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Running
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Zero
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import logging
import math
import torch
import torch.nn as nn
logger = logging.getLogger(__name__)
@torch.jit.script
def _fused_tanh_sigmoid(h):
a, b = h.chunk(2, dim=1)
h = a.tanh() * b.sigmoid()
return h
class WNLayer(nn.Module):
"""
A DiffWave-like WN
"""
def __init__(self, hidden_dim, local_dim, global_dim, kernel_size, dilation):
super().__init__()
local_output_dim = hidden_dim * 2
if global_dim is not None:
self.gconv = nn.Conv1d(global_dim, hidden_dim, 1)
if local_dim is not None:
self.lconv = nn.Conv1d(local_dim, local_output_dim, 1)
self.dconv = nn.Conv1d(hidden_dim, local_output_dim, kernel_size, dilation=dilation, padding="same")
self.out = nn.Conv1d(hidden_dim, 2 * hidden_dim, kernel_size=1)
def forward(self, z, l, g):
identity = z
if g is not None:
if g.dim() == 2:
g = g.unsqueeze(-1)
z = z + self.gconv(g)
z = self.dconv(z)
if l is not None:
z = z + self.lconv(l)
z = _fused_tanh_sigmoid(z)
h = self.out(z)
z, s = h.chunk(2, dim=1)
o = (z + identity) / math.sqrt(2)
return o, s
class WN(nn.Module):
def __init__(
self,
input_dim,
output_dim,
local_dim=None,
global_dim=None,
n_layers=30,
kernel_size=3,
dilation_cycle=5,
hidden_dim=512,
):
super().__init__()
assert kernel_size % 2 == 1
assert hidden_dim % 2 == 0
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.local_dim = local_dim
self.global_dim = global_dim
self.start = nn.Conv1d(input_dim, hidden_dim, 1)
if local_dim is not None:
self.local_norm = nn.InstanceNorm1d(local_dim)
self.layers = nn.ModuleList(
[
WNLayer(
hidden_dim=hidden_dim,
local_dim=local_dim,
global_dim=global_dim,
kernel_size=kernel_size,
dilation=2 ** (i % dilation_cycle),
)
for i in range(n_layers)
]
)
self.end = nn.Conv1d(hidden_dim, output_dim, 1)
def forward(self, z, l=None, g=None):
"""
Args:
z: input (b c t)
l: local condition (b c t)
g: global condition (b d)
"""
z = self.start(z)
if l is not None:
l = self.local_norm(l)
# Skips
s_list = []
for layer in self.layers:
z, s = layer(z, l, g)
s_list.append(s)
s_list = torch.stack(s_list, dim=0).sum(dim=0)
s_list = s_list / math.sqrt(len(self.layers))
o = self.end(s_list)
return o
def summarize(self, length=100):
from ptflops import get_model_complexity_info
x = torch.randn(1, self.input_dim, length)
macs, params = get_model_complexity_info(
self,
(self.input_dim, length),
as_strings=True,
print_per_layer_stat=True,
verbose=True,
)
print(f"Input shape: {x.shape}")
print(f"Computational complexity: {macs}")
print(f"Number of parameters: {params}")
if __name__ == "__main__":
model = WN(input_dim=64, output_dim=64)
model.summarize()
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