BatchNorm -> BatchNorm2D

ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py

@@ -18,7 +18,7 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
-from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
+from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
 from paddle.regularizer import L2Decay
 from paddle.nn.initializer import KaimingNormal
 from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
@@ -70,9 +70,9 @@ class ConvBNLayer(TheseusLayer):
             weight_attr=ParamAttr(initializer=KaimingNormal()),
             bias_attr=False)
 
-        self.bn = BatchNorm(
+        self.bn = BatchNorm2D(
             out_channels,
-            param_attr=ParamAttr(regularizer=L2Decay(0.0)),
+            weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
             bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
         if self.use_act:
             self.act = nn.ReLU()
@@ -393,4 +393,4 @@ def PPLCNetV2_base(pretrained=False, use_ssld=False, **kwargs):
     """
     model = PPLCNetV2(scale=1.0, depths=[2, 2, 6, 2], **kwargs)
     _load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
-    return model
+    return model
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