init

finetune/mmseg/models/necks/multilevel_neck.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmengine.model.weight_init import xavier_init
+
+from mmseg.registry import MODELS
+from ..utils import resize
+
+
+@MODELS.register_module()
+class MultiLevelNeck(nn.Module):
+    """MultiLevelNeck.
+
+    A neck structure connect vit backbone and decoder_heads.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale).
+        scales (List[float]): Scale factors for each input feature map.
+            Default: [0.5, 1, 2, 4]
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        act_cfg (dict): Config dict for activation layer in ConvModule.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 scales=[0.5, 1, 2, 4],
+                 norm_cfg=None,
+                 act_cfg=None):
+        super().__init__()
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.scales = scales
+        self.num_outs = len(scales)
+        self.lateral_convs = nn.ModuleList()
+        self.convs = nn.ModuleList()
+        for in_channel in in_channels:
+            self.lateral_convs.append(
+                ConvModule(
+                    in_channel,
+                    out_channels,
+                    kernel_size=1,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+        for _ in range(self.num_outs):
+            self.convs.append(
+                ConvModule(
+                    out_channels,
+                    out_channels,
+                    kernel_size=3,
+                    padding=1,
+                    stride=1,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+
+    # default init_weights for conv(msra) and norm in ConvModule
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                xavier_init(m, distribution='uniform')
+
+    def forward(self, inputs):
+        assert len(inputs) == len(self.in_channels)
+        inputs = [
+            lateral_conv(inputs[i])
+            for i, lateral_conv in enumerate(self.lateral_convs)
+        ]
+        # for len(inputs) not equal to self.num_outs
+        if len(inputs) == 1:
+            inputs = [inputs[0] for _ in range(self.num_outs)]
+        outs = []
+        for i in range(self.num_outs):
+            x_resize = resize(
+                inputs[i], scale_factor=self.scales[i], mode='bilinear')
+            outs.append(self.convs[i](x_resize))
+        return tuple(outs)