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流程

• 自我介绍
• 介绍项目
• 介绍论文
• 写代码

问题

请你写出项目中用到的模型代码，Resnet50

面试官：写出一个单元就好了。
实际面试过程中写出伪代码就好，
源代码：vision/torchvision/models/resnet.py

class BasicBlock(nn.Module):expansion: int = 1def __init__(self,inplanes: int,planes: int,stride: int = 1,downsample: Optional[nn.Module] = None,groups: int = 1,base_width: int = 64,dilation: int = 1,norm_layer: Optional[Callable[..., nn.Module]] = None,) -> None:super().__init__()if norm_layer is None:norm_layer = nn.BatchNorm2dif groups != 1 or base_width != 64:raise ValueError("BasicBlock only supports groups=1 and base_width=64")if dilation > 1:raise NotImplementedError("Dilation > 1 not supported in BasicBlock")# Both self.conv1 and self.downsample layers downsample the input when stride != 1self.conv1 = conv3x3(inplanes, planes, stride)self.bn1 = norm_layer(planes)self.relu = nn.ReLU(inplace=True)self.conv2 = conv3x3(planes, planes)self.bn2 = norm_layer(planes)self.downsample = downsampleself.stride = stridedef forward(self, x: Tensor) -> Tensor:identity = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)if self.downsample is not None:identity = self.downsample(x)out += identityout = self.relu(out)return out

里面的精华部分如下，我跳着写：

def __init__():self.conv1 = conv3x3(inplanes, planes, stride)self.bn1 = norm_layer(planes)self.relu = nn.ReLU(inplace = True)self.conv2 = conv3x3(planes, planes)self.bn2 = norm_layer(planes)self.downsample = downsampleself.stride = stridedef forward(self, x: Tensor) -> Tensor:identity = xout = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)if self.downsample is not None:identity = self.downsample(x)out += identityout = self.relu(out)return out

在面试官的提示下我写了大概这样的伪代码：

def forward(x):identity = xout = conv2d(x)out = batchnorm(out)out = relu(out)out = conv2d(x)out = batchnorm(out)out += identityout = relu(out)return out

面试结果还没出，不保证我这样写是正确的！

进一步了解Resnet50，来自B站的同济子豪兄【精读AI论文】ResNet深度残差网络

• 有几种不好的现象：

（1）网络退化现象：把网络加深之后，效果反而变差了

用人话说明：一个孩子报名了课外辅导班，结果不仅作业写得更差了，考试也更差了；（学多了反而导致结果更糟糕）

（2）过拟合现象：训练集表现很棒，测试集很差

用人话说明：一个孩子作业做的很棒，一上考场就发挥失常；

把你做的工作里面的模型替换成ViT能行吗？

有了解过Stable difussion，transformer吗？

有一点点
【渣渣讲课】试图做一个正常讲解Latent / Stable Diffusion的成年人

总结

一面会重点针对简历上写的论文和项目，以及考察一些和岗位相关的前沿知识，坐在实验室是绝对绝对感受不到这些的！要勇敢踏出第一步；

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