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_()