# Copyright (c) 2022 NVIDIA CORPORATION.
# Licensed under the MIT license.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from src.utils import weight_scaling_init
# Transformer (encoder) https://github.com/jadore801120/attention-is-all-you-need-pytorch
# Original Copyright 2017 Victor Huang
# MIT License (https://opensource.org/licenses/MIT)
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, -1e9)
attn = self.dropout(F.softmax(attn, dim=-1))
output = torch.matmul(attn, v)
return output, attn
class MultiHeadAttention(nn.Module):
''' Multi-Head Attention module '''
def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.w_qs = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_ks = nn.Linear(d_model, n_head * d_k, bias=False)
self.w_vs = nn.Linear(d_model, n_head * d_v, bias=False)
self.fc = nn.Linear(n_head * d_v, d_model, bias=False)
self.attention = ScaledDotProductAttention(temperature=d_k ** 0.5)
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(self, q, k, v, mask=None):
d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
residual = q
# Pass through the pre-attention projection: b x lq x (n*dv)
# Separate different heads: b x lq x n x dv
q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
# Transpose for attention dot product: b x n x lq x dv
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
mask = mask.unsqueeze(1) # For head axis broadcasting.
q, attn = self.attention(q, k, v, mask=mask)
# Transpose to move the head dimension back: b x lq x n x dv
# Combine the last two dimensions to concatenate all the heads together: b x lq x (n*dv)
q = q.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
q = self.dropout(self.fc(q))
q += residual
q = self.layer_norm(q)
return q, attn
class PositionwiseFeedForward(nn.Module):
''' A two-feed-forward-layer module '''
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid) # position-wise
self.w_2 = nn.Linear(d_hid, d_in) # position-wise
self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
residual = x
x = self.w_2(F.relu(self.w_1(x)))
x = self.dropout(x)
x += residual
x = self.layer_norm(x)
return x
def get_subsequent_mask(seq):
''' For masking out the subsequent info. '''
sz_b, len_s = seq.size()
subsequent_mask = (1 - torch.triu(
torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
return subsequent_mask
class PositionalEncoding(nn.Module):
def __init__(self, d_hid, n_position=200):
super(PositionalEncoding, self).__init__()
# Not a parameter
self.register_buffer('pos_table', self._get_sinusoid_encoding_table(n_position, d_hid))
def _get_sinusoid_encoding_table(self, n_position, d_hid):
''' Sinusoid position encoding table '''
# TODO: make it with torch instead of numpy
def get_position_angle_vec(position):
return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)]
sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)])
sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i
sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1
return torch.FloatTensor(sinusoid_table).unsqueeze(0)
def forward(self, x):
return x + self.pos_table[:, :x.size(1)].clone().detach()
class EncoderLayer(nn.Module):
''' Compose with two layers '''
def __init__(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.0):
super(EncoderLayer, self).__init__()
self.slf_attn = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)
self.pos_ffn = PositionwiseFeedForward(d_model, d_inner, dropout=dropout)
def forward(self, enc_input, slf_attn_mask=None):
enc_output, enc_slf_attn = self.slf_attn(
enc_input, enc_input, enc_input, mask=slf_attn_mask)
enc_output = self.pos_ffn(enc_output)
return enc_output, enc_slf_attn
class TransformerEncoder(nn.Module):
''' A encoder model with self attention mechanism. '''
def __init__(
self, d_word_vec=512, n_layers=2, n_head=8, d_k=64, d_v=64,
d_model=512, d_inner=2048, dropout=0.1, n_position=624, scale_emb=False):
super().__init__()
# self.src_word_emb = nn.Embedding(n_src_vocab, d_word_vec, padding_idx=pad_idx)
if n_position > 0:
self.position_enc = PositionalEncoding(d_word_vec, n_position=n_position)
else:
self.position_enc = lambda x: x
self.dropout = nn.Dropout(p=dropout)
self.layer_stack = nn.ModuleList([
EncoderLayer(d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
for _ in range(n_layers)])
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
self.scale_emb = scale_emb
self.d_model = d_model
def forward(self, src_seq, src_mask, return_attns=False):
enc_slf_attn_list = []
# -- Forward
# enc_output = self.src_word_emb(src_seq)
enc_output = src_seq
if self.scale_emb:
enc_output *= self.d_model ** 0.5
enc_output = self.dropout(self.position_enc(enc_output))
enc_output = self.layer_norm(enc_output)
for enc_layer in self.layer_stack:
