class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
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=MlpSoftmaxDecoder(input_dim=layer_dim, lstm_dim=layer_dim, mlp_hidden_dim=layer_dim,
trg_embed_dim=layer_dim, vocab_size=100,
bridge=CopyBridge(dec_dim=layer_dim, dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator(beam=1)
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()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(self.src_data[0], 0,
forced_trg_ids=self.trg_data[0])
dy.renew_cg()
train_loss = self.model.calc_loss(src=self.src_data[0],
trg=outputs[0].actions,
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(-outputs[0].score, train_loss, places=4)
class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
layer_dim = 512
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
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=MlpSoftmaxDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn_layer=UniLSTMSeqTransducer(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn_layer"),
mlp_layer=MLP(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_rnn_dim=layer_dim,
vocab_size=100,
yaml_path="model.decoder.rnn_layer"),
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
self.model.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()
self.model.initialize_generator()
outputs = self.model.generate_output(self.src_data[0],
0,
BeamSearch(beam_size=1),
forced_trg_ids=self.trg_data[0])
output_score1 = outputs[0].score
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(self.src_data[0],
0,
GreedySearch(),
forced_trg_ids=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
xnmt.events.clear()
ParamManager.init_param_col()
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
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=MlpSoftmaxDecoder(input_dim=layer_dim,
trg_embed_dim=layer_dim,
rnn_layer=UniLSTMSeqTransducer(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_input_dim=layer_dim,
yaml_path="model.decoder.rnn_layer"),
mlp_layer=MLP(
input_dim=layer_dim,
hidden_dim=layer_dim,
decoder_rnn_dim=layer_dim,
vocab_size=100,
yaml_path="model.decoder.rnn_layer"),
bridge=CopyBridge(dec_dim=layer_dim,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator()
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_output(
self.src_data[sent_id],
sent_id,
self.search,
forced_trg_ids=self.trg_data[sent_id])
self.assertItemsEqual(self.trg_data[sent_id], outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context,
vocab_size=100,
bridge=CopyBridge(self.model_context,
dec_layers=1)),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def test_greedy_vs_beam(self):
dy.renew_cg()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score1 = outputs[0].score
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score2 = outputs[0].score
self.assertAlmostEqual(output_score1, output_score2)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context,
vocab_size=100,
bridge=CopyBridge(self.model_context,
dec_layers=1)),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.model.initialize_generator()
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def test_single(self):
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[0],
0,
forced_trg_ids=self.training_corpus.train_trg_data[0])
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_loss(
src=self.training_corpus.train_src_data[0],
trg=outputs[0].actions).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)
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):
xnmt.events.clear()
self.model_context = ModelContext()
self.model_context.dynet_param_collection = PersistentParamCollection(
"some_file", 1)
self.model = DefaultTranslator(
src_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
encoder=BiLSTMSeqTransducer(self.model_context),
attender=MlpAttender(self.model_context),
trg_embedder=SimpleWordEmbedder(self.model_context,
vocab_size=100),
decoder=MlpSoftmaxDecoder(self.model_context, vocab_size=100),
)
self.model.initialize_training_strategy(TrainingStrategy())
self.model.set_train(False)
self.model.initialize_generator()
self.training_corpus = BilingualTrainingCorpus(
train_src="examples/data/head.ja",
train_trg="examples/data/head.en",
dev_src="examples/data/head.ja",
dev_trg="examples/data/head.en")
self.corpus_parser = BilingualCorpusParser(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
training_corpus=self.training_corpus)
def assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate_output(
self.training_corpus.train_src_data[sent_id],
sent_id,
forced_trg_ids=self.training_corpus.train_trg_data[sent_id])
self.assertItemsEqual(self.training_corpus.train_trg_data[sent_id],
outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
class TestGreedyVsBeam(unittest.TestCase):
"""
Test if greedy search produces same output as beam search with beam 1.
"""
def setUp(self):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.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()
self.model.initialize_generator(beam=1)
outputs = self.model.generate_output(self.src_data[0],
0,
forced_trg_ids=self.trg_data[0])
output_score1 = outputs[0].score
dy.renew_cg()
self.model.initialize_generator()
outputs = self.model.generate_output(self.src_data[0],
0,
forced_trg_ids=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):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator(beam=1)
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 assert_forced_decoding(self, sent_id):
dy.renew_cg()
outputs = self.model.generate_output(
self.src_data[sent_id],
sent_id,
forced_trg_ids=self.trg_data[sent_id])
self.assertItemsEqual(self.trg_data[sent_id], outputs[0].actions)
def test_forced_decoding(self):
for i in range(1):
self.assert_forced_decoding(sent_id=i)
class TestFreeDecodingLoss(unittest.TestCase):
def setUp(self):
xnmt.events.clear()
self.exp_global = ExpGlobal(
dynet_param_collection=PersistentParamCollection("some_file", 1))
self.model = DefaultTranslator(
src_reader=PlainTextReader(),
trg_reader=PlainTextReader(),
src_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
encoder=BiLSTMSeqTransducer(exp_global=self.exp_global),
attender=MlpAttender(exp_global=self.exp_global),
trg_embedder=SimpleWordEmbedder(exp_global=self.exp_global,
vocab_size=100),
decoder=MlpSoftmaxDecoder(exp_global=self.exp_global,
vocab_size=100,
bridge=CopyBridge(
exp_global=self.exp_global,
dec_layers=1)),
)
self.model.set_train(False)
self.model.initialize_generator()
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()
self.model.initialize_generator()
outputs = self.model.generate_output(self.src_data[0],
0,
forced_trg_ids=self.trg_data[0])
output_score = outputs[0].score
dy.renew_cg()
train_loss = self.model.calc_loss(
src=self.src_data[0],
trg=outputs[0].actions,
loss_calculator=LossCalculator()).value()
self.assertAlmostEqual(-output_score, train_loss, places=5)
