From 97d648c255316ec1fff5d82e46749076faabdd2d Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期一, 15 一月 2024 15:41:25 +0800
Subject: [PATCH] code optimize, model update, scripts
---
funasr/models/campplus/components.py | 80 ++++++++++++++++++++-------------------
1 files changed, 41 insertions(+), 39 deletions(-)
diff --git a/funasr/models/campplus/components.py b/funasr/models/campplus/components.py
index 43d366e..8db9aef 100644
--- a/funasr/models/campplus/components.py
+++ b/funasr/models/campplus/components.py
@@ -1,41 +1,43 @@
-# 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)
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as cp
-from torch import nn
-class BasicResBlock(nn.Module):
+class BasicResBlock(torch.nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicResBlock, self).__init__()
- self.conv1 = nn.Conv2d(in_planes,
+ self.conv1 = torch.nn.Conv2d(in_planes,
planes,
kernel_size=3,
stride=(stride, 1),
padding=1,
bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes,
+ self.bn1 = torch.nn.BatchNorm2d(planes)
+ self.conv2 = torch.nn.Conv2d(planes,
planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
+ self.bn2 = torch.nn.BatchNorm2d(planes)
- self.shortcut = nn.Sequential()
+ self.shortcut = torch.nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
- self.shortcut = nn.Sequential(
- nn.Conv2d(in_planes,
+ self.shortcut = torch.nn.Sequential(
+ torch.nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=(stride, 1),
bias=False),
- nn.BatchNorm2d(self.expansion * planes))
+ torch.nn.BatchNorm2d(self.expansion * planes))
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
@@ -45,7 +47,7 @@
return out
-class FCM(nn.Module):
+class FCM(torch.nn.Module):
def __init__(self,
block=BasicResBlock,
num_blocks=[2, 2],
@@ -53,14 +55,14 @@
feat_dim=80):
super(FCM, self).__init__()
self.in_planes = m_channels
- self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(m_channels)
+ self.conv1 = torch.nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
+ self.bn1 = torch.nn.BatchNorm2d(m_channels)
self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
- self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(m_channels)
+ self.conv2 = torch.nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
+ self.bn2 = torch.nn.BatchNorm2d(m_channels)
self.out_channels = m_channels * (feat_dim // 8)
def _make_layer(self, block, planes, num_blocks, stride):
@@ -69,7 +71,7 @@
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
- return nn.Sequential(*layers)
+ return torch.nn.Sequential(*layers)
def forward(self, x):
x = x.unsqueeze(1)
@@ -84,17 +86,17 @@
def get_nonlinear(config_str, channels):
- nonlinear = nn.Sequential()
+ nonlinear = torch.nn.Sequential()
for name in config_str.split('-'):
if name == 'relu':
- nonlinear.add_module('relu', nn.ReLU(inplace=True))
+ nonlinear.add_module('relu', torch.nn.ReLU(inplace=True))
elif name == 'prelu':
- nonlinear.add_module('prelu', nn.PReLU(channels))
+ nonlinear.add_module('prelu', torch.nn.PReLU(channels))
elif name == 'batchnorm':
- nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels))
+ nonlinear.add_module('batchnorm', torch.nn.BatchNorm1d(channels))
elif name == 'batchnorm_':
nonlinear.add_module('batchnorm',
- nn.BatchNorm1d(channels, affine=False))
+ torch.nn.BatchNorm1d(channels, affine=False))
else:
raise ValueError('Unexpected module ({}).'.format(name))
return nonlinear
@@ -109,12 +111,12 @@
return stats
-class StatsPool(nn.Module):
+class StatsPool(torch.nn.Module):
def forward(self, x):
return statistics_pooling(x)
-class TDNNLayer(nn.Module):
+class TDNNLayer(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
@@ -129,7 +131,7 @@
assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
kernel_size)
padding = (kernel_size - 1) // 2 * dilation
- self.linear = nn.Conv1d(in_channels,
+ self.linear = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size,
stride=stride,
@@ -144,7 +146,7 @@
return x
-class CAMLayer(nn.Module):
+class CAMLayer(torch.nn.Module):
def __init__(self,
bn_channels,
out_channels,
@@ -155,17 +157,17 @@
bias,
reduction=2):
super(CAMLayer, self).__init__()
- self.linear_local = nn.Conv1d(bn_channels,
+ self.linear_local = torch.nn.Conv1d(bn_channels,
out_channels,
kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
bias=bias)
- self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1)
- self.relu = nn.ReLU(inplace=True)
- self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1)
- self.sigmoid = nn.Sigmoid()
+ self.linear1 = torch.nn.Conv1d(bn_channels, bn_channels // reduction, 1)
+ self.relu = torch.nn.ReLU(inplace=True)
+ self.linear2 = torch.nn.Conv1d(bn_channels // reduction, out_channels, 1)
+ self.sigmoid = torch.nn.Sigmoid()
def forward(self, x):
y = self.linear_local(x)
@@ -187,7 +189,7 @@
return seg
-class CAMDenseTDNNLayer(nn.Module):
+class CAMDenseTDNNLayer(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
@@ -204,7 +206,7 @@
padding = (kernel_size - 1) // 2 * dilation
self.memory_efficient = memory_efficient
self.nonlinear1 = get_nonlinear(config_str, in_channels)
- self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
+ self.linear1 = torch.nn.Conv1d(in_channels, bn_channels, 1, bias=False)
self.nonlinear2 = get_nonlinear(config_str, bn_channels)
self.cam_layer = CAMLayer(bn_channels,
out_channels,
@@ -226,7 +228,7 @@
return x
-class CAMDenseTDNNBlock(nn.ModuleList):
+class CAMDenseTDNNBlock(torch.nn.ModuleList):
def __init__(self,
num_layers,
in_channels,
@@ -257,7 +259,7 @@
return x
-class TransitLayer(nn.Module):
+class TransitLayer(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
@@ -265,7 +267,7 @@
config_str='batchnorm-relu'):
super(TransitLayer, self).__init__()
self.nonlinear = get_nonlinear(config_str, in_channels)
- self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+ self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
def forward(self, x):
x = self.nonlinear(x)
@@ -273,14 +275,14 @@
return x
-class DenseLayer(nn.Module):
+class DenseLayer(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
bias=False,
config_str='batchnorm-relu'):
super(DenseLayer, self).__init__()
- self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+ self.linear = torch.nn.Conv1d(in_channels, out_channels, 1, bias=bias)
self.nonlinear = get_nonlinear(config_str, out_channels)
def forward(self, x):
--
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