[to #42322933]support model revision

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9300163

modelscope/hub/file_download.py

@@ -141,7 +141,9 @@ def model_file_download(
         cached_file_path = cache.get_file_by_path_and_commit_id(
             file_path, revision)
         if cached_file_path is not None:
-            logger.info('The specified file is in cache, skip downloading!')
+            file_name = os.path.basename(cached_file_path)
+            logger.info(
+                f'File {file_name} already in cache, skip downloading!')
             return cached_file_path  # the file is in cache.
         is_commit_id = True
     # we need to download again

modelscope/hub/snapshot_download.py

@@ -1,13 +1,11 @@
 import os
 import tempfile
-from glob import glob
 from pathlib import Path
 from typing import Dict, Optional, Union
 
 from modelscope.utils.logger import get_logger
 from .api import HubApi, ModelScopeConfig
-from .constants import DEFAULT_MODELSCOPE_GROUP, MODEL_ID_SEPARATOR
-from .errors import NotExistError, RequestError, raise_on_error
+from .errors import NotExistError
 from .file_download import (get_file_download_url, http_get_file,
                             http_user_agent)
 from .utils.caching import ModelFileSystemCache
@@ -98,8 +96,9 @@ def snapshot_download(model_id: str,
                 continue
             # check model_file is exist in cache, if exist, skip download, otherwise download
             if cache.exists(model_file):
+                file_name = os.path.basename(model_file['Name'])
                 logger.info(
-                    'The specified file is in cache, skip downloading!')
+                    f'File {file_name} already in cache, skip downloading!')
                 continue
 
             # get download url

modelscope/models/base.py

@@ -33,7 +33,7 @@ class Model(ABC):
         standard model outputs.
 
         Args:
-            inputs:  input data
+            input:  input data
 
         Return:
             dict of results:  a dict containing outputs of model, each
@@ -50,9 +50,17 @@ class Model(ABC):
         """ Instantiate a model from local directory or remote model repo. Note
         that when loading from remote, the model revision can be specified.
         """
+        prefetched = kwargs.get('model_prefetched')
+        if prefetched is not None:
+            kwargs.pop('model_prefetched')
+
         if osp.exists(model_name_or_path):
             local_model_dir = model_name_or_path
         else:
+            if prefetched is True:
+                raise RuntimeError(
+                    'Expecting model is pre-fetched locally, but is not found.'
+                )
             local_model_dir = snapshot_download(model_name_or_path, revision)
         logger.info(f'initialize model from {local_model_dir}')
         cfg = Config.from_file(

modelscope/pipelines/audio/ans_pipeline.py

@@ -37,7 +37,8 @@ class ANSPipeline(Pipeline):
     SAMPLE_RATE = 16000
 
     def __init__(self, model):
-        r"""
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
         Args:
             model: model id on modelscope hub.
         """

modelscope/pipelines/audio/kws_kwsbp_pipeline.py

@@ -1,7 +1,4 @@
-import io
 import os
-import shutil
-import stat
 import subprocess
 from typing import Any, Dict, List, Union
 
@@ -28,7 +25,10 @@ class KeyWordSpottingKwsbpPipeline(Pipeline):
                  model: Union[Model, str] = None,
                  preprocessor: WavToLists = None,
                  **kwargs):
-        """use `model` and `preprocessor` to create a kws pipeline for prediction
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
         """
 
         model = model if isinstance(model,
@@ -39,6 +39,7 @@ class KeyWordSpottingKwsbpPipeline(Pipeline):
             model=model,
             preprocessor=preprocessor,
             **kwargs)
+
         assert model is not None, 'kws model should be provided'
 
         self._preprocessor = preprocessor

modelscope/pipelines/audio/linear_aec_pipeline.py

@@ -63,7 +63,8 @@ class LinearAECPipeline(Pipeline):
     """
 
     def __init__(self, model):
-        r"""
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
         Args:
             model: model id on modelscope hub.
         """

modelscope/pipelines/audio/text_to_speech_pipeline.py

@@ -1,17 +1,12 @@
-import time
 from typing import Any, Dict, List
 
 import numpy as np
 
 from modelscope.metainfo import Pipelines
-from modelscope.models import Model
-from modelscope.models.audio.tts.am import SambertNetHifi16k
-from modelscope.models.audio.tts.vocoder import Hifigan16k
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
-from modelscope.preprocessors import (Preprocessor, TextToTacotronSymbols,
-                                      build_preprocessor)
-from modelscope.utils.constant import Fields, Tasks
+from modelscope.preprocessors import Preprocessor, TextToTacotronSymbols
+from modelscope.utils.constant import Tasks
 
