# resultType ='second'
# fileTypes = ['results', 'accuracy', 'evalQA', 'evalQuesType', 'evalAnsType']
# vqaVal = VQA(annFile, quesFile)
dataDir = './../VQA'
taskType = 'MultipleChoice'
dataType = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType = 'train2014'
annFile = '%s/Annotations/%s_%s_annotations.json' % (dataDir, dataType,
dataSubType)
quesFile = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType,
dataType, dataSubType)
imgDir = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType)
vqaTrain = VQA(annFile, quesFile)
dummyano = vqaTrain.dataset['annotations']
answerFeatures = ld.createAnswerFeatures(dummyano)
sys.path.insert(0, '%s/PythonHelperTools/vqaTools' % (dataDir))
sys.path.insert(0, '%s/PythonEvaluationTools' % (dataDir))
dataDir = './../VQA'
taskType2 = 'MultipleChoice'
dataType2 = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType2 = 'val2014'
annFile2 = '%s/Annotations/%s_%s_annotations.json' % (dataDir, dataType,
dataSubType2)
quesFile2 = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType,
dataType, dataSubType2)
imgDir2 = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType2)
modelReader = open('./model_definition_100iter.json')
def evalResults():
dataDir = './../VQA'
taskType = 'MultipleChoice'
dataType = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType = 'train2014'
annFile = '%s/Annotations/%s_%s_annotations.json' % (dataDir, dataType,
dataSubType)
quesFile = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType,
dataType, dataSubType)
imgDir = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType)
vqaTrain = VQA(annFile, quesFile)
dummyano = vqaTrain.dataset['annotations']
answerFeatures = ld.createAnswerFeatures(dummyano)
dataDir2 = './../VQA'
taskType2 = 'MultipleChoice'
dataType2 = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType2 = 'analysis1'
annFile2 = '%s/Annotations/%s_%s_annotations.json' % (dataDir2, dataType2,
dataSubType2)
quesFile2 = '%s/Questions/%s_%s_%s_questions.json' % (
dataDir2, taskType2, dataType2, dataSubType2)
imgDir2 = '%s/Images/%s/%s/' % (dataDir2, dataType2, dataSubType2)
modelReader = open('./model_definition_100iter.json')
json_read = modelReader.read()
model = model_from_json(json_read)
model.load_weights('./model_weights_100iter.h5py')
vqaVal = VQA(annFile2, quesFile2)
FILE_INDEX = 0
total = 0.0
correct = 0.0
resultsDicts = []
x_test = []
y_test = []
glove_word_vec_file = './../glove/glove.6B.300d.txt'
word_vec_dict = ld.readGloveData(glove_word_vec_file)
imageDict = pramod.generateDictionary(tfile)
feats = sio.loadmat('./../features/coco/vgg_feats.mat')['feats']
for quesID, annotation in vqaVal.qa.iteritems():
# print quesID
# if quesID not in vqaVal.qqa.keys():
# continue
question = vqaVal.qqa[quesID]
# print question
questionVector = ld.getBOWVector(
question['question'].strip().replace('?', ' ?').split(),
word_vec_dict)
imgID = annotation['image_id']
imageVector = np.asarray(feats[:, imageDict[imgID]])
temp_dict = {}
ansString = annotation['multiple_choice_answer']
temp_dict['question_id'] = quesID
# answerVector = ld.getAnswerVector(ansString, answerFeatures)
temp_x_test = np.append(imageVector, questionVector)
# temp_y_test = answerVector
x_test = np.asarray([temp_x_test])
# y_test = np.asarray([temp_y_test])
predictions = model.predict_classes(x_test, verbose=False)
temp_dict['answer'] = answerFeatures[predictions[0]]
resultsDicts.append(temp_dict)
writer = open('./../Results/MultipleChoice_mscoco_analysis1_results.json',
'w')
json_dump = json.dumps(resultsDicts)
writer.write(json_dump)
def evalResults():
dataDir = './../VQA'
taskType = 'MultipleChoice'
dataType = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType = 'train2014'
annFile = '%s/Annotations/%s_%s_annotations.json' % (dataDir, dataType, dataSubType)
quesFile = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType, dataType, dataSubType)
