Parameters of DilatedEncoder (#7812)

* Parameters of DilatedEncoder

Add the original fixed parameter block_dilations as an incoming parameter

* [Fix] Add default value to block_dilations parameter of DilatedEncoder and fully function annotation

* [Fix] The incoming parameter of calling DilatedEncoder in the test_dilated_encoder function is missing block_dilations

* Update test_necks.py

[fix] tests/test_models/test_necks.py:237:80: E501 line too long (84 > 79 characters)

Author: Shanyaliux

configs/yolof/yolof_r50_c5_8x8_1x_coco.py

@@ -21,7 +21,8 @@
         in_channels=2048,
         out_channels=512,
         block_mid_channels=128,
-        num_residual_blocks=4),
+        num_residual_blocks=4,
+        block_dilations=[2, 4, 6, 8]),
     bbox_head=dict(
         type='YOLOFHead',
         num_classes=80,

mmdet/models/necks/dilated_encoder.py

@@ -62,16 +62,17 @@ class DilatedEncoder(nn.Module):
         out_channels (int): The number of output channels.
         block_mid_channels (int): The number of middle block output channels
         num_residual_blocks (int): The number of residual blocks.
+        block_dilations (list): The list of residual blocks dilation.
     """
 
     def __init__(self, in_channels, out_channels, block_mid_channels,
-                 num_residual_blocks):
+                 num_residual_blocks, block_dilations):
         super(DilatedEncoder, self).__init__()
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.block_mid_channels = block_mid_channels
         self.num_residual_blocks = num_residual_blocks
-        self.block_dilations = [2, 4, 6, 8]
+        self.block_dilations = block_dilations
         self._init_layers()
 
     def _init_layers(self):

tests/test_models/test_necks.py

@@ -234,7 +234,8 @@ def test_dilated_encoder():
     in_channels = 16
     out_channels = 32
     out_shape = 34
-    dilated_encoder = DilatedEncoder(in_channels, out_channels, 16, 2)
+    dilated_encoder = DilatedEncoder(in_channels, out_channels, 16, 2, 
+                                     [2, 4, 6, 8])
     feat = [torch.rand(1, in_channels, 34, 34)]
     out_feat = dilated_encoder(feat)[0]
     assert out_feat.shape == (1, out_channels, out_shape, out_shape)