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)
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) 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(30)
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)
elif args.svcnn_training_mode == 'load': cnet.cuda() log_dir = args.prefix + 'ckpt/svcnn/model-00025.pth' # log_dir = ckpt_dir + '/20/mvcnn_stage_1/mvcnn/model-00025.pth' model = torch.load(log_dir) cnet.load_state_dict(model) print('SVCNN trained model loaded!') # STAGE 2 log_dir = run_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, constraint=args.constraint, w_m=args.w_m, T=args.T, preType=args.preType) del cnet new_params = list(cnet_2.main_net.parameters()) + list(cnet_2.main_net.parameters()) if args.preType != None else cnet_2.main_net.parameters() params = new_params if args.freeze else cnet_2.parameters() optimizer = optim.Adam(params, lr=args.lr/8*args.batchSize, 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) 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(30)
# 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, cnet, pool_mode='no', nclasses=40, cnn_name=args.cnn_name, num_views=args.num_views) del cnet cnet_2 = torch.nn.DataParallel(cnet_2, device_ids=[0]) # 0,1 optimizer = optim.Adam(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.999)) # bitch_size: 原文为8,内存不足改为6 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,
'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 optimizer = optim.SGD(cnet_2.parameters(), lr=args.lr, weight_decay=args.weight_decay) 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,