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generator.py
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generator.py
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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
from utils import init_weights, get_padding
LRELU_SLOPE = 0.1
class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5, 7)):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
padding=get_padding(kernel_size, dilation[2]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[3],
padding=get_padding(kernel_size, dilation[3])))
])
self.convs1.apply(init_weights)
self.convs2 = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
padding=get_padding(kernel_size, 1)))
])
self.convs2.apply(init_weights)
def forward(self, x):
for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
xt = c2(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs1:
remove_weight_norm(l)
for l in self.convs2:
remove_weight_norm(l)
class ResBlock2(torch.nn.Module):
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.h = h
self.convs = nn.ModuleList([
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=get_padding(kernel_size, dilation[0]))),
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
padding=get_padding(kernel_size, dilation[1])))
])
self.convs.apply(init_weights)
def forward(self, x):
for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
xt = c(xt)
x = xt + x
return x
def remove_weight_norm(self):
for l in self.convs:
remove_weight_norm(l)
class FreGAN(torch.nn.Module):
def __init__(self, h, top_k=4):
super(FreGAN, self).__init__()
self.num_kernels = len(h.resblock_kernel_sizes)
self.num_upsamples = len(h.upsample_rates)
self.upsample_rates = h.upsample_rates
self.up_kernels = h.upsample_kernel_sizes
self.cond_level = self.num_upsamples - top_k
self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
resblock = ResBlock1
self.ups = nn.ModuleList()
self.cond_up = nn.ModuleList()
self.res_output = nn.ModuleList()
upsample_ = 1
kr = 80
for i, (u, k) in enumerate(zip(self.upsample_rates, self.up_kernels)):
self.ups.append(weight_norm(
ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
k, u, padding=(k - u) // 2)))
if i > (self.num_upsamples - top_k):
self.res_output.append(
nn.Sequential(
nn.Upsample(scale_factor=u, mode='nearest'),
weight_norm(nn.Conv1d(h.upsample_initial_channel // (2 ** i),
h.upsample_initial_channel // (2 ** (i + 1)), 1))
)
)
if i >= (self.num_upsamples - top_k):
self.cond_up.append(
weight_norm(
ConvTranspose1d(kr, h.upsample_initial_channel // (2 ** i),
self.up_kernels[i - 1], self.upsample_rates[i - 1],
padding=(self.up_kernels[i - 1] - self.upsample_rates[i - 1]) // 2))
)
kr = h.upsample_initial_channel // (2 ** i)
upsample_ *= u
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = h.upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
self.resblocks.append(resblock(ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
self.ups.apply(init_weights)
self.conv_post.apply(init_weights)
self.cond_up.apply(init_weights)
self.res_output.apply(init_weights)
def forward(self, x):
mel = x
x = self.conv_pre(x)
output = None
for i in range(self.num_upsamples):
if i >= self.cond_level:
mel = self.cond_up[i - self.cond_level](mel)
x += mel
if i > self.cond_level:
if output is None:
output = self.res_output[i - self.cond_level - 1](x)
else:
output = self.res_output[i - self.cond_level - 1](output)
x = F.leaky_relu(x, LRELU_SLOPE)
x = self.ups[i](x)
xs = None
for j in range(self.num_kernels):
if xs is None:
xs = self.resblocks[i * self.num_kernels + j](x)
else:
xs += self.resblocks[i * self.num_kernels + j](x)
x = xs / self.num_kernels
if output is not None:
output = output + x
x = F.leaky_relu(output)
x = self.conv_post(x)
x = torch.tanh(x)
return x
def remove_weight_norm(self):
print('Removing weight norm...')
for l in self.ups:
remove_weight_norm(l)
for l in self.resblocks:
l.remove_weight_norm()
for l in self.cond_up:
remove_weight_norm(l)
for l in self.res_output:
remove_weight_norm(l[1])
remove_weight_norm(self.conv_pre)
remove_weight_norm(self.conv_post)
'''
to run this, fix
from . import ResStack
into
from res_stack import ResStack
'''
if __name__ == '__main__':
'''
torch.Size([3, 80, 10])
torch.Size([3, 1, 2000])
4527362
'''
with open('config.json') as f:
data = f.read()
from utils import AttrDict
import json
json_config = json.loads(data)
h = AttrDict(json_config)
model = FreGAN(h)
c = torch.randn(3, 80, 10) # (B, channels, T).
print(c.shape)
y = model(c) # (B, 1, T ** prod(upsample_scales)
print(y.shape)
assert y.shape == torch.Size([3, 1, 2560]) # For normal melgan torch.Size([3, 1, 2560])
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(pytorch_total_params)