适合新手搭建ResNet50残差网络的架构图+代码（最全）
网上的教程大多复杂难懂，不适合新手，本来神经网络就难，这些教程本身更难，对新手极度不友好，因此自己做的这个架构图和写的代码，面向新手，大神跳过
文章来源：https://www.toymoban.com/news/detail-622987.html
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'''
后续还会上传ResNet30，FCN，UNet等架构图和代码。文章来源地址https://www.toymoban.com/news/detail-622987.html
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