如何使用Pytorch完成图像分类任务详解_python_

import requests import os import urllib class Spider_baidu_image(): def __init__(self): self.url = 'http://image.baidu.com/search/acjson?' self.headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.\ 3497.81 Safari/537.36'} self.headers_image = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.\ 3497.81 Safari/537.36', 'Referer': 'http://image.baidu.com/search/index?tn=baiduimage&ipn=r&ct=201326592&cl=2&lm=-1&st=-1&fm=result&fr=&sf=1&fmq=1557124645631_R&pv=&ic=&nc=1&z=&hd=1&latest=0©right=0&se=1&showtab=0&fb=0&width=&height=&face=0&istype=2&ie=utf-8&sid=&word=%E8%83%A1%E6%AD%8C'} self.keyword = input("请输入搜索图片关键字:") self.paginator = int(input("请输入搜索页数，每页30张图片：")) def get_param(self): """ 获取url请求的参数，存入列表并返回 :return: """ keyword = urllib.parse.quote(self.keyword) params = [] for i in range(1, self.paginator + 1): params.append( 'tn=resultjson_com&ipn=rj&ct=201326592&is=&fp=result&queryWord={}&cl=2&lm=-1&ie=utf-8&oe=utf-8&adpicid=&st=-1&z=&ic=&hd=1&latest=0©right=0&word={}&s=&se=&tab=&width=&height=&face=0&istype=2&qc=&nc=1&fr=&expermode=&force=&cg=star&pn={}&rn=30&gsm=78&1557125391211='.format( keyword, keyword, 30 * i)) return params def get_urls(self, params): """ 由url参数返回各个url拼接后的响应，存入列表并返回 :return: """ urls = [] for i in params: urls.append(self.url + i) return urls def get_image_url(self, urls): image_url = [] for url in urls: json_data = requests.get(url, headers=self.headers).json() json_data = json_data.get('data') for i in json_data: if i: image_url.append(i.get('thumbURL')) return image_url def get_image(self, image_url): """ 根据图片url，在本地目录下新建一个以搜索关键字命名的文件夹，然后将每一个图片存入。 :param image_url: :return: """ cwd = os.getcwd() file_name = os.path.join(cwd, self.keyword) if not os.path.exists(self.keyword): os.mkdir(file_name) for index, url in enumerate(image_url, start=1): with open(file_name + '\\{}.jpg'.format(index), 'wb') as f: f.write(requests.get(url, headers=self.headers_image).content) if index != 0 and index % 30 == 0: print('{}第{}页下载完成'.format(self.keyword, index / 30)) def __call__(self, *args, **kwargs): params = self.get_param() urls = self.get_urls(params) image_url = self.get_image_url(urls) self.get_image(image_url) if __name__ == '__main__': spider = Spider_baidu_image() spider()

transforms.Compose([ transforms.CenterCrop(84), transforms.ToTensor(), ])

data_transforms = { 'train': transforms.Compose([ transforms.Resize(84), transforms.CenterCrop(84), # 转换成tensor向量 transforms.ToTensor(), # 对图像进行归一化操作 # [0.485, 0.456, 0.406]，RGB通道的均值与标准差 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(84), transforms.CenterCrop(84), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), }

class my_Data_Set(nn.Module): def __init__(self, txt, transform=None, target_transform=None, loader=None): super(my_Data_Set, self).__init__() # 打开存储图像名与标签的txt文件 fp = open(txt, 'r') images = [] labels = [] # 将图像名和图像标签对应存储起来 for line in fp: line.strip('\n') line.rstrip() information = line.split() images.append(information[0]) labels.append(int(information[1])) self.images = images self.labels = labels self.transform = transform self.target_transform = target_transform self.loader = loader # 重写这个函数用来进行图像数据的读取 def __getitem__(self, item): # 获取图像名和标签 imageName = self.images[item] label = self.labels[item] # 读入图像信息 image = self.loader(imageName) # 处理图像数据 if self.transform is not None: image = self.transform(image) return image, label # 重写这个函数，来看数据集中含有多少数据 def __len__(self): return len(self.images) # 生成Pytorch所需的DataLoader数据输入格式 train_dataset = my_Data_Set('train.txt', transform=data_transforms['train'], loader=Load_Image_Information) test_dataset = my_Data_Set('val.txt', transform=data_transforms['val'], loader=Load_Image_Information) train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=10, shuffle=True)

# 验证是否生成DataLoader格式数据 for data in train_loader: inputs, labels = data print(inputs) print(labels) for data in test_loader: inputs, labels = data print(inputs) print(labels)

import torch from torch.utils.data import DataLoader from torchvision import transforms, datasets import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable from torch.utils.data import Dataset import numpy as np import os from PIL import Image import warnings import matplotlib.pyplot as plt warnings.filterwarnings("ignore") plt.ion()

class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 18 * 18, 800) self.fc2 = nn.Linear(800, 120) self.fc3 = nn.Linear(120, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 18 * 18) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x net = Net()

#训练 cirterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.5) for epoch in range(50): running_loss = 0.0 for i, data in enumerate(train_loader, 0): inputs, labels = data inputs, labels = Variable(inputs), Variable(labels) optimizer.zero_grad() # 优化器清零 outputs = net(inputs) loss = cirterion(outputs, labels) loss.backward() optimizer.step() #优化 running_loss += loss.item() if i % 200 == 199: print('[%d %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 200)) running_loss = 0.0 print('finished training!')

#测试 correct = 0 total = 0 with torch.no_grad(): for data in test_loader: images, labels = data outputs = net(Variable(images)) _, predicted = torch.max(outputs.data, dim=1) total += labels.size(0) correct += (predicted == labels).sum() print('Accuracy of the network on the 400 test images: %d %%' % (100 * correct / 
                
提示：
                    本文由神整理自网络，如有侵权请联系本站删除！
                    

                    本站声明： 

                    1、本站所有资源均来源于互联网，不保证100%完整、不提供任何技术支持； 

                    2、本站所发布的文章以及附件仅限用于学习和研究目的;不得将用于商业或者非法用途；否则由此产生的法律后果，本站概不负责！
                
                

                

                                            
上一篇：pytorch模型转onnx模型的方法详解_python_
                                                                
下一篇：Python 模拟死锁的常见实例详解_python_