[to #43112771] requirements check and lazy import support

modelscope/hub/utils/utils.py

@@ -4,6 +4,7 @@ from modelscope.hub.constants import (DEFAULT_MODELSCOPE_DOMAIN,
                                       DEFAULT_MODELSCOPE_GROUP,
                                       MODEL_ID_SEPARATOR,
                                       MODELSCOPE_URL_SCHEME)
+from modelscope.utils.utils import get_default_cache_dir
 
 
 def model_id_to_group_owner_name(model_id):
@@ -21,8 +22,7 @@ def get_cache_dir():
     cache dir precedence:
         function parameter > enviroment > ~/.cache/modelscope/hub
     """
-    default_cache_dir = os.path.expanduser(
-        os.path.join('~/.cache', 'modelscope'))
+    default_cache_dir = get_default_cache_dir()
     return os.getenv('MODELSCOPE_CACHE', os.path.join(default_cache_dir,
                                                       'hub'))
 

modelscope/metrics/__init__.py

@@ -1,8 +1,36 @@
-from .base import Metric
-from .builder import METRICS, build_metric, task_default_metrics
-from .image_color_enhance_metric import ImageColorEnhanceMetric
-from .image_denoise_metric import ImageDenoiseMetric
-from .image_instance_segmentation_metric import \
-    ImageInstanceSegmentationCOCOMetric
-from .sequence_classification_metric import SequenceClassificationMetric
-from .text_generation_metric import TextGenerationMetric
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .base import Metric
+    from .builder import METRICS, build_metric, task_default_metrics
+    from .image_color_enhance_metric import ImageColorEnhanceMetric
+    from .image_denoise_metric import ImageDenoiseMetric
+    from .image_instance_segmentation_metric import \
+        ImageInstanceSegmentationCOCOMetric
+    from .sequence_classification_metric import SequenceClassificationMetric
+    from .text_generation_metric import TextGenerationMetric
+
+else:
+    _import_structure = {
+        'base': ['Metric'],
+        'builder': ['METRICS', 'build_metric', 'task_default_metrics'],
+        'image_color_enhance_metric': ['ImageColorEnhanceMetric'],
+        'image_denoise_metric': ['ImageDenoiseMetric'],
+        'image_instance_segmentation_metric':
+        ['ImageInstanceSegmentationCOCOMetric'],
+        'sequence_classification_metric': ['SequenceClassificationMetric'],
+        'text_generation_metric': ['TextGenerationMetric'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/__init__.py

@@ -1,39 +1,12 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
 from modelscope.utils.error import (AUDIO_IMPORT_ERROR,
                                     TENSORFLOW_IMPORT_WARNING)
-from .base import Model
-from .builder import MODELS, build_model
+from modelscope.utils.import_utils import is_torch_available
+from . import audio, cv, multi_modal, nlp
+from .base import Head, Model
+from .builder import BACKBONES, HEADS, MODELS, build_model
 
-try:
-    from .audio.ans.frcrn import FRCRNModel
-    from .audio.asr import GenericAutomaticSpeechRecognition
-    from .audio.kws import GenericKeyWordSpotting
-    from .audio.tts import SambertHifigan
-except ModuleNotFoundError as e:
-    print(AUDIO_IMPORT_ERROR.format(e))
-
-try:
-    from .nlp.csanmt_for_translation import CsanmtForTranslation
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'tensorflow'":
-        print(TENSORFLOW_IMPORT_WARNING.format('CsanmtForTranslation'))
-    else:
-        raise ModuleNotFoundError(e)
-
-try:
-    from .multi_modal import OfaForImageCaptioning
-    from .cv import NAFNetForImageDenoise
-    from .nlp import (BertForMaskedLM, BertForSequenceClassification,
-                      SbertForNLI, SbertForSentenceSimilarity,
-                      SbertForSentimentClassification,
-                      SbertForTokenClassification,
-                      SbertForZeroShotClassification, SpaceForDialogIntent,
-                      SpaceForDialogModeling, SpaceForDialogStateTracking,
-                      StructBertForMaskedLM, VecoForMaskedLM)
-    from .nlp.backbones import SbertModel
-    from .nlp.heads import SequenceClassificationHead
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'pytorch'":
-        pass
-    else:
-        raise ModuleNotFoundError(e)
+if is_torch_available():
+    from .base import TorchModel, TorchHead

modelscope/models/audio/__init__.py

@@ -0,0 +1,3 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+from . import ans, asr, kws, tts

modelscope/models/audio/ans/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .frcrn import FRCRNModel
+
+else:
+    _import_structure = {
+        'frcrn': ['FRCRNModel'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/audio/asr/__init__.py

@@ -1 +1,23 @@
-from .generic_automatic_speech_recognition import *  # noqa F403
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .generic_automatic_speech_recognition import GenericAutomaticSpeechRecognition
+
+else:
+    _import_structure = {
+        'generic_automatic_speech_recognition':
+        ['GenericAutomaticSpeechRecognition'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/audio/kws/__init__.py

@@ -1 +1,22 @@
-from .generic_key_word_spotting import *  # noqa F403
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .generic_key_word_spotting import GenericKeyWordSpotting
+
+else:
+    _import_structure = {
+        'generic_key_word_spotting': ['GenericKeyWordSpotting'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/audio/tts/__init__.py

@@ -1 +1,22 @@
-from .sambert_hifi import *  # noqa F403
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .sambert_hifi import SambertHifigan
+
+else:
+    _import_structure = {
+        'sambert_hifi': ['SambertHifigan'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/base/base_torch_model.py

@@ -6,7 +6,6 @@ import torch
 from torch import nn
 
 from modelscope.utils.logger import get_logger
-from modelscope.utils.torch_utils import create_device
 from .base_model import Model
 
 logger = get_logger(__name__)

modelscope/models/cv/__init__.py

@@ -1,3 +1,5 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from .image_color_enhance.image_color_enhance import ImageColorEnhance
-from .image_denoise.nafnet_for_image_denoise import *  # noqa F403
+from . import (action_recognition, animal_recognition, cartoon,
+               cmdssl_video_embedding, face_generation, image_color_enhance,
+               image_colorization, image_denoise, image_instance_segmentation,
+               super_resolution, virual_tryon)

modelscope/models/cv/action_recognition/__init__.py

@@ -0,0 +1,25 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+
+    from .models import BaseVideoModel
+    from .tada_convnext import TadaConvNeXt
+
+else:
+    _import_structure = {
+        'models': ['BaseVideoModel'],
+        'tada_convnext': ['TadaConvNeXt'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/animal_recognition/__init__.py

@@ -0,0 +1,25 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+
+    from .resnet import ResNet, Bottleneck
+    from .splat import SplAtConv2d
+
+else:
+    _import_structure = {
+        'resnet': ['ResNet', 'Bottleneck'],
+        'splat': ['SplAtConv2d']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/cartoon/__init__.py

@@ -0,0 +1,28 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .facelib.facer import FaceAna
+    from .mtcnn_pytorch.src.align_trans import (get_reference_facial_points,
+                                                warp_and_crop_face)
+    from .utils import (get_f5p, padTo16x, resize_size)
+
+else:
+    _import_structure = {
+        'facelib.facer': ['FaceAna'],
+        'mtcnn_pytorch.src.align_trans':
+        ['get_reference_facial_points', 'warp_and_crop_face'],
+        'utils': ['get_f5p', 'padTo16x', 'resize_size']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/cmdssl_video_embedding/__init__.py

@@ -1,3 +1,26 @@
-from .c3d import C3D
-from .resnet2p1d import resnet26_2p1d
-from .resnet3d import resnet26_3d
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .c3d import C3D
+    from .resnet2p1d import resnet26_2p1d
+    from .resnet3d import resnet26_3d
+
+else:
+    _import_structure = {
+        'c3d': ['C3D'],
+        'resnet2p1d': ['resnet26_2p1d'],
+        'resnet3d': ['resnet26_3d']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/face_generation/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .stylegan2 import Generator
+
+else:
+    _import_structure = {
+        'stylegan2': ['Generator'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/image_color_enhance/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .image_color_enhance import ImageColorEnhance
+
+else:
+    _import_structure = {
+        'image_color_enhance': ['ImageColorEnhance'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/image_colorization/__init__.py

@@ -0,0 +1,24 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .unet import DynamicUnetWide, DynamicUnetDeep
+    from .utils import NormType
+
+else:
+    _import_structure = {
+        'unet': ['DynamicUnetWide', 'DynamicUnetDeep'],
+        'utils': ['NormType']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/image_denoise/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .nafnet_for_image_denoise import NAFNetForImageDenoise
+
+else:
+    _import_structure = {'nafnet_for_image_denoise': ['NAFNetForImageDenoise']}
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/image_instance_segmentation/__init__.py

@@ -1,2 +1,27 @@
-from .cascade_mask_rcnn_swin import CascadeMaskRCNNSwin
-from .model import CascadeMaskRCNNSwinModel
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .cascade_mask_rcnn_swin import CascadeMaskRCNNSwin
+    from .model import CascadeMaskRCNNSwinModel
+    from .postprocess_utils import get_img_ins_seg_result
+    from .datasets import ImageInstanceSegmentationCocoDataset
+else:
+    _import_structure = {
+        'cascade_mask_rcnn_swin': ['CascadeMaskRCNNSwin'],
+        'model': ['CascadeMaskRCNNSwinModel'],
+        'postprocess_utils': ['get_img_ins_seg_result'],
+        'datasets': ['ImageInstanceSegmentationCocoDataset']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/image_instance_segmentation/backbones/__init__.py

@@ -1 +1,22 @@
-from .swin_transformer import SwinTransformer
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .swin_transformer import SwinTransformer
+
+else:
+    _import_structure = {
+        'swin_transformer': ['SwinTransformer'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/super_resolution/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .rrdbnet_arch import RRDBNet
+
+else:
+    _import_structure = {'rrdbnet_arch': ['RRDBNet']}
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/cv/virual_tryon/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .sdafnet import SDAFNet_Tryon
+
+else:
+    _import_structure = {'sdafnet': ['SDAFNet_Tryon']}
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/multi_modal/__init__.py

@@ -1,8 +1,35 @@
-from .clip.clip_model import CLIPForMultiModalEmbedding
-from .gemm.gemm_model import GEMMForMultiModalEmbedding
-from .imagen.imagen_model import ImagenForTextToImageSynthesis
-from .mmr.models.clip_for_multi_model_video_embedding import \
-    VideoCLIPForMultiModalEmbedding
-from .mplug_for_visual_question_answering import \
-    MPlugForVisualQuestionAnswering
-from .ofa_for_image_captioning_model import OfaForImageCaptioning
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+
+    from .clip import CLIPForMultiModalEmbedding
+    from .gemm import GEMMForMultiModalEmbedding
+    from .imagen import ImagenForTextToImageSynthesis
+    from .mmr import VideoCLIPForMultiModalEmbedding
+    from .mplug_for_visual_question_answering import \
+        MPlugForVisualQuestionAnswering
+    from .ofa_for_image_captioning_model import OfaForImageCaptioning
+
+else:
+    _import_structure = {
+        'clip': ['CLIPForMultiModalEmbedding'],
+        'imagen': ['ImagenForTextToImageSynthesis'],
+        'gemm': ['GEMMForMultiModalEmbedding'],
+        'mmr': ['VideoCLIPForMultiModalEmbedding'],
+        'mplug_for_visual_question_answering':
+        ['MPlugForVisualQuestionAnswering'],
+        'ofa_for_image_captioning_model': ['OfaForImageCaptioning']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/multi_modal/clip/__init__.py

