解释class ResNet50(torch.nn.Module): def __init__(self,in_channels=2,classes=125): super(ResNet50, self).__init__() self.features = torch.nn.Sequential( torch.nn.Conv1d(in_channels,64,kernel_size=7,stride=2,padding=3), torch.nn.MaxPool1d(3,2,1), Bottlrneck(64,64,256,False), Bottlrneck(256,64,256,False), Bottlrneck(256,64,256,False), # Bottlrneck(256,128,512, True), Bottlrneck(512,128,512, False), Bottlrneck(512,128,512, False), Bottlrneck(512,128,512, False), # Bottlrneck(512,256,1024, True), Bottlrneck(1024,256,1024, False), Bottlrneck(1024,256,1024, False), Bottlrneck(1024,256,1024, False), Bottlrneck(1024,256,1024, False), Bottlrneck(1024,256,1024, False), # Bottlrneck(1024,512,2048, True), Bottlrneck(2048,512,2048, False), Bottlrneck(2048,512,2048, False), torch.nn.AdaptiveAvgPool1d(1) ) self.classifer = torch.nn.Sequential( torch.nn.Linear(2048,classes) ) def forward(self,x): # 定义前向的函数运算即可 x = self.features(x) #torch.sqrt(x + 1e-8) # print("forward(self,x)_________________________________________________________x=",x) x = x.view(-1,2048) x = self.classifer(x) return x
时间: 2023-06-13 16:07:25 浏览: 123
fb.resnet.torch-master.zip_ResNet_ResNet深度学习_facebook_torch resn
这是一个 PyTorch 中的 ResNet50 模型,用于对输入数据进行分类。它的输入通道数为2,输出类别数为125。在模型中,首先进行一维卷积操作,然后添加了多个残差块(Bottleneck),每个残差块包含了多个卷积层和批量归一化(BatchNorm)层。最后使用自适应平均池化层将特征图降为一维,并通过全连接层输出分类结果。在前向传播过程中,输入数据通过模型的 features 部分进行特征提取,然后将特征图展平,再通过模型的 classifier 部分进行分类。
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