Add Conv1d layer, update SequenceBatchGenerator

layers.py

@@ -45,6 +45,22 @@ class Layer(nn.Module):
         return output
 
 
+class Conv1d(Layer):
+    def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
+                 stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs):
+        super().__init__(activation, batch_norm)
+
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
+                              bias=not self.batch_norm, **kwargs)
+        self.batch_norm = nn.BatchNorm1d(
+            out_channels,
+            momentum=Layer.BATCH_NORM_MOMENTUM,
+            track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None
+
+    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
+        return super().forward(self.conv(input_data))
+
+
 class Conv2d(Layer):
     def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
                  stride: Union[int, Tuple[int, int]] = 1, activation=0, batch_norm=None, **kwargs):