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pcdet/models/backbones_3d/focal_sparse_conv/SemanticSeg/basic_blocks.py

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+import torch.nn as nn
+
+class BasicBlock1D(nn.Module):
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        """
+        Initializes convolutional block
+        Args:
+            in_channels: int, Number of input channels
+            out_channels: int, Number of output channels
+            **kwargs: Dict, Extra arguments for nn.Conv2d
+        """
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.conv = nn.Conv1d(in_channels=in_channels,
+                              out_channels=out_channels,
+                              **kwargs)
+        self.bn = nn.BatchNorm1d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, features):
+        """
+        Applies convolutional block
+        Args:
+            features: (B, C_in, H, W), Input features
+        Returns:
+            x: (B, C_out, H, W), Output features
+        """
+        x = self.conv(features)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+class BasicBlock2D(nn.Module):
+
+    def __init__(self, in_channels, out_channels, **kwargs):
+        """
+        Initializes convolutional block
+        Args:
+            in_channels: int, Number of input channels
+            out_channels: int, Number of output channels
+            **kwargs: Dict, Extra arguments for nn.Conv2d
+        """
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.conv = nn.Conv2d(in_channels=in_channels,
+                              out_channels=out_channels,
+                              **kwargs)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, features):
+        """
+        Applies convolutional block
+        Args:
+            features: (B, C_in, H, W), Input features
+        Returns:
+            x: (B, C_out, H, W), Output features
+        """
+        x = self.conv(features)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x