merge feat/nlp

modelscope/hub/constants.py

@@ -1,5 +1,5 @@
 MODELSCOPE_URL_SCHEME = 'http://'
-DEFAULT_MODELSCOPE_DOMAIN = '101.201.119.157:32330'
+DEFAULT_MODELSCOPE_DOMAIN = '47.94.223.21:31090'
 DEFAULT_MODELSCOPE_GITLAB_DOMAIN = '101.201.119.157:31102'
 
 DEFAULT_MODELSCOPE_GROUP = 'damo'

modelscope/metainfo.py

@@ -16,6 +16,7 @@ class Models(object):
     palm = 'palm-v2'
     structbert = 'structbert'
     veco = 'veco'
+    space = 'space'
 
     # audio models
     sambert_hifi_16k = 'sambert-hifi-16k'
@@ -67,7 +68,7 @@ class Pipelines(object):
     kws_kwsbp = 'kws-kwsbp'
 
     # multi-modal tasks
-    image_caption = 'image-caption'
+    image_caption = 'image-captioning'
     multi_modal_embedding = 'multi-modal-embedding'
     visual_question_answering = 'visual-question-answering'
 
@@ -105,6 +106,9 @@ class Preprocessors(object):
     token_cls_tokenizer = 'token-cls-tokenizer'
     nli_tokenizer = 'nli-tokenizer'
     sen_cls_tokenizer = 'sen-cls-tokenizer'
+    dialog_intent_preprocessor = 'dialog-intent-preprocessor'
+    dialog_modeling_preprocessor = 'dialog-modeling-preprocessor'
+    dialog_state_tracking_preprocessor = 'dialog_state_tracking_preprocessor'
     sbert_token_cls_tokenizer = 'sbert-token-cls-tokenizer'
     zero_shot_cls_tokenizer = 'zero-shot-cls-tokenizer'
 

modelscope/models/__init__.py

@@ -9,6 +9,7 @@ from .builder import MODELS, build_model
 from .multi_modal import OfaForImageCaptioning
 from .nlp import (BertForMaskedLM, BertForSequenceClassification, SbertForNLI,
                   SbertForSentenceSimilarity, SbertForSentimentClassification,
-                  SbertForTokenClassification, SpaceForDialogIntentModel,
-                  SpaceForDialogModelingModel, SpaceForDialogStateTracking,
-                  StructBertForMaskedLM, VecoForMaskedLM)
+                  SbertForTokenClassification, SbertForZeroShotClassification,
+                  SpaceForDialogIntent, SpaceForDialogModeling,
+                  SpaceForDialogStateTracking, StructBertForMaskedLM,
+                  VecoForMaskedLM)

modelscope/models/nlp/space/dialog_intent_prediction_model.py

@@ -1,6 +1,9 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import os
 from typing import Any, Dict
 
+from ....metainfo import Models
 from ....preprocessors.space.fields.intent_field import IntentBPETextField
 from ....trainers.nlp.space.trainer.intent_trainer import IntentTrainer
 from ....utils.config import Config
@@ -10,19 +13,18 @@ from ...builder import MODELS
 from .model.generator import Generator
 from .model.model_base import SpaceModelBase
 
-__all__ = ['SpaceForDialogIntentModel']
+__all__ = ['SpaceForDialogIntent']
 
 
-@MODELS.register_module(Tasks.dialog_intent_prediction, module_name=r'space')
-class SpaceForDialogIntentModel(Model):
+@MODELS.register_module(
+    Tasks.dialog_intent_prediction, module_name=Models.space)
+class SpaceForDialogIntent(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the test generation model from the `model_dir` path.
 
         Args:
             model_dir (str): the model path.
-            model_cls (Optional[Any], optional): model loader, if None, use the
-                default loader to load model weights, by default None.
         """
 
         super().__init__(model_dir, *args, **kwargs)

modelscope/models/nlp/space/dialog_modeling_model.py

@@ -1,6 +1,9 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import os
 from typing import Any, Dict, Optional
 
+from ....metainfo import Models
 from ....preprocessors.space.fields.gen_field import MultiWOZBPETextField
 from ....trainers.nlp.space.trainer.gen_trainer import MultiWOZTrainer
 from ....utils.config import Config
@@ -10,19 +13,17 @@ from ...builder import MODELS
 from .model.generator import Generator
 from .model.model_base import SpaceModelBase
 
-__all__ = ['SpaceForDialogModelingModel']
+__all__ = ['SpaceForDialogModeling']
 
 
-@MODELS.register_module(Tasks.dialog_modeling, module_name=r'space')
-class SpaceForDialogModelingModel(Model):
+@MODELS.register_module(Tasks.dialog_modeling, module_name=Models.space)
+class SpaceForDialogModeling(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the test generation model from the `model_dir` path.
 
