七月在线 《关键点检测概览与环境配置》

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 inlinetransform = 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 xcost = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
n_epochs = 5for epoch in range(n_epochs):running_loss = 0.0running_correct = 0print("Epoch {}/{}".format(epoch, n_epochs))print("-"*10)for data in data_loader_train:X_train, y_train = dataX_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.datarunning_correct += torch.sum(pred == y_train.data)testing_correct = 0for data in data_loader_test:X_test, y_test = dataX_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

本文来自互联网用户投稿，文章观点仅代表作者本人，不代表本站立场，不承担相关法律责任。如若转载，请注明出处。 如若内容造成侵权/违法违规/事实不符，请点击【内容举报】进行投诉反馈！