def test_is_tensor_equal():
"""
测试两个 tensor 是否相等
:return:
"""
x = torch.tensor([1, 2, 3])
y = torch.tensor([1, 2, 3])
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=0)
ASSERT.assertTrue(equal)
x = torch.tensor([1, 2, 3])
y = torch.tensor([2, 2, 3])
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=0)
ASSERT.assertFalse(equal)
x = torch.tensor([1.0001, 2.0001, 3.0001])
y = torch.tensor([1., 2., 3.])
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=1e-3)
ASSERT.assertTrue(equal)
equal = tensor_util.is_tensor_equal(tensor1=x, tensor2=y, epsilon=1e-4)
ASSERT.assertFalse(equal)
def test_bert_model_collate(mrc_msra_ner_dataset, paper_mrc_msra_ner_dataset):
max_length = 128
bert_dir = "data/pretrained/bert/chinese_roberta_wwm_large_ext_pytorch"
bert_dir = os.path.join(ROOT_PATH, bert_dir)
bert_model_collate = BertModelCollate(tokenizer=bert_tokenizer(bert_dir),
max_length=max_length)
instances = [instance for instance in mrc_msra_ner_dataset]
model_inputs = bert_model_collate(instances=instances)
inputs = model_inputs.model_inputs
paper_instances = [instance for instance in paper_mrc_msra_ner_dataset]
paper_model_inputs = collate_to_max_length(paper_instances)
paper_token_ids = paper_model_inputs[0]
token_ids = inputs["input_ids"]
ASSERT.assertTrue(is_tensor_equal(paper_token_ids, token_ids, epsilon=0))
paper_type_ids = paper_model_inputs[1]
type_ids = inputs["token_type_ids"]
ASSERT.assertTrue(is_tensor_equal(paper_type_ids, type_ids, epsilon=0))
paper_start_label_indices = paper_model_inputs[2]
start_label_indices = model_inputs.labels["start_position_labels"]
ASSERT.assertTrue(
is_tensor_equal(paper_start_label_indices,
start_label_indices,
epsilon=0))
paper_end_label_indices = paper_model_inputs[3]
end_label_indices = model_inputs.labels["end_position_labels"]
ASSERT.assertTrue(
is_tensor_equal(paper_end_label_indices, end_label_indices, epsilon=0))
paper_start_label_mask = paper_model_inputs[4]
sequence_mask = inputs["sequence_mask"].long()
ASSERT.assertTrue(
is_tensor_equal(paper_start_label_mask, sequence_mask, epsilon=0))
paper_end_label_mask = paper_model_inputs[5]
sequence_mask = inputs["sequence_mask"].long()
ASSERT.assertTrue(
is_tensor_equal(paper_end_label_mask, sequence_mask, epsilon=0))
paper_match_labels = paper_model_inputs[6]
match_labels = model_inputs.labels["match_position_labels"]
ASSERT.assertTrue(
is_tensor_equal(paper_match_labels, match_labels, epsilon=0))
def test_gat_without_hidden():
"""
测试 gat
:return:
"""
torch.manual_seed(7)
torch.cuda.manual_seed_all(7)
in_features = 2
out_features = 4
gat = GAT(in_features=in_features,
out_features=out_features,
dropout=0.,
alpha=0.1,
num_heads=3,
hidden_size=None)
nodes = torch.tensor([[[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]],
[[0.7, 0.8], [0.9, 0.10], [0.11, 0.12]]],
dtype=torch.float)
adj = torch.tensor(
[[[0, 1, 0], [1, 0, 0], [0, 0, 0]], [[0, 1, 1], [1, 0, 1], [1, 1, 0]]],
dtype=torch.long)
output_nodes: torch.Tensor = gat(nodes=nodes, adj=adj)
expect_size = (nodes.size(0), nodes.size(1), out_features)
ASSERT.assertEqual(expect_size, output_nodes.size())
expect = torch.tensor([[[-1.6478, -0.3935, -2.6613, -2.7653],
[-1.3204, -0.8394, -1.8519, -1.9375],
[-1.6478, -0.3935, -2.6613, -2.7653]],
[[-1.9897, -0.4203, -2.4447, -2.1232],
[-2.1944, -0.1897, -3.4053, -3.5697],
[-2.9364, -0.0878, -4.1695, -4.1617]]],
dtype=torch.float)
ASSERT.assertTrue(
tensor_util.is_tensor_equal(expect, output_nodes, epsilon=1e-4))
def test_gat_with_hidden():
"""
测试 gat
:return:
"""
torch.manual_seed(7)
torch.cuda.manual_seed_all(7)
in_features = 2
out_features = 4
gat = GAT(in_features=in_features,
out_features=out_features,
dropout=0.,
alpha=0.1,
num_heads=3,
hidden_size=3)
