class TestListField(AllenNlpTestCase): def setUp(self): self.vocab = Vocabulary() self.vocab.add_token_to_namespace("this", "words") self.vocab.add_token_to_namespace("is", "words") self.vocab.add_token_to_namespace("a", "words") self.vocab.add_token_to_namespace("sentence", "words") self.vocab.add_token_to_namespace("s", "characters") self.vocab.add_token_to_namespace("e", "characters") self.vocab.add_token_to_namespace("n", "characters") self.vocab.add_token_to_namespace("t", "characters") self.vocab.add_token_to_namespace("c", "characters") for label in ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"]: self.vocab.add_token_to_namespace(label, "labels") self.word_indexer = {"words": SingleIdTokenIndexer("words")} self.words_and_characters_indexers = { "words": SingleIdTokenIndexer("words"), "characters": TokenCharactersIndexer("characters", min_padding_length=1), } self.field1 = TextField( [Token(t) for t in ["this", "is", "a", "sentence"]], self.word_indexer) self.field2 = TextField( [Token(t) for t in ["this", "is", "a", "different", "sentence"]], self.word_indexer) self.field3 = TextField( [Token(t) for t in ["this", "is", "another", "sentence"]], self.word_indexer) self.empty_text_field = self.field1.empty_field() self.index_field = IndexField(1, self.field1) self.empty_index_field = self.index_field.empty_field() self.sequence_label_field = SequenceLabelField([1, 1, 0, 1], self.field1) self.empty_sequence_label_field = self.sequence_label_field.empty_field( ) tokenizer = SpacyTokenizer() tokens = tokenizer.tokenize("Foo") text_field = TextField(tokens, self.word_indexer) empty_list_field = ListField([text_field.empty_field()]) empty_fields = {"list_tensor": empty_list_field} self.empty_instance = Instance(empty_fields) non_empty_list_field = ListField([text_field]) non_empty_fields = {"list_tensor": non_empty_list_field} self.non_empty_instance = Instance(non_empty_fields) super().setUp() def test_get_padding_lengths(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) lengths = list_field.get_padding_lengths() assert lengths == {"num_fields": 3, "list_words___tokens": 5} def test_list_field_can_handle_empty_text_fields(self): list_field = ListField( [self.field1, self.field2, self.empty_text_field]) list_field.index(self.vocab) tensor_dict = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal( tensor_dict["words"]["tokens"].detach().cpu().numpy(), numpy.array([[2, 3, 4, 5, 0], [2, 3, 4, 1, 5], [0, 0, 0, 0, 0]]), ) def test_list_field_can_handle_empty_index_fields(self): list_field = ListField( [self.index_field, self.index_field, self.empty_index_field]) list_field.index(self.vocab) tensor = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal(tensor.detach().cpu().numpy(), numpy.array([[1], [1], [-1]])) def test_list_field_can_handle_empty_sequence_label_fields(self): list_field = ListField([ self.sequence_label_field, self.sequence_label_field, self.empty_sequence_label_field ]) list_field.index(self.vocab) tensor = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal( tensor.detach().cpu().numpy(), numpy.array([[1, 1, 0, 1], [1, 1, 0, 1], [0, 0, 0, 0]])) def test_all_fields_padded_to_max_length(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) tensor_dict = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][0].detach().cpu().numpy(), numpy.array([2, 3, 4, 5, 0])) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][1].detach().cpu().numpy(), numpy.array([2, 3, 4, 1, 5])) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][2].detach().cpu().numpy(), numpy.array([2, 3, 1, 5, 0])) def test_nested_list_fields_are_padded_correctly(self): nested_field1 = ListField( [LabelField(c) for c in ["a", "b", "c", "d", "e"]]) nested_field2 = ListField( [LabelField(c) for c in ["f", "g", "h", "i", "j", "k"]]) list_field = ListField( [nested_field1.empty_field(), nested_field1, nested_field2]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() assert