add model type check and give easy-to-understand error prompts

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11498502

modelscope/pipelines/cv/image_defrcn_fewshot_pipeline.py

@@ -7,6 +7,7 @@ import numpy as np
 import torch
 
 from modelscope.metainfo import Pipelines
+from modelscope.models.base.base_model import Model
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
@@ -44,6 +45,10 @@ class ImageDefrcnDetectionPipeline(Pipeline):
         """
         super().__init__(model=model, auto_collate=False, **kwargs)
 
+        assert isinstance(
+            self.model, Model
+        ), f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         model_path = os.path.join(self.model.model_dir,
                                   ModelFile.TORCH_MODEL_FILE)
         self.model.model = self._load_pretrained(

modelscope/pipelines/cv/image_instance_segmentation_pipeline.py

@@ -8,6 +8,7 @@ import torch
 from PIL import Image
 
 from modelscope.metainfo import Pipelines
+from modelscope.models.base.base_model import Model
 from modelscope.models.cv.image_instance_segmentation import (
     CascadeMaskRCNNSwinModel, get_img_ins_seg_result)
 from modelscope.pipelines.base import Input, Pipeline
@@ -40,6 +41,8 @@ class ImageInstanceSegmentationPipeline(Pipeline):
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
 
         if preprocessor is None:
+            assert isinstance(self.model, Model), \
+                f'please check whether model config exists in {ModelFile.CONFIGURATION}'
             config_path = os.path.join(self.model.model_dir,
                                        ModelFile.CONFIGURATION)
             cfg = Config.from_file(config_path)

modelscope/pipelines/cv/language_guided_video_summarization_pipeline.py

@@ -14,6 +14,7 @@ import torch
 from PIL import Image
 
 from modelscope.metainfo import Pipelines
+from modelscope.models.base.base_model import Model
 from modelscope.models.cv.language_guided_video_summarization import \
     ClipItVideoSummarization
 from modelscope.models.cv.language_guided_video_summarization.summarizer import (
@@ -44,8 +45,9 @@ class LanguageGuidedVideoSummarizationPipeline(Pipeline):
         """
         super().__init__(model=model, auto_collate=False, **kwargs)
         logger.info(f'loading model from {model}')
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         self.model_dir = self.model.model_dir
-
         self.tmp_dir = kwargs.get('tmp_dir', None)
         if self.tmp_dir is None:
             self.tmp_dir = tempfile.TemporaryDirectory().name

modelscope/pipelines/multi_modal/image_captioning_pipeline.py

@@ -10,7 +10,7 @@ from modelscope.pipelines.builder import PIPELINES
 from modelscope.pipelines.util import batch_process
 from modelscope.preprocessors import (MPlugPreprocessor, OfaPreprocessor,
                                       Preprocessor)
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
 
 logger = get_logger()
@@ -31,7 +31,10 @@ class ImageCaptioningPipeline(Pipeline):
         """
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.model.eval()
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
+
             if isinstance(self.model, OfaForAllTasks):
                 self.preprocessor = OfaPreprocessor(self.model.model_dir)
             elif isinstance(self.model, MPlugForAllTasks):

modelscope/pipelines/multi_modal/image_text_retrieval_pipeline.py

@@ -8,7 +8,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Model, Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import MPlugPreprocessor, Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
 
 logger = get_logger()
@@ -30,6 +30,8 @@ class ImageTextRetrievalPipeline(Pipeline):
         """
         super().__init__(model=model, preprocessor=preprocessor, **kwargs)
         self.model.eval()
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = MPlugPreprocessor(self.model.model_dir)
 

modelscope/pipelines/multi_modal/mgeo_ranking_pipeline.py

@@ -11,7 +11,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['MGeoRankingPipeline']
 
@@ -46,6 +46,8 @@ class MGeoRankingPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/faq_question_answering_pipeline.py

@@ -8,7 +8,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['FaqQuestionAnsweringPipeline']
 
@@ -38,6 +38,8 @@ class FaqQuestionAnsweringPipeline(Pipeline):
             config_file=config_file,
             device=device,
             auto_collate=auto_collate)
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir, **kwargs)

modelscope/pipelines/nlp/feature_extraction_pipeline.py

@@ -55,6 +55,8 @@ class FeatureExtractionPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/fill_mask_pipeline.py

@@ -10,7 +10,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline, Tensor
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['FillMaskPipeline']
 
@@ -61,6 +61,8 @@ class FillMaskPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/information_extraction_pipeline.py

@@ -9,7 +9,7 @@ from modelscope.models import Model
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['InformationExtractionPipeline']
 
@@ -45,6 +45,9 @@ class InformationExtractionPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if self.preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/named_entity_recognition_pipeline.py

@@ -7,7 +7,7 @@ from modelscope.models import Model
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.pipelines.nlp import TokenClassificationPipeline
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['NamedEntityRecognitionPipeline']
 
