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import math
from collections import OrderedDict
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from packaging.version import parse as V
from torch.nn import init
from torch.nn.parameter import Parameter
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class Network(nn.Module):
def __init__(self, n_srcs=1, win=512, n_mics=4, n_layers=12, att_dim=64, hidden_dim=256, n_head=4, emb_dim=64, emb_ks=4, emb_hs=1, dropout=0.1, eps=1.0e-5):
super().__init__()
self.n_srcs = n_srcs
self.win = win
self.hop = win // 2
self.n_layers = n_layers
self.n_mics = n_mics
self.emb_dim = emb_dim
assert win % 2 == 0
t_ksize = 3
ks, padding = (t_ksize, 3), (t_ksize // 2, 1)
self.conv = nn.Sequential(
nn.Conv2d(2 * n_mics, emb_dim * n_head, ks, padding=padding),
nn.GroupNorm(1, emb_dim * n_head, eps=eps),
InverseDenseBlock2d(emb_dim * n_head, emb_dim, n_head)
)
self.blocks = nn.ModuleList([])
for idx in range(n_layers):
self.blocks.append(DeFTANblock(idx, emb_dim, emb_ks, emb_hs, att_dim, hidden_dim, n_head, dropout, eps))
self.deconv = nn.Sequential(
nn.Conv2d(emb_dim, 2 * n_srcs * n_head, ks, padding=padding),
InverseDenseBlock2d(2 * n_srcs * n_head, 2 * n_srcs, n_head))
def pad_signal(self, input):
# input is the waveforms: (B, T) or (B, 1, T)
# reshape and padding
if input.dim() not in [2, 3]:
raise RuntimeError("Input can only be 2 or 3 dimensional.")
if input.dim() == 2:
input = input.unsqueeze(1)
batch_size = input.size(0)
nchannel = input.size(1)
nsample = input.size(2)
rest = self.win - (self.hop + nsample % self.win) % self.win
if rest > 0:
pad = Variable(torch.zeros(batch_size, nchannel, rest)).type(input.type())
input = torch.cat([input, pad], 2)
pad_aux = Variable(torch.zeros(batch_size, nchannel, self.hop)).type(input.type())
input = torch.cat([pad_aux, input, pad_aux], 2)
return input, rest
def forward(self, input: Union[torch.Tensor]) -> Tuple[List[Union[torch.Tensor]], torch.Tensor, OrderedDict]:
input, rest = self.pad_signal(input)
B, M, N = input.size() # batch B, mic M, time samples N
mix_std_ = torch.std(input, dim=(1, 2), keepdim=True) # [B, 1, 1]
input = input / mix_std_ # RMS normalization
stft_input = torch.stft(input.view([-1, N]), n_fft=self.win, hop_length=self.hop, window=torch.hann_window(self.win).type(input.type()), return_complex=False)
_, F, T, _ = stft_input.size() # B*M , F= num freqs, T= num frame, 2= real imag
xi = stft_input.view([B, M, F, T, 2]) # B*M, F, T, 2 -> B, M, F, T, 2
xi = xi.permute(0, 1, 4, 3, 2).contiguous() # [B, M, 2, T, F]
batch = xi.view([B, M * 2, T, F]) # [B, 2*M, T, F]
batch = self.conv(batch) # [B, C, T, F]
for ii in range(self.n_layers):
batch = self.blocks[ii](batch) # [B, C, T, F]
batch = self.deconv(batch).view([B, self.n_srcs, 2, T, F]).view([B * self.n_srcs, 2, T, F])
batch = batch.permute(0, 3, 2, 1).type(input.type()) # [B*n_srcs, 2, T, F] -> [B*n_srcs, F, T, 2]
istft_input = torch.complex(batch[:, :, :, 0], batch[:, :, :, 1])
istft_output = torch.istft(istft_input, n_fft=self.win, hop_length=self.hop, window=torch.hann_window(self.win).type(input.type()), return_complex=False)
output = istft_output[:, self.hop:-(rest + self.hop)].unsqueeze(1) # [B*n_srcs, 1, N]
output = output.view([B, self.n_srcs, -1]) # [B, n_srcs, N]
output = output * mix_std_ # reverse the RMS normalization
return output
class InverseDenseBlock1d(nn.Module):
def __init__(self, in_channels, out_channels, groups):
super().__init__()
assert in_channels // out_channels == groups
self.in_channels = in_channels
self.out_channels = out_channels
