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# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
ERes2Net incorporates both local and global feature fusion techniques to improve the performance.
The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
"""
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
import pooling_layers as pooling_layers
from fusion import AFF
class ReLU(nn.Hardtanh):
def __init__(self, inplace=False):
super(ReLU, self).__init__(0, 20, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ inplace_str + ')'
class BasicBlockERes2Net(nn.Module):
expansion = 2
def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
super(BasicBlockERes2Net, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
self.nums = scale
convs=[]
bns=[]
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes))
self.stride = stride
self.width = width
self.scale = scale
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
for i in range(self.nums):
if i==0:
sp = spx[i]
else:
sp = sp + spx[i]
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out,sp),1)
out = self.conv3(out)
out = self.bn3(out)
residual = self.shortcut(x)
out += residual
out = self.relu(out)
return out
class BasicBlockERes2Net_diff_AFF(nn.Module):
expansion = 2
def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
super(BasicBlockERes2Net_diff_AFF, self).__init__()
width = int(math.floor(planes*(baseWidth/64.0)))
self.conv1 = nn.Conv2d(in_planes, width*scale, kernel_size=1, stride=stride, bias=False)
self.bn1 = nn.BatchNorm2d(width*scale)
self.nums = scale
convs=[]
fuse_models=[]
bns=[]
for i in range(self.nums):
convs.append(nn.Conv2d(width, width, kernel_size=3, padding=1, bias=False))
bns.append(nn.BatchNorm2d(width))
for j in range(self.nums - 1):
fuse_models.append(AFF(channels=width))
self.convs = nn.ModuleList(convs)
self.bns = nn.ModuleList(bns)
self.fuse_models = nn.ModuleList(fuse_models)
self.relu = ReLU(inplace=True)
self.conv3 = nn.Conv2d(width*scale, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes*self.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes))
self.stride = stride
self.width = width
self.scale = scale
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
spx = torch.split(out,self.width,1)
for i in range(self.nums):
if i==0:
sp = spx[i]
else:
sp = self.fuse_models[i-1](sp, spx[i])
sp = self.convs[i](sp)
sp = self.relu(self.bns[i](sp))
if i==0:
out = sp
else:
out = torch.cat((out,sp),1)
out = self.conv3(out)
out = self.bn3(out)
residual = self.shortcut(x)
out += residual
out = self.relu(out)
return out
class ERes2Net(nn.Module):
def __init__(self,
block=BasicBlockERes2Net,
block_fuse=BasicBlockERes2Net_diff_AFF,
num_blocks=[3, 4, 6, 3],
m_channels=32,
feat_dim=80,
embedding_size=192,
pooling_func='TSTP',
two_emb_layer=False):
super(ERes2Net, self).__init__()
self.in_planes = m_channels
self.feat_dim = feat_dim
self.embedding_size = embedding_size
self.stats_dim = int(feat_dim / 8) * m_channels * 8
self.two_emb_layer = two_emb_layer
self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(m_channels)
self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block_fuse, m_channels * 4, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
# Downsampling module for each layer
self.layer1_downsample = nn.Conv2d(m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1, bias=False)
self.layer2_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False)
self.layer3_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False)
# Bottom-up fusion module
self.fuse_mode12 = AFF(channels=m_channels * 4)
self.fuse_mode123 = AFF(channels=m_channels * 8)
self.fuse_mode1234 = AFF(channels=m_channels * 16)
self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
self.pool = getattr(pooling_layers, pooling_func)(
in_dim=self.stats_dim * block.expansion)
self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
embedding_size)
if self.two_emb_layer:
self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
self.seg_2 = nn.Linear(embedding_size, embedding_size)
else:
self.seg_bn_1 = nn.Identity()
self.seg_2 = nn.Identity()
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
x = x.unsqueeze_(1)
out = F.relu(self.bn1(self.conv1(x)))
out1 = self.layer1(out)
out2 = self.layer2(out1)
out1_downsample = self.layer1_downsample(out1)
fuse_out12 = self.fuse_mode12(out2, out1_downsample)
out3 = self.layer3(out2)
fuse_out12_downsample = self.layer2_downsample(fuse_out12)
fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
out4 = self.layer4(out3)
fuse_out123_downsample = self.layer3_downsample(fuse_out123)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
stats = self.pool(fuse_out1234)
embed_a = self.seg_1(stats)
if self.two_emb_layer:
out = F.relu(embed_a)
out = self.seg_bn_1(out)
embed_b = self.seg_2(out)
return embed_b
else:
return embed_a
def forward3(self, x):
x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
x = x.unsqueeze_(1)
out = F.relu(self.bn1(self.conv1(x)))
out1 = self.layer1(out)
out2 = self.layer2(out1)
out1_downsample = self.layer1_downsample(out1)
fuse_out12 = self.fuse_mode12(out2, out1_downsample)
out3 = self.layer3(out2)
fuse_out12_downsample = self.layer2_downsample(fuse_out12)
fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
out4 = self.layer4(out3)
fuse_out123_downsample = self.layer3_downsample(fuse_out123)
fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1,end_dim=2).mean(-1)
return fuse_out1234
if __name__ == '__main__':
x = torch.zeros(10, 300, 80)
model = ERes2Net(feat_dim=80, embedding_size=192, pooling_func='TSTP')
model.eval()
out = model(x)
print(out.shape) # torch.Size([10, 192])
num_params = sum(param.numel() for param in model.parameters())
print("{} M".format(num_params / 1e6)) # 6.61M
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