文章目录
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 = 1
def __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.BatchNorm2d
if 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 != 1
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 = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = 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 += identity
out = 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 = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = 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 += identity
out = 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 = x
out = conv2d(x)
out = batchnorm(out)
out = relu(out)
out = conv2d(x)
out = batchnorm(out)
out += identity
out = 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？
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；
