def testGetClass(self):
mp = model_registry.GetClass('test.DummyModel')
self.assertEqual('Train', mp.Train().name)
self.assertEqual('Dev', mp.Dev().name)
self.assertEqual('Test', mp.Test().name)
self.assertIsNotNone(mp.Task())
self.assertIsNotNone(mp.Model())
with self.assertRaises(LookupError):
# Not yet registered.
model_registry.GetClass('something.does.not.exist')
def _testOneModelParams(self, name):
cls = model_registry.GetClass(name)
p = cls.Model()
self.assertTrue(issubclass(p.cls, base_model.BaseModel))
self.assertTrue(p.model is not None)
for dataset in ('Train', 'Dev', 'Test'):
input_p = cls.GetDatasetParams(dataset)
if issubclass(p.cls, base_model.SingleTaskModel):
self.assertTrue(
issubclass(input_p.cls, base_input_generator.BaseInputGenerator),
'Error in %s' % dataset)
if (dataset != 'Train') and issubclass(
input_p.cls, base_input_generator.BaseSequenceInputGenerator) and (
input_p.num_samples != 0):
self.assertEquals(
input_p.num_batcher_threads, 1,
'num_batcher_threads too large in %s. Decoder '
'or eval runs over this set might not span '
'exactly one epoch.' % dataset)
else:
self.assertTrue(issubclass(p.cls, base_model.MultiTaskModel))
for _, v in input_p.IterParams():
self.assertTrue(
issubclass(v.cls, base_input_generator.BaseInputGenerator),
'Error in %s' % dataset)
def testGetModelParamsClass(self):
cls = model_registry.GetClass('test.DummyModel')
self.assertTrue(issubclass(cls, base_model_params.SingleTaskModelParams))