计算机视觉工具：Detectron2学习手册（二）训练自己的数据集_detectron2注册pascal voc格式数据集

作者：Monodyee | 2024-03-31 16:12:11

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detectron2注册pascal voc格式数据集

Detectron2环境配置，在本专栏上一篇文章

环境配置

一　数据集准备

由于我的数据及标注类型是PASCAL VOC格式，现需要做一个格式转化，转化为COCO数据集格式，其实Detectron2是支持对PASCAL-VOC类型数据进行训练的，但为了更加好的队训练结果进行评价，这里还是使用COCO格式数据集：
原文件夹目录如下：

Annotations
– 000001.xml
– 000002.xml
– …
ImageSets
– Main
— train.txt
— val.txt
JPEGImages
– 000001.jpg
– 000002.jog
–…
labels
– 000001.txt
– 000002.txt
– …
这里只需要Annotations(Annotations-train,Annotations-val)和JPEGImages(JPEGImages-train,JPEGImages-val)两个文件夹，将其分为训练集和测试集，对其转化为COCO格式.

创建COCO数据集

cd detectron2/detecrton2/data/datasets
mkdir coco
cd coco
mkdir annotations train2017 val2017
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PASCAL-VOC转COCO

#!/usr/bin/python

# pip install lxml

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

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 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__":
    import argparse

    parser = argparse.ArgumentParser(
        description="Convert Pascal VOC annotation to COCO format."
    )
    parser.add_argument("xml_dir", help="Directory path to xml files.", type=str)
    parser.add_argument("json_file", help="Output COCO format json file.", type=str)
    args = parser.parse_args()
    xml_files = glob.glob(os.path.join(args.xml_dir, "*.xml"))

    # If you want to do train/test split, you can pass a subset of xml files to convert function.
    print("Number of xml files: {}".format(len(xml_files)))
    convert(xml_files, args.json_file)
    print("Success: {}".format(args.json_file))
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python voc2coco.py ./Annotations-train ./annotations/instances-train2017.json
python voc2coco.py ./Annotations-val ./annotations/instances-val2017.json
cp ./JPEGImages-train/*.jpg ./train2017
cp ./JPEGImages-val/*.jpg ./val2017
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数据及制作完毕．

COCO格式数据集注册

（１）修改detectron2/detectron2/data/datasets/builtin.py文件

# ==== Predefined datasets and splits for COCO ==========
_PREDEFINED_SPLITS_COCO = {}
_PREDEFINED_SPLITS_COCO["coco"] = {
    "coco_2017_train": ("/home/xxx/detectron2/detectron2/data/datasets/coco/train2017", "/home/xxx/detectron2/detectron2/data/datasets/coco/annotations/instances_train2017.json"),
    "coco_2017_val": ("/home/xxx/detectron2/detectron2/data/datasets/coco/val2017", "/home/xxx/detectron2/detectron2/data/datasets/coco/annotations/instances_val2017.json"),
    "coco_2017_val_100": ("/home/xxx/detectron2/detectron2/data/datasets/coco/val2017", "/home/xxx/detectron2/detectron2/data/datasets/coco/annotations/instances_val2017.json"),
}
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注释掉源代码中的_PREDEFINED_SPLITS_COCO[“coco”]，对应修改为自己的数据集．
填写绝对路径

（２）修改detectron2/detectron2/data/datasets/builtin_meta.py文件

将COCO数据集定义的变量直接转化为自己数据集的变量，同时将源代码修改为_base

COCO_CATEGORIES = [
    {"color": [120, 166, 157], "isthing": 1, "id": 0, "name": "lateral"},
    {"color": [120, 166, 157], "isthing": 1, "id": 1, "name": "sit"},
    {"color": [120, 166, 157], "isthing": 1, "id": 2, "name": "stand"},
    {"color": [120, 166, 157], "isthing": 1, "id": 3, "name": "sternum"},
]
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id是从０开始的，如果id从１开始，需要将第一个目标种类设置为＂background＂．

def _get_coco_instances_meta():
    thing_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 1]
    thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
    assert len(thing_ids) == 4, len(thing_ids)　#　注意这里断言语句要与自己的类别数相同
    # Mapping from the incontiguous COCO category id to an id in [0, 79]
    thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
    thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
    ret = {
        "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
        "thing_classes": thing_classes,
        "thing_colors": thing_colors,
    }
    return ret
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def _get_coco_panoptic_separated_meta():
    """
    Returns metadata for "separated" version of the panoptic segmentation dataset.
    """
    stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0]
    assert len(stuff_ids) == 0, len(stuff_ids)　#　注意这里没有背景类时候，应设置为０