imgDir = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType)
vqaTrain = VQA(annFile, quesFile)
dummyano = vqaTrain.dataset['annotations']
answerFeatures = ld.createAnswerFeatures(dummyano)
dataDir2 = './../VQA'
taskType2 = 'MultipleChoice'
dataType2 = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType2 = 'val2014'
# number = '100'
annFile2 = '%s/Annotations/%s_%s_annotations.json' % (dataDir2, dataType2, dataSubType2)
quesFile2 = '%s/Questions/%s_%s_%s_questions.json' % (dataDir2, taskType2, dataType2, dataSubType2)
resultFile = './../Results/MultipleChoice_mscoco_analysis1_second_results.json'
imgDir2 = '%s/Images/%s/%s/' % (dataDir2, dataType2, dataSubType2)
modelReader = open('./model_definition_100iter.json')
json_read = modelReader.read()
model = model_from_json(json_read)
model.load_weights('./model_weights_100iter.h5py')
vqaVal = VQA(annFile2, quesFile2)
FILE_INDEX = 0
total = 0.0
correct = 0.0
resultsDicts = []
x_test = []
y_test = []
glove_word_vec_file = './../glove/glove.6B.300d.txt'
word_vec_dict = ld.readGloveData(glove_word_vec_file)
imageDict = pramod.generateDictionary(tfile)
feats = sio.loadmat('./../features/coco/vgg_feats.mat')['feats']
for quesID, annotation in vqaVal.qa.iteritems():
# print quesID
# if quesID not in vqaVal.qqa.keys():
# continue
question = vqaVal.qqa[quesID]
choicesList = vqaVal.qqa[quesID]['multiple_choices']
# print choicesList
setChoices = set(choicesList)
setAnswers = set(answerFeatures)
choiceAndAnswer = list(setChoices.intersection(setAnswers))
choiceIndex = []
for choice in choiceAndAnswer:
choiceIndex.append(answerFeatures.index(choice))
#print choiceIndex
questionVector = ld.getBOWVector(question['question'].strip().replace('?', ' ?').split(), word_vec_dict)
imgID = annotation['image_id']
imageVector = np.asarray(feats[:, imageDict[imgID]])
temp_dict = {}
ansString = annotation['multiple_choice_answer']
temp_dict['question_id'] = quesID
# answerVector = ld.getAnswerVector(ansString, answerFeatures)
temp_x_test = np.append(imageVector, questionVector)
# temp_y_test = answerVector
x_test = np.asarray([temp_x_test])
# y_test = np.asarray([temp_y_test])
predictions = model.predict_classes(x_test, verbose = False)
predict_probaResult = model.predict_proba(x_test,verbose = False)
# print "###############Sanity Check############"
# print predict_probaResult.size
# print predict_probaResult
# print predict_probaResult[7]
# print predict_probaResult
maxPred = 0.0
# print "#######################################"
print choiceIndex
for item in choiceIndex:
print len(choiceIndex), item,answerFeatures[item]
for item in choiceIndex:
print item,answerFeatures[item],predict_probaResult[0][item]
if(maxPred < predict_probaResult[0][item]):
maxPred = predict_probaResult[0][item]
maxIndex = item
print maxPred, maxIndex, answerFeatures[maxIndex]
# temp_dict['answer'] = answerFeatures[predictions[0]]
temp_dict['answer'] = answerFeatures[maxIndex]
resultsDicts.append(temp_dict)
writer = open(resultFile, 'w')
json_dump = json.dumps(resultsDicts)
writer.write(json_dump)
import sys
sys.path.insert(0, './../VQA/PythonHelperTools')
from vqaTools.vqa import VQA
dataDir = './../VQA'
taskType = 'MultipleChoice'
dataType = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType = 'train2014'
annFile = '%s/Annotations/%s_%s_annotations.json' % (dataDir, dataType,
dataSubType)
quesFile = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType,
dataType, dataSubType)
imgDir = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType)
vqaTrain = VQA(annFile, quesFile)
dummyano = vqaTrain.dataset['annotations']
answerFeatures = utilities.createAnswerFeatures(dummyano)
vqaVal = VQA(annFile, quesFile)
# In[4]:
dataset = []
for quesID, annotation in vqaVal.qa.iteritems():
question = vqaVal.qqa[quesID]
question_text = question['question'].strip().replace('?', ' ?').split()
imgID = annotation['image_id']