 __all__ = ['TextToSpeechSambertHifigan16kPipeline']
 
@@ -24,6 +19,11 @@ class TextToSpeechSambertHifigan16kPipeline(Pipeline):
                  model: List[str] = None,
                  preprocessor: Preprocessor = None,
                  **kwargs):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         assert len(model) == 3, 'model number should be 3'
         if preprocessor is None:
             lang_type = 'pinyin'

modelscope/pipelines/base.py

@@ -17,9 +17,6 @@ Tensor = Union['torch.Tensor', 'tf.Tensor']
 Input = Union[str, tuple, MsDataset, 'PIL.Image.Image', 'numpy.ndarray']
 InputModel = Union[str, Model]
 
-output_keys = [
-]  # 对于不同task的pipeline，规定标准化的输出key，用以对接postprocess,同时也用来标准化postprocess后输出的key
-
 logger = get_logger()
 
 
@@ -28,9 +25,9 @@ class Pipeline(ABC):
     def initiate_single_model(self, model):
         logger.info(f'initiate model from {model}')
         if isinstance(model, str) and is_official_hub_path(model):
-            model = snapshot_download(
-                model) if not osp.exists(model) else model
-            return Model.from_pretrained(model) if is_model(model) else model
+            # expecting model has been prefetched to local cache beforehand
+            return Model.from_pretrained(
+                model, model_prefetched=True) if is_model(model) else model
         elif isinstance(model, Model):
             return model
         else:

modelscope/pipelines/builder.py

@@ -1,7 +1,8 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from typing import List, Union
+from typing import List, Optional, Union
 
+from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.metainfo import Pipelines
 from modelscope.models.base import Model
 from modelscope.utils.config import Config, ConfigDict
@@ -67,6 +68,21 @@ DEFAULT_MODEL_FOR_PIPELINE = {
 }
 
 
+def normalize_model_input(model, model_revision):
+    """ normalize the input model, to ensure that a model str is a valid local path: in other words,
+    for model represented by a model id, the model shall be downloaded locally
+    """
+    if isinstance(model, str) and is_official_hub_path(model, model_revision):
+        # note that if there is already a local copy, snapshot_download will check and skip downloading
+        model = snapshot_download(model, revision=model_revision)
+    elif isinstance(model, list) and isinstance(model[0], str):
+        for idx in range(len(model)):
+            if is_official_hub_path(model[idx], model_revision):
+                model[idx] = snapshot_download(
+                    model[idx], revision=model_revision)
+    return model
+
+
 def build_pipeline(cfg: ConfigDict,
                    task_name: str = None,
                    default_args: dict = None):
@@ -89,8 +105,9 @@ def pipeline(task: str = None,
              pipeline_name: str = None,
              framework: str = None,
              device: int = -1,
+             model_revision: Optional[str] = 'master',
              **kwargs) -> Pipeline:
-    """ Factory method to build a obj:`Pipeline`.
+    """ Factory method to build an obj:`Pipeline`.
 
 
     Args:
@@ -100,6 +117,8 @@ def pipeline(task: str = None,
         config_file (str, optional): path to config file.
         pipeline_name (str, optional): pipeline class name or alias name.
         framework (str, optional): framework type.
+        model_revision: revision of model(s) if getting from model hub, for multiple models, expecting
+        all models to have the same revision
         device (int, optional): which device is used to do inference.
 
     Return:
@@ -123,14 +142,18 @@ def pipeline(task: str = None,
     assert isinstance(model, (type(None), str, Model, list)), \
         f'model should be either None, str, List[str], Model, or List[Model], but got {type(model)}'
 
+    model = normalize_model_input(model, model_revision)
+
     if pipeline_name is None:
         # get default pipeline for this task
         if isinstance(model, str) \
            or (isinstance(model, list) and isinstance(model[0], str)):
-            if is_official_hub_path(model):
+            if is_official_hub_path(model, revision=model_revision):
                 # read config file from hub and parse
-                cfg = read_config(model) if isinstance(
-                    model, str) else read_config(model[0])
+                cfg = read_config(
+                    model, revision=model_revision) if isinstance(
+                        model, str) else read_config(
+                            model[0], revision=model_revision)
                 assert hasattr(
                     cfg,
                     'pipeline'), 'pipeline config is missing from config file.'
@@ -152,7 +175,7 @@ def pipeline(task: str = None,
             pipeline_name = first_model.pipeline.type
         else:
             pipeline_name, default_model_repo = get_default_pipeline_info(task)
-            model = default_model_repo
+            model = normalize_model_input(default_model_repo, model_revision)
 
     cfg = ConfigDict(type=pipeline_name, model=model)
 

modelscope/pipelines/cv/action_recognition_pipeline.py

@@ -23,6 +23,11 @@ logger = get_logger()
 class ActionRecognitionPipeline(Pipeline):
 
     def __init__(self, model: str):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         super().__init__(model=model)
         model_path = osp.join(self.model, ModelFile.TORCH_MODEL_FILE)
         logger.info(f'loading model from {model_path}')

modelscope/pipelines/cv/animal_recog_pipeline.py

@@ -1,5 +1,4 @@
 import os.path as osp
-import tempfile
 from typing import Any, Dict
 
 import cv2
@@ -8,13 +7,12 @@ import torch
 from PIL import Image
 from torchvision import transforms
 
