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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import copy | |
| import logging | |
| import numpy as np | |
| import pycocotools.mask as mask_util | |
| import torch | |
| from torch.nn import functional as F | |
| from detectron2.config import configurable | |
| from detectron2.data import detection_utils as utils | |
| from detectron2.data import transforms as T | |
| from detectron2.projects.point_rend import ColorAugSSDTransform | |
| from detectron2.structures import BitMasks, Instances, polygons_to_bitmask | |
| __all__ = ["MaskFormerInstanceDatasetMapper"] | |
| class MaskFormerInstanceDatasetMapper: | |
| """ | |
| A callable which takes a dataset dict in Detectron2 Dataset format, | |
| and map it into a format used by MaskFormer for instance segmentation. | |
| The callable currently does the following: | |
| 1. Read the image from "file_name" | |
| 2. Applies geometric transforms to the image and annotation | |
| 3. Find and applies suitable cropping to the image and annotation | |
| 4. Prepare image and annotation to Tensors | |
| """ | |
| def __init__( | |
| self, | |
| is_train=True, | |
| *, | |
| augmentations, | |
| image_format, | |
| size_divisibility, | |
| ): | |
| """ | |
| NOTE: this interface is experimental. | |
| Args: | |
| is_train: for 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`. | |
| size_divisibility: pad image size to be divisible by this value | |
| """ | |
| self.is_train = is_train | |
| self.tfm_gens = augmentations | |
| self.img_format = image_format | |
| self.size_divisibility = size_divisibility | |
| logger = logging.getLogger(__name__) | |
| mode = "training" if is_train else "inference" | |
| logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}") | |
| def from_config(cls, cfg, is_train=True): | |
| # Build augmentation | |
| augs = [ | |
| T.ResizeShortestEdge( | |
| cfg.INPUT.MIN_SIZE_TRAIN, | |
| cfg.INPUT.MAX_SIZE_TRAIN, | |
| cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING, | |
| ) | |
| ] | |
| if cfg.INPUT.CROP.ENABLED: | |
| augs.append( | |
| T.RandomCrop( | |
| cfg.INPUT.CROP.TYPE, | |
| cfg.INPUT.CROP.SIZE, | |
| ) | |
| ) | |
| if cfg.INPUT.COLOR_AUG_SSD: | |
| augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT)) | |
| augs.append(T.RandomFlip()) | |
| ret = { | |
| "is_train": is_train, | |
| "augmentations": augs, | |
| "image_format": cfg.INPUT.FORMAT, | |
| "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY, | |
| } | |
| return ret | |
| 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 | |
| """ | |
| assert self.is_train, "MaskFormerPanopticDatasetMapper should only be used for training!" | |
| dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below | |
| image = utils.read_image(dataset_dict["file_name"], format=self.img_format) | |
| utils.check_image_size(dataset_dict, image) | |
| aug_input = T.AugInput(image) | |
| aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input) | |
| image = aug_input.image | |
| # transform instnace masks | |
| assert "annotations" in dataset_dict | |
| for anno in dataset_dict["annotations"]: | |
| anno.pop("keypoints", None) | |
| annos = [ | |
| utils.transform_instance_annotations(obj, transforms, image.shape[:2]) | |
| for obj in dataset_dict.pop("annotations") | |
| if obj.get("iscrowd", 0) == 0 | |
| ] | |
| if len(annos): | |
| assert "segmentation" in annos[0] | |
| segms = [obj["segmentation"] for obj in annos] | |
| masks = [] | |
| for segm in segms: | |
| if isinstance(segm, list): | |
| # polygon | |
| masks.append(polygons_to_bitmask(segm, *image.shape[:2])) | |
| elif isinstance(segm, dict): | |
| # COCO RLE | |
| masks.append(mask_util.decode(segm)) | |
| elif isinstance(segm, np.ndarray): | |
| assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format( | |
| segm.ndim | |
| ) | |
| # mask array | |
| masks.append(segm) | |
| else: | |
| raise ValueError( | |
| "Cannot convert segmentation of type '{}' to BitMasks!" | |
| "Supported types are: polygons as list[list[float] or ndarray]," | |
| " COCO-style RLE as a dict, or a binary segmentation mask " | |
| " in a 2D numpy array of shape HxW.".format(type(segm)) | |
| ) | |
| # Pad image and segmentation label here! | |
| image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1))) | |
| masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks] | |
| classes = [int(obj["category_id"]) for obj in annos] | |
| classes = torch.tensor(classes, dtype=torch.int64) | |
| if self.size_divisibility > 0: | |
| image_size = (image.shape[-2], image.shape[-1]) | |
| padding_size = [ | |
| 0, | |
| self.size_divisibility - image_size[1], | |
| 0, | |
| self.size_divisibility - image_size[0], | |
| ] | |
| # pad image | |
| image = F.pad(image, padding_size, value=128).contiguous() | |
| # pad mask | |
| masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks] | |
| image_shape = (image.shape[-2], image.shape[-1]) # 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"] = image | |
| # Prepare per-category binary masks | |
| instances = Instances(image_shape) | |
| instances.gt_classes = classes | |
| if len(masks) == 0: | |
| # Some image does not have annotation (all ignored) | |
| instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1])) | |
| else: | |
| masks = BitMasks(torch.stack(masks)) | |
| instances.gt_masks = masks.tensor | |
| dataset_dict["instances"] = instances | |
| return dataset_dict | |