From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords
---
funasr/models/eres2net/eres2net.py | 167 +++++++++++++++++++++----------------------------------
1 files changed, 65 insertions(+), 102 deletions(-)
diff --git a/funasr/models/eres2net/eres2net.py b/funasr/models/eres2net/eres2net.py
index 3ea9fdf..de6c2bb 100644
--- a/funasr/models/eres2net/eres2net.py
+++ b/funasr/models/eres2net/eres2net.py
@@ -26,21 +26,18 @@
super(ReLU, self).__init__(0, 20, inplace)
def __repr__(self):
- inplace_str = 'inplace' if self.inplace else ''
- return self.__class__.__name__ + ' (' \
- + inplace_str + ')'
+ inplace_str = "inplace" if self.inplace else ""
+ return self.__class__.__name__ + " (" + inplace_str + ")"
def conv1x1(in_planes, out_planes, stride=1):
"1x1 convolution without padding"
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
- padding=0, bias=False)
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
+ return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlockERes2Net(nn.Module):
@@ -67,12 +64,11 @@
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))
+ 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
@@ -135,12 +131,11 @@
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))
+ 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
@@ -176,15 +171,17 @@
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):
+ 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
@@ -192,48 +189,32 @@
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.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)
+ 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)
+ 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)
+ 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)
@@ -298,12 +279,11 @@
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))
+ 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
@@ -340,14 +320,16 @@
class Res2Net(nn.Module):
- def __init__(self,
- block=BasicBlockRes2Net,
- num_blocks=[3, 4, 6, 3],
- m_channels=32,
- feat_dim=80,
- embedding_size=192,
- pooling_func='TSTP',
- two_emb_layer=False):
+ def __init__(
+ self,
+ block=BasicBlockRes2Net,
+ num_blocks=[3, 4, 6, 3],
+ m_channels=32,
+ feat_dim=80,
+ embedding_size=192,
+ pooling_func="TSTP",
+ two_emb_layer=False,
+ ):
super(Res2Net, self).__init__()
self.in_planes = m_channels
self.feat_dim = feat_dim
@@ -355,35 +337,16 @@
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.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,
- m_channels * 4,
- num_blocks[2],
- stride=2)
- self.layer4 = self._make_layer(block,
- m_channels * 8,
- num_blocks[3],
- stride=2)
+ 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, m_channels * 4, num_blocks[2], stride=2)
+ self.layer4 = self._make_layer(block, m_channels * 8, num_blocks[3], stride=2)
- 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)
+ 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)
--
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