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Chenyun pytorch learning notes_ Build RESNET with 50 lines of code
2022-06-27 23:57:00 【51CTO】
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
# Residual fast Residual network formula a^[L+2] = g(a^[L]+z^[L+2])
class ResidualBlock(nn.Module):
def __init__(self, inchannel, outchannel, stride=1, shortcut=None): #shortcut=None Corresponding to the solid line of cross layer connection in the figure , Corresponding residual network formula a^[L+2] = g(a^[L]+z^[L+2]), Otherwise, it should be
# The dotted line of the first residual block after the number of channels changes , At this time, the corresponding residual formula is a^[L+2] = g(z^[L+1]+z^[L+2])
nn.Module.__init__(self)
self.left = nn.Sequential(# obtain z^[L+2]
nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
nn.BatchNorm2d(outchannel),
nn.ReLU(inplace= True),
nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
nn.BatchNorm2d(outchannel))
self.right = shortcut# Decide whether the cross layer connection is a solid line or a dotted line
def forward(self, x):
out = self.left(x)
residual = x if self.right is None else self.right(x)
out += residual
return F.relu(out) #a^[L+2] = g(a^[L]+z^[L+2])
# ResNet34
class ResNet(nn.Module):
def __init__(self, num_classes=1000):
nn.Module.__init__(self)
# The first few layers of image conversion ( Network input part )
self.pre = nn.Sequential(# Corresponding to the part in the figure before starting the residual processing
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(3, 2, 1)
)
# Intermediate convolution part
self.layer1 = self._make_layer(64, 64, 3)
self.layer2 = self._make_layer(64, 128, 4, stride=2)#stride=2 Representing the first layer of each residual error 2/
self.layer3 = self._make_layer(128, 256, 6, stride=2)
self.layer4 = self._make_layer(256, 512, 3, stride=2)
# The average pooling
self.avgpool = nn.AvgPool2d(7, stride=1)
# Full connection for classification
self.fc = nn.Linear(512, 1000)
def _make_layer(self, inchannel, outchannel, block_num, stride=1):
# Adjust the number of input and output channels to be consistent . Like the second one layer when , The first residual fast input is 64, Output is 128
shortcut = nn.Sequential(# The first cross layer straight line corresponding to the fast residuals of the same number of channels of each type is a dotted line
nn.Conv2d(inchannel, outchannel, 1, stride, bias=False),
nn.BatchNorm2d(outchannel))
layers = []
layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
for i in range(1, block_num):
layers.append(ResidualBlock(outchannel, outchannel))
return nn.Sequential(*layers)
def forward(self, x):
x = self.pre(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)#torch.Size([1, 512])
return self.fc(x)
model = ResNet()
input = t.autograd.Variable(t.randn(1, 3, 224, 224))
o = model(input)
print(o)
model = models.resnet34()# Call toolkit solid line residual network
o1 = model(input)
print(o1)
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output
D:\anaconda\anaconda\pythonw.exe D:/Code/Python/pytorch Introduction and practice / Chapter four _ Neural network toolbox nn/ build ResNet.py
tensor([[-3.7385e-01, -2.2010e-01, 8.9218e-01, -6.6067e-01, -5.8422e-01,
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2.4529e-01, 1.2500e+00, -1.6038e-01, -3.3023e-01, 3.6957e-01,
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grad_fn=<AddmmBackward>)
Process finished with exit code 0
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