import copy import datetime import json import os import random import re import sys # 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)" 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")) print(datetime.datetime.utcnow() - time_t) 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.dataset["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] # print set of question ids that do not have corresponding annotations # 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 class VQAEval: def __init__(self, vqa=None, vqaRes=None, n=2): self.n = n self.accuracy = {} self.evalQA = {} self.evalQuesType = {} self.evalAnsType = {} self.vqa = vqa self.vqaRes = vqaRes if vqaRes is not None: self.params = {"question_id": vqaRes.getQuesIds()} self.contractions = { "aint": "ain't", "arent": "aren't", "cant": "can't", "couldve": "could've", "couldnt": "couldn't", "couldn'tve": "couldn't've", "couldnt've": "couldn't've", "didnt": "didn't", "doesnt": "doesn't", "dont": "don't", "hadnt": "hadn't", "hadnt've": "hadn't've", "hadn'tve": "hadn't've", "hasnt": "hasn't", "havent": "haven't", "hed": "he'd", "hed've": "he'd've", "he'dve": "he'd've", "hes": "he's", "howd": "how'd", "howll": "how'll", "hows": "how's", "Id've": "I'd've", "I'dve": "I'd've", "Im": "I'm", "Ive": "I've", "isnt": "isn't", "itd": "it'd", "itd've": "it'd've", "it'dve": "it'd've", "itll": "it'll", "let's": "let's", "maam": "ma'am", "mightnt": "mightn't", "mightnt've": "mightn't've", "mightn'tve": "mightn't've", "mightve": "might've", "mustnt": "mustn't", "mustve": "must've", "neednt": "needn't", "notve": "not've", "oclock": "o'clock", "oughtnt": "oughtn't", "ow's'at": "'ow's'at", "'ows'at": "'ow's'at", "'ow'sat": "'ow's'at", "shant": "shan't", "shed've": "she'd've", "she'dve": "she'd've", "she's": "she's", "shouldve": "should've", "shouldnt": "shouldn't", "shouldnt've": "shouldn't've", "shouldn'tve": "shouldn't've", "somebody'd": "somebodyd", "somebodyd've": "somebody'd've", "somebody'dve": "somebody'd've", "somebodyll": "somebody'll", "somebodys": "somebody's", "someoned": "someone'd", "someoned've": "someone'd've", "someone'dve": "someone'd've", "someonell": "someone'll", "someones": "someone's", "somethingd": "something'd", "somethingd've": "something'd've", "something'dve": "something'd've", "somethingll": "something'll", "thats": "that's", "thered": "there'd", "thered've": "there'd've", "there'dve": "there'd've", "therere": "there're", "theres": "there's", "theyd": "they'd", "theyd've": "they'd've", "they'dve": "they'd've", "theyll": "they'll", "theyre": "they're", "theyve": "they've", "twas": "'twas", "wasnt": "wasn't", "wed've": "we'd've", "we'dve": "we'd've", "weve": "we've", "werent": "weren't", "whatll": "what'll", "whatre": "what're", "whats": "what's", "whatve": "what've", "whens": "when's", "whered": "where'd", "wheres": "where's", "whereve": "where've", "whod": "who'd", "whod've": "who'd've", "who'dve": "who'd've", "wholl": "who'll", "whos": "who's", "whove": "who've", "whyll": "why'll", "whyre": "why're", "whys": "why's", "wont": "won't", "wouldve": "would've", "wouldnt": "wouldn't", "wouldnt've": "wouldn't've", "wouldn'tve": "wouldn't've", "yall": "y'all", "yall'll": "y'all'll", "y'allll": "y'all'll", "yall'd've": "y'all'd've", "y'alld've": "y'all'd've", "y'all'dve": "y'all'd've", "youd": "you'd", "youd've": "you'd've", "you'dve": "you'd've", "youll": "you'll", "youre": "you're", "youve": "you've", } self.manualMap = { "none": "0", "zero": "0", "one": "1", "two": "2", "three": "3", "four": "4", "five": "5", "six": "6", "seven": "7", "eight": "8", "nine": "9", "ten": "10", } self.articles = ["a", "an", "the"] self.periodStrip = re.compile("(?!