基于pytorch框架 实现ResNet网络 （参考pytorch官方文档）
from torch import nn
import torch
import torchvision.models
from torch.utils.tensorboard import SummaryWriter
def con3x3(in_channel, out_channel, stride = 1 ):
return nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False)
def con1x1(in_channel, out_channel, stride = 1):
return nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channel, out_channel, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = con3x3(in_channel, out_channel, stride=stride)
self.bn1 = nn.BatchNorm2d(out_channel)
self.conv2 = con3x3(out_channel, out_channel)
self.bn2 = nn.BatchNorm2d(out_channel)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
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 = out + identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channel, out_channel, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = con1x1(in_channel, out_channel)
self.bn1 = nn.BatchNorm2d(out_channel)
self.conv2 = con3x3(out_channel, out_channel, stride=stride)
self.bn2 = nn.BatchNorm2d(out_channel)
self.conv3 = con1x1(out_channel,out_channel * self.expansion)
self.bn3 = nn.BatchNorm2d(out_channel * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out = out + identity
out = self.relu(out)
return out
class ResNet(nn.Module):
"""
__init__
block: 堆叠的基本模块[BasicBlock, Bottelneck]
block_num: 基本模块堆叠个数
num_classes: 全连接之后的类别个数
"""
def __init__(self, block, block_num, num_classes=1000):
super(ResNet, self).__init__()
self.in_channel = 64
self.conv1 = nn.Conv2d(in_channels=3, out_channels=self.in_channel, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(self.in_channel)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block=block, channel=64, block_num=block_num[0], stride=1)
self.layer2 = self._make_layer(block=block, channel=128, block_num=block_num[1], stride=2)
self.layer3 = self._make_layer(block=block, channel=256, block_num=block_num[2], stride=2)
self.layer4 = self._make_layer(block=block, channel=512, block_num=block_num[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, channel, block_num, stride=1):
"""
block: 堆叠的基本模块
channel: 每个stage中堆叠模块的第一个卷积的卷积核个数，对于 resnet 来说分别是:64,128,256,512
block_num: 当期stage堆叠block个数
stride: 默认卷积步长
"""
downsample = None
if stride != 1 or self.in_channel != channel * block.expansion:
downsample = nn.Sequential(
con1x1(self.in_channel, channel * block.expansion, stride=stride),
nn.BatchNorm2d(channel * block.expansion),
)
layers = []
layers.append(block(in_channel=self.in_channel, out_channel=channel, downsample=downsample, stride=stride))
self.in_channel = channel * block.expansion
for _ in range(1, block_num):
layers.append(block(in_channel=self.in_channel, out_channel=channel))
return 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.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
def resnet18(num_classes=10):
return ResNet(block=BasicBlock, block_num=[2, 2, 2, 2], num_classes=num_classes)
def resnet34(num_classes=10):
return ResNet(block=BasicBlock, block_num=[3, 4, 6, 3], num_classes=num_classes)
def resnet50(num_classes=10):
return ResNet(block=Bottleneck, block_num=[3, 4, 6, 3], num_classes=num_classes)
def resnet101(num_classes=10):
return ResNet(block=Bottleneck, block_num=[3, 4, 23, 3], num_classes=num_classes)
def resnet52(num_classes=10):
return ResNet(block=Bottleneck, block_num=[3, 8, 36, 3], num_classes=num_classes)
