resnet pytorch代码_resnet pytorch

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我们。

本文标题: PyTorch实现ResNet50、ResNet101和ResNet152示例

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