def get_dataset(name, train=True, path=None, background=True, all=False): if name == "omniglot": train_transform = transforms.Compose( [transforms.Resize((84, 84)), transforms.ToTensor()]) if path is None: return om.Omniglot("../data/omni", background=background, download=True, train=train, transform=train_transform, all=all) else: return om.Omniglot(path, download=True, background=train, transform=train_transform) else: print("Unsupported Dataset") assert False
def get_dataset( name, train=True, path=None, background=True, all=False, prefetch_gpu=False, device=None, resize=None, augment=False, normalize=False, ): if name == "omniglot": if resize is None: logger.info("Using image size 84") resize = 84 else: logger.info(f"Using image size {resize}") if augment: logger.info("Using data augmentation") train_transform = transforms.Compose([ transforms.Resize(resize), transforms.RandomCrop(resize, padding=8), transforms.ToTensor(), transforms.Normalize(train_train_mean, train_train_std), ]) # warmup_transform = transforms.Compose( # [ # transforms.Resize(resize), # transforms.ToTensor(), # transforms.Normalize(train_train_mean, train_train_std), # ] # ) # warmup_steps = 0 else: logger.info("NO augmentation") train_transform = transforms.Compose([ transforms.Resize(resize), transforms.ToTensor(), transforms.Normalize(train_train_mean, train_train_std), ]) warmup_transform = None warmup_steps = 0 if path is None: return om.Omniglot( "../data/omni", background=background, download=True, train=train, transform=train_transform, all=all, prefetch_gpu=prefetch_gpu, device=device, warmup_transform=warmup_transform, warmup_steps=warmup_steps, ) else: return om.Omniglot( path, background=background, download=True, train=train, transform=train_transform, all=all, prefetch_gpu=prefetch_gpu, device=device, warmup_transform=warmup_transform, warmup_steps=warmup_steps, ) else: print("Unsupported Dataset") assert False