首次体验Pytorch，本文参考于：github and PyTorch 中文网人脸相似度对比
本文主要熟悉Pytorch大致流程，修改了读取数据部分。没有采用原作者的ImageFolder方法： ImageFolder(root, transform=None, target_transform=None, loader=default_loader)。而是采用了一种更自由的方法，利用了Dataset 和 DataLoader 自由实现，更加适合于不同数据的预处理导入工作。
Siamese网络不用多说，就是两个共享参数的CNN。每次的输入是一对图像+1个label，共3个值。注意label=0或1（又称正负样本），表示输入的两张图片match（匹配、同一个人）或no-match（不匹配、非同一人）。 下图是Siamese基本结构，图是其他论文随便找的，输入看做两张图片就好。只不过下图是两个光普段而已。
1. 数据处理
数据采用的是AT&T人脸数据。共40个人，每个人有10张脸。数据下载：AT&T
首先解压后发现文件夹下共40个文件夹，每个文件夹里有10张pgm图片。这里生成一个包含图片路径的train.txt文件共后续调用：
def convert(train=True):
if(train):
f=open(Config.txt_root, 'w')
data_path=root+'/train/'
if(not os.path.exists(data_path)):
os.makedirs(data_path)
for i in range(40):
for j in range(10):
img_path = data_path+'s'+str(i+1)+'/'+str(j+1)+'.pgm'
f.write(img_path+' '+str(i)+'\n')
f.close()
生成结果：每行前面为每张图片的完整路径， 后面数字为类别标签0~39。train文件夹下为s1~s40共40个子文件夹。
2. 定制个性化数据集
这一步骤主要继承了类Dataset，然后重写getitem和len方法即可：
class MyDataset(Dataset): # 集成Dataset类以定制
def init(self, txt, transform=None, target_transform=None, should_invert=False):
self.transform = transform
self.target_transform = target_transform
self.should_invert = should_invert
self.txt = txt # 之前生成的train.txt
def getitem(self, index):
line = linecache.getline(self.txt, random.randint(1, self.len())) # 随机选择一个人脸
line.strip('\n')
img0_list= line.split()
should_get_same_class = random.randint(0,1) # 随机数0或1，是否选择同一个人的脸，这里为了保证尽量使匹配和非匹配数据大致平衡（正负类样本相当）
if should_get_same_class: # 执行的话就挑一张同一个人的脸作为匹配样本对
while True:
img1_list = linecache.getline(self.txt, random.randint(1, self.len())).strip('\n').split()
if img0_list【1】==img1_list【1】:
break
else: # else就是随意挑一个人的脸作为非匹配样本对，当然也可能抽到同一个人的脸，概率较小而已
img1_list = linecache.getline(self.txt, random.randint(1,self.len())).strip('\n').split()
img0 = Image.open(img0_list【0】) # img_list都是大小为2的列表，list【0】为图像, list【1】为label
img1 = Image.open(img1_list【0】)
img0 = img0.convert("L") # 转为灰度
img1 = img1.convert("L")
if self.should_invert: # 是否进行像素反转操作，即0变1,1变0
img0 = PIL.ImageOps.invert(img0)
img1 = PIL.ImageOps.invert(img1)
if self.transform is not None: # 非常方便的transform操作，在实例化时可以进行任意定制
img0 = self.transform(img0)
img1 = self.transform(img1)
return img0, img1 , torch.from_numpy(np.array(【int(img1_list【1】!=img0_list【1】)】,dtype=np.float32)) # 注意一定要返回数据+标签， 这里返回一对图像+label（应由numpy转为tensor）
def len(self): # 数据总长
fh = open(self.txt, 'r')
num = len(fh.readlines())
fh.close()
return num
3. 制作双塔CNN
class SiameseNetwork(nn.Module):
def init(self):
super(SiameseNetwork, self).init()
self.cnn1 = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(1, 4, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(4),
nn.Dropout2d(p=.2),
nn.ReflectionPad2d(1),
nn.Conv2d(4, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
nn.Dropout2d(p=.2),
nn.ReflectionPad2d(1),
nn.Conv2d(8, 8, kernel_size=3),
nn.ReLU(inplace=True),
nn.BatchNorm2d(8),
nn.Dropout2d(p=.2),
)
self.fc1 = nn.Sequential(
nn.Linear(8100100, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 500),
nn.ReLU(inplace=True),
nn.Linear(500, 5)
)
def forward_once(self, x):
output = self.cnn1(x)
output = output.view(output.size()【0】, -1)
output = self.fc1(output)
return output
def forward(self, input1, input2):
output1 = self.forward_once(input1)
output2 = self.forward_once(input2)
return output1, output2
很简单，没说的，注意前向传播是两张图同时输入进行。
4. 定制对比损失函数
# Custom Contrastive Loss
class ContrastiveLoss(torch.nn.Module):
"""
Contrastive loss function.
