DETR(DEtection TRansforme)调试记录

Posted 2023-03-05

下载地址：
​​​ https://github.com/facebookresearch/detr​​ 调试过程开始了

环境配置

我们依旧使用的是NVIDIA T4 GPU 服务器

创建conda环境

conda create -n detr python=3.8

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

在安装依赖时报错：

WARNING conda.core.path_actions:verify(962): Unable to create
environments file. Path not writable.

采用命令sudo chown -R 1000:1000 /home/×××（yourname）/.conda 给予conda权限
对于博主而言即为：
sudo chown -R 1000:1000 /home/ubuntu/.conda
注意，在给予权限后需要重启一下。
但重启后发现依旧存在问题，如下，我们给了权限后依旧于事无补。

NoWritablePkgsDirError: No writeable pkgs directories configured.
  - /usr/local/miniconda3/pkgs
  - /home/ubuntu/.conda/pkgs

此时博主通过pip list发现，其实该包早已经安装成功了。

这种情况博主也是第一次遇到，至于原因：
首先，所有的包，不论base环境还是虚拟环境都是放在pkgs文件夹下。如果虚拟环境需要安装的包与pkgs中已有的包版本完全一样，则不会再下载，而是通过硬盘链接直接找到该包，反之当一个包被多个环境使用时，从某一个环境卸载该包也不会将其从pkgs文件夹删除。

随后便是将代码上传到服务器上。

然后我们使用Pycharm的Terminal连接服务器，激活环境并切换至detr代码存放目录

按照要求下载对应依赖包

pip install -r requirements.txt 
 -i https://mirrors.bfsu.edu.cn/pypi/web/simple/

但在执行这个命令时总是报错：

This error originates from a subprocess, and is likely not a problem
with pip.

而在详细看过错误信息后发现实际上其是由于无法下载coco的相关文件从而无法继续，而我们并不打算使用原始的COCO数据集。那么我可以考虑直接按照我们需要的依赖包即可。也就是下面几个:

pip install scipy
pip install onnx
pip install onnxruntime
pip install submitit  分布式训练可以使用Slurm和submitit,这里我们不需要

安装pycocotools和scipy，用于在COCO数据集上进行evaluation及训练

pip install pycocotools #使用这条命令安装pycocotools

数据集处理

随后我们需要将我们的自定义数据集转换为COCO格式。并顺带完成训练集与验证集的划分：

# coding:utf-8

# pip install lxml

import os
import glob
import json
import shutil
import numpy as np
import xml.etree.ElementTree as ET

path2 = "E:/graduate/datasets/cadcd/VOC2023/VOC2023/COCO"

START_BOUNDING_BOX_ID = 1


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


def get_and_check(root, name, length):
    vars = root.findall(name)
    if len(vars) == 0:
        raise NotImplementedError(Can not find %s in %s. % (name, root.tag))
    if length > 0 and len(vars) != length:
        raise NotImplementedError(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 convert(xml_list, json_file):
    json_dict = "images": [], "type": "instances", "annotations": [], "categories": []
    categories = pre_define_categories.copy()
    bnd_id = START_BOUNDING_BOX_ID
    all_categories = 
    for index, line in enumerate(xml_list):
        # print("Processing %s"%(line))
        xml_f = line
        tree = ET.parse(xml_f)
        root = tree.getroot()

        filename = os.path.basename(xml_f)[:-4] + ".jpg"
        image_id = 20190000001 + index
        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)
        ## Cruuently 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 in all_categories:
                all_categories[category] += 1
            else:
                all_categories[category] = 1
            if category not in categories:
                if only_care_pre_define_categories:
                    continue
                new_id = len(categories) + 1
                print(
                    "[warning] category  not in pre_define_categories(), create new id:  automatically".format(
                        category, pre_define_categories, new_id))
                categories[category] = new_id
            category_id = categories[category]
            bndbox = get_and_check(obj, bndbox, 1)
            xmin = int(float(get_and_check(bndbox, xmin, 1).text))
            ymin = int(float(get_and_check(bndbox, ymin, 1).text))
            xmax = int(float(get_and_check(bndbox, xmax, 1).text))
            ymax = int(float(get_and_check(bndbox, ymax, 1).text))
            #assert (xmax > xmin), "xmax <= xmin, ".format(line)
            if xmax <= xmin:
                continue
            if ymax <= ymin:
                continue
            #assert (ymax > ymin), "ymax <= ymin, ".format(line)
            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)
    json_fp = open(json_file, w)
    json_str = json.dumps(json_dict)
    json_fp.write(json_str)
    json_fp.close()
    print("------------create  done--------------".format(json_file))
    print("find  categories:  -->>> your pre_define_categories : ".format(len(all_categories),
                                                                                  all_categories.keys(),
                                                                                  len(pre_define_categories),
                                                                                  pre_define_categories.keys()))
    print("category: id --> ".format(categories))
    print(categories.keys())
    print(categories.values())