         Args:
             model_dir (str): the model path.
-            model_cls (Optional[Any], optional): model loader, if None, use the
-                default loader to load model weights, by default None.
         """
 
         super().__init__(model_dir, *args, **kwargs)

modelscope/models/nlp/space/model/gen_unified_transformer.py

@@ -1,6 +1,5 @@
-"""
-IntentUnifiedTransformer
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import torch
 
 from .unified_transformer import UnifiedTransformer

modelscope/models/nlp/space/model/generator.py

@@ -1,6 +1,4 @@
-"""
-Generator class.
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import math
 

modelscope/models/nlp/space/model/intent_unified_transformer.py

@@ -1,6 +1,5 @@
-"""
-IntentUnifiedTransformer
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

modelscope/models/nlp/space/model/model_base.py

@@ -1,6 +1,5 @@
-"""
-Model base
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import os
 
 import torch.nn as nn

modelscope/models/nlp/space/model/unified_transformer.py

@@ -1,6 +1,4 @@
-"""
-UnifiedTransformer
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import numpy as np
 import torch

modelscope/models/nlp/space/modules/embedder.py

@@ -1,6 +1,4 @@
-"""
-Embedder class.
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import torch
 import torch.nn as nn

modelscope/models/nlp/space/modules/feedforward.py

@@ -1,6 +1,4 @@
-"""
-FeedForward class.
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import torch
 import torch.nn as nn

modelscope/models/nlp/space/modules/functions.py

@@ -1,6 +1,4 @@
-"""
-Helpful functions.
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import numpy as np
 import torch

modelscope/models/nlp/space/modules/multihead_attention.py

@@ -1,6 +1,4 @@
-"""
-MultiheadAttention class.
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import torch
 import torch.nn as nn
@@ -53,8 +51,6 @@ class MultiheadAttention(nn.Module):
         if mask is not None:
             '''
             mask: [batch size, num_heads, seq_len, seq_len]
-            mask后两维(seq_len, seq_len)矩阵来看，其中有的行可能都是true(1)，对应句子中<pad>位看的行
-            导致softmax后该行的每个位置的attn prob都为1/n而非0，所以此处需重置为0
 
             >>> F.softmax([-1e10, -100, -100])
             >>> [0.00, 0.50, 0.50]

modelscope/models/nlp/space/modules/transformer_block.py

@@ -1,6 +1,4 @@
-"""
-TransformerBlock class.
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
 
 import torch
 import torch.nn as nn

modelscope/msdatasets/config.py

@@ -19,4 +19,4 @@ DOWNLOADED_DATASETS_PATH = Path(
     os.getenv('DOWNLOADED_DATASETS_PATH', DEFAULT_DOWNLOADED_DATASETS_PATH))
 
 MS_HUB_ENDPOINT = os.environ.get('MS_HUB_ENDPOINT',
-                                 'http://123.57.189.90:31752')
+                                 'http://47.94.223.21:31752')

modelscope/pipelines/builder.py

@@ -36,6 +36,11 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     Tasks.zero_shot_classification:
     (Pipelines.zero_shot_classification,
      'damo/nlp_structbert_zero-shot-classification_chinese-base'),
+    Tasks.dialog_intent_prediction:
+    (Pipelines.dialog_intent_prediction,
+     'damo/nlp_space_dialog-intent-prediction'),
+    Tasks.dialog_modeling: (Pipelines.dialog_modeling,
+                            'damo/nlp_space_dialog-modeling'),
     Tasks.image_captioning: (Pipelines.image_caption,
                              'damo/ofa_image-caption_coco_large_en'),
     Tasks.image_generation:

modelscope/pipelines/nlp/dialog_intent_prediction_pipeline.py

@@ -1,7 +1,9 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 from typing import Any, Dict
 
 from ...metainfo import Pipelines
-from ...models.nlp import SpaceForDialogIntentModel
+from ...models.nlp import SpaceForDialogIntent
 from ...preprocessors import DialogIntentPredictionPreprocessor
 from ...utils.constant import Tasks
 from ..base import Pipeline
@@ -15,7 +17,7 @@ __all__ = ['DialogIntentPredictionPipeline']
     module_name=Pipelines.dialog_intent_prediction)
 class DialogIntentPredictionPipeline(Pipeline):
 
