class ResNet(nn.Module): def __init__(self, block, num_blocks, num_classes=2): super(ResNet, self).__init__() self.in_channels = 64 self.senet = senet(3) self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self.make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self.make_layer(block, 128, num_blocks[1], stride=2) self.layer3 = self.make_layer(block, 256, num_blocks[2], stride=2) self.layer4 = self.make_layer(block, 512, num_blocks[3], stride=2) self.sppcab = SSPCAB(512) self.avgpool= SpatialPyramidPooling(output_sizes=[1]) self.fc = nn.Linear(512 * block.expansion, num_classes) def make_layer(self, block, out_channels, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) layers = [] for stride in strides: layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion return nn.Sequential(*layers) def forward(self, x): out = self.senet(x) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.sppcab(out) out = self.avgpool(out) out = torch.flatten(out, 1) out = self.fc(out) return out。这个作用为单分类是不是少了些什么
时间: 2024-02-03 19:02:53 浏览: 24
这个代码实现的确可以用于单分类任务,但是需要根据具体的数据集和任务进行一些修改和调整。
首先,需要将 `num_classes` 参数设置为 1,因为单分类任务只有一个类别。其次,需要根据数据集的特点和任务需求,选择合适的损失函数和评价指标。在二分类任务中,常用的损失函数包括二元交叉熵(BCE)和交叉熵(CE),评价指标包括精确度、召回率、F1 值等。最后,还需要根据数据集的大小和复杂度,调整模型的超参数,如学习率、批大小、训练轮数等。
另外,在这段代码中,`senet` 和 `SSPCAB` 都是自定义的模块,需要先导入相关代码或定义相关模块,才能正常运行。
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
```
为以下的每句代码做注释:class ResNet(nn.Module): def __init__(self, block, blocks_num, num_classes=1000, include_top=True): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') def _make_layer(self, block, channel, block_num, stride=1): downsample = None if stride != 1 or self.in_channel != channel * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x
这段代码定义了一个名为 ResNet 的类,继承自 nn.Module 类。ResNet 是一个深度卷积神经网络模型,常用于图像分类任务。
在 __init__ 方法中,首先定义了一些基本参数:
- block:指定 ResNet 中的基本块类型,如 BasicBlock 或 Bottleneck。
- blocks_num:指定每个 stage 中基本块的数量,是一个长度为 4 的列表。
- num_classes:指定模型输出分类数目,默认为 1000。
- include_top:指定是否包含顶层分类器,默认为 True。
接着在 __init__ 中,定义了一些基本组件:
- in_channel:表示当前卷积层的输入通道数,初始化为 64。
- conv1:第一个卷积层,输入 3 通道,输出 self.in_channel 个通道,卷积核大小为 7,步长为 2,padding 为 3。
- bn1:卷积层后面紧跟的批归一化层。
- relu:ReLU 激活函数。
- maxpool:最大池化层,尺寸为 3x3,步长为 2,padding 为 1。
- layer1 - layer4:四个 stage,每个 stage 由多个基本块组成。通过调用 _make_layer 方法生成,该方法会返回一个 Sequential 对象。
- avgpool:全局平均池化层,将特征图转换为 1x1 大小的张量。
- fc:全连接层,将特征向量映射到 num_classes 维空间上。
接下来是 _make_layer 方法,用于生成 ResNet 的每个 stage。该方法包含以下参数:
- block:基本块类型。
- channel:该 stage 中第一个基本块的输出通道数。
- block_num:该 stage 中基本块的数量。
- stride:该 stage 中第一个基本块的步长,默认为 1。
_make_layer 方法首先会根据 stride 和 in_channel 是否等于 channel * block.expansion 构建 downsample 层。如果两者不相等,则 downsample 为一个包含一个卷积层和一个批归一化层的 Sequential 对象。否则,downsample 为 None。
接着,_make_layer 方法通过循环调用 block 方法构建基本块,并将其加入 layers 列表中。其中,第一个基本块的步长由 stride 参数指定,后续基本块步长均为 1。最后,_make_layer 方法返回一个 Sequential 对象,该对象包含所有生成的基本块。
最后是 forward 方法,用于前向传播计算。该方法首先执行一些基本卷积操作,如卷积、批归一化和 ReLU 激活。然后,将特征图 x 依次经过四个 stage,中间可能存在池化和下采样操作。最后,如果 include_top=True,则通过全局平均池化层和全连接层将特征向量映射到 num_classes 维空间上。最终返回分类结果。