nodes = torch.tensor([[[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]],
[[0.7, 0.8], [0.9, 0.10], [0.11, 0.12]]],
dtype=torch.float)
adj = torch.tensor(
[[[0, 1, 0], [1, 0, 0], [0, 0, 0]], [[0, 1, 1], [1, 0, 1], [1, 1, 0]]],
dtype=torch.long)
output_nodes: torch.Tensor = gat(nodes=nodes, adj=adj)
expect_size = (nodes.size(0), nodes.size(1), out_features)
ASSERT.assertEqual(expect_size, output_nodes.size())
expect = torch.tensor([[[-1.3835, -1.4764, -1.2033, -1.5113],
[-1.3316, -1.5785, -1.1564, -1.5368],
[-1.3475, -1.5467, -1.1706, -1.5279]],
[[-1.3388, -1.6693, -1.4427, -1.1610],
[-1.4288, -1.6525, -1.6607, -0.9707],
[-1.4320, -1.4422, -1.6465, -1.1025]]])
ASSERT.assertTrue(
tensor_util.is_tensor_equal(expect, output_nodes, epsilon=1e-4))
def test_graph_attention_layer():
torch.manual_seed(7)
torch.cuda.manual_seed_all(7)
in_features = 2
out_features = 4
gat_layer = GraphAttentionLayer(in_features=in_features,
out_features=out_features,
dropout=0.0,
alpha=0.1)
nodes = torch.tensor([[[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]],
[[0.7, 0.8], [0.9, 0.10], [0.11, 0.12]]],
dtype=torch.float)
adj = torch.tensor(
[[[0, 1, 0], [1, 0, 0], [0, 0, 0]], [[0, 1, 1], [1, 0, 1], [1, 1, 0]]],
dtype=torch.long)
outputs: torch.Tensor = gat_layer(input=nodes, adj=adj)
expect_size = (nodes.size(0), nodes.size(1), out_features)
ASSERT.assertEqual(expect_size, outputs.size())
# 下面的 expect 是从原论文中测试得到的结果，直接拿来用
expect = torch.tensor([[[0.2831, 0.3588, -0.5131, -0.2058],
[0.1606, 0.1292, -0.2264, -0.0951],
[0.2831, 0.3588, -0.5131, -0.2058]],
[[-0.0748, 0.5025, -0.3840, -0.1192],
[0.2959, 0.4624, -0.6123, -0.2405],
[0.1505, 0.8668, -0.8609, -0.3059]]],
dtype=torch.float)
ASSERT.assertTrue(
tensor_util.is_tensor_equal(expect, outputs, epsilon=1e-4))
def test_flat_model_collate(lattice_ner_demo_dataset,
character_pretrained_embedding_loader,
gaz_pretrained_embedding_loader):
"""
测试 flat model collate
:return:
"""
# 仅仅取前两个作为测试
batch_instances = lattice_ner_demo_dataset[0:2]
vocabulary_collate = VocabularyCollate()
collate_result = vocabulary_collate(batch_instances)
characters = collate_result["tokens"]
sequence_label = collate_result["sequence_labels"]
character_vocabulary = Vocabulary(tokens=characters,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
character_vocabulary = PretrainedVocabulary(
vocabulary=character_vocabulary,
pretrained_word_embedding_loader=character_pretrained_embedding_loader)
label_vocabulary = LabelVocabulary(labels=sequence_label,
padding=LabelVocabulary.PADDING)
gazetter = Gazetteer(
gaz_pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetter)
gaz_words = gaz_vocabulary_collate(batch_instances)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
flat_vocabulary = FlatPretrainedVocabulary(
character_pretrained_vocabulary=character_vocabulary,
gaz_word_pretrained_vocabulary=gaz_vocabulary)
flat_model_collate = FLATModelCollate(token_vocabulary=flat_vocabulary,
gazetter=gazetter,
label_vocabulary=label_vocabulary)
model_inputs = flat_model_collate(batch_instances)
logging.debug(json2str(model_inputs.model_inputs["metadata"]))
metadata_0 = model_inputs.model_inputs["metadata"][0]
sentence = "陈元呼吁加强国际合作推动世界经济发展"
# 陈元呼吁加强国际合作推动世界经济发展
expect_squeeze_gaz_words_0 = [
"陈元", "呼吁", "吁加", "加强", "强国", "国际", "合作", "推动", "世界", "经济", "发展"
]
squeeze_gaz_words_0 = metadata_0["squeeze_gaz_words"]
ASSERT.assertListEqual(expect_squeeze_gaz_words_0, squeeze_gaz_words_0)
expect_tokens = [character
for character in sentence] + expect_squeeze_gaz_words_0
tokens = metadata_0["tokens"]
ASSERT.assertListEqual(expect_tokens, tokens)
character_pos_begin = [index for index in range(len(sentence))]
character_pos_end = [index for index in range(len(sentence))]
squeeze_gaz_words_begin = list()
