ref: 247ea8367395a95e5be2a7a9c5dfb8025a3f367e
dir: /dnn/torch/osce/models/td_discriminator.py/
"""
MIT License
Copyright (c) 2020 Jungil Kong
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
# This is an adaptation of the HiFi-Gan discriminators derived from https://github.com/jik876/hifi-gan
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
def get_padding(kernel_size, dilation=1):
return int((kernel_size*dilation - dilation)/2)
LRELU_SLOPE = 0.1
class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False, max_channels=1024):
super(DiscriminatorP, self).__init__()
self.max_channels = max_channels
self.period = period
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(min(self.max_channels, 128), min(self.max_channels, 512), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(min(self.max_channels, 512), min(self.max_channels, 1024), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
norm_f(Conv2d(min(self.max_channels, 1024), min(self.max_channels, 1024), (kernel_size, 1), 1, padding=(2, 0))),
])
self.conv_post = norm_f(Conv2d(min(self.max_channels, 1024), 1, (3, 1), 1, padding=(1, 0)))
def forward(self, x):
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
output = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, LRELU_SLOPE)
output.append(x)
x = self.conv_post(x)
output.append(x)
return output
class MultiPeriodDiscriminator(torch.nn.Module):
def __init__(self, max_channels=1024):
super(MultiPeriodDiscriminator, self).__init__()
self.discriminators = nn.ModuleList([
DiscriminatorP(2, max_channels=max_channels),
DiscriminatorP(3, max_channels=max_channels),
DiscriminatorP(5, max_channels=max_channels),
DiscriminatorP(7, max_channels=max_channels),
DiscriminatorP(11, max_channels=max_channels),
])
def forward(self, y):
outputs = []
for disc in self.discriminators:
outputs.append(disc(y))
return outputs
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False, max_channels=1024):
super(DiscriminatorS, self).__init__()
self.max_channels = max_channels
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv1d(1, min(self.max_channels, 128), 15, 1, padding=7)),
norm_f(Conv1d(min(self.max_channels, 128), min(self.max_channels, 128), 41, 2, groups=4, padding=20)),
norm_f(Conv1d(min(self.max_channels, 128), min(self.max_channels, 256), 41, 2, groups=16, padding=20)),
norm_f(Conv1d(min(self.max_channels, 256), min(self.max_channels, 512), 41, 4, groups=16, padding=20)),
norm_f(Conv1d(min(self.max_channels, 512), min(self.max_channels, 1024), 41, 4, groups=16, padding=20)),
norm_f(Conv1d(min(self.max_channels, 1024), min(self.max_channels, 1024), 41, 1, groups=16, padding=20)),
norm_f(Conv1d(min(self.max_channels, 1024), min(self.max_channels, 1024), 5, 1, padding=2)),
])
self.conv_post = norm_f(Conv1d(min(self.max_channels, 1024), 1, 3, 1, padding=1))
def forward(self, x):
output = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, LRELU_SLOPE)
output.append(x)
x = self.conv_post(x)
output.append(x)
return output
class MultiScaleDiscriminator(torch.nn.Module):
def __init__(self, max_channels=1024):
super(MultiScaleDiscriminator, self).__init__()
self.discriminators = nn.ModuleList([
DiscriminatorS(use_spectral_norm=True, max_channels=max_channels),
DiscriminatorS(max_channels=max_channels),
DiscriminatorS(max_channels=max_channels),
])
self.meanpools = nn.ModuleList([
AvgPool1d(4, 2, padding=2),
AvgPool1d(4, 2, padding=2)
])
def forward(self, y):
outputs = []
for disc in self.discriminators:
outputs.append(disc(y))
return outputs
class TDMultiResolutionDiscriminator(torch.nn.Module):
def __init__(self, max_channels=1024, **kwargs):
super().__init__()
print(f"{max_channels=}")
self.msd = MultiScaleDiscriminator(max_channels=max_channels)
self.mpd = MultiPeriodDiscriminator(max_channels=max_channels)
def forward(self, y):
return self.msd(y) + self.mpd(y)