PyTorch实现ResNet50、ResNet101和ResNet152示例

PyTorch:https://github.com/shanglianlm0525/PyTorch-Networksimporttorchimporttorch.nnasnnimporttorchvisionimportnumpyasnp

PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks

import torch
import torch.nn as nn
import torchvision
import numpy as np

print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)

__all__ = ['ResNet50', 'ResNet101','ResNet152']

def Conv1(in_planes, places, stride=2):
 return nn.Sequential(
 nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),
 nn.BatchNorm2d(places),
 nn.ReLU(inplace=True),
 nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 )

class Bottleneck(nn.Module):
 def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):
 super(Bottleneck,self).__init__()
 self.expansion = expansion
 self.downsampling = downsampling

 self.bottleneck = nn.Sequential(
 nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),
 nn.BatchNorm2d(places),
 nn.ReLU(inplace=True),
 nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),
 nn.BatchNorm2d(places),
 nn.ReLU(inplace=True),
 nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),
 nn.BatchNorm2d(places*self.expansion),
 )

 if self.downsampling:
 self.downsample = nn.Sequential(
 nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),
 nn.BatchNorm2d(places*self.expansion)
 )
 self.relu = nn.ReLU(inplace=True)
 def forward(self, x):
 residual = x
 out = self.bottleneck(x)

 if self.downsampling:
 residual = self.downsample(x)

 out += residual
 out = self.relu(out)
 return out

class ResNet(nn.Module):
 def __init__(self,blocks, num_classes=1000, expansion = 4):
 super(ResNet,self).__init__()
 self.expansion = expansion

 self.conv1 = Conv1(in_planes = 3, places= 64)

 self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)
 self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)
 self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)
 self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)

 self.avgpool = nn.AvgPool2d(7, stride=1)
 self.fc = nn.Linear(2048,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.init.constant_(m.weight, 1)
 nn.init.constant_(m.bias, 0)

 def make_layer(self, in_places, places, block, stride):
 layers = []
 layers.append(Bottleneck(in_places, places,stride, downsampling =True))
 for i in range(1, block):
 layers.append(Bottleneck(places*self.expansion, places))

 return nn.Sequential(*layers)


 def forward(self, x):
 x = self.conv1(x)

 x = self.layer1(x)
 x = self.layer2(x)
 x = self.layer3(x)
 x = self.layer4(x)

 x = self.avgpool(x)
 x = x.view(x.size(0), -1)
 x = self.fc(x)
 return x

def ResNet50():
 return ResNet([3, 4, 6, 3])

def ResNet101():
 return ResNet([3, 4, 23, 3])

def ResNet152():
 return ResNet([3, 8, 36, 3])


if __name__=='__main__':
 #model = torchvision.models.resnet50()
 model = ResNet50()
 print(model)

 input = torch.randn(1, 3, 224, 224)
 out = model(input)
 print(out.shape)

以上这篇PyTorch实现ResNet50、ResNet101和ResNet152示例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。