@@ -0,0 +1 @@
+from .clip_model import CLIPForMultiModalEmbedding

modelscope/models/multi_modal/gemm/__init__.py

@@ -0,0 +1 @@
+from .gemm_model import GEMMForMultiModalEmbedding

modelscope/models/multi_modal/imagen/__init__.py

@@ -0,0 +1 @@
+from .imagen_model import ImagenForTextToImageSynthesis

modelscope/models/multi_modal/mmr/__init__.py

@@ -0,0 +1 @@
+from .models import VideoCLIPForMultiModalEmbedding

modelscope/models/multi_modal/mmr/models/__init__.py

@@ -0,0 +1 @@
+from .clip_for_mm_video_embedding import VideoCLIPForMultiModalEmbedding

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

@@ -11,13 +11,11 @@ from PIL import Image
 from modelscope.metainfo import Models
 from modelscope.models.base import Model
 from modelscope.models.builder import MODELS
-from modelscope.models.multi_modal.mmr.dataloaders.rawvideo_util import \
-    RawVideoExtractor
-from modelscope.models.multi_modal.mmr.models.modeling import CLIP4Clip
-from modelscope.models.multi_modal.mmr.models.tokenization_clip import \
-    SimpleTokenizer as ClipTokenizer
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
+from ..dataloaders.rawvideo_util import RawVideoExtractor
+from .modeling import CLIP4Clip
+from .tokenization_clip import SimpleTokenizer as ClipTokenizer
 
 logger = get_logger()
 

modelscope/models/nlp/__init__.py

@@ -1,25 +1,59 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from modelscope.utils.error import TENSORFLOW_IMPORT_WARNING
-from .backbones import *  # noqa F403
-from .bert_for_sequence_classification import *  # noqa F403
-from .heads import *  # noqa F403
-from .masked_language import *  # noqa F403
-from .nncrf_for_named_entity_recognition import *  # noqa F403
-from .palm_for_text_generation import *  # noqa F403
-from .sbert_for_nli import *  # noqa F403
-from .sbert_for_sentence_similarity import *  # noqa F403
-from .sbert_for_sentiment_classification import *  # noqa F403
-from .sbert_for_token_classification import *  # noqa F403
-from .sbert_for_zero_shot_classification import *  # noqa F403
-from .sequence_classification import *  # noqa F403
-from .space_for_dialog_intent_prediction import *  # noqa F403
-from .space_for_dialog_modeling import *  # noqa F403
-from .space_for_dialog_state_tracking import *  # noqa F403
+from typing import TYPE_CHECKING
 
-try:
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .backbones import (SbertModel, SpaceGenerator, SpaceModelBase)
+    from .heads import SequenceClassificationHead
+    from .bert_for_sequence_classification import BertForSequenceClassification
     from .csanmt_for_translation import CsanmtForTranslation
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'tensorflow'":
-        print(TENSORFLOW_IMPORT_WARNING.format('CsanmtForTranslation'))
-    else:
-        raise ModuleNotFoundError(e)
+    from .masked_language import (StructBertForMaskedLM, VecoForMaskedLM,
+                                  BertForMaskedLM)
+    from .nncrf_for_named_entity_recognition import TransformerCRFForNamedEntityRecognition
+    from .palm_for_text_generation import PalmForTextGeneration
+    from .sbert_for_nli import SbertForNLI
+    from .sbert_for_sentence_similarity import SbertForSentenceSimilarity
+    from .sbert_for_sentiment_classification import SbertForSentimentClassification
+    from .sbert_for_token_classification import SbertForTokenClassification
+    from .sbert_for_zero_shot_classification import SbertForZeroShotClassification
+    from .sequence_classification import SequenceClassificationModel
+    from .space_for_dialog_intent_prediction import SpaceForDialogIntent
+    from .space_for_dialog_modeling import SpaceForDialogModeling
+    from .space_for_dialog_state_tracking import SpaceForDialogStateTracking
+    from .task_model import SingleBackboneTaskModelBase
+
+else:
+    _import_structure = {
+        'backbones': ['SbertModel', 'SpaceGenerator', 'SpaceModelBase'],
+        'heads': ['SequenceClassificationHead'],
+        'csanmt_for_translation': ['CsanmtForTranslation'],
+        'bert_for_sequence_classification': ['BertForSequenceClassification'],
+        'masked_language':
+        ['StructBertForMaskedLM', 'VecoForMaskedLM', 'BertForMaskedLM'],
+        'nncrf_for_named_entity_recognition':
+        ['TransformerCRFForNamedEntityRecognition'],
+        'palm_for_text_generation': ['PalmForTextGeneration'],
+        'sbert_for_nli': ['SbertForNLI'],
+        'sbert_for_sentence_similarity': ['SbertForSentenceSimilarity'],
+        'sbert_for_sentiment_classification':
+        ['SbertForSentimentClassification'],
+        'sbert_for_token_classification': ['SbertForTokenClassification'],
+        'sbert_for_zero_shot_classification':
+        ['SbertForZeroShotClassification'],
+        'sequence_classification': ['SequenceClassificationModel'],
+        'space_for_dialog_intent_prediction': ['SpaceForDialogIntent'],
+        'space_for_dialog_modeling': ['SpaceForDialogModeling'],
+        'space_for_dialog_state_tracking': ['SpaceForDialogStateTracking'],
+        'task_model': ['SingleBackboneTaskModelBase'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/nlp/backbones/__init__.py

@@ -1,4 +1,23 @@
-from .space import SpaceGenerator, SpaceModelBase
-from .structbert import SbertModel
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
 
-__all__ = ['SbertModel', 'SpaceGenerator', 'SpaceModelBase']
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .space import SpaceGenerator, SpaceModelBase
+    from .structbert import SbertModel
+else:
+    _import_structure = {
+        'space': ['SpaceGenerator', 'SpaceModelBase'],
+        'structbert': ['SbertModel']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/nlp/heads/__init__.py

@@ -1,3 +1,21 @@
-from .sequence_classification_head import SequenceClassificationHead
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
 
-__all__ = ['SequenceClassificationHead']
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .sequence_classification_head import SequenceClassificationHead
+else:
+    _import_structure = {
+        'sequence_classification_head': ['SequenceClassificationHead']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/models/nlp/space_for_dialog_intent_prediction.py

@@ -6,11 +6,10 @@ from typing import Any, Dict
 from modelscope.metainfo import Models
 from modelscope.models.base import Model, Tensor
 from modelscope.models.builder import MODELS
-from modelscope.preprocessors.space.fields.intent_field import \
-    IntentBPETextField
+from modelscope.models.nlp.backbones import SpaceGenerator, SpaceModelBase
+from modelscope.preprocessors.space import IntentBPETextField
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
-from .backbones import SpaceGenerator, SpaceModelBase
 
 __all__ = ['SpaceForDialogIntent']
 

modelscope/models/nlp/space_for_dialog_modeling.py

@@ -6,11 +6,10 @@ from typing import Any, Dict, Optional
 from modelscope.metainfo import Models
 from modelscope.models.base import Model, Tensor
 from modelscope.models.builder import MODELS
-from modelscope.preprocessors.space.fields.gen_field import \
-    MultiWOZBPETextField
+from modelscope.models.nlp.backbones import SpaceGenerator, SpaceModelBase
+from modelscope.preprocessors.space import MultiWOZBPETextField
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
-from .backbones import SpaceGenerator, SpaceModelBase
 
 __all__ = ['SpaceForDialogModeling']
 

modelscope/msdatasets/image_denoise_data/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .image_denoise_dataset import PairedImageDataset
+
+else:
+    _import_structure = {
+        'image_denoise_dataset': ['PairedImageDataset'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/pipelines/__init__.py

@@ -1,12 +1,7 @@
-from modelscope.utils.error import AUDIO_IMPORT_ERROR
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+from . import audio, cv, multi_modal, nlp
 from .base import Pipeline
 from .builder import pipeline
-from .cv import *  # noqa F403
-from .multi_modal import *  # noqa F403
-from .nlp import *  # noqa F403
-
-try:
-    from .audio import LinearAECPipeline
-    from .audio.ans_pipeline import ANSPipeline
-except ModuleNotFoundError as e:
-    print(AUDIO_IMPORT_ERROR.format(e))

modelscope/pipelines/audio/__init__.py

@@ -1,21 +1,30 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
 
-from modelscope.utils.error import TENSORFLOW_IMPORT_ERROR
+from modelscope.utils.import_utils import LazyImportModule
 
-try:
+if TYPE_CHECKING:
+    from .ans_pipeline import ANSPipeline
     from .asr_inference_pipeline import AutomaticSpeechRecognitionPipeline
-    from .kws_kwsbp_pipeline import *  # noqa F403
+    from .kws_kwsbp_pipeline import KeyWordSpottingKwsbpPipeline
     from .linear_aec_pipeline import LinearAECPipeline
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'torch'":
-        pass
-    else:
-        raise ModuleNotFoundError(e)
+    from .text_to_speech_pipeline import TextToSpeechSambertHifiganPipeline
 
-try:
-    from .text_to_speech_pipeline import *  # noqa F403
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'tensorflow'":
-        print(TENSORFLOW_IMPORT_ERROR.format('tts'))
-    else:
-        raise ModuleNotFoundError(e)
+else:
+    _import_structure = {
+        'ans_pipeline': ['ANSPipeline'],
+        'asr_inference_pipeline': ['AutomaticSpeechRecognitionPipeline'],
+        'kws_kwsbp_pipeline': ['KeyWordSpottingKwsbpPipeline'],
+        'linear_aec_pipeline': ['LinearAECPipeline'],
+        'text_to_speech_pipeline': ['TextToSpeechSambertHifiganPipeline'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/pipelines/audio/ans_pipeline.py

@@ -11,6 +11,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.utils.constant import Tasks
+from modelscope.utils.torch_utils import create_device
 
 
 def audio_norm(x):

modelscope/pipelines/audio/linear_aec_pipeline.py

@@ -12,7 +12,7 @@ from modelscope.metainfo import Pipelines
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors.audio import LinearAECAndFbank
+from modelscope.preprocessors import LinearAECAndFbank
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
 

modelscope/pipelines/base.py

@@ -26,7 +26,7 @@ if is_tf_available():
     import tensorflow as tf
 
 Tensor = Union['torch.Tensor', 'tf.Tensor']
-Input = Union[str, tuple, MsDataset, 'PIL.Image.Image', 'numpy.ndarray']
+Input = Union[str, tuple, MsDataset, 'Image.Image', 'numpy.ndarray']
 InputModel = Union[str, Model]
 
 logger = get_logger()
@@ -233,7 +233,7 @@ class Pipeline(ABC):
 
         """
         from torch.utils.data.dataloader import default_collate
-        from modelscope.preprocessors.space.dst_processors import InputFeatures
+        from modelscope.preprocessors import InputFeatures
         if isinstance(data, dict) or isinstance(data, Mapping):
             return type(data)(
                 {k: self._collate_fn(v)

modelscope/pipelines/cv/__init__.py

@@ -1,33 +1,48 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
 
-from modelscope.utils.error import TENSORFLOW_IMPORT_ERROR
+from modelscope.utils.import_utils import LazyImportModule
 
