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RepCodec/repcodec/layers/conv_layer.py

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+# Copyright (c) ByteDance, Inc. and its affiliates.
+# Copyright (c) Chutong Meng
+#
+# This source code is licensed under the CC BY-NC license found in the
+# LICENSE file in the root directory of this source tree.
+# Based on AudioDec (https://github.com/facebookresearch/AudioDec)
+
+import torch.nn as nn
+
+
+class Conv1d1x1(nn.Conv1d):
+    """1x1 Conv1d."""
+
+    def __init__(self, in_channels, out_channels, bias=True):
+        super(Conv1d1x1, self).__init__(in_channels, out_channels, kernel_size=1, bias=bias)
+
+
+class Conv1d(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            kernel_size: int,
+            stride: int = 1,
+            padding: int = -1,
+            dilation: int = 1,
+            groups: int = 1,
+            bias: bool = True
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        if padding < 0:
+            padding = (kernel_size - 1) // 2 * dilation
+        self.dilation = dilation
+        self.conv = nn.Conv1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Float tensor variable with the shape  (B, C, T).
+        Returns:
+            Tensor: Float tensor variable with the shape (B, C, T).
+        """
+        x = self.conv(x)
+        return x
+
+
+class ConvTranspose1d(nn.Module):
+    def __init__(
+            self,
+            in_channels: int,
+            out_channels: int,
+            kernel_size: int,
+            stride: int,
+            padding=-1,
+            output_padding=-1,
+            groups=1,
+            bias=True,
+    ):
+        super().__init__()
+        if padding < 0:
+            padding = (stride + 1) // 2
+        if output_padding < 0:
+            output_padding = 1 if stride % 2 else 0
+        self.deconv = nn.ConvTranspose1d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            output_padding=output_padding,
+            groups=groups,
+            bias=bias,
+        )
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Float tensor variable with the shape  (B, C, T).
+        Returns:
+            Tensor: Float tensor variable with the shape (B, C', T').
+        """
+        x = self.deconv(x)
+        return x