[人工智能]Spatial Transformer Networks(STN)-代码实现

Spatial Transformer Networks(STN)-代码实现

• pytorch为了方便实现STN，里面封装了affine_grid和grid_sample两个高级API。

• STN的基本步骤是：
  

• $Localisationnet$(参数预测)：
  Localisation net模块通过$CNN$提取图像的特征来预测变换矩阵$\theta$

• $Gridgenerator$(坐标映射):
  Grid generator模块就是利用Localisation net模块回归出来的$\theta$参数来对图片中的位置进行变换，输入图片到输出图片之间的变换，需要特别注意的是这里指的是图片像素所对应的位置。

• $Sampler$(像素的采集):
  Sampler就是用来解决Grid generator模块变换出现小数位置的问题的。针对这种情况，STN采用的是双线性插值(Bilinear Interpolation)，下面我们来介绍一下这个算法

1. STN层的实现

from torchvision import transforms
import torch.nn.functional as F
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

#读取图片
img = Image.open("img/test.jpg")
#将图片转换为torch tensor
img_tensor = transforms.ToTensor()(img)

#定义平移变换矩阵
#0.1表示将图片向左平移图片宽的百分比
#0.2表示将图片向上平移图片高的百分比
theta = torch.tensor([[1,0,0.1],[0,1,0.2]],
                     dtype=torch.float)
#根据变换矩阵来计算变换后图片的对应位置
grid = F.affine_grid(theta.unsqueeze(0),
               img_tensor.unsqueeze(0).size(),align_corners=True)
#默认使用双向性插值，可以通过mode参数设置
output = F.grid_sample(img_tensor.unsqueeze(0),
			   grid,align_corners=True)

plt.figure()
plt.subplot(1,2,1)
plt.imshow(np.array(img))
plt.title("original image")

plt.subplot(1,2,2)
plt.imshow(output[0].numpy().transpose(1,2,0))
plt.title("stn transform image")

plt.show()

2. STN+CNN

当输入图片通过STN模块之后获得变换后的图片，然后我们再将变换后的图片输入到$CNN$网络中，通过损失函数计算$loss$，然后计算梯度更新$\theta$参数，最终STN模块会学习到如何矫正图片。

2.1 参数设置

config.py

import argparse

def parse_args():
    parse = argparse.ArgumentParser("config stn args")
    parse.add_argument("--lr",default=0.01,
    type=float,help="learning rate")
    parse.add_argument("--epoch_nums",default=20,
    type=int,help="iterated epochs")
    parse.add_argument("--use_stn",default=True,
    type=bool,help="whether to use STN module")
    parse.add_argument("--batch_size",default=64,
    type=int,help="batch size")
    parse.add_argument("--use_eval",default=True,
    type=bool,help="whether to evaluate")
    parse.add_argument("--use_visual",default=True,
    type=bool,help="visual STN transform image")
    parse.add_argument("--use_gpu",default=True,
    type=bool,help="whether to use GPU")
    parse.add_argument("--show_net_construct",default=False,
    type=bool,help="print net construct info")
    return parse.parse_args()

2.2 加载数据

DataLoader.py

import torch
from torchvision import datasets,transforms
import numpy as np


def get_dataloader(batch_size):
    # 加载数据集
    # 如果GPU可用就用GPU,否则用CPU
    device = torch.device("cuda" if torch.cuda.is_available()
    					   else "cpu")
    # 加载训练集
    train_dataloader = torch.utils.data.DataLoader(
        datasets.MNIST(root="D:\PyCharm\PyCharm_Project\STN", train=True, download=True,
                       transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                       ])), batch_size=batch_size, shuffle=True)

    # 加载测试集
    test_dataloader = torch.utils.data.DataLoader(
        datasets.MNIST(root="D:\PyCharm\PyCharm_Project\STN", train=False,
                       transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                       ])), batch_size=batch_size, shuffle=True)

    return train_dataloader,test_dataloader


def tensor_to_array(img_tensor):
    img_array = img_tensor.numpy().transpose((1,2,0))
    mean = np.array([0.485,0.456,0.406])
    std = np.array([0.229,0.224,0.225])
    img_array = std * img_array + mean
    img = np.clip(img_array,0,1)
    return img

2.3 定义网络

Net.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class STN_Net(nn.Module):
    def __init__(self,use_stn=True):
        super(STN_Net, self).__init__()
        self.conv1 = nn.Conv2d(1,10,kernel_size=5)
        self.conv2 = nn.Conv2d(10,20,kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320,50)
        self.fc2 = nn.Linear(50,10)
        #用来判断是否使用STN
        self._use_stn = use_stn

