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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 random
import numpy as np
import torch
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, Boxes, Instances
from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
from detectron2.data import MetadataCatalog
from pycocotools import mask
from utils.prompt_engineering import prompt_engineering
from modeling.language.misc import text_noun_with_prompt_all
from modeling.utils import configurable
__all__ = ["COCOPanopticNewBaselineDatasetMapper"]
def build_transform_gen(cfg, is_train):
"""
Create a list of default :class:`Augmentation` from config.
Now it includes resizing and flipping.
Returns:
list[Augmentation]
"""
assert is_train, "Only support training augmentation"
cfg_input = cfg['INPUT']
image_size = cfg_input['IMAGE_SIZE']
min_scale = cfg_input['MIN_SCALE']
max_scale = cfg_input['MAX_SCALE']
augmentation = []
if cfg_input['RANDOM_FLIP'] != "none":
augmentation.append(
T.RandomFlip(
horizontal=cfg_input['RANDOM_FLIP'] == "horizontal",
vertical=cfg_input['RANDOM_FLIP'] == "vertical",
)
)
augmentation.extend([
T.ResizeScale(
min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
),
T.FixedSizeCrop(crop_size=(image_size, image_size)),
])
return augmentation
# This is specifically designed for the COCO dataset.
class COCOPanopticNewBaselineDatasetMapper:
"""
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,
*,
tfm_gens,
image_format,
caption_thres,
grounding,
max_grounding_num,
):
"""
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.tfm_gens = tfm_gens
logging.getLogger(__name__).info(
"[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(
str(self.tfm_gens)
)
)
self.img_format = image_format
self.is_train = is_train
self.caption_thres = caption_thres
self.grounding = grounding
self.max_grounding_num = max_grounding_num
self.caption_similarity = torch.load(MetadataCatalog.get('logistic').get('caption_similarity_pth'))
@classmethod
def from_config(cls, cfg, is_train=True):
# Build augmentation
tfm_gens = build_transform_gen(cfg, is_train)
ret = {
"is_train": is_train,
"tfm_gens": tfm_gens,
"image_format": cfg['INPUT']['FORMAT'],
"caption_thres": cfg['MODEL']['DECODER']['CAPTION']['SIM_THRES'],
"grounding": cfg['MODEL']['DECODER']['GROUNDING']['ENABLED'],
"max_grounding_num": cfg['MODEL']['DECODER']['GROUNDING']['MAX_LEN'],
}
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)
image, transforms = T.apply_transform_gens(self.tfm_gens, 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)))
# Add caption noun that is not in coco set to target
captions = dataset_dict["captions"]
captions_noun = []
for caption in captions:
nouns = np.array(text_noun_with_prompt_all(caption, phrase_prob=0.0, append_text=False)[1])
cap_similarity = np.array([self.caption_similarity[noun][0] for noun in nouns])
captions_noun.append(nouns[cap_similarity < self.caption_thres].tolist())
dataset_dict["captions_noun"] = captions_noun
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"])
is_things = [COCO_CATEGORIES[idx]['isthing'] for idx in classes]
classes = np.array(classes)
is_things = np.array(is_things)
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
instances.is_things = torch.tensor(is_things, 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]))
instances.gt_boxes = Boxes(torch.zeros((0, 4)))
else:
masks = BitMasks(
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
)
instances.gt_masks = masks.tensor
instances.gt_boxes = masks.get_bounding_boxes()
dataset_dict["instances"] = instances
if self.grounding:
grounding_anno = dataset_dict['grounding_info']
grounding_len = random.randint(1, self.max_grounding_num-1)
if len(grounding_anno) > 0:
masks_grd = []
texts_grd = []
mode = 'text'
random.shuffle(grounding_anno)
for ann in grounding_anno:
rle = mask.frPyObjects(
ann['segmentation'], dataset_dict['height'], dataset_dict['width'])
m = mask.decode(rle)
# sometimes there are multiple binary map (corresponding to multiple segs)
m = np.sum(m, axis=2)
m = m.astype(np.uint8) # convert to np.uint8
m = transforms.apply_segmentation(m[:,:,None])[:,:,0]
masks_grd += [m]
# random select a sentence of a single annotation.
rand_index = random.randint(0, len(ann['sentences'])-1)
texts_grd += [ann['sentences'][rand_index]['raw'].lower()]
max_len = min(grounding_len, len(texts_grd))
indices = np.random.permutation(max_len)
texts_grd = list(np.array(texts_grd)[indices])
masks_grd = torch.tensor(np.stack(masks_grd)[indices])
hash_grd = np.array([hash(txt) for txt in texts_grd])
else:
masks_grd = instances.gt_masks
mode = 'class'
if len(masks_grd) == 0:
masks_grd = torch.tensor([])
texts_grd = ['none']
hash_grd = np.array([hash(txt) for txt in texts_grd])
else:
texts_grd = np.array([COCO_CATEGORIES[idx]['name'] for idx in classes])
hash_grd = np.array([hash(txt) for txt in texts_grd])
unique_hash_grd = np.unique(hash_grd)
np.random.shuffle(unique_hash_grd)
max_len = min(grounding_len, len(unique_hash_grd))
indices = np.random.permutation(max_len)
selected_unique_hash_grd = unique_hash_grd[indices]
selected_mask = np.in1d(hash_grd, selected_unique_hash_grd)
texts_grd = texts_grd[selected_mask]
hash_grd = hash_grd[selected_mask]
masks_grd = masks_grd[selected_mask]
texts_grd = [prompt_engineering(text.replace('-other','').replace('-merged','').replace('-stuff',''), topk=10000, suffix='.') \
for text in texts_grd]
groundings = {'masks': masks_grd, 'texts': texts_grd, 'mode': mode, 'hash': hash_grd}
dataset_dict["groundings"] = groundings
return dataset_dict
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