models/detectron2/platform_detector_setup.py

import sys, os, distutils.core

# os.system('python -m pip install pyyaml==5.3.1')
# dist = distutils.core.run_setup("./detectron2/setup.py")
# temp = ' '.join([f"'{x}'" for x in dist.install_requires])
# cmd = "python -m pip install {0}".format(temp)
# os.system(cmd)
sys.path.insert(0, os.path.abspath('./detectron2'))

import detectron2
import cv2

from detectron2.utils.logger import setup_logger
setup_logger()

# from detectron2.modeling import build_model
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.data.datasets import register_coco_instances

def get_platform_detector():
    cfg = get_cfg()
    cfg.OUTPUT_DIR = "./output/platform/"
    # model = build_model(cfg)  # returns a torch.nn.Module
    cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
    cfg.DATASETS.TEST = ()
    cfg.DATALOADER.NUM_WORKERS = 2
    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")  # Let training initialize from model zoo
    cfg.SOLVER.IMS_PER_BATCH = 2  # This is the real "batch size" commonly known to deep learning people
    cfg.SOLVER.BASE_LR = 0.00025  # pick a good LR
    cfg.SOLVER.MAX_ITER = 300    # 300 iterations seems good enough for this toy dataset; you will need to train longer for a practical dataset
    cfg.SOLVER.STEPS = []        # do not decay learning rate
    cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # The "RoIHead batch size". 128 is faster, and good enough for this toy dataset (default: 512)
    cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
    cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")  # path to the model we just trained
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7   # set a custom testing threshold
    predictor = DefaultPredictor(cfg)
    return predictor

# register_coco_instances("springboard_trains", {}, "./coco_annotations/springboard/train.json", "../data/Boards/spring")
# register_coco_instances("springboard_vals", {}, "./coco_annotations/springboard/val.json", "../data/Boards/spring")

# from detectron2.utils.visualizer import ColorMode
# splash_metadata = MetadataCatalog.get('springboard_vals')
# dataset_dicts = DatasetCatalog.get("springboard_vals")