-    def __init__(self, model: SpaceForDialogIntentModel,
+    def __init__(self, model: SpaceForDialogIntent,
                  preprocessor: DialogIntentPredictionPreprocessor, **kwargs):
         """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
 
@@ -26,7 +28,7 @@ class DialogIntentPredictionPipeline(Pipeline):
 
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.model = model
-        # self.tokenizer = preprocessor.tokenizer
+        self.categories = preprocessor.categories
 
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]:
         """process the prediction results
@@ -41,6 +43,10 @@ class DialogIntentPredictionPipeline(Pipeline):
         pred = inputs['pred']
         pos = np.where(pred == np.max(pred))
 
-        result = {'pred': pred, 'label': pos[0]}
+        result = {
+            'pred': pred,
+            'label_pos': pos[0],
+            'label': self.categories[pos[0][0]]
+        }
 
         return result

modelscope/pipelines/nlp/dialog_modeling_pipeline.py

@@ -1,7 +1,9 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 from typing import Any, Dict, Optional
 
 from ...metainfo import Pipelines
-from ...models.nlp import SpaceForDialogModelingModel
+from ...models.nlp import SpaceForDialogModeling
 from ...preprocessors import DialogModelingPreprocessor
 from ...utils.constant import Tasks
 from ..base import Pipeline, Tensor
@@ -14,7 +16,7 @@ __all__ = ['DialogModelingPipeline']
     Tasks.dialog_modeling, module_name=Pipelines.dialog_modeling)
 class DialogModelingPipeline(Pipeline):
 
-    def __init__(self, model: SpaceForDialogModelingModel,
+    def __init__(self, model: SpaceForDialogModeling,
                  preprocessor: DialogModelingPreprocessor, **kwargs):
         """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
 
@@ -40,7 +42,6 @@ class DialogModelingPipeline(Pipeline):
             inputs['resp'])
         assert len(sys_rsp) > 2
         sys_rsp = sys_rsp[1:len(sys_rsp) - 1]
-        # sys_rsp = self.preprocessor.text_field.tokenizer.
 
         inputs['sys'] = sys_rsp
 

modelscope/pipelines/outputs.py

@@ -108,14 +108,19 @@ TASK_OUTPUTS = {
     #   }
     Tasks.sentiment_classification: ['scores', 'labels'],
 
+    # zero-shot classification result for single sample
+    #   {
+    #       "labels": ["happy", "sad", "calm", "angry"],
+    #       "scores": [0.9, 0.1, 0.05, 0.05]
+    #   }
+    Tasks.zero_shot_classification: ['scores', 'labels'],
+
     # nli result for single sample
     #   {
     #       "labels": ["happy", "sad", "calm", "angry"],
     #       "scores": [0.9, 0.1, 0.05, 0.05]
     #   }
     Tasks.nli: ['scores', 'labels'],
-    Tasks.dialog_modeling: [],
-    Tasks.dialog_intent_prediction: [],
 
     # {
     #     "dialog_states": {
@@ -153,6 +158,31 @@ TASK_OUTPUTS = {
     # }
     Tasks.dialog_state_tracking: ['dialog_states'],
 
