def test_pretrained_vocabulary(pretrained_vocabulary):
"""
测试预训练词汇表
"""
ASSERT.assertEqual(4, pretrained_vocabulary.size)
ASSERT.assertEqual(4, len(pretrained_vocabulary))
ASSERT.assertEqual(2, pretrained_vocabulary.index("我"))
ASSERT.assertEqual(3, pretrained_vocabulary.index("美丽"))
ASSERT.assertEqual((pretrained_vocabulary.size, 3),
pretrained_vocabulary.embedding_matrix.size())
expect_embedding_dict = {
"a": [1.0, 2.0, 3.0],
"b": [4.0, 5.0, 6.0],
"美丽": [7.0, 8.0, 9.0]
}
ASSERT.assertListEqual(
expect_embedding_dict["美丽"], pretrained_vocabulary.embedding_matrix[
pretrained_vocabulary.index("美丽")].tolist())
zero_vec = [0.] * 3
for index in [
pretrained_vocabulary.index("我"),
pretrained_vocabulary.padding_index,
pretrained_vocabulary.index(pretrained_vocabulary.unk)
]:
ASSERT.assertListEqual(
zero_vec, pretrained_vocabulary.embedding_matrix[index].tolist())
def __call__(self, instances: Iterable[Instance]) -> ModelInputs:
x = list()
labels = list()
for instance in instances:
x_data = instance["x"]
x.append(torch.tensor([x_data], dtype=torch.float))
if x_data - 50 > 0:
labels.append(1)
else:
labels.append(0)
x = torch.stack(x)
batch_size = x.size(0)
ASSERT.assertEqual(x.dim(), 2)
ASSERT.assertListEqual([batch_size, 1], [x.size(0), x.size(1)])
labels = torch.tensor(labels)
ASSERT.assertEqual(labels.dim(), 1)
ASSERT.assertEqual(batch_size, labels.size(0))
model_inputs = ModelInputs(batch_size=batch_size,
model_inputs={"x": x},
labels=labels)
return model_inputs
def test_decode_decode_label_index_to_span():
"""
测试解码 golden label index
:return:
"""
vocabulary = LabelVocabulary([["B-T", "B-T", "B-T", "I-T", "I-T", "O"]],
padding=LabelVocabulary.PADDING)
b_index = vocabulary.index("B-T")
ASSERT.assertEqual(0, b_index)
i_index = vocabulary.index("I-T")
ASSERT.assertEqual(1, i_index)
o_index = vocabulary.index("O")
ASSERT.assertEqual(2, o_index)
golden_labels = torch.tensor([[0, 1, 2, 0],
[2, 0, 1, 1]])
expect = [[{"label": "T", "begin": 0, "end": 2}, {"label": "T", "begin": 3, "end": 4}],
[{"label": "T", "begin": 1, "end": 4}]]
spans = BIO.decode_label_index_to_span(batch_sequence_label_index=golden_labels,
mask=None,
vocabulary=vocabulary)
ASSERT.assertListEqual(expect, spans)
def test_decode():
"""
测试 模型输出的 batch logits 解码
:return:
"""
# [[O, B, I], [B, B, I], [B, I, I], [B, I, O]]
batch_sequence_logits = torch.tensor([[[0.2, 0.3, 0.4], [0.7, 0.2, 0.3], [0.2, 0.3, 0.1]],
[[0.8, 0.3, 0.4], [0.7, 0.2, 0.3], [0.2, 0.3, 0.1]],
[[0.8, 0.3, 0.4], [0.1, 0.7, 0.3], [0.2, 0.3, 0.1]],
[[0.8, 0.3, 0.4], [0.1, 0.7, 0.3], [0.2, 0.3, 0.5]]],
dtype=torch.float)
expect = [[{"label": "T", "begin": 1, "end": 3}],
[{"label": "T", "begin": 0, "end": 1}, {"label": "T", "begin": 1, "end": 3}],
[{"label": "T", "begin": 0, "end": 3}],
[{"label": "T", "begin": 0, "end": 2}]]
vocabulary = LabelVocabulary([["B-T", "B-T", "B-T", "I-T", "I-T", "O"]],
padding=LabelVocabulary.PADDING)
b_index = vocabulary.index("B-T")
ASSERT.assertEqual(0, b_index)
i_index = vocabulary.index("I-T")
ASSERT.assertEqual(1, i_index)
o_index = vocabulary.index("O")
ASSERT.assertEqual(2, o_index)
spans = BIO.decode(batch_sequence_logits=batch_sequence_logits,
mask=None,
vocabulary=vocabulary)
ASSERT.assertListEqual(expect, spans)
def test_decode_one_sequence_label_to_span():
"""
测试对 sequence label 解码成 span 字典
:return:
"""
