def main():
parser = argparse.ArgumentParser()
parser.add_argument('--sc-root', type=str, required=True)
parser.add_argument('--experiments-root', type=str, required=True)
parser.add_argument('--run', type=str, required=True)
parser.add_argument('--device', type=str)
args = parser.parse_args()
if not args.device:
args.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
common_init()
experimenter = Experimenter(sc_root=args.sc_root,
experiments_root=args.experiments_root,
experiment_name=args.run,
device=args.device)
if args.run == 'compare_ae_vae_source_label':
experimenter.compare_ae_vae_source_label()
elif args.run == 'vary_num_classes':
experimenter.vary_num_classes()
else:
raise Exception('invalid experiment')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--sc09-mixture-root', type=str, required=True)
parser.add_argument('--checkpoints', type=str, required=True)
parser.add_argument('--supervision', choices=['label', 'source'], default='label')
parser.add_argument('--batch-size', type=int, default=100)
parser.add_argument('--latent-size', type=int, default=128)
parser.add_argument('--num-filters', type=int, default=128)
parser.add_argument('--beta', type=float, default=10)
parser.add_argument('--ae', action='store_true')
parser.add_argument('--report-interval', type=int, default=200)
parser.add_argument('--patience', type=int, default=10)
parser.add_argument('--device', type=str)
args = parser.parse_args()
if not args.device:
args.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
common_init()
nvae_trainer = NVAETrainer(args.sc09_mixture_root, args.device)
nvae_trainer.run(checkpoints=args.checkpoints,
supervision=args.supervision,
batch_size=args.batch_size,
latent_size=args.latent_size,
num_filters=args.num_filters,
beta=args.beta,
ae=args.ae,
report_interval=args.report_interval,
patience=args.patience)
def setUp(self):
common_init(self)
self.a = 2.0
self.b = 8.0
self.xval = 4.0
self.yval = 16.0
self.overflow_buf = torch.cuda.IntTensor(1).zero_()
self.ref = torch.cuda.FloatTensor([136.0])
def setUp(self):
common_init(self)
self.a = 2.0
self.b = 8.0
self.xval = 4.0
self.yval = 16.0
self.overflow_buf = torch.cuda.IntTensor(1).zero_()
self.ref = torch.full((1, ), 136.0, device="cuda", dtype=torch.float32)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--sc09-mixture-root', type=str, required=True)
parser.add_argument('--checkpoints', type=str, required=True)
parser.add_argument('--partition', type=str, default='testing')
parser.add_argument('--size', type=int)
parser.add_argument('--step', type=int)
parser.add_argument('--device', type=str)
args = parser.parse_args()
if not args.device:
args.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
common_init()
nvae_tester = NVAETester(sc09_mixture_root=args.sc09_mixture_root,
partition=args.partition,
size=args.size,
device=args.device)
nvae_tester.run(checkpoints=args.checkpoints, step=args.step)
def setUp(self):
self.scale = 4.0
self.overflow_buf = torch.cuda.IntTensor(1).zero_()
self.ref = torch.cuda.FloatTensor([1.0])
common_init(self)
def setUp(self):
self.x = torch.ones((2), device='cuda', dtype=torch.float32)
common_init(self)
def setUp(self):
self.handle = amp.init(enabled=True, patch_type=torch.half)
common_init(self)
def setUp(self):
self.handle = amp.init(enabled=True)
common_init(self)
def setUp(self):
self.handle = amp.init(enabled=True)
self.x = torch.ones((2, 8), device='cuda', dtype=torch.float32)
common_init(self)
def setUp(self):
common_init(self)
self.val = 4.0
self.overflow_buf = torch.cuda.IntTensor(1).zero_()
