class TestSimultaneousTranslation(unittest.TestCase):
def setUp(self):
# Seeding
numpy.random.seed(2)
random.seed(2)
layer_dim = 32
xnmt.events.clear()
ParamManager.init_param_col()
self.src_reader = PlainTextReader(vocab=Vocab(vocab_file="examples/data/head.ja.vocab"))
self.trg_reader = PlainTextReader(vocab=Vocab(vocab_file="examples/data/head.en.vocab"))
self.layer_dim = layer_dim
self.src_data = list(self.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(self.trg_reader.read_sents("examples/data/head.en"))
self.input_vocab_size = len(self.src_reader.vocab.i2w)
self.output_vocab_size = len(self.trg_reader.vocab.i2w)
self.loss_calculator = loss_calculators.MLELoss()
self.model = SimultaneousTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=self.input_vocab_size),
encoder=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(input_dim=layer_dim,
rnn=UniLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim,
decoder_input_dim=layer_dim, yaml_path="decoder"),
transform=AuxNonLinear(input_dim=layer_dim, output_dim=layer_dim,
aux_input_dim=layer_dim),
scorer=Softmax(vocab_size=self.output_vocab_size, input_dim=layer_dim),
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=self.output_vocab_size),
bridge=NoBridge(dec_dim=layer_dim, dec_layers=1)),
policy_train_oracle=False,
policy_test_oracle=False,
read_before_write=True,
)
event_trigger.set_train(True)
my_batcher = batchers.TrgBatcher(batch_size=3)
self.src, self.trg = my_batcher.pack(self.src_data, self.trg_data)
dy.renew_cg(immediate_compute=True, check_validity=True)
def test_train_nll(self):
event_trigger.set_train(True)
mle_loss = loss_calculators.MLELoss()
mle_loss.calc_loss(self.model, self.src[0], self.trg[0])
event_trigger.set_train(False)
self.model.generate(batchers.mark_as_batch([self.src_data[0]]), GreedySearch())
def test_simult_beam(self):
event_trigger.set_train(False)
mle_loss = loss_calculators.MLELoss()
mle_loss.calc_loss(self.model, self.src[0], self.trg[0])
self.model.generate(batchers.mark_as_batch([self.src_data[0]]), BeamSearch(beam_size=2))
class PretrainedSimpleWordEmbedderSanityTest(unittest.TestCase):
def setUp(self):
events.clear()
self.input_reader = PlainTextReader(vocab=Vocab(vocab_file="examples/data/head.ja.vocab"))
list(self.input_reader.read_sents('examples/data/head.ja'))
ParamManager.init_param_col()
def test_load(self):
"""
Checks that the embeddings can be loaded, have the right dimension, and that one line matches.
"""
embedder = LookupEmbedder(init_fastext='examples/data/wiki.ja.vec.small', emb_dim=300, vocab=self.input_reader.vocab)
# self.assertEqual(embedder.embeddings.shape()[::-1], (self.input_reader.vocab_size(), 300))
with open('examples/data/wiki.ja.vec.small', encoding='utf-8') as vecfile:
test_line = next(islice(vecfile, 9, None)).split() # Select the vector for '日'
test_word = test_line[0]
test_id = self.input_reader.vocab.w2i[test_word]
test_emb = test_line[1:]
self.assertTrue(np.allclose(embedder.embeddings.batch([test_id]).npvalue().tolist(),
np.array(test_emb, dtype=float).tolist(), rtol=1e-5))
class TestTruncatedBatchTraining(unittest.TestCase):
def setUp(self):
xnmt.events.clear()
ParamManager.init_param_col()
self.src_reader = PlainTextReader(vocab=Vocab(
vocab_file="examples/data/head.ja.vocab"))
self.trg_reader = PlainTextReader(vocab=Vocab(
vocab_file="examples/data/head.en.vocab"))
self.src_data = list(
self.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.trg_reader.read_sents("examples/data/head.en"))
def assert_single_loss_equals_batch_loss(self,
model,
pad_src_to_multiple=1):
"""
Tests whether single loss equals batch loss.
