1 在Pytorch中保存和加载模型

1.1 在训练中保存检查点：

def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
    print("=> Saving checkpoint")
    torch.save(state, filename)

设置在epoch==2的时候保存检查点

for epoch in range(num_epochs):

    if epoch == 2:
        checkpoint = {'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()}
        save_checkpoint(checkpoint)

    for batch_idx, (data, targets) in enumerate(train_loader):
        # 将数据放到cuda中
        data = data.to(device)
        targets = targets.to(device)

        # forward
        lables = model(data)
        loss = criterion(lables, targets)

        # backward
        optimizer.zero_grad()
        loss.backward()

        # 梯度下降
        optimizer.step()

得到文件.pth.tar文件

1.2 加载检查点

def load_checkpoint(checkpoint):
    print("=> Loading checkpoint")
    model.load_state_dict(checkpoint['state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer'])

load_model = True

if load_model:
    load_checkpoint(torch.load("my_checkpoint.pth.tar"))

2 使用PyTorch构建数据集

这里以蜜蜂和蚂蚁的图片数据集为例：

将蚂蚁的标签设置为0，蜜蜂为1，存入csv文件中，代码如下：

import os
import pandas as pd

# 定义两个文件夹的路径
ant_folder = r""  # 替换为ant文件夹的实际路径
bees_folder = r""  # 替换为bees文件夹的实际路径

# 初始化两个空列表，用于存储文件名和标签
file_names = []
labels = []

# 处理ant文件夹
for filename in os.listdir(ant_folder):
    if filename.endswith(".jpg"):  # 假设所有图片都是.jpg格式
        file_names.append(filename)
        labels.append(0)  # ant的标签为0

# 处理bees文件夹
for filename in os.listdir(bees_folder):
    if filename.endswith(".jpg"):
        file_names.append(filename)
        labels.append(1)  # bees的标签为1

# 创建一个DataFrame对象
data = {'Image_Name': file_names, 'Label': labels}
df = pd.DataFrame(data)

# 将DataFrame保存为CSV文件
csv_filename = "image_labels.csv"  # 保存的CSV文件名
df.to_csv(csv_filename, index=False)

print(f"CSV文件已创建: {csv_filename}")

得到csv文件如下：

编写自定义数据集类：

import os
import pandas
import pandas as pd
import torch
from torch.utils.data import Dataset
from skimage import io

class AntsAndBeesDataset(Dataset):
    def __init__(self, csv_file, root_dir, transform=None):
        self.annotations = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.annotations) #397

    def __getitem__(self, index):
        img_path = os.path.join(self.root_dir,self.annotations.iloc[index, 0]);
        image = io.imread(img_path)
        y_yable = int(self.annotations.iloc[index, 1])

        if self.transform:
            image = self.transform(image)

        return (image, y_yable)

在训练的代码中加载自定义的数据集类

dataset = AntsAndBeesDataset(csv_file='', root_dir='',transform=transformers.Totensor())
train_set, test_set = torch.utils.data.random_split(dataset, [278, 119]); #训练集和测试集
train_loader = DataLoader(dataset=train_set, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_set, batch_size=batch_size, shuffle=False)