detectron2 mask训练代码

以下是一个基本的 detectron2 mask 训练代码示例。

# 导入必要的库
import os
import numpy as np
import cv2

from detectron2.structures import BoxMode
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.engine import DefaultTrainer, DefaultPredictor
from detectron2.config import get_cfg
from detectron2.data.datasets import register_coco_instances
from detectron2.data import DatasetMapper
from detectron2.data.catalog import DatasetCatalog, MetadataCatalog
from detectron2.modeling import build_model
from detectron2.utils.logger import setup_logger
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.utils.events import CommonMetricPrinter, JSONWriter, TensorboardXWriter
from detectron2.engine import  DefaultPredictor


# 注册数据集
register_coco_instances("my_dataset_train",{}, os.path.join("path/to/train/annotation/file.json"), os.path.join("path/to/train/images/folder"))
register_coco_instances("my_dataset_val",{}, os.path.join("path/to/val/annotation/file.json"), os.path.join("path/to/val/images/folder"))


def custom_dataset_mapper(dataset_dict):
    dataset_dict = copy.deepcopy(dataset_dict)

    # 将数据集中的像素值和目标映射到 0-255 范围内。
    image = dataset_dict["image"]
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    annotations = dataset_dict['annotations']
    for annotation in annotations:
        annotation['bbox_mode'] = BoxMode.XYWH_ABS

    return {"image": image, "annotations": annotations, "height": image.shape[0], "width": image.shape[1]}

# 设置模型配置
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("my_dataset_train",)
cfg.DATASETS.TEST = ("my_dataset_val",)

cfg.DATALOADER.NUM_WORKERS = 4
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.0025
cfg.SOLVER.MAX_ITER = 1000
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1

# 向数据集目录中注册元数据
MetadataCatalog.get("my_dataset_train").set(thing_classes=["class1", "class2"])
MetadataCatalog.get("my_dataset_val").set(thing_classes=["class1", "class2"])

# 配置训练器
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)

# 训练模型
trainer.train()

# 进行预测
predictor = DefaultPredictor(cfg)
metadata = MetadataCatalog.get("my_dataset_val")

dataset_dicts = DatasetCatalog.get("my_dataset_val")
for d in random.sample(dataset_dicts, 3):
    im = cv2.imread(d["file_name"])
    outputs = predictor(im)
    v = Visualizer(im[:, :, ::-1], metadata=metadata, scale=1.2)
    v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    cv2.imshow('Image',v.get_image()[:,:,::-1])
    cv2.waitKey(0)

请注意，训练模型的具体内容可能会因您使用的模型而异，代码中的一些配置和参数也可能需要根据您的需求进行修改。建议使用 detectron2 官方文档作为参考。