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# Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
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.structures import BitMasks, Instances
from .mask_former_semantic_dataset_mapper import MaskFormerSemanticDatasetMapper
__all__ = ["MaskFormerPanopticDatasetMapper"]
class MaskFormerPanopticDatasetMapper(MaskFormerSemanticDatasetMapper):
"""
A callable which takes a dataset dict in Detectron2 Dataset format,
and map it into a format used by MaskFormer for panoptic 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
"""
@configurable
def __init__(
self,
is_train=True,
*,
augmentations,
image_format,
ignore_label,
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`.
ignore_label: the label that is ignored to evaluation
size_divisibility: pad image size to be divisible by this value
"""
super().__init__(
is_train,
augmentations=augmentations,
image_format=image_format,
ignore_label=ignore_label,
size_divisibility=size_divisibility,
)
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)
# semantic segmentation
if "sem_seg_file_name" in dataset_dict:
# PyTorch transformation not implemented for uint16, so converting it to double first
sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
else:
sem_seg_gt = None
# panoptic segmentation
if "pan_seg_file_name" in dataset_dict:
pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
segments_info = dataset_dict["segments_info"]
else:
pan_seg_gt = None
segments_info = None
if pan_seg_gt is None:
raise ValueError(
"Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format(
dataset_dict["file_name"]
)
)
aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
image = aug_input.image
if sem_seg_gt is not None:
sem_seg_gt = aug_input.sem_seg
# apply the same transformation to panoptic segmentation
pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
from panopticapi.utils import rgb2id
pan_seg_gt = rgb2id(pan_seg_gt)
# Pad image and segmentation label here!
image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
if sem_seg_gt is not None:
sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long"))
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],
]
image = F.pad(image, padding_size, value=128).contiguous()
if sem_seg_gt is not None:
sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
pan_seg_gt = F.pad(
pan_seg_gt, padding_size, value=0
).contiguous() # 0 is the VOID panoptic label
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
if sem_seg_gt is not None:
dataset_dict["sem_seg"] = sem_seg_gt.long()
if "annotations" in dataset_dict:
raise ValueError("Pemantic segmentation dataset should not have 'annotations'.")
# Prepare per-category binary masks
pan_seg_gt = pan_seg_gt.numpy()
instances = Instances(image_shape)
classes = []
masks = []
for segment_info in segments_info:
class_id = segment_info["category_id"]
if not segment_info["iscrowd"]:
classes.append(class_id)
masks.append(pan_seg_gt == segment_info["id"])
classes = np.array(classes)
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
if len(masks) == 0:
# Some image does not have annotation (all ignored)
instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
else:
masks = BitMasks(
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
)
instances.gt_masks = masks.tensor
dataset_dict["instances"] = instances
return dataset_dict
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