class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
layer_dim = 512
events.clear()
ParamManager.init_param_col()
src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
trg_vocab = Vocab(vocab_file="examples/data/head.en.vocab")
self.model = DefaultTranslator(
src_reader=PlainTextReader(vocab=src_vocab),
trg_reader=PlainTextReader(vocab=trg_vocab),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
event_trigger.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_greedy_vs_beam(self):
dy.renew_cg()
outputs = self.model.generate(
batchers.mark_as_batch([self.src_data[0]]),
BeamSearch(beam_size=1),
forced_trg_ids=batchers.mark_as_batch([self.trg_data[0]]))
output_score1 = outputs[0].score
dy.renew_cg()
outputs = self.model.generate(
batchers.mark_as_batch([self.src_data[0]]),
GreedySearch(),
forced_trg_ids=batchers.mark_as_batch([self.trg_data[0]]))
output_score2 = outputs[0].score
self.assertAlmostEqual(output_score1, output_score2)
class TestForcedDecodingOutputs(unittest.TestCase):
def assertItemsEqual(self, l1, l2):
self.assertEqual(len(l1), len(l2))
for i in range(len(l1)):
self.assertEqual(l1[i], l2[i])
def setUp(self):
layer_dim = 512
events.clear()
ParamManager.init_param_col()
src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
trg_vocab = Vocab(vocab_file="examples/data/head.en.vocab")
self.model = DefaultTranslator(
src_reader=PlainTextReader(vocab=src_vocab),
trg_reader=PlainTextReader(vocab=trg_vocab),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
event_trigger.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
self.search = GreedySearch()
def assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate(
batchers.mark_as_batch([self.src_data[sent_id]]),
self.search,
forced_trg_ids=batchers.mark_as_batch([self.trg_data[sent_id]]))
self.assertItemsEqual(self.trg_data[sent_id].words, outputs[0].words)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
layer_dim = 512
events.clear()
ParamManager.init_param_col()
src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
trg_vocab = Vocab(vocab_file="examples/data/head.en.vocab")
self.model = DefaultTranslator(
src_reader=PlainTextReader(vocab=src_vocab),
trg_reader=PlainTextReader(vocab=trg_vocab),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
trg_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn"),
transform=NonLinear(input_dim=layer_dim * 2,
output_dim=layer_dim),
scorer=Softmax(input_dim=layer_dim, vocab_size=100),
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
event_trigger.set_train(False)
self.src_data = list(
self.model.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.model.trg_reader.read_sents("examples/data/head.en"))
def test_single(self):
dy.renew_cg()
outputs = self.model.generate(
batchers.mark_as_batch([self.src_data[0]]),
GreedySearch(),
forced_trg_ids=batchers.mark_as_batch([self.trg_data[0]]))
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_nll(src=self.src_data[0],
trg=outputs[0]).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)