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pcdet/models/backbones_2d/fuser/convfuser.py

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+import torch
+from torch import nn
+
+
+class ConvFuser(nn.Module):
+    def __init__(self,model_cfg) -> None:
+        super().__init__()
+        self.model_cfg = model_cfg
+        in_channel = self.model_cfg.IN_CHANNEL
+        out_channel = self.model_cfg.OUT_CHANNEL
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channel, out_channel, 3, padding=1, bias=False),
+            nn.BatchNorm2d(out_channel),
+            nn.ReLU(True)
+            )
+        
+    def forward(self,batch_dict):
+        """
+        Args:
+            batch_dict:
+                spatial_features_img (tensor): Bev features from image modality
+                spatial_features (tensor): Bev features from lidar modality
+
+        Returns:
+            batch_dict:
+                spatial_features (tensor): Bev features after muli-modal fusion
+        """
+        img_bev = batch_dict['spatial_features_img']
+        lidar_bev = batch_dict['spatial_features']
+        cat_bev = torch.cat([img_bev,lidar_bev],dim=1)
+        mm_bev = self.conv(cat_bev)
+        batch_dict['spatial_features'] = mm_bev
+        return batch_dict