enc_output, enc_slf_attn = enc_layer(enc_output, slf_attn_mask=src_mask)
enc_slf_attn_list += [enc_slf_attn] if return_attns else []
if return_attns:
return enc_output, enc_slf_attn_list
return enc_output
# CleanUNet architecture
def padding(x, D, K, S):
"""padding zeroes to x so that denoised audio has the same length"""
L = x.shape[-1]
for _ in range(D):
if L < K:
L = 1
else:
L = 1 + np.ceil((L - K) / S)
for _ in range(D):
L = (L - 1) * S + K
L = int(L)
x = F.pad(x, (0, L - x.shape[-1]))
return x
class DenoisingModel(nn.Module):
""" CleanUNet architecture. """
def __init__(self, channels_input=1, channels_output=1,
channels_H=64, max_H=768,
encoder_n_layers=8, kernel_size=4, stride=2,
tsfm_n_layers=3,
tsfm_n_head=8,
tsfm_d_model=512,
tsfm_d_inner=2048):
"""
Parameters:
channels_input (int): input channels
channels_output (int): output channels
channels_H (int): middle channels H that controls capacity
max_H (int): maximum H
encoder_n_layers (int): number of encoder/decoder layers D
kernel_size (int): kernel size K
stride (int): stride S
tsfm_n_layers (int): number of self attention blocks N
tsfm_n_head (int): number of heads in each self attention block
tsfm_d_model (int): d_model of self attention
tsfm_d_inner (int): d_inner of self attention
"""
super(DenoisingModel, self).__init__()
self.channels_input = channels_input
self.channels_output = channels_output
self.channels_H = channels_H
self.max_H = max_H
self.encoder_n_layers = encoder_n_layers
self.kernel_size = kernel_size
self.stride = stride
self.tsfm_n_layers = tsfm_n_layers
self.tsfm_n_head = tsfm_n_head
self.tsfm_d_model = tsfm_d_model
self.tsfm_d_inner = tsfm_d_inner
# encoder and decoder
self.encoder = nn.ModuleList()
self.decoder = nn.ModuleList()
for i in range(encoder_n_layers):
self.encoder.append(nn.Sequential(
nn.Conv1d(channels_input, channels_H, kernel_size, stride),
nn.ReLU(),
nn.Conv1d(channels_H, channels_H * 2, 1),
nn.GLU(dim=1)
))
channels_input = channels_H
if i == 0:
# no relu at end
self.decoder.append(nn.Sequential(
nn.Conv1d(channels_H, channels_H * 2, 1),
nn.GLU(dim=1),
nn.ConvTranspose1d(channels_H, channels_output, kernel_size, stride)
))
else:
self.decoder.insert(0, nn.Sequential(
nn.Conv1d(channels_H, channels_H * 2, 1),
nn.GLU(dim=1),
nn.ConvTranspose1d(channels_H, channels_output, kernel_size, stride),
nn.ReLU()
))
channels_output = channels_H
# double H but keep below max_H
channels_H *= 2
channels_H = min(channels_H, max_H)
# self attention block
self.tsfm_conv1 = nn.Conv1d(channels_output, tsfm_d_model, kernel_size=1)
self.tsfm_encoder = TransformerEncoder(d_word_vec=tsfm_d_model,
n_layers=tsfm_n_layers,
n_head=tsfm_n_head,
d_k=tsfm_d_model // tsfm_n_head,
d_v=tsfm_d_model // tsfm_n_head,
d_model=tsfm_d_model,
d_inner=tsfm_d_inner,
dropout=0.0,
n_position=0,
scale_emb=False)
self.tsfm_conv2 = nn.Conv1d(tsfm_d_model, channels_output, kernel_size=1)
# weight scaling initialization
for layer in self.modules():
if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
weight_scaling_init(layer)
def forward(self, noisy_audio):
# (B, L) -> (B, C, L)
if len(noisy_audio.shape) == 2:
noisy_audio = noisy_audio.unsqueeze(1)
B, C, L = noisy_audio.shape
assert C == 1
# normalization and padding
std = noisy_audio.std(dim=2, keepdim=True) + 1e-3
noisy_audio /= std
x = padding(noisy_audio, self.encoder_n_layers, self.kernel_size, self.stride)
# encoder
skip_connections = []
for downsampling_block in self.encoder:
x = downsampling_block(x)
skip_connections.append(x)
skip_connections = skip_connections[::-1]
# attention mask for causal inference; for non-causal, set attn_mask to None
len_s = x.shape[-1] # length at bottleneck
attn_mask = (1 - torch.triu(torch.ones((1, len_s, len_s), device=x.device), diagonal=1)).bool()
x = self.tsfm_conv1(x) # C 1024 -> 512
x = x.permute(0, 2, 1)
x = self.tsfm_encoder(x, src_mask=attn_mask)
x = x.permute(0, 2, 1)
x = self.tsfm_conv2(x) # C 512 -> 1024
# decoder
for i, upsampling_block in enumerate(self.decoder):
skip_i = skip_connections[i]
x += skip_i[:, :, :x.shape[-1]]
x = upsampling_block(x)
x = x[:, :, :L] * std
return x
if __name__ == '__main__':
import json
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default='configs/DNS-large-full.json',
help='JSON file for configuration')
args = parser.parse_args()
with open(args.config) as f:
data = f.read()
config = json.loads(data)
network_config = config["network_config"]
model = CleanUNet(**network_config).cuda()
from util import print_size
print_size(model, keyword="tsfm")
input_data = torch.ones([4,1,int(4.5*16000)]).cuda()
output = model(input_data)
print(output.shape)
y = torch.rand([4,1,int(4.5*16000)]).cuda()
loss = torch.nn.MSELoss()(y, output)
loss.backward()
print(loss.item())