复现ResNeXt50和Reset50 pytorch代码
【摘要】
Reset50和ResNeXt50网络图
Reset50 101 152 pytorch代码复现
import torchimport torch.nn as nnimport torchvisionimport numpy as np print("PyTorch Version: ",torch.__version__)pr...
Reset50和ResNeXt50网络图
Reset50 101 152 pytorch代码复现
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import torch
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import torch.nn as nn
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import torchvision
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import numpy as np
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print("PyTorch Version: ",torch.__version__)
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print("Torchvision Version: ",torchvision.__version__)
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__all__ = ['ResNet50', 'ResNet101','ResNet152']
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def Conv1(in_planes, places, stride=2):
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return nn.Sequential(
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nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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)
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class Bottleneck(nn.Module):
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def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 4):
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super(Bottleneck,self).__init__()
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self.expansion = expansion
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self.downsampling = downsampling
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self.bottleneck = nn.Sequential(
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nn.Conv2d(in_channels=in_places,out_channels=places,kernel_size=1,stride=1, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False),
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nn.BatchNorm2d(places),
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nn.ReLU(inplace=True),
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nn.Conv2d(in_channels=places, out_channels=places*self.expansion, kernel_size=1, stride=1, bias=False),
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nn.BatchNorm2d(places*self.expansion),
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)
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if self.downsampling:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_channels=in_places, out_channels=places*self.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(places*self.expansion)
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)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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residual = x
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out = self.bottleneck(x)
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if self.downsampling:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self,blocks, num_classes=1000, expansion = 4):
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super(ResNet,self).__init__()
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self.expansion = expansion
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self.conv1 = Conv1(in_planes = 3, places= 64)
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self.layer1 = self.make_layer(in_places = 64, places= 64, block=blocks[0], stride=1)
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self.layer2 = self.make_layer(in_places = 256,places=128, block=blocks[1], stride=2)
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self.layer3 = self.make_layer(in_places=512,places=256, block=blocks[2], stride=2)
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self.layer4 = self.make_layer(in_places=1024,places=512, block=blocks[3], stride=2)
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self.avgpool = nn.AvgPool2d(7, stride=1)
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self.fc = nn.Linear(2048,num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def make_layer(self, in_places, places, block, stride):
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layers = []
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layers.append(Bottleneck(in_places, places,stride, downsampling =True))
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for i in range(1, block):
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layers.append(Bottleneck(places*self.expansion, places))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def ResNet50():
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return ResNet([3, 4, 6, 3])
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def ResNet101():
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return ResNet([3, 4, 23, 3])
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def ResNet152():
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return ResNet([3, 8, 36, 3])
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if __name__=='__main__':
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#model = torchvision.models.resnet50()
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model = ResNet50()
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print(model)
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input = torch.randn(1, 3, 224, 224)
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out = model(input)
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print(out.shape)
ResNeXt50 pytorch代码
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import torch
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import torch.nn as nn
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class Block(nn.Module):
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def __init__(self,in_channels, out_channels, stride=1, is_shortcut=False):
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super(Block,self).__init__()
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self.relu = nn.ReLU(inplace=True)
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self.is_shortcut = is_shortcut
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self.conv1 = nn.Sequential(
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nn.Conv2d(in_channels, out_channels // 2, kernel_size=1,stride=stride,bias=False),
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nn.BatchNorm2d(out_channels // 2),
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nn.ReLU()
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)
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self.conv2 = nn.Sequential(
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nn.Conv2d(out_channels // 2, out_channels // 2, kernel_size=3, stride=1, padding=1, groups=32,
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bias=False),
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nn.BatchNorm2d(out_channels // 2),
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nn.ReLU()
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)
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self.conv3 = nn.Sequential(
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nn.Conv2d(out_channels // 2, out_channels, kernel_size=1,stride=1,bias=False),
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nn.BatchNorm2d(out_channels),
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)
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if is_shortcut:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=stride,bias=1),
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nn.BatchNorm2d(out_channels)
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)
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def forward(self, x):
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x_shortcut = x
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x = self.conv1(x)
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x = self.conv2(x)
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x = self.conv3(x)
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if self.is_shortcut:
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x_shortcut = self.shortcut(x_shortcut)
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x = x + x_shortcut
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x = self.relu(x)
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return x
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class Resnext(nn.Module):
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def __init__(self,num_classes,layer=[3,4,6,3]):
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super(Resnext,self).__init__()
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self.conv1 = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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)
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self.conv2 = self._make_layer(64,256,1,num=layer[0])
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self.conv3 = self._make_layer(256,512,2,num=layer[1])
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self.conv4 = self._make_layer(512,1024,2,num=layer[2])
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self.conv5 = self._make_layer(1024,2048,2,num=layer[3])
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self.global_average_pool = nn.AvgPool2d(kernel_size=7, stride=1)
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self.fc = nn.Linear(2048,num_classes)
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = self.conv3(x)
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x = self.conv4(x)
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x = self.conv5(x)
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x = self.global_average_pool(x)
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x = torch.flatten(x,1)
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x = self.fc(x)
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return x
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def _make_layer(self,in_channels,out_channels,stride,num):
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layers = []
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block_1=Block(in_channels, out_channels,stride=stride,is_shortcut=True)
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layers.append(block_1)
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for i in range(1, num):
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layers.append(Block(out_channels,out_channels,stride=1,is_shortcut=False))
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return nn.Sequential(*layers)
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net = Resnext(10)
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x = torch.rand((10, 3, 224, 224))
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for name,layer in net.named_children():
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if name != "fc":
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x = layer(x)
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print(name, 'output shaoe:', x.shape)
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else:
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x = x.view(x.size(0), -1)
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x = layer(x)
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print(name, 'output shaoe:', x.shape)
文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/115477386
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