    # For semantic segmentation, this mapping maps from contiguous stuff id
    # (in [0, 53], used in models) to ids in the dataset (used for processing results)
    # The id 0 is mapped to an extra category "thing".
    stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)}
    # When converting COCO panoptic annotations to semantic annotations
    # We label the "thing" category to 0
    stuff_dataset_id_to_contiguous_id[0] = 0
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（３）修改detectron2/tools/train_net.py

cd detectron2/tools
cp train_net.py train.py
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#引入以下注释
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets.coco import load_coco_json
import pycocotools
import os
import logging
import os
from collections import OrderedDict
import torch

import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
from detectron2.evaluation import (
    CityscapesInstanceEvaluator,
    CityscapesSemSegEvaluator,
    COCOEvaluator,
    COCOPanopticEvaluator,
    DatasetEvaluators,
    LVISEvaluator,
    PascalVOCDetectionEvaluator,
    SemSegEvaluator,
    verify_results,
)
from detectron2.modeling import GeneralizedRCNNWithTTA

#声明类别，尽量保持
CLASS_NAMES =["lateral","sit","stand","sternum"]
# 数据集路径
DATASET_ROOT = '/home/xxx/detectron2/detectron2/data/datasets/coco'
ANN_ROOT = os.path.join(DATASET_ROOT, 'annotations')

TRAIN_PATH = os.path.join(DATASET_ROOT, 'train2017')
VAL_PATH = os.path.join(DATASET_ROOT, 'val2017')

TRAIN_JSON = os.path.join(ANN_ROOT, 'instances_train2017.json')
#VAL_JSON = os.path.join(ANN_ROOT, 'val.json')
VAL_JSON = os.path.join(ANN_ROOT, 'instances_val2017.json')

# 声明数据集的子集
PREDEFINED_SPLITS_DATASET = {
    "coco_2017_train": (TRAIN_PATH, TRAIN_JSON),
    "coco_2017_val": (VAL_PATH, VAL_JSON),
}
#===========以下有两种注册数据集的方法，本人直接用的第二个plain_register_dataset的方式 也可以用register_dataset的形式==================
#注册数据集（这一步就是将自定义数据集注册进Detectron2）
def register_dataset():
    """
    purpose: register all splits of dataset with PREDEFINED_SPLITS_DATASET
    """
    for key, (image_root, json_file) in PREDEFINED_SPLITS_DATASET.items():
        register_dataset_instances(name=key,
                                   json_file=json_file,
                                   image_root=image_root)


#注册数据集实例，加载数据集中的对象实例
def register_dataset_instances(name, json_file, image_root):
    """
    purpose: register dataset to DatasetCatalog,
             register metadata to MetadataCatalog and set attribute
    """
    DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
    MetadataCatalog.get(name).set(json_file=json_file,
                                  image_root=image_root,
                                  evaluator_type="coco")

#=============================
# 注册数据集和元数据
def plain_register_dataset():
    #训练集
    DatasetCatalog.register("coco_2017_train", lambda: load_coco_json(TRAIN_JSON, TRAIN_PATH))
    MetadataCatalog.get("coco_2017_train").set(thing_classes=CLASS_NAMES,  # 可以选择开启，但是不能显示中文，这里需要注意，中文的话最好关闭
                                                    evaluator_type='coco', # 指定评估方式
                                                    json_file=TRAIN_JSON,
                                                    image_root=TRAIN_PATH)

