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# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
import copy
import logging
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.transforms import TransformGen
from detectron2.structures import BitMasks, Instances
__all__ = ["DETRPanopticDatasetMapper"]
def build_transform_gen(cfg, is_train):
"""
Create a list of :class:`TransformGen` from config.
Returns:
list[TransformGen]
"""
if is_train:
min_size = cfg.INPUT.MIN_SIZE_TRAIN
max_size = cfg.INPUT.MAX_SIZE_TRAIN
sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
else:
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
sample_style = "choice"
if sample_style == "range":
assert len(min_size) == 2, "more than 2 ({}) min_size(s) are provided for ranges".format(
len(min_size)
)
logger = logging.getLogger(__name__)
tfm_gens = []
if is_train:
tfm_gens.append(T.RandomFlip())
tfm_gens.append(T.ResizeShortestEdge(min_size, max_size, sample_style))
if is_train:
logger.info("TransformGens used in training: " + str(tfm_gens))
return tfm_gens
# This is specifically designed for the COCO dataset.
class DETRPanopticDatasetMapper:
"""
A callable which takes a dataset dict in Detectron2 Dataset format,
and map it into a format used by MaskFormer.
This dataset mapper applies the same transformation as DETR for COCO 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,
*,
crop_gen,
tfm_gens,
image_format,
):
"""
NOTE: this interface is experimental.
Args:
is_train: for training or inference
augmentations: a list of augmentations or deterministic transforms to apply
crop_gen: crop augmentation
tfm_gens: data augmentation
image_format: an image format supported by :func:`detection_utils.read_image`.
"""
self.crop_gen = crop_gen
self.tfm_gens = tfm_gens
logging.getLogger(__name__).info(
"[DETRPanopticDatasetMapper] Full TransformGens used in training: {}, crop: {}".format(
str(self.tfm_gens), str(self.crop_gen)
)
)
self.img_format = image_format
self.is_train = is_train
@classmethod
def from_config(cls, cfg, is_train=True):
# Build augmentation
if cfg.INPUT.CROP.ENABLED and is_train:
crop_gen = [
T.ResizeShortestEdge([400, 500, 600], sample_style="choice"),
T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE),
]
else:
crop_gen = None
tfm_gens = build_transform_gen(cfg, is_train)
ret = {
"is_train": is_train,
"crop_gen": crop_gen,
"tfm_gens": tfm_gens,
"image_format": cfg.INPUT.FORMAT,
}
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
"""
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)
if self.crop_gen is None:
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
else:
if np.random.rand() > 0.5:
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
else:
image, transforms = T.apply_transform_gens(
self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:], image
)
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 not self.is_train:
# USER: Modify this if you want to keep them for some reason.
dataset_dict.pop("annotations", None)
return dataset_dict
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"]
# 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)
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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