+    # {'pred': array([2.62349960e-03, 4.12110658e-03, 4.12748595e-05, 3.77560973e-05,
+    #        1.08599677e-04, 1.72710388e-05, 2.95618793e-05, 1.93638436e-04,
+    #        6.45841064e-05, 1.15997791e-04, 5.11605394e-05, 9.87020373e-01,
+    #        2.66957268e-05, 4.72324500e-05, 9.74208378e-05, 4.18022355e-05,
+    #        2.97343540e-05, 5.81317654e-05, 5.44203431e-05, 6.28319322e-05,
+    #        7.34537680e-05, 6.61411541e-05, 3.62534920e-05, 8.58885178e-05,
+    #        8.24327726e-05, 4.66077945e-05, 5.32869453e-05, 4.16190960e-05,
+    #        5.97518992e-05, 3.92273068e-05, 3.44069012e-05, 9.92335918e-05,
+    #        9.25978165e-05, 6.26462061e-05, 3.32317031e-05, 1.32061413e-03,
+    #        2.01607945e-05, 3.36636294e-05, 3.99156743e-05, 5.84108493e-05,
+    #        2.53432900e-05, 4.95731190e-04, 2.64443643e-05, 4.46992999e-05,
+    #        2.42672231e-05, 4.75615161e-05, 2.66230145e-05, 4.00083954e-05,
+    #        2.90536875e-04, 4.23891543e-05, 8.63691166e-05, 4.98188965e-05,
+    #        3.47019341e-05, 4.52718523e-05, 4.20905781e-05, 5.50173208e-05,
+    #        4.92360487e-05, 3.56021264e-05, 2.13957210e-05, 6.17428886e-05,
+    #        1.43893281e-04, 7.32152112e-05, 2.91354867e-04, 2.46623786e-05,
+    #        3.61441926e-05, 3.38475402e-05, 3.44323053e-05, 5.70138109e-05,
+    #        4.31488479e-05, 4.94503947e-05, 4.30105974e-05, 1.00963116e-04,
+    #        2.82062047e-05, 1.15582036e-04, 4.48261271e-05, 3.99339879e-05,
+    #        7.27692823e-05], dtype=float32), 'label_pos': array([11]), 'label': 'lost_or_stolen_card'}
+    Tasks.dialog_intent_prediction: ['pred', 'label_pos', 'label'],
+
+    # sys : ['you', 'are', 'welcome', '.', 'have', 'a', 'great', 'day', '!']
+    Tasks.dialog_modeling: ['sys'],
+
     # ============ audio tasks ===================
 
     # audio processed for single file in PCM format

modelscope/preprocessors/nlp.py

@@ -15,7 +15,7 @@ __all__ = [
     'Tokenize', 'SequenceClassificationPreprocessor',
     'TextGenerationPreprocessor', 'TokenClassifcationPreprocessor',
     'NLIPreprocessor', 'SentimentClassificationPreprocessor',
-    'FillMaskPreprocessor'
+    'FillMaskPreprocessor', 'ZeroShotClassificationPreprocessor'
 ]
 
 
@@ -421,3 +421,47 @@ class TokenClassifcationPreprocessor(Preprocessor):
             'attention_mask': attention_mask,
             'token_type_ids': token_type_ids
         }
+
+
+@PREPROCESSORS.register_module(
+    Fields.nlp, module_name=Preprocessors.zero_shot_cls_tokenizer)
+class ZeroShotClassificationPreprocessor(Preprocessor):
+
+    def __init__(self, model_dir: str, *args, **kwargs):
+        """preprocess the data via the vocab.txt from the `model_dir` path
+
+        Args:
+            model_dir (str): model path
+        """
+
+        super().__init__(*args, **kwargs)
+
+        from sofa import SbertTokenizer
+        self.model_dir: str = model_dir
+        self.sequence_length = kwargs.pop('sequence_length', 512)
+        self.tokenizer = SbertTokenizer.from_pretrained(self.model_dir)
+
+    @type_assert(object, str)
+    def __call__(self, data: str, hypothesis_template: str,
+                 candidate_labels: list) -> Dict[str, Any]:
+        """process the raw input data
+
+        Args:
+            data (str): a sentence
+                Example:
+                    'you are so handsome.'
+
+        Returns:
+            Dict[str, Any]: the preprocessed data
+        """
+        pairs = [[data, hypothesis_template.format(label)]
+                 for label in candidate_labels]
+
+        features = self.tokenizer(
+            pairs,
+            padding=True,
+            truncation=True,
+            max_length=self.sequence_length,
+            return_tensors='pt',
+            truncation_strategy='only_first')
+        return features

modelscope/preprocessors/space/dialog_intent_prediction_preprocessor.py

@@ -3,6 +3,9 @@
 import os
 from typing import Any, Dict
 
+import json
+
+from ...metainfo import Preprocessors
 from ...utils.config import Config
 from ...utils.constant import Fields, ModelFile
 from ...utils.type_assert import type_assert
@@ -13,7 +16,8 @@ from .fields.intent_field import IntentBPETextField
 __all__ = ['DialogIntentPredictionPreprocessor']
 
