[to #42322933] pre commit fix

modelscope/models/nlp/__init__.py

@@ -1,4 +1,4 @@
 from .sentence_similarity_model import *  # noqa F403
 from .sequence_classification_model import *  # noqa F403
 from .text_generation_model import *  # noqa F403
-from .zero_shot_classification_model import *
+from .zero_shot_classification_model import *  # noqa F403

modelscope/models/nlp/zero_shot_classification_model.py

@@ -1,6 +1,7 @@
 from typing import Any, Dict
-import torch
+
 import numpy as np
+import torch
 
 from modelscope.utils.constant import Tasks
 from ..base import Model
@@ -10,7 +11,8 @@ __all__ = ['BertForZeroShotClassification']
 
 
 @MODELS.register_module(
-    Tasks.zero_shot_classification, module_name=r'bert-zero-shot-classification')
+    Tasks.zero_shot_classification,
+    module_name=r'bert-zero-shot-classification')
 class BertForZeroShotClassification(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
@@ -40,6 +42,6 @@ class BertForZeroShotClassification(Model):
         """
         with torch.no_grad():
             outputs = self.model(**input)
-        logits = outputs["logits"].numpy()
+        logits = outputs['logits'].numpy()
         res = {'logits': logits}
         return res

modelscope/pipelines/builder.py

@@ -20,7 +20,8 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     Tasks.text_classification:
     ('bert-sentiment-analysis', 'damo/bert-base-sst2'),
     Tasks.zero_shot_classification:
-    ('bert-zero-shot-classification', 'damo/nlp_structbert_zero-shot-classification_chinese-base'),
+    ('bert-zero-shot-classification',
+     'damo/nlp_structbert_zero-shot-classification_chinese-base'),
     Tasks.text_generation: ('palm', 'damo/nlp_palm_text-generation_chinese'),
     Tasks.image_captioning: ('ofa', None),
     Tasks.image_generation:

modelscope/pipelines/nlp/__init__.py

@@ -1,4 +1,4 @@
 from .sentence_similarity_pipeline import *  # noqa F403
 from .sequence_classification_pipeline import *  # noqa F403
 from .text_generation_pipeline import *  # noqa F403
-from .zero_shot_classification_pipeline import *
+from .zero_shot_classification_pipeline import *  # noqa F403

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -4,6 +4,7 @@ from typing import Any, Dict, Union
 
 import json
 import numpy as np
+from scipy.special import softmax
 
 from modelscope.models.nlp import BertForZeroShotClassification
 from modelscope.preprocessors import ZeroShotClassificationPreprocessor
@@ -11,7 +12,6 @@ from modelscope.utils.constant import Tasks
 from ...models import Model
 from ..base import Input, Pipeline
 from ..builder import PIPELINES
-from scipy.special import softmax
 
 __all__ = ['ZeroShotClassificationPipeline']
 
@@ -39,16 +39,15 @@ class ZeroShotClassificationPipeline(Pipeline):
 
         self.entailment_id = 0
         self.contradiction_id = 2
-        self.candidate_labels = kwargs.pop("candidate_labels")
-        self.hypothesis_template = kwargs.pop('hypothesis_template', "{}")
+        self.candidate_labels = kwargs.pop('candidate_labels')
+        self.hypothesis_template = kwargs.pop('hypothesis_template', '{}')
         self.multi_label = kwargs.pop('multi_label', False)
 
         if preprocessor is None:
             preprocessor = ZeroShotClassificationPreprocessor(
                 sc_model.model_dir,
                 candidate_labels=self.candidate_labels,
-                hypothesis_template=self.hypothesis_template
-            )
+                hypothesis_template=self.hypothesis_template)
         super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
 
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
@@ -72,7 +71,7 @@ class ZeroShotClassificationPipeline(Pipeline):
 
         reversed_index = list(reversed(scores.argsort()))
         result = {
-            "labels": [self.candidate_labels[i] for i in reversed_index],
-            "scores": [scores[i].item() for i in reversed_index],
+            'labels': [self.candidate_labels[i] for i in reversed_index],
+            'scores': [scores[i].item() for i in reversed_index],
         }
         return result

modelscope/preprocessors/nlp.py

@@ -12,8 +12,7 @@ from .builder import PREPROCESSORS
 
 __all__ = [
     'Tokenize', 'SequenceClassificationPreprocessor',
-    'TextGenerationPreprocessor',
-    "ZeroShotClassificationPreprocessor"
+    'TextGenerationPreprocessor', 'ZeroShotClassificationPreprocessor'
 ]
 
