110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
# Copyright (c) ByteDance, Inc. and its affiliates.
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# Copyright (c) Chutong Meng
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#
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# This source code is licensed under the CC BY-NC license found in the
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# LICENSE file in the root directory of this source tree.
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# Based on AudioDec (https://github.com/facebookresearch/AudioDec)
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import torch
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import torch.nn as nn
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from ..layers.conv_layer import Conv1d, ConvTranspose1d
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from ..modules.residual_unit import ResidualUnit
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class DecoderBlock(nn.Module):
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""" Decoder block (no up-sampling) """
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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stride: int,
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dilations=(1, 1),
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unit_kernel_size=3,
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bias=True
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):
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super().__init__()
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if stride == 1:
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self.conv = Conv1d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3, # fix kernel=3 when stride=1 for unchanged shape
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stride=stride,
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bias=bias,
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)
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else:
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self.conv = ConvTranspose1d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(2 * stride),
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stride=stride,
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bias=bias,
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)
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self.res_units = torch.nn.ModuleList()
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for idx, dilation in enumerate(dilations):
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self.res_units += [
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ResidualUnit(out_channels, out_channels,
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kernel_size=unit_kernel_size,
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dilation=dilation)
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]
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self.num_res = len(self.res_units)
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def forward(self, x):
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x = self.conv(x)
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for idx in range(self.num_res):
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x = self.res_units[idx](x)
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return x
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class Decoder(nn.Module):
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def __init__(
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self,
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code_dim: int,
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output_channels: int,
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decode_channels: int,
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channel_ratios=(1, 1),
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strides=(1, 1),
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kernel_size=3,
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bias=True,
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block_dilations=(1, 1),
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unit_kernel_size=3,
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):
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super().__init__()
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assert len(channel_ratios) == len(strides)
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self.conv1 = Conv1d(
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in_channels=code_dim,
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out_channels=int(decode_channels * channel_ratios[0]),
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kernel_size=kernel_size,
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stride=1,
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bias=False
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)
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self.conv_blocks = torch.nn.ModuleList()
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for idx, stride in enumerate(strides):
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in_channels = int(decode_channels * channel_ratios[idx])
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if idx < (len(channel_ratios) - 1):
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out_channels = int(decode_channels * channel_ratios[idx + 1])
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else:
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out_channels = decode_channels
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self.conv_blocks += [
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DecoderBlock(
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in_channels, out_channels, stride,
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dilations=block_dilations, unit_kernel_size=unit_kernel_size,
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bias=bias
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)
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]
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self.num_blocks = len(self.conv_blocks)
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self.conv2 = Conv1d(out_channels, output_channels, kernel_size, 1, bias=False)
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def forward(self, z):
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x = self.conv1(z)
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for i in range(self.num_blocks):
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x = self.conv_blocks[i](x)
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x = self.conv2(x)
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return x
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