<=\d)(\.)(?!\d)") self.commaStrip = re.compile("(\d)(\,)(\d)") self.punct = [ ";", r"/", "[", "]", '"', "{", "}", "(", ")", "=", "+", "\\", "_", "-", ">", "<", "@", "`", ",", "?", "!", ] def evaluate(self, quesIds=None): if quesIds == None: quesIds = [quesId for quesId in self.params["question_id"]] gts = {} res = {} for quesId in quesIds: gts[quesId] = self.vqa.qa[quesId] res[quesId] = self.vqaRes.qa[quesId] # ================================================= # Compute accuracy # ================================================= accQA = [] accQuesType = {} accAnsType = {} print("computing accuracy") step = 0 for quesId in quesIds: for ansDic in gts[quesId]["answers"]: ansDic["answer"] = ansDic["answer"].replace("\n", " ") ansDic["answer"] = ansDic["answer"].replace("\t", " ") ansDic["answer"] = ansDic["answer"].strip() resAns = res[quesId]["answer"] resAns = resAns.replace("\n", " ") resAns = resAns.replace("\t", " ") resAns = resAns.strip() gtAcc = [] gtAnswers = [ans["answer"] for ans in gts[quesId]["answers"]] if len(set(gtAnswers)) > 1: for ansDic in gts[quesId]["answers"]: ansDic["answer"] = self.processPunctuation(ansDic["answer"]) ansDic["answer"] = self.processDigitArticle(ansDic["answer"]) resAns = self.processPunctuation(resAns) resAns = self.processDigitArticle(resAns) for gtAnsDatum in gts[quesId]["answers"]: otherGTAns = [ item for item in gts[quesId]["answers"] if item != gtAnsDatum ] matchingAns = [item for item in otherGTAns if item["answer"] == resAns] acc = min(1, float(len(matchingAns)) / 3) gtAcc.append(acc) quesType = gts[quesId]["question_type"] ansType = gts[quesId]["answer_type"] avgGTAcc = float(sum(gtAcc)) / len(gtAcc) accQA.append(avgGTAcc) if quesType not in accQuesType: accQuesType[quesType] = [] accQuesType[quesType].append(avgGTAcc) if ansType not in accAnsType: accAnsType[ansType] = [] accAnsType[ansType].append(avgGTAcc) self.setEvalQA(quesId, avgGTAcc) self.setEvalQuesType(quesId, quesType, avgGTAcc) self.setEvalAnsType(quesId, ansType, avgGTAcc) if step % 100 == 0: self.updateProgress(step / float(len(quesIds))) step = step + 1 self.setAccuracy(accQA, accQuesType, accAnsType) print("Done computing accuracy") def processPunctuation(self, inText): outText = inText for p in self.punct: if (p + " " in inText or " " + p in inText) or ( re.search(self.commaStrip, inText) != None ): outText = outText.replace(p, "") else: outText = outText.replace(p, " ") outText = self.periodStrip.sub("", outText, re.UNICODE) return outText def processDigitArticle(self, inText): outText = [] tempText = inText.lower().split() for word in tempText: word = self.manualMap.setdefault(word, word) if word not in self.articles: outText.append(word) else: pass for wordId, word in enumerate(outText): if word in self.contractions: outText[wordId] = self.contractions[word] outText = " ".join(outText) return outText def setAccuracy(self, accQA, accQuesType, accAnsType): self.accuracy["overall"] = round(100 * float(sum(accQA)) / len(accQA), self.n) self.accuracy["perQuestionType"] = { quesType: round( 100 * float(sum(accQuesType[quesType])) / len(accQuesType[quesType]), self.n, ) for quesType in