def setUp(self):
super(SmallNORBTestLighting, self).setUp(
data_wrapper=smallnorb.SmallNORBData("label_lighting"),
num_classes=6,
expected_num_samples=dict(
train=24300,
val=12150,
trainval=36450,
test=12150,
),
required_tensors_shapes={
"image": (96, 96, 3),
"label": (),
},
tfds_label_key_map="label_lighting")
def setUp(self):
super(SmallNORBTestAzimuth, self).setUp(
data_wrapper=smallnorb.SmallNORBData("label_azimuth"),
num_classes=18,
expected_num_samples=dict(
train=24300,
val=12150,
trainval=36450,
test=12150,
train800val200=1000,
train800=800,
val200=200,
),
required_tensors_shapes={
"image": (96, 96, 3),
"label": (),
},
tfds_label_key_map="label_azimuth")
def test_incorrect_classes(self):
with self.assertRaisesWithLiteralMatch(
ValueError,
"invalid_attribute is not a valid attribute to predict."):
smallnorb.SmallNORBData("invalid_attribute")