if __name__ == __main__:
    classes = [car, truck, bus, person,sign]
    pre_define_categories = 
    for i, cls in enumerate(classes):
        pre_define_categories[cls] = i + 1
    # pre_define_categories = a1: 1, a3: 2, a6: 3, a9: 4, "a10": 5
    only_care_pre_define_categories = True
    # only_care_pre_define_categories = False

    train_ratio = 0.9
    save_json_train = train2023.json
    save_json_val = val2023.json
    xml_dir = "E:/graduate/datasets/cadcd/VOC2023/VOC2023/Annotations"

    xml_list = glob.glob(xml_dir + "/*.xml")
    xml_list = np.sort(xml_list)
    np.random.seed(100)
    np.random.shuffle(xml_list)

    train_num = int(len(xml_list) * train_ratio)
    xml_list_train = xml_list[:train_num]
    xml_list_val = xml_list[train_num:]

    convert(xml_list_train, save_json_train)
    convert(xml_list_val, save_json_val)

    if os.path.exists(path2 + "/annotations"):
        shutil.rmtree(path2 + "/annotations")
    os.makedirs(path2 + "/annotations")
    if os.path.exists(path2 + "/images/train2023"):
        shutil.rmtree(path2 + "/images/train2023")
    os.makedirs(path2 + "/images/train2023")
    if os.path.exists(path2 + "/images/val2023"):
        shutil.rmtree(path2 + "/images/val2023")
    os.makedirs(path2 + "/images/val2023")

    f1 = open("train.txt", "w")
    for xml in xml_list_train:
        img = xml[:-4] + ".png"
        img=img.replace("Annotations", "JPEGImages")
        print(img)
        f1.write(os.path.basename(xml)[:-4] + "\\n")
        shutil.copyfile(img, path2 + "/images/train2023/" + os.path.basename(img))

    f2 = open("test.txt", "w")
    for xml in xml_list_val:
        img = xml[:-4] + ".png"
        img=img.replace("Annotations", "JPEGImages")
        f2.write(os.path.basename(xml)[:-4] + "\\n")
        shutil.copyfile(img, path2 + "/images/val2023/" + os.path.basename(img))
    f1.close()
    f2.close()
    print("-------------------------------")
    print("train number:", len(xml_list_train))
    print("val number:", len(xml_list_val))

此外还可通过Annocations来进行划分的，此时不进行训练集，验证集的划分。
关于COCO数据集中的参数解析

 
    "info" : info,
    "licenses" : [license],
    "images" : [image],
    "annotations" : [annataton],
    "categories" : [category]

最终完成标注转换与数据集划分：

代码修改

此时我们需要修改​​model/detr.py​​中的num_classes改成你的物体种类的数目。这里一定要修改，否则回报权重值不匹配的错误。

随后为了让我们加快训练我们可以去官网下载预训练模型：

下载完成后执行下面代码来修改一下模型：

import torch
pretrained_weights  = torch.load(detr-r50-e632da11.pth)

num_class = 6 #这里是你的物体数+1，因为背景也算一个
pretrained_weights["model"]["class_embed.weight"].resize_(num_class+1, 256)
pretrained_weights["model"]["class_embed.bias"].resize_(num_class+1)
torch.save(pretrained_weights, "detr-r50_%d.pth"%num_class)

随后我们修改下main.py的配置文件中的路径：

parser.add_argument(--coco_path, default=/home/ubuntu/detr/data/,type=str)

修改预训练模型

这里有个疑惑，在本地运行时使用默认的resnet-50正常，但在服务器上却报错。后来考虑大概是有用先前在本地运行时下载了resnet-50模型的原因。这里我们的骨干网络依旧使用resnet-50

parser.add_argument(--backbone, default=resnet-50, type=str,
                        help="Name of the convolutional backbone to use")

修改resume为自己的预训练权重文件路径

parser.add_argument(--resume, default=detr-r50_6.pth, help=resume from checkpoint)

随后再修改epoch=100与batch-size=8，num_workers=6。
GPU使用情况如下：由此可见，该模型很吃配置。

然后修改datasets/coco.py中的文件路径与标注路径：

PATHS = 
        "train": (root / "images/train2023", root / "annotations" / ftrain2023.json),
        "val": (root / "images/val2023", root / "annotations" / fval2023.json),
    

完成后运行main.py即可，运行成功：

补充

显存溢出了，正如作者所说，真的很吃配置。
RuntimeError: CUDA out of memory. Tried to allocate 34.00 MiB (GPU 0; 14.76 GiB total capacity; 13.63 GiB already allocated; 19.75 MiB free; 13.77 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

结语

个人觉得在训练那块还是改main.py文件比较好，需要改的地方挺多，我觉得需要修改的主要有–epoch(轮次)、–num_workers（主要看你电脑性能怎么样，好点可以调高些）、–output_dir（输出的模型权重，pth文件）、–dataset_file（数据存放位置）、–coco_path（coco数据集的位置）和–resume（预训练权重文件位置）。