def init_test_dataset(self):
dist_loader_text = "distributed" if self.args.get_distributed() else ""
self.logger.debug(f"Loading '{dist_loader_text}' MNIST test data")
self.test_dataset = datasets.MNIST(
root=self.get_args().get_data_path(),
train=False,
download=True,
transform=transforms.Compose([transforms.ToTensor()]))
self.test_sampler = get_sampler(self.test_dataset, self.args)
self.test_loader = DataLoader(self.test_dataset,
batch_size=self.args.test_batch_size,
sampler=self.test_sampler)
def init_test_dataset(self):
dist_loader_text = "distributed" if self.args.get_distributed() else ""
self.logger.debug(f"Loading '{dist_loader_text}' Shakespeare test data")
dataset = 'shakespeare'
data_root: str = None
pickle_root: str = None
pdataset = PickleDataset(dataset_name=dataset, data_root=data_root, pickle_root=pickle_root)
# client_id = None
client_id = self.args.rank
self.test_dataset = pdataset.get_dataset_pickle(dataset_type="test", client_id=client_id)
self.test_sampler = get_sampler(self.test_dataset, self.args)
self.test_loader = DataLoader(self.test_dataset, batch_size=self.args.test_batch_size, sampler=self.test_sampler)
def init_test_dataset(self):
dist_loader_text = "distributed" if self.args.get_distributed() else ""
self.logger.debug(f"Loading '{dist_loader_text}' CIFAR100 test data")
normalize = transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276])
transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
self.test_dataset = datasets.CIFAR100(root=self.get_args().get_data_path(), train=False, download=True,
transform=transform)
self.test_sampler = get_sampler(self.test_dataset, self.args)
self.test_loader = DataLoader(self.test_dataset, batch_size=self.args.test_batch_size, sampler=self.test_sampler)
def load_test_dataset(self):
self.logger.debug("Loading CIFAR100 test data")
normalize = transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276])
transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
test_dataset = datasets.CIFAR100(root=self.get_args().get_data_path(), train=False, download=True,
transform=transform)
sampler = get_sampler(self.test_dataset, self.args)
test_loader = DataLoader(test_dataset, batch_size=len(test_dataset), sampler=sampler)
self.args.set_sampler(sampler)
test_data = self.get_tuple_from_data_loader(test_loader)
self.logger.debug("Finished loading CIFAR10 test data")
return test_data
def load_train_dataset(self):
dist_loader_text = "distributed" if self.args.get_distributed() else ""
self.logger.debug(f"Loading '{dist_loader_text}' CIFAR100 train data")
normalize = transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276])
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize
])
train_dataset = datasets.CIFAR100(root=self.get_args().get_data_path(), train=True, download=True,
transform=transform)
sampler = get_sampler(self.test_dataset, self.args)
train_loader = DataLoader(train_dataset, batch_size=len(train_dataset), sampler=sampler)
self.args.set_sampler(sampler)
train_data = self.get_tuple_from_data_loader(train_loader)
dist_loader_text = "distributed" if self.args.get_distributed() else ""
self.logger.debug(f"Finished loading '{dist_loader_text}' CIFAR100 train data")
return train_data
