fix a type in aspp (#414)

* fix a type in aspp

* fix a typo

dygraph/paddleseg/models/ann.py

@@ -18,7 +18,7 @@ import paddle
 import paddle.nn.functional as F
 from paddle import nn
 
-from paddleseg.cvlibs import manager
+from paddleseg.cvlibs import manager, param_init
 from paddleseg.models.common.layer_libs import ConvBNReLU, ConvBN, AuxLayer
 from paddleseg.utils import utils
 
@@ -294,7 +294,7 @@ class SelfAttentionBlock_AFNB(nn.Layer):
         key_channels (int): the key channels in self-attention block.
         value_channels (int): the value channels in self-attention block.
         out_channels (int): out channels of AFNB module.
-        scale (int): pooling size. Defaut to 1.
+        scale (int): pooling size. Default to 1.
         psp_size (tuple): the out size of pooled feature maps. Default to (1, 3, 6, 8).
     """
 
@@ -374,7 +374,7 @@ class SelfAttentionBlock_APNB(nn.Layer):
         out_channels (int): out channels of APNB module.
         key_channels (int): the key channels in self-attention block.
         value_channels (int): the value channels in self-attention block.
-        scale (int): pooling size. Defaut to 1.
+        scale (int): pooling size. Default to 1.
         psp_size (tuple): the out size of pooled feature maps. Default to (1, 3, 6, 8).
     """
 

dygraph/paddleseg/models/common/pyramid_pool.py

@@ -79,7 +79,9 @@ class ASPPModule(nn.Layer):
 
         outputs = []
         for block in self.aspp_blocks:
-            outputs.append(block(x))
+            y = block(x)
+            y = F.resize_bilinear(y, out_shape=x.shape[2:])
+            outputs.append(y)
 
         if self.image_pooling:
             img_avg = self.global_avg_pool(x)

dygraph/paddleseg/models/deeplab.py

@@ -197,7 +197,7 @@ class DeepLabV3Head(nn.Layer):
             image_pooling=True)
 
         self.cls = nn.Conv2d(
-            in_channels=backbone_channels[0],
+            in_channels=aspp_out_channels,
             out_channels=num_classes,
             kernel_size=1)
 
@@ -209,6 +209,7 @@ class DeepLabV3Head(nn.Layer):
         logit_list = []
 
         x = feat_list[self.backbone_indices[0]]
+        x = self.aspp(x)
         logit = self.cls(x)
         logit_list.append(logit)