def test(config): log_dir = os.path.join(config.log_dir, config.name + '_stage_2') val_path = os.path.join(config.data, "*/test") val_dataset = MultiviewImgDataset(val_path, scale_aug=False, rot_aug=False, num_views=config.num_views) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=config.stage2_batch_size, shuffle=False, num_workers=0) pretraining = not config.no_pretraining cnet = SVCNN(config.name, nclasses=config.num_classes, cnn_name=config.cnn_name, pretraining=pretraining) cnet_2 = MVCNN(config.name, cnet, nclasses=config.num_classes, cnn_name=config.cnn_name, num_views=config.num_views) cnet_2.load( os.path.join(log_dir, config.snapshot_prefix + str(config.weights))) optimizer = optim.Adam(cnet_2.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay, betas=(0.9, 0.999)) trainer = ModelNetTrainer(cnet_2, None, val_loader, optimizer, nn.CrossEntropyLoss(), config, log_dir, num_views=config.num_views) labels, predictions = trainer.update_validation_accuracy(config.weights, test=True) import Evaluation_tools as et eval_file = os.path.join(config.log_dir, '{}.txt'.format(config.name)) et.write_eval_file(config.data, eval_file, predictions, labels, config.name) et.make_matrix(config.data, eval_file, config.log_dir)
def stage1(): log_dir = os.path.join(args.log_path, args.name, args.name+'_stage_1') create_folder(log_dir) optimizer = optim.Adam(cnet.parameters(), lr=args.lr, weight_decay=args.weight_decay) train_dataset = SingleImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=0) val_dataset = SingleImgDataset(args.val_path, scale_aug=False, rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=0) print('num_train_files: '+str(len(train_dataset.filepaths))) print('num_val_files: '+str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1) trainer.train(args.epoch)
def train_3d_single(): # STAGE 1 print('Stage_1 begin:') log_dir = args.name + '_stage_1' create_folder(log_dir) svcnn = SVCNN(args.name, nclasses=40, pretraining=True, cnn_name=args.cnn_name) optimizer = optim.Adam(svcnn.parameters(), lr=args.lr, weight_decay=args.weight_decay) train_file = open(args.single_train_path) train_list = json.load(train_file) train_dataset = SingleImgDataset(train_list, scale_aug=False, rot_aug=False) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=0) test_file = open(args.single_test_path) test_list = json.load(test_file) val_dataset = SingleImgDataset(test_list, scale_aug=False, rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=0) print('num_train_files: ' + str(len(train_dataset.data_list))) print('num_val_files: ' + str(len(val_dataset.data_list))) trainer = ModelNetTrainer(svcnn, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1) trainer.train(10) return svcnn
def train_3d_multi(svcnn): print('Stage_2 begin:') log_dir = args.name + '_stage_2' create_folder(log_dir) gvcnn = GVCNN(args.name, svcnn, nclasses=40, num_views=args.num_views) del svcnn optimizer = optim.Adam(gvcnn.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999)) train_file = open(args.multi_train_path) train_list = json.load(train_file) train_dataset = MultiviewImgDataset(train_list, scale_aug=False, rot_aug=False, num_views=args.num_views) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=True, num_workers=0) test_file = open(args.multi_test_path) test_list = json.load(test_file) val_dataset = MultiviewImgDataset(test_list, scale_aug=False, rot_aug=False, num_views=args.num_views) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0) print('num_train_files: ' + str(len(train_dataset.data_list))) print('num_val_files: ' + str(len(val_dataset.data_list))) trainer = ModelNetTrainer(gvcnn, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'gvcnn', log_dir, num_views=args.num_views) trainer.train(15)
