remove extra line break

dygraph/paddleseg/models/ann.py

@@ -73,7 +73,6 @@ class ANN(nn.Layer):
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input):
-
         feat_list = self.backbone(input)
         logit_list = self.head(feat_list)
         return [
@@ -154,7 +153,6 @@ class ANNHead(nn.Layer):
         self.init_weight()
 
     def forward(self, feat_list):
-
         logit_list = []
         low_level_x = feat_list[self.backbone_indices[0]]
         high_level_x = feat_list[self.backbone_indices[1]]

dygraph/paddleseg/models/common/activation.py

@@ -53,7 +53,6 @@ class Activation(nn.Layer):
                     act, act_dict.keys()))
 
     def forward(self, x):
-
         if self._act is not None:
             return self.act_func(x)
         else:

dygraph/paddleseg/models/common/pyramid_pool.py

@@ -44,7 +44,6 @@ class ASPPModule(nn.Layer):
         self.aspp_blocks = []
 
         for ratio in aspp_ratios:
-
             if sep_conv and ratio > 1:
                 conv_func = layer_libs.SeparableConvBNReLU
             else:
@@ -76,7 +75,6 @@ class ASPPModule(nn.Layer):
         self.dropout = nn.Dropout(p=0.1)  # drop rate
 
     def forward(self, x):
-
         outputs = []
         for block in self.aspp_blocks:
             y = block(x)
@@ -141,7 +139,6 @@ class PPModule(nn.Layer):
         After pooling, the channels are reduced to 1/len(bin_sizes) immediately, while some other implementations
         keep the channels to be same.
 
-
         Args:
             in_channels (int): the number of intput channels to pyramid pooling module.
             size (int): the out size of the pooled layer.

dygraph/paddleseg/models/deeplab.py

@@ -73,7 +73,6 @@ class DeepLabV3P(nn.Layer):
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input):
-
         feat_list = self.backbone(input)
         logit_list = self.head(feat_list)
         return [
@@ -122,7 +121,6 @@ class DeepLabV3PHead(nn.Layer):
         self.init_weight()
 
     def forward(self, feat_list):
-
         logit_list = []
         low_level_feat = feat_list[self.backbone_indices[0]]
         x = feat_list[self.backbone_indices[1]]
@@ -171,7 +169,6 @@ class DeepLabV3(nn.Layer):
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input):
-
         feat_list = self.backbone(input)
         logit_list = self.head(feat_list)
         return [
@@ -205,9 +202,7 @@ class DeepLabV3Head(nn.Layer):
         self.init_weight()
 
     def forward(self, feat_list):
-
         logit_list = []
-
         x = feat_list[self.backbone_indices[0]]
         x = self.aspp(x)
         logit = self.cls(x)

dygraph/paddleseg/models/fast_scnn.py

@@ -61,7 +61,6 @@ class FastSCNN(nn.Layer):
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input, label=None):
-
         logit_list = []
         higher_res_features = self.learning_to_downsample(input)
         x = self.global_feature_extractor(higher_res_features)
@@ -274,9 +273,7 @@ class FeatureFusionModule(nn.Layer):
         low_res_input = F.resize_bilinear(input=low_res_input, scale=4)
         low_res_input = self.dwconv(low_res_input)
         low_res_input = self.conv_low_res(low_res_input)
-
         high_res_input = self.conv_high_res(high_res_input)
-
         x = high_res_input + low_res_input
 
         return self.relu(x)

dygraph/paddleseg/models/gcnet.py

@@ -70,7 +70,6 @@ class GCNet(nn.Layer):
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input):
-
         feat_list = self.backbone(input)
         logit_list = self.head(feat_list)
         return [
@@ -142,7 +141,6 @@ class GCNetHead(nn.Layer):
         self.init_weight()
 
     def forward(self, feat_list):
-
         logit_list = []
         x = feat_list[self.backbone_indices[1]]
 

dygraph/paddleseg/models/pspnet.py

@@ -70,7 +70,6 @@ class PSPNet(nn.Layer):
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input):
-
         feat_list = self.backbone(input)
         logit_list = self.head(feat_list)
         return [
@@ -130,9 +129,7 @@ class PSPNetHead(nn.Layer):
         self.init_weight()
 
     def forward(self, feat_list):
-
         logit_list = []
-
         x = feat_list[self.backbone_indices[1]]
         x = self.psp_module(x)
         x = F.dropout(x, p=0.1)  # dropout_prob