Truncating src / trg sents to same length so no masking is necessary
"""
batch_size = 5
src_sents = self.src_data[:batch_size]
src_min = min([x.sent_len() for x in src_sents])
src_sents_trunc = [s.words[:src_min] for s in src_sents]
for single_sent in src_sents_trunc:
single_sent[src_min - 1] = Vocab.ES
while len(single_sent) % pad_src_to_multiple != 0:
single_sent.append(Vocab.ES)
trg_sents = self.trg_data[:batch_size]
trg_min = min([x.sent_len() for x in trg_sents])
trg_sents_trunc = [s.words[:trg_min] for s in trg_sents]
for single_sent in trg_sents_trunc:
single_sent[trg_min - 1] = Vocab.ES
src_sents_trunc = [
sent.SimpleSentence(words=s) for s in src_sents_trunc
]
trg_sents_trunc = [
sent.SimpleSentence(words=s) for s in trg_sents_trunc
]
single_loss = 0.0
for sent_id in range(batch_size):
dy.renew_cg()
train_loss, _ = MLELoss().calc_loss(
model=model,
src=src_sents_trunc[sent_id],
trg=trg_sents_trunc[sent_id]).compute()
single_loss += train_loss.value()
dy.renew_cg()
batched_loss, _ = MLELoss().calc_loss(
model=model,
src=mark_as_batch(src_sents_trunc),
trg=mark_as_batch(trg_sents_trunc)).compute()
self.assertAlmostEqual(single_loss,
np.sum(batched_loss.value()),
places=4)
def test_loss_model1(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(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),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model)
def test_loss_model2(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
layers=3),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model, pad_src_to_multiple=4)
def test_loss_model3(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
layers=3),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model)
def test_loss_model4(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=DotAttender(),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model)
class TestBatchTraining(unittest.TestCase):
def setUp(self):
xnmt.events.clear()
ParamManager.init_param_col()
self.src_reader = PlainTextReader(vocab=Vocab(
vocab_file="examples/data/head.ja.vocab"))
self.trg_reader = PlainTextReader(vocab=Vocab(
vocab_file="examples/data/head.en.vocab"))
self.src_data = list(
self.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.trg_reader.read_sents("examples/data/head.en"))
def assert_single_loss_equals_batch_loss(self,
model,
pad_src_to_multiple=1):
"""
Tests whether single loss equals batch loss.
Here we don't truncate the target side and use masking.
"""
batch_size = 5
src_sents = self.src_data[:batch_size]
src_min = min([x.sent_len() for x in src_sents])
src_sents_trunc = [s.words[:src_min] for s in src_sents]
for single_sent in src_sents_trunc:
single_sent[src_min - 1] = Vocab.ES
while len(single_sent) % pad_src_to_multiple != 0:
single_sent.append(Vocab.ES)
trg_sents = sorted(self.trg_data[:batch_size],
key=lambda x: x.sent_len(),
reverse=True)
trg_max = max([x.sent_len() for x in trg_sents])
np_arr = np.zeros([batch_size, trg_max])
for i in range(batch_size):
for j in range(trg_sents[i].sent_len(), trg_max):
np_arr[i, j] = 1.0
trg_masks = Mask(np_arr)
trg_sents_padded = [[w for w in s] + [Vocab.ES] *
(trg_max - s.sent_len()) for s in trg_sents]
src_sents_trunc = [
sent.SimpleSentence(words=s) for s in src_sents_trunc
]
trg_sents_padded = [
sent.SimpleSentence(words=s) for s in trg_sents_padded
]
single_loss = 0.0
for sent_id in range(batch_size):
dy.renew_cg()
train_loss, _ = MLELoss().calc_loss(
model=model,
src=src_sents_trunc[sent_id],
trg=trg_sents[sent_id]).compute()
single_loss += train_loss.value()
dy.renew_cg()
batched_loss, _ = MLELoss().calc_loss(
model=model,
src=mark_as_batch(src_sents_trunc),
trg=mark_as_batch(trg_sents_padded, trg_masks)).compute()
self.assertAlmostEqual(single_loss,
np.sum(batched_loss.value()),
places=4)
def test_loss_model1(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(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),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model)
def test_loss_model2(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(layers=3,
input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model, pad_src_to_multiple=4)
