【DETR】DETR训练VOC数据集/自定义数据集

一、数据准备

DETR用的是COCO格式的数据集。
如果要用DETR训练自己的数据集，直接利用Labelimg标注成COCO格式。
如果是VOC数据集的话，要做一个格式转换。网上一大堆格式转换的代码都很乱，所以自己写了一个针对VOC数据集的转换。

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COCO数据集的格式类似这样，annotations文件夹里面有对应的train、val数据集的json文件。train2017则是训练集图片，其他同理。

VOC数据集的存放方式是这样的，转换格式就是找出Main文件夹下用于目标检测的图片。

Main文件夹下有train.txt文件，记录了训练集的图片。val.txt记录了验证集的图片

只需要修改注释中的两个路径即可（创建文件夹时没有加判断语句严谨一点应该加上）。

import os
import shutil
import sys
import json
import glob
import xml.etree.ElementTree as ET


START_BOUNDING_BOX_ID = 1
# PRE_DEFINE_CATEGORIES = None
# If necessary, pre-define category and its id
PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
                         "bottle": 5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
                         "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
                         "motorbike": 14, "person": 15, "pottedplant": 16,
                         "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}


def get(root, name):
    vars = root.findall(name)
    return vars


def get_and_check(root, name, length):
    vars = root.findall(name)
    if len(vars) == 0:
        raise ValueError("Can not find %s in %s." % (name, root.tag))
    if length > 0 and len(vars) != length:
        raise ValueError(
            "The size of %s is supposed to be %d, but is %d."
            % (name, length, len(vars))
        )
    if length == 1:
        vars = vars[0]
    return vars


def get_filename_as_int(filename):
    try:
        filename = filename.replace("\\", "/")
        filename = os.path.splitext(os.path.basename(filename))[0]
        return int(filename)
    except:
        raise ValueError(
            "Filename %s is supposed to be an integer." % (filename))


def get_categories(xml_files):
    """Generate category name to id mapping from a list of xml files.

    Arguments:
        xml_files {list} -- A list of xml file paths.

    Returns:
        dict -- category name to id mapping.
    """
    classes_names = []
    for xml_file in xml_files:
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for member in root.findall("object"):
            classes_names.append(member[0].text)
    classes_names = list(set(classes_names))
    classes_names.sort()
    return {name: i for i, name in enumerate(classes_names)}


def convert(xml_files, json_file):
    json_dict = {"images": [], "type": "instances",
                 "annotations": [], "categories": []}
    if PRE_DEFINE_CATEGORIES is not None:
        categories = PRE_DEFINE_CATEGORIES
    else:
        categories = get_categories(xml_files)
    bnd_id = START_BOUNDING_BOX_ID
    for xml_file in xml_files:
        tree = ET.parse(xml_file)
        root = tree.getroot()
        path = get(root, "path")
        if len(path) == 1:
            filename = os.path.basename(path[0].text)
        elif len(path) == 0:
            filename = get_and_check(root, "filename", 1).text
        else:
            raise ValueError("%d paths found in %s" % (len(path), xml_file))
        # The filename must be a number
        image_id = get_filename_as_int(filename)
        size = get_and_check(root, "size", 1)
        width = int(get_and_check(size, "width", 1).text)
        height = int(get_and_check(size, "height", 1).text)
        image = {
            "file_name": filename,
            "height": height,
            "width": width,
            "id": image_id,
        }
        json_dict["images"].append(image)
        # Currently we do not support segmentation.
        #  segmented = get_and_check(root, 'segmented', 1).text
        #  assert segmented == '0'
        for obj in get(root, "object"):
            category = get_and_check(obj, "name", 1).text
            if category not in categories:
                new_id = len(categories)
                categories[category] = new_id
            category_id = categories[category]
            bndbox = get_and_check(obj, "bndbox", 1)
            xmin = int(get_and_check(bndbox, "xmin", 1).text) - 1
            ymin = int(get_and_check(bndbox, "ymin", 1).text) - 1
            xmax = int(get_and_check(bndbox, "xmax", 1).text)
            ymax = int(get_and_check(bndbox, "ymax", 1).text)
            assert xmax > xmin
            assert ymax > ymin
            o_width = abs(xmax - xmin)
            o_height = abs(ymax - ymin)
            ann = {
                "area": o_width * o_height,
                "iscrowd": 0,
                "image_id": image_id,
                "bbox": [xmin, ymin, o_width, o_height],
                "category_id": category_id,
                "id": bnd_id,
                "ignore": 0,
                "segmentation": [],
            }
            json_dict["annotations"].append(ann)
            bnd_id = bnd_id + 1

    for cate, cid in categories.items():
        cat = {"supercategory": "none", "id": cid, "name": cate}
        json_dict["categories"].append(cat)

    os.makedirs(os.path.dirname(json_file), exist_ok=True)
    json_fp = open(json_file, "w")
    json_str = json.dumps(json_dict)
    json_fp.write(json_str)
    json_fp.close()


if __name__ == "__main__":
    #  只需修改以下两个路径
    #  VOC数据集根目录
    voc_path = "VOC2012"
    
    #  保存coco格式数据集根目录
    save_coco_path = "VOC2COCO"
    
    #  VOC只分了训练集和验证集即train.txt和val.txt
    data_type_list = ["train", "val"]
    for data_type in data_type_list:
        os.makedirs(os.path.join(save_coco_path, data_type+"2017"))
        os.makedirs(os.path.join(save_coco_path, data_type+"_xml"))
        with open(os.path.join(voc_path, "ImageSets\Main", data_type+".txt"), "r") as f:
            txt_ls = f.readlines()
        txt_ls = [i.strip() for i in txt_ls]
        for i in os.listdir(os.path.join(voc_path, "JPEGImages")):
            if os.path.splitext(i)[0] in txt_ls:
                shutil.copy(os.path.join(voc_path, "JPEGImages", i),
                            os.path.join(save_coco_path, data_type+"2017", i))
                shutil.copy(os.path.join(voc_path, "Annotations", i[:-4]+".xml"), os.path.join(
                    save_coco_path, data_type+"_xml", i[:-4]+".xml"))
        xml_path = os.path.join(save_coco_path, data_type+"_xml")
        xml_files = glob.glob(os.path.join(xml_path, "*.xml"))
        convert(xml_files, os.path.join(save_coco_path,
                "annotations", "instances_"+data_type+"2017.json"))
        shutil.rmtree(xml_path)

结果如图所示，在voc2coco文件夹下有三个文件：

二、配置DETR

修改main.py文件中的参数、超参数：

这个最好不改，就设为coco。去修改models/detr.py 文件的num_classes(大概在三百多行)。这里作者也解释了num_classes其实并不是类别数，因为coco只有80类，因为coco的id是不连续的，coco数据集最大的ID是90，所以原论文时写的MAX ID +1 即91。对于我们自定义的和转化的VOC数据集num_classes就是类别数。

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coco_path改成自己的coco路径。

其中预训练权重需要修改一下，coco是80类，不能直接加载官方的模型。voc是20类。把num_classes改成21。传入得到的detr_r50_21.pth新的权重文件。

import torch
pretrained_weights=torch.load('detr-r50-e632da11.pth')
num_classes=21
pretrained_weights["model"]["class_embed.weight"].resize_(num_classes+1,256)
pretrained_weights["model"]["class_embed.bias"].resize_(num_classes+1)
torch.save(pretrained_weights,"detr_r50_%d.path"%num_classes)

运行日志（特别难训练）：

References

VOC2COCO代码参考Github
DETR预训练模型