padding_lengths == {"num_fields": 3, "list_num_fields": 6} tensor = list_field.as_tensor(padding_lengths).detach().cpu().numpy() numpy.testing.assert_almost_equal( tensor, [[-1, -1, -1, -1, -1, -1], [0, 1, 2, 3, 4, -1], [5, 6, 7, 8, 9, 10]]) def test_fields_can_pad_to_greater_than_max_length(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() padding_lengths["list_words___tokens"] = 7 padding_lengths["num_fields"] = 5 tensor_dict = list_field.as_tensor(padding_lengths) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][0].detach().cpu().numpy(), numpy.array([2, 3, 4, 5, 0, 0, 0]), ) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][1].detach().cpu().numpy(), numpy.array([2, 3, 4, 1, 5, 0, 0]), ) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][2].detach().cpu().numpy(), numpy.array([2, 3, 1, 5, 0, 0, 0]), ) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][3].detach().cpu().numpy(), numpy.array([0, 0, 0, 0, 0, 0, 0]), ) numpy.testing.assert_array_almost_equal( tensor_dict["words"]["tokens"][4].detach().cpu().numpy(), numpy.array([0, 0, 0, 0, 0, 0, 0]), ) def test_as_tensor_can_handle_multiple_token_indexers(self): self.field1._token_indexers = self.words_and_characters_indexers self.field2._token_indexers = self.words_and_characters_indexers self.field3._token_indexers = self.words_and_characters_indexers list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() tensor_dict = list_field.as_tensor(padding_lengths) words = tensor_dict["words"]["tokens"].detach().cpu().numpy() characters = tensor_dict["characters"]["token_characters"].detach( ).cpu().numpy() numpy.testing.assert_array_almost_equal( words, numpy.array([[2, 3, 4, 5, 0], [2, 3, 4, 1, 5], [2, 3, 1, 5, 0]])) numpy.testing.assert_array_almost_equal( characters[0], numpy.array([ [5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], ]), ) numpy.testing.assert_array_almost_equal( characters[1], numpy.array([ [5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 3, 1, 3, 4, 5], [2, 3, 4, 5, 3, 4, 6, 3, 0], ]), ) numpy.testing.assert_array_almost_equal( characters[2], numpy.array([ [5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 4, 1, 5, 1, 3, 1, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], ]), ) def test_as_tensor_can_handle_multiple_token_indexers_and_empty_fields( self): self.field1._token_indexers = self.words_and_characters_indexers self.field2._token_indexers = self.words_and_characters_indexers self.field3._token_indexers = self.words_and_characters_indexers list_field = ListField( [self.field1.empty_field(), self.field1, self.field2]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() tensor_dict = list_field.as_tensor(padding_lengths) words = tensor_dict["words"]["tokens"].detach().cpu().numpy() characters = tensor_dict["characters"]["token_characters"].detach( ).cpu().numpy() numpy.testing.assert_array_almost_equal( words, numpy.array([[0, 0, 0, 0, 0], [2, 3, 4, 5, 0], [2, 3, 4, 1, 5]])) numpy.testing.assert_array_almost_equal(characters[0], numpy.zeros([5, 9])) numpy.testing.assert_array_almost_equal( characters[1], numpy.array([ [5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], ]), ) numpy.testing.assert_array_almost_equal( characters[2], numpy.array([ [5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 3, 1, 3, 4, 5], [2, 3, 4, 5, 3, 4, 6, 3, 0], ]), ) def test_printing_doesnt_crash(self): list_field = ListField([self.field1, self.field2]) print(list_field) def test_sequence_methods(self): list_field = ListField([self.field1, self.field2, self.field3]) assert len(list_field) == 3 assert list_field[1] == self.field2 assert [f for f in list_field ] == [self.field1, self.field2, self.field3] def test_empty_list_can_be_tensorized(self): tokenizer = SpacyTokenizer() tokens = tokenizer.tokenize("Foo") text_field = TextField(tokens, self.word_indexer) list_field = ListField([text_field.empty_field()]) fields = { "list": list_field, "bar": TextField(tokenizer.tokenize("BAR"), self.word_indexer), } instance = Instance(fields) instance.index_fields(self.vocab) instance.as_tensor_dict() def test_batch_with_some_empty_lists_works(self): dataset = AllennlpDataset( [self.empty_instance, self.non_empty_instance], self.vocab) model = DummyModel(self.vocab) model.eval() loader = DataLoader(dataset, batch_size=2) batch = next(iter(loader)) model.forward(**batch) # This use case may seem a bit peculiar. It's intended for situations where # you have sparse inputs that are used as additional features for some # prediction, and they are sparse enough that they can be empty for some # cases. It would be silly to try to handle these as None in your model; it # makes a whole lot more sense to just have a minimally-sized tensor that # gets entirely masked and has no effect on the rest of the model. def test_batch_of_entirely_empty_lists_works(self): dataset = AllennlpDataset([self.empty_instance, self.empty_instance], self.vocab) model = DummyModel(self.vocab) model.eval() loader = DataLoader(dataset, batch_size=2) batch = next(iter(loader)) model.forward(**batch)
class TestListField(AllenNlpTestCase): def setUp(self): self.vocab = Vocabulary() self.vocab.add_token_to_namespace("this", "words") self.vocab.add_token_to_namespace("is", "words") self.vocab.add_token_to_namespace("a", "words") self.vocab.add_token_to_namespace("sentence", 'words') self.vocab.add_token_to_namespace("s", 'characters') self.vocab.add_token_to_namespace("e", 'characters') self.vocab.add_token_to_namespace("n", 'characters') self.vocab.add_token_to_namespace("t", 'characters') self.vocab.add_token_to_namespace("c", 'characters') for label in ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']: self.vocab.add_token_to_namespace(label, 'labels') self.word_indexer = {"words": SingleIdTokenIndexer("words")} self.words_and_characters_indexers = {"words": SingleIdTokenIndexer("words"), "characters": TokenCharactersIndexer("characters")} self.field1 = TextField([Token(t) for t in ["this", "is", "a", "sentence"]], self.word_indexer) self.field2 = TextField([Token(t) for t in ["this", "is", "a", "different", "sentence"]], self.word_indexer) self.field3 = TextField([Token(t) for t in ["this", "is", "another", "sentence"]], self.word_indexer) self.empty_text_field = self.field1.empty_field() self.index_field = IndexField(1, self.field1) self.empty_index_field = self.index_field.empty_field() self.sequence_label_field = SequenceLabelField([1, 1, 0, 1], self.field1) self.empty_sequence_label_field = self.sequence_label_field.empty_field() super(TestListField, self).setUp() def test_get_padding_lengths(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) lengths = list_field.get_padding_lengths() assert lengths == {"num_fields": 3, "list_words_length": 5, "list_num_tokens": 5} def test_list_field_can_handle_empty_text_fields(self): list_field = ListField([self.field1, self.field2, self.empty_text_field]) list_field.index(self.vocab) tensor_dict = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal(tensor_dict["words"].detach().cpu().numpy(), numpy.array([[2, 3, 4, 5, 0], [2, 3, 4, 1, 5], [0, 0, 0, 0, 0]])) def test_list_field_can_handle_empty_index_fields(self): list_field = ListField([self.index_field, self.index_field, self.empty_index_field]) list_field.index(self.vocab) tensor = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal(tensor.detach().cpu().numpy(), numpy.array([[1], [1], [-1]])) def test_list_field_can_handle_empty_sequence_label_fields(self): list_field = ListField([self.sequence_label_field, self.sequence_label_field, self.empty_sequence_label_field]) list_field.index(self.vocab) tensor = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal(tensor.detach().cpu().numpy(), numpy.array([[1, 1, 0, 1], [1, 1, 0, 1], [0, 0, 0, 0]])) def