    #DatasetCatalog.register("coco_my_val", lambda: load_coco_json(VAL_JSON, VAL_PATH, "coco_2017_val"))
    #验证/测试集
    DatasetCatalog.register("coco_2017_val", lambda: load_coco_json(VAL_JSON, VAL_PATH))
    MetadataCatalog.get("coco_2017_val").set(thing_classes=CLASS_NAMES, # 可以选择开启，但是不能显示中文，这里需要注意，中文的话最好关闭
                                                evaluator_type='coco', # 指定评估方式
                                                json_file=VAL_JSON,
                                                image_root=VAL_PATH)
# 查看数据集标注，可视化检查数据集标注是否正确，
#这个也可以自己写脚本判断，其实就是判断标注框是否超越图像边界
#可选择使用此方法
def checkout_dataset_annotation(name="coco_2017_val"):
    #dataset_dicts = load_coco_json(TRAIN_JSON, TRAIN_PATH, name)
    dataset_dicts = load_coco_json(TRAIN_JSON, TRAIN_PATH)
    print(len(dataset_dicts))
    for i, d in enumerate(dataset_dicts,0):
        #print(d)
        img = cv2.imread(d["file_name"])
        visualizer = Visualizer(img[:, :, ::-1], metadata=MetadataCatalog.get(name), scale=1.5)
        vis = visualizer.draw_dataset_dict(d)
        #cv2.imshow('show', vis.get_image()[:, :, ::-1])
        cv2.imwrite('out/'+str(i) + '.jpg',vis.get_image()[:, :, ::-1])
        #cv2.waitKey(0)
        if i == 200:
            break


class Trainer(DefaultTrainer):
    """
    We use the "DefaultTrainer" which contains pre-defined default logic for
    standard training workflow. They may not work for you, especially if you
    are working on a new research project. In that case you can write your
    own training loop. You can use "tools/plain_train_net.py" as an example.
    """

    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        """
        Create evaluator(s) for a given dataset.
        This uses the special metadata "evaluator_type" associated with each builtin dataset.
        For your own dataset, you can simply create an evaluator manually in your
        script and do not have to worry about the hacky if-else logic here.
        """
        if output_folder is None:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
        evaluator_list = []
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
        if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
            evaluator_list.append(
                SemSegEvaluator(
                    dataset_name,
                    distributed=True,
                    num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
                    ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
                    output_dir=output_folder,
                )
            )
        if evaluator_type in ["coco", "coco_panoptic_seg"]:
            evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
        if evaluator_type == "coco_panoptic_seg":
            evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
        if evaluator_type == "cityscapes_instance":
            assert (
                torch.cuda.device_count() >= comm.get_rank()
            ), "CityscapesEvaluator currently do not work with multiple machines."
            return CityscapesInstanceEvaluator(dataset_name)
        if evaluator_type == "cityscapes_sem_seg":
            assert (
                torch.cuda.device_count() >= comm.get_rank()
            ), "CityscapesEvaluator currently do not work with multiple machines."
            return CityscapesSemSegEvaluator(dataset_name)
        elif evaluator_type == "pascal_voc":
            return PascalVOCDetectionEvaluator(dataset_name)
        elif evaluator_type == "lvis":
            return LVISEvaluator(dataset_name, cfg, True, output_folder)
        if len(evaluator_list) == 0:
            raise NotImplementedError(
                "no Evaluator for the dataset {} with the type {}".format(
                    dataset_name, evaluator_type
                )
            )
        elif len(evaluator_list) == 1:
            return evaluator_list[0]
        return DatasetEvaluators(evaluator_list)

    @classmethod
    def test_with_TTA(cls, cfg, model):
        logger = logging.getLogger("detectron2.trainer")
        # In the end of training, run an evaluation with TTA
        # Only support some R-CNN models.
        logger.info("Running inference with test-time augmentation ...")
        model = GeneralizedRCNNWithTTA(cfg, model)
        evaluators = [
            cls.build_evaluator(
                cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
            )
            for name in cfg.DATASETS.TEST
        ]
        res = cls.test(cfg, model, evaluators)
        res = OrderedDict({k + "_TTA": v for k, v in res.items()})
        return res

def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    args.config_file = "../configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml"
    cfg.merge_from_file(args.config_file)   # 从config file 覆盖配置
    cfg.merge_from_list(args.opts)          # 从CLI参数 覆盖配置