 
-@PREPROCESSORS.register_module(Fields.nlp, module_name=r'space-intent')
+@PREPROCESSORS.register_module(
+    Fields.nlp, module_name=Preprocessors.dialog_intent_preprocessor)
 class DialogIntentPredictionPreprocessor(Preprocessor):
 
     def __init__(self, model_dir: str, *args, **kwargs):
@@ -30,6 +34,11 @@ class DialogIntentPredictionPreprocessor(Preprocessor):
         self.text_field = IntentBPETextField(
             self.model_dir, config=self.config)
 
+        self.categories = None
+        with open(os.path.join(self.model_dir, 'categories.json'), 'r') as f:
+            self.categories = json.load(f)
+        assert len(self.categories) == 77
+
     @type_assert(object, str)
     def __call__(self, data: str) -> Dict[str, Any]:
         """process the raw input data

modelscope/preprocessors/space/dialog_modeling_preprocessor.py

@@ -3,6 +3,7 @@
 import os
 from typing import Any, Dict
 
+from ...metainfo import Preprocessors
 from ...utils.config import Config
 from ...utils.constant import Fields, ModelFile
 from ...utils.type_assert import type_assert
@@ -13,7 +14,8 @@ from .fields.gen_field import MultiWOZBPETextField
 __all__ = ['DialogModelingPreprocessor']
 
 
-@PREPROCESSORS.register_module(Fields.nlp, module_name=r'space-modeling')
+@PREPROCESSORS.register_module(
+    Fields.nlp, module_name=Preprocessors.dialog_modeling_preprocessor)
 class DialogModelingPreprocessor(Preprocessor):
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/preprocessors/space/fields/gen_field.py

@@ -1,6 +1,5 @@
-"""
-Field class
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import os
 import random
 from collections import OrderedDict
@@ -8,7 +7,6 @@ from itertools import chain
 
 import numpy as np
 
-from ....utils.constant import ModelFile
 from ....utils.nlp.space import ontology, utils
 from ....utils.nlp.space.db_ops import MultiWozDB
 from ....utils.nlp.space.utils import list2np

modelscope/preprocessors/space/fields/intent_field.py

@@ -1,6 +1,5 @@
-"""
-Intent Field class
-"""
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
 import glob
 import multiprocessing
 import os

modelscope/utils/nlp/space/db_ops.py

@@ -308,14 +308,6 @@ if __name__ == '__main__':
             'attraction': 5,
             'train': 1,
         }
-        # for ent in res:
-        #     if reidx.get(domain):
-        #         report.append(ent[reidx[domain]])
-        # for ent in res:
-        #     if 'name' in ent:
-        #         report.append(ent['name'])
-        #     if 'trainid' in ent:
-        #         report.append(ent['trainid'])
         print(constraints)
         print(res)
         print('count:', len(res), '\nnames:', report)

modelscope/utils/nlp/space/ontology.py

@@ -123,19 +123,6 @@ dialog_act_all_slots = all_slots + ['choice', 'open']
 # no need of this, just covert slot to [slot] e.g. pricerange -> [pricerange]
 slot_name_to_slot_token = {}
 
-# special slot tokens in responses
-# not use at the momoent
-slot_name_to_value_token = {
-    # 'entrance fee': '[value_price]',
-    # 'pricerange': '[value_price]',
-    # 'arriveby': '[value_time]',
-    # 'leaveat': '[value_time]',
-    # 'departure': '[value_place]',
-    # 'destination': '[value_place]',
-    # 'stay': 'count',
-    # 'people': 'count'
-}
-
 # eos tokens definition
 eos_tokens = {
     'user': '<eos_u>',

modelscope/utils/nlp/space/utils.py

@@ -53,16 +53,9 @@ def clean_replace(s, r, t, forward=True, backward=False):
             return s, -1
         return s[:idx] + t + s[idx_r:], idx_r
 
