File size: 3,397 Bytes
5af1f06 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
# 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
import torch.nn as nn
from RepCodec.repcodec.layers.conv_layer import Conv1d, ConvTranspose1d
from RepCodec.repcodec.modules.residual_unit import ResidualUnit
class DecoderBlock(nn.Module):
""" Decoder block (no up-sampling) """
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int,
dilations=(1, 1),
unit_kernel_size=3,
bias=True
):
super().__init__()
if stride == 1:
self.conv = Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3, # fix kernel=3 when stride=1 for unchanged shape
stride=stride,
bias=bias,
)
else:
self.conv = ConvTranspose1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(2 * stride),
stride=stride,
bias=bias,
)
self.res_units = torch.nn.ModuleList()
for idx, dilation in enumerate(dilations):
self.res_units += [
ResidualUnit(out_channels, out_channels,
kernel_size=unit_kernel_size,
dilation=dilation)
]
self.num_res = len(self.res_units)
def forward(self, x):
x = self.conv(x)
for idx in range(self.num_res):
x = self.res_units[idx](x)
return x
class Decoder(nn.Module):
def __init__(
self,
code_dim: int,
output_channels: int,
decode_channels: int,
channel_ratios=(1, 1),
strides=(1, 1),
kernel_size=3,
bias=True,
block_dilations=(1, 1),
unit_kernel_size=3,
):
super().__init__()
assert len(channel_ratios) == len(strides)
self.conv1 = Conv1d(
in_channels=code_dim,
out_channels=int(decode_channels * channel_ratios[0]),
kernel_size=kernel_size,
stride=1,
bias=False
)
self.conv_blocks = torch.nn.ModuleList()
for idx, stride in enumerate(strides):
in_channels = int(decode_channels * channel_ratios[idx])
if idx < (len(channel_ratios) - 1):
out_channels = int(decode_channels * channel_ratios[idx + 1])
else:
out_channels = decode_channels
self.conv_blocks += [
DecoderBlock(
in_channels, out_channels, stride,
dilations=block_dilations, unit_kernel_size=unit_kernel_size,
bias=bias
)
]
self.num_blocks = len(self.conv_blocks)
self.conv2 = Conv1d(out_channels, output_channels, kernel_size, 1, bias=False)
def forward(self, z):
x = self.conv1(z)
for i in range(self.num_blocks):
x = self.conv_blocks[i](x)
x = self.conv2(x)
return x
|