Wrap Conv1d no bias layer

2 files changed, 23 insertions, 9 deletions

diff --git a/models/layers.py b/models/layers.py
index a0e35f0..2be93ad 100644
--- a/models/layers.py
+++ b/models/layers.py
@@ -24,3 +24,19 @@ class FocalConv2d(nn.Module):
         z = x.split(split_size, dim=2)
         z = torch.cat([self.conv(_) for _ in z], dim=2)
         return z
+
+
+class BasicConv1d(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            kernel_size: Union[int, Tuple[int]],
+            **kwargs
+    ):
+        super(BasicConv1d, self).__init__()
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size,
+                              bias=False, **kwargs)
+
+    def forward(self, x):
+        return self.conv(x)
diff --git a/models/tfa.py b/models/tfa.py
index 2e4e656..72bfcfb 100644
--- a/models/tfa.py
+++ b/models/tfa.py
@@ -3,6 +3,8 @@ import copy
 import torch
 from torch import nn as nn
 
+from models.layers import BasicConv1d
+
 
 class TemporalFeatureAggregator(nn.Module):
     def __init__(
@@ -17,11 +19,9 @@ class TemporalFeatureAggregator(nn.Module):
 
         # MTB1
         conv3x1 = nn.Sequential(
-            nn.Conv1d(in_channels, hidden_dim,
-                      kernel_size=3, padding=1, bias=False),
+            BasicConv1d(in_channels, hidden_dim, kernel_size=3, padding=1),
             nn.LeakyReLU(inplace=True),
-            nn.Conv1d(hidden_dim, in_channels,
-                      kernel_size=1, padding=0, bias=False)
+            BasicConv1d(hidden_dim, in_channels, kernel_size=1, padding=0)
         )
         self.conv1d3x1 = self._parted(conv3x1)
         self.avg_pool3x1 = nn.AvgPool1d(kernel_size=3, stride=1, padding=1)
@@ -29,11 +29,9 @@ class TemporalFeatureAggregator(nn.Module):
 
         # MTB2
         conv3x3 = nn.Sequential(
-            nn.Conv1d(in_channels, hidden_dim,
-                      kernel_size=3, padding=1, bias=False),
+            BasicConv1d(in_channels, hidden_dim, kernel_size=3, padding=1),
             nn.LeakyReLU(inplace=True),
-            nn.Conv1d(hidden_dim, in_channels,
-                      kernel_size=3, padding=1, bias=False)
+            BasicConv1d(hidden_dim, in_channels, kernel_size=3, padding=1)
         )
         self.conv1d3x3 = self._parted(conv3x3)
         self.avg_pool3x3 = nn.AvgPool1d(kernel_size=5, stride=1, padding=2)
@@ -76,4 +74,4 @@ class TemporalFeatureAggregator(nn.Module):
 
         # Temporal Pooling
         ret = (feature3x1 + feature3x3).max(-1)[0]
-        return ret
\ No newline at end of file
+        return ret