def evaluate_and_dump_predictions(pred, qids, qfile, afile, ix_ans_dict, filename):
"""
dumps predictions to some default file
:param pred: list of predictions, like [1, 2, 3, 2, ...]. one number for each example
:param qids: question ids in the same order of predictions, they need to align and match
:param qfile:
:param afile:
:param ix_ans_dict:
:return:
"""
assert len(pred) == len(qids), "Number of predictions need to match number of question IDs"
answers = []
for i, val in enumerate(pred):
qa_pair = {}
qa_pair['question_id'] = int(qids[i])
qa_pair['answer'] = ix_ans_dict[str(val + 1)] # note indexing diff between python and torch
answers.append(qa_pair)
vqa = VQA(afile, qfile)
fod = open(filename, 'wb')
json.dump(answers, fod)
fod.close()
# VQA evaluation
vqaRes = vqa.loadRes(filename, qfile)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
acc = vqaEval.accuracy['overall']
print("Overall Accuracy is: %.02f\n" % acc)
return acc
def vqaEval(config=Config(), epoch_list=range(10)):
accuracy_dic = {}
best_accuracy, best_epoch = 0.0, -1
# set up file names and paths
annFile = config.selected_val_annotations_path
quesFile = config.selected_val_questions_path
for epoch in epoch_list:
resFile = config.result_path % (epoch)
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(resFile, quesFile)
vqaEval = VQAEval(
vqa, vqaRes, n=2
) #n is precision of accuracy (number of places after decimal), default is 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()
# print accuracies
accuracy = vqaEval.accuracy['overall']
print "Overall Accuracy is: %.02f\n" % (accuracy)
"""
print "Per Question Type Accuracy is the following:"
for quesType in vqaEval.accuracy['perQuestionType']:
print "%s : %.02f" %(quesType, vqaEval.accuracy['perQuestionType'][quesType])
print "\n"
"""
accuracy_dic[epoch] = {'overall': accuracy}
print "Per Answer Type Accuracy is the following:"
for ansType in vqaEval.accuracy['perAnswerType']:
accuracy_dic[epoch][ansType] = vqaEval.accuracy['perAnswerType'][
ansType]
#print "%s : %.02f" %(ansType, vqaEval.accuracy['perAnswerType'][ansType])
if accuracy > best_accuracy:
best_accuracy = accuracy
best_epoch = epoch
#print "** Done for every epoch! **"
#print "Accuracy Dictionry"
#print accuracy_dic
print "Best Epoch is %d with Accuracy %.02f" % (best_epoch, best_accuracy)
return accuracy_dic
def vqaEval(config = Config(), epoch_list = range(10)):
accuracy_dic = {}
best_accuracy, best_epoch = 0.0, -1
# set up file names and paths
annFile = config.selected_val_annotations_path
quesFile = config.selected_val_questions_path
for epoch in epoch_list:
resFile = config.result_path%(epoch)
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(resFile, quesFile)
vqaEval = VQAEval(vqa, vqaRes, n=2) #n is precision of accuracy (number of places after decimal), default is 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()
# print accuracies
accuracy = vqaEval.accuracy['overall']
print "Overall Accuracy is: %.02f\n" %(accuracy)
"""
print "Per Question Type Accuracy is the following:"
for quesType in vqaEval.accuracy['perQuestionType']:
print "%s : %.02f" %(quesType, vqaEval.accuracy['perQuestionType'][quesType])
print "\n"
"""
accuracy_dic[epoch] = {'overall' : accuracy}
print "Per Answer Type Accuracy is the following:"
for ansType in vqaEval.accuracy['perAnswerType']:
accuracy_dic[epoch][ansType] = vqaEval.accuracy['perAnswerType'][ansType]
#print "%s : %.02f" %(ansType, vqaEval.accuracy['perAnswerType'][ansType])
if accuracy > best_accuracy:
best_accuracy = accuracy
best_epoch = epoch
#print "** Done for every epoch! **"
#print "Accuracy Dictionry"
#print accuracy_dic
print "Best Epoch is %d with Accuracy %.02f"%(best_epoch, best_accuracy)
return accuracy_dic
def exec_validation(device_id, mode, it='', visualize=False):
caffe.set_device(device_id)
caffe.set_mode_gpu()
net = caffe.Net('./result/proto_test.prototxt',\
'./result/tmp.caffemodel',\
caffe.TEST)
dp = VQADataProvider(mode=mode,batchsize=64)
total_questions = len(dp.getQuesIds())
epoch = 0
pred_list = []
testloss_list = []
stat_list = []
while epoch == 0:
t_word, t_cont, t_img_feature, t_answer, t_glove_matrix, t_qid_list, t_iid_list, epoch = dp.get_batch_vec()
net.blobs['data'].data[...] = np.transpose(t_word,(1,0))
net.blobs['cont'].data[...] = np.transpose(t_cont,(1,0))
