from tkinter.messagebox import NO import torch import json from collections import defaultdict from PIL import Image, ImageDraw from copy import deepcopy import os import torchvision.transforms as transforms import torchvision from .base_dataset import BaseDataset, check_filenames_in_zipdata, recalculate_box_and_verify_if_valid from io import BytesIO import random def check_unique(images, fields): for field in fields: temp_list = [] for img_info in images: temp_list.append(img_info[field]) assert len(set(temp_list)) == len(temp_list), field def clean_data(data): for data_info in data: data_info.pop("original_img_id", None) data_info.pop("original_id", None) data_info.pop("sentence_id", None) # sentence id for each image (multiple sentences for one image) data_info.pop("dataset_name", None) data_info.pop("data_source", None) data_info["data_id"] = data_info.pop("id") def clean_annotations(annotations): for anno_info in annotations: anno_info.pop("iscrowd", None) # I have checked that all 0 for flickr, vg, coco anno_info.pop("category_id", None) # I have checked that all 1 for flickr vg. This is not always 1 for coco, but I do not think we need this annotation anno_info.pop("area", None) # anno_info.pop("id", None) anno_info["data_id"] = anno_info.pop("image_id") def draw_box(img, boxes): draw = ImageDraw.Draw(img) for box in boxes: draw.rectangle([box[0], box[1], box[2], box[3]], outline ="red", width=2) # x0 y0 x1 y1 return img def xyhw2xyxy(box): x0, y0, w, h = box return [ x0, y0, x0+w, y0+h ] class GroundingDataset(BaseDataset): def __init__(self, image_root, json_path, annotation_embedding_path, prob_real_caption=1, image_size=256, min_box_size=0.01, max_boxes_per_data=8, max_images=None, # set as 30K used to eval random_crop = False, random_flip = True, ): super().__init__(image_root, random_crop, random_flip, image_size) self.image_root = image_root self.json_path = json_path self.annotation_embedding_path = annotation_embedding_path self.prob_real_caption = prob_real_caption self.min_box_size = min_box_size self.max_boxes_per_data = max_boxes_per_data self.max_images = max_images # Load raw data with open(json_path, 'r') as f: json_raw = json.load(f) # keys: 'info', 'images', 'licenses', 'categories', 'annotations' self.data = json_raw["images"] # donot name it images, which is misleading self.annotations = json_raw["annotations"] # Load preprocessed name embedding if 'bert' in annotation_embedding_path: self.embedding_len = 1280 elif 'clip' in annotation_embedding_path: self.embedding_len = 768 else: assert False # clean data and annotation check_unique( self.data, ['id'] ) check_unique( self.annotations, ['id'] ) clean_data(self.data) clean_annotations(self.annotations) self.data_id_list = [ datum['data_id'] for datum in self.data ] self.data = { datum['data_id']:datum for datum in self.data } # map self.data from a list into a dict # data point to its annotation mapping self.data_id_to_annos = defaultdict(list) for anno in self.annotations: self.data_id_to_annos[ anno["data_id"] ].append(anno) # These are not used that offen, but are useful in some cases self.file_names = [] # all training images self.file_name_to_data_ids = defaultdict(list) # for each image, there are multiple data points (captions) for data_id in self.data_id_list: fine_name = self.data[data_id]["file_name"] self.file_names.append(fine_name) self.file_name_to_data_ids[fine_name].append(data_id) self.file_names = list(set(self.file_names)) if self.max_images is not None: "This is only used as COCO2017P evulation, when we set max_images as 30k" assert False, 'I have commented out the following code to save cpu memory' # new_data_id_list = [] # new_file_name_to_data_ids = defaultdict(list) # self.file_names = self.file_names[0:self.max_images] # for file_name in self.file_names: # data_id = self.file_name_to_data_ids[file_name][0] # new_data_id_list.append(data_id) # new_file_name_to_data_ids[file_name].append(data_id) # self.data_id_list = new_data_id_list # self.file_name_to_data_ids = new_file_name_to_data_ids # Check if all filenames can be found in the zip file # all_filenames = [self.data[idx]['file_name'] for idx in self.data_id_list ] # check_filenames_in_zipdata(all_filenames, image_root) def total_images(self): return len(self.file_names) def __getitem__(self, index): if self.max_boxes_per_data > 99: assert False, "Are you sure setting such large number of boxes?" out = {} data_id = self.data_id_list[index] out['id'] = data_id # Image and caption file_name = self.data[data_id]['file_name'] image = self.fetch_image(file_name) image_tensor, trans_info = self.transform_image(image) out["image"] = image_tensor if random.uniform(0, 1) < self.prob_real_caption: out["caption"] = self.data[data_id]["caption"] else: out["caption"] = "" annos = deepcopy(self.data_id_to_annos[data_id]) areas = [] all_boxes = [] all_masks = [] all_positive_embeddings = [] for anno in annos: x, y, w, h = anno['bbox'] valid, (x0, y0, x1, y1) = recalculate_box_and_verify_if_valid(x, y, w, h, trans_info, self.image_size, self.min_box_size) if valid: areas.append( (x1-x0)*(y1-y0) ) all_boxes.append( torch.tensor([x0,y0,x1,y1]) / self.image_size ) # scale to 0-1 all_masks.append(1) all_positive_embeddings.append( torch.load(os.path.join(self.annotation_embedding_path,str(anno["id"])), map_location='cpu' ) ) wanted_idxs = torch.tensor(areas).sort(descending=True)[1] wanted_idxs = wanted_idxs[0:self.max_boxes_per_data] boxes = torch.zeros(self.max_boxes_per_data, 4) masks = torch.zeros(self.max_boxes_per_data) positive_embeddings = torch.zeros(self.max_boxes_per_data, self.embedding_len) for i, idx in enumerate(wanted_idxs): boxes[i] = all_boxes[idx] masks[i] = all_masks[idx] positive_embeddings[i] = all_positive_embeddings[idx] out["boxes"] = boxes out["masks"] = masks out["positive_embeddings"] = positive_embeddings return out def __len__(self): return len(self.data_id_list)