基于python实现resnet_【深度学习】基于Pytorch的ResNet实现
1. ResNet理論
殘差學習基本單元：
在ImageNet上的結果：
效果會隨著模型層數的提升而下降，當更深的網絡能夠開始收斂時，就會出現降級問題：隨著網絡深度的增加，準確度變得飽和(這可能不足為奇)，然后迅速降級。
ResNet模型：
2. pytorch實現
2.1 基礎卷積
conv3$\times\(3 和conv1\)\times$1 基礎模塊
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)
參數解釋：
in_channel: 輸入的通道數目
out_channel:輸出的通道數目
stride, padding: 步長和補0
dilation: 空洞卷積中的參數
groups: 從輸入通道到輸出通道的阻塞連接數
feature size 計算：
output = (intput - filter_size + 2 x padding) / stride + 1
空洞卷積實際卷積核大小：
K = K + (K-1)x(R-1)
K 是原始卷積核大小
R 是空洞卷積參數的空洞率(普通卷積為1)
2.2 模塊
- resnet34
- _resnet
- ResNet
- _make_layer
- block
- Bottleneck
- BasicBlock
Bottlenect
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
BasicBlock
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
2.3 使用ResNet模塊進行遷移學習
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
使用模塊直接生成一個類即可，比如訓練的時候：
cnn = RES101()
cnn.train() # 改為訓練模式
prediction = cnn(img) #進行預測
目前先寫這么多，看過了源碼以后感覺寫的很好，不僅僅有論文中最基礎的部分，還有一些額外的功能，模塊的組織也很整齊。
平時使用一般都進行遷移學習，使用的話可以把上述幾個類中pretrained=False參數改為True.
總結
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