-    # source, replace, target = s, r, t
-    # count = 0
     sidx = 0
     while sidx != -1:
         s, sidx = clean_replace_single(s, r, t, forward, backward, sidx)
-        # count += 1
-        # print(s, sidx)
-        # if count == 20:
-        #     print(source, '\n', replace, '\n', target)
-        #     quit()
     return s
 
 
@@ -193,14 +186,3 @@ class MultiWOZVocab(object):
             return self._idx2word[idx]
         else:
             return self._idx2word[idx] + '(o)'
-
-    # def sentence_decode(self, index_list, eos=None, indicate_oov=False):
-    #     l = [self.decode(_, indicate_oov) for _ in index_list]
-    #     if not eos or eos not in l:
-    #         return ' '.join(l)
-    #     else:
-    #         idx = l.index(eos)
-    #         return ' '.join(l[:idx])
-    #
-    # def nl_decode(self, l, eos=None):
-    #     return [self.sentence_decode(_, eos) + '\n' for _ in l]

tests/pipelines/test_dialog_intent_prediction.py

@@ -3,10 +3,11 @@ import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
-from modelscope.models.nlp import SpaceForDialogIntentModel
+from modelscope.models.nlp import SpaceForDialogIntent
 from modelscope.pipelines import DialogIntentPredictionPipeline, pipeline
 from modelscope.preprocessors import DialogIntentPredictionPreprocessor
 from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import test_level
 
 
 class DialogIntentPredictionTest(unittest.TestCase):
@@ -16,11 +17,11 @@ class DialogIntentPredictionTest(unittest.TestCase):
         'I still have not received my new card, I ordered over a week ago.'
     ]
 
-    @unittest.skip('test with snapshot_download')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run(self):
         cache_path = snapshot_download(self.model_id)
         preprocessor = DialogIntentPredictionPreprocessor(model_dir=cache_path)
-        model = SpaceForDialogIntentModel(
+        model = SpaceForDialogIntent(
             model_dir=cache_path,
             text_field=preprocessor.text_field,
             config=preprocessor.config)
@@ -37,6 +38,7 @@ class DialogIntentPredictionTest(unittest.TestCase):
         for my_pipeline, item in list(zip(pipelines, self.test_case)):
             print(my_pipeline(item))
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
         preprocessor = DialogIntentPredictionPreprocessor(

tests/pipelines/test_dialog_modeling.py

@@ -1,15 +1,13 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-import os
-import os.path as osp
-import tempfile
 import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
-from modelscope.models.nlp import SpaceForDialogModelingModel
+from modelscope.models.nlp import SpaceForDialogModeling
 from modelscope.pipelines import DialogModelingPipeline, pipeline
 from modelscope.preprocessors import DialogModelingPreprocessor
 from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import test_level
 
 
 class DialogModelingTest(unittest.TestCase):
@@ -91,13 +89,13 @@ class DialogModelingTest(unittest.TestCase):
         }
     }
 
-    @unittest.skip('test with snapshot_download')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run(self):
 
         cache_path = snapshot_download(self.model_id)
 
         preprocessor = DialogModelingPreprocessor(model_dir=cache_path)
-        model = SpaceForDialogModelingModel(
+        model = SpaceForDialogModeling(
             model_dir=cache_path,
             text_field=preprocessor.text_field,
             config=preprocessor.config)
@@ -120,6 +118,7 @@ class DialogModelingTest(unittest.TestCase):
             })
             print('sys : {}'.format(result['sys']))
 
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
         preprocessor = DialogModelingPreprocessor(model_dir=model.model_dir)

tests/pipelines/test_nli.py

@@ -29,7 +29,7 @@ class NLITest(unittest.TestCase):
             f'sentence1: {self.sentence1}\nsentence2: {self.sentence2}\n'
             f'pipeline1: {pipeline2(input=(self.sentence1, self.sentence2))}')
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
         tokenizer = NLIPreprocessor(model.model_dir)

tests/pipelines/test_sentiment_classification.py

@@ -42,7 +42,7 @@ class SentimentClassificationTest(unittest.TestCase):
             preprocessor=tokenizer)
         print(pipeline_ins(input=self.sentence1))
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_model_name(self):
         pipeline_ins = pipeline(
             task=Tasks.sentiment_classification, model=self.model_id)