PyTorch实现ResNet50、ResNet101和ResNet152示例
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2020-09-18
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PyTorch实现实现ResNet50、、ResNet101和和ResNet152示例示例
今天小编就为大家分享一篇PyTorch实现ResNet50、ResNet101和ResNet152示例，具有很好的参考价值，希望对大家
有所帮助。一起跟随小编过来看看吧
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)
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