-try:
+if TYPE_CHECKING:
     from .action_recognition_pipeline import ActionRecognitionPipeline
     from .animal_recog_pipeline import AnimalRecogPipeline
     from .cmdssl_video_embedding_pipleline import CMDSSLVideoEmbeddingPipeline
+    from .face_image_generation_pipeline import FaceImageGenerationPipeline
+    from .image_cartoon_pipeline import ImageCartoonPipeline
     from .image_denoise_pipeline import ImageDenoisePipeline
     from .image_color_enhance_pipeline import ImageColorEnhancePipeline
-    from .virtual_tryon_pipeline import VirtualTryonPipeline
     from .image_colorization_pipeline import ImageColorizationPipeline
-    from .image_super_resolution_pipeline import ImageSuperResolutionPipeline
-    from .face_image_generation_pipeline import FaceImageGenerationPipeline
     from .image_instance_segmentation_pipeline import ImageInstanceSegmentationPipeline
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'torch'":
-        pass
-    else:
-        raise ModuleNotFoundError(e)
-
-try:
-    from .image_cartoon_pipeline import ImageCartoonPipeline
     from .image_matting_pipeline import ImageMattingPipeline
+    from .image_super_resolution_pipeline import ImageSuperResolutionPipeline
     from .style_transfer_pipeline import StyleTransferPipeline
     from .ocr_detection_pipeline import OCRDetectionPipeline
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'tensorflow'":
-        print(
-            TENSORFLOW_IMPORT_ERROR.format(
-                'image-cartoon image-matting ocr-detection style-transfer'))
-    else:
-        raise ModuleNotFoundError(e)
+    from .virtual_tryon_pipeline import VirtualTryonPipeline
+else:
+    _import_structure = {
+        'action_recognition_pipeline': ['ActionRecognitionPipeline'],
+        'animal_recog_pipeline': ['AnimalRecogPipeline'],
+        'cmdssl_video_embedding_pipleline': ['CMDSSLVideoEmbeddingPipeline'],
+        'image_color_enhance_pipeline': ['ImageColorEnhancePipeline'],
+        'virtual_tryon_pipeline': ['VirtualTryonPipeline'],
+        'image_colorization_pipeline': ['ImageColorizationPipeline'],
+        'image_super_resolution_pipeline': ['ImageSuperResolutionPipeline'],
+        'image_denoise_pipeline': ['ImageDenoisePipeline'],
+        'face_image_generation_pipeline': ['FaceImageGenerationPipeline'],
+        'image_cartoon_pipeline': ['ImageCartoonPipeline'],
+        'image_matting_pipeline': ['ImageMattingPipeline'],
+        'style_transfer_pipeline': ['StyleTransferPipeline'],
+        'ocr_detection_pipeline': ['OCRDetectionPipeline'],
+        'image_instance_segmentation_pipeline':
+        ['ImageInstanceSegmentationPipeline'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/pipelines/cv/action_recognition_pipeline.py

@@ -5,11 +5,11 @@ from typing import Any, Dict
 import torch
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.action_recognition.models import BaseVideoModel
+from modelscope.models.cv.action_recognition import BaseVideoModel
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors.video import ReadVideoData
+from modelscope.preprocessors import ReadVideoData
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger

modelscope/pipelines/cv/animal_recog_pipeline.py

@@ -9,11 +9,11 @@ from torchvision import transforms
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.animal_recognition import resnet
+from modelscope.models.cv.animal_recognition import Bottleneck, ResNet
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors import LoadImage, load_image
+from modelscope.preprocessors import LoadImage
 from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 
@@ -34,8 +34,8 @@ class AnimalRecogPipeline(Pipeline):
         import torch
 
         def resnest101(**kwargs):
-            model = resnet.ResNet(
-                resnet.Bottleneck, [3, 4, 23, 3],
+            model = ResNet(
+                Bottleneck, [3, 4, 23, 3],
                 radix=2,
                 groups=1,
                 bottleneck_width=64,

modelscope/pipelines/cv/cmdssl_video_embedding_pipleline.py

@@ -7,8 +7,7 @@ import torchvision.transforms.functional as TF
 from PIL import Image
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.cmdssl_video_embedding.resnet2p1d import \
-    resnet26_2p1d
+from modelscope.models.cv.cmdssl_video_embedding import resnet26_2p1d
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES

modelscope/pipelines/cv/face_image_generation_pipeline.py

@@ -7,7 +7,7 @@ import PIL
 import torch
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.face_generation import stylegan2
+from modelscope.models.cv.face_generation import Generator
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
@@ -36,7 +36,7 @@ class FaceImageGenerationPipeline(Pipeline):
         self.channel_multiplier = 2
         self.truncation = 0.7
         self.truncation_mean = 4096
-        self.generator = stylegan2.Generator(
+        self.generator = Generator(
             self.size,
             self.latent,
             self.n_mlp,

modelscope/pipelines/cv/image_cartoon_pipeline.py

@@ -6,10 +6,10 @@ import numpy as np
 import tensorflow as tf
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.cartoon.facelib.facer import FaceAna
-from modelscope.models.cv.cartoon.mtcnn_pytorch.src.align_trans import (
-    get_reference_facial_points, warp_and_crop_face)
-from modelscope.models.cv.cartoon.utils import get_f5p, padTo16x, resize_size
+from modelscope.models.cv.cartoon import (FaceAna, get_f5p,
+                                          get_reference_facial_points,
+                                          padTo16x, resize_size,
+                                          warp_and_crop_face)
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES

modelscope/pipelines/cv/image_color_enhance_pipeline.py

@@ -5,8 +5,7 @@ from torchvision import transforms
 
 from modelscope.metainfo import Pipelines
 from modelscope.models.base import Model
-from modelscope.models.cv.image_color_enhance.image_color_enhance import \
-    ImageColorEnhance
+from modelscope.models.cv.image_color_enhance import ImageColorEnhance
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES

modelscope/pipelines/cv/image_colorization_pipeline.py

@@ -7,8 +7,8 @@ import torch
 from torchvision import models, transforms
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.image_colorization import unet
-from modelscope.models.cv.image_colorization.utils import NormType
+from modelscope.models.cv.image_colorization import (DynamicUnetDeep,
+                                                     DynamicUnetWide, NormType)
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
@@ -46,7 +46,7 @@ class ImageColorizationPipeline(Pipeline):
         if self.model_type == 'stable':
             body = models.resnet101(pretrained=True)
             body = torch.nn.Sequential(*list(body.children())[:self.cut])
-            self.model = unet.DynamicUnetWide(
+            self.model = DynamicUnetWide(
                 body,
                 n_classes=3,
                 blur=True,
@@ -61,7 +61,7 @@ class ImageColorizationPipeline(Pipeline):
         else:
             body = models.resnet34(pretrained=True)
             body = torch.nn.Sequential(*list(body.children())[:cut])
-            model = unet.DynamicUnetDeep(
+            model = DynamicUnetDeep(
                 body,
                 n_classes=3,
                 blur=True,
@@ -84,7 +84,7 @@ class ImageColorizationPipeline(Pipeline):
     def preprocess(self, input: Input) -> Dict[str, Any]:
         if isinstance(input, str):
             img = load_image(input).convert('LA').convert('RGB')
-        elif isinstance(input, PIL.Image.Image):
+        elif isinstance(input, Image.Image):
             img = input.convert('LA').convert('RGB')
         elif isinstance(input, np.ndarray):
             if len(input.shape) == 2:

modelscope/pipelines/cv/image_denoise_pipeline.py

@@ -5,7 +5,7 @@ from torchvision import transforms
 
 from modelscope.metainfo import Pipelines
 from modelscope.models import Model
-from modelscope.models.cv import NAFNetForImageDenoise
+from modelscope.models.cv.image_denoise import NAFNetForImageDenoise
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES

modelscope/pipelines/cv/image_instance_segmentation_pipeline.py

@@ -7,10 +7,8 @@ import torch
 from PIL import Image
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.image_instance_segmentation.model import \
-    CascadeMaskRCNNSwinModel
-from modelscope.models.cv.image_instance_segmentation.postprocess_utils import \
-    get_img_ins_seg_result
+from modelscope.models.cv.image_instance_segmentation import (
+    CascadeMaskRCNNSwinModel, get_img_ins_seg_result)
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import (ImageInstanceSegmentationPreprocessor,

modelscope/pipelines/cv/image_super_resolution_pipeline.py

@@ -6,11 +6,11 @@ import PIL
 import torch
 
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.super_resolution import rrdbnet_arch
+from modelscope.models.cv.super_resolution import RRDBNet
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors import LoadImage, load_image
+from modelscope.preprocessors import LoadImage
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
 
@@ -32,7 +32,7 @@ class ImageSuperResolutionPipeline(Pipeline):
         self.num_feat = 64
         self.num_block = 23
         self.scale = 4
-        self.sr_model = rrdbnet_arch.RRDBNet(
+        self.sr_model = RRDBNet(
             num_in_ch=3,
             num_out_ch=3,
             num_feat=self.num_feat,

modelscope/pipelines/cv/ocr_detection_pipeline.py

@@ -12,7 +12,9 @@ from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import LoadImage
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.logger import get_logger
-from .ocr_utils import model_resnet_mutex_v4_linewithchar, ops, utils
+from .ocr_utils import (SegLinkDetector, cal_width, combine_segments_python,
+                        decode_segments_links_python, nms_python,
+                        rboxes_to_polygons)
 
 if tf.__version__ >= '2.0':
     tf = tf.compat.v1
@@ -66,7 +68,7 @@ class OCRDetectionPipeline(Pipeline):
                 0.997, global_step)
 
             # detector
-            detector = model_resnet_mutex_v4_linewithchar.SegLinkDetector()
+            detector = SegLinkDetector()
             all_maps = detector.build_model(
                 self.input_images, is_training=False)
 
@@ -90,7 +92,7 @@ class OCRDetectionPipeline(Pipeline):
 
             # decode segments and links
             image_size = tf.shape(self.input_images)[1:3]
-            segments, group_indices, segment_counts, _ = ops.decode_segments_links_python(
+            segments, group_indices, segment_counts, _ = decode_segments_links_python(
                 image_size,
                 all_nodes,
                 all_links,
@@ -98,7 +100,7 @@ class OCRDetectionPipeline(Pipeline):
                 anchor_sizes=list(detector.anchor_sizes))
 
             # combine segments
-            combined_rboxes, combined_counts = ops.combine_segments_python(
+            combined_rboxes, combined_counts = combine_segments_python(
                 segments, group_indices, segment_counts)
             self.output['combined_rboxes'] = combined_rboxes
             self.output['combined_counts'] = combined_counts
@@ -145,7 +147,7 @@ class OCRDetectionPipeline(Pipeline):
         # convert rboxes to polygons and find its coordinates on the original image
         orig_h, orig_w = inputs['orig_size']
         resize_h, resize_w = inputs['resize_size']
-        polygons = utils.rboxes_to_polygons(rboxes)
+        polygons = rboxes_to_polygons(rboxes)
         scale_y = float(orig_h) / float(resize_h)
         scale_x = float(orig_w) / float(resize_w)
 
@@ -157,8 +159,8 @@ class OCRDetectionPipeline(Pipeline):
         polygons = np.round(polygons).astype(np.int32)
 
         # nms
-        dt_n9 = [o + [utils.cal_width(o)] for o in polygons.tolist()]
-        dt_nms = utils.nms_python(dt_n9)
+        dt_n9 = [o + [cal_width(o)] for o in polygons.tolist()]
+        dt_nms = nms_python(dt_n9)
         dt_polygons = np.array([o[:8] for o in dt_nms])
 
         result = {OutputKeys.POLYGONS: dt_polygons}

modelscope/pipelines/cv/ocr_utils/__init__.py

@@ -0,0 +1,25 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .model_resnet_mutex_v4_linewithchar import SegLinkDetector
+    from .ops import decode_segments_links_python, combine_segments_python
+    from .utils import rboxes_to_polygons, cal_width, nms_python
+else:
+    _import_structure = {
+        'model_resnet_mutex_v4_linewithchar': ['SegLinkDetector'],
+        'ops': ['decode_segments_links_python', 'combine_segments_python'],
+        'utils': ['rboxes_to_polygons', 'cal_width', 'nms_python']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/pipelines/cv/virtual_tryon_pipeline.py