self.groups = groups
self.blocks = nn.ModuleList([])
for idx in range(groups):
self.blocks.append(nn.Sequential(
nn.Conv1d(out_channels * ((idx > 0) + 1), out_channels, kernel_size=3, padding=1),
nn.GroupNorm(1, out_channels, 1e-5),
nn.PReLU(out_channels)
))
def forward(self, x):
B, C, L = x.size()
g = self.groups
x = x.view(B, g, C//g, L).transpose(1, 2).reshape(B, C, L)
skip = x[:, ::g, :]
for idx in range(g):
output = self.blocks[idx](skip)
skip = torch.cat([output, x[:, idx+1::g, :]], dim=1)
return output
class InverseDenseBlock2d(nn.Module):
def __init__(self, in_channels, out_channels, groups):
super().__init__()
assert in_channels // out_channels == groups
self.in_channels = in_channels
self.out_channels = out_channels
self.groups = groups
self.blocks = nn.ModuleList([])
for idx in range(groups):
self.blocks.append(nn.Sequential(
nn.Conv2d(out_channels * ((idx > 0) + 1), out_channels, kernel_size=(3, 3), padding=(1, 1)),
nn.GroupNorm(1, out_channels, 1e-5),
nn.PReLU(out_channels)
))
def forward(self, x):
B, C, T, Q = x.size()
g = self.groups
x = x.view(B, g, C//g, T, Q).transpose(1, 2).reshape(B, C, T, Q)
skip = x[:, ::g, :, :]
for idx in range(g):
output = self.blocks[idx](skip)
skip = torch.cat([output, x[:, idx+1::g, :, :]], dim=1)
return output
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class Attention(nn.Module):
def __init__(self, dim, heads, dim_head, dropout):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.cv_qk = nn.Sequential(
nn.Conv1d(dim, dim * 2, kernel_size=3, padding=1, bias = False),
nn.GLU(dim=1))
self.to_q = nn.Linear(dim, inner_dim, bias = False)
self.to_k = nn.Linear(dim, inner_dim, bias=False)
self.to_v = nn.Linear(dim, inner_dim, bias = False)
self.att_drop = nn.Dropout(dropout)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x):
qk = self.cv_qk(x.transpose(1, 2)).transpose(1, 2)
q = rearrange(self.to_q(qk), 'b n (h d) -> b h n d', h = self.heads)
k = rearrange(self.to_k(qk), 'b n (h d) -> b h n d', h=self.heads)
v = rearrange(self.to_v(x), 'b n (h d) -> b h n d', h = self.heads)
weight = torch.matmul(F.softmax(k, dim=2).transpose(-1, -2), v) * self.scale
out = torch.matmul(F.softmax(q, dim=3), self.att_drop(weight))
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, idx, dropout):
super().__init__()
self.PW1 = nn.Sequential(
nn.Linear(dim, hidden_dim//2),
nn.GELU(),
nn.Dropout(dropout)
)
self.PW2 = nn.Sequential(
nn.Linear(dim, hidden_dim//2),
nn.GELU(),
nn.Dropout(dropout)
)
self.DW_Conv = nn.Sequential(
nn.Conv1d(hidden_dim//2, hidden_dim//2, kernel_size=5, dilation=2**idx, padding='same'),
nn.GroupNorm(1, hidden_dim//2, 1e-5),
nn.PReLU(hidden_dim//2)
)
self.PW3 = nn.Sequential(
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
ffw_out = self.PW1(x)
dw_out = self.DW_Conv(self.PW2(x).transpose(1, 2)).transpose(1, 2)
out = self.PW3(torch.cat((ffw_out, dw_out), dim=2))
return out
class DeFTANblock(nn.Module):
def __getitem__(self, key):
return getattr(self, key)
def __init__(self, idx, emb_dim, emb_ks, emb_hs, att_dim, hidden_dim, n_head, dropout, eps):
super().__init__()
in_channels = emb_dim * emb_ks
self.intra_norm = LayerNormalization4D(emb_dim, eps)
self.intra_inv = InverseDenseBlock1d(in_channels, emb_dim, emb_ks)
self.intra_att = PreNorm(emb_dim, Attention(emb_dim, n_head, att_dim, dropout))
self.intra_ffw = PreNorm(emb_dim, FeedForward(emb_dim, hidden_dim, idx, dropout))
self.intra_linear = nn.ConvTranspose1d(emb_dim, emb_dim, emb_ks, stride=emb_hs)
self.inter_norm = LayerNormalization4D(emb_dim, eps)
