def test_py_lstm_mask(self):
model = DefaultTranslator(
src_reader=self.src_reader,
trg_reader=self.trg_reader,
src_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
encoder=PyramidalLSTMSeqTransducer(self.exp_global, layers=1),
attender=MlpAttender(self.exp_global),
trg_embedder=SimpleWordEmbedder(self.exp_global, vocab_size=100),
decoder=MlpSoftmaxDecoder(self.exp_global, vocab_size=100),
)
batcher = xnmt.batcher.TrgBatcher(batch_size=3)
train_src, _ = \
batcher.pack(self.src_data, self.trg_data)
self.set_train(True)
for sent_i in range(3):
dy.renew_cg()
src = train_src[sent_i]
self.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder(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_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),
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),
)
self.set_train(True)
for sent_i in range(10):
dy.renew_cg()
src = self.src_data[sent_i].get_padded_sent(
Vocab.ES, 4 - (len(self.src_data[sent_i]) % 4))
self.start_sent(src)
embeddings = model.src_embedder.embed_sent(src)
encodings = model.encoder(embeddings)
self.assertEqual(int(math.ceil(len(embeddings) / float(4))),
len(encodings))