@@ -11,7 +11,7 @@ from PIL import Image
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.metainfo import Pipelines
-from modelscope.models.cv.virual_tryon.sdafnet import SDAFNet_Tryon
+from modelscope.models.cv.virual_tryon import SDAFNet_Tryon
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES

modelscope/pipelines/multi_modal/__init__.py

@@ -1,14 +1,36 @@
-try:
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .generative_multi_modal_embedding_pipeline import GEMMMultiModalEmbeddingPipeline
     from .image_captioning_pipeline import ImageCaptionPipeline
     from .multi_modal_embedding_pipeline import MultiModalEmbeddingPipeline
-    from .generative_multi_modal_embedding_pipeline import GEMMMultiModalEmbeddingPipeline
     from .text_to_image_synthesis_pipeline import TextToImageSynthesisPipeline
     from .video_multi_modal_embedding_pipeline import \
         VideoMultiModalEmbeddingPipeline
-    from .visual_question_answering_pipeline import \
-        VisualQuestionAnsweringPipeline
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'torch'":
-        pass
-    else:
-        raise ModuleNotFoundError(e)
+    from .visual_question_answering_pipeline import VisualQuestionAnsweringPipeline
+
+else:
+    _import_structure = {
+        'image_captioning_pipeline': ['ImageCaptionPipeline'],
+        'multi_modal_embedding_pipeline': ['MultiModalEmbeddingPipeline'],
+        'text_to_image_synthesis_pipeline': ['TextToImageSynthesisPipeline'],
+        'visual_question_answering_pipeline':
+        ['VisualQuestionAnsweringPipeline'],
+        'video_multi_modal_embedding_pipeline':
+        ['VideoMultiModalEmbeddingPipeline'],
+        'generative_multi_modal_embedding_pipeline':
+        ['GEMMMultiModalEmbeddingPipeline']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/pipelines/nlp/__init__.py

@@ -1,30 +1,50 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
 
-from modelscope.utils.error import TENSORFLOW_IMPORT_WARNING
+from modelscope.utils.import_utils import LazyImportModule
 
-try:
-    from .translation_pipeline import *  # noqa F403
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'tensorflow'":
-        print(TENSORFLOW_IMPORT_WARNING.format('translation'))
-    else:
-        raise ModuleNotFoundError(e)
+if TYPE_CHECKING:
+    from .dialog_intent_prediction_pipeline import DialogIntentPredictionPipeline
+    from .dialog_modeling_pipeline import DialogModelingPipeline
+    from .dialog_state_tracking_pipeline import DialogStateTrackingPipeline
+    from .fill_mask_pipeline import FillMaskPipeline
+    from .named_entity_recognition_pipeline import NamedEntityRecognitionPipeline
+    from .nli_pipeline import NLIPipeline
+    from .sentence_similarity_pipeline import SentenceSimilarityPipeline
+    from .sentiment_classification_pipeline import SentimentClassificationPipeline
+    from .sequence_classification_pipeline import SequenceClassificationPipeline
+    from .text_generation_pipeline import TextGenerationPipeline
+    from .translation_pipeline import TranslationPipeline
+    from .word_segmentation_pipeline import WordSegmentationPipeline
+    from .zero_shot_classification_pipeline import ZeroShotClassificationPipeline
 
-try:
-    from .dialog_intent_prediction_pipeline import *  # noqa F403
-    from .dialog_modeling_pipeline import *  # noqa F403
-    from .dialog_state_tracking_pipeline import *  # noqa F403
-    from .fill_mask_pipeline import *  # noqa F403
-    from .named_entity_recognition_pipeline import *  # noqa F403
-    from .nli_pipeline import *  # noqa F403
-    from .sentence_similarity_pipeline import *  # noqa F403
-    from .sentiment_classification_pipeline import *  # noqa F403
-    from .sequence_classification_pipeline import *  # noqa F403
-    from .text_generation_pipeline import *  # noqa F403
-    from .word_segmentation_pipeline import *  # noqa F403
-    from .zero_shot_classification_pipeline import *  # noqa F403
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'torch'":
-        pass
-    else:
-        raise ModuleNotFoundError(e)
+else:
+    _import_structure = {
+        'dialog_intent_prediction_pipeline':
+        ['DialogIntentPredictionPipeline'],
+        'dialog_modeling_pipeline': ['DialogModelingPipeline'],
+        'dialog_state_tracking_pipeline': ['DialogStateTrackingPipeline'],
+        'fill_mask_pipeline': ['FillMaskPipeline'],
+        'nli_pipeline': ['NLIPipeline'],
+        'sentence_similarity_pipeline': ['SentenceSimilarityPipeline'],
+        'sentiment_classification_pipeline':
+        ['SentimentClassificationPipeline'],
+        'sequence_classification_pipeline': ['SequenceClassificationPipeline'],
+        'text_generation_pipeline': ['TextGenerationPipeline'],
+        'word_segmentation_pipeline': ['WordSegmentationPipeline'],
+        'zero_shot_classification_pipeline':
+        ['ZeroShotClassificationPipeline'],
+        'named_entity_recognition_pipeline':
+        ['NamedEntityRecognitionPipeline'],
+        'translation_pipeline': ['TranslationPipeline'],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/pipelines/nlp/dialog_state_tracking_pipeline.py

@@ -1,7 +1,8 @@
 from typing import Any, Dict, Union
 
 from modelscope.metainfo import Pipelines
-from modelscope.models import Model, SpaceForDialogStateTracking
+from modelscope.models import Model
+from modelscope.models.nlp import SpaceForDialogStateTracking
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES

modelscope/preprocessors/__init__.py

@@ -1,29 +1,67 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
 
-from modelscope.utils.error import AUDIO_IMPORT_ERROR, TENSORFLOW_IMPORT_ERROR
-from .asr import WavToScp
-from .base import Preprocessor
-from .builder import PREPROCESSORS, build_preprocessor
-from .common import Compose
-from .image import LoadImage, load_image
-from .kws import WavToLists
+from modelscope.utils.import_utils import LazyImportModule
 
-try:
+if TYPE_CHECKING:
+    from .base import Preprocessor
+    from .builder import PREPROCESSORS, build_preprocessor
+    from .common import Compose
+    from .asr import WavToScp
     from .audio import LinearAECAndFbank
-except ModuleNotFoundError as e:
-    print(AUDIO_IMPORT_ERROR.format(e))
+    from .image import (LoadImage, load_image,
+                        ImageColorEnhanceFinetunePreprocessor,
+                        ImageInstanceSegmentationPreprocessor,
+                        ImageDenoisePreprocessor)
+    from .kws import WavToLists
+    from .multi_modal import (OfaImageCaptionPreprocessor,
+                              MPlugVisualQuestionAnsweringPreprocessor)
+    from .nlp import (Tokenize, SequenceClassificationPreprocessor,
+                      TextGenerationPreprocessor,
+                      TokenClassificationPreprocessor, NLIPreprocessor,
+                      SentimentClassificationPreprocessor,
+                      SentenceSimilarityPreprocessor, FillMaskPreprocessor,
+                      ZeroShotClassificationPreprocessor, NERPreprocessor)
+    from .space import (DialogIntentPredictionPreprocessor,
+                        DialogModelingPreprocessor,
+                        DialogStateTrackingPreprocessor)
+    from .video import ReadVideoData
 
-try:
-    from .multi_modal import *  # noqa F403
-    from .nlp import *  # noqa F403
-    from .space.dialog_intent_prediction_preprocessor import *  # noqa F403
-    from .space.dialog_modeling_preprocessor import *  # noqa F403
-    from .space.dialog_state_tracking_preprocessor import *  # noqa F403
-    from .image import ImageColorEnhanceFinetunePreprocessor
-    from .image import ImageInstanceSegmentationPreprocessor
-    from .image import ImageDenoisePreprocessor
-except ModuleNotFoundError as e:
-    if str(e) == "No module named 'tensorflow'":
-        print(TENSORFLOW_IMPORT_ERROR.format('tts'))
-    else:
-        raise ModuleNotFoundError(e)
+else:
+    _import_structure = {
+        'base': ['Preprocessor'],
+        'builder': ['PREPROCESSORS', 'build_preprocessor'],
+        'common': ['Compose'],
+        'asr': ['WavToScp'],
+        'video': ['ReadVideoData'],
+        'image': [
+            'LoadImage', 'load_image', 'ImageColorEnhanceFinetunePreprocessor',
+            'ImageInstanceSegmentationPreprocessor', 'ImageDenoisePreprocessor'
+        ],
+        'kws': ['WavToLists'],
+        'multi_modal': [
+            'OfaImageCaptionPreprocessor',
+            'MPlugVisualQuestionAnsweringPreprocessor'
+        ],
+        'nlp': [
+            'Tokenize', 'SequenceClassificationPreprocessor',
+            'TextGenerationPreprocessor', 'TokenClassificationPreprocessor',
+            'NLIPreprocessor', 'SentimentClassificationPreprocessor',
+            'SentenceSimilarityPreprocessor', 'FillMaskPreprocessor',
+            'ZeroShotClassificationPreprocessor', 'NERPreprocessor'
+        ],
+        'space': [
+            'DialogIntentPredictionPreprocessor', 'DialogModelingPreprocessor',
+            'DialogStateTrackingPreprocessor', 'InputFeatures'
+        ],
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/preprocessors/space/__init__.py

@@ -0,0 +1,33 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule
+
+if TYPE_CHECKING:
+    from .dialog_intent_prediction_preprocessor import \
+        DialogIntentPredictionPreprocessor
+    from .dialog_modeling_preprocessor import DialogModelingPreprocessor
+    from .dialog_state_tracking_preprocessor import DialogStateTrackingPreprocessor
+    from .dst_processors import InputFeatures
+    from .fields import MultiWOZBPETextField, IntentBPETextField
+
+else:
+    _import_structure = {
+        'dialog_intent_prediction_preprocessor':
+        ['DialogIntentPredictionPreprocessor'],
+        'dialog_modeling_preprocessor': ['DialogModelingPreprocessor'],
+        'dialog_state_tracking_preprocessor':
+        ['DialogStateTrackingPreprocessor'],
+        'dst_processors': ['InputFeatures'],
+        'fields': ['MultiWOZBPETextField', 'IntentBPETextField']
+    }
+
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/preprocessors/space/fields/__init__.py

