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| """ | |
| Copyright (c) 2022, salesforce.com, inc. | |
| All rights reserved. | |
| SPDX-License-Identifier: BSD-3-Clause | |
| For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause | |
| """ | |
| __author__ = "aagrawal" | |
| __version__ = "0.9" | |
| # Interface for accessing the VQA dataset. | |
| # This code is based on the code written by Tsung-Yi Lin for MSCOCO Python API available at the following link: | |
| # (https://github.com/pdollar/coco/blob/master/PythonAPI/pycocotools/coco.py). | |
| # The following functions are defined: | |
| # VQA - VQA class that loads VQA annotation file and prepares data structures. | |
| # getQuesIds - Get question ids that satisfy given filter conditions. | |
| # getImgIds - Get image ids that satisfy given filter conditions. | |
| # loadQA - Load questions and answers with the specified question ids. | |
| # showQA - Display the specified questions and answers. | |
| # loadRes - Load result file and create result object. | |
| # Help on each function can be accessed by: "help(COCO.function)" | |
| import json | |
| import datetime | |
| import copy | |
| class VQA: | |
| def __init__(self, annotation_file=None, question_file=None): | |
| """ | |
| Constructor of VQA helper class for reading and visualizing questions and answers. | |
| :param annotation_file (str): location of VQA annotation file | |
| :return: | |
| """ | |
| # load dataset | |
| self.dataset = {} | |
| self.questions = {} | |
| self.qa = {} | |
| self.qqa = {} | |
| self.imgToQA = {} | |
| if not annotation_file == None and not question_file == None: | |
| print("loading VQA annotations and questions into memory...") | |
| time_t = datetime.datetime.utcnow() | |
| dataset = json.load(open(annotation_file, "r")) | |
| questions = json.load(open(question_file, "r")) | |
| self.dataset = dataset | |
| self.questions = questions | |
| self.createIndex() | |
| def createIndex(self): | |
| # create index | |
| print("creating index...") | |
| imgToQA = {ann["image_id"]: [] for ann in self.dataset["annotations"]} | |
| qa = {ann["question_id"]: [] for ann in self.dataset["annotations"]} | |
| qqa = {ann["question_id"]: [] for ann in self.dataset["annotations"]} | |
| for ann in self.dataset["annotations"]: | |
| imgToQA[ann["image_id"]] += [ann] | |
| qa[ann["question_id"]] = ann | |
| for ques in self.questions["questions"]: | |
| qqa[ques["question_id"]] = ques | |
| print("index created!") | |
| # create class members | |
| self.qa = qa | |
| self.qqa = qqa | |
| self.imgToQA = imgToQA | |
| def info(self): | |
| """ | |
| Print information about the VQA annotation file. | |
| :return: | |
| """ | |
| for key, value in self.datset["info"].items(): | |
| print("%s: %s" % (key, value)) | |
| def getQuesIds(self, imgIds=[], quesTypes=[], ansTypes=[]): | |
| """ | |
| Get question ids that satisfy given filter conditions. default skips that filter | |
| :param imgIds (int array) : get question ids for given imgs | |
| quesTypes (str array) : get question ids for given question types | |
| ansTypes (str array) : get question ids for given answer types | |
| :return: ids (int array) : integer array of question ids | |
| """ | |
| imgIds = imgIds if type(imgIds) == list else [imgIds] | |
| quesTypes = quesTypes if type(quesTypes) == list else [quesTypes] | |
| ansTypes = ansTypes if type(ansTypes) == list else [ansTypes] | |
| if len(imgIds) == len(quesTypes) == len(ansTypes) == 0: | |
| anns = self.dataset["annotations"] | |
| else: | |
| if not len(imgIds) == 0: | |
| anns = sum( | |
| [self.imgToQA[imgId] for imgId in imgIds if imgId in self.imgToQA], | |
| [], | |
| ) | |
| else: | |
| anns = self.dataset["annotations"] | |
| anns = ( | |
| anns | |
| if len(quesTypes) == 0 | |
| else [ann for ann in anns if ann["question_type"] in quesTypes] | |
| ) | |
| anns = ( | |
| anns | |
| if len(ansTypes) == 0 | |
| else [ann for ann in anns if ann["answer_type"] in ansTypes] | |
