fix create logger with module file path and avoid import mmcv in pipeline base

1. fix create logger with module file path
2. avoid import mmcv in collate_fn

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11397356

modelscope/models/multi_modal/vldoc/modeling_layout_roberta.py

@@ -26,7 +26,7 @@ from transformers.modeling_utils import (PreTrainedModel,
                                          prune_linear_layer)
 from transformers.utils import logging
 
-logger = logging.get_logger(__name__)
+logger = logging.get_logger()
 
 
 class LayoutRobertaConfig(PretrainedConfig):

modelscope/models/nlp/unite/configuration_unite.py

@@ -6,7 +6,7 @@ from enum import Enum
 from modelscope.utils import logger as logging
 from modelscope.utils.config import Config
 
-logger = logging.get_logger(__name__)
+logger = logging.get_logger()
 
 
 class EvaluationMode(Enum):

modelscope/pipelines/base.py

@@ -501,7 +501,10 @@ def collate_fn(data, device):
 
     """
     from torch.utils.data.dataloader import default_collate
-    from modelscope.preprocessors.nlp import InputFeatures
+
+    def get_class_name(obj):
+        return obj.__class__.__name__
+
     if isinstance(data, dict) or isinstance(data, Mapping):
         # add compatibility for img_metas for mmlab models
         return type(data)({
@@ -524,11 +527,11 @@ def collate_fn(data, device):
         return data.to(device)
     elif isinstance(data, (bytes, str, int, float, bool, type(None))):
         return data
-    elif isinstance(data, InputFeatures):
+    elif get_class_name(data) == 'InputFeatures':
+        # modelscope.preprocessors.nlp.InputFeatures
+        return data
+    elif get_class_name(data) == 'DataContainer':
+        # mmcv.parallel.DataContainer
         return data
     else:
-        from mmcv.parallel import DataContainer
-        if isinstance(data, DataContainer):
-            return data
-        else:
-            raise ValueError(f'Unsupported data type {type(data)}')
+        raise ValueError(f'Unsupported data type {type(data)}')

modelscope/utils/cv/image_utils.py

@@ -11,7 +11,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.preprocessors.image import load_image
 from modelscope.utils import logger as logging
 
-logger = logging.get_logger(__name__)
+logger = logging.get_logger()
 
 
 def numpy_to_cv2img(img_array):

tests/msdatasets/test_dataset_delete.py

@@ -9,7 +9,7 @@ from modelscope.msdatasets import MsDataset
 from modelscope.utils import logger as logging
 from modelscope.utils.test_utils import test_level
 
-logger = logging.get_logger(__name__)
+logger = logging.get_logger()
 
 KEY_EXTRACTED = 'extracted'
 EXPECTED_MSG = 'success'

tests/msdatasets/test_dataset_upload.py

@@ -12,7 +12,7 @@ from modelscope.utils.constant import (DEFAULT_DATASET_REVISION, DownloadMode,
                                        ModelFile)
 from modelscope.utils.test_utils import test_level
 
-logger = logging.get_logger(__name__)
+logger = logging.get_logger()
 
 KEY_EXTRACTED = 'extracted'