@@ -0,0 +1,2 @@
+from .gen_field import MultiWOZBPETextField
+from .intent_field import IntentBPETextField

modelscope/task_datasets/__init__.py

@@ -1,3 +1,25 @@
-from .base import TaskDataset
-from .builder import build_task_dataset
-from .torch_base_dataset import TorchTaskDataset
+# Copyright (c) Alibaba, Inc. and its affiliates.
+from typing import TYPE_CHECKING
+
+from modelscope.utils.import_utils import LazyImportModule, is_torch_available
+
+if TYPE_CHECKING:
+    from .base import TaskDataset
+    from .builder import TASK_DATASETS, build_task_dataset
+    from .torch_base_dataset import TorchTaskDataset
+
+else:
+    _import_structure = {
+        'base': ['TaskDataset'],
+        'builder': ['TASK_DATASETS', 'build_task_dataset'],
+        'torch_base_dataset': ['TorchTaskDataset'],
+    }
+    import sys
+
+    sys.modules[__name__] = LazyImportModule(
+        __name__,
+        globals()['__file__'],
+        _import_structure,
+        module_spec=__spec__,
+        extra_objects={},
+    )

modelscope/utils/ast_utils.py

@@ -0,0 +1,598 @@
+import ast
+import contextlib
+import hashlib
+import importlib
+import os
+import os.path as osp
+import time
+from functools import reduce
+from typing import Generator, Union
+
+import gast
+import json
+
+from modelscope import __version__
+from modelscope.fileio.file import LocalStorage
+from modelscope.metainfo import (Heads, Metrics, Models, Pipelines,
+                                 Preprocessors, TaskModels, Trainers)
+from modelscope.utils.constant import Fields, Tasks
+from modelscope.utils.logger import get_logger
+from modelscope.utils.registry import default_group
+from modelscope.utils.utils import get_default_cache_dir
+
+logger = get_logger()
+storage = LocalStorage()
+
+# get the path of package 'modelscope'
+MODELSCOPE_PATH = '/'.join(os.path.dirname(__file__).split('/')[:-1])
+REGISTER_MODULE = 'register_module'
+IGNORED_PACKAGES = ['modelscope', '.']
+SCAN_SUB_FOLDERS = [
+    'models', 'metrics', 'pipelines', 'preprocessors', 'task_datasets'
+]
+INDEXER_FILE = 'ast_indexer'
+DECORATOR_KEY = 'decorators'
+FROM_IMPORT_KEY = 'from_imports'
+IMPORT_KEY = 'imports'
+FILE_NAME_KEY = 'filepath'
+VERSION_KEY = 'version'
+MD5_KEY = 'md5'
+INDEX_KEY = 'index'
+REQUIREMENT_KEY = 'requirements'
+MODULE_KEY = 'module'
+
+
+class AstScaning(object):
+
+    def __init__(self) -> None:
+        self.result_import = dict()
+        self.result_from_import = dict()
+        self.result_decorator = []
+
+    def _is_sub_node(self, node: object) -> bool:
+        return isinstance(node,
+                          ast.AST) and not isinstance(node, ast.expr_context)
+
+    def _is_leaf(self, node: ast.AST) -> bool:
+        for field in node._fields:
+            attr = getattr(node, field)
+            if self._is_sub_node(attr):
+                return False
+            elif isinstance(attr, (list, tuple)):
+                for val in attr:
+                    if self._is_sub_node(val):
+                        return False
+        else:
+            return True
+
+    def _fields(self, n: ast.AST, show_offsets: bool = True) -> tuple:
+        if show_offsets:
+            return n._attributes + n._fields
+        else:
+            return n._fields
+
+    def _leaf(self, node: ast.AST, show_offsets: bool = True) -> str:
+        output = dict()
+        local_print = list()
+        if isinstance(node, ast.AST):
+            local_dict = dict()
+            for field in self._fields(node, show_offsets=show_offsets):
+                field_output, field_prints = self._leaf(
+                    getattr(node, field), show_offsets=show_offsets)
+                local_dict[field] = field_output
+                local_print.append('{}={}'.format(field, field_prints))
+
+            prints = '{}({})'.format(
+                type(node).__name__,
+                ', '.join(local_print),
+            )
+            output[type(node).__name__] = local_dict
+            return output, prints
+        elif isinstance(node, list):
+            if '_fields' not in node:
+                return node, repr(node)
+            for item in node:
+                item_output, item_prints = self._leaf(
+                    getattr(node, item), show_offsets=show_offsets)
+                local_print.append(item_prints)
+            return node, '[{}]'.format(', '.join(local_print), )
+        else:
+            return node, repr(node)
+
+    def _refresh(self):
+        self.result_import = dict()
+        self.result_from_import = dict()
+        self.result_decorator = []
+
+    def scan_ast(self, node: Union[ast.AST, None, str]):
+        self._setup_global()
+        self.scan_import(node, indent='  ', show_offsets=False)
+
+    def scan_import(
+        self,
+        node: Union[ast.AST, None, str],
+        indent: Union[str, int] = '    ',
+        show_offsets: bool = True,
+        _indent: int = 0,
+        parent_node_name: str = '',
+    ) -> tuple:
+        if node is None:
+            return node, repr(node)
+        elif self._is_leaf(node):
+            return self._leaf(node, show_offsets=show_offsets)
+        else:
+            if isinstance(indent, int):
+                indent_s = indent * ' '
+            else:
+                indent_s = indent
+
+            class state:
+                indent = _indent
+
+            @contextlib.contextmanager
+            def indented() -> Generator[None, None, None]:
+                state.indent += 1
+                yield
+                state.indent -= 1
+
+            def indentstr() -> str:
+                return state.indent * indent_s
+
+            def _scan_import(el: Union[ast.AST, None, str],
+                             _indent: int = 0,
+                             parent_node_name: str = '') -> str:
+                return self.scan_import(
+                    el,
+                    indent=indent,
+                    show_offsets=show_offsets,
+                    _indent=_indent,
+                    parent_node_name=parent_node_name)
+
+            out = type(node).__name__ + '(\n'
+            outputs = dict()
+            # add relative path expression
+            if type(node).__name__ == 'ImportFrom':
+                level = getattr(node, 'level')
+                if level >= 1:
+                    path_level = ''.join(['.'] * level)
+                    setattr(node, 'level', 0)
+                    module_name = getattr(node, 'module')
+                    if module_name is None:
+                        setattr(node, 'module', path_level)
+                    else:
+                        setattr(node, 'module', path_level + module_name)
+            with indented():
+                for field in self._fields(node, show_offsets=show_offsets):
+                    attr = getattr(node, field)
+                    if attr == []:
+                        representation = '[]'
+                        outputs[field] = []
+                    elif (isinstance(attr, list) and len(attr) == 1
+                          and isinstance(attr[0], ast.AST)
+                          and self._is_leaf(attr[0])):
+                        local_out, local_print = _scan_import(attr[0])
+                        representation = f'[{local_print}]'
+                        outputs[field] = local_out
+
+                    elif isinstance(attr, list):
+                        representation = '[\n'
+                        el_dict = dict()
+                        with indented():
+                            for el in attr:
+                                local_out, local_print = _scan_import(
+                                    el, state.indent,
+                                    type(el).__name__)
+                                representation += '{}{},\n'.format(
+                                    indentstr(),
+                                    local_print,
+                                )
+                                name = type(el).__name__
+                                if (name == 'Import' or name == 'ImportFrom'
+                                        or parent_node_name == 'ImportFrom'):
+                                    if name not in el_dict:
+                                        el_dict[name] = []
+                                    el_dict[name].append(local_out)
+                        representation += indentstr() + ']'
+                        outputs[field] = el_dict
+                    elif isinstance(attr, ast.AST):
+                        output, representation = _scan_import(
+                            attr, state.indent)
+                        outputs[field] = output
+                    else:
+                        representation = repr(attr)
+                        outputs[field] = attr
+
+                    if (type(node).__name__ == 'Import'
+                            or type(node).__name__ == 'ImportFrom'):
+                        if type(node).__name__ == 'ImportFrom':
+                            if field == 'module':
+                                self.result_from_import[
+                                    outputs[field]] = dict()
+                            if field == 'names':
+                                if isinstance(outputs[field]['alias'], list):
+                                    item_name = []
+                                    for item in outputs[field]['alias']:
+                                        local_name = item['alias']['name']
+                                        item_name.append(local_name)
+                                    self.result_from_import[
+                                        outputs['module']] = item_name
+                                else:
+                                    local_name = outputs[field]['alias'][
+                                        'name']
+                                    self.result_from_import[
+                                        outputs['module']] = [local_name]
+
+                        if type(node).__name__ == 'Import':
+                            final_dict = outputs[field]['alias']
+                            self.result_import[outputs[field]['alias']
+                                               ['name']] = final_dict
+
+                    if 'decorator_list' == field and attr != []:
+                        self.result_decorator.extend(attr)
+
+                    out += f'{indentstr()}{field}={representation},\n'
+
+            out += indentstr() + ')'
+            return {
+                IMPORT_KEY: self.result_import,
+                FROM_IMPORT_KEY: self.result_from_import,
+                DECORATOR_KEY: self.result_decorator
+            }, out
+
+    def _parse_decorator(self, node: ast.AST) -> tuple:
+
+        def _get_attribute_item(node: ast.AST) -> tuple:
+            value, id, attr = None, None, None
+            if type(node).__name__ == 'Attribute':
+                value = getattr(node, 'value')
+                id = getattr(value, 'id')
+                attr = getattr(node, 'attr')
+            if type(node).__name__ == 'Name':
+                id = getattr(node, 'id')
+            return id, attr
+
+        def _get_args_name(nodes: list) -> list:
+            result = []
+            for node in nodes:
+                result.append(_get_attribute_item(node))
+            return result
+
+        def _get_keyword_name(nodes: ast.AST) -> list:
+            result = []
+            for node in nodes:
+                if type(node).__name__ == 'keyword':
+                    attribute_node = getattr(node, 'value')
+                    if type(attribute_node).__name__ == 'Str':
+                        result.append((attribute_node.s, None))
+                    else:
+                        result.append(_get_attribute_item(attribute_node))
+            return result
+
+        functions = _get_attribute_item(node.func)
+        args_list = _get_args_name(node.args)
+        keyword_list = _get_keyword_name(node.keywords)
+        return functions, args_list, keyword_list
+
+    def _get_registry_value(self, key_item):
+        if key_item is None:
+            return None
+        if key_item == 'default_group':
+            return default_group
+        split_list = key_item.split('.')
+        # in the case, the key_item is raw data, not registred
+        if len(split_list) == 1:
+            return key_item
+        else:
+            return getattr(eval(split_list[0]), split_list[1])
+
+    def _registry_indexer(self, parsed_input: tuple) -> tuple:
+        """format registry information to a tuple indexer
+
+        Return:
+            tuple: (MODELS, Tasks.text-classification, Models.structbert)
+        """
+        functions, args_list, keyword_list = parsed_input
+
+        # ignore decocators other than register_module
+        if REGISTER_MODULE != functions[1]:
+            return None
+        output = [functions[0]]
+
+        if len(args_list) == 0 and len(keyword_list) == 0:
+            args_list.append(None)
+        if len(keyword_list) == 0 and len(args_list) == 1:
+            args_list.append(None)
+
+        args_list.extend(keyword_list)
+
+        for item in args_list:
+            # the case empty input
+            if item is None:
+                output.append(None)
+            # the case (default_group)
+            elif item[1] is None:
+                output.append(item[0])
+            else:
+                output.append('.'.join(item))
+        return (output[0], self._get_registry_value(output[1]),
+                self._get_registry_value(output[2]))
+
+    def parse_decorators(self, nodes: list) -> list:
+        """parse the AST nodes of decorators object to registry indexer
+
+        Args:
+            nodes (list): list of AST decorator nodes
+
+        Returns:
+            list: list of registry indexer
+        """
+        results = []
+        for node in nodes:
+            if type(node).__name__ != 'Call':
+                continue
+            parse_output = self._parse_decorator(node)
+            index = self._registry_indexer(parse_output)
+            if None is not index:
+                results.append(index)
+        return results
+
+    def generate_ast(self, file):
+        self._refresh()
+        with open(file, 'r') as code:
+            data = code.readlines()
+        data = ''.join(data)
+
+        node = gast.parse(data)
+        output, _ = self.scan_import(node, indent='  ', show_offsets=False)
+        output[DECORATOR_KEY] = self.parse_decorators(output[DECORATOR_KEY])
+        return output
+
+
+class FilesAstScaning(object):
+
+    def __init__(self) -> None:
+        self.astScaner = AstScaning()
+        self.file_dirs = []
+
+    def _parse_import_path(self,
+                           import_package: str,
+                           current_path: str = None) -> str:
+        """
+        Args:
+            import_package (str): relative import or abs import
+            current_path (str): path/to/current/file
+        """
+        if import_package.startswith(IGNORED_PACKAGES[0]):
+            return MODELSCOPE_PATH + '/' + '/'.join(
+                import_package.split('.')[1:]) + '.py'
+        elif import_package.startswith(IGNORED_PACKAGES[1]):
+            current_path_list = current_path.split('/')
+            import_package_list = import_package.split('.')
+            level = 0
+            for index, item in enumerate(import_package_list):
+                if item != '':
+                    level = index
+                    break
+
+            abs_path_list = current_path_list[0:-level]
+            abs_path_list.extend(import_package_list[index:])
+            return '/' + '/'.join(abs_path_list) + '.py'
+        else:
+            return current_path
+
+    def _traversal_import(
+        self,
+        import_abs_path,
+    ):
+        pass
+
+    def parse_import(self, scan_result: dict) -> list:
+        """parse import and from import dicts to a third party package list
+
+        Args:
+            scan_result (dict): including the import and from import result
+
+        Returns:
+            list: a list of package ignored 'modelscope' and relative path import
+        """
+        output = []
+        output.extend(list(scan_result[IMPORT_KEY].keys()))
+        output.extend(list(scan_result[FROM_IMPORT_KEY].keys()))
+
+        # get the package name
+        for index, item in enumerate(output):
+            if '' == item.split('.')[0]:
+                output[index] = '.'
+            else:
+                output[index] = item.split('.')[0]
+
+        ignored = set()
+        for item in output:
+            for ignored_package in IGNORED_PACKAGES:
+                if item.startswith(ignored_package):
+                    ignored.add(item)
+        return list(set(output) - set(ignored))
+
+    def traversal_files(self, path, check_sub_dir):
+        self.file_dirs = []
+        if check_sub_dir is None or len(check_sub_dir) == 0:
+            self._traversal_files(path)
+
+        for item in check_sub_dir:
+            sub_dir = os.path.join(path, item)
+            if os.path.isdir(sub_dir):
+                self._traversal_files(sub_dir)
+
+    def _traversal_files(self, path):
+        dir_list = os.scandir(path)
+        for item in dir_list:
+            if item.name.startswith('__'):
+                continue
+            if item.is_dir():
+                self._traversal_files(item.path)
+            elif item.is_file() and item.name.endswith('.py'):
+                self.file_dirs.append(item.path)
+
+    def _get_single_file_scan_result(self, file):
+        output = self.astScaner.generate_ast(file)
+        import_list = self.parse_import(output)
+        return output[DECORATOR_KEY], import_list
+
+    def _inverted_index(self, forward_index):
+        inverted_index = dict()
+        for index in forward_index:
+            for item in forward_index[index][DECORATOR_KEY]:
+                inverted_index[item] = {
+                    FILE_NAME_KEY: index,
+                    IMPORT_KEY: forward_index[index][IMPORT_KEY],
+                    MODULE_KEY: forward_index[index][MODULE_KEY],
+                }
+        return inverted_index
+
+    def _module_import(self, forward_index):
+        module_import = dict()
+        for index, value_dict in forward_index.items():
+            module_import[value_dict[MODULE_KEY]] = value_dict[IMPORT_KEY]
+        return module_import
+
+    def get_files_scan_results(self,
+                               target_dir=MODELSCOPE_PATH,
+                               target_folders=SCAN_SUB_FOLDERS):
+        """the entry method of the ast scan method
+
+        Args:
+            target_dir (str, optional): the absolute path of the target directory to be scaned. Defaults to None.
+            target_folder (list, optional): the list of
+            sub-folders to be scaned in the target folder.
+            Defaults to SCAN_SUB_FOLDERS.
+
+        Returns:
+            dict: indexer of registry
+        """
+
+        self.traversal_files(target_dir, target_folders)
+        start = time.time()
+        logger.info(
+            f'AST-Scaning the path "{target_dir}" with the following sub folders {target_folders}'
+        )
+
+        result = dict()
+        for file in self.file_dirs:
+            filepath = file[file.find('modelscope'):]
+            module_name = filepath.replace(osp.sep, '.').replace('.py', '')
+            decorator_list, import_list = self._get_single_file_scan_result(
+                file)
+            result[file] = {
+                DECORATOR_KEY: decorator_list,
+                IMPORT_KEY: import_list,
+                MODULE_KEY: module_name
+            }
+        inverted_index_with_results = self._inverted_index(result)
+        module_import = self._module_import(result)
+        index = {
+            INDEX_KEY: inverted_index_with_results,
+            REQUIREMENT_KEY: module_import
+        }
+        logger.info(
+            f'Scaning done! A number of {len(inverted_index_with_results)}'
+            f' files indexed! Time consumed {time.time()-start}s')
+        return index
+
+    def files_mtime_md5(self,
+                        target_path=MODELSCOPE_PATH,
+                        target_subfolder=SCAN_SUB_FOLDERS):
+        self.file_dirs = []
+        self.traversal_files(target_path, target_subfolder)
+        files_mtime = []
+        for item in self.file_dirs:
+            files_mtime.append(os.path.getmtime(item))
+        result_str = reduce(lambda x, y: str(x) + str(y), files_mtime, '')
+        md5 = hashlib.md5(result_str.encode())
+        return md5.hexdigest()
+
+
+fileScaner = FilesAstScaning()
+
+
+def _save_index(index, file_path):
+    # convert tuple key to str key
+    index[INDEX_KEY] = {str(k): v for k, v in index[INDEX_KEY].items()}
+    index[VERSION_KEY] = __version__
+    index[MD5_KEY] = fileScaner.files_mtime_md5()
+    json_index = json.dumps(index)
+    storage.write(json_index.encode(), file_path)
+    index[INDEX_KEY] = {
+        ast.literal_eval(k): v
+        for k, v in index[INDEX_KEY].items()
+    }
+
+
+def _load_index(file_path):
+    bytes_index = storage.read(file_path)
+    wrapped_index = json.loads(bytes_index)
+    # convert str key to tuple key
+    wrapped_index[INDEX_KEY] = {
+        ast.literal_eval(k): v
+        for k, v in wrapped_index[INDEX_KEY].items()
+    }
+    return wrapped_index
+
+
+def load_index(force_rebuild=False):
+    """get the index from scan results or cache
+
+    Args:
+        force_rebuild: If set true, rebuild and load index
+    Returns:
+        dict: the index information for all registred modules, including key:
+        index, requirments, version and md5, the detail is shown below example:
+        {
+            'index': {
+                ('MODELS', 'nlp', 'bert'):{
+                    'filepath' : 'path/to/the/registered/model', 'imports':
+                    ['os', 'torch', 'typeing'] 'module':
+                    'modelscope.models.nlp.bert'
+                },
+                ...
+            }, 'requirments': {
+                'modelscope.models.nlp.bert': ['os', 'torch', 'typeing'],
+                'modelscope.models.nlp.structbert': ['os', 'torch', 'typeing'],
+                ...
+            }, 'version': '0.2.3', 'md5': '8616924970fe6bc119d1562832625612',
+        }
+    """
+    cache_dir = os.getenv('MODELSCOPE_CACHE', get_default_cache_dir())
+    file_path = os.path.join(cache_dir, INDEXER_FILE)
+    logger.info(f'Loading ast index from {file_path}')
+    index = None
+    if not force_rebuild and os.path.exists(file_path):
+        wrapped_index = _load_index(file_path)
+        md5 = fileScaner.files_mtime_md5()
+        if (wrapped_index[VERSION_KEY] == __version__
+                and wrapped_index[MD5_KEY] == md5):
+            index = wrapped_index
+
+    if index is None:
+        if force_rebuild:
+            logger.info('Force rebuilding ast index')
+        else:
+            logger.info(
+                f'No valid ast index found from {file_path}, rebuilding ast index!'
+            )
+        index = fileScaner.get_files_scan_results()
+        _save_index(index, file_path)
+    return index
+
+
+def check_import_module_avaliable(module_dicts: dict) -> list:
+    missed_module = []
+    for module in module_dicts.keys():
+        loader = importlib.find_loader(module)
+        if loader is None:
+            missed_module.append(module)
+    return missed_module
+
+
+if __name__ == '__main__':
+    index = load_index()
+    print(index)