-from modelscope.fileio import File
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.metainfo import Pipelines
 from modelscope.models.cv.animal_recognition import resnet
 from modelscope.pipelines.base import Input
 from modelscope.preprocessors import load_image
-from modelscope.utils.constant import ModelFile, Tasks
+from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 from ..base import Pipeline
 from ..builder import PIPELINES
@@ -28,6 +26,11 @@ logger = get_logger()
 class AnimalRecogPipeline(Pipeline):
 
     def __init__(self, model: str):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         super().__init__(model=model)
         import torch
 

modelscope/pipelines/cv/cmdssl_video_embedding_pipleline.py

@@ -1,11 +1,8 @@
-import math
 import os.path as osp
 from typing import Any, Dict
 
-import cv2
 import decord
 import numpy as np
-import PIL
 import torch
 import torchvision.transforms.functional as TF
 from decord import VideoReader, cpu
@@ -30,6 +27,11 @@ logger = get_logger()
 class CMDSSLVideoEmbeddingPipeline(Pipeline):
 
     def __init__(self, model: str):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         super().__init__(model=model)
         model_path = osp.join(self.model, ModelFile.TORCH_MODEL_FILE)
         logger.info(f'loading model from {model_path}')

modelscope/pipelines/cv/image_cartoon_pipeline.py

@@ -31,6 +31,11 @@ logger = get_logger()
 class ImageCartoonPipeline(Pipeline):
 
     def __init__(self, model: str):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         super().__init__(model=model)
         self.facer = FaceAna(self.model)
         self.sess_anime_head = self.load_sess(

modelscope/pipelines/cv/image_matting_pipeline.py

@@ -22,6 +22,11 @@ logger = get_logger()
 class ImageMattingPipeline(Pipeline):
 
     def __init__(self, model: str):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         super().__init__(model=model)
         import tensorflow as tf
         if tf.__version__ >= '2.0':

modelscope/pipelines/cv/ocr_detection_pipeline.py

@@ -1,8 +1,5 @@
-import math
-import os
 import os.path as osp
-import sys
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any, Dict
 
 import cv2
 import numpy as np
@@ -48,6 +45,11 @@ tf.app.flags.DEFINE_float('link_threshold', 0.6,
 class OCRDetectionPipeline(Pipeline):
 
     def __init__(self, model: str):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         super().__init__(model=model)
         tf.reset_default_graph()
         model_path = osp.join(

modelscope/pipelines/multi_modal/image_captioning_pipeline.py

@@ -18,7 +18,12 @@ class ImageCaptionPipeline(Pipeline):
                  model: Union[Model, str],
                  preprocessor: [Preprocessor] = None,
                  **kwargs):
-        super().__init__()
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
+        super().__init__(model=model)
         assert isinstance(model, str) or isinstance(model, Model), \
             'model must be a single str or OfaForImageCaptioning'
         if isinstance(model, str):

modelscope/pipelines/multi_modal/multi_modal_embedding_pipeline.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Union
+from typing import Any, Dict
 
 from modelscope.metainfo import Pipelines
 from modelscope.pipelines.base import Input
@@ -15,6 +15,11 @@ logger = get_logger()
 class MultiModalEmbeddingPipeline(Pipeline):
 
     def __init__(self, model: str, device_id: int = -1):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         if isinstance(model, str):
             pipe_model = Model.from_pretrained(model)
         elif isinstance(model, Model):

modelscope/pipelines/multi_modal/text_to_image_synthesis_pipeline.py

@@ -19,6 +19,11 @@ logger = get_logger()
 class TextToImageSynthesisPipeline(Pipeline):
 
     def __init__(self, model: str, **kwargs):
+        """
+        use `model` and `preprocessor` to create a kws pipeline for prediction
+        Args:
+            model: model id on modelscope hub.
+        """
         device_id = 0 if torch.cuda.is_available() else -1
         if isinstance(model, str):
             pipe_model = Model.from_pretrained(model, device_id=device_id)

modelscope/pipelines/nlp/dialog_modeling_pipeline.py

@@ -1,6 +1,6 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-from typing import Any, Dict, Union
+from typing import Dict, Union
 
 from ...metainfo import Pipelines
 from ...models import Model
@@ -22,7 +22,7 @@ class DialogModelingPipeline(Pipeline):
                  model: Union[SpaceForDialogModeling, str],
                  preprocessor: DialogModelingPreprocessor = None,
                  **kwargs):
-        """use `model` and `preprocessor` to create a dialog modleing pipeline for dialog response generation
+        """use `model` and `preprocessor` to create a dialog modeling pipeline for dialog response generation
 
