resnet50代码_ResNet代码详解
代码学习第一天！ fighting!
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
# 这个文件内包括6中不同的网络架构
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
# 每一种架构下都有训练好的可以用的参数文件
model_urls = {
'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',
'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
'resnet101': 'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth',
}
# 常见的3x3卷积
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
# 这是残差网络中的basicblock，实现的功能如下方解释：
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None): # inplanes代表输入通道数，planes代表输出通道数。
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out1.BasicBlock类中的init()函数是先定义网络架构，forward()的函数是前向传播，实现的功能就是残差块，如下图所示：
2.Bottleneck类是另一种blcok类型，同上，init()函数是预定义网络架构，forward函数是进行前向传播。该block中有三个卷积，分别是1x1,3x3,1x1，分别完成的功能就是维度压缩，卷积，恢复维度！故bottleneck实现的功能就是对通道数进行压缩，再放大。注意：这里的plane不再是输出的通道数，输出通道数应该就是plane*expansion，即4*plane。
class Bottleneck(nn.Module):
expansion = 4 # 输出通道数的倍乘
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
这两个class讲清楚的话，后面的网络主体架构就还蛮好理解的了，6中架构之间的不同在于basicblock和bottlenek之间的不同以及block的输入参数的不同。因为ResNet一般有4个stack，每一个stack里面都是block的堆叠，所以[3, 4, 6, 3]就是每一个stack里面堆叠block的个数，故而造就了不同深度的ResNet。
resnet18： ResNet(BasicBlock, [2, 2, 2, 2])
resnet34： ResNet(BasicBlock, [3, 4, 6, 3])
resnet50：ResNet(Bottleneck, [3, 4, 6, 3])
resnet101:ResNet(Bottleneck, [3, 4, 23, 3])
resnet152:ResNet(Bottleneck, [3, 8, 36, 3])
def resnet18(pretrained=False):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3])
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
最后的ResNet类其实可以根据列表大小来构建不同深度的resnet网络架构。resnet一共有5个阶段，第一阶段是一个7x7的卷积，stride=2，然后再经过池化层，得到的特征图大小变为原图的1/4。_make_layer()函数用来产生4个layer，可以根据输入的layers列表来创建网络。
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000): # layers=参数列表 block选择不同的类
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample)) # 每个blocks的第一个residual结构保存在layers列表中。
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes)) #该部分是将每个blocks的剩下residual 结构保存在layers列表中，这样就完成了一个blocks的构造。
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(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) # 将输出结果展成一行
x = self.fc(x)
return x下面我将展示resnet18的部分结构：
如上图所示：先经过一个7x7的卷积，然后送入（layer1）,里面包括两个basicblock，每一个basicblock里面都是两个3x3的卷积，下面再接相同类型的layer2,3,4。之后再接一个平均池化层和全连接层就完成了resnet-18的整个架构。
其他结构依旧可以调用上面的函数进行查询。
2019-8-16更新完毕！