diff --git a/ppdet/modeling/backbones/darknet.py b/ppdet/modeling/backbones/darknet.py
index 246529699ba8d99d08f05e4bbf24699fa10a80b7..ac236b30b813fb36fb63aec85d35bff2b66f7e83 100755
--- a/ppdet/modeling/backbones/darknet.py
+++ b/ppdet/modeling/backbones/darknet.py
@@ -149,9 +149,14 @@ class BasicBlock(nn.Layer):
 
         super(BasicBlock, self).__init__()
 
+        assert ch_in == ch_out and (ch_in % 2) == 0, \
+            f"ch_in and ch_out should be the same even int, but the input \'ch_in is {ch_in}, \'ch_out is {ch_out}"
+        # example:
+        # --------------{conv1} --> {conv2}
+        # channel route: 10-->5 --> 5-->10
         self.conv1 = ConvBNLayer(
             ch_in=ch_in,
-            ch_out=ch_out,
+            ch_out=int(ch_out/2),
             filter_size=1,
             stride=1,
             padding=0,
@@ -160,8 +165,8 @@ class BasicBlock(nn.Layer):
             freeze_norm=freeze_norm,
             data_format=data_format)
         self.conv2 = ConvBNLayer(
-            ch_in=ch_out,
-            ch_out=ch_out * 2,
+            ch_in=int(ch_out/2),
+            ch_out=ch_out ,
             filter_size=3,
             stride=1,
             padding=1,
@@ -215,7 +220,7 @@ class Blocks(nn.Layer):
             res_out = self.add_sublayer(
                 block_name,
                 BasicBlock(
-                    ch_out * 2,
+                    ch_out,
                     ch_out,
                     norm_type=norm_type,
                     norm_decay=norm_decay,
@@ -296,7 +301,7 @@ class DarkNet(nn.Layer):
                 name,
                 Blocks(
                     int(ch_in[i]),
-                    32 * (2**i),
+                    int(ch_in[i]),
                     stage,
                     norm_type=norm_type,
                     norm_decay=norm_decay,
@@ -305,14 +310,14 @@ class DarkNet(nn.Layer):
                     name=name))
             self.darknet_conv_block_list.append(conv_block)
             if i in return_idx:
-                self._out_channels.append(64 * (2**i))
+                self._out_channels.append(int(ch_in[i]))
         for i in range(num_stages - 1):
             down_name = 'stage.{}.downsample'.format(i)
             downsample = self.add_sublayer(
                 down_name,
                 DownSample(
-                    ch_in=32 * (2**(i + 1)),
-                    ch_out=32 * (2**(i + 2)),
+                    ch_in=int(ch_in[i]),
+                    ch_out=int(ch_in[i+1]),
                     norm_type=norm_type,
                     norm_decay=norm_decay,
                     freeze_norm=freeze_norm,