PyTorch实现ResNet50、ResNet101和ResNet152示例_githubresnet资源-CSDN文库

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PyTorch实现实现ResNet50、、ResNet101和和ResNet152示例示例

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:

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