net.blobs['img_feature'].data[...] = t_img_feature
net.blobs['label'].data[...] = t_answer
net.blobs['glove'].data[...] = np.transpose(t_glove_matrix, (1,0,2))
net.forward()
t_pred_list = net.blobs['prediction'].data.argmax(axis=1)
t_pred_str = [dp.vec_to_answer(pred_symbol) for pred_symbol in t_pred_list]
testloss_list.append(net.blobs['loss'].data)
for qid, iid, ans, pred in zip(t_qid_list, t_iid_list, t_answer.tolist(), t_pred_str):
pred_list.append({u'answer':pred, u'question_id': int(dp.getStrippedQuesId(qid))})
if visualize:
q_list = dp.seq_to_list(dp.getQuesStr(qid))
if mode == 'test-dev' or 'test':
ans_str = ''
ans_list = ['']*10
else:
ans_str = dp.vec_to_answer(ans)
ans_list = [ dp.getAnsObj(qid)[i]['answer'] for i in xrange(10)]
stat_list.append({\
'qid' : qid,
'q_list' : q_list,
'iid' : iid,
'answer': ans_str,
'ans_list': ans_list,
'pred' : pred })
percent = 100 * float(len(pred_list)) / total_questions
sys.stdout.write('\r' + ('%.2f' % percent) + '%')
sys.stdout.flush()
mean_testloss = np.array(testloss_list).mean()
if mode == 'val':
valFile = './result/val2015_resfile'
with open(valFile, 'w') as f:
json.dump(pred_list, f)
if visualize:
visualize_failures(stat_list,mode)
annFile = config.DATA_PATHS['val']['ans_file']
quesFile = config.DATA_PATHS['val']['ques_file']
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(valFile, quesFile)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
acc_overall = vqaEval.accuracy['overall']
acc_perQuestionType = vqaEval.accuracy['perQuestionType']
acc_perAnswerType = vqaEval.accuracy['perAnswerType']
return mean_testloss, acc_overall, acc_perQuestionType, acc_perAnswerType
elif mode == 'test-dev':
filename = './result/vqa_OpenEnded_mscoco_test-dev2015_v3t'+str(it).zfill(8)+'_results'
with open(filename+'.json', 'w') as f:
json.dump(pred_list, f)
if visualize:
visualize_failures(stat_list,mode)
elif mode == 'test':
filename = './result/vqa_OpenEnded_mscoco_test2015_v3c'+str(it).zfill(8)+'_results'
with open(filename+'.json', 'w') as f:
json.dump(pred_list, f)
if visualize:
visualize_failures(stat_list,mode)
dataType ='mscoco' # 'mscoco' only for v1.0. 'mscoco' for real and 'abstract_v002' for abstract for v1.0.
dataSubType ='train2014'
annFile ='%s/Annotations/%s%s_%s_annotations.json'%(dataDir, versionType, dataType, dataSubType)
quesFile ='%s/Questions/%s%s_%s_%s_questions.json'%(dataDir, versionType, taskType, dataType, dataSubType)
imgDir ='%s/Images/%s/%s/' %(dataDir, dataType, dataSubType)
resultType ='fake'
fileTypes = ['results', 'accuracy', 'evalQA', 'evalQuesType', 'evalAnsType']
# An example result json file has been provided in './Results' folder.
[resFile, accuracyFile, evalQAFile, evalQuesTypeFile, evalAnsTypeFile] = ['%s/Results/%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(annFile, quesFile)
vqaRes = vqa.loadRes(resFile, quesFile)
# create vqaEval object by taking vqa and vqaRes
vqaEval = VQAEval(vqa, vqaRes, n=2) #n is precision of accuracy (number of places after decimal), default is 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()
# print accuracies
print("\n")
print("Overall Accuracy is: %.02f\n" %(vqaEval.accuracy['overall']))
print("Per Question Type Accuracy is the following:")
def exec_validation(device_id, mode, it='', visualize=False):
caffe.set_device(device_id)
caffe.set_mode_gpu()
net = caffe.Net('./result/proto_test.prototxt',\
'./result/tmp.caffemodel',\
caffe.TEST)
dp = VQADataProvider(mode=mode, batchsize=64)
total_questions = len(dp.getQuesIds())
epoch = 0
pred_list = []
testloss_list = []
stat_list = []
while epoch == 0:
t_word, t_cont, t_img_feature, t_answer, t_qid_list, t_iid_list, epoch = dp.get_batch_vec(
)
net.blobs['data'].data[...] = np.transpose(t_word, (1, 0))
net.blobs['cont'].data[...] = np.transpose(t_cont, (1, 0))
net.blobs['img_feature'].data[...] = t_img_feature
net.blobs['label'].data[...] = t_answer
net.forward()
t_pred_list = net.blobs['prediction'].data.argmax(axis=1)
t_pred_str = [
dp.vec_to_answer(pred_symbol) for pred_symbol in t_pred_list
]
testloss_list.append(net.blobs['loss'].data)
for qid, iid, ans, pred in zip(t_qid_list, t_iid_list,
t_answer.tolist(), t_pred_str):
pred_list.append({
'answer': pred,
'question_id': int(dp.getStrippedQuesId(qid))
})
if visualize:
q_list = dp.seