update layer_libs.py

dygraph/paddleseg/models/common/layer_libs.py

@@ -20,10 +20,10 @@ from paddle.nn import Conv2d
 from paddle.nn import SyncBatchNorm as BatchNorm
 
 
-class ConvBNRelu(nn.Layer):
+class ConvBNReLU(nn.Layer):
     def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
 
-        super(ConvBNRelu, self).__init__()
+        super(ConvBNReLU, self).__init__()
 
         self._conv = Conv2d(in_channels, out_channels, kernel_size, **kwargs)
 
@@ -40,9 +40,7 @@ class ConvBN(nn.Layer):
     def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
 
         super(ConvBN, self).__init__()
-
         self._conv = Conv2d(in_channels, out_channels, kernel_size, **kwargs)
-
         self._batch_norm = BatchNorm(out_channels)
 
     def forward(self, x):
@@ -69,16 +67,16 @@ class ConvReluPool(nn.Layer):
         return x
 
 
-class DepthwiseConvBNRelu(nn.Layer):
+class DepthwiseConvBNReLU(nn.Layer):
     def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
-        super(DepthwiseConvBNRelu, self).__init__()
+        super(DepthwiseConvBNReLU, self).__init__()
         self.depthwise_conv = ConvBN(
             in_channels,
             out_channels=in_channels,
             kernel_size=kernel_size,
             groups=in_channels,
             **kwargs)
-        self.piontwise_conv = ConvBNRelu(
+        self.piontwise_conv = ConvBNReLU(
             in_channels, out_channels, kernel_size=1, groups=1)
 
     def forward(self, x):
@@ -105,7 +103,7 @@ class AuxLayer(nn.Layer):
                  dropout_prob=0.1):
         super(AuxLayer, self).__init__()
 
-        self.conv_bn_relu = ConvBNRelu(
+        self.conv_bn_relu = ConvBNReLU(
             in_channels=in_channels,
             out_channels=inter_channels,
             kernel_size=3,