解释下列代码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), torch.nn.Dropout(0.1), # 添加Dropout层 Bottlrneck(256,128,512, True), Bottlrneck(512,128,512, False), Bottlrneck(512,128,512, False), Bottlrneck(512,128,512, False), torch.nn.Dropout(0.1), # 添加Dropout层 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), torch.nn.Dropout(0.1), # 添加Dropout层 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) x = x.view(-1,2048) x = self.classifer(x) return x
时间: 2024-04-28 08:19:15 浏览: 206
这段代码定义了一个名为 ResNet50 的神经网络模型,该模型基于 ResNet50 架构,用于音频信号分类任务。模型输入的通道数为 in_channels,输出的分类数为 classes。模型的前半部分是一个 Sequential 容器,其中包含了多个卷积层和残差块,后半部分是一个全连接层,用于将卷积层的输出映射为类别概率。在前半部分的残差块中,使用了 Bottleneck 结构,该结构包含了 1x1、3x3 和 1x1 的卷积层,用于降低计算复杂度。在模型的前半部分,还添加了 Dropout 层,用于防止过拟合。最后,模型的 forward 函数定义了模型的前向计算过程,其中通过 features 层提取特征,通过 classifer 层进行分类。最终输出分类概率。
相关问题
class BasicBlock2D(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock2D, self).__init__() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def __init__(self, num_classes=1000): super(ResNet18_2D, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层
为了用稀疏表示替换全连接层,需要先将特征图展平,然后使用稀疏矩阵与其进行乘法运算。
以下是修改后的代码:
```
import torch.sparse as sparse
class ResNet18_2D(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet18_2D, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1)
self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2)
self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2)
self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
# 替换全连接层
self.fc = nn.Identity()
def _make_layer(self, block, out_channels, num_blocks, stride):
layers = []
layers.append(block(self.in_channels, out_channels, stride))
self.in_channels = out_channels * block.expansion
for _ in range(1, num_blocks):
layers.append(block(self.in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.maxpool(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avgpool(out)
out = out.view(out.size(0), -1)
# 使用稀疏矩阵与展平后的特征图进行乘法运算
out = sparse.mm(out, nn.Parameter(torch.randn(out.size(1), 512)))
return out
```
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
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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