return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) def resnet50(num_classes=1000, include_top=True): # https://download.pytorch.org/models/resnet50-19c8e357.pth return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_to...
class ResNet(nn.Module): #参数block指明残差块是两层或三层,参数layers指明每个卷积层需要的残差块数量,num_classes指明分类数,zero_init_residual是否初始化为0 def __init__(self, block, layers, num_classes=1000, zero_init_residual=False): super(ResNet, self).__init__() self.inplanes = 64 ...
classResNet(nn.Module):def__init__(self,in_chans,block,num_block,num_classes=1000):super().__init__()#传入的模块resnet 18/34和resnet 50/101/152不一样self.block=blockself.in_channels=64self.conv1=nn.Sequential(nn.Conv2d(in_chans,64,kernel_size=7,stride=2,padding=3,bias=False),...
Module): def __init__(self, block, layers, num_classes=1000): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=True) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool...
# 总的残差网络模型class ResNet(nn.Module):def __init__(self,block,block_num,num_classes=1000,include_top=True):super(ResNet,self).__init__()self.include_top=include_topself.in_channel=64# 第一个卷积层,使用7*7的卷积核,步长为2,使数据维度减半self.conv1=nn.Conv2d(in_channels=3,out...
超深的网络结构 提出Residual模块 使用Batch Normalization加速训练(丢弃Dropout) 梯度消失或梯度爆炸 退化问题 Residual架构 其中bottleneck的1×11×1卷积用来降低维度和升高维度 Batch Normalization Batch Normalization的目的是使得一批(Batch)数据的特征图均满足均值为0,方差为1的分布规律。
classResNet(nn.Module):def__init__(self,block,layers,num_classes=1000):self.inplanes=64# 每一个block的输入通道数目super(ResNet,self).__init__()self.conv1=nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(64)self.relu=nn.ReLU(inplace=True)self....
def__init__(self, block, layers, num_classes=1000): 在初始化的时候,有两个参数,block和layers。 block有两种,一种是Bottleneck,一种是Basicblock。在resnet18和resnet34中调用的是BasicBlock类,在resnet50,resnet101,resnet152中调用的是Bottleneck类。
classResNet(nn.Module):def__init__(self,block,layers,num_classes=1000):self.inplanes=64super(ResNet,self).__init__()self.conv1=nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(64)self.relu=nn.ReLU(inplace=True)self.maxpool=nn.MaxPool2d(kernel...
definit(self, num_classes=1000): super(ResNet34, self).init() ### 先做 7x7 卷积 self.pre =nn.Sequential( nn.Conv2d(3, 64, 7, 2 ,3, bias=False),### 输入 3 通道,输出 64 通道,卷积核7x7,步长2,padding 3 nn.BatchNorm2d(64), ...