    # 更改配置参数
    cfg.DATASETS.TRAIN = ("coco_2017_train",) # 训练数据集名称
    cfg.DATASETS.TEST = ("coco_2017_val",)
    cfg.DATALOADER.NUM_WORKERS = 4  # 单线程

    cfg.INPUT.CROP.ENABLED = True
    cfg.INPUT.MAX_SIZE_TRAIN = 640 # 训练图片输入的最大尺寸
    cfg.INPUT.MAX_SIZE_TEST = 640 # 测试数据输入的最大尺寸
    cfg.INPUT.MIN_SIZE_TRAIN = (512, 768) # 训练图片输入的最小尺寸，可以设定为多尺度训练
    cfg.INPUT.MIN_SIZE_TEST = 640
    #cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING，其存在两种配置，分别为 choice 与 range ：
    # range 让图像的短边从 512-768随机选择
    #choice ： 把输入图像转化为指定的，有限的几种图片大小进行训练，即短边只能为 512或者768
    cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING = 'range'
#  本句一定要看下注释！！！！！！！！
    cfg.MODEL.RETINANET.NUM_CLASSES = 4  # 类别数+1（因为有background，也就是你的 cate id 从 1 开始，如果您的数据集Json下标从 0 开始，这个改为您对应的类别就行，不用再加背景类！！！！！）
    #cfg.MODEL.WEIGHTS="/home/yourstorePath/.pth"
    cfg.MODEL.WEIGHTS = "../model_zoo/model_final_721ade.pkl"    # 预训练模型权重
    #cfg.MODEL.WEIGHTS = "output/model_0002248.pth"  #测试时使用的模型，看自己训练结果的outputs
    cfg.SOLVER.IMS_PER_BATCH = 4  # batch_size=2; iters_in_one_epoch = dataset_imgs/batch_size

    # 根据训练数据总数目以及batch_size，计算出每个epoch需要的迭代次数
    #9000为你的训练数据的总数目，可自定义
    ITERS_IN_ONE_EPOCH = int(1000 / cfg.SOLVER.IMS_PER_BATCH)

    # 指定最大迭代次数
    cfg.SOLVER.MAX_ITER = (ITERS_IN_ONE_EPOCH * 12) - 1 # 12 epochs，
    # 初始学习率
    cfg.SOLVER.BASE_LR = 0.002
    # 优化器动能
    cfg.SOLVER.MOMENTUM = 0.9
    #权重衰减
    cfg.SOLVER.WEIGHT_DECAY = 0.0001
    cfg.SOLVER.WEIGHT_DECAY_NORM = 0.0
    # 学习率衰减倍数
    cfg.SOLVER.GAMMA = 0.1
    # 迭代到指定次数，学习率进行衰减
    cfg.SOLVER.STEPS = (800,)
    # 在训练之前，会做一个热身运动，学习率慢慢增加初始学习率
    cfg.SOLVER.WARMUP_FACTOR = 1.0 / 1000
    # 热身迭代次数
    cfg.SOLVER.WARMUP_ITERS = 100

    cfg.SOLVER.WARMUP_METHOD = "linear"
    # 保存模型文件的命名数据减1
    cfg.SOLVER.CHECKPOINT_PERIOD = ITERS_IN_ONE_EPOCH - 1

    # 迭代到指定次数，进行一次评估
    cfg.TEST.EVAL_PERIOD = ITERS_IN_ONE_EPOCH
    #cfg.TEST.EVAL_PERIOD = 100

    #cfg.merge_from_file(args.config_file)
    #cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg

def main(args):
    cfg = setup(args)

    if args.eval_only:
        model = Trainer.build_model(cfg)
        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        res = Trainer.test(cfg, model)
        if cfg.TEST.AUG.ENABLED:
            res.update(Trainer.test_with_TTA(cfg, model))
        if comm.is_main_process():
            verify_results(cfg, res)
        return res

    """
    If you'd like to do anything fancier than the standard training logic,
    consider writing your own training loop (see plain_train_net.py) or
    subclassing the trainer.
    """
    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    if cfg.TEST.AUG.ENABLED:
        trainer.register_hooks(
            [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))]
        )
    return trainer.train()


if __name__ == "__main__":
    args = default_argument_parser().parse_args()
    print("Command Line Args:", args)
    launch(
        main,
        args.num_gpus,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        args=(args,),
    )
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直接复制就行了．

(4)训练

python train.py --num-gpus 1
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在这里插入图片描述

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