 
@@ -190,8 +189,8 @@ class ZeroShotClassificationPreprocessor(Preprocessor):
         from sofa import SbertTokenizer
         self.model_dir: str = model_dir
         self.sequence_length = kwargs.pop('sequence_length', 512)
-        self.candidate_labels = kwargs.pop("candidate_labels")
-        self.hypothesis_template = kwargs.pop('hypothesis_template', "{}")
+        self.candidate_labels = kwargs.pop('candidate_labels')
+        self.hypothesis_template = kwargs.pop('hypothesis_template', '{}')
         self.tokenizer = SbertTokenizer.from_pretrained(self.model_dir)
 
     @type_assert(object, str)
@@ -206,7 +205,8 @@ class ZeroShotClassificationPreprocessor(Preprocessor):
         Returns:
             Dict[str, Any]: the preprocessed data
         """
-        pairs = [[data, self.hypothesis_template.format(label)] for label in self.candidate_labels]
+        pairs = [[data, self.hypothesis_template.format(label)]
+                 for label in self.candidate_labels]
 
         features = self.tokenizer(
             pairs,
@@ -214,7 +214,5 @@ class ZeroShotClassificationPreprocessor(Preprocessor):
             truncation=True,
             max_length=self.sequence_length,
             return_tensors='pt',
-            truncation_strategy='only_first'
-        )
+            truncation_strategy='only_first')
         return features
-

tests/pipelines/test_zero_shot_classification.py

@@ -13,13 +13,13 @@ from modelscope.utils.constant import Tasks
 class ZeroShotClassificationTest(unittest.TestCase):
     model_id = 'damo/nlp_structbert_zero-shot-classification_chinese-base'
     sentence = '全新突破 解放军运20版空中加油机曝光'
-    candidate_labels = ["文化", "体育", "娱乐", "财经", "家居", "汽车", "教育", "科技", "军事"]
+    candidate_labels = ['文化', '体育', '娱乐', '财经', '家居', '汽车', '教育', '科技', '军事']
 
     def test_run_from_local(self):
         cache_path = snapshot_download(self.model_id)
-        tokenizer = ZeroShotClassificationPreprocessor(cache_path, candidate_labels=self.candidate_labels)
-        model = BertForZeroShotClassification(
-            cache_path, tokenizer=tokenizer)
+        tokenizer = ZeroShotClassificationPreprocessor(
+            cache_path, candidate_labels=self.candidate_labels)
+        model = BertForZeroShotClassification(cache_path, tokenizer=tokenizer)
         pipeline1 = ZeroShotClassificationPipeline(
             model,
             preprocessor=tokenizer,
@@ -29,8 +29,7 @@ class ZeroShotClassificationTest(unittest.TestCase):
             Tasks.zero_shot_classification,
             model=model,
             preprocessor=tokenizer,
-            candidate_labels=self.candidate_labels
-        )
+            candidate_labels=self.candidate_labels)
 
         print(f'sentence: {self.sentence}\n'
               f'pipeline1:{pipeline1(input=self.sentence)}')
@@ -40,21 +39,20 @@ class ZeroShotClassificationTest(unittest.TestCase):
 
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
-        tokenizer = ZeroShotClassificationPreprocessor(model.model_dir, candidate_labels=self.candidate_labels)
+        tokenizer = ZeroShotClassificationPreprocessor(
+            model.model_dir, candidate_labels=self.candidate_labels)
         pipeline_ins = pipeline(
             task=Tasks.zero_shot_classification,
             model=model,
             preprocessor=tokenizer,
-            candidate_labels=self.candidate_labels
-        )
+            candidate_labels=self.candidate_labels)
         print(pipeline_ins(input=self.sentence))
 
     def test_run_with_model_name(self):
         pipeline_ins = pipeline(
             task=Tasks.zero_shot_classification,
             model=self.model_id,
-            candidate_labels=self.candidate_labels
-        )
+            candidate_labels=self.candidate_labels)
         print(pipeline_ins(input=self.sentence))
 
     def test_run_with_default_model(self):