Module nn.tests.test_loader

Classes

class TestDataLoaders (methodName='runTest')

A class whose instances are single test cases.

By default, the test code itself should be placed in a method named 'runTest'.

If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.

Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.

If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.

When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

Expand source code

class TestDataLoaders(unittest.TestCase):
    def setUp(self):
        # test with more like a 3x3 image
        self.x_train = torch.tensor(
            [
                [[1, 2, 1], [1, 2, 1], [1, 2, 1]],
                [[3, 4, 1], [1, 2, 1], [1, 2, 1]],
                [[5, 6, 1], [1, 2, 1], [1, 2, 1]],
                [[7, 8, 1], [1, 2, 1], [1, 2, 1]],
            ]
        )
        self.y_train = torch.tensor([0, 1, 0, 1])
        self.batch_size = 2
        self.dls = DataLoaders(self.x_train, self.y_train, self.batch_size)

    def test_data_loader_length(self):
        self.assertEqual(len(self.dls), len(self.x_train) // self.batch_size)

    def test_data_loader_iteration(self):
        batches = list(self.dls)
        self.assertEqual(len(batches), len(self.x_train) // self.batch_size)
        for x_batch, y_batch in batches:
            self.assertEqual(len(x_batch), self.batch_size)
            self.assertEqual(len(y_batch), self.batch_size)

    def test_validation_data(self):
        x_val = torch.tensor([[9, 10], [11, 12]])
        y_val = torch.tensor([0, 1])

        dls = DataLoaders(
            self.x_train, self.y_train, self.batch_size, x_val=x_val, y_val=y_val
        )
        vdls = dls.get_validation_data()
        self.assertIsNotNone(vdls)

Ancestors

• unittest.case.TestCase

Methods

def setUp(self)

Hook method for setting up the test fixture before exercising it.

Expand source code

def setUp(self):
    # test with more like a 3x3 image
    self.x_train = torch.tensor(
        [
            [[1, 2, 1], [1, 2, 1], [1, 2, 1]],
            [[3, 4, 1], [1, 2, 1], [1, 2, 1]],
            [[5, 6, 1], [1, 2, 1], [1, 2, 1]],
            [[7, 8, 1], [1, 2, 1], [1, 2, 1]],
        ]
    )
    self.y_train = torch.tensor([0, 1, 0, 1])
    self.batch_size = 2
    self.dls = DataLoaders(self.x_train, self.y_train, self.batch_size)

def test_data_loader_iteration(self)

Expand source code

def test_data_loader_iteration(self):
    batches = list(self.dls)
    self.assertEqual(len(batches), len(self.x_train) // self.batch_size)
    for x_batch, y_batch in batches:
        self.assertEqual(len(x_batch), self.batch_size)
        self.assertEqual(len(y_batch), self.batch_size)

def test_data_loader_length(self)

Expand source code

def test_data_loader_length(self):
    self.assertEqual(len(self.dls), len(self.x_train) // self.batch_size)

def test_validation_data(self)

Expand source code

def test_validation_data(self):
    x_val = torch.tensor([[9, 10], [11, 12]])
    y_val = torch.tensor([0, 1])

    dls = DataLoaders(
        self.x_train, self.y_train, self.batch_size, x_val=x_val, y_val=y_val
    )
    vdls = dls.get_validation_data()
    self.assertIsNotNone(vdls)