Merge branch 'dev'

model.py

@@ -1,12 +1,13 @@
 """
 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
+单独运行本文件将会输出模型结构
+目前的话是一个36层的模型，模型总量应该是在10M左右 如果到时候还是欠拟合的话再考虑去做更深的结构
 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
 
@@ -22,7 +23,7 @@ class Resblock(nn.Module):
         """
         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)
+        self.conv2 = nn.Conv2d(output_channels,output_channels,kernel_size=3,padding=1,stride=1)
         if use_1x1conv:
             self.conv3 = nn.Conv2d(input_channels, output_channels,kernel_size=1, stride=strides)
         else:
@@ -38,6 +39,189 @@ class Resblock(nn.Module):
         return F.relu(Y)
 
 class Net():
-    def
+    """
+    模型的主要结构就在这里了，到时也好该和调用
+    现在必须实现的方法：
+    目前还是以图片缩放到256＊256构建残差块
+    """
+    net = nn.Sequential()
+    def resnet_block(self,input_channels, num_channels, num_residuals,
+                     first_block=False):
+        """
+        :param input_channels: 输入维度
+        :param num_channels: 输出维度
+        :param num_residuals: 单个残差层的残差块数
+        :param first_block: 第一块不用下采样 特殊控制
+        :return: list[nn.Module]
+        """
+        blk = []
+
+        for i in range(num_residuals):
+            if i == 0 and not first_block:
+                blk.append(Resblock(input_channels, num_channels,
+                                use_1x1conv=True, strides=2))
+            else:
+                blk.append(Resblock(num_channels, num_channels))
+        return blk
+    def __init__(self):
+        b1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
+                            nn.BatchNorm2d(64), nn.ReLU(),
+                            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+                            )
+        """
+        7×7 卷积层，输出通道 64，步长 2，填充 3
+        (3×256×256)->(64×128×128)
+        批归一化 relu层 
+        最大池化 
+        (64×128×128)->(64×64×64)
+        """
+        b2 = nn.Sequential(*self.resnet_block(64, 64, num_residuals=3, first_block=True))
+        b3 = nn.Sequential(*self.resnet_block(64, 128, num_residuals=4))
+        b4 = nn.Sequential(*self.resnet_block(128, 256, num_residuals=6))
+        b5 = nn.Sequential(*self.resnet_block(256, 512, num_residuals=3))
+        self.net = nn.Sequential(b1, b2, b3, b4, b5,nn.AdaptiveAvgPool2d((1,1)),nn.Flatten(), nn.Linear(512, 4))
+    def get_network(self):
+        return self.net
+
+"""
+Sequential(
+  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
+  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (2): ReLU()
+  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+)
+Sequential output shape:	torch.Size([1, 64, 64, 64])
+Sequential(
+  (0): Resblock(
+    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (1): Resblock(
+    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (2): Resblock(
+    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+)
+Sequential output shape:	torch.Size([1, 64, 64, 64])
+Sequential(
+  (0): Resblock(
+    (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv3): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
+    (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (1): Resblock(
+    (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (2): Resblock(
+    (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (3): Resblock(
+    (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+)
+Sequential output shape:	torch.Size([1, 128, 32, 32])
+Sequential(
+  (0): Resblock(
+    (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
+    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (1): Resblock(
+    (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (2): Resblock(
+    (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (3): Resblock(
+    (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (4): Resblock(
+    (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (5): Resblock(
+    (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+)
+Sequential output shape:	torch.Size([1, 256, 16, 16])
+Sequential(
+  (0): Resblock(
+    (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
+    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
+    (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (1): Resblock(
+    (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+  (2): Resblock(
+    (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+    (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  )
+)
+Sequential output shape:	torch.Size([1, 512, 8, 8])
+AdaptiveAvgPool2d(output_size=(1, 1))
+AdaptiveAvgPool2d output shape:	torch.Size([1, 512, 1, 1])
+Flatten(start_dim=1, end_dim=-1)
+Flatten output shape:	torch.Size([1, 512])
+Linear(in_features=512, out_features=4, bias=True)
+Linear output shape:	torch.Size([1, 4])
+"""
+
+if __name__ == '__main__':
+    Net_new = Net()
+    X = torch.rand(size=(1, 3, 256, 256))
+    for layer in Net_new.get_network():
+        print(layer)
+        X = layer(X)
+        print(layer.__class__.__name__, 'output shape:\t', X.shape)
+
+
+
+
+
+