pytorch实践小结

根据土堆教程学习情况，即学即练。要求：获取桃（水蜜桃，油桃和黄桃）数据集的收集，划分训练集和测试集并完成分类任务。

1. 获取数据（爬虫）

代码来源： http://t.csdnimg.cn/CcEW6

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 29 10:17:50 2023
@author: MatpyMaster
"""
import requests
import os
import re
 
 
def get_images_from_baidu(keyword, page_num, save_dir):
    header = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'}
    # 请求的 url
    url = 'https://image.baidu.com/search/acjson?'
    n = 0
    for pn in range(0, 30 * page_num, 30):
        # 请求参数
        param = {'tn': 'resultjson_com',
                 'logid': '7603311155072595725',
                 'ipn': 'rj',
                 'ct': 201326592,
                 'is': '',
                 'fp': 'result',
                 'queryWord': keyword,
                 'cl': 2,
                 'lm': -1,
                 'ie': 'utf-8',
                 'oe': 'utf-8',
                 'adpicid': '',
                 'st': -1,
                 'z': '',
                 'ic': '',
                 'hd': '',
                 'latest': '',
                 'copyright': '',
                 'word': keyword,
                 's': '',
                 'se': '',
                 'tab': '',
                 'width': '',
                 'height': '',
                 'face': 0,
                 'istype': 2,
                 'qc': '',
                 'nc': '1',
                 'fr': '',
                 'expermode': '',
                 'force': '',
                 'cg': '',  # 这个参数没公开，但是不可少
                 'pn': pn,  # 显示：30-60-90
                 'rn': '30',  # 每页显示 30 条
                 'gsm': '1e',
                 '1618827096642': ''
                 }
        request = requests.get(url=url, headers=header, params=param)
        if request.status_code == 200:
            print('Request success.')
        request.encoding = 'utf-8'
        # 正则方式提取图片链接
        html = request.text
        image_url_list = re.findall('"thumbURL":"(.*?)",', html, re.S)
 
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
 
        for image_url in image_url_list:
            image_data = requests.get(url=image_url, headers=header).content
            with open(os.path.join(save_dir, f'{n:06d}.jpg'), 'wb') as fp:
                fp.write(image_data)
            n = n + 1
 
 
if __name__ == "__main__":
    keyword = '黄桃'
    page_num = 1
    page_num = int(page_num)
    save_dir = '.\\图片\\' + keyword
    get_images_from_baidu(keyword, page_num, save_dir)

2. train.py

import os
import time
from torch.utils.tensorboard import SummaryWriter
import torch.optim
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import torchvision
from torch import nn
 
#指定设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
#获取数据
class PeachDate(Dataset):
    def __init__(self,root_dir,label_dir):
        self.root_dir = root_dir
        self.label_dir = label_dir
        self.path = os.path.join(root_dir,label_dir) #完整路径
        self.img_list = os.listdir(self.path) #图片列表
        self.label_mapping = {'honey_peach': 0, 'Huangtao': 1, 'nectarine': 2}
 
    def __getitem__(self, idx):
        img_name = self.img_list[idx]
        img_path = os.path.join(self.root_dir,self.label_dir,img_name) #单张图片完整路径
        img_read = Image.open(img_path) #读取图片
        trans_resize = torchvision.transforms.Resize((32,32)) #统一尺寸
        trans_tensor = torchvision.transforms.ToTensor() #转换图片类型
        if img_read.mode != 'RGB': # 确保通道数为3（RGB）
            img_read = img_read.convert('RGB')
        img_resize = trans_resize(img_read)
        img = trans_tensor(img_resize)
        label_str = self.label_dir
        label_int = self.label_mapping[label_str]
        return img, label_int
 
    def __len__(self):
        return len(self.img_list)
 
train_root_dir = "dataset/train"
test_root_dir = "dataset/test"
 
train_honeypeach = PeachDate(train_root_dir,"honey_peach")
train_Huangtao = PeachDate(train_root_dir,"Huangtao")
train_nectarine =  PeachDate(train_root_dir,"nectarine")
train_data = train_honeypeach + train_Huangtao + train_nectarine
 
test_honeypeach = PeachDate(test_root_dir,"honey_peach")
test_Huangtao = PeachDate(test_root_dir,"Huangtao")
test_nectarine =  PeachDate(test_root_dir,"nectarine")
test_data = test_honeypeach + test_Huangtao + test_nectarine
 