modelscope/utils/check_requirements.py

@@ -1,79 +0,0 @@
-# Copyright (c) Alibaba, Inc. and its affiliates.
-
-from modelscope.utils.constant import Fields, Requirements
-from modelscope.utils.import_utils import requires
-
-
-def get_msg(field):
-    msg = f'\n{field} requirements not installed, please execute ' \
-          f'`pip install requirements/{field}.txt` or ' \
-          f'`pip install modelscope[{field}]`'
-    return msg
-
-
-class NLPModuleNotFoundError(ModuleNotFoundError):
-
-    def __init__(self, e: ModuleNotFoundError) -> None:
-        e.msg += get_msg(Fields.nlp)
-        super().__init__(e)
-
-
-class CVModuleNotFoundError(ModuleNotFoundError):
-
-    def __init__(self, e: ModuleNotFoundError) -> None:
-        e.msg += get_msg(Fields.cv)
-        super().__init__(e)
-
-
-class AudioModuleNotFoundError(ModuleNotFoundError):
-
-    def __init__(self, e: ModuleNotFoundError) -> None:
-        e.msg += get_msg(Fields.audio)
-        super().__init__(e)
-
-
-class MultiModalModuleNotFoundError(ModuleNotFoundError):
-
-    def __init__(self, e: ModuleNotFoundError) -> None:
-        e.msg += get_msg(Fields.multi_modal)
-        super().__init__(e)
-
-
-def check_nlp():
-    try:
-        requires('nlp models', (
-            Requirements.torch,
-            Requirements.tokenizers,
-        ))
-    except ImportError as e:
-        raise NLPModuleNotFoundError(e)
-
-
-def check_cv():
-    try:
-        requires('cv models', (
-            Requirements.torch,
-            Requirements.tokenizers,
-        ))
-    except ImportError as e:
-        raise CVModuleNotFoundError(e)
-
-
-def check_audio():
-    try:
-        requires('audio models', (
-            Requirements.torch,
-            Requirements.tf,
-        ))
-    except ImportError as e:
-        raise AudioModuleNotFoundError(e)
-
-
-def check_multi_modal():
-    try:
-        requires('multi-modal models', (
-            Requirements.torch,
-            Requirements.tokenizers,
-        ))
-    except ImportError as e:
-        raise MultiModalModuleNotFoundError(e)

modelscope/utils/error.py

@@ -75,3 +75,19 @@ SCIPY_IMPORT_ERROR = """
 {0} requires the scipy library but it was not found in your environment. You can install it with pip:
 `pip install scipy`
 """
+
+# docstyle-ignore
+OPENCV_IMPORT_ERROR = """
+{0} requires the opencv library but it was not found in your environment. You can install it with pip:
+`pip install opencv-python`
+"""
+
+PILLOW_IMPORT_ERROR = """
+{0} requires the Pillow library but it was not found in your environment. You can install it with pip:
+`pip install Pillow`
+"""
+
+GENERAL_IMPORT_ERROR = """
+{0} requires the REQ library but it was not found in your environment. You can install it with pip:
+`pip install REQ`
+"""

modelscope/utils/import_utils.py

@@ -2,11 +2,10 @@
 # Part of the implementation is borrowed from huggingface/transformers.
 import ast
 import functools
-import importlib.util
+import importlib
 import os
 import os.path as osp
 import sys
-import types
 from collections import OrderedDict
 from functools import wraps
 from importlib import import_module
@@ -14,18 +13,15 @@ from itertools import chain
 from types import ModuleType
 from typing import Any
 