sequence_label_list = list()
expect_list = list()
sequence_label = ["B-T", "I-T", "O-T"]
expect = [{"label": "T", "begin": 0, "end": 2}]
sequence_label_list.append(sequence_label)
expect_list.append(expect)
sequence_label = ["B-T", "I-T", "I-T"]
expect = [{"label": "T", "begin": 0, "end": 3}]
sequence_label_list.append(sequence_label)
expect_list.append(expect)
sequence_label = ["B-T", "I-T", "I-T", "B-T"]
expect = [{"label": "T", "begin": 0, "end": 3},
{"label": "T", "begin": 3, "end": 4}]
sequence_label_list.append(sequence_label)
expect_list.append(expect)
for expect, sequence_label in zip(expect_list, sequence_label_list):
span = BIO.decode_one_sequence_label_to_span(sequence_label)
ASSERT.assertListEqual(expect, span)
def test_bmes_to_bio():
"""
测试 BMES schema 转换成 bio
:return:
"""
bmes = ["B-T", "M-T", "E-T", "O", "S-T", "B-T", "E-T"]
expect_bio = ["B-T", "I-T", "I-T", "O", "B-T", "B-T", "I-T"]
bio_sequence_label = bio_schema.bmes_to_bio(bmes)
ASSERT.assertListEqual(expect_bio, bio_sequence_label)
def test_crf_label_index_decoder_with_constraint(crf_data):
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.uint8)
crf_label_index_decoder = CRFLabelIndexDecoder(
crf=crf_data.constraint_crf,
label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 3, 3], [2, 3, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
def test_max_label_index_decoder():
"""
测试 max label index
:return:
"""
decoder = MaxLabelIndexDecoder()
logits = torch.tensor([[0.1, 0.9], [0.3, 0.7], [0.8, 0.2]])
label_indices = decoder(logits=logits)
expect = [1, 1, 0]
ASSERT.assertListEqual(expect, label_indices.tolist())
def test_crf_label_index_decoder(crf_data):
"""
测试 crf label index decoder
:param crf_data: crf data
:return:
"""
mask = torch.tensor([[1, 1, 1], [1, 1, 0]], dtype=torch.long)
crf_label_index_decoder = CRFLabelIndexDecoder(
crf=crf_data.crf, label_vocabulary=crf_data.label_vocabulary)
label_indices = crf_label_index_decoder(logits=crf_data.logits, mask=mask)
padding_index = crf_data.label_vocabulary.padding_index
expect = [[2, 4, 3], [4, 2, padding_index]]
ASSERT.assertListEqual(expect, label_indices.tolist())
def test_decode_one_sequence_logits_to_label():
"""
测试 decode sequence label
:return:
"""
sequence_logits_list = list()
expect_list = list()
sequence_logits = torch.tensor([[0.2, 0.3, 0.4], [0.7, 0.2, 0.3], [0.2, 0.3, 0.1]],
dtype=torch.float) # O B I 正常
expect = ["O", "B-T", "I-T"]
sequence_logits_list.append(sequence_logits)
expect_list.append(expect)
sequence_logits = torch.tensor([[0.9, 0.3, 0.4], [0.2, 0.8, 0.3], [0.2, 0.3, 0.1]],
dtype=torch.float)
expect = ["B-T", "I-T", "I-T"]
sequence_logits_list.append(sequence_logits)
expect_list.append(expect)
sequence_logits = torch.tensor([[0.9, 0.3, 0.4], [0.2, 0.8, 0.3], [0.2, 0.3, 0.9]],
dtype=torch.float)
expect = ["B-T", "I-T", "O"]
sequence_logits_list.append(sequence_logits)
expect_list.append(expect)
vocabulary = LabelVocabulary([["B-T", "B-T", "B-T", "I-T", "I-T", "O"]],
padding=LabelVocabulary.PADDING)
b_index = vocabulary.index("B-T")
ASSERT.assertEqual(0, b_index)
i_index = vocabulary.index("I-T")
ASSERT.assertEqual(1, i_index)
o_index = vocabulary.index("O")
ASSERT.assertEqual(2, o_index)
for sequence_logits, expect in zip(sequence_logits_list, expect_list):
sequence_label, sequence_label_indices = BIO.decode_one_sequence_logits_to_label(
