七月在线 《关键点检测概览与环境配置》

Posted 2022-02-11 刘润森！

七月在线 课程：https://www.julyedu.com/course/getDetail/262

什么是关键点？

关键点定义：关键点也称为兴趣点，它是2D图像、3D点云或曲面模型上，可以通过定义检测标准来获取的具有稳定性、区别性的点集。关键点检测涉及同时检测人和定位他们的关键点。关键点与兴趣点相同。它们是空间位置或图像中的点，它们定义了图像中有趣或突出的内容。它们对图像旋转、收缩、平移、失真等是不变的。

关键点的意义？

加快后续识别、追踪等数据的处理速度。

环境配置

nvidia GPU 配置：

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

code ： MNIST

MNIST实战！

import torch
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import os
import torchvision
import numpy as np
from torch.autograd import Variable
import random
%matplotlib inline

transform = transforms.Compose([
transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])

data_train = datasets.MNIST(root = "./data/",
                            transform=transform,
                            train = True,
                            download = True)

data_test = datasets.MNIST(root="./data/",
                           transform = transform,
                           train = False)

data_loader_train = torch.utils.data.DataLoader(dataset=data_train,
                                                batch_size = 64,
                                                shuffle = True,
                                                num_workers=2)

data_loader_test = torch.utils.data.DataLoader(dataset=data_test,
                                               batch_size = 64,
                                               shuffle = True,
                                               num_workers=2)

images, labels = next(iter(data_loader_train))
img = torchvision.utils.make_grid(images)

img = img.numpy().transpose(1,2,0)
std = [0.5,0.5,0.5]
mean = [0.5,0.5,0.5]
img = img*std+mean
print([labels[i] for i in range(64)])
plt.imshow(img)

class Model(torch.nn.Module):
    
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = torch.nn.Sequential(torch.nn.Conv2d(1,64,kernel_size=3,stride=1,padding=1),
                                         torch.nn.ReLU(),
                                         torch.nn.Conv2d(64,128,kernel_size=3,stride=1,padding=1),
                                         torch.nn.ReLU(),
                                         torch.nn.MaxPool2d(stride=2,kernel_size=2))
        self.dense = torch.nn.Sequential(torch.nn.Linear(14*14*128,1024),
                                         torch.nn.ReLU(),
                                         torch.nn.Dropout(p=0.5),
                                         torch.nn.Linear(1024, 10))
    def forward(self, x):
        x = self.conv1(x)
        x = x.view(-1, 14*14*128)
        x = self.dense(x)
        return x
        
cost = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
n_epochs = 5

for epoch in range(n_epochs):
    running_loss = 0.0
    running_correct = 0
    print("Epoch /".format(epoch, n_epochs))
    print("-"*10)
    for data in data_loader_train:
        X_train, y_train = data
        X_train, y_train = Variable(X_train), Variable(y_train)
        outputs = model(X_train)
        _,pred = torch.max(outputs.data, 1)
        optimizer.zero_grad()
        loss = cost(outputs, y_train)
        
        loss.backward()
        optimizer.step() #进行单次优化
        running_loss += loss.data
        running_correct += torch.sum(pred == y_train.data)
    testing_correct = 0
    for data in data_loader_test:
        X_test, y_test = data
        X_test, y_test = Variable(X_test), Variable(y_test)
        outputs = model(X_test)
        _, pred = torch.max(outputs.data, 1)
        testing_correct += torch.sum(pred == y_test.data)
    print("Loss is::.4f, Train Accuracy is::.4f%, Test Accuracy is::.4f".format(running_loss/len(data_train),
                                                                                      100*running_correct/len(data_train),
                                                                                      100*testing_correct/len(data_test)))
torch.save(model.state_dict(), "model_parameter.pkl")

reference resources

• https://paperswithcode.com/sota/keypoint-detection-on-coco-test-dev