Architecture diagram + code suitable for novices to build ResNet50 residual network (most complete)
Most of the tutorials on the Internet are complex and difficult to understand and are not suitable for novices. Neural networks are inherently difficult. These tutorials themselves are more difficult and extremely unfriendly to novices. Therefore, the architecture diagram and code I have made are geared towards novices and should be skipped by experts.
from torch import nn
import torch
from torchviz import make_dot
class box(nn.Module):
def __init__(self, in_channels, index=999, stride=1, downsample=False):
super(box, self).__init__()
last_stride = 2 # 虚残差中卷积核的步距
if downsample: # 虚残差结构
f_out_channnels = in_channels * 2
out_channels = int(in_channels / 2)
if index == 0: # here is first core
in_channels = int(in_channels / 2) # 第一层设置为128，是方便了后面的统一处理
out_channels = in_channels
f_out_channnels = in_channels * 4
last_stride = 1
stride = 1
else: # 实残差
f_out_channnels = in_channels * 1
out_channels = int(in_channels / 4)
self.downsample = downsample
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.relu = nn.ReLU(inplace=True)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(in_channels=out_channels, out_channels=f_out_channnels, kernel_size=1, stride=1, padding=0)
self.bn3 = nn.BatchNorm2d(f_out_channnels)
self.fe = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=f_out_channnels, kernel_size=1, stride=last_stride, padding=0, bias=False),
nn.BatchNorm2d(f_out_channnels),
)
def forward(self, x):
identity = x
if self.downsample:
identity = self.fe(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.bn3(x)
out = x + identity
out = self.relu(out)
return out
class New50(nn.Module):
def __init__(self,in_out, num_classes=4):
super(New50, 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_size=3, stride=2, padding=1)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1)
self.fc = nn.Linear(512 * 4 , num_classes)
layers = []
for index, z in enumerate(in_out):
in_ch = z[0] # 这里通道/2
layers.append(box(in_channels=in_ch, stride=2, downsample=z[2], index=index)) # 这里处理第一层
for i in range(1, z[1]):
layers.append(box(in_channels=z[3])) # 这里处理其他两层
# print(layers)
self.fes = 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.fes(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
in_out = [(128, 3, True, 256), (256, 4, True, 512), (512, 6, True, 1024), (1024, 3, True, 2048)]
s = New50(in_out=in_out)
def resnet500():
return New50(in_out=in_out)
'''每层的第一层输入
每层重复的次数
是否走虚残差
每层的第二个卷积核的输入'''
ResNet30, FCN, UNet and other architecture diagrams and codes will be uploaded in the future.