add feat_channels in backbone

dygraph/paddleseg/models/backbones/resnet_vd.py

@@ -224,9 +224,9 @@ class ResNet_vd(nn.Layer):
                         ] if layers >= 50 else [64, 64, 128, 256]
         num_filters = [64, 128, 256, 512]
 
-        # for channels of returned stage
-        self.backbone_channels = [c * 4 for c in num_filters
-                                  ] if layers >= 50 else num_filters
+        # for channels of four returned stages
+        self.feat_channels = [c * 4 for c in num_filters
+                              ] if layers >= 50 else num_filters
 
         dilation_dict = None
         if output_stride == 8:
@@ -319,7 +319,7 @@ class ResNet_vd(nn.Layer):
                     block_list.append(basic_block)
                     shortcut = True
                 self.stage_list.append(block_list)
-                
+
         utils.load_pretrained_model(self, pretrained)
 
     def forward(self, inputs):
@@ -336,8 +336,6 @@ class ResNet_vd(nn.Layer):
             feat_list.append(y)
 
         return feat_list
-    
-
 
 
 @manager.BACKBONES.add_component

dygraph/paddleseg/models/danet.py

@@ -190,7 +190,7 @@ class DANet(nn.Layer):
 
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.channels[i] for i in backbone_indices]
+        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
 
         self.head = DAHead(num_classes=num_classes, in_channels=in_channels)
 

dygraph/paddleseg/models/deeplab.py

@@ -62,14 +62,13 @@ class DeepLabV3P(nn.Layer):
         super(DeepLabV3P, self).__init__()
 
         self.backbone = backbone
-        backbone_channels = backbone.backbone_channels
+        backbone_channels = [
+            backbone.feat_channels[i] for i in backbone_indices
+        ]
 
-        self.head = DeepLabV3PHead(
-            num_classes,
-            backbone_indices,
-            backbone_channels,
-            aspp_ratios,
-            aspp_out_channels)
+        self.head = DeepLabV3PHead(num_classes, backbone_indices,
+                                   backbone_channels, aspp_ratios,
+                                   aspp_out_channels)
 
         utils.load_entire_model(self, pretrained)
 
@@ -81,6 +80,7 @@ class DeepLabV3P(nn.Layer):
             F.resize_bilinear(logit, input.shape[2:]) for logit in logit_list
         ]
 
+
 class DeepLabV3PHead(nn.Layer):
     """
     The DeepLabV3PHead implementation based on PaddlePaddle.
@@ -110,14 +110,14 @@ class DeepLabV3PHead(nn.Layer):
                  aspp_out_channels=256):
 
         super(DeepLabV3PHead, self).__init__()
-        
+
         self.aspp = pyramid_pool.ASPPModule(
             aspp_ratios,
-            backbone_channels[backbone_indices[1]],
+            backbone_channels[1],
             aspp_out_channels,
             sep_conv=True,
             image_pooling=True)
-        self.decoder = Decoder(num_classes, backbone_channels[backbone_indices[0]])
+        self.decoder = Decoder(num_classes, backbone_channels[0])
         self.backbone_indices = backbone_indices
         self.init_weight()
 
@@ -135,6 +135,7 @@ class DeepLabV3PHead(nn.Layer):
     def init_weight(self):
         pass
 
+
 @manager.MODELS.add_component
 class DeepLabV3(nn.Layer):
     """
@@ -147,7 +148,7 @@ class DeepLabV3(nn.Layer):
     Args:
         Refer to DeepLabV3P above 
     """
-    
+
     def __init__(self,
                  num_classes,
                  backbone,
@@ -159,15 +160,14 @@ class DeepLabV3(nn.Layer):
         super(DeepLabV3, self).__init__()
 
         self.backbone = backbone
-        backbone_channels = backbone.backbone_channels
+        backbone_channels = [
+            backbone.feat_channels[i] for i in backbone_indices
+        ]
+
+        self.head = DeepLabV3Head(num_classes, backbone_indices,
+                                  backbone_channels, aspp_ratios,
+                                  aspp_out_channels)
 
-        self.head = DeepLabV3Head(
-            num_classes,
-            backbone_indices,
-            backbone_channels,
-            aspp_ratios,
-            aspp_out_channels)
-        
         utils.load_entire_model(self, pretrained)
 
     def forward(self, input):
@@ -191,13 +191,13 @@ class DeepLabV3Head(nn.Layer):
 
         self.aspp = pyramid_pool.ASPPModule(
             aspp_ratios,
-            backbone_channels[backbone_indices[0]],
+            backbone_channels[0],
             aspp_out_channels,
             sep_conv=False,
             image_pooling=True)
 
         self.cls = nn.Conv2d(
-            in_channels=backbone_channels[backbone_indices[0]],
+            in_channels=backbone_channels[0],
             out_channels=num_classes,
             kernel_size=1)
 

dygraph/paddleseg/models/ocrnet.py

@@ -203,7 +203,7 @@ class OCRNet(nn.Layer):
 
         self.backbone = backbone
         self.backbone_indices = backbone_indices
-        in_channels = [self.backbone.channels[i] for i in backbone_indices]
+        in_channels = [self.backbone.feat_channels[i] for i in backbone_indices]
 
         self.head = OCRHead(
             num_classes=num_classes,