fix ShuffleNet problem

hubconf.py

@@ -12,14 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 dependencies = ['paddle', 'numpy']
 
 import paddle
 
 from ppcls.modeling.architectures import alexnet as _alexnet
-from ppcls.modeling.architectures import vgg as _vgg 
-from ppcls.modeling.architectures import resnet as _resnet 
+from ppcls.modeling.architectures import vgg as _vgg
+from ppcls.modeling.architectures import resnet as _resnet
 from ppcls.modeling.architectures import squeezenet as _squeezenet
 from ppcls.modeling.architectures import densenet as _densenet
 from ppcls.modeling.architectures import inception_v3 as _inception_v3
@@ -32,13 +31,13 @@ from ppcls.modeling.architectures import mobilenet_v3 as _mobilenet_v3
 from ppcls.modeling.architectures import resnext as _resnext
 
 
-
 def _load_pretrained_parameters(model, name):
-    url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'.format(name)
+    url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/{}_pretrained.pdparams'.format(
+        name)
     path = paddle.utils.download.get_weights_path_from_url(url)
     model.set_state_dict(paddle.load(path))
     return model
-    
+
 
 def AlexNet(pretrained=False, **kwargs):
     """
@@ -182,7 +181,7 @@ def ResNet50(pretrained=False, **kwargs):
     model = _resnet.ResNet50(**kwargs)
     if pretrained:
         model = _load_pretrained_parameters(model, 'ResNet50')
-        
+
     return model
 
 
@@ -404,19 +403,19 @@ def GoogLeNet(pretrained=False, **kwargs):
     return model
 
 
-def ShuffleNet(pretrained=False, **kwargs):
+def ShuffleNetV2_x0_25(pretrained=False, **kwargs):
     """
-    ShuffleNet
+    ShuffleNetV2_x0_25
     Args:
         pretrained: bool=False. If `True` load pretrained parameters, `False` otherwise.
         kwargs: 
             class_dim: int=1000. Output dim of last fc layer.
     Returns:
-        model: nn.Layer. Specific `ShuffleNet` model depends on args.
+        model: nn.Layer. Specific `ShuffleNetV2_x0_25` model depends on args.
     """
-    model = _shufflenet_v2.ShuffleNet(**kwargs)
+    model = _shufflenet_v2.ShuffleNetV2_x0_25(**kwargs)
     if pretrained:
-        model = _load_pretrained_parameters(model, 'ShuffleNet')
+        model = _load_pretrained_parameters(model, 'ShuffleNetV2_x0_25')
 
     return model
 
@@ -744,7 +743,6 @@ def MobileNetV3_small_x1_25(pretrained=False, **kwargs):
     return model
 
 
-
 def ResNeXt101_32x4d(pretrained=False, **kwargs):
     """
     ResNeXt101_32x4d