Spaces:
Sleeping
Sleeping
File size: 8,634 Bytes
2ada650 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 |
"""
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
|