def eval_vqa(pred_list, verbose, extra_vars, split):
"""
VQA challenge metrics
:param pred_list: dictionary of hypothesis sentences (id, sentence)
:param verbose: if greater than 0 the metric measures are printed out
:param extra_vars: extra variables
extra_vars['quesFile'] - path to the .json file where the questions are stored
extra_vars['annFile'] - path to the .json file where the annotated answers are stored
extra_vars['question_ids'] - question identifiers
:param split: split on which we are evaluating
:return: Dictionary of VQA accuracies
"""
import datetime
import os
from pycocoevalcap.vqa import vqaEval, visual_qa
from read_write import list2vqa
quesFile = extra_vars[split]['quesFile']
annFile = extra_vars[split]['annFile']
# create temporal resFile
resFile = 'tmp_res_file_' + str(datetime.datetime.now()) + '.json'
list2vqa(resFile, pred_list, extra_vars[split]['question_ids'])
# create vqa object and vqaRes object
vqa_ = visual_qa.VQA(annFile, quesFile)
vqaRes = vqa_.loadRes(resFile, quesFile)
vqaEval_ = vqaEval.VQAEval(
vqa_, vqaRes, n=2
) # n is precision of accuracy (number of places after decimal), default is 2
vqaEval_.evaluate()
os.remove(resFile) # remove temporal file
# get results
acc_overall = vqaEval_.accuracy['overall']
acc_yes_no = vqaEval_.accuracy['perAnswerType']['yes/no']
acc_number = vqaEval_.accuracy['perAnswerType']['number']
acc_other = vqaEval_.accuracy['perAnswerType']['other']
# acc_per_class = vqaEval_.accuracy['perAnswerClass']
# acc_class_normalized = vqaEval_.accuracy['classNormalizedOverall']
if verbose > 0:
logging.info(
'VQA Metric: Accuracy yes/no is {0}, other is {1}, number is {2}, overall is {3}'
.format(acc_yes_no, acc_other, acc_number,
acc_overall)) # , acc_class_normalized))
return {
'overall accuracy': acc_overall,
'yes/no accuracy': acc_yes_no,
'number accuracy': acc_number,
'other accuracy': acc_other
}
def eval_vqa(pred_list, verbose, extra_vars, split):
"""
VQA challenge metrics
:param pred_list: dictionary of hypothesis sentences (id, sentence)
:param verbose: if greater than 0 the metric measures are printed out
:param extra_vars: extra variables
extra_vars['quesFile'] - path to the .json file where the questions are stored
extra_vars['annFile'] - path to the .json file where the annotated answers are stored
extra_vars['question_ids'] - question identifiers
:param split: split on which we are evaluating
:return: Dictionary of VQA accuracies
"""
import datetime
import os
from pycocoevalcap.vqa import vqaEval, visual_qa
from read_write import list2vqa
quesFile = extra_vars[split]['quesFile']
annFile = extra_vars[split]['annFile']
# create temporal resFile
resFile = 'tmp_res_file_' + str(datetime.datetime.now()) + '.json'
list2vqa(resFile, pred_list, extra_vars[split]['question_ids'])
# create vqa object and vqaRes object
vqa_ = visual_qa.VQA(annFile, quesFile)
vqaRes = vqa_.loadRes(resFile, quesFile)
vqaEval_ = vqaEval.VQAEval(vqa_, vqaRes,
n=2) # n is precision of accuracy (number of places after decimal), default is 2
vqaEval_.evaluate()
os.remove(resFile) # remove temporal file
# get results
acc_overall = vqaEval_.accuracy['overall']
acc_yes_no = vqaEval_.accuracy['perAnswerType']['yes/no']
acc_number = vqaEval_.accuracy['perAnswerType']['number']
acc_other = vqaEval_.accuracy['perAnswerType']['other']
# acc_per_class = vqaEval_.accuracy['perAnswerClass']
# acc_class_normalized = vqaEval_.accuracy['classNormalizedOverall']
if verbose > 0:
logging.info('VQA Metric: Accuracy yes/no is {0}, other is {1}, number is {2}, overall is {3}'.
format(acc_yes_no, acc_other, acc_number, acc_overall)) # , acc_class_normalized))
return {'overall accuracy': acc_overall,
'yes/no accuracy': acc_yes_no,
'number accuracy': acc_number,
'other accuracy': acc_other}
def eval_vqa(pred_list, verbose, extra_vars, split):
'''
VQA challenge metric
# Arguments
gt_list, dictionary of reference sentences (id, sentence)
pred_list, dictionary of hypothesis sentences (id, sentence)
verbose - if greater than 0 the metric measures are printed out
extra_vars - extra variables, here are:
extra_vars['quesFile'] - path to the .json file where the questions are stored
extra_vars['annFile'] - path to the .json file where the annotated answers are stored
extra_vars['question_ids'] - question identifiers
'''
quesFile = extra_vars[split]['quesFile']
annFile = extra_vars[split]['annFile']
# create temporal resFile
resFile = 'tmp_res_file.json'
list2vqa(resFile, pred_list, extra_vars[split]['question_ids'])
# create vqa object and vqaRes object
vqa_ = visual_qa.VQA(annFile, quesFile)
vqaRes = vqa_.loadRes(resFile, quesFile)
vqaEval_ = vqaEval.VQAEval(
vqa_, vqaRes, n=2
) # n is precision of accuracy (number of places after decimal), default is 2
vqaEval_.evaluate()
os.remove(resFile) # remove temporal file
# get results
acc_overall = vqaEval_.accuracy['overall']
acc_yes_no = vqaEval_.accuracy['perAnswerType']['yes/no']
acc_number = vqaEval_.accuracy['perAnswerType']['number']
acc_other = vqaEval_.accuracy['perAnswerType']['other']
# acc_per_class = vqaEval_.accuracy['perAnswerClass']
# acc_class_normalized = vqaEval_.accuracy['classNormalizedOverall']
if verbose > 0:
logging.info('VQA Metric: Accuracy yes/no is {0}, other is {1}, number is {2}, overall is {3}'. \
format(acc_yes_no, acc_other, acc_number, acc_overall)) # , acc_class_normalized))
return {
'overall accuracy': acc_overall,
'yes/no accuracy': acc_yes_no,
'number accuracy': acc_number,
'other accuracy': acc_other
}