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l2l.data.transforms.NWays(valid_dataset, ways),
l2l.data.transforms.KShots(valid_dataset, 2*shots),
l2l.data.transforms.LoadData(valid_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
l2l.data.transforms.RemapLabels(valid_dataset),
]
valid_tasks = l2l.data.TaskDataset(valid_dataset,
task_transforms=valid_transforms,
num_tasks=600)
test_transforms = [
l2l.data.transforms.NWays(test_dataset, ways),
l2l.data.transforms.KShots(test_dataset, 2*shots),
l2l.data.transforms.LoadData(test_dataset),
l2l.data.transforms.RemapLabels(test_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
]
test_tasks = l2l.data.TaskDataset(test_dataset,
task_transforms=test_transforms,
num_tasks=600)
# Create model
model = l2l.vision.models.MiniImagenetCNN(ways)
model.to(device)
maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
opt = optim.Adam(maml.parameters(), meta_lr)
loss = nn.CrossEntropyLoss(size_average=True, reduction='mean')
for iteration in range(num_iterations):
opt.zero_grad()
meta_train_error = 0.0
meta_train_accuracy = 0.0
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),
l2l.data.transforms.KShots(valid_dataset, 2*shots),
l2l.data.transforms.LoadData(valid_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
l2l.data.transforms.RemapLabels(valid_dataset),
]
valid_tasks = l2l.data.TaskDataset(valid_dataset,
task_transforms=valid_transforms,
num_tasks=600)
test_transforms = [
l2l.data.transforms.NWays(test_dataset, ways),
l2l.data.transforms.KShots(test_dataset, 2*shots),
l2l.data.transforms.LoadData(test_dataset),
l2l.data.transforms.RemapLabels(test_dataset),
l2l.data.transforms.ConsecutiveLabels(train_dataset),
]
test_tasks = l2l.data.TaskDataset(test_dataset,
task_transforms=test_transforms,
num_tasks=600)
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)
meta_model = l2l.algorithms.MAML(model, lr=maml_lr)
opt = optim.Adam(meta_model.parameters(), lr=lr)
loss_func = nn.NLLLoss(reduction='mean')
for iteration in range(iterations):
iteration_error = 0.0
iteration_acc = 0.0
for _ in range(tps):
learner = meta_model.clone()
train_task = train_tasks.sample()
data, labels = train_task
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),
ConsecutiveLabels(train_dataset),
RemapLabels(valid_dataset),
]
valid_tasks = l2l.data.TaskDataset(valid_dataset,
task_transforms=valid_transforms,
num_tasks=600)
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),
ConsecutiveLabels(train_dataset),
RemapLabels(valid_dataset),
]
valid_tasks = l2l.data.TaskDataset(valid_dataset,
task_transforms=valid_transforms,
num_tasks=600)
test_transforms = [
NWays(test_dataset, ways),
KShots(test_dataset, 2*shots),
LoadData(test_dataset),
RemapLabels(test_dataset),
ConsecutiveLabels(train_dataset),
]
test_tasks = l2l.data.TaskDataset(test_dataset,
task_transforms=test_transforms,
num_tasks=600)
l2l.data.transforms.LoadData(dataset),
l2l.data.transforms.RemapLabels(dataset),
l2l.data.transforms.ConsecutiveLabels(dataset),
l2l.vision.transforms.RandomClassRotation(dataset, [0.0, 90.0, 180.0, 270.0])
]
valid_tasks = l2l.data.TaskDataset(dataset,
task_transforms=valid_transforms,
num_tasks=1024)
test_transforms = [
l2l.data.transforms.FilterLabels(dataset, classes[1200:]),
l2l.data.transforms.NWays(dataset, ways),
l2l.data.transforms.KShots(dataset, 2*shots),
l2l.data.transforms.LoadData(dataset),
l2l.data.transforms.RemapLabels(dataset),
l2l.data.transforms.ConsecutiveLabels(dataset),
l2l.vision.transforms.RandomClassRotation(dataset, [0.0, 90.0, 180.0, 270.0])
]
test_tasks = l2l.data.TaskDataset(dataset,
task_transforms=test_transforms,
num_tasks=1024)
# Create model
model = l2l.vision.models.OmniglotFC(28 ** 2, ways)
model.to(device)
maml = l2l.algorithms.MAML(model, lr=fast_lr, first_order=False)
opt = optim.Adam(maml.parameters(), meta_lr)
loss = nn.CrossEntropyLoss(reduction='mean')
for iteration in range(num_iterations):
opt.zero_grad()
meta_train_error = 0.0