squeeze_gaz_words_end = list()
for squeeze_gaz_word in squeeze_gaz_words_0:
index = sentence.find(squeeze_gaz_word)
squeeze_gaz_words_begin.append(index)
squeeze_gaz_words_end.append(index + len(squeeze_gaz_word) - 1)
pos_begin = model_inputs.model_inputs["pos_begin"][0]
pos_end = model_inputs.model_inputs["pos_end"][0]
expect_pos_begin = character_pos_begin + squeeze_gaz_words_begin
expect_pos_begin += [0] * (pos_begin.size(0) - len(expect_pos_begin))
expect_pos_begin = torch.tensor(expect_pos_begin)
expect_pos_end = character_pos_end + squeeze_gaz_words_end
expect_pos_end += [0] * (pos_end.size(0) - len(expect_pos_end))
expect_pos_end = torch.tensor(expect_pos_end)
ASSERT.assertTrue(tensor_util.is_tensor_equal(expect_pos_begin, pos_begin))
ASSERT.assertTrue(tensor_util.is_tensor_equal(expect_pos_end, pos_end))
expect_character_length = len(sentence)
expect_squeeze_gaz_word_length = len(expect_squeeze_gaz_words_0)
character_length = model_inputs.model_inputs["sequence_length"][0]
squeeze_word_length = model_inputs.model_inputs["squeeze_gaz_word_length"][
0]
ASSERT.assertEqual(expect_character_length, character_length.item())
ASSERT.assertEqual(expect_squeeze_gaz_word_length,
squeeze_word_length.item())
def test_bilstm_gat_model_collate(lattice_ner_demo_dataset,
gaz_pretrained_embedding_loader):
"""
测试 bilstm gat model collate
:return:
"""
# 仅仅取前两个作为测试
batch_instances = lattice_ner_demo_dataset[0:2]
vocabulary_collate = VocabularyCollate()
collate_result = vocabulary_collate(batch_instances)
tokens = collate_result["tokens"]
sequence_label = collate_result["sequence_labels"]
token_vocabulary = Vocabulary(tokens=tokens,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
label_vocabulary = LabelVocabulary(labels=sequence_label,
padding=LabelVocabulary.PADDING)
gazetter = Gazetteer(
gaz_pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
gaz_vocabulary_collate = GazVocabularyCollate(gazetteer=gazetter)
gaz_words = gaz_vocabulary_collate(batch_instances)
gaz_vocabulary = Vocabulary(tokens=gaz_words,
padding=Vocabulary.PADDING,
unk=Vocabulary.UNK,
special_first=True)
gaz_vocabulary = PretrainedVocabulary(
vocabulary=gaz_vocabulary,
pretrained_word_embedding_loader=gaz_pretrained_embedding_loader)
bilstm_gat_model_collate = BiLstmGATModelCollate(
token_vocabulary=token_vocabulary,
gazetter=gazetter,
gaz_vocabulary=gaz_vocabulary,
label_vocabulary=label_vocabulary)
model_inputs = bilstm_gat_model_collate(batch_instances)
logging.debug(json2str(model_inputs.model_inputs["metadata"]))
t_graph_0 = model_inputs.model_inputs["t_graph"][0]
c_graph_0 = model_inputs.model_inputs["c_graph"][0]
l_graph_0 = model_inputs.model_inputs["l_graph"][0]
expect_t_graph_tensor = torch.tensor(expect_t_graph, dtype=torch.uint8)
ASSERT.assertTrue(is_tensor_equal(expect_t_graph_tensor, t_graph_0))
expect_c_graph_tensor = torch.tensor(expect_c_graph, dtype=torch.uint8)
ASSERT.assertTrue(is_tensor_equal(expect_c_graph_tensor, c_graph_0))
expect_l_graph_tensor = torch.tensor(expect_l_graph, dtype=torch.uint8)
ASSERT.assertTrue(is_tensor_equal(expect_l_graph_tensor, l_graph_0))
gaz_words_indices = model_inputs.model_inputs["gaz_words"]
ASSERT.assertEqual((2, 11), gaz_words_indices.size())
metadata_0 = model_inputs.model_inputs["metadata"][0]
# 陈元呼吁加强国际合作推动世界经济发展
expect_squeeze_gaz_words_0 = [
"陈元", "呼吁", "吁加", "加强", "强国", "国际", "合作", "推动", "世界", "经济", "发展"
]
sequeeze_gaz_words_0 = metadata_0["sequeeze_gaz_words"]
ASSERT.assertListEqual(expect_squeeze_gaz_words_0, sequeeze_gaz_words_0)
expect_squeeze_gaz_words_indices_0 = torch.tensor(
[gaz_vocabulary.index(word) for word in expect_squeeze_gaz_words_0],
dtype=torch.long)
ASSERT.assertTrue(
is_tensor_equal(expect_squeeze_gaz_words_indices_0,
gaz_words_indices[0]))