-import json
 from packaging import version
 
-from modelscope.utils.constant import Fields
-from modelscope.utils.error import (PROTOBUF_IMPORT_ERROR,
-                                    PYTORCH_IMPORT_ERROR, SCIPY_IMPORT_ERROR,
-                                    SENTENCEPIECE_IMPORT_ERROR,
-                                    SKLEARN_IMPORT_ERROR,
-                                    TENSORFLOW_IMPORT_ERROR, TIMM_IMPORT_ERROR,
-                                    TOKENIZERS_IMPORT_ERROR)
+from modelscope.utils.ast_utils import (INDEX_KEY, MODULE_KEY, REQUIREMENT_KEY,
+                                        load_index)
+from modelscope.utils.error import *  # noqa
 from modelscope.utils.logger import get_logger
 
+logger = get_logger(__name__)
+
 if sys.version_info < (3, 8):
     import importlib_metadata
 else:
@@ -33,6 +29,8 @@ else:
 
 logger = get_logger()
 
+AST_INDEX = None
+
 
 def import_modules_from_file(py_file: str):
     """ Import module from a certrain file
@@ -250,18 +248,44 @@ def is_tf_available():
     return _tf_available
 
 
+def is_opencv_available():
+    return importlib.util.find_spec('cv2') is not None
+
+
+def is_pillow_available():
+    return importlib.util.find_spec('PIL.Image') is not None
+
+
+def is_package_available(pkg_name):
+    return importlib.util.find_spec(pkg_name) is not None
+
+
+def is_espnet_available(pkg_name):
+    return importlib.util.find_spec('espnet2') is not None \
+        and importlib.util.find_spec('espnet')
+
+
 REQUIREMENTS_MAAPING = OrderedDict([
     ('protobuf', (is_protobuf_available, PROTOBUF_IMPORT_ERROR)),
     ('sentencepiece', (is_sentencepiece_available,
                        SENTENCEPIECE_IMPORT_ERROR)),
     ('sklearn', (is_sklearn_available, SKLEARN_IMPORT_ERROR)),
     ('tf', (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
+    ('tensorflow', (is_tf_available, TENSORFLOW_IMPORT_ERROR)),
     ('timm', (is_timm_available, TIMM_IMPORT_ERROR)),
     ('tokenizers', (is_tokenizers_available, TOKENIZERS_IMPORT_ERROR)),
     ('torch', (is_torch_available, PYTORCH_IMPORT_ERROR)),
     ('scipy', (is_scipy_available, SCIPY_IMPORT_ERROR)),
+    ('cv2', (is_opencv_available, OPENCV_IMPORT_ERROR)),
+    ('PIL', (is_pillow_available, PILLOW_IMPORT_ERROR)),
+    ('espnet2', (is_espnet_available,
+                 GENERAL_IMPORT_ERROR.replace('REQ', 'espnet'))),
+    ('espnet', (is_espnet_available,
+                GENERAL_IMPORT_ERROR.replace('REQ', 'espnet'))),
 ])
 
+SYSTEM_PACKAGE = set(['os', 'sys', 'typing'])
+
 
 def requires(obj, requirements):
     if not isinstance(requirements, (list, tuple)):
@@ -271,7 +295,18 @@ def requires(obj, requirements):
     else:
         name = obj.__name__ if hasattr(obj,
                                        '__name__') else obj.__class__.__name__
-    checks = (REQUIREMENTS_MAAPING[req] for req in requirements)
+    checks = []
+    for req in requirements:
+        if req == '' or req in SYSTEM_PACKAGE:
+            continue
+        if req in REQUIREMENTS_MAAPING:
+            check = REQUIREMENTS_MAAPING[req]
+        else:
+            check_fn = functools.partial(is_package_available, req)
+            err_msg = GENERAL_IMPORT_ERROR.replace('REQ', req)
+            check = (check_fn, err_msg)
+        checks.append(check)
+
     failed = [msg.format(name) for available, msg in checks if not available()]
     if failed:
         raise ImportError(''.join(failed))
@@ -299,3 +334,99 @@ def tf_required(func):
             raise ImportError(f'Method `{func.__name__}` requires TF.')
 
     return wrapper
+
+
+class LazyImportModule(ModuleType):
+    AST_INDEX = None
+    if AST_INDEX is None:
+        AST_INDEX = load_index()
+
+    def __init__(self,
+                 name,
+                 module_file,
+                 import_structure,
+                 module_spec=None,
+                 extra_objects=None,
+                 try_to_pre_import=False):
+        super().__init__(name)
+        self._modules = set(import_structure.keys())
+        self._class_to_module = {}
+        for key, values in import_structure.items():
+            for value in values:
+                self._class_to_module[value] = key
+        # Needed for autocompletion in an IDE
+        self.__all__ = list(import_structure.keys()) + list(
+            chain(*import_structure.values()))
+        self.__file__ = module_file
+        self.__spec__ = module_spec
+        self.__path__ = [os.path.dirname(module_file)]
+        self._objects = {} if extra_objects is None else extra_objects
+        self._name = name
+        self._import_structure = import_structure
+        if try_to_pre_import:
+            self._try_to_import()
+
+    def _try_to_import(self):
+        for sub_module in self._class_to_module.keys():
+            try:
+                getattr(self, sub_module)
+            except Exception as e:
+                logger.warn(
+                    f'pre load module {sub_module} error, please check {e}')
+
+    # Needed for autocompletion in an IDE
+    def __dir__(self):
+        result = super().__dir__()
+        # The elements of self.__all__ that are submodules may or may not be in the dir already, depending on whether
+        # they have been accessed or not. So we only add the elements of self.__all__ that are not already in the dir.
+        for attr in self.__all__:
+            if attr not in result:
+                result.append(attr)
+        return result
+
+    def __getattr__(self, name: str) -> Any:
+        if name in self._objects:
+            return self._objects[name]
+        if name in self._modules:
+            value = self._get_module(name)
+        elif name in self._class_to_module.keys():
+            module = self._get_module(self._class_to_module[name])
+            value = getattr(module, name)
+        else:
+            raise AttributeError(
+                f'module {self.__name__} has no attribute {name}')
+
+        setattr(self, name, value)
+        return value
+
+    def _get_module(self, module_name: str):
+        try:
+            # check requirements before module import
+            module_name_full = self.__name__ + '.' + module_name
+            if module_name_full in LazyImportModule.AST_INDEX[REQUIREMENT_KEY]:
+                requirements = LazyImportModule.AST_INDEX[REQUIREMENT_KEY][
+                    module_name_full]
+                requires(module_name_full, requirements)
+            return importlib.import_module('.' + module_name, self.__name__)
+        except Exception as e:
+            raise RuntimeError(
+                f'Failed to import {self.__name__}.{module_name} because of the following error '
+                f'(look up to see its traceback):\n{e}') from e
+
+    def __reduce__(self):
+        return self.__class__, (self._name, self.__file__,
+                                self._import_structure)
+
+    @staticmethod
+    def import_module(signature):
+        """ import a lazy import module using signature
+
+        Args:
+            signature (tuple): a tuple of str, (registry_name, registry_group_name, module_name)
+        """
+        if signature in LazyImportModule.AST_INDEX[INDEX_KEY]:
+            mod_index = LazyImportModule.AST_INDEX[INDEX_KEY][signature]
+            module_name = mod_index[MODULE_KEY]
+            importlib.import_module(module_name)
+        else:
+            logger.warning(f'{signature} not found in ast index file')

modelscope/utils/registry.py

@@ -1,14 +1,15 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
+import importlib
 import inspect
 from typing import List, Tuple, Union
 
-from modelscope.utils.import_utils import requires
 from modelscope.utils.logger import get_logger
 
 TYPE_NAME = 'type'
 default_group = 'default'
 logger = get_logger()
+AST_INDEX = None
 
 
 class Registry(object):
@@ -55,14 +56,10 @@ class Registry(object):
     def _register_module(self,
                          group_key=default_group,
                          module_name=None,
-                         module_cls=None,
-                         requirements=None):
+                         module_cls=None):
         assert isinstance(group_key,
                           str), 'group_key is required and must be str'
 
-        if requirements is not None:
-            requires(module_cls, requirements)
-
         if group_key not in self._modules:
             self._modules[group_key] = dict()
 
@@ -81,8 +78,7 @@ class Registry(object):
     def register_module(self,
                         group_key: str = default_group,
                         module_name: str = None,
-                        module_cls: type = None,
-                        requirements: Union[List, Tuple] = None):
+                        module_cls: type = None):
         """ Register module
 
         Example:
@@ -106,7 +102,6 @@ class Registry(object):
                 default group name is 'default'
             module_name: Module name
             module_cls: Module class object
-            requirements: Module necessary requirements
 
         """
         if not (module_name is None or isinstance(module_name, str)):
@@ -116,8 +111,7 @@ class Registry(object):
             self._register_module(
                 group_key=group_key,
                 module_name=module_name,
-                module_cls=module_cls,
-                requirements=requirements)
+                module_cls=module_cls)
             return module_cls
 
         # if module_cls is None, should return a decorator function
@@ -125,8 +119,7 @@ class Registry(object):
             self._register_module(
                 group_key=group_key,
                 module_name=module_name,
-                module_cls=module_cls,
-                requirements=requirements)
+                module_cls=module_cls)
             return module_cls
 
         return _register
@@ -178,6 +171,11 @@ def build_from_cfg(cfg,
         raise TypeError('default_args must be a dict or None, '
                         f'but got {type(default_args)}')
 
+    # dynamic load installation reqruiements for this module
+    from modelscope.utils.import_utils import LazyImportModule
+    sig = (registry.name.upper(), group_key, cfg['type'])
+    LazyImportModule.import_module(sig)
+
     args = cfg.copy()
     if default_args is not None:
         for name, value in default_args.items():

modelscope/utils/utils.py

@@ -1,6 +1,7 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 import inspect
+import os
 
 
 def if_func_receive_dict_inputs(func, inputs):
@@ -26,3 +27,12 @@ def if_func_receive_dict_inputs(func, inputs):
         return True
     else:
         return False
+
+
+def get_default_cache_dir():
+    """
+    default base dir: '~/.cache/modelscope'
+    """
+    default_cache_dir = os.path.expanduser(
+        os.path.join('~/.cache', 'modelscope'))
+    return default_cache_dir

requirements/audio.txt

@@ -9,6 +9,7 @@ librosa
 lxml
 matplotlib
 nara_wpe
+nltk
 numpy<=1.18
 # protobuf version beyond 3.20.0 is not compatible with TensorFlow 1.x, therefore is discouraged.
 protobuf>3,<3.21.0

requirements/runtime.txt

@@ -3,6 +3,7 @@ datasets
 easydict
 einops
 filelock>=3.3.0
+gast>=0.5.3
 numpy
 opencv-python
 Pillow>=6.2.0

setup.cfg

@@ -21,5 +21,5 @@ ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids
 [flake8]
 select = B,C,E,F,P,T4,W,B9
 max-line-length = 120
-ignore = F401,F821,W503
+ignore = F401,F405,F821,W503
 exclude = docs/src,*.pyi,.git

tests/pipelines/test_base.py

@@ -4,7 +4,7 @@ import unittest
 from typing import Any, Dict, List, Tuple, Union
 
 import numpy as np
-import PIL
+from PIL import Image
 
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines import Pipeline, pipeline
@@ -54,7 +54,7 @@ class CustomPipelineTest(unittest.TestCase):
                 """ Provide default implementation based on preprocess_cfg and user can reimplement it
 
