fix style issues

modelscope/hub/utils/utils.py

@@ -2,10 +2,11 @@
 
 import hashlib
 import os
-import requests
 from datetime import datetime
 from typing import Optional
 
+import requests
+
 from modelscope.hub.api import ModelScopeConfig
 from modelscope.hub.constants import (DEFAULT_MODELSCOPE_DOMAIN,
                                       DEFAULT_MODELSCOPE_GROUP,

modelscope/pipelines/base.py

@@ -10,6 +10,7 @@ from typing import Any, Dict, Generator, List, Mapping, Union
 
 import numpy as np
 
+from modelscope.hub.utils.utils import create_library_statistics
 from modelscope.models.base import Model
 from modelscope.msdatasets import MsDataset
 from modelscope.outputs import TASK_OUTPUTS
@@ -23,7 +24,6 @@ from modelscope.utils.hub import read_config, snapshot_download
 from modelscope.utils.import_utils import is_tf_available, is_torch_available
 from modelscope.utils.logger import get_logger
 from modelscope.utils.torch_utils import _find_free_port, _is_free_port
-from modelscope.hub.utils.utils import create_library_statistics
 from .util import is_model, is_official_hub_path
 
 if is_torch_available():
@@ -154,7 +154,7 @@ class Pipeline(ABC):
         # modelscope library developer will handle this function
         for single_model in self.models:
             if hasattr(single_model, 'name'):
-                create_library_statistics("pipeline", single_model.name, None)
+                create_library_statistics('pipeline', single_model.name, None)
         # place model to cpu or gpu
         if (self.model or (self.has_multiple_models and self.models[0])):
             if not self._model_prepare:

modelscope/trainers/trainer.py

@@ -14,8 +14,8 @@ from torch.utils.data import DataLoader, Dataset
 from torch.utils.data.dataloader import default_collate
 from torch.utils.data.distributed import DistributedSampler
 
-from modelscope.hub.utils.utils import create_library_statistics
 from modelscope.hub.snapshot_download import snapshot_download
+from modelscope.hub.utils.utils import create_library_statistics
 from modelscope.metainfo import Trainers
 from modelscope.metrics import build_metric, task_default_metrics
 from modelscope.models.base import Model, TorchModel
@@ -438,7 +438,7 @@ class EpochBasedTrainer(BaseTrainer):
     def train(self, checkpoint_path=None, *args, **kwargs):
         self._mode = ModeKeys.TRAIN
         if hasattr(self.model, 'name'):
-            create_library_statistics("train", self.model.name, None)
+            create_library_statistics('train', self.model.name, None)
 
         if self.train_dataset is None:
             self.train_dataloader = self.get_train_dataloader()
@@ -460,7 +460,7 @@ class EpochBasedTrainer(BaseTrainer):
 
     def evaluate(self, checkpoint_path=None):
         if hasattr(self.model, 'name'):
-            create_library_statistics("evaluate", self.model.name, None)
+            create_library_statistics('evaluate', self.model.name, None)
         if checkpoint_path is not None and os.path.isfile(checkpoint_path):
             from modelscope.trainers.hooks import CheckpointHook
             CheckpointHook.load_checkpoint(checkpoint_path, self)