def sample(self, B, N, K, **kwargs): dataset = self.get_MiniImagenet(train=False) loader = get_random_data_loader( self.get_MiniImagenet(False), B, N, K, 1, TEST_NUM_PER_CLASS, **kwargs) # transform=self.transform, **kwargs) return next(iter(loader))
def get_train_loader(self): return get_random_data_loader(self.get_ImageNet32(), self.B, self.N, self.K, self.num_steps, TRAIN_NUM_PER_CLASS, classes=range(TRAIN_NUM_CLASSES))
def get_train_loader(self): classes = range(TRAIN_NUM_CLASSES) if self.novel else None return get_random_data_loader(self.get_EMNIST(), self.B, self.N, self.K, self.num_steps, TRAIN_NUM_PER_CLASS, classes=classes)
def get_train_loader(self): if self.phase == 1: classes = range(TRAIN_NUM_CLASSES // 2) else: classes = range(TRAIN_NUM_CLASSES) return get_random_data_loader(self.get_EMNIST(), self.B, self.N, self.K, self.num_steps, TRAIN_NUM_PER_CLASS, classes=classes)
def sample(self, B, N, K, **kwargs): classes = range(TRAIN_NUM_CLASSES, TRAIN_NUM_CLASSES + TEST_NUM_CLASSES_NOVEL) \ if self.novel else None loader = get_random_data_loader(self.get_EMNIST(False), B, N, K, 1, TEST_NUM_PER_CLASS, classes=classes, **kwargs) return next(iter(loader))
def get_train_loader(self): dataset = EMNIST(os.path.join(datasets_path, 'emnist'), split='balanced', download=True, train=True) def transform(x): return torch.bernoulli(x.unsqueeze(-3).float().div(255)) classes = torch.arange(20) if self.novel else None return get_random_data_loader(dataset, self.B, self.N, self.K, self.num_steps, TRAIN_NUM_PER_CLASS, transform=transform, classes=classes)
def sample(self, B, N, K, **kwargs): dataset = EMNIST(os.path.join(datasets_path, 'emnist'), split='balanced', download=True, train=False) def transform(x): return torch.bernoulli(x.unsqueeze(-3).float().div(255)) loader = get_random_data_loader( dataset, B, N, K, 1, TEST_NUM_PER_CLASS, transform=transform, classes=(torch.arange(20, 47) if self.novel else None), transform=transform, **kwargs) return next(iter(loader))
def get_train_loader(self): return get_random_data_loader(self.get_Kkanji(), self.B, self.N, self.K, self.num_steps, TRAIN_NUM_PER_CLASS)
def sample(self, B, N, K, **kwargs): loader = get_random_data_loader(self.get_Kkanji(False), B, N, K, 1, TEST_NUM_PER_CLASS, **kwargs) return next(iter(loader))