Source code for

# Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
from typing import List, Optional, Union
import torch

from detectron2.config import configurable

from . import detection_utils as utils
from . import transforms as T

This file contains the default mapping that's applied to "dataset dicts".

__all__ = ["DatasetMapper"]

[docs]class DatasetMapper: """ A callable which takes a dataset dict in Detectron2 Dataset format, and map it into a format used by the model. This is the default callable to be used to map your dataset dict into training data. You may need to follow it to implement your own one for customized logic, such as a different way to read or transform images. See :doc:`/tutorials/data_loading` for details. The callable currently does the following: 1. Read the image from "file_name" 2. Applies cropping/geometric transforms to the image and annotations 3. Prepare data and annotations to Tensor and :class:`Instances` """
[docs] @configurable def __init__( self, is_train: bool, *, augmentations: List[Union[T.Augmentation, T.Transform]], image_format: str, use_instance_mask: bool = False, use_keypoint: bool = False, instance_mask_format: str = "polygon", keypoint_hflip_indices: Optional[np.ndarray] = None, precomputed_proposal_topk: Optional[int] = None, recompute_boxes: bool = False, ): """ NOTE: this interface is experimental. Args: is_train: whether it's used in training or inference augmentations: a list of augmentations or deterministic transforms to apply image_format: an image format supported by :func:`detection_utils.read_image`. use_instance_mask: whether to process instance segmentation annotations, if available use_keypoint: whether to process keypoint annotations if available instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation masks into this format. keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices` precomputed_proposal_topk: if given, will load pre-computed proposals from dataset_dict and keep the top k proposals for each image. recompute_boxes: whether to overwrite bounding box annotations by computing tight bounding boxes from instance mask annotations. """ if recompute_boxes: assert use_instance_mask, "recompute_boxes requires instance masks" # fmt: off self.is_train = is_train self.augmentations = T.AugmentationList(augmentations) self.image_format = image_format self.use_instance_mask = use_instance_mask self.instance_mask_format = instance_mask_format self.use_keypoint = use_keypoint self.keypoint_hflip_indices = keypoint_hflip_indices self.proposal_topk = precomputed_proposal_topk self.recompute_boxes = recompute_boxes # fmt: on logger = logging.getLogger(__name__) mode = "training" if is_train else "inference""[DatasetMapper] Augmentations used in {mode}: {augmentations}")
[docs] @classmethod def from_config(cls, cfg, is_train: bool = True): augs = utils.build_augmentation(cfg, is_train) if cfg.INPUT.CROP.ENABLED and is_train: augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE)) recompute_boxes = cfg.MODEL.MASK_ON else: recompute_boxes = False ret = { "is_train": is_train, "augmentations": augs, "image_format": cfg.INPUT.FORMAT, "use_instance_mask": cfg.MODEL.MASK_ON, "instance_mask_format": cfg.INPUT.MASK_FORMAT, "use_keypoint": cfg.MODEL.KEYPOINT_ON, "recompute_boxes": recompute_boxes, } if cfg.MODEL.KEYPOINT_ON: ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) if cfg.MODEL.LOAD_PROPOSALS: ret["precomputed_proposal_topk"] = ( cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN if is_train else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST ) return ret
def _transform_annotations(self, dataset_dict, transforms, image_shape): # USER: Modify this if you want to keep them for some reason. for anno in dataset_dict["annotations"]: if not self.use_instance_mask: anno.pop("segmentation", None) if not self.use_keypoint: anno.pop("keypoints", None) # USER: Implement additional transformations if you have other types of data annos = [ utils.transform_instance_annotations( obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices ) for obj in dataset_dict.pop("annotations") if obj.get("iscrowd", 0) == 0 ] instances = utils.annotations_to_instances( annos, image_shape, mask_format=self.instance_mask_format ) # After transforms such as cropping are applied, the bounding box may no longer # tightly bound the object. As an example, imagine a triangle object # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to # the intersection of original bounding box and the cropping box. if self.recompute_boxes: instances.gt_boxes = instances.gt_masks.get_bounding_boxes() dataset_dict["instances"] = utils.filter_empty_instances(instances)
[docs] def __call__(self, dataset_dict): """ Args: dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. Returns: dict: a format that builtin models in detectron2 accept """ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below # USER: Write your own image loading if it's not from a file image = utils.read_image(dataset_dict["file_name"], format=self.image_format) utils.check_image_size(dataset_dict, image) # USER: Remove if you don't do semantic/panoptic segmentation. if "sem_seg_file_name" in dataset_dict: sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2) else: sem_seg_gt = None aug_input = T.AugInput(image, sem_seg=sem_seg_gt) transforms = self.augmentations(aug_input) image, sem_seg_gt = aug_input.image, aug_input.sem_seg image_shape = image.shape[:2] # h, w # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory, # but not efficient on large generic data structures due to the use of pickle & mp.Queue. # Therefore it's important to use torch.Tensor. dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) if sem_seg_gt is not None: dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long")) # USER: Remove if you don't use pre-computed proposals. # Most users would not need this feature. if self.proposal_topk is not None: utils.transform_proposals( dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk ) if not self.is_train: # USER: Modify this if you want to keep them for some reason. dataset_dict.pop("annotations", None) dataset_dict.pop("sem_seg_file_name", None) return dataset_dict if "annotations" in dataset_dict: self._transform_annotations(dataset_dict, transforms, image_shape) return dataset_dict