test_all_fields_padded_to_max_length(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) tensor_dict = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_almost_equal(tensor_dict["words"][0].detach().cpu().numpy(), numpy.array([2, 3, 4, 5, 0])) numpy.testing.assert_array_almost_equal(tensor_dict["words"][1].detach().cpu().numpy(), numpy.array([2, 3, 4, 1, 5])) numpy.testing.assert_array_almost_equal(tensor_dict["words"][2].detach().cpu().numpy(), numpy.array([2, 3, 1, 5, 0])) def test_nested_list_fields_are_padded_correctly(self): nested_field1 = ListField([LabelField(c) for c in ['a', 'b', 'c', 'd', 'e']]) nested_field2 = ListField([LabelField(c) for c in ['f', 'g', 'h', 'i', 'j', 'k']]) list_field = ListField([nested_field1.empty_field(), nested_field1, nested_field2]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() assert padding_lengths == {'num_fields': 3, 'list_num_fields': 6} tensor = list_field.as_tensor(padding_lengths).detach().cpu().numpy() numpy.testing.assert_almost_equal(tensor, [[-1, -1, -1, -1, -1, -1], [0, 1, 2, 3, 4, -1], [5, 6, 7, 8, 9, 10]]) def test_fields_can_pad_to_greater_than_max_length(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() padding_lengths["list_words_length"] = 7 padding_lengths["num_fields"] = 5 tensor_dict = list_field.as_tensor(padding_lengths) numpy.testing.assert_array_almost_equal(tensor_dict["words"][0].detach().cpu().numpy(), numpy.array([2, 3, 4, 5, 0, 0, 0])) numpy.testing.assert_array_almost_equal(tensor_dict["words"][1].detach().cpu().numpy(), numpy.array([2, 3, 4, 1, 5, 0, 0])) numpy.testing.assert_array_almost_equal(tensor_dict["words"][2].detach().cpu().numpy(), numpy.array([2, 3, 1, 5, 0, 0, 0])) numpy.testing.assert_array_almost_equal(tensor_dict["words"][3].detach().cpu().numpy(), numpy.array([0, 0, 0, 0, 0, 0, 0])) numpy.testing.assert_array_almost_equal(tensor_dict["words"][4].detach().cpu().numpy(), numpy.array([0, 0, 0, 0, 0, 0, 0])) def test_as_tensor_can_handle_multiple_token_indexers(self): # pylint: disable=protected-access self.field1._token_indexers = self.words_and_characters_indexers self.field2._token_indexers = self.words_and_characters_indexers self.field3._token_indexers = self.words_and_characters_indexers list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() tensor_dict = list_field.as_tensor(padding_lengths) words = tensor_dict["words"].detach().cpu().numpy() characters = tensor_dict["characters"].detach().cpu().numpy() numpy.testing.assert_array_almost_equal(words, numpy.array([[2, 3, 4, 5, 0], [2, 3, 4, 1, 5], [2, 3, 1, 5, 0]])) numpy.testing.assert_array_almost_equal(characters[0], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]])) numpy.testing.assert_array_almost_equal(characters[1], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 3, 1, 3, 4, 5], [2, 3, 4, 5, 3, 4, 6, 3, 0]])) numpy.testing.assert_array_almost_equal(characters[2], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 4, 1, 5, 1, 3, 1, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]])) def test_as_tensor_can_handle_multiple_token_indexers_and_empty_fields(self): # pylint: disable=protected-access self.field1._token_indexers = self.words_and_characters_indexers self.field2._token_indexers = self.words_and_characters_indexers self.field3._token_indexers = self.words_and_characters_indexers list_field = ListField([self.field1.empty_field(), self.field1, self.field2]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() tensor_dict = list_field.as_tensor(padding_lengths) words = tensor_dict["words"].detach().cpu().numpy() characters = tensor_dict["characters"].detach().cpu().numpy() numpy.testing.assert_array_almost_equal(words, numpy.array([[0, 0, 0, 0, 0], [2, 3, 4, 5, 0], [2, 3, 4, 1, 5]])) numpy.testing.assert_array_almost_equal(characters[0], numpy.zeros([5, 9])) numpy.testing.assert_array_almost_equal(characters[1], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]])) numpy.testing.assert_array_almost_equal(characters[2], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 3, 1, 3, 4, 5], [2, 3, 4, 5, 3, 4, 6, 3, 0]])) def test_printing_doesnt_crash(self): list_field = ListField([self.field1, self.field2]) print(list_field) def test_sequence_methods(self): list_field = ListField([self.field1, self.field2, self.field3]) assert len(list_field) == 3 assert list_field[1] == self.field2 assert [f for f in list_field] == [self.field1, self.field2, self.field3]
