Merge branch 'feat/nlp_refactor' of http://gitlab.alibaba-inc.com/Ali-MaaS/MaaS-lib into feat/nlp_refactor

modelscope/models/__init__.py

@@ -1,10 +1,10 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from .audio.tts.am import SambertNetHifi16k
-from .audio.tts.vocoder import Hifigan16k
+# from .audio.tts.am import SambertNetHifi16k
+# from .audio.tts.vocoder import Hifigan16k
 from .base import Model
 from .builder import MODELS, build_model
-from .multi_model import OfaForImageCaptioning
+# from .multi_model import OfaForImageCaptioning
 from .nlp import (
     BertForSequenceClassification,
     SbertForNLI,
@@ -13,5 +13,5 @@ from .nlp import (
     SbertForZeroShotClassification,
     StructBertForMaskedLM,
     VecoForMaskedLM,
-    StructBertForTokenClassification,
+    SbertForTokenClassification,
 )

modelscope/models/nlp/sbert_for_token_classification.py

@@ -24,10 +24,10 @@ class SbertForTokenClassification(Model):
         """
         super().__init__(model_dir, *args, **kwargs)
         self.model_dir = model_dir
-        from sofa import SbertConfig, SbertForTokenClassification
-        self.model = SbertForTokenClassification.from_pretrained(
+        import sofa
+        self.model = sofa.SbertForTokenClassification.from_pretrained(
             self.model_dir)
-        self.config = SbertConfig.from_pretrained(self.model_dir)
+        self.config = sofa.SbertConfig.from_pretrained(self.model_dir)
 
     def forward(self, input: Dict[str,
                                   Any]) -> Dict[str, Union[str, np.ndarray]]:
@@ -46,7 +46,7 @@ class SbertForTokenClassification(Model):
                     }
         """
         input_ids = torch.tensor(input['input_ids']).unsqueeze(0)
-        return self.model(input_ids)
+        return {**self.model(input_ids), 'text': input['text']}
 
     def postprocess(self, input: Dict[str, Tensor], **kwargs) -> Dict[str, Tensor]:
         logits = input["logits"]

modelscope/pipelines/builder.py

@@ -25,15 +25,15 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     (Pipelines.sentence_similarity,
      'damo/nlp_structbert_sentence-similarity_chinese-base'),
     Tasks.image_matting: ('image-matting', 'damo/cv_unet_image-matting'),
-    Tasks.nli: ('nlp_structbert_nli_chinese-base',
+    Tasks.nli: (Pipelines.nli,
                 'damo/nlp_structbert_nli_chinese-base'),
     Tasks.sentiment_classification:
-    ('sbert-sentiment-classification',
+    (Pipelines.sentiment_classification,
      'damo/nlp_structbert_sentiment-classification_chinese-base'),
     Tasks.text_classification: ('bert-sentiment-analysis',
                                 'damo/bert-base-sst2'),
     Tasks.zero_shot_classification:
-    ('bert-zero-shot-classification',
+    (Pipelines.zero_shot_classification,
      'damo/nlp_structbert_zero-shot-classification_chinese-base'),
     Tasks.image_matting: (Pipelines.image_matting,
                           'damo/cv_unet_image-matting'),
@@ -48,7 +48,7 @@ DEFAULT_MODEL_FOR_PIPELINE = {
      'damo/cv_unet_person-image-cartoon_compound-models'),
     Tasks.ocr_detection: (Pipelines.ocr_detection,
                           'damo/cv_resnet18_ocr-detection-line-level_damo'),
-    Tasks.fill_mask: ('veco', 'damo/nlp_veco_fill-mask_large')
+    Tasks.fill_mask: (Pipelines.fill_mask, 'damo/nlp_veco_fill-mask_large')
 }
 
 

tests/pipelines/test_word_segmentation.py

@@ -4,7 +4,7 @@ import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
-from modelscope.models.nlp import StructBertForTokenClassification
+from modelscope.models.nlp import SbertForTokenClassification
 from modelscope.pipelines import WordSegmentationPipeline, pipeline
 from modelscope.preprocessors import TokenClassifcationPreprocessor
 from modelscope.utils.constant import Tasks
@@ -19,7 +19,7 @@ class WordSegmentationTest(unittest.TestCase):
     def test_run_by_direct_model_download(self):
         cache_path = snapshot_download(self.model_id)
         tokenizer = TokenClassifcationPreprocessor(cache_path)
-        model = StructBertForTokenClassification(
+        model = SbertForTokenClassification(
             cache_path, tokenizer=tokenizer)
         pipeline1 = WordSegmentationPipeline(model, preprocessor=tokenizer)
         pipeline2 = pipeline(

tests/pipelines/test_zero_shot_classification.py

@@ -3,7 +3,7 @@ import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
-from modelscope.models.nlp import BertForZeroShotClassification
+from modelscope.models.nlp import SbertForZeroShotClassification
 from modelscope.pipelines import ZeroShotClassificationPipeline, pipeline
 from modelscope.preprocessors import ZeroShotClassificationPreprocessor
 from modelscope.utils.constant import Tasks
@@ -18,7 +18,7 @@ class ZeroShotClassificationTest(unittest.TestCase):
     def test_run_from_local(self):
         cache_path = snapshot_download(self.model_id)
         tokenizer = ZeroShotClassificationPreprocessor(cache_path)
-        model = BertForZeroShotClassification(cache_path, tokenizer=tokenizer)
+        model = SbertForZeroShotClassification(cache_path, tokenizer=tokenizer)
         pipeline1 = ZeroShotClassificationPipeline(
             model, preprocessor=tokenizer)
         pipeline2 = pipeline(