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)
log_dir = args.name create_folder(args.name) config_f = open(os.path.join(log_dir, 'config.json'), 'w') json.dump(vars(args), config_f) config_f.close() # STAGE 1 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
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)
config_f = open(os.path.join(log_dir, 'config.json'), 'w') json.dump(vars(args), config_f) config_f.close() # STAGE 1 log_dir = args.name+'_stage_1' create_folder(log_dir) cnet = SVCNN(args.name, nclasses=len(classnames), pretraining=pretraining, cnn_name=args.cnn_name) #if torch.cuda.device_count()>1: # print('Use',torch.cuda.device_count(),'GPUs') # cnet = nn.DataParallel(cnet) optimizer = optim.Adam(cnet.parameters(), lr=args.lr, weight_decay=args.weight_decay) train_dataset = SingleImgDataset(args.train_path, classnames,objs, 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, classnames,objs,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) # STAGE 2 log_dir = args.name+'_stage_2' create_folder(log_dir) cnet_2 = MVCNN(args.name, cnet, nclasses=len(classnames), cnn_name=args.cnn_name, num_views=args.num_views)