        #localisation net
        #从输入图像中提取特征
        #输入图片的shape为(-1,1,28,28)
        self.localization = nn.Sequential(
            #卷积输出shape为(-1,8,22,22)
            nn.Conv2d(1,8,kernel_size=7),
            #最大池化输出shape为(-1,1,11,11)
            nn.MaxPool2d(2,stride=2),
            nn.ReLU(True),
            #卷积输出shape为(-1,10,7,7)
            nn.Conv2d(8,10,kernel_size=5),
            #最大池化层输出shape为(-1,10,3,3)
            nn.MaxPool2d(2,stride=2),
            nn.ReLU(True)
        )
        #利用全连接层回归\theta参数
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3,32),
            nn.ReLU(True),
            nn.Linear(32,2*3)
        )

        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1,0,0,0,1,0]
        ,dtype=torch.float))

    def stn(self,x):
        #提取输入图像中的特征
        xs = self.localization(x)
        xs = xs.view(-1,10*3*3)
        #回归theta参数
        theta = self.fc_loc(xs)
        theta = theta.view(-1,2,3)

        #利用theta参数计算变换后图片的位置
        grid = F.affine_grid(theta,x.size())
        #根据输入图片计算变换后图片位置填充的像素值
        x = F.grid_sample(x,grid)

        return x

    def forward(self,x):
        #使用STN模块
        if self._use_stn:
            x = self.stn(x)
        #利用STN矫正过的图片来进行图片的分类
        #经过conv1卷积输出的shape为(-1,10,24,24)
        #经过max pool的输出shape为(-1,10,12,12)
        x = F.relu(F.max_pool2d(self.conv1(x),2))
        #经过conv2卷积输出的shape为(-1,20,8,8)
        #经过max pool的输出shape为(-1,20,4,4)
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)),2))
        x = x.view(-1,320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x,training=self.training)
        x = self.fc2(x)

        return F.log_softmax(x,dim=1)

2.4 训练模型

train.py

import torch,torchvision
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


def train(net,epoch_nums,lr,train_dataloader,per_batch,device):
    #使用训练模式
    net.train()
    #选择梯度下降优化算法
    optimizer = optim.SGD(net.parameters(),lr=lr)
    #训练模型
    for epoch in range(epoch_nums):
        for batch_idx,(data,label) in enumerate(train_dataloader):
            data,label = data.to(device),label.to(device)

            optimizer.zero_grad()
            pred = net(data)
            loss = F.nll_loss(pred,label)
            loss.backward()
            optimizer.step()

            if batch_idx % per_batch == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch, batch_idx * len(data), len(train_dataloader.dataset),
                           100. * batch_idx / len(train_dataloader), loss.item()))

2.5 评估模型

evaluate.py

import torch
import torch.nn.functional as F


def evaluate(net,test_dataloader,device):
    with torch.no_grad():
        #使用评估模式
        net.eval()
        eval_loss = 0
        eval_acc = 0
        for data,label in test_dataloader:
            data,label = data.to(device),label.to(device)
            pred = net(data)

            eval_loss += F.nll_loss(pred,label,
            size_average=False).item()
            pred_label = pred.max(1,keepdim=True)[1]
            eval_acc += pred_label.eq(label.view_as(pred_label)
            ).sum().item()

        eval_loss /= len(test_dataloader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
              .format(eval_loss, eval_acc, len(test_dataloader.dataset),
                      100. * eval_acc / len(test_dataloader.dataset)))

2.6 可视化

Visualize.py

import torch,torchvision
import matplotlib.pyplot as plt
from DataLoader import tensor_to_array


def visualize_stn(net,dataloader,device):
    with torch.no_grad():
        data = next(iter(dataloader))[0].to(device)

        input_tensor = data.cpu()
        t_input_tensor = net.stn(data).cpu()

        in_grid = tensor_to_array(torchvision.utils.make_grid(
        input_tensor))
        out_grid = tensor_to_array(torchvision.utils.make_grid(
        t_input_tensor))

        f,axarr = plt.subplots(1,2)
        axarr[0].imshow(in_grid)
        axarr[0].set_title("input images")

        axarr[1].imshow(out_grid)
        axarr[1].set_title("stn transformed images")

        plt.show()

2.7 主函数

MAIN.py

import torch
from Net import STN_Net
from Visualize import visualize_stn
from train import train
from config import parse_args
from DataLoader import get_dataloader
from evaluate import evaluate

if __name__ == "__main__":
    args = parse_args()
    if args.use_gpu and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    #加载数据集
    train_loader,test_loader = get_dataloader(args.batch_size)
    #创建网络
    net = STN_Net(args.use_stn).to(device)
    #训练模型
    train(net,args.epoch_nums,args.lr,train_loader
    ,args.batch_size,device)
    if args.use_eval:
        #评估模型
        evaluate(net,test_loader,device)
    if args.use_visual:
        #可视化展示效果
        visualize_stn(net,test_loader,device)

Test set: Average loss: 0.0423, Accuracy: 9868/10000 (99%)

参考

1. 通俗易懂的Spatial Transformer Networks(STN)(一)
2. 通俗易懂的Spatial Transformer Networks(STN)(二)
3. SPATIAL TRANSFORMER NETWORKS TUTORIAL