| ) | |
| ids = [ann["question_id"] for ann in anns] | |
| return ids | |
| def getImgIds(self, quesIds=[], quesTypes=[], ansTypes=[]): | |
| """ | |
| Get image ids that satisfy given filter conditions. default skips that filter | |
| :param quesIds (int array) : get image ids for given question ids | |
| quesTypes (str array) : get image ids for given question types | |
| ansTypes (str array) : get image ids for given answer types | |
| :return: ids (int array) : integer array of image ids | |
| """ | |
| quesIds = quesIds if type(quesIds) == list else [quesIds] | |
| quesTypes = quesTypes if type(quesTypes) == list else [quesTypes] | |
| ansTypes = ansTypes if type(ansTypes) == list else [ansTypes] | |
| if len(quesIds) == len(quesTypes) == len(ansTypes) == 0: | |
| anns = self.dataset["annotations"] | |
| else: | |
| if not len(quesIds) == 0: | |
| anns = sum( | |
| [self.qa[quesId] for quesId in quesIds if quesId in self.qa], [] | |
| ) | |
| else: | |
| anns = self.dataset["annotations"] | |
| anns = ( | |
| anns | |
| if len(quesTypes) == 0 | |
| else [ann for ann in anns if ann["question_type"] in quesTypes] | |
| ) | |
| anns = ( | |
| anns | |
| if len(ansTypes) == 0 | |
| else [ann for ann in anns if ann["answer_type"] in ansTypes] | |
| ) | |
| ids = [ann["image_id"] for ann in anns] | |
| return ids | |
| def loadQA(self, ids=[]): | |
| """ | |
| Load questions and answers with the specified question ids. | |
| :param ids (int array) : integer ids specifying question ids | |
| :return: qa (object array) : loaded qa objects | |
| """ | |
| if type(ids) == list: | |
| return [self.qa[id] for id in ids] | |
| elif type(ids) == int: | |
| return [self.qa[ids]] | |
| def showQA(self, anns): | |
| """ | |
| Display the specified annotations. | |
| :param anns (array of object): annotations to display | |
| :return: None | |
| """ | |
| if len(anns) == 0: | |
| return 0 | |
| for ann in anns: | |
| quesId = ann["question_id"] | |
| print("Question: %s" % (self.qqa[quesId]["question"])) | |
| for ans in ann["answers"]: | |
| print("Answer %d: %s" % (ans["answer_id"], ans["answer"])) | |
| def loadRes(self, resFile, quesFile): | |
| """ | |
| Load result file and return a result object. | |
| :param resFile (str) : file name of result file | |
| :return: res (obj) : result api object | |
| """ | |
| res = VQA() | |
| res.questions = json.load(open(quesFile)) | |
| res.dataset["info"] = copy.deepcopy(self.questions["info"]) | |
| res.dataset["task_type"] = copy.deepcopy(self.questions["task_type"]) | |
| res.dataset["data_type"] = copy.deepcopy(self.questions["data_type"]) | |
| res.dataset["data_subtype"] = copy.deepcopy(self.questions["data_subtype"]) | |
| res.dataset["license"] = copy.deepcopy(self.questions["license"]) | |
| print("Loading and preparing results... ") | |
| time_t = datetime.datetime.utcnow() | |
| anns = json.load(open(resFile)) | |
| assert type(anns) == list, "results is not an array of objects" | |
| annsQuesIds = [ann["question_id"] for ann in anns] | |
| assert set(annsQuesIds) == set( | |
| self.getQuesIds() | |
| ), "Results do not correspond to current VQA set. Either the results do not have predictions for all question ids in annotation file or there is atleast one question id that does not belong to the question ids in the annotation file." | |
| for ann in anns: | |
| quesId = ann["question_id"] | |
| if res.dataset["task_type"] == "Multiple Choice": | |
| assert ( | |
| ann["answer"] in self.qqa[quesId]["multiple_choices"] | |
| ), "predicted answer is not one of the multiple choices" | |
| qaAnn = self.qa[quesId] | |
| ann["image_id"] = qaAnn["image_id"] | |
| ann["question_type"] = qaAnn["question_type"] | |
| ann["answer_type"] = qaAnn["answer_type"] | |
| print( | |
| "DONE (t=%0.2fs)" % ((datetime.datetime.utcnow() - time_t).total_seconds()) | |
| ) | |
| res.dataset["annotations"] = anns | |
| res.createIndex() | |
| return res | |