#数据长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为:{}".format(train_data_size),"测试集的长度为:{}".format(test_data_size))
 
# 打包数据
train_dataloader = DataLoader(train_data,8,shuffle=True)
test_dataloader = DataLoader(test_data,4,shuffle=True)
 
 
#创建网络模型 (CIF改写)
class PeachNn(nn.Module):
    def __init__(self):
        super(PeachNn, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3,32,5,1,padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32,32,5,1,padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32,64,5,1,padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(64*4*4,64),
            nn.Linear(64,10),
            nn.Linear(10,3)
        )
 
    def forward(self,x):
        x = self.model(x)
        return x
 
peachnn = PeachNn()
peachnn.to(device)
 
#损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn.to(device)
 
#优化器
learing_rate = 0.01
optimzer = torch.optim.SGD(peachnn.parameters(),lr=learing_rate)
 
#其他参数
total_train_step = 0
total_test_step = 0
epoch = 10
 
#添加tensorboard
writer = SummaryWriter("./logs")
 
#开始计时
start_time = time.time()
 
for i in range(epoch):
    print("-------第{}轮训练开始".format(i+1))
 
    #训练
    peachnn.train()
    for data in train_dataloader:
        imgs,targets = data
        imgs = imgs.to(device)
        targets = targets.to(device)
        outputs = peachnn(imgs)
        loss = loss_fn(outputs,targets) #损失
 
        #优化器调优
        optimzer.zero_grad()
        loss.backward()
        optimzer.step()
 
        total_train_step = total_train_step+1
 
        if total_train_step % 10 == 0: #每训练十次打印一次
            end_time = time.time()
            print("训练时长为:{}".format(end_time - start_time))
            print('训练第{}次,loss:{}'.format(total_train_step, loss.item()))
 
            writer.add_scalar("train_loss", loss, total_train_step)  # 将损失写入tensorboard
 
    peachnn.eval() #测试开始
    total_test_loss = 0
    total_accuracy = 0
    with torch.no_grad():
        for data in test_dataloader:
            imgs,targets = data
            imgs = imgs.to(device)
            targets = targets.to(device)
            outputs = peachnn(imgs)
            loss = loss_fn(outputs,targets)
            accuracy = (outputs.argmax(1) == targets).sum()
            total_test_loss = total_test_loss + loss.item()
            total_accuracy = total_accuracy + accuracy
 
    print("整体测试集上的损失为:{}".format(total_test_loss))
    print("测试集上的准确率为:{}".format(total_accuracy/test_data_size))
 
    writer.add_scalar("total_test_loss",total_test_loss,total_test_step)
    writer.add_scalar("total_accuracy",total_accuracy,total_test_step)
 
    total_test_step = total_test_step+1
 
    #保存模型
    torch.save(peachnn,"peachnn_{}.pt".format(i))
    print("第{}轮模型已保存".format(i))
 
writer.close()

3. test.py

import torch
from PIL import Image
import torchvision
from torch import nn
 
img_path = "dataset/test/Huangtao/000002.jpg"
img_read = Image.open(img_path)
img = img_read.convert("RGB")
 
transforms =  torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),torchvision.transforms.ToTensor()])
image = transforms(img)
 
print(image.shape)
 
#创建网络模型 (CIF改写)
class PeachNn(nn.Module):
    def __init__(self):
        super(PeachNn, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3,32,5,1,padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32,32,5,1,padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32,64,5,1,padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(64*4*4,64),
            nn.Linear(64,10),
            nn.Linear(10,3)
        )
 
    def forward(self,x):
        x = self.model(x)
        return x
 
peahcnn = PeachNn()
# model = torch.load("peachnn_9.pt",map_location=torch.device("cuda"))
model = torch.load("peachnn_9.pt")
print(model)
 
image = torch.reshape(image,(1,3,32,32))
model.eval()
with torch.no_grad():
    output = peahcnn(image)
 
print(output)
print(output.argmax(1))

特别感谢

CSDN 
  土堆老师 ＆ MatpyMaster