[Unix] 【深度学习】基于Pytorch的ResNet实现

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• ​​1. ResNet理论​​
  
• ​​2. pytorch实现​​
  
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  • ​​2.1 基础卷积​​
    
  • ​​2.2 模块​​
    
  • ​​2.3 使用ResNet模块进行迁移学习​​
    
    
    
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def conv3x3(in_channel, out_channel, stride=1, groups=1, dilation=1):
  return nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_channel, out_channel, stride=1):
  return nn.Conv2d(in_channel, out_channel, kernel_size=1, bias=False)

in_channel: 输入的通道数目
out_channel:输出的通道数目
stride, padding: 步长和补0
dilation: 空洞卷积中的参数
groups: 从输入通道到输出通道的阻塞连接数

feature size 计算：
output = (intput - filter_size + 2 x padding) / stride + 1

K = K + (K-1)x(R-1)
K 是原始卷积核大小
R 是空洞卷积参数的空洞率(普通卷积为1)

- resnet34
  - _resnet
  - ResNet
    - _make_layer
    - block 
      - Bottleneck
      - BasicBlock

class Bottleneck(nn.Module):
  expansion = 4
  __constants__ = ['downsample']
  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
         base_width=64, dilation=1, norm_layer=None):
    super(Bottleneck, self).__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    width = int(planes * (base_width / 64.)) * groups
    # Both self.conv2 and self.downsample layers downsample the input when stride != 1
    self.conv1 = conv1x1(inplanes, width)
    self.bn1 = norm_layer(width)
    self.conv2 = conv3x3(width, width, stride, groups, dilation)
    self.bn2 = norm_layer(width)
    self.conv3 = conv1x1(width, planes * self.expansion)
    self.bn3 = norm_layer(planes * self.expansion)
    self.relu = nn.ReLU(inplace=True)
    self.downsample = downsample
    self.stride = stride
  def forward(self, x):
    identity = 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:
      identity = self.downsample(x)
    out += identity
    out = self.relu(out)
    return out

class BasicBlock(nn.Module):
  expansion = 1
  __constants__ = ['downsample']
  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
         base_width=64, dilation=1, norm_layer=None):
    super(BasicBlock, self).__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    if groups != 1 or base_width != 64:
      raise ValueError('BasicBlock only supports groups=1 and base_width=64')
    if dilation > 1:
      raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
    # Both self.conv1 and self.downsample layers downsample the input when stride != 1
    self.conv1 = conv3x3(inplanes, planes, stride)
    self.bn1 = norm_layer(planes)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, planes)
    self.bn2 = norm_layer(planes)
    self.downsample = downsample
    self.stride = stride
  def forward(self, x):
    identity = 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:
      identity = self.downsample(x)
    out += identity
    out = self.relu(out)
    return out

import torchvision.models as models
import torch.nn as nn
class RES18(nn.Module):
  def __init__(self):
    super(RES18, self).__init__()
    self.num_cls = settings.MAX_CAPTCHA*settings.ALL_CHAR_SET_LEN
    self.base = torchvision.models.resnet18(pretrained=False)
    self.base.fc = nn.Linear(self.base.fc.in_features, self.num_cls)
  def forward(self, x):
    out = self.base(x)
    return out
class RES34(nn.Module):
  def __init__(self):
    super(RES34, self).__init__()
    self.num_cls = settings.MAX_CAPTCHA*settings.ALL_CHAR_SET_LEN
    self.base = torchvision.models.resnet34(pretrained=False)
    self.base.fc = nn.Linear(self.base.fc.in_features, self.num_cls)
  def forward(self, x):
    out = self.base(x)
    return out
class RES50(nn.Module):
  def __init__(self):
    super(RES50, self).__init__()
    self.num_cls = settings.MAX_CAPTCHA*settings.ALL_CHAR_SET_LEN
    self.base = torchvision.models.resnet50(pretrained=False)
    self.base.fc = nn.Linear(self.base.fc.in_features, self.num_cls)
  def forward(self, x):
    out = self.base(x)
    return out
class RES101(nn.Module):
  def __init__(self):
    super(RES101, self).__init__()
    self.num_cls = settings.MAX_CAPTCHA*settings.ALL_CHAR_SET_LEN
    self.base = torchvision.models.resnet101(pretrained=False)
    self.base.fc = nn.Linear(self.base.fc.in_features, self.num_cls)
  def forward(self, x):
    out = self.base(x)
    return out
class RES152(nn.Module):
  def __init__(self):
    super(RES152, self).__init__()
    self.num_cls = settings.MAX_CAPTCHA*settings.ALL_CHAR_SET_LEN
    self.base = torchvision.models.resnet152(pretrained=False)
    self.base.fc = nn.Linear(self.base.fc.in_features, self.num_cls)
  def forward(self, x):
    out = self.base(x)
    return out

cnn = RES101()
cnn.train() # 改为训练模式
prediction = cnn(img) #进行预测