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resnet.py
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import torch.nn as nn
import torch
import torch.nn.functional as F
# 定义残差结构（18、34）
class BasicBlock(nn.Module):
# 主分支卷积核个数有没有发生变化 对于resnet18、34 、每个残差块的卷积核个数不变
expansion = 1
# 初始化
# in_channel=输入特征层=卷积核channel；out_channel=输出特征矩阵channel=卷积核个数
# downsample 下采样参数 对应虚线残差结构 （每一层第一个残差结构 都有降维的作用）
def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
super(BasicBlock, self).__init__()
# stride=1 实线残差结构 stride=2 虚线残差结构
# 使用BN 不需要bias
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channel)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,
kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channel)
self.downsample = downsample
# 前向传播
def forward(self, x):
# 定义恒等映射
identity = x
# 如果没有下采样（ 对应的就是实线的残差结构 ）如果有（虚线） 就执行下采样 得到shortcut connection分支的输出
if self.downsample is not None:
identity = self.downsample(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out
#定义残差结构（50、101、152）
class Bottleneck(nn.Module):
"""
注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。
但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，
这么做的好处是能够在top1上提升大概0.5%的准确率。
可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch
"""
# 每个残差块第三个卷积核个数是第一个卷积核个数的四倍
expansion = 4
def __init__(self, in_channel, out_channel, stride=1, downsample=None,
groups=1, width_per_group=64):
super(Bottleneck, self).__init__()
width = int(out_channel * (width_per_group / 64.)) * groups
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,
kernel_size=1, stride=1, bias=False) # squeeze channels
self.bn1 = nn.BatchNorm2d(width)
# -----------------------------------------
# 这里的stride根据传入的参数调整 虚线是2 实线是1
self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,
kernel_size=3, stride=stride, bias=False, padding=1)
self.bn2 = nn.BatchNorm2d(width)
# -----------------------------------------
self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,
kernel_size=1, stride=1, bias=False) # unsqueeze channels
self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def forward(self, x):
identity = x
if self.downsample is not None:
identity = self.downsample(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)
out += identity
out = self.relu(out)
return out
# resnet整个网络的框架
class ResNet(nn.Module):
def __init__(self,
# 不同层用不同的block
block,
# 残差结构数目 列表的形式 对照论文中的表格比如34层 3 4 6 3
blocks_num,
# 训练集分类个数
num_classes=1000,
groups=1,
width_per_group=64):
super(ResNet, self).__init__()
self.in_channel = 64
self.groups = groups
self.width_per_group = width_per_group
# 输入是RGB图像 所以这里是3
self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,
padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(self.in_channel)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# layer1 对应表格中conv2 对应参数
self.layer1 = self._make_layer(block, 64, blocks_num[0])
self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)
self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)
self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# channel对应表格中每个conv块第一个卷积的卷积核个数
def _make_layer(self, block, channel, block_num, stride=1):
downsample = None
if stride != 1 or self.in_channel != channel * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(channel * block.expansion))
# 定义一个空的列表
layers = []
layers.append(block(self.in_channel,
channel,
downsample=downsample,
stride=stride,
groups=self.groups,
width_per_group=self.width_per_group))
self.in_channel = channel * block.expansion
# 通过for语句循环剩余的实线残差结构
for _ in range(1, block_num):
layers.append(block(self.in_channel,
channel,
groups=self.groups,
width_per_group=self.width_per_group))
# 非关键字参数
return nn.Sequential(*layers)
def forward(self, x):
# 对tensor数据进行处理
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_map = self.layer4(x)
x_feature = self.avgpool(x_map).flatten(1)
return x_map, x_feature