Based on:
"""
def init(self, margin=2.0):
super(ContrastiveLoss, self).init()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
loss_contrastive = torch.mean((1-label) torch.pow(euclidean_distance, 2) + # calmp夹断用法
(label) torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))
return loss_contrastive
上面的损失函数为自己制作的，公式源于lecun文章：
Loss =
DW=
m为容忍度， Dw为两张图片的欧氏距离。
5. 训练一波
train_data = MyDataset(txt = Config.txt_root,transform=transforms.Compose(
【transforms.Resize((100,100)),transforms.ToTensor()】), should_invert=False) #Resize到100,100
train_dataloader = DataLoader(dataset=train_data, shuffle=True, num_workers=2, batch_size = Config.train_batch_size)
net = SiameseNetwork().cuda() # GPU加速
criterion = ContrastiveLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0005)
counter = 【】
loss_history =【】
iteration_number =0
for epoch in range(0, Config.train_number_epochs):
for i, data in enumerate(train_dataloader, 0):
img0, img1, label = data
img0, img1, label = Variable(img0).cuda(), Variable(img1).cuda(), Variable(label).cuda()
output1, output2 = net(img0, img1)
optimizer.zero_grad()
loss_contrastive = criterion(output1, output2, label)
loss_contrastive.backward()
optimizer.step()
if i%10 == 0:
print("Epoch:{}, Current loss {}\n".format(epoch,loss_contrastive.data【0】))
iteration_number += 10
//代码效果参考：http://www.jhylw.com.cn/583124999.html
counter.append(iteration_number)loss_history.append(loss_contrastive.data【0】)
show_plot(counter, loss_history) # plot 损失函数变化曲线
损失函数结果图：
batch_size=32, epoches=20, lr=0.001 batch_size=32, epoches=30, lr=0.0005
全部代码：
#!/usr/bin/env python3
# -- coding: utf-8 --
"""
Created on Wed Jan 24 10:00:24 2018
Paper: Siamese Neural Networks for One-shot Image Recognition
links:
"""
import torch
from torch.autograd import Variable
import os
import random
import linecache
import numpy as np
import //代码效果参考：http://www.jhylw.com.cn/055937059.html
torchvisionfrom torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import PIL.ImageOps
import matplotlib.pyplot as plt
class Config():
root = '/home/lps/Spyder/data_faces/'
txt_root = '/home/lps/Spyder/data_faces/train.txt'
train_batch_size = 32
train_number_epochs = 30
# Helper functions
def imshow(img,text=None,should_save=False):
npimg = img.numpy()
plt.axis("off")
if text:
plt.text(75, 8, text, style='italic',fontweight='bold',
bbox={'facecolor':'white', 'alpha':0.8, 'pad':10})
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
def show_plot(iteration,loss):
plt.plot(iteration,loss)
plt.show()
def convert(train=True):
if(train):
f=open(Config.txt_root, 'w')
data_path=root+'/train/'
if(not os.path.exists(data_path)):
os.makedirs(data_path)
for i in range(40):
for j in range(10):
img_path = data_path+'s'+str(i+1)+'/'+str(j+1)+'.pgm'
f.write(img_path+' '+str(i)+'\n')
f.close()
#convert(True)
# ready the dataset, Not use ImageFolder as the author did
class MyDataset(Dataset):
def init(self, txt, transform=None, target_transform=None, should_invert=False):
self.transform = transform
self.target_transform = target_transform
self.should_invert = should_invert
self.txt = txt
def getitem(self, index):
line = linecache.getline(self.txt, random.randint(1, self.len()))
line.strip('\n')
img0_list= line.split()
should_get_same_class = random.randint(0,1)
if should_get_same_class:
while True:
img1_list = linecache.getline(self.txt, random.randint(1, self.len())).strip('\n').split()
if img0_list【1】==img1_list【1】:
break
else:
img1_list = linecache.getline(self.txt, random.randint(1,self.len())).strip('\n').split()
img0 = Image.open(img0_list【0】)
img1 = Image.open(img1_list【0】)
img0 = img0.convert("L")
img1 = img1.convert("L")
if self.should_invert:
img0 = PIL.ImageOps.invert(img0)
img1 = PIL.ImageOps.invert(img1)
if self.transform is not None:
img0 = self.transform(img0)
img1 = self.transform(img1)