sequence_logits=sequence_logits,
vocabulary=vocabulary)
ASSERT.assertListEqual(sequence_label, expect)
expect_indices = [vocabulary.index(label) for label in expect]
ASSERT.assertListEqual(sequence_label_indices, expect_indices)
def test_ibo1_to_bio():
"""
测试 ibo1 转换到 bio
:return:
"""
ibo1 = [
"I-L1", "I-L1", "O", "I-L1", "I-L2", "O", "I-L1", "I-L1", "I-L1",
"B-L1", "I-L1", "O", "B-L1", "I-L1", "O"
]
expect_bio = [
"B-L1", "I-L1", "O", "B-L1", "B-L2", "O", "B-L1", "I-L1", "I-L1",
"B-L1", "I-L1", "O", "B-L1", "I-L1", "O"
]
bio_sequence = bio_schema.ibo1_to_bio(ibo1)
ASSERT.assertListEqual(expect_bio, bio_sequence)
def test_synchronized_data():
"""
测试 to_synchronized_data 和 from_synchronized_data
:return:
"""
demo_metric = _DemoF1Metric()
sync_data, op = demo_metric.to_synchronized_data()
true_positives = sync_data["true_positives"]
false_positives = sync_data["false_positives"]
false_negatives = sync_data["false_negatives"]
expect_values = [v for _, v in demo_metric.true_positives.items()]
ASSERT.assertListEqual(expect_values, true_positives.tolist())
expect_values = [v for _, v in demo_metric.false_positives.items()]
ASSERT.assertListEqual(expect_values, false_positives.tolist())
expect_values = [v for _, v in demo_metric._false_negatives.items()]
ASSERT.assertListEqual(expect_values, false_negatives.tolist())
expect_true_positives = dict(demo_metric.true_positives)
expect_false_positives = dict(demo_metric.false_positives)
expect_false_negatives = dict(demo_metric.false_negatives)
demo_metric.from_synchronized_data(sync_data=sync_data, reduce_op=op)
ASSERT.assertDictEqual(expect_true_positives, demo_metric.true_positives)
ASSERT.assertDictEqual(expect_false_positives, demo_metric.false_positives)
ASSERT.assertDictEqual(expect_false_negatives, demo_metric.false_negatives)
def test_span_intersection():
span_list1 = [{"label": "T", "begin": 0, "end": 2}, {"label": "T", "begin": 3, "end": 4}]
span_list2 = [{"label": "T", "begin": 0, "end": 2}]
intersetction = BIO.span_intersection(span_list1=span_list1,
span_list2=span_list2)
expect = [{"label": "T", "begin": 0, "end": 2}]
ASSERT.assertListEqual(expect, intersetction)
span_list1 = [{"label": "T", "begin": 0, "end": 2}, {"label": "T", "begin": 3, "end": 4}]
span_list2 = [{"label": "T", "begin": 9, "end": 10}]
intersetction = BIO.span_intersection(span_list1=span_list1,
span_list2=span_list2)
expect = []
ASSERT.assertListEqual(expect, intersetction)
span_list1 = [{"label": "T", "begin": 0, "end": 2}, {"label": "T", "begin": 3, "end": 4}]
span_list2 = []
intersetction = BIO.span_intersection(span_list1=span_list1,
span_list2=span_list2)
expect = []
ASSERT.assertListEqual(expect, intersetction)
def test_synchronized_data():
"""
测试 from_synchronized_data 和 to_synchronized_data
:return:
"""
acc_metric = AccMetric()
sync_data, op = acc_metric.to_synchronized_data()
ASSERT.assertEqual((2,), sync_data.size())
ASSERT.assertEqual(0, sync_data[0].item())
ASSERT.assertEqual(0, sync_data[1].item())
# 对应的label是 [1, 1, 0, 1]
logits = torch.tensor([[1., 2.], [3., 4.], [5, 4.], [3., 7.]], dtype=torch.float)
prediction_labels = torch.argmax(logits, dim=-1)
golden_labels = torch.tensor([0, 1, 1, 0], dtype=torch.long)
acc_metric(prediction_labels=prediction_labels, gold_labels=golden_labels, mask=None)
