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cuda=False,
seed=42,
):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
device = th.device('cpu')
if cuda and th.cuda.device_count():
th.cuda.manual_seed(seed)
device = th.device('cuda')
# Create Datasets
train_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='train')
valid_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='validation')
test_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='test')
train_dataset = l2l.data.MetaDataset(train_dataset)
valid_dataset = l2l.data.MetaDataset(valid_dataset)
test_dataset = l2l.data.MetaDataset(test_dataset)
train_transforms = [
l2l.data.transforms.NWays(train_dataset, ways),
l2l.data.transforms.KShots(train_dataset, 2*shots),
l2l.data.transforms.LoadData(train_dataset),
l2l.data.transforms.RemapLabels(train_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
]
train_tasks = l2l.data.TaskDataset(train_dataset,
task_transforms=train_transforms,
num_tasks=20000)
valid_transforms = [
l2l.data.transforms.NWays(valid_dataset, ways),
def setUpClass(cls) -> None:
cls.ds = TestDatasets()
cls.meta_tensor_dataset = MetaDataset(cls.ds.tensor_dataset)
cls.meta_str_dataset = MetaDataset(cls.ds.str_dataset)
cls.meta_alpha_dataset = MetaDataset(cls.ds.alphabet_dataset)
cls.mnist_dataset = MetaDataset(cls.ds.get_mnist())
cls.omniglot_dataset = MetaDataset(cls.ds.get_omniglot())
seed=42,
):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
device = th.device('cpu')
if cuda and th.cuda.device_count():
th.cuda.manual_seed(seed)
device = th.device('cuda')
# Create Datasets
train_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='train')
valid_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='validation')
test_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='test')
train_dataset = l2l.data.MetaDataset(train_dataset)
valid_dataset = l2l.data.MetaDataset(valid_dataset)
test_dataset = l2l.data.MetaDataset(test_dataset)
train_transforms = [
NWays(train_dataset, ways),
KShots(train_dataset, 2*shots),
LoadData(train_dataset),
RemapLabels(train_dataset),
ConsecutiveLabels(train_dataset),
]
train_tasks = l2l.data.TaskDataset(train_dataset,
task_transforms=train_transforms,
num_tasks=20000)
valid_transforms = [
NWays(valid_dataset, ways),
KShots(valid_dataset, 2*shots),
def main(lr=0.005, maml_lr=0.01, iterations=1000, ways=5, shots=1, tps=32, fas=5, device=torch.device("cpu"),
download_location='./data'):
transformations = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
lambda x: x.view(1, 28, 28),
])
mnist_train = l2l.data.MetaDataset(MNIST(download_location,
train=True,
download=True,
transform=transformations))
train_tasks = l2l.data.TaskDataset(mnist_train,
task_transforms=[
l2l.data.transforms.NWays(mnist_train, ways),
l2l.data.transforms.KShots(mnist_train, 2*shots),
l2l.data.transforms.LoadData(mnist_train),
l2l.data.transforms.RemapLabels(mnist_train),
l2l.data.transforms.ConsecutiveLabels(mnist_train),
],
num_tasks=1000)
model = Net(ways)
model.to(device)
def main(lr=0.005, maml_lr=0.01, iterations=1000, ways=5, shots=1, tps=32, fas=5, device=torch.device("cpu"),
download_location="/tmp/mnist", test=False):
transformations = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
lambda x: x.view(1, 1, 28, 28),
])
mnist_train = l2l.data.MetaDataset(MNIST(download_location, train=True, download=True, transform=transformations))
# mnist_test = MNIST(file_location, train=False, download=True, transform=transformations)
train_gen = l2l.data.TaskGenerator(mnist_train, ways=ways, tasks=10000)
# test_gen = l2l.data.TaskGenerator(mnist_test, ways=ways)
model = Net(ways)
model.to(device)
meta_model = l2l.algorithms.MAML(model, lr=maml_lr)
opt = optim.Adam(meta_model.parameters(), lr=lr)
loss_func = nn.NLLLoss(reduction="sum")
tqdm_bar = tqdm(range(iterations))
for iteration in tqdm_bar:
iteration_error = 0.0
iteration_acc = 0.0
for _ in range(tps):
):
random.seed(seed)
np.random.seed(seed)
th.manual_seed(seed)
device = th.device('cpu')
if cuda and th.cuda.device_count():
th.cuda.manual_seed(seed)
device = th.device('cuda')
# Create Datasets
train_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='train')
valid_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='validation')
test_dataset = l2l.vision.datasets.MiniImagenet(root='./data', mode='test')
train_dataset = l2l.data.MetaDataset(train_dataset)
valid_dataset = l2l.data.MetaDataset(valid_dataset)
test_dataset = l2l.data.MetaDataset(test_dataset)
train_transforms = [
NWays(train_dataset, ways),
KShots(train_dataset, 2*shots),
LoadData(train_dataset),
RemapLabels(train_dataset),
ConsecutiveLabels(train_dataset),
]
train_tasks = l2l.data.TaskDataset(train_dataset,
task_transforms=train_transforms,
num_tasks=20000)
valid_transforms = [
NWays(valid_dataset, ways),
KShots(valid_dataset, 2*shots),
LoadData(valid_dataset),