def test_loss_model3(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(layers=3,
input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=LookupEmbedder(emb_dim=layer_dim, vocab_size=100),
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.assert_single_loss_equals_batch_loss(model)
class TestEncoder(unittest.TestCase):
def setUp(self):
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.src_reader = PlainTextReader(vocab=src_vocab)
self.trg_reader = PlainTextReader(vocab=trg_vocab)
self.src_data = list(
self.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(
self.trg_reader.read_sents("examples/data/head.en"))
def assert_in_out_len_equal(self, model):
dy.renew_cg()
event_trigger.set_train(True)
src = self.src_data[0]
event_trigger.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder.transduce(embeddings)
self.assertEqual(len(embeddings), len(encodings))
def test_bi_lstm_encoder_len(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
layers=3),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
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)),
)
self.assert_in_out_len_equal(model)
def test_uni_lstm_encoder_len(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=UniLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
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)),
)
self.assert_in_out_len_equal(model)
# TODO: Update this to the new residual LSTM transducer framework
# def test_res_lstm_encoder_len(self):
# layer_dim = 512
# model = DefaultTranslator(
# src_reader=self.src_reader,
# trg_reader=self.trg_reader,
# src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
# encoder=ResidualLSTMSeqTransducer(input_dim=layer_dim, hidden_dim=layer_dim, layers=3),
# attender=MlpAttender(input_dim=layer_dim, state_dim=layer_dim, hidden_dim=layer_dim),
# decoder=AutoRegressiveDecoder(input_dim=layer_dim,
# embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
# 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)),
# )
# self.assert_in_out_len_equal(model)
def test_py_lstm_encoder_len(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
layers=3),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
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(True)
for sent_i in range(10):
dy.renew_cg()
src = self.src_data[sent_i].create_padded_sent(
4 - (self.src_data[sent_i].sent_len() % 4))
event_trigger.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder.transduce(embeddings)
self.assertEqual(int(math.ceil(len(embeddings) / float(4))),
len(encodings))
def test_py_lstm_mask(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
layers=1),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
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)),
)
batcher = batchers.TrgBatcher(batch_size=3)
train_src, _ = \
batcher.pack(self.src_data, self.trg_data)
event_trigger.set_train(True)
for sent_i in range(3):
dy.renew_cg()
src = train_src[sent_i]
event_trigger.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder.transduce(embeddings)
if train_src[sent_i].mask is None:
assert encodings.mask is None
else:
np.testing.assert_array_almost_equal(
train_src[sent_i].mask.np_arr, encodings.mask.np_arr)
def test_multihead_attention_encoder_len(self):
layer_dim = 512
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
encoder=MultiHeadAttentionSeqTransducer(input_dim=layer_dim),
attender=MlpAttender(input_dim=layer_dim,
state_dim=layer_dim,
hidden_dim=layer_dim),
decoder=AutoRegressiveDecoder(
input_dim=layer_dim,
embedder=SimpleWordEmbedder(emb_dim=layer_dim, vocab_size=100),
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)),
)
self.assert_in_out_len_equal(model)
class TestEmbedder(unittest.TestCase):
def setUp(self):
# Seeding
np.random.seed(2)
random.seed(2)
layer_dim = 4
xnmt.events.clear()
ParamManager.init_param_col()
self.src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
self.src_char_vocab = CharVocab(vocab_file="examples/data/head.ja.vocab")
self.ngram_vocab = Vocab(vocab_file="examples/data/head.ngramcount.ja")
self.trg_vocab = Vocab(vocab_file="examples/data/head.en.vocab")
