import matplotlib if not args.use_gui: matplotlib.use('Agg') else: from matplotlib import pyplot as plt plt.ion() plt.show() import os from dataset import * from vlae import VLadder from trainer import NoisyTrainer if args.gpus is not '': os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus dataset = MnistDataset() model = VLadder(dataset, name=args.netname, reg=args.reg, batch_size=args.batch_size, restart=args.restart) trainer = NoisyTrainer(model, dataset, args) trainer.train() # TODO remove #if args.no_train: # trainer.visualize() #else: # trainer.train()
# Log + print error and quit gracefully if there was one if error: print(error_msg) LOG.error(error_msg) exit(-1) # Create the dataset object if args.dataset == 'mnist': dataset = MnistDataset() elif args.dataset == 'lsun': dataset = LSUNDataset(db_path=args.db_path) elif args.dataset == 'celebA': dataset = CelebADataset(db_path=args.db_path) elif args.dataset == 'svhn': dataset = SVHNDataset(db_path=args.db_path) else: LOG.error("Unknown dataset") exit(-1) # Construct network and trainer, then let it fly num_gpus = len(args.gpus.split(',')) model = SequentialVAE(dataset, name=args.netname, batch_size=args.batch_size, logger=LOG, version=args.version, base_dir=base_dir, num_gpus=num_gpus) trainer = NoisyTrainer(model, dataset, args, LOG, base_dir) trainer.train()
from trainer import NoisyTrainer import tensorflow as tf import numpy as np tf.set_random_seed(0) np.random.seed(0) os.environ['CUDA_VISIBLE_DEVICES'] = '-1' if args.gpus is not '': os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus if args.dataset == 'mnist': dataset = MnistDataset() elif args.dataset == 'lsun': dataset = LSUNDataset(db_path=args.db_path) elif args.dataset == 'celebA': dataset = CelebADataset(db_path=args.db_path) elif args.dataset == 'svhn': dataset = SVHNDataset(db_path=args.db_path) else: print("Unknown dataset") exit(-1) model = VLadder(dataset, name=args.netname, reg=args.reg, batch_size=args.batch_size, restart=not args.no_train) trainer = NoisyTrainer(model, dataset, args) if args.no_train: trainer.visualize() else: trainer.train()
if not args.use_gui: matplotlib.use('Agg') else: from matplotlib import pyplot as plt plt.ion() plt.show() from dataset import * from sequential_vae import SequentialVAE from trainer import NoisyTrainer if args.gpus is not '': os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus if args.dataset == 'mnist': dataset = MnistDataset() elif args.dataset == 'lsun': dataset = LSUNDataset(db_path=args.db_path) elif args.dataset == 'celebA': dataset = CelebADataset(db_path=args.db_path) elif args.dataset == 'svhn': dataset = SVHNDataset(db_path=args.db_path) else: print("Unknown dataset") exit(-1) model = SequentialVAE(dataset, name=args.netname, batch_size=args.batch_size) trainer = NoisyTrainer(model, dataset, args) trainer.train()