@@ -55,6 +55,10 @@ class NamedEntityRecognitionPipeline(TokenClassificationPipeline):
             config_file=config_file,
             device=device,
             auto_collate=auto_collate)
+
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/sentence_embedding_pipeline.py

@@ -10,7 +10,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['SentenceEmbeddingPipeline']
 
@@ -42,6 +42,10 @@ class SentenceEmbeddingPipeline(Pipeline):
             config_file=config_file,
             device=device,
             auto_collate=auto_collate)
+
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/table_question_answering_pipeline.py

@@ -53,6 +53,9 @@ class TableQuestionAnsweringPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             self.preprocessor = TableQuestionAnsweringPreprocessor(
                 self.model.model_dir, **kwargs)

modelscope/pipelines/nlp/text_classification_pipeline.py

@@ -11,7 +11,7 @@ from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.pipelines.util import batch_process
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Fields, Tasks
+from modelscope.utils.constant import Fields, ModelFile, Tasks
 from modelscope.utils.logger import get_logger
 
 logger = get_logger()
@@ -63,6 +63,9 @@ class TextClassificationPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             if self.model.__class__.__name__ == 'OfaForAllTasks':
                 self.preprocessor = Preprocessor.from_pretrained(

modelscope/pipelines/nlp/text_error_correction_pipeline.py

@@ -11,7 +11,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline, Tensor
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['TextErrorCorrectionPipeline']
 
@@ -49,6 +49,8 @@ class TextErrorCorrectionPipeline(Pipeline):
             config_file=config_file,
             device=device,
             auto_collate=auto_collate)
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir, **kwargs)

modelscope/pipelines/nlp/text_generation_pipeline.py

@@ -13,7 +13,7 @@ from modelscope.pipelines.base import Pipeline, Tensor
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
 from modelscope.utils.chinese_utils import remove_space_between_chinese_chars
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.hub import Config, read_config
 
 __all__ = ['TextGenerationPipeline', 'TextGenerationT5Pipeline']
@@ -60,6 +60,8 @@ class TextGenerationPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir, first_sequence=first_sequence, **kwargs)

modelscope/pipelines/nlp/text_ranking_pipeline.py

@@ -11,7 +11,7 @@ from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import (Preprocessor,
                                       TextRankingTransformersPreprocessor)
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['TextRankingPipeline']
 
@@ -45,6 +45,9 @@ class TextRankingPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/token_classification_pipeline.py

@@ -11,7 +11,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.tensor_utils import (torch_nested_detach,
                                            torch_nested_numpify)
 
@@ -53,6 +53,9 @@ class TokenClassificationPipeline(Pipeline):
             device=device,
             auto_collate=auto_collate)
 
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             self.preprocessor = Preprocessor.from_pretrained(
                 self.model.model_dir,

modelscope/pipelines/nlp/translation_evaluation_pipeline.py

@@ -46,6 +46,8 @@ class TranslationEvaluationPipeline(Pipeline):
 
         self.eval_mode = eval_mode
         self.checking_eval_mode()
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
 
         self.preprocessor = TranslationEvaluationPreprocessor(
             self.model.model_dir,

modelscope/pipelines/nlp/translation_pipeline.py

@@ -38,6 +38,9 @@ class TranslationPipeline(Pipeline):
             model: A Model instance.
         """
         super().__init__(model=model, **kwargs)
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         model = self.model.model_dir
         tf.reset_default_graph()
 

modelscope/pipelines/nlp/word_segmentation_pipeline.py

@@ -13,7 +13,7 @@ from modelscope.pipelines.nlp import TokenClassificationPipeline
 from modelscope.preprocessors import (
     Preprocessor, TokenClassificationTransformersPreprocessor,
     WordSegmentationPreprocessorThai)
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.tensor_utils import (torch_nested_detach,
                                            torch_nested_numpify)
 
@@ -100,6 +100,10 @@ class WordSegmentationThaiPipeline(MultilingualWordSegmentationPipeline):
             config_file=config_file,
             device=device,
             auto_collate=auto_collate)
+
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             self.preprocessor = WordSegmentationPreprocessorThai(
                 self.model.model_dir,

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -11,7 +11,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import Preprocessor
-from modelscope.utils.constant import Tasks
+from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['ZeroShotClassificationPipeline']
 
@@ -69,6 +69,10 @@ class ZeroShotClassificationPipeline(Pipeline):
             auto_collate=auto_collate)
         self.entailment_id = 0
         self.contradiction_id = 2
+
+        assert isinstance(self.model, Model), \
+            f'please check whether model config exists in {ModelFile.CONFIGURATION}'
+
         if preprocessor is None:
             sequence_length = kwargs.pop('sequence_length', 512)
             self.preprocessor = Preprocessor.from_pretrained(