         Args:
             model (SpaceForDialogModeling): a model instance

modelscope/pipelines/nlp/sentiment_classification_pipeline.py

@@ -1,8 +1,5 @@
-import os
-import uuid
 from typing import Any, Dict, Union
 
-import json
 import numpy as np
 import torch
 
@@ -11,7 +8,7 @@ from ...models import Model
 from ...models.nlp import SbertForSentimentClassification
 from ...preprocessors import SentimentClassificationPreprocessor
 from ...utils.constant import Tasks
-from ..base import Input, Pipeline
+from ..base import Pipeline
 from ..builder import PIPELINES
 from ..outputs import OutputKeys
 

modelscope/pipelines/nlp/translation_pipeline.py

@@ -1,12 +1,11 @@
 import os.path as osp
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict
 
 import numpy as np
 import tensorflow as tf
 
 from ...hub.snapshot_download import snapshot_download
 from ...metainfo import Pipelines
-from ...models import Model
 from ...models.nlp import CsanmtForTranslation
 from ...utils.constant import ModelFile, Tasks
 from ...utils.logger import get_logger

modelscope/pipelines/nlp/zero_shot_classification_pipeline.py

@@ -1,9 +1,5 @@
-import os
-import uuid
 from typing import Any, Dict, Union
 
-import json
-import numpy as np
 import torch
 from scipy.special import softmax
 
@@ -12,7 +8,7 @@ from ...models import Model
 from ...models.nlp import SbertForZeroShotClassification
 from ...preprocessors import ZeroShotClassificationPreprocessor
 from ...utils.constant import Tasks
-from ..base import Input, Pipeline
+from ..base import Pipeline
 from ..builder import PIPELINES
 from ..outputs import OutputKeys
 
@@ -30,7 +26,7 @@ class ZeroShotClassificationPipeline(Pipeline):
                  **kwargs):
         """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
         Args:
-            model (SbertForSentimentClassification): a model instance
+            model (SbertForZeroShotClassification): a model instance
             preprocessor (SentimentClassificationPreprocessor): a preprocessor instance
         """
         assert isinstance(model, str) or isinstance(model, SbertForZeroShotClassification), \

modelscope/pipelines/util.py

@@ -1,6 +1,6 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os.path as osp
-from typing import List, Union
+from typing import List, Optional, Union
 
 from modelscope.hub.api import HubApi
 from modelscope.hub.file_download import model_file_download
@@ -20,8 +20,9 @@ def is_config_has_model(cfg_file):
         return False
 
 
-def is_official_hub_path(path: Union[str, List]):
-    """ Whether path is a official hub name or a valid local
+def is_official_hub_path(path: Union[str, List],
+                         revision: Optional[str] = 'master'):
+    """ Whether path is an official hub name or a valid local
     path to official hub directory.
     """
 
@@ -31,7 +32,7 @@ def is_official_hub_path(path: Union[str, List]):
             return osp.exists(cfg_file)
         else:
             try:
-                _ = HubApi().get_model(path)
+                _ = HubApi().get_model(path, revision=revision)
                 return True
             except Exception:
                 return False

modelscope/utils/hub.py

@@ -42,7 +42,7 @@ def create_model_if_not_exist(
         return True
 
 
-def read_config(model_id_or_path: str):
+def read_config(model_id_or_path: str, revision: Optional[str] = 'master'):
     """ Read config from hub or local path
 
     Args:
@@ -52,8 +52,8 @@ def read_config(model_id_or_path: str):
         config (:obj:`Config`): config object
     """
     if not os.path.exists(model_id_or_path):
-        local_path = model_file_download(model_id_or_path,
-                                         ModelFile.CONFIGURATION)
+        local_path = model_file_download(
+            model_id_or_path, ModelFile.CONFIGURATION, revision=revision)
     else:
         local_path = os.path.join(model_id_or_path, ModelFile.CONFIGURATION)
 

tests/pipelines/test_image_matting.py

@@ -1,6 +1,5 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import os.path as osp
-import shutil
 import tempfile
 import unittest
 
@@ -47,7 +46,8 @@ class ImageMattingTest(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_modelhub(self):
-        img_matting = pipeline(Tasks.image_matting, model=self.model_id)
+        img_matting = pipeline(
+            Tasks.image_matting, model=self.model_id, model_revision='beta')
 
         result = img_matting('data/test/images/image_matting.png')
         cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])