def test_index_field_empty_field_works(self): index_field = IndexField(4, self.text) empty_index = index_field.empty_field() assert empty_index.sequence_index == -1
class TestListField(AllenNlpTestCase): def setUp(self): self.vocab = Vocabulary() self.vocab.add_token_to_namespace("this", "words") self.vocab.add_token_to_namespace("is", "words") self.vocab.add_token_to_namespace("a", "words") self.vocab.add_token_to_namespace("sentence", 'words') self.vocab.add_token_to_namespace("s", 'characters') self.vocab.add_token_to_namespace("e", 'characters') self.vocab.add_token_to_namespace("n", 'characters') self.vocab.add_token_to_namespace("t", 'characters') self.vocab.add_token_to_namespace("c", 'characters') for label in ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']: self.vocab.add_token_to_namespace(label, 'labels') self.word_indexer = {"words": SingleIdTokenIndexer("words")} self.words_and_characters_indexers = { "words": SingleIdTokenIndexer("words"), "characters": TokenCharactersIndexer("characters") } self.field1 = TextField( [Token(t) for t in ["this", "is", "a", "sentence"]], self.word_indexer) self.field2 = TextField( [Token(t) for t in ["this", "is", "a", "different", "sentence"]], self.word_indexer) self.field3 = TextField( [Token(t) for t in ["this", "is", "another", "sentence"]], self.word_indexer) self.empty_text_field = self.field1.empty_field() self.index_field = IndexField(1, self.field1) self.empty_index_field = self.index_field.empty_field() self.sequence_label_field = SequenceLabelField([1, 1, 0, 1], self.field1) self.empty_sequence_label_field = self.sequence_label_field.empty_field( ) super(TestListField, self).setUp() def test_get_padding_lengths(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) lengths = list_field.get_padding_lengths() assert lengths == {"num_fields": 3, "list_num_tokens": 5} def test_list_field_can_handle_empty_text_fields(self): list_field = ListField( [self.field1, self.field2, self.empty_text_field]) list_field.index(self.vocab) tensor_dict = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal( tensor_dict["words"].detach().cpu().numpy(), numpy.array([[2, 3, 4, 5, 0], [2, 3, 4, 1, 5], [0, 0, 0, 0, 0]])) def test_list_field_can_handle_empty_index_fields(self): list_field = ListField( [self.index_field, self.index_field, self.empty_index_field]) list_field.index(self.vocab) tensor = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal(tensor.detach().cpu().numpy(), numpy.array([[1], [1], [-1]])) def test_list_field_can_handle_empty_sequence_label_fields(self): list_field = ListField([ self.sequence_label_field, self.sequence_label_field, self.empty_sequence_label_field ]) list_field.index(self.vocab) tensor = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_equal( tensor.detach().cpu().numpy(), numpy.array([[1, 1, 0, 1], [1, 1, 0, 1], [0, 0, 0, 0]])) def test_all_fields_padded_to_max_length(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) tensor_dict = list_field.as_tensor(list_field.get_padding_lengths()) numpy.testing.assert_array_almost_equal( tensor_dict["words"][0].detach().cpu().numpy(), numpy.array([2, 3, 4, 5, 0])) numpy.testing.assert_array_almost_equal( tensor_dict["words"][1].detach().cpu().numpy(), numpy.array([2, 3, 4, 1, 5])) numpy.testing.assert_array_almost_equal( tensor_dict["words"][2].detach().cpu().numpy(), numpy.array([2, 3, 1, 5, 0])) def test_nested_list_fields_are_padded_correctly(self): nested_field1 = ListField( [LabelField(c) for c in ['a', 'b', 'c', 'd', 'e']]) nested_field2 = ListField( [LabelField(c) for c in ['f', 'g', 'h', 'i', 'j', 'k']]) list_field = ListField( [nested_field1.empty_field(), nested_field1, nested_field2]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() assert padding_lengths == {'num_fields': 3, 'list_num_fields': 6} tensor = list_field.as_tensor(padding_lengths).detach().cpu().numpy() numpy.testing.assert_almost_equal( tensor, [[-1, -1, -1, -1, -1, -1], [0, 1, 2, 3, 4, -1], [5, 6, 7, 8, 9, 10]]) def test_fields_can_pad_to_greater_than_max_length(self): list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() padding_lengths["list_num_tokens"] = 7 padding_lengths["num_fields"] = 5 tensor_dict = list_field.as_tensor(padding_lengths) numpy.testing.assert_array_almost_equal( tensor_dict["words"][0].detach().cpu().numpy(), numpy.array([2, 3, 4, 5, 0, 0, 0])) numpy.testing.assert_array_almost_equal( tensor_dict["words"][1].detach().cpu().numpy(), numpy.array([2, 3, 4, 1, 5, 0, 0])) numpy.testing.assert_array_almost_equal( tensor_dict["words"][2].detach().cpu().numpy(), numpy.array([2, 3, 1, 5, 0, 0, 0])) numpy.testing.assert_array_almost_equal( tensor_dict["words"][3].detach().cpu().numpy(), numpy.array([0, 0, 0, 0, 0, 0, 0])) numpy.testing.assert_array_almost_equal( tensor_dict["words"][4].detach().cpu().numpy(), numpy.array([0, 0, 0, 0, 0, 0, 0])) def test_as_tensor_can_handle_multiple_token_indexers(self): # pylint: disable=protected-access self.field1._token_indexers = self.words_and_characters_indexers self.field2._token_indexers = self.words_and_characters_indexers self.field3._token_indexers = self.words_and_characters_indexers list_field = ListField([self.field1, self.field2, self.field3]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() tensor_dict = list_field.as_tensor(padding_lengths) words = tensor_dict["words"].detach().cpu().numpy() characters = tensor_dict["characters"].detach().cpu().numpy() numpy.testing.assert_array_almost_equal( words, numpy.array([[2, 3, 4, 5, 0], [2, 3, 4, 1, 5], [2, 3, 1, 5, 0]])) numpy.testing.assert_array_almost_equal( characters[0], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]])) numpy.testing.assert_array_almost_equal( characters[1], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 3, 1, 3, 4, 5], [2, 3, 4, 5, 3, 4, 6, 3, 0]])) numpy.testing.assert_array_almost_equal( characters[2], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 4, 1, 5, 1, 3, 1, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]])) def test_as_tensor_can_handle_multiple_token_indexers_and_empty_fields( self): # pylint: disable=protected-access self.field1._token_indexers = self.words_and_characters_indexers self.field2._token_indexers = self.words_and_characters_indexers self.field3._token_indexers = self.words_and_characters_indexers list_field = ListField( [self.field1.empty_field(), self.field1, self.field2]) list_field.index(self.vocab) padding_lengths = list_field.get_padding_lengths() tensor_dict = list_field.as_tensor(padding_lengths) words = tensor_dict["words"].detach().cpu().numpy() characters = tensor_dict["characters"].detach().cpu().numpy() numpy.testing.assert_array_almost_equal( words, numpy.array([[0, 0, 0, 0, 0], [2, 3, 4, 5, 0], [2, 3, 4, 1, 5]])) numpy.testing.assert_array_almost_equal(characters[0], numpy.zeros([5, 9])) numpy.testing.assert_array_almost_equal( characters[1], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [2, 3, 4, 5, 3, 4, 6, 3, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]])) numpy.testing.assert_array_almost_equal( characters[2], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0], [1, 2, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 3, 1, 3, 4, 5], [2, 3, 4, 5, 3, 4, 6, 3, 0]])) def test_printing_doesnt_crash(self): list_field = ListField([self.field1, self.field2]) print(list_field)