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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)
"""
To further improve the short-duration feature extraction capability of ERes2Net, we expand the channel dimension
within each stage. However, this modification also increases the number of model parameters and computational complexity.
To alleviate this problem, we propose an improved ERes2NetV2 by pruning redundant structures, ultimately reducing
both the model parameters and its computational cost.
"""
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 BasicBlockERes2NetV2(nn.Module):
def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
super(BasicBlockERes2NetV2, 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
self.expansion = expansion
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 BasicBlockERes2NetV2AFF(nn.Module):
def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
super(BasicBlockERes2NetV2AFF, 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
self.expansion = expansion
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, r=4))
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 ERes2NetV2(nn.Module):
def __init__(self,
block=BasicBlockERes2NetV2,
block_fuse=BasicBlockERes2NetV2AFF,
num_blocks=[3, 4, 6, 3],
m_channels=64,
feat_dim=80,
embedding_size=192,
baseWidth=26,
scale=2,
expansion=2,
pooling_func='TSTP',
two_emb_layer=False):
super(ERes2NetV2, 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.baseWidth = baseWidth
self.scale = scale
self.expansion = expansion
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
self.layer3_ds = nn.Conv2d(m_channels * 4 * self.expansion, m_channels * 8 * self.expansion, kernel_size=3, \
padding=1, stride=2, bias=False)
# Bottom-up fusion module
self.fuse34 = AFF(channels=m_channels * 8 * self.expansion, r=4)
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 * self.expansion)
self.seg_1 = nn.Linear(self.stats_dim * self.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, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion))
self.in_planes = planes * self.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)
out3 = self.layer3(out2)
out4 = self.layer4(out3)
out3_ds = self.layer3_ds(out3)
fuse_out34 = self.fuse34(out4, out3_ds)
stats = self.pool(fuse_out34)
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)
out3 = self.layer3(out2)
out4 = self.layer4(out3)
out3_ds = self.layer3_ds(out3)
fuse_out34 = self.fuse34(out4, out3_ds)
# print(111111111,fuse_out34.shape)#111111111 torch.Size([16, 2048, 10, 72])
return fuse_out34.flatten(start_dim=1,end_dim=2).mean(-1)
# stats = self.pool(fuse_out34)
#
# 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
if __name__ == '__main__':
x = torch.randn(1, 300, 80)
model = ERes2NetV2(feat_dim=80, embedding_size=192, m_channels=64, baseWidth=26, scale=2, expansion=2)
model.eval()
y = model(x)
print(y.size())
macs, num_params = profile(model, inputs=(x, ))
print("Params: {} M".format(num_params / 1e6)) # 17.86 M
print("MACs: {} G".format(macs / 1e9)) # 12.69 G
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