fe_refer.write('\n') elif item[1] == '4': for i in range(5): fe_follow.write(str(item[0][i].cpu().numpy())) fe_follow.write(' ') fe_follow.write(str(item[1].cpu().numpy())) fe_follow.write(' ') fe_follow.write(str(item[2].cpu().numpy())) fe_follow.write('\n') elif item[1] == '5': for i in range(5): fe_share.write(str(item[0][i].cpu().numpy())) fe_share.write(' ') fe_share.write(str(item[1].cpu().numpy())) fe_share.write(' ') fe_share.write(str(item[2].cpu().numpy())) fe_share.write('\n')
def main(args): args.cuda = args.use_cuda and torch.cuda.is_available() train_set, validate_set, test_set, train_loader, validate_loader, test_loader = get_data.get_data_resnet_msgpassing_balanced_lstm(args) model_args = {'roi_feature_size':args.roi_feature_size,'edge_feature_size': args.roi_feature_size, 'node_feature_size': args.roi_feature_size, 'message_size': args.message_size, 'link_hidden_size': args.link_hidden_size, 'link_hidden_layers': args.link_hidden_layers, 'propagate_layers': args.propagate_layers, 'big_attr_classes': args.big_attr_class_num, 'lstm_hidden_size':args.lstm_hidden_size} model = models.HGNN_resnet_msgpassing_balanced_lstm(model_args) optimizer=torch.optim.Adam(model.parameters(),lr=args.lr) criterion=torch.nn.CrossEntropyLoss() if args.cuda: model=model.cuda() criterion=criterion.cuda( ) if args.load_best_checkpoint: loaded_checkpoint=utils.load_best_checkpoint(args,model,optimizer,path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer=loaded_checkpoint if args.load_last_checkpoint: loaded_checkpoint=utils.load_last_checkpoint(args,model,optimizer,path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer=loaded_checkpoint train_error_history=list() train_loss_history=list() val_error_history=list() val_loss_history=list() best_epoch_error=np.inf for epoch in range(args.start_epoch, args.epochs): train_error_rate_cur_epoch, train_loss_cur_epoch=train(train_loader,model,criterion,optimizer,epoch,args) train_error_history.append(train_error_rate_cur_epoch) train_loss_history.append(train_loss_cur_epoch) val_error_rate_cur_epoch, val_loss_cur_epoch=validate(validate_loader,model,criterion,args) val_error_history.append(val_error_rate_cur_epoch) val_loss_history.append(val_loss_cur_epoch) if epoch>0 and epoch%1==0: args.lr*=args.lr_decay for param_group in optimizer.param_groups: param_group['lr']=args.lr is_best=val_error_rate_cur_epoch<best_epoch_error best_epoch_error=min(val_error_rate_cur_epoch,best_epoch_error) avg_epoch_error=np.mean(val_error_history) utils.save_checkpoint({ 'epoch':epoch+1, 'state_dict':model.state_dict(), 'best_epoch_error':best_epoch_error, 'avg_epoch_error':avg_epoch_error, 'optimizer':optimizer.state_dict(),},is_best=is_best,directory=args.resume) print('best_epoch_error: {}, avg_epoch_error: {}'.format(best_epoch_error, avg_epoch_error)) # test #loaded_checkpoint=utils.load_best_checkpoint(args,model,optimizer,path=args.resume) loaded_checkpoint = utils.load_last_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer=loaded_checkpoint test(test_loader,model,args)
def main(args): args.cuda = args.use_cuda and torch.cuda.is_available() train_set, validate_set, test_set, train_loader, validate_loader, test_loader = get_data.get_data_resnet_fc( args) model = models.HGNN_resnet_fc() # TODO: try to use the step policy for Adam, also consider the step interval optimizer = torch.optim.Adam(model.parameters(), lr=args.lr) # TODO: related to line 99 (e.g. max for auc, min for loss; try to check the definition of this method) scheduler = ReduceLROnPlateau(optimizer, factor=args.lr_decay, patience=1, verbose=True, mode='min') # TODO; double check this loss, also the output of the network criterion = torch.nn.CrossEntropyLoss() # criterion=torch.nn.MSELoss() #------------------------ # use multi-gpu if args.cuda and torch.cuda.device_count() > 1: print("Now Using ", len(args.device_ids), " GPUs!") #model=model.to(device_ids[0]) model = torch.nn.DataParallel(model, device_ids=args.device_ids, output_device=args.device_ids[0]).cuda() criterion = criterion.cuda() elif args.cuda: model = model.cuda() criterion = criterion.cuda() if args.load_best_checkpoint: loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint if args.load_last_checkpoint: loaded_checkpoint = utils.load_last_checkpoint( args, model, optimizer, path=args.resume, version=args.model_load_version) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint # for param_group in optimizer.param_groups: # param_group['lr'] = args.lr #------------------------------------------------------------------------------ # Train since = time.time() train_epoch_loss_all = [] val_epoch_loss_all = [] best_loss = np.inf avg_epoch_loss = np.inf for epoch in range(args.start_epoch, args.epochs): train_epoch_loss = train(train_loader, model, criterion, optimizer, epoch, args) train_epoch_loss_all.append(train_epoch_loss) #visdom_viz(vis, train_epoch_loss_all, win=0, ylabel='Training Epoch Loss', title=args.project_name, color='green') val_epoch_loss = validate(validate_loader, model, criterion, epoch, args) val_epoch_loss_all.append(val_epoch_loss) #visdom_viz(vis, val_epoch_loss_all, win=1, ylabel='Validation Epoch Loss', title=args.project_name,color='blue') print( 'Epoch {}/{} Training Loss: {:.4f} Validation Loss: {:.4f}'.format( epoch, args.epochs - 1, train_epoch_loss, val_epoch_loss)) print('*' * 15) #TODO: reducing lr when there is no gains on validation metric results (e.g. auc, loss) scheduler.step(val_epoch_loss) is_best = val_epoch_loss < best_loss if is_best: best_loss = val_epoch_loss avg_epoch_loss = np.mean(val_epoch_loss_all) utils.save_checkpoint( { 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'best_epoch_error': best_loss, 'avg_epoch_error': avg_epoch_loss, 'optimizer': optimizer.state_dict() }, is_best=is_best, directory=args.resume, version='epoch_{}'.format(str(epoch))) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best Val Loss: {}, Final Avg Val Loss: {}'.format( best_loss, avg_epoch_loss)) #------------------------------------------------------------------------------------------------------------- # test loaded_checkpoint = utils.load_best_checkpoint(args, model, optimizer, path=args.resume) if loaded_checkpoint: args, best_epoch_error, avg_epoch_error, model, optimizer = loaded_checkpoint pred_label, gt_label, test_loss = test(test_loader, model, criterion, args) print("Test Epoch Loss {}".format(test_loss)) # save test results if not isdir(args.save_test_res): os.mkdir(args.save_test_res) with open(os.path.join(args.save_test_res, 'raw_test_results.pkl'), 'w') as f: pickle.dump([pred_label, gt_label, test_loss], f) #todo: check get_test_metric recall, precision, F_one_score, acc, avg_acc, ConfMat = get_test_metric( pred_label, gt_label, args) print( '[====Test results Small Attr====] \n recall: {} \n precision: {} \n F1 score: {} \n acc: {} \n avg acc: {} \n Confusion Matrix: \n {}' .format(recall, precision, F_one_score, acc, avg_acc, ConfMat))