[to #42322933]specifiy torch model to inherit from TorchModel

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9588834

modelscope/models/audio/ans/frcrn.py

@@ -6,7 +6,8 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import ModelFile, Tasks
 from .conv_stft import ConviSTFT, ConvSTFT
@@ -59,7 +60,7 @@ class FTB(nn.Module):
 
 @MODELS.register_module(
     Tasks.speech_signal_process, module_name=Models.speech_frcrn_ans_cirm_16k)
-class FRCRNModel(Model):
+class FRCRNModel(TorchModel):
     r""" A decorator of FRCRN for integrating into modelscope framework """
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/models/cv/action_recognition/models.py

@@ -1,4 +1,3 @@
-import torch
 import torch.nn as nn
 
 from .tada_convnext import TadaConvNeXt

modelscope/models/multi_modal/clip/clip_model.py

@@ -13,7 +13,7 @@ from torch.distributed.nn.functional import \
 from torchvision.transforms import Compose, Normalize, Resize, ToTensor
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model
+from modelscope.models import TorchModel
 from modelscope.models.builder import MODELS
 from modelscope.models.multi_modal.clip.clip_bert import TextTransformer
 from modelscope.models.multi_modal.clip.clip_vit import VisionTransformer
@@ -116,7 +116,7 @@ class CLIPModel(nn.Module):
 
 
 @MODELS.register_module(Tasks.multi_modal_embedding, module_name=Models.clip)
-class CLIPForMultiModalEmbedding(Model):
+class CLIPForMultiModalEmbedding(TorchModel):
 
     def __init__(self, model_dir, device_id=-1):
         super().__init__(model_dir=model_dir, device_id=device_id)

modelscope/models/multi_modal/imagen/imagen_model.py

@@ -6,10 +6,9 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from PIL import Image
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model
+from modelscope.models import Model
 from modelscope.models.builder import MODELS
 from modelscope.models.multi_modal.imagen.diffusion import (GaussianDiffusion,
                                                             beta_schedule)

modelscope/models/multi_modal/mmr/models/clip_for_mm_video_embedding.py

@@ -1,4 +1,3 @@
-import os
 import random
 from os.path import exists
 from typing import Any, Dict
@@ -9,7 +8,7 @@ import torch
 from PIL import Image
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model
+from modelscope.models import TorchModel
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
@@ -22,7 +21,7 @@ logger = get_logger()
 
 @MODELS.register_module(
     Tasks.video_multi_modal_embedding, module_name=Models.video_clip)
-class VideoCLIPForMultiModalEmbedding(Model):
+class VideoCLIPForMultiModalEmbedding(TorchModel):
 
     def __init__(self, model_dir, device_id=-1):
         super().__init__(model_dir=model_dir, device_id=device_id)

modelscope/models/multi_modal/mplug_for_visual_question_answering.py

@@ -1,7 +1,8 @@
 from typing import Dict
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import Tasks
 
@@ -10,7 +11,7 @@ __all__ = ['MPlugForVisualQuestionAnswering']
 
 @MODELS.register_module(
     Tasks.visual_question_answering, module_name=Models.mplug)
-class MPlugForVisualQuestionAnswering(Model):
+class MPlugForVisualQuestionAnswering(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the mplug model from the `model_dir` path.

modelscope/models/multi_modal/ofa_for_all_tasks.py

@@ -8,7 +8,8 @@ import torch.cuda
 import torch.nn.functional as F
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.outputs import OutputKeys
 from modelscope.preprocessors.ofa.utils.collate import collate_tokens
@@ -32,7 +33,7 @@ __all__ = ['OfaForAllTasks']
 @MODELS.register_module(Tasks.image_classification, module_name=Models.ofa)
 @MODELS.register_module(Tasks.summarization, module_name=Models.ofa)
 @MODELS.register_module(Tasks.text_classification, module_name=Models.ofa)
-class OfaForAllTasks(Model):
+class OfaForAllTasks(TorchModel):
 
     def __init__(self, model_dir, *args, **kwargs):
         super().__init__(model_dir=model_dir, *args, **kwargs)

modelscope/models/nlp/bert_for_sequence_classification.py

@@ -5,7 +5,7 @@ import json
 import numpy as np
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model
+from modelscope.models import TorchModel
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import Tasks
 
@@ -13,7 +13,7 @@ __all__ = ['BertForSequenceClassification']
 
 
 @MODELS.register_module(Tasks.text_classification, module_name=Models.bert)
-class BertForSequenceClassification(Model):
+class BertForSequenceClassification(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         # Model.__init__(self, model_dir, model_cls, first_sequence, *args, **kwargs)

modelscope/models/nlp/csanmt_for_translation.py

@@ -1,10 +1,7 @@
 import math
-import os
 from collections import namedtuple
-from typing import Any, Dict
+from typing import Dict
 
-import json
-import numpy as np
 import tensorflow as tf
 
 from modelscope.metainfo import Models

modelscope/models/nlp/masked_language.py

@@ -1,16 +1,17 @@
-from typing import Any, Dict, Optional, Union
+from typing import Dict
 
 import numpy as np
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import Tasks
 
 __all__ = ['BertForMaskedLM', 'StructBertForMaskedLM', 'VecoForMaskedLM']
 
 
-class MaskedLanguageModelBase(Model):
+class MaskedLanguageModelBase(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         super().__init__(model_dir, *args, **kwargs)

modelscope/models/nlp/nncrf_for_named_entity_recognition.py

@@ -1,15 +1,12 @@
 import os
 from typing import Any, Dict, List, Optional
 
-import json
-import numpy as np
 import torch
 import torch.nn as nn
-from torch.autograd import Variable
 from transformers import AutoConfig, AutoModel
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model
+from modelscope.models import TorchModel
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import ModelFile, Tasks
 
@@ -18,7 +15,7 @@ __all__ = ['TransformerCRFForNamedEntityRecognition']
 
 @MODELS.register_module(
     Tasks.named_entity_recognition, module_name=Models.tcrf)
-class TransformerCRFForNamedEntityRecognition(Model):
+class TransformerCRFForNamedEntityRecognition(TorchModel):
 
     def __init__(self, model_dir, *args, **kwargs):
         super().__init__(model_dir, *args, **kwargs)

modelscope/models/nlp/sbert_for_sequence_classification.py

@@ -7,7 +7,7 @@ import torch
 from sofa.models.sbert.modeling_sbert import SbertModel, SbertPreTrainedModel
 from torch import nn
 
-from modelscope.models.base import Model
+from modelscope.models import TorchModel
 
 
 class SbertTextClassfier(SbertPreTrainedModel):
@@ -43,7 +43,7 @@ class SbertTextClassfier(SbertPreTrainedModel):
         return SbertTextClassfier.from_pretrained(model_dir, **model_args)
 
 
-class SbertForSequenceClassificationBase(Model):
+class SbertForSequenceClassificationBase(TorchModel):
 
     def __init__(self, model_dir: str, model_args=None, *args, **kwargs):
         super().__init__(model_dir, *args, **kwargs)

modelscope/models/nlp/sbert_for_token_classification.py

@@ -4,7 +4,8 @@ import numpy as np
 import torch
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import Tasks
 
@@ -12,7 +13,7 @@ __all__ = ['SbertForTokenClassification']
 
 
 @MODELS.register_module(Tasks.word_segmentation, module_name=Models.structbert)
-class SbertForTokenClassification(Model):
+class SbertForTokenClassification(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the word segmentation model from the `model_dir` path.

modelscope/models/nlp/sbert_for_zero_shot_classification.py

@@ -3,7 +3,7 @@ from typing import Any, Dict
 import numpy as np
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model
+from modelscope.models import TorchModel
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import Tasks
 
@@ -12,7 +12,7 @@ __all__ = ['SbertForZeroShotClassification']
 
 @MODELS.register_module(
     Tasks.zero_shot_classification, module_name=Models.structbert)
-class SbertForZeroShotClassification(Model):
+class SbertForZeroShotClassification(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the zero shot classification model from the `model_dir` path.

modelscope/models/nlp/space_for_dialog_intent_prediction.py

@@ -1,10 +1,11 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 import os
-from typing import Any, Dict
+from typing import Dict
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.models.nlp.backbones import SpaceGenerator, SpaceModelBase
 from modelscope.preprocessors.space import IntentBPETextField
@@ -16,7 +17,7 @@ __all__ = ['SpaceForDialogIntent']
 
 @MODELS.register_module(
     Tasks.dialog_intent_prediction, module_name=Models.space)
-class SpaceForDialogIntent(Model):
+class SpaceForDialogIntent(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the test generation model from the `model_dir` path.

modelscope/models/nlp/space_for_dialog_modeling.py

@@ -1,10 +1,11 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 import os
-from typing import Any, Dict, Optional
+from typing import Dict
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.models.nlp.backbones import SpaceGenerator, SpaceModelBase
 from modelscope.preprocessors.space import MultiWOZBPETextField
@@ -15,7 +16,7 @@ __all__ = ['SpaceForDialogModeling']
 
 
 @MODELS.register_module(Tasks.dialog_modeling, module_name=Models.space)
-class SpaceForDialogModeling(Model):
+class SpaceForDialogModeling(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the test generation model from the `model_dir` path.

modelscope/models/nlp/space_for_dialog_state_tracking.py

@@ -1,17 +1,16 @@
-import os
-from typing import Any, Dict
+from typing import Dict
 
 from modelscope.metainfo import Models
-from modelscope.models.base import Model, Tensor
+from modelscope.models import TorchModel
+from modelscope.models.base import Tensor
 from modelscope.models.builder import MODELS
 from modelscope.utils.constant import Tasks
-from modelscope.utils.nlp.space.utils_dst import batch_to_device
 
 __all__ = ['SpaceForDialogStateTracking']
 
 
 @MODELS.register_module(Tasks.dialog_state_tracking, module_name=Models.space)
-class SpaceForDialogStateTracking(Model):
+class SpaceForDialogStateTracking(TorchModel):
 
     def __init__(self, model_dir: str, *args, **kwargs):
         """initialize the test generation model from the `model_dir` path.