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。

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。

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）：

三、绘图

在util文件夹下有plot_utils.py文件，可以绘制损失和mAP曲线。

在plot_utils.py文件中加入代码运行即可：

if __name__ == "__main__":
	# 路径更换为保存输出的eval路径
	# mAP曲线
    files=list(Path("./outputs/eval").glob("*.pth"))
    plot_precision_recall(files)
    plt.show()
    # 路径更换为保存输出的路径
    # 损失曲线
    plot_logs(Path("./output"))
    plt.show()

四、推理

训练完毕后我们会得到一个checkpoint.pth的文件，可以用自己训练得到的模型来推理图片，代码如下：

import argparse
import numpy as np
from models.detr import DETR
from models.backbone import Backbone, build_backbone
from models.transformer import build_transformer
from PIL import Image
import cv2
import matplotlib.pyplot as plt
import torch
import torchvision.transforms as T
torch.set_grad_enabled(False)
def get_args_parser():
    parser = argparse.ArgumentParser('Set transformer detector', add_help=False)
    parser.add_argument('--lr', default=1e-4, type=float)
    parser.add_argument('--lr_backbone', default=1e-5, type=float)
    parser.add_argument('--batch_size', default=2, type=int)
    parser.add_argument('--weight_decay', default=1e-4, type=float)
    parser.add_argument('--epochs', default=300, type=int)
    parser.add_argument('--lr_drop', default=200, type=int)
    parser.add_argument('--clip_max_norm', default=0.1, type=float,
                        help='gradient clipping max norm')
    # Model parameters
    parser.add_argument('--frozen_weights', type=str, default=None,
                        help="Path to the pretrained model. If set, only the mask head will be trained")
    # * Backbone
    parser.add_argument('--backbone', default='resnet50', type=str,
                        help="Name of the convolutional backbone to use")
    parser.add_argument('--dilation', action='store_true',
                        help="If true, we replace stride with dilation in the last convolutional block (DC5)")
    parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
                        help="Type of positional embedding to use on top of the image features")
    # * Transformer
    parser.add_argument('--enc_layers', default=6, type=int,
                        help="Number of encoding layers in the transformer")
    parser.add_argument('--dec_layers', default=6, type=int,
                        help="Number of decoding layers in the transformer")
    parser.add_argument('--dim_feedforward', default=2048, type=int,
                        help="Intermediate size of the feedforward layers in the transformer blocks")
    parser.add_argument('--hidden_dim', default=256, type=int,
                        help="Size of the embeddings (dimension of the transformer)")
    parser.add_argument('--dropout', default=0.1, type=float,
                        help="Dropout applied in the transformer")
    parser.add_argument('--nheads', default=8, type=int,
                        help="Number of attention heads inside the transformer's attentions")
    parser.add_argument('--num_queries', default=100, type=int,
                        help="Number of query slots")
    parser.add_argument('--pre_norm', action='store_true')
    # * Segmentation
    parser.add_argument('--masks', action='store_true',
                        help="Train segmentation head if the flag is provided")
    # Loss
    parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
                        help="Disables auxiliary decoding losses (loss at each layer)")
    # * Matcher
    parser.add_argument('--set_cost_class', default=1, type=float,
                        help="Class coefficient in the matching cost")
    parser.add_argument('--set_cost_bbox', default=5, type=float,
                        help="L1 box coefficient in the matching cost")
    parser.add_argument('--set_cost_giou', default=2, type=float,
                        help="giou box coefficient in the matching cost")
    # * Loss coefficients
    parser.add_argument('--mask_loss_coef', default=1, type=float)
    parser.add_argument('--dice_loss_coef', default=1, type=float)
    parser.add_argument('--bbox_loss_coef', default=5, type=float)
    parser.add_argument('--giou_loss_coef', default=2, type=float)
    parser.add_argument('--eos_coef', default=0.1, type=float,
                        help="Relative classification weight of the no-object class")
    # dataset parameters
    parser.add_argument('--dataset_file', default='coco')
    parser.add_argument('--coco_path', type=str, default=r"F:\DLdata\VOC2COCO")
    parser.add_argument('--coco_panoptic_path', type=str)
    parser.add_argument('--remove_difficult', action='store_true')
    parser.add_argument('--output_dir', default='./output',
                        help='path where to save, empty for no saving')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--resume', default='detr_r50_21.path', help='resume from checkpoint')
    # parser.add_argument('--resume', default='detr-r50-e632da11.pth', help='resume from checkpoint')
    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', action='store_true')
    parser.add_argument('--num_workers', default=0, type=int)
    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    return parser
COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], [0.494, 0.184, 0.556],
          [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]
transform_input = T.Compose([T.Resize(800),
                             T.ToTensor(),
                             T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=1)
def rescale_bboxes(out_bbox, size):
    img_w, img_h = size
    b = box_cxcywh_to_xyxy(out_bbox)
    b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32, device="cuda")
    return b
def plot_results(pil_img, prob, boxes, img_save_path):
    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100
    for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
                                   fill=False, color=c, linewidth=3))
        cl = p.argmax()
        text = f'{CLASSES[cl]}:      {p[cl]:0.2f}'
        ax.text(xmin, ymin, text, fontsize=9,
                bbox=dict(facecolor='yellow', alpha=0.5))
    plt.savefig(img_save_path)
    plt.axis('off')
    plt.show()
def main(num_classes, chenkpoint_path, img_path, img_save_path, num_queries=100):
    parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args()
    backbone = build_backbone(args)
    transform = build_transformer(args)
    model = DETR(backbone=backbone, transformer=transform, num_classes=num_classes, num_queries=100)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    model_path = chenkpoint_path
    model_data = torch.load(model_path)['model']
    model.load_state_dict(model_data)
    model.eval()
    path = img_path
    im = cv2.imread(path)
    im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
    img = transform_input(im).unsqueeze(0)
    outputs = model(img.to(device))
    probs = outputs['pred_logits'].softmax(-1)[0, :, :-1]
    # 可修改阈值,只输出概率大于0.7的物体
    keep = probs.max(-1).values > 0.7
    # print(probs[keep])
    bboxes_scaled = rescale_bboxes(outputs['pred_boxes'][0, keep], im.size)
    ori_img = np.array(im)
    plot_results(ori_img, probs[keep], bboxes_scaled, img_save_path)
if __name__ == "__main__":
    CLASSES = ['N/A', "aeroplane", "bicycle", "bird", "boat",
               "bottle", "bus", "car", "cat", "chair",
               "cow", "diningtable", "dog", "horse",
               "motorbike", "person", "pottedplant",
               "sheep", "sofa", "train", "tvmonitor"]
    main(num_classes=21, chenkpoint_path="checkpoint.pth", img_path="test.png",
         img_save_path="result2.png")

几点说明：
1.CLASSES是我们数据集对应的类别名，注意自己标注的顺序一定写对。第一个类别是背景类，这个是固定的，所有数据集都要有。
2.
num_classes：类别数+1
chenkpoint_path：保存的权重文件
img_path：测试的图片路径
img_save_path：保存结果路径
3.可修改阈值，论文中默认只输出概率大于0.7的物体。

用VOC数据集训练的模型推理效果：
（VOC数据集中没有自行车一类所以识别不出来）

五、一些小bug

UserWarning: floordiv is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the ‘trunc’ function NOT ‘floor’). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode=‘trunc’), or for actual floor division, use torch.div(a, b, rounding_mode=‘floor’).
这时一个torch版本原因导致的一个函数问题，报了一个警告。
将models/position_encoding.py文件中的第44行改成如下形式即可。

References

VOC2COCO代码参考Github
DETR预训练模型