ansString = annotation['multiple_choice_answer']
dataset.append({
'question': question_text,
def evalResults():
dataDir = './../VQA'
taskType = 'MultipleChoice'
dataType = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType = 'train2014'
annFile = '%s/Annotations/%s_%s_annotations.json' % (dataDir, dataType, dataSubType)
quesFile = '%s/Questions/%s_%s_%s_questions.json' % (dataDir, taskType, dataType, dataSubType)
imgDir = '%s/Images/%s/%s/' % (dataDir, dataType, dataSubType)
vqaTrain = VQA(annFile, quesFile)
dummyano = vqaTrain.dataset['annotations']
answerFeatures = ld.createAnswerFeatures(dummyano)
dataDir2 = './../VQA'
taskType2 = 'MultipleChoice'
dataType2 = 'mscoco' # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType2 = 'analysis1'
annFile2 = '%s/Annotations/%s_%s_annotations.json' % (dataDir2, dataType2, dataSubType2)
quesFile2 = '%s/Questions/%s_%s_%s_questions.json' % (dataDir2, taskType2, dataType2, dataSubType2)
imgDir2 = '%s/Images/%s/%s/' % (dataDir2, dataType2, dataSubType2)
modelReader = open('./model_definition_100iter.json')
json_read = modelReader.read()
model = model_from_json(json_read)
model.load_weights('./model_weights_100iter.h5py')
vqaVal = VQA(annFile2, quesFile2)
FILE_INDEX = 0
total = 0.0
correct = 0.0
resultsDicts = []
x_test = []
y_test = []
glove_word_vec_file = './../glove/glove.6B.300d.txt'
word_vec_dict = ld.readGloveData(glove_word_vec_file)
imageDict = pramod.generateDictionary(tfile)
feats = sio.loadmat('./../features/coco/vgg_feats.mat')['feats']
for quesID, annotation in vqaVal.qa.iteritems():
# print quesID
# if quesID not in vqaVal.qqa.keys():
# continue
question = vqaVal.qqa[quesID]
# print question
questionVector = ld.getBOWVector(question['question'].strip().replace('?', ' ?').split(), word_vec_dict)
imgID = annotation['image_id']
imageVector = np.asarray(feats[:, imageDict[imgID]])
temp_dict = {}
ansString = annotation['multiple_choice_answer']
temp_dict['question_id'] = quesID
# answerVector = ld.getAnswerVector(ansString, answerFeatures)
temp_x_test = np.append(imageVector, questionVector)
# temp_y_test = answerVector
x_test = np.asarray([temp_x_test])
# y_test = np.asarray([temp_y_test])
predictions = model.predict_classes(x_test, verbose = False)
temp_dict['answer'] = answerFeatures[predictions[0]]
resultsDicts.append(temp_dict)
writer = open('./../Results/MultipleChoice_mscoco_analysis1_results.json', 'w')
json_dump = json.dumps(resultsDicts)
writer.write(json_dump)
# quesFile ='%s/Questions/%s_%s_%s_questions.json'%(dataDir, taskType, dataType, dataSubType)
# imgDir ='%s/Images/%s/%s/' %(dataDir, dataType, dataSubType)
# resultType ='second'
# fileTypes = ['results', 'accuracy', 'evalQA', 'evalQuesType', 'evalAnsType']
# vqaVal = VQA(annFile, quesFile)
dataDir = "./../VQA"
taskType = "MultipleChoice"
dataType = "mscoco" # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType = "train2014"
annFile = "%s/Annotations/%s_%s_annotations.json" % (dataDir, dataType, dataSubType)
quesFile = "%s/Questions/%s_%s_%s_questions.json" % (dataDir, taskType, dataType, dataSubType)
imgDir = "%s/Images/%s/%s/" % (dataDir, dataType, dataSubType)
vqaTrain = VQA(annFile, quesFile)
dummyano = vqaTrain.dataset["annotations"]
answerFeatures = ld.createAnswerFeatures(dummyano)
sys.path.insert(0, "%s/PythonHelperTools/vqaTools" % (dataDir))
sys.path.insert(0, "%s/PythonEvaluationTools" % (dataDir))
dataDir = "./../VQA"
taskType2 = "MultipleChoice"
dataType2 = "mscoco" # 'mscoco' for real and 'abstract_v002' for abstract
dataSubType2 = "val2014"
annFile2 = "%s/Annotations/%s_%s_annotations.json" % (dataDir, dataType, dataSubType2)
quesFile2 = "%s/Questions/%s_%s_%s_questions.json" % (dataDir, taskType, dataType, dataSubType2)
imgDir2 = "%s/Images/%s/%s/" % (dataDir, dataType, dataSubType2)
modelReader = open("./model_definition_100iter.json")
json_read = modelReader.read()
model = model_from_json(json_read)
