def test_infinite_tasks(self):
data = th.randn(NUM_DATA, X_SHAPE)
labels = th.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = TensorDataset(data, labels)
task_dataset = TaskDataset(
dataset, task_transforms=[LoadData(dataset), random_subset])
self.assertEqual(len(task_dataset), 1)
prev = task_dataset.sample()
for i, task in enumerate(task_dataset):
self.assertFalse(task_equal(prev, task))
prev = task
if i > 4:
break
def test_instanciation(self):
data = th.randn(NUM_DATA, X_SHAPE)
labels = th.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = TensorDataset(data, labels)
task_dataset = TaskDataset(dataset,
task_transforms=[LoadData(dataset)],
num_tasks=NUM_TASKS)
self.assertEqual(len(task_dataset), NUM_TASKS)
def test_load_data(self):
data = torch.randn(NUM_DATA, X_SHAPE)
labels = torch.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = MetaDataset(TensorDataset(data, labels))
task_dataset = TaskDataset(dataset,
task_transforms=[LoadData(dataset)],
num_tasks=NUM_TASKS)
for task in task_dataset:
self.assertTrue(isinstance(task[0], torch.Tensor))
self.assertTrue(isinstance(task[1], torch.Tensor))
def test_filter_labels(self):
data = torch.randn(NUM_DATA, X_SHAPE)
labels = torch.randint(0, Y_SHAPE, (NUM_DATA, ))
chosen_labels = random.sample(list(range(Y_SHAPE)), k=Y_SHAPE // 2)
dataset = MetaDataset(TensorDataset(data, labels))
task_dataset = TaskDataset(dataset,
task_transforms=[
FilterLabels(dataset, chosen_labels),
LoadData(dataset)
],
num_tasks=NUM_TASKS)
for task in task_dataset:
for label in task[1]:
self.assertTrue(label in chosen_labels)
def test_n_ways(self):
data = torch.randn(NUM_DATA, X_SHAPE)
labels = torch.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = MetaDataset(TensorDataset(data, labels))
for ways in range(1, 10):
task_dataset = TaskDataset(
dataset,
task_transforms=[NWays(dataset, n=ways),
LoadData(dataset)],
num_tasks=NUM_TASKS)
for task in task_dataset:
bins = task[1].bincount()
num_classes = len(bins) - (bins == 0).sum()
self.assertEqual(num_classes, ways)
def test_remap_labels(self):
data = torch.randn(NUM_DATA, X_SHAPE)
labels = torch.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = MetaDataset(TensorDataset(data, labels))
for ways in range(1, 5):
task_dataset = TaskDataset(dataset,
task_transforms=[
NWays(dataset, ways),
LoadData(dataset),
RemapLabels(dataset)
],
num_tasks=NUM_TASKS)
for task in task_dataset:
for label in range(ways):
self.assertTrue(label in task[1])
def test_dataloader(self):
data = th.randn(NUM_DATA, X_SHAPE)
labels = th.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = TensorDataset(data, labels)
task_dataset = TaskDataset(
dataset,
task_transforms=[LoadData(dataset), random_subset],
num_tasks=NUM_TASKS)
task_loader = DataLoader(task_dataset,
shuffle=True,
batch_size=META_BSZ,
num_workers=WORKERS,
drop_last=True)
for task_batch in task_loader:
self.assertEqual(task_batch[0].shape, (META_BSZ, X_SHAPE))
self.assertEqual(task_batch[1].shape, (META_BSZ, 1))
def test_task_transforms(self):
data = th.randn(NUM_DATA, X_SHAPE)
labels = th.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = TensorDataset(data, labels)
task_dataset = TaskDataset(
dataset,
task_transforms=[LoadData(dataset), random_subset],
num_tasks=NUM_TASKS)
for task in task_dataset:
# Tests transforms on the task_description
self.assertEqual(len(task[0]), SUBSET_SIZE)
self.assertEqual(len(task[1]), SUBSET_SIZE)
# Tests transforms on the data
self.assertEqual(task[0].size(1), X_SHAPE)
self.assertLessEqual(task[1].max(), Y_SHAPE - 1)
self.assertGreaterEqual(task[1].max(), 0)
def test_k_shots(self):
data = torch.randn(NUM_DATA, X_SHAPE)
labels = torch.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = MetaDataset(TensorDataset(data, labels))
for replacement in [False, True]:
for shots in range(1, 10):
task_dataset = TaskDataset(dataset,
task_transforms=[
KShots(dataset,
k=shots,
replacement=replacement),
LoadData(dataset)
],
num_tasks=NUM_TASKS)
for task in task_dataset:
bins = task[1].bincount()
correct = (bins == shots).sum()
self.assertEqual(correct, Y_SHAPE)
def test_task_caching(self):
data = th.randn(NUM_DATA, X_SHAPE)
labels = th.randint(0, Y_SHAPE, (NUM_DATA, ))
dataset = TensorDataset(data, labels)
task_dataset = TaskDataset(dataset,
task_transforms=[LoadData(dataset)],
num_tasks=NUM_TASKS)
tasks = []
for i, task in enumerate(task_dataset, 1):
tasks.append(task)
self.assertEqual(i, NUM_TASKS)
for ref, task in zip(tasks, task_dataset):
self.assertTrue(task_equal(ref, task))
for i in range(NUM_TASKS):
ref = tasks[i]
task = task_dataset[i]
self.assertTrue(task_equal(ref, task))
def build_taskset(datasets, k=4):
"""
Function to build a learn2learn TaskDataset
datasest : list -- a list of the different datasets to create training tasks from
"""
MetaDS = l2l.data.UnionMetaDataset(
[l2l.data.MetaDataset(MicroDataset(t)) for t in datasets])
dataset = l2l.data.MetaDataset(MetaDS)
transforms = [
l2l.data.transforms.NWays(dataset, n=2),
l2l.data.transforms.KShots(dataset, k=k, replacement=True),
l2l.data.transforms.LoadData(dataset)
]
return (TaskDataset(dataset,
transforms,
num_tasks=(len(datasets) * 2 *
(len(datasets) * 2 - 1)),
task_collate=collate))