def test_sit_multi_dataset_merge(self):
split_mapping = [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
mnist_train = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
train=True, download=True)
mnist_test = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
train=False, download=True)
train_part1 = make_nc_transformation_subset(
mnist_train, None, None, range(5))
train_part2 = make_nc_transformation_subset(
mnist_train, None, None, range(5, 10))
train_part2 = AvalancheSubset(
train_part2, class_mapping=split_mapping)
test_part1 = make_nc_transformation_subset(
mnist_test, None, None, range(5))
test_part2 = make_nc_transformation_subset(
mnist_test, None, None, range(5, 10))
test_part2 = AvalancheSubset(test_part2,
class_mapping=split_mapping)
my_nc_scenario = nc_scenario(
[train_part1, train_part2], [test_part1, test_part2], 5,
task_labels=False, shuffle=True, seed=1234)
self.assertEqual(5, my_nc_scenario.n_experiences)
self.assertEqual(10, my_nc_scenario.n_classes)
for batch_id in range(5):
self.assertEqual(
2, len(my_nc_scenario.classes_in_experience[batch_id]))
all_classes = set()
for batch_id in range(5):
all_classes.update(my_nc_scenario.classes_in_experience[batch_id])
self.assertEqual(10, len(all_classes))
def test_sit_multi_dataset_merge(self):
split_mapping = [0, 1, 2, 3, 4, 0, 1, 2, 3, 4]
mnist_train = MNIST(
root=default_dataset_location("mnist"),
train=True,
download=True,
)
mnist_test = MNIST(
root=default_dataset_location("mnist"),
train=False,
download=True,
)
train_part1 = make_nc_transformation_subset(
mnist_train, None, None, range(5)
)
train_part2 = make_nc_transformation_subset(
mnist_train, None, None, range(5, 10)
)
train_part2 = AvalancheSubset(train_part2, class_mapping=split_mapping)
test_part1 = make_nc_transformation_subset(
mnist_test, None, None, range(5)
)
test_part2 = make_nc_transformation_subset(
mnist_test, None, None, range(5, 10)
)
test_part2 = AvalancheSubset(test_part2, class_mapping=split_mapping)
my_nc_benchmark = nc_benchmark(
[train_part1, train_part2],
[test_part1, test_part2],
5,
task_labels=False,
shuffle=True,
seed=1234,
)
self.assertEqual(5, my_nc_benchmark.n_experiences)
self.assertEqual(10, my_nc_benchmark.n_classes)
for batch_id in range(5):
self.assertEqual(
2, len(my_nc_benchmark.classes_in_experience["train"][batch_id])
)
all_classes = set()
for batch_id in range(5):
all_classes.update(
my_nc_benchmark.classes_in_experience["train"][batch_id]
)
self.assertEqual(10, len(all_classes))
def test_sit_multi_dataset_one_batch_per_set(self):
split_mapping = [0, 1, 2, 0, 1, 2, 3, 4, 5, 6]
mnist_train = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=True,
download=True,
)
mnist_test = MNIST(
root=expanduser("~") + "/.avalanche/data/mnist/",
train=False,
download=True,
)
train_part1 = make_nc_transformation_subset(mnist_train, None, None,
range(3))
train_part2 = make_nc_transformation_subset(mnist_train, None, None,
range(3, 10))
train_part2 = AvalancheSubset(train_part2, class_mapping=split_mapping)
test_part1 = make_nc_transformation_subset(mnist_test, None, None,
range(3))
test_part2 = make_nc_transformation_subset(mnist_test, None, None,
range(3, 10))
test_part2 = AvalancheSubset(test_part2, class_mapping=split_mapping)
my_nc_benchmark = nc_benchmark(
[train_part1, train_part2],
[test_part1, test_part2],
2,
task_labels=False,
shuffle=True,
seed=1234,
one_dataset_per_exp=True,
)
self.assertEqual(2, my_nc_benchmark.n_experiences)
self.assertEqual(10, my_nc_benchmark.n_classes)
all_classes = set()
for batch_id in range(2):
all_classes.update(
my_nc_benchmark.classes_in_experience["train"][batch_id])
self.assertEqual(10, len(all_classes))
self.assertTrue(
(my_nc_benchmark.classes_in_experience["train"][0] == {0, 1, 2}
and my_nc_benchmark.classes_in_experience["train"][1] == set(
range(3, 10))) or
(my_nc_benchmark.classes_in_experience["train"][0] == set(
range(3, 10)) and
my_nc_benchmark.classes_in_experience["train"][1] == {0, 1, 2}))
def test_mt_multi_dataset_one_task_per_set(self):
split_mapping = [0, 1, 2, 0, 1, 2, 3, 4, 5, 6]
mnist_train = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
train=True,
download=True)
mnist_test = MNIST(root=expanduser("~") + "/.avalanche/data/mnist/",
train=False,
download=True)
train_part1 = make_nc_transformation_subset(mnist_train, None, None,
range(3))
train_part2 = make_nc_transformation_subset(mnist_train, None, None,
range(3, 10))
train_part2 = AvalancheSubset(train_part2, class_mapping=split_mapping)
test_part1 = make_nc_transformation_subset(mnist_test, None, None,
range(3))
test_part2 = make_nc_transformation_subset(mnist_test, None, None,
range(3, 10))
test_part2 = AvalancheSubset(test_part2, class_mapping=split_mapping)
my_nc_benchmark = nc_benchmark([train_part1, train_part2],
[test_part1, test_part2],
2,
task_labels=True,
seed=1234,
class_ids_from_zero_in_each_exp=True,
one_dataset_per_exp=True)
self.assertEqual(2, my_nc_benchmark.n_experiences)
self.assertEqual(10, my_nc_benchmark.n_classes)
self.assertEqual(2, len(my_nc_benchmark.train_stream))
self.assertEqual(2, len(my_nc_benchmark.test_stream))
exp_classes_train = []
exp_classes_test = []
all_classes_train = set()
all_classes_test = set()
task_info: NCExperience
for task_id, task_info in enumerate(my_nc_benchmark.train_stream):
self.assertLessEqual(task_id, 1)
all_classes_train.update(
my_nc_benchmark.classes_in_experience['train'][task_id])
exp_classes_train.append(task_info.classes_in_this_experience)
self.assertEqual(7, len(all_classes_train))
for task_id, task_info in enumerate(my_nc_benchmark.test_stream):
self.assertLessEqual(task_id, 1)
all_classes_test.update(
my_nc_benchmark.classes_in_experience['test'][task_id])
exp_classes_test.append(task_info.classes_in_this_experience)
self.assertEqual(7, len(all_classes_test))
self.assertTrue(
(my_nc_benchmark.classes_in_experience['train'][0] == {0, 1, 2}
and my_nc_benchmark.classes_in_experience['train'][1] == set(
range(0, 7))) or
(my_nc_benchmark.classes_in_experience['train'][0] == set(
range(0, 7)) and
my_nc_benchmark.classes_in_experience['train'][1] == {0, 1, 2}))
exp_classes_ref1 = [list(range(3)), list(range(7))]
exp_classes_ref2 = [list(range(7)), list(range(3))]
self.assertTrue(exp_classes_train == exp_classes_ref1
or exp_classes_train == exp_classes_ref2)
if exp_classes_train == exp_classes_ref1:
self.assertTrue(exp_classes_test == exp_classes_ref1)
else:
self.assertTrue(exp_classes_test == exp_classes_ref2)