self.inter_inv = InverseDenseBlock1d(in_channels, emb_dim, emb_ks)
self.inter_att = PreNorm(emb_dim, Attention(emb_dim, n_head, att_dim, dropout))
self.inter_ffw = PreNorm(emb_dim, FeedForward(emb_dim, hidden_dim, idx, dropout))
self.inter_linear = nn.ConvTranspose1d(emb_dim, emb_dim, emb_ks, stride=emb_hs)
self.emb_dim = emb_dim
self.emb_ks = emb_ks
self.emb_hs = emb_hs
self.n_head = n_head
def forward(self, x):
B, C, old_T, old_Q = x.shape
T = math.ceil((old_T - self.emb_ks) / self.emb_hs) * self.emb_hs + self.emb_ks
Q = math.ceil((old_Q - self.emb_ks) / self.emb_hs) * self.emb_hs + self.emb_ks
x = F.pad(x, (0, Q - old_Q, 0, T - old_T))
# F-transformer
input_ = x
intra_rnn = self.intra_norm(input_) # [B, C, T, Q]
intra_rnn = intra_rnn.transpose(1, 2).contiguous().view(B * T, C, Q) # [BT, C, Q]
intra_rnn = F.unfold(intra_rnn[..., None], (self.emb_ks, 1), stride=(self.emb_hs, 1)) # [BT, C*emb_ks, -1]
intra_rnn = self.intra_inv(intra_rnn) # [BT, C, -1]
intra_rnn = intra_rnn.transpose(1, 2) # [BT, -1, C]
intra_rnn = self.intra_att(intra_rnn) + intra_rnn
intra_rnn = self.intra_ffw(intra_rnn) + intra_rnn
intra_rnn = intra_rnn.transpose(1, 2) # [BT, H, -1]
intra_rnn = self.intra_linear(intra_rnn) # [BT, C, Q]
intra_rnn = intra_rnn.view([B, T, C, Q])
intra_rnn = intra_rnn.transpose(1, 2).contiguous() # [B, C, T, Q]
intra_rnn = intra_rnn + input_ # [B, C, T, Q]
# T-transformer
input_ = intra_rnn
inter_rnn = self.inter_norm(input_) # [B, C, T, F]
inter_rnn = inter_rnn.permute(0, 3, 1, 2).contiguous().view(B * Q, C, T) # [BF, C, T]
inter_rnn = F.unfold(inter_rnn[..., None], (self.emb_ks, 1), stride=(self.emb_hs, 1)) # [BF, C*emb_ks, -1]
inter_rnn = self.inter_inv(inter_rnn) # [BF, C, -1]
inter_rnn = inter_rnn.transpose(1, 2) # [BF, -1, C]
inter_rnn = self.inter_att(inter_rnn) + inter_rnn
inter_rnn = self.inter_ffw(inter_rnn) + inter_rnn
inter_rnn = inter_rnn.transpose(1, 2) # [BF, H, -1]
inter_rnn = self.inter_linear(inter_rnn) # [BF, C, T]
inter_rnn = inter_rnn.view([B, Q, C, T])
inter_rnn = inter_rnn.permute(0, 2, 3, 1).contiguous() # [B, C, T, Q]
inter_rnn = inter_rnn + input_ # [B, C, T, Q]
return inter_rnn
class LayerNormalization4D(nn.Module):
def __init__(self, input_dimension, eps=1e-5):
super().__init__()
param_size = [1, input_dimension, 1, 1]
self.gamma = Parameter(torch.Tensor(*param_size).to(torch.float32))
self.beta = Parameter(torch.Tensor(*param_size).to(torch.float32))
init.ones_(self.gamma)
init.zeros_(self.beta)
self.eps = eps
def forward(self, x):
if x.ndim == 4:
_, C, _, _ = x.shape
stat_dim = (1,)
else:
raise ValueError("Expect x to have 4 dimensions, but got {}".format(x.ndim))
mu_ = x.mean(dim=stat_dim, keepdim=True) # [B,1,T,F]
std_ = torch.sqrt(
x.var(dim=stat_dim, unbiased=False, keepdim=True) + self.eps
) # [B,1,T,F]
x_hat = ((x - mu_) / std_) * self.gamma + self.beta
return x_hat
class LayerNormalization4DCF(nn.Module):
def __init__(self, input_dimension, eps=1e-5):
super().__init__()
assert len(input_dimension) == 2
param_size = [1, input_dimension[0], 1, input_dimension[1]]
self.gamma = Parameter(torch.Tensor(*param_size).to(torch.float32))
self.beta = Parameter(torch.Tensor(*param_size).to(torch.float32))
init.ones_(self.gamma)
init.zeros_(self.beta)
self.eps = eps
def forward(self, x):
if x.ndim == 4:
stat_dim = (1, 3)
else:
raise ValueError("Expect x to have 4 dimensions, but got {}".format(x.ndim))
mu_ = x.mean(dim=stat_dim, keepdim=True) # [B,1,T,1]
std_ = torch.sqrt(
x.var(dim=stat_dim, unbiased=False, keepdim=True) + self.eps
) # [B,1,T,F]
x_hat = ((x - mu_) / std_) * self.gamma + self.beta
return x_hat |