self.src_reader = CharFromWordTextReader(vocab= self.src_vocab, char_vocab= self.src_char_vocab)
self.trg_reader = PlainTextReader(vocab=self.trg_vocab)
self.layer_dim = layer_dim
self.src_data = list(self.src_reader.read_sents("examples/data/head.ja"))
self.trg_data = list(self.trg_reader.read_sents("examples/data/head.en"))
self.src, self.trg = batchers.TrgBatcher(batch_size=3).pack(self.src_data, self.trg_data)
dy.renew_cg(immediate_compute=True, check_validity=True)
def test_lookup_composer(self):
embedder = LookupEmbedder(emb_dim=self.layer_dim, vocab_size=100)
embedder.embed_sent(self.src[1])
embedder.embed(self.src[1][1][1])
def test_sum_composer(self):
embedder = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=SumComposer(),
char_vocab=self.src_char_vocab)
embedder.embed_sent(self.src[1])
def test_avg_composer(self):
embedder = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=AverageComposer(),
char_vocab=self.src_char_vocab)
embedder.embed_sent(self.src[1])
def test_max_composer(self):
embedder = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=MaxComposer(),
char_vocab=self.src_char_vocab)
embedder.embed_sent(self.src[1])
def test_conv_composer(self):
composer = ConvolutionComposer(ngram_size=2,
transform=NonLinear(self.layer_dim, self.layer_dim, activation="relu"),
embed_dim=self.layer_dim,
hidden_dim=self.layer_dim)
embedder = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=composer,
char_vocab=self.src_char_vocab)
embedder.embed_sent(self.src[1])
def test_transducer_composer(self):
composer = SeqTransducerComposer(seq_transducer=BiLSTMSeqTransducer(input_dim=self.layer_dim,
hidden_dim=self.layer_dim))
embedder = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=composer,
char_vocab=self.src_char_vocab)
event_trigger.set_train(True)
event_trigger.start_sent(self.src[1])
embedder.embed_sent(self.src[1])
def test_bagofwords_embedder(self):
embedder = BagOfWordsEmbedder(self.layer_dim, char_vocab=self.src_char_vocab, ngram_vocab= self.ngram_vocab, ngram_size=3)
event_trigger.set_train(True)
event_trigger.start_sent(self.src[1])
embedder.embed_sent(self.src[1])
def test_bagofwords_embedder_with_word_vocab(self):
embedder = BagOfWordsEmbedder(self.layer_dim, word_vocab=self.src_vocab, ngram_vocab= self.ngram_vocab, ngram_size=3)
event_trigger.set_train(True)
event_trigger.start_sent(self.src[1])
embedder.embed_sent(self.src[1])
def test_dyer_composer(self):
composer = DyerHeadComposer(fwd_combinator=UniLSTMSeqTransducer(input_dim=self.layer_dim, hidden_dim=self.layer_dim),
bwd_combinator=UniLSTMSeqTransducer(input_dim=self.layer_dim, hidden_dim=self.layer_dim),
transform=AuxNonLinear(input_dim=self.layer_dim,
output_dim=self.layer_dim,
aux_input_dim=self.layer_dim))
embedder = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=composer,
char_vocab=self.src_char_vocab)
event_trigger.set_train(True)
event_trigger.start_sent(self.src[1])
embedder.embed_sent(self.src[1])
def test_composite_composer(self):
composer = DyerHeadComposer(fwd_combinator=UniLSTMSeqTransducer(input_dim=self.layer_dim, hidden_dim=self.layer_dim),
bwd_combinator=UniLSTMSeqTransducer(input_dim=self.layer_dim, hidden_dim=self.layer_dim),
transform=AuxNonLinear(input_dim=self.layer_dim,
output_dim=self.layer_dim,
aux_input_dim=self.layer_dim))
embedder_1 = CharCompositionEmbedder(emb_dim=self.layer_dim,
composer=composer,
char_vocab=self.src_char_vocab)
embedder_2 = LookupEmbedder(emb_dim=self.layer_dim, vocab_size=100)
embedder = CompositeEmbedder(embedders=[embedder_1, embedder_2])
event_trigger.set_train(True)
event_trigger.start_sent(self.src[1])
embedder.embed_sent(self.src[1])
embedder.embed(self.src[1][0].words[0])
def test_segmented_word(self):
a = SegmentedWord([1,2,3], 10)
b = SegmentedWord([1,2,3], 10)
c = SegmentedWord([2,3,4], 10)
d = SegmentedWord([1,2,3], 9)
self.assertEqual(a, b)
self.assertEqual(a, [1,2,3])
self.assertEqual(a, 10)
self.assertNotEqual(a, c)
self.assertNotEqual(a, d)
self.assertNotEqual(type(self.src[0][0][0]), SegmentedWord)
self.assertEqual(type(self.src[0][0].words[0]), SegmentedWord)
