Merge branch 'dev'

model.py

@@ -0,0 +1,43 @@
+"""
+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
+
+