log_dir = args.name+'_stage_1' create_folder(log_dir) cnet = SVCNN(args.name, nclasses=40, pretraining=pretraining, cnn_name=args.cnn_name) optimizer = optim.Adam(cnet.parameters(), lr=args.lr, weight_decay=args.weight_decay) n_models_train = args.num_models*args.num_views train_dataset = SingleImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=0) val_dataset = SingleImgDataset(args.val_path, scale_aug=False, rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=0) print('num_train_files: '+str(len(train_dataset.filepaths))) print('num_val_files: '+str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1) trainer.train(30) # STAGE 2 log_dir = args.name+'_stage_2' create_folder(log_dir) cnet_2 = MVCNN(args.name, cnet, nclasses=40, cnn_name=args.cnn_name, num_views=args.num_views) del cnet optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999)) train_dataset = MultiviewImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)# shuffle needs to be false! it's done within the trainer val_dataset = MultiviewImgDataset(args.val_path, scale_aug=False, rot_aug=False, num_views=args.num_views,test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)
shuffle=False, num_workers=0) val_dataset = KmeanImgDataset(args.val_path) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0) print('num_train_files: ' + str(len(train_dataset.filepaths))) print('num_val_files: ' + str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'mvcnn', log_dir, num_views=args.num_views) trainer.train_kmean_threeview(30) else: # test path = '~/mvcnn_pytorch-master_ECCV2018_backup_2019_11_22/MVCNN_kmean_cat_no_sort_128/' modelfile = 'model-00002.pth' pretraining = not args.no_pretraining log_dir = args.name cnet = SVCNN(args.name, nclasses=40, pretraining=pretraining, cnn_name=args.cnn_name)
def train(config): log(config.log_file, 'Starting...') pretraining = not config.no_pretraining log_dir = config.name create_folder(config.name) log(config.log_file, '--------------stage 1--------------') # STAGE 1 log_dir = os.path.join(config.log_dir, config.name + '_stage_1') create_folder(log_dir) cnet = SVCNN(config, pretraining=pretraining) optimizer = optim.Adam(cnet.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay) train_path = os.path.join(config.data, "*/train") train_dataset = SingleImgDataset(train_path, config, scale_aug=False, rot_aug=False) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config.stage1_batch_size, shuffle=True, num_workers=0) val_path = os.path.join(config.data, "*/test") val_dataset = SingleImgDataset(val_path, config, scale_aug=False, rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=config.stage1_batch_size, shuffle=False, num_workers=0) log(config.log_file, 'num_train_files: ' + str(len(train_dataset.filepaths))) log(config.log_file, 'num_val_files: ' + str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), config, log_dir, num_views=1) trainer.train(config, config.stage1_batch_size) #cnet.load(os.path.join(log_dir, config.snapshot_prefix + str(30))) # STAGE 2 log(config.log_file, '--------------stage 2--------------') log_dir = os.path.join(config.log_dir, config.name + '_stage_2') create_folder(log_dir) cnet_2 = MVCNN(cnet, config) del cnet optimizer = optim.Adam(cnet_2.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay, betas=(0.9, 0.999)) train_dataset = MultiviewImgDataset(train_path, config, scale_aug=False, rot_aug=False) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config.stage2_batch_size, shuffle=False, num_workers=0 ) # shuffle needs to be false! it's done within the trainer val_dataset = MultiviewImgDataset(val_path, config, scale_aug=False, rot_aug=False) val_loader = torch.utils.data.DataLoader( val_dataset, batch_size=config.stage2_batch_size, shuffle=False, num_workers=0) log(config.log_file, 'num_train_files: ' + str(len(train_dataset.filepaths))) log(config.log_file, 'num_val_files: ' + str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), config, log_dir, num_views=config.num_views) trainer.train(config, config.stage2_batch_size)
val_dataset = SingleImgDataset(args.val_path, scale_aug=False, rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=0) print('num_train_files: '+str(len(train_dataset.filepaths))) print('num_val_files: '+str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1) trainer.train(args.epoch) if not args.skip_stage1: stage1() # STAGE 2 log_dir = os.path.join(args.log_path, args.name, args.name+'_stage_2') create_folder(log_dir) cnet_2 = MVCNN(args.name, cnet, nclasses=args.num_class, cnn_name=args.cnn_name, num_views=args.num_views) del cnet optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999)) train_dataset = MultiviewImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views, num_class=args.num_class) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)# shuffle needs to be false! it's done within the trainer val_dataset = MultiviewImgDataset(args.val_path, scale_aug=False, rot_aug=False, num_views=args.num_views, num_class=args.num_class) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0) print('####stage_2####') print('num_train_files: '+str(len(train_dataset.filepaths))) print('num_val_files: '+str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'mvcnn', log_dir, num_views=args.num_views, num_class=args.num_class) trainer.train(args.epoch)
rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=48, shuffle=False, num_workers=10) # 训练集和测试集分别为9843和2468再乖以12 print('num_train_files: ' + str(len(train_dataset.filepaths))) print('num_val_files: ' + str(len(val_dataset.filepaths))) # 这里只是定义一个训练器,记录数据,输出loss和acc, svcnn和num_view=1即只要单个图像输入 trainer = ModelNetTrainer(cnet_, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'svcnn', log_dir, num_views=1) tic1 = time.clock() trainer.train(n_epochs=0) # 测试时设为1,看能否完整跑完两个阶段 toc1 = time.clock() print('The training time of first stage: %d m' % ((toc1 - tic1) / 60)) # STAGE 2 print('###################Stage 2####################') log_dir = args.name + '_stage_2' create_folder(log_dir) # cnet_2与cnet采用相同的网络 cnet_2 = MVCNN(args.name,
val_dataset = KmeanImgDataset(args.val_path, fea_type=args.fea_type, dataset=dataset) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0) print('num_train_files: ' + str(len(train_dataset.filepaths))) print('num_val_files: ' + str(len(val_dataset.filepaths))) trainer = ModelNetTrainer((nem, cnet_2), train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), None, log_dir, num_views=args.num_views, class_num=class_num) trainer.train_nem_mvcnn(args.epoch) else: # test path = '/mnt/cloud_disk/huangjj/exp_mvcnn/' + args.name modelfile = args.modelfile pretraining = not args.no_pretraining log_dir = args.name cnet = SVCNN(args.name, nclasses=class_num, pretraining=pretraining,
val_dataset = SingleImgDataset(args.val_path, scale_aug=False, rot_aug=False, test_mode=True) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False, num_workers=0) print('num_train_files: ' + str(len(train_dataset.filepaths))) print('num_val_files: ' + str(len(val_dataset.filepaths))) trainer = ModelNetTrainer(cnet, train_loader, val_loader, optimizer_model, optimizer_centerloss, softmax_loss, center_loss, 'svcnn', log_dir, num_views=1) trainer.train(20) # STAGE 2 log_dir = 'smtcloss_wonor_' + args.name + '_stage_2_' + localtime create_folder(log_dir) cnet_2 = MVCNN(args.name, cnet, nclasses=40, cnn_name=args.cnn_name, num_views=args.num_views) del cnet
# cnet_2.module.load_state_dict(torch.load("Vgg11_Seg_white_stage_2/Vgg11_Seg_white/model-00027.pth")) # cnet_2.module.eval() # else: # cnet_2.load_state_dict(torch.load("Vgg11_Seg_white_stage_2/Vgg11_Seg_white/model-00027.pth")) # cnet_2.eval() ### ------------------------------------------------------------------------------------------------------------- if use_dataparallel: cnet_2 = nn.DataParallel(cnet_2) cnet_2.to(device) optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999)) train_dataset = MultiviewImgDataset(args.train_path, scale_aug=False, rot_aug=False, num_models=n_models_train, num_views=args.num_views,KNU_data=args.KNU_Data,pixel_augmentation=args.pixel_augmentation) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0)# shuffle needs to be false! it's done within the trainer val_dataset = MultiviewImgDataset(args.val_path, scale_aug=False, rot_aug=False, num_views=args.num_views,KNU_data=args.KNU_Data,pixel_augmentation=args.pixel_augmentation) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batchSize, shuffle=False, num_workers=0) print('num_train_files: '+str(len(train_dataset.filepaths))) print('num_val_files: '+str(len(val_dataset.filepaths))) if(args.loss_type == 'focal_loss'): focal_loss = FocalLoss(gamma=2, alpha=0.25) trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, focal_loss, 'mvcnn', log_dir, num_views=args.num_views, nClasses=nclasses) else: trainer = ModelNetTrainer(cnet_2, train_loader, val_loader, optimizer, nn.CrossEntropyLoss(), 'mvcnn', log_dir, num_views=args.num_views, nClasses=nclasses) trainer.train(30, use_dataparallel)