change api to utils

modelscope/hub/api.py

@@ -646,29 +646,6 @@ class HubApi:
     def check_local_cookies(self, use_cookies) -> CookieJar:
         return self._check_cookie(use_cookies=use_cookies)
 
-    def create_library_statistics(self,
-                                  method: str,
-                                  name: str,
-                                  cn_name: Optional[str]):
-        """
-            create library statistics. called by train()/evaluate()/pipeline()
-
-        Args:
-            method (str): called methed name,i.e train/evaluate/pipeline
-            name (str): model name, for example: damo/cv_unet_person-image-cartoon_compound-models
-            cn_name (str): model name in chinese, for example: 达摩卡通化模型
-        Raises:
-            ValueError: If user_cookies is True, but no local cookie.
-
-        Returns:
-            None
-        """
-        path = f'{self.endpoint}/api/v1/statistics/library'
-        headers = {'user-agent': ModelScopeConfig.get_user_agent()}
-        params = {"Method": method, "Name": name, "CnName": cn_name}
-        r = requests.post(path, params=params, headers=headers)
-        r.raise_for_status()
-        return
 
 
 class ModelScopeConfig:

modelscope/hub/utils/utils.py

@@ -4,6 +4,7 @@ import hashlib
 import os
 from datetime import datetime
 from typing import Optional
+import requests
 
 from modelscope.hub.constants import (DEFAULT_MODELSCOPE_DOMAIN,
                                       DEFAULT_MODELSCOPE_GROUP,
@@ -12,6 +13,7 @@ from modelscope.hub.constants import (DEFAULT_MODELSCOPE_DOMAIN,
 from modelscope.hub.errors import FileIntegrityError
 from modelscope.utils.file_utils import get_default_cache_dir
 from modelscope.utils.logger import get_logger
+from modelscope.hub.api import ModelScopeConfig
 
 logger = get_logger()
 
@@ -85,3 +87,14 @@ def file_integrity_validation(file_path, expected_sha256):
         msg = 'File %s integrity check failed, the download may be incomplete, please try again.' % file_path
         logger.error(msg)
         raise FileIntegrityError(msg)
+
+
+def create_library_statistics(method: str,
+                              name: str,
+                              cn_name: Optional[str]):
+    path = f'{get_endpoint()}/api/v1/statistics/library'
+    headers = {'user-agent': ModelScopeConfig.get_user_agent()}
+    params = {"Method": method, "Name": name, "CnName": cn_name}
+    r = requests.post(path, params=params, headers=headers)
+    r.raise_for_status()
+    return

modelscope/pipelines/base.py

@@ -23,7 +23,7 @@ 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.api import HubApi
+from modelscope.hub.utils.utils import create_library_statistics
 from .util import is_model, is_official_hub_path
 
 if is_torch_available():
@@ -152,9 +152,8 @@ class Pipeline(ABC):
                  **kwargs) -> Union[Dict[str, Any], Generator]:
         # model provider should leave it as it is
         # modelscope library developer will handle this function
-        _api = HubApi()
         model_name = self.cfg.task
-        _api.create_library_statistics("pipeline", model_name, None)
+        create_library_statistics("pipeline", 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

@@ -39,7 +39,7 @@ from modelscope.utils.logger import get_logger
 from modelscope.utils.registry import build_from_cfg
 from modelscope.utils.torch_utils import (get_dist_info, get_local_rank,
                                           init_dist, set_random_seed)
-from modelscope.hub.api import HubApi
+from modelscope.hub.utils.utils import create_library_statistics
 from .base import BaseTrainer
 from .builder import TRAINERS
 from .default_config import merge_cfg
@@ -437,9 +437,8 @@ class EpochBasedTrainer(BaseTrainer):
 
     def train(self, checkpoint_path=None, *args, **kwargs):
         self._mode = ModeKeys.TRAIN
-        _api = HubApi()
         model_name = self.cfg.task
-        _api.create_library_statistics("train", model_name, None)
+        create_library_statistics("train", model_name, None)
 
         if self.train_dataset is None:
             self.train_dataloader = self.get_train_dataloader()
@@ -460,9 +459,8 @@ class EpochBasedTrainer(BaseTrainer):
         self.train_loop(self.train_dataloader)
 
     def evaluate(self, checkpoint_path=None):
-        _api = HubApi()
         model_name = self.cfg.task
-        _api.create_library_statistics("evaluate", model_name, None)
+        create_library_statistics("evaluate", 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)