CustomMapper.py

import detectron2
from detectron2.utils.logger import setup_logger
import torch
setup_logger()
import detectron2.data.transforms as T
from detectron2.data import detection_utils as utils

import copy

def polyp_training_mapper(dataset_dict):
    # Implement a mapper, similar to the default DatasetMapper, but with your own customizations
    dataset_dict = copy.deepcopy(dataset_dict)  # it will be modified by code below
    image = utils.read_image(dataset_dict["file_name"], format="BGR")
    transform_list = [
                      #T.Resize((800,800)),
                      T.RandomFlip(prob=0.5, horizontal=False, vertical=True),
                      T.RandomFlip(prob=0.5, horizontal=True, vertical=False), 
                      ]
    image, transforms = T.apply_transform_gens(transform_list, image)
    dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))

    annos = [
        utils.transform_instance_annotations(obj, transforms, image.shape[:2])
        for obj in dataset_dict.pop("annotations")
        if obj.get("iscrowd", 0) == 0
    ]
    instances = utils.annotations_to_instances(annos, image.shape[:2])
    dataset_dict["instances"] = utils.filter_empty_instances(instances)
    return dataset_dict