accQuesType } self.accuracy["perAnswerType"] = { ansType: round( 100 * float(sum(accAnsType[ansType])) / len(accAnsType[ansType]), self.n ) for ansType in accAnsType } def setEvalQA(self, quesId, acc): self.evalQA[quesId] = round(100 * acc, self.n) def setEvalQuesType(self, quesId, quesType, acc): if quesType not in self.evalQuesType: self.evalQuesType[quesType] = {} self.evalQuesType[quesType][quesId] = round(100 * acc, self.n) def setEvalAnsType(self, quesId, ansType, acc): if ansType not in self.evalAnsType: self.evalAnsType[ansType] = {} self.evalAnsType[ansType][quesId] = round(100 * acc, self.n) def updateProgress(self, progress): barLength = 20 status = "" if isinstance(progress, int): progress = float(progress) if not isinstance(progress, float): progress = 0 status = "error: progress var must be float\r\n" if progress < 0: progress = 0 status = "Halt...\r\n" if progress >= 1: progress = 1 status = "Done...\r\n" block = int(round(barLength * progress)) text = "\rFinshed Percent: [{0}] {1}% {2}".format( "#" * block + "-" * (barLength - block), int(progress * 100), status ) sys.stdout.write(text) sys.stdout.flush() def compute_vqa_accuracy(result_json_path, question_json_path, annotation_json_path, vqa_dataset): """Compute the VQA accuracy metric. Args: predictions (List): list of predictions ground_truth (List[List]): list of all possible ground truth answers Returns: float: VQA accuracy """ # coding: utf-8 # dataDir = data_dir # set up file names and paths # versionType = 'v2_' # this should be '' when using VQA v2.0 dataset # 'OpenEnded' only for v2.0. 'OpenEnded' or 'MultipleChoice' for v1.0 # taskType = 'OpenEnded' # 'mscoco' only for v1.0. 'mscoco' for real and 'abstract_v002' for abstract for v1.0. # dataType = 'mscoco' # dataSubType = 'train2014' # annFile = '%s/%s%s_%s_annotations.json' % ( # dataDir, versionType, dataType, dataSubType) # quesFile = '%s/%s%s_%s_%s_questions.json' % ( # dataDir, versionType, taskType, dataType, dataSubType) # imgDir = '%s/%s/%s/' % (dataDir, dataType, dataSubType) # resultType = res_file_name # fileTypes = ['results', 'accuracy', # 'evalQA', 'evalQuesType', 'evalAnsType'] # An example result json file has been provided in './Results' folder. # [resFile, accuracyFile, evalQAFile, evalQuesTypeFile, evalAnsTypeFile] = ['%s/%s%s_%s_%s_%s_%s.json' % (dataDir, versionType, taskType, dataType, dataSubType, # resultType, fileType) for fileType in fileTypes] # create vqa object and vqaRes object vqa = VQA(annotation_json_path, question_json_path) vqaRes = vqa.loadRes(result_json_path, question_json_path) # create vqaEval object by taking vqa and vqaRes # n is precision of accuracy (number of places after decimal), default is 2 vqaEval = VQAEval(vqa, vqaRes, n=2) # evaluate results """ If you have a list of question ids on which you would like to evaluate your results, pass it as a list to below function By default it uses all the question ids in annotation file """ vqaEval.evaluate() return vqaEval.accuracy["overall"] def postprocess_vqa_generation(predictions): return re.split("Question|Answer", predictions, 1)[0] def compute_gqa_accuracy(results): acc = [] vqa_tool = VQAEval() for res in results: gt_ans = res["answers"] pred = res["answer"] pred = vqa_tool.processPunctuation(pred) pred = vqa_tool.processDigitArticle(pred) vqa_acc = 1 if pred == gt_ans else 0 acc.append(vqa_acc) accuracy = sum(acc) / len(acc) return accuracy