def test_get_embedding_layer_crashes_when_embedding_file_has_header(self):
vocab = Vocabulary()
embeddings_filename = self.TEST_DIR + "embeddings.gz"
with gzip.open(embeddings_filename, 'wb') as embeddings_file:
embeddings_file.write("dimensionality 3\n".encode('utf-8'))
embeddings_file.write("word1 1.0 2.3 -1.0\n".encode('utf-8'))
embeddings_file.write("word2 0.1 0.4 -4.0\n".encode('utf-8'))
with pytest.raises(Exception):
get_pretrained_embedding_layer(embeddings_filename, vocab)
def test_get_embedding_layer_uses_correct_embedding_dim(self):
vocab = Vocabulary()
embeddings_filename = self.TEST_DIR + "embeddings.gz"
with gzip.open(embeddings_filename, 'wb') as embeddings_file:
embeddings_file.write("word1 1.0 2.3 -1.0\n".encode('utf-8'))
embeddings_file.write("word2 0.1 0.4 -4.0\n".encode('utf-8'))
embedding_layer = get_pretrained_embedding_layer(
embeddings_filename, vocab)
assert embedding_layer.embedding_dim == 3
with gzip.open(embeddings_filename, 'wb') as embeddings_file:
embeddings_file.write("word1 1.0 2.3 -1.0 3.1\n".encode('utf-8'))
embeddings_file.write("word2 0.1 0.4 -4.0 -1.2\n".encode('utf-8'))
embedding_layer = get_pretrained_embedding_layer(
embeddings_filename, vocab)
assert embedding_layer.embedding_dim == 4
def test_get_embedding_layer_initializes_unseen_words_randomly_not_zero(
self):
vocab = Vocabulary()
vocab.add_token_to_namespace("word2")
embeddings_filename = self.TEST_DIR + "embeddings.gz"
with gzip.open(embeddings_filename, 'wb') as embeddings_file:
embeddings_file.write("word 1.0 2.3 -1.0\n".encode('utf-8'))
embedding_layer = get_pretrained_embedding_layer(
embeddings_filename, vocab)
word_vector = embedding_layer.weight.data[vocab.get_token_index(
"word2")]
assert not numpy.allclose(word_vector.numpy(),
numpy.array([0.0, 0.0, 0.0]))
def test_get_embedding_layer_skips_inconsistent_lines(self):
vocab = Vocabulary()
vocab.add_token_to_namespace("word1")
vocab.add_token_to_namespace("word2")
embeddings_filename = self.TEST_DIR + "embeddings.gz"
with gzip.open(embeddings_filename, 'wb') as embeddings_file:
embeddings_file.write("word1 1.0 2.3 -1.0\n".encode('utf-8'))
embeddings_file.write("word2 0.1 0.4 \n".encode('utf-8'))
embedding_layer = get_pretrained_embedding_layer(
embeddings_filename, vocab)
word_vector = embedding_layer.weight.data[vocab.get_token_index(
"word2")]
assert not numpy.allclose(word_vector.numpy()[:2],
numpy.array([0.1, 0.4]))
def test_forward_works_with_projection_layer(self):
vocab = Vocabulary()
vocab.add_token_to_namespace('the')
vocab.add_token_to_namespace('a')
embedding_layer = get_pretrained_embedding_layer(
'tests/fixtures/glove.6B.300d.sample.txt.gz',
vocab,
projection_dim=20)
input_tensor = Variable(torch.LongTensor([[3, 2, 1, 0]]))
embedded = embedding_layer(input_tensor).data.numpy()
assert embedded.shape == (1, 4, 20)
input_tensor = Variable(torch.LongTensor([[[3, 2, 1, 0]]]))
embedded = embedding_layer(input_tensor).data.numpy()
assert embedded.shape == (1, 1, 4, 20)