                 """
-                if not isinstance(input, PIL.Image.Image):
+                if not isinstance(input, Image.Image):
                     from modelscope.preprocessors import load_image
                     data_dict = {'img': load_image(input), 'url': input}
                 else:

tests/pipelines/test_csanmt_translation.py

@@ -3,7 +3,7 @@ import shutil
 import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
-from modelscope.pipelines import TranslationPipeline, pipeline
+from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
 

tests/pipelines/test_dialog_intent_prediction.py

@@ -4,7 +4,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SpaceForDialogIntent
-from modelscope.pipelines import DialogIntentPredictionPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import DialogIntentPredictionPipeline
 from modelscope.preprocessors import DialogIntentPredictionPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_dialog_modeling.py

@@ -5,7 +5,8 @@ from typing import List
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SpaceForDialogModeling
-from modelscope.pipelines import DialogModelingPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import DialogModelingPipeline
 from modelscope.preprocessors import DialogModelingPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_dialog_state_tracking.py

@@ -3,8 +3,10 @@ import unittest
 from typing import List
 
 from modelscope.hub.snapshot_download import snapshot_download
-from modelscope.models import Model, SpaceForDialogStateTracking
-from modelscope.pipelines import DialogStateTrackingPipeline, pipeline
+from modelscope.models import Model
+from modelscope.models.nlp import SpaceForDialogStateTracking
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import DialogStateTrackingPipeline
 from modelscope.preprocessors import DialogStateTrackingPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_fill_mask.py

@@ -5,7 +5,8 @@ from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import (BertForMaskedLM, StructBertForMaskedLM,
                                    VecoForMaskedLM)
-from modelscope.pipelines import FillMaskPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import FillMaskPipeline
 from modelscope.preprocessors import FillMaskPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_image_denoise.py

@@ -7,7 +7,8 @@ from PIL import Image
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.outputs import OutputKeys
-from modelscope.pipelines import ImageDenoisePipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.cv import ImageDenoisePipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
 

tests/pipelines/test_image_instance_segmentation.py

@@ -4,10 +4,11 @@ import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
-from modelscope.models.cv.image_instance_segmentation.model import \
-    CascadeMaskRCNNSwinModel
+from modelscope.models.cv.image_instance_segmentation import (
+    CascadeMaskRCNNSwinModel, get_img_ins_seg_result)
 from modelscope.outputs import OutputKeys
-from modelscope.pipelines import ImageInstanceSegmentationPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.cv import ImageInstanceSegmentationPipeline
 from modelscope.preprocessors import build_preprocessor
 from modelscope.utils.config import Config
 from modelscope.utils.constant import Fields, ModelFile, Tasks

tests/pipelines/test_named_entity_recognition.py

@@ -4,7 +4,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import TransformerCRFForNamedEntityRecognition
-from modelscope.pipelines import NamedEntityRecognitionPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import NamedEntityRecognitionPipeline
 from modelscope.preprocessors import NERPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_nli.py

@@ -4,7 +4,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SbertForNLI
-from modelscope.pipelines import NLIPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import NLIPipeline
 from modelscope.preprocessors import NLIPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_sentence_similarity.py

@@ -5,7 +5,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SbertForSentenceSimilarity
-from modelscope.pipelines import SentenceSimilarityPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import SentenceSimilarityPipeline
 from modelscope.preprocessors import SentenceSimilarityPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_sentiment_classification.py

@@ -5,7 +5,8 @@ from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import (SbertForSentimentClassification,
                                    SequenceClassificationModel)
-from modelscope.pipelines import SentimentClassificationPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import SentimentClassificationPipeline
 from modelscope.preprocessors import SentimentClassificationPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_text_classification.py

@@ -4,7 +4,8 @@ import unittest
 
 from modelscope.models import Model
 from modelscope.msdatasets import MsDataset
-from modelscope.pipelines import SequenceClassificationPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import SequenceClassificationPipeline
 from modelscope.preprocessors import SequenceClassificationPreprocessor
 from modelscope.utils.constant import Hubs, Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_text_generation.py

@@ -4,7 +4,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import PalmForTextGeneration
-from modelscope.pipelines import TextGenerationPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import TextGenerationPipeline
 from modelscope.preprocessors import TextGenerationPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_visual_question_answering.py

@@ -5,7 +5,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.multi_modal import MPlugForVisualQuestionAnswering
-from modelscope.pipelines import VisualQuestionAnsweringPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.multi_modal import VisualQuestionAnsweringPipeline
 from modelscope.preprocessors import MPlugVisualQuestionAnsweringPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_word_segmentation.py

@@ -5,7 +5,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SbertForTokenClassification
-from modelscope.pipelines import WordSegmentationPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import WordSegmentationPipeline
 from modelscope.preprocessors import TokenClassificationPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_zero_shot_classification.py

@@ -4,7 +4,8 @@ import unittest
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SbertForZeroShotClassification
-from modelscope.pipelines import ZeroShotClassificationPipeline, pipeline
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.nlp import ZeroShotClassificationPipeline
 from modelscope.preprocessors import ZeroShotClassificationPreprocessor
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/preprocessors/test_image.py

@@ -2,7 +2,7 @@
 
 import unittest
 
-import PIL
+from PIL import Image
 
 from modelscope.preprocessors import load_image
 
@@ -11,7 +11,7 @@ class ImagePreprocessorTest(unittest.TestCase):
 
     def test_load(self):
         img = load_image('data/test/images/image_matting.png')
-        self.assertTrue(isinstance(img, PIL.Image.Image))
+        self.assertTrue(isinstance(img, Image.Image))
         self.assertEqual(img.size, (948, 533))
 
 

tests/trainers/test_image_color_enhance_trainer.py

@@ -11,8 +11,7 @@ import torch
 from torch.utils import data as data
 
 from modelscope.hub.snapshot_download import snapshot_download
-from modelscope.models.cv.image_color_enhance.image_color_enhance import \
-    ImageColorEnhance
+from modelscope.models.cv.image_color_enhance import ImageColorEnhance
 from modelscope.trainers import build_trainer
 from modelscope.utils.constant import ModelFile
 from modelscope.utils.test_utils import test_level

tests/trainers/test_image_denoise_trainer.py

@@ -5,9 +5,8 @@ import tempfile
 import unittest
 
 from modelscope.hub.snapshot_download import snapshot_download
-from modelscope.models import NAFNetForImageDenoise
-from modelscope.msdatasets.image_denoise_data.image_denoise_dataset import \
-    PairedImageDataset
+from modelscope.models.cv.image_denoise import NAFNetForImageDenoise
+from modelscope.msdatasets.image_denoise_data import PairedImageDataset
 from modelscope.trainers import build_trainer
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile

tests/trainers/test_image_instance_segmentation_trainer.py

@@ -7,10 +7,8 @@ import zipfile
 from functools import partial
 
 from modelscope.hub.snapshot_download import snapshot_download
-from modelscope.models.cv.image_instance_segmentation import \
-    CascadeMaskRCNNSwinModel
-from modelscope.models.cv.image_instance_segmentation.datasets import \
-    ImageInstanceSegmentationCocoDataset
+from modelscope.models.cv.image_instance_segmentation import (
+    CascadeMaskRCNNSwinModel, ImageInstanceSegmentationCocoDataset)
 from modelscope.trainers import build_trainer
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile

tests/utils/test_ast.py

@@ -0,0 +1,97 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+
+import os
+import shutil
+import tempfile
+import time
+import unittest
+
+import gast
+
+from modelscope.utils.ast_utils import AstScaning, FilesAstScaning, load_index
+
+MODELSCOPE_PATH = '/'.join(
+    os.path.dirname(__file__).split('/')[:-2]) + '/modelscope'
+
+
+class AstScaningTest(unittest.TestCase):
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+        self.tmp_dir = tempfile.TemporaryDirectory().name
+        self.test_file = os.path.join(self.tmp_dir, 'test.py')
+        if not os.path.exists(self.tmp_dir):
+            os.makedirs(self.tmp_dir)
+
+    def tearDown(self):
+        super().tearDown()
+        shutil.rmtree(self.tmp_dir)
+
+    def test_ast_scaning_class(self):
+        astScaner = AstScaning()
+        pipeline_file = os.path.join(MODELSCOPE_PATH, 'pipelines', 'nlp',
+                                     'sequence_classification_pipeline.py')
+        output = astScaner.generate_ast(pipeline_file)
+        self.assertTrue(output['imports'] is not None)
+        self.assertTrue(output['from_imports'] is not None)
+        self.assertTrue(output['decorators'] is not None)
+        imports, from_imports, decorators = output['imports'], output[
+            'from_imports'], output['decorators']
+        self.assertIsInstance(imports, dict)
+        self.assertIsInstance(from_imports, dict)
+        self.assertIsInstance(decorators, list)
+        self.assertListEqual(
+            list(set(imports.keys()) - set(['typing', 'numpy'])), [])
+        self.assertEqual(len(from_imports.keys()), 9)
+        self.assertTrue(from_imports['modelscope.metainfo'] is not None)
+        self.assertEqual(from_imports['modelscope.metainfo'], ['Pipelines'])
+        self.assertEqual(
+            decorators,
+            [('PIPELINES', 'text-classification', 'sentiment-analysis')])
+
+    def test_files_scaning_method(self):
+        fileScaner = FilesAstScaning()
+        output = fileScaner.get_files_scan_results()
+        self.assertTrue(output['index'] is not None)
+        self.assertTrue(output['requirements'] is not None)
+        index, requirements = output['index'], output['requirements']
+        self.assertIsInstance(index, dict)
+        self.assertIsInstance(requirements, dict)
+        self.assertIsInstance(list(index.keys())[0], tuple)
+        index_0 = list(index.keys())[0]
+        self.assertIsInstance(index[index_0], dict)
+        self.assertTrue(index[index_0]['imports'] is not None)
+        self.assertIsInstance(index[index_0]['imports'], list)
+        self.assertTrue(index[index_0]['module'] is not None)
+        self.assertIsInstance(index[index_0]['module'], str)
+        index_0 = list(requirements.keys())[0]
+        self.assertIsInstance(requirements[index_0], list)
+
+    def test_file_mtime_md5_method(self):
+        fileScaner = FilesAstScaning()
+        # create first file
+        with open(self.test_file, 'w', encoding='utf-8') as f:
+            f.write('This is the new test!')
+
+        md5_1 = fileScaner.files_mtime_md5(self.tmp_dir, [])
+        md5_2 = fileScaner.files_mtime_md5(self.tmp_dir, [])
+        self.assertEqual(md5_1, md5_2)
+        time.sleep(2)
+        # case of revise
+        with open(self.test_file, 'w', encoding='utf-8') as f:
+            f.write('test again')
+        md5_3 = fileScaner.files_mtime_md5(self.tmp_dir, [])
+        self.assertNotEqual(md5_1, md5_3)
+
+        # case of create
+        self.test_file_new = os.path.join(self.tmp_dir, 'test_1.py')
+        time.sleep(2)
+        with open(self.test_file_new, 'w', encoding='utf-8') as f:
+            f.write('test again')
+        md5_4 = fileScaner.files_mtime_md5(self.tmp_dir, [])
+        self.assertNotEqual(md5_1, md5_4)
+        self.assertNotEqual(md5_3, md5_4)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/utils/test_check_requirements.py

@@ -1,22 +0,0 @@
-# Copyright (c) Alibaba, Inc. and its affiliates.
-
-import unittest
-from typing import List, Union
-
-from modelscope.utils.check_requirements import NLPModuleNotFoundError, get_msg
-from modelscope.utils.constant import Fields
-
-
-class ImportUtilsTest(unittest.TestCase):
-
-    def test_type_module_not_found(self):
-        with self.assertRaises(NLPModuleNotFoundError) as ctx:
-            try:
-                import not_found
-            except ModuleNotFoundError as e:
-                raise NLPModuleNotFoundError(e)
-        self.assertTrue(get_msg(Fields.nlp) in ctx.exception.msg.msg)
-
-
-if __name__ == '__main__':
-    unittest.main()