def test_detokenizing_with_no_tokenizer_fails(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
with self.assertRaises(ValueError):
sm.detokenize("hello world")
def test_detokenizing_with_no_tokenizer_fails(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
with self.assertRaises(ValueError):
sm.detokenize("hello world")
def test_run(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
inp = [{
"src": "hello how are you today"
}, {
"src": "good morning to you ."
}]
results, scores, n_best, time, aligns = sm.run(inp)
self.assertIsInstance(results, list)
for sentence_string in results:
self.assertIsInstance(sentence_string, string_types)
self.assertIsInstance(scores, list)
for elem in scores:
self.assertIsInstance(elem, float)
self.assertIsInstance(aligns, list)
for align_list in aligns:
for align_string in align_list:
if align_string is not None:
self.assertIsInstance(align_string, string_types)
self.assertEqual(len(results), len(scores))
self.assertEqual(len(scores), len(inp) * n_best)
self.assertEqual(len(time), 1)
self.assertIsInstance(time, dict)
self.assertIn("translation", time)
def test_load_model_on_init_and_unload(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
self.assertTrue(sm.loaded)
self.assertIsInstance(sm.translator, Translator)
sm.unload()
self.assertFalse(sm.loaded)
def test_load_model_on_init_and_unload(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
self.assertTrue(sm.loaded)
self.assertIsInstance(sm.translator, Translator)
sm.unload()
self.assertFalse(sm.loaded)
def test_moving_to_gpu_and_back(self):
torch.cuda.set_device(torch.device("cuda", 0))
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
sm.to_gpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 0)
sm.to_cpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
def test_run(self):
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
inp = [{"src": "hello how are you today"},
{"src": "good morning to you ."}]
results, scores, n_best, time = sm.run(inp)
self.assertIsInstance(results, list)
for sentence_string in results:
self.assertIsInstance(sentence_string, string_types)
self.assertIsInstance(scores, list)
for elem in scores:
self.assertIsInstance(elem, float)
self.assertEqual(len(results), len(scores))
self.assertEqual(len(scores), len(inp))
self.assertEqual(n_best, 1)
self.assertEqual(len(time), 1)
self.assertIsInstance(time, dict)
self.assertIn("translation", time)
def test_moving_to_gpu_and_back(self):
torch.cuda.set_device(torch.device("cuda", 0))
model_id = 0
opt = {"models": ["test_model.pt"]}
model_root = TEST_DIR
sm = ServerModel(opt, model_id, model_root=model_root, load=True)
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
sm.to_gpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cuda")
self.assertEqual(p.device.index, 0)
sm.to_cpu()
for p in sm.translator.model.parameters():
self.assertEqual(p.device.type, "cpu")
def test_nbest_init_fails(self):
model_id = 0
opt = {"models": ["test_model.pt"], "n_best": 2}
model_root = TEST_DIR
with self.assertRaises(ValueError):
ServerModel(opt, model_id, model_root=model_root, load=True)