【目标检测】自定义Dataset方法(VOC数据集)——pytorch实现

推荐参考：
以VOC2007数据集为例：

import os
import torch
from torch.utils.data import Dataset
from PIL import Image

from lxml import etree
from xml.etree import ElementTree

"""
VOC2007数据集格式:
└──VOCdevkit
    └──VOC2007
        └──JPEGImages
            └──0.jpg
            └──1.jpg
            └──2.jpg
            └──...
        └──Annotations
            └──0.xml
            └──1.xml
            └──2.xml
            └──...
        └──ImageSets
            └──Main
                └──train.txt
                └──val.txt
                └──trainval.txt
                └──test.txt
      
"""

'''
xml文件信息(例):
<annotation>
    <folder>JPEGImages</folder>
    <filename>0.jpg</filename>
    <path>X:/.../.../VOCdevkit/VOC2007/JPEGImages/0.jpg</path>
    <source>
        <database>Unknown</database>
    </source>
    <size>
        <width>600</width>
        <height>800</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>peach</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>100</xmin>
            <ymin>250</ymin>
            <xmax>150</xmax>
            <ymax>260</ymax>
        </bndbox>
    </object>
    <object>
        <name>cat</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>200</xmin>
            <ymin>50</ymin>
            <xmax>550</xmax>
            <ymax>370</ymax>
        </bndbox>
    </object>
</annotation>
'''


class CustomDataset(Dataset):  # 自定义数据集
    def __init__(self, root, transforms=None, dataset_property="train"):  # 初始化方法
        self.root = root  # 数据路径，应指向".../.../VOCdevkit"
        self.transforms = transforms  # 预处理方法，一般来说需要传入，注意区分训练数据和验证数据的预处理方法
        self.images_dir = os.path.join(self.root,