def test_get(self):
loader = registry.get(self.dataset_hparams, train=True)
self.assertIsInstance(loader, base.DataLoader)
self.assertEqual(len(loader), 1000)
minibatch, labels = next(iter(loader))
self.assertEqual(minibatch.numpy().shape[0], self.dataset_hparams.batch_size)
loader = registry.get(self.dataset_hparams, train=False)
self.assertIsInstance(loader, base.DataLoader)
self.assertEqual(len(loader), 200)
def test_do_not_augment(self):
self.dataset_hparams.do_not_augment = True
loader = registry.get(self.dataset_hparams, train=True)
self.assertIsInstance(loader, base.DataLoader)
minibatch, labels = next(iter(loader))
self.assertEqual(minibatch.numpy().shape[0], self.dataset_hparams.batch_size)
def test_integration(self):
default_hparams = registry.get_default_hparams('cifar_resnet_20')
model = registry.get(default_hparams.model_hparams)
self.assertEqual(self.count_parameters(model), 272474)
cifar10 = datasets.registry.get(default_hparams.dataset_hparams, train=True)
minibatch, labels = next(iter(cifar10))
loss = model.loss_criterion(model(minibatch), labels)
loss.backward()
def test_get_subsample(self):
self.dataset_hparams.transformation_seed = 0
self.dataset_hparams.subsample_fraction = 0.1
loader = registry.get(self.dataset_hparams, train=True)
self.assertIsInstance(loader, base.DataLoader)
self.assertEqual(len(loader), 100)
minibatch, labels = next(iter(loader))
self.assertEqual(minibatch.numpy().shape[0], self.dataset_hparams.batch_size)
def test_get_unsupervised_labels(self):
self.dataset_hparams.transformation_seed = 0
self.dataset_hparams.unsupervised_labels = 'rotation'
loader = registry.get(self.dataset_hparams, train=True)
self.assertIsInstance(loader, base.DataLoader)
self.assertEqual(len(loader), 1000)
minibatch, labels = next(iter(loader))
self.assertEqual(np.max(labels.numpy()), 3)
self.assertEqual(minibatch.numpy().shape[0], self.dataset_hparams.batch_size)
def test_get_random_labels(self):
self.dataset_hparams.transformation_seed = 0
self.dataset_hparams.random_labels_fraction = 1.0
self.dataset_hparams.do_not_augment = True
loader = registry.get(self.dataset_hparams, train=True)
self.assertIsInstance(loader, base.DataLoader)
self.assertEqual(len(loader), 1000)
minibatch, labels = next(iter(loader))
self.assertEqual(minibatch.numpy().shape[0], self.dataset_hparams.batch_size)
def test_integration(self):
default_hparams = registry.get_default_hparams('mnist_lenet_300_100')
model = registry.get(default_hparams.model_hparams)
self.assertEqual(self.count_parameters(model), 266610)
mnist = datasets.registry.get(default_hparams.dataset_hparams,
train=True)
minibatch, labels = next(iter(mnist))
loss = model.loss_criterion(model(minibatch), labels)
loss.backward()