# acc = 1/4
sync_data, op = acc_metric.to_synchronized_data()
ASSERT.assertListEqual([1, 4], sync_data.tolist())
acc_metric.from_synchronized_data(sync_data=sync_data, reduce_op=op)
acc = acc_metric.metric
expect = 1/4
ASSERT.assertAlmostEqual(expect, acc[AccMetric.ACC])
new_sync_data, op = acc_metric.to_synchronized_data()
ASSERT.assertListEqual(sync_data.tolist(), new_sync_data.tolist())
def test_decode_one_sequence_logits_to_label_abnormal():
"""
测试异常case
:return:
"""
# [0.2, 0.5, 0.4] argmax 解码是 I 这是异常的 case, 整个序列是: I B O
# 而 decode_sequence_lable_bio 会将 概率值 是 0.4 的 也就是 O 作为标签输出 来修订这个个错误
sequence_logits = torch.tensor([[0.2, 0.5, 0.4], [0.7, 0.2, 0.3], [0.2, 0.3, 0.1]],
dtype=torch.float)
vocabulary = LabelVocabulary([["B-T", "B-T", "B-T", "I-T", "I-T", "O"]],
padding=LabelVocabulary.PADDING)
b_index = vocabulary.index("B-T")
ASSERT.assertEqual(0, b_index)
i_index = vocabulary.index("I-T")
ASSERT.assertEqual(1, i_index)
o_index = vocabulary.index("O")
ASSERT.assertEqual(2, o_index)
sequence_label, sequence_label_indices = BIO.decode_one_sequence_logits_to_label(sequence_logits=sequence_logits,
vocabulary=vocabulary)
expect = ["O", "B-T", "I-T"]
expect_indices = [vocabulary.index(label) for label in expect]
ASSERT.assertListEqual(expect, sequence_label)
ASSERT.assertListEqual(expect_indices, sequence_label_indices)
# argmax 解码是 I I I 经过修订后是: O O B
sequence_logits = torch.tensor([[0.2, 0.5, 0.4], [0.2, 0.9, 0.3], [0.2, 0.3, 0.1]],
dtype=torch.float)
sequence_label, sequence_label_indices = BIO.decode_one_sequence_logits_to_label(sequence_logits=sequence_logits,
vocabulary=vocabulary)
expect = ["O", "O", "B-T"]
expect_indices = [vocabulary.index(label) for label in expect]
ASSERT.assertListEqual(expect, sequence_label)
ASSERT.assertListEqual(expect_indices, sequence_label_indices)
def test_sequence_label_decoder():
"""
测试 sequence label decoder
:return:
"""
sequence_label_list = list()
expect_spans = list()
sequence_label = ["B-T", "I-T", "O-T"]
expect = [{"label": "T", "begin": 0, "end": 2}]
sequence_label_list.append(sequence_label)
expect_spans.append(expect)
sequence_label = ["B-T", "I-T", "I-T"]
expect = [{"label": "T", "begin": 0, "end": 3}]
sequence_label_list.append(sequence_label)
expect_spans.append(expect)
sequence_label = ["B-T", "I-T", "I-T", "B-T"]
expect = [{
"label": "T",
"begin": 0,
"end": 3
}, {
"label": "T",
"begin": 3,
"end": 4
}]
sequence_label_list.append(sequence_label)
expect_spans.append(expect)
label_vocabulary = LabelVocabulary(sequence_label_list,
padding=LabelVocabulary.PADDING)
sequence_label_indices = list()
mask_list = list()
max_sequence_len = 4
for sequence_labels in sequence_label_list:
sequence_label_index = [
label_vocabulary.index(label) for label in sequence_labels
]
mask = [1] * len(sequence_label_index) + [0] * (
max_sequence_len - len(sequence_label_index))
sequence_label_index.extend(
[label_vocabulary.padding_index] *
(max_sequence_len - len(sequence_label_index)))
sequence_label_indices.append(sequence_label_index)
mask_list.append(mask)
sequence_label_indices = torch.tensor(sequence_label_indices,
dtype=torch.long)
mask = torch.tensor(mask_list, dtype=torch.uint8)
decoder = SequenceLabelDecoder(label_vocabulary=label_vocabulary)
spans = decoder(label_indices=sequence_label_indices, mask=mask)
ASSERT.assertListEqual(expect_spans, spans)
