43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
"""
|
||
这个文件是模型的定义文件，请不要擅自修改，如有疑问微信群里反馈
|
||
author : yukun-hh
|
||
date : 2026-4-10
|
||
|
||
"""
|
||
#神经网络模型库
|
||
import torch
|
||
from modelscope.msdatasets.dataset_cls.custom_datasets.audio.kws_nearfield_processor import padding
|
||
from torch import nn
|
||
from torch.nn import functional as F
|
||
|
||
#残差块
|
||
class Resblock(nn.Module):
|
||
def __init__(self, input_channels,output_channels,use_1x1conv=False,strides=1):
|
||
"""
|
||
|
||
:param input_channels: 进入残差块时的原通道
|
||
:param output_channels: 输出时的通道数
|
||
:param use_1x1conv: 如果输入和输出通道不相等时，需要用一个1x1的卷积层对原来的输入进行一个通道提升
|
||
:param strides: 默认1，如果大于1起到缩小张量的作用
|
||
"""
|
||
super().__init__()
|
||
self.conv1 = nn.Conv2d(input_channels,output_channels,kernel_size=3,padding=1,stride=strides)
|
||
self.conv2 = nn.Conv2d(output_channels,output_channels,kernel_size=3,padding=1,stride=strides)
|
||
if use_1x1conv:
|
||
self.conv3 = nn.Conv2d(input_channels, output_channels,kernel_size=1, stride=strides)
|
||
else:
|
||
self.conv3 = None
|
||
self.bn1 = nn.BatchNorm2d(output_channels)
|
||
self.bn2 = nn.BatchNorm2d(output_channels)
|
||
def forward(self,X):
|
||
Y = F.relu(self.bn1(self.conv1(X)))
|
||
Y = self.bn2(self.conv2(Y))
|
||
if self.conv3 is not None:
|
||
X = self.conv3(X)
|
||
Y += X
|
||
return F.relu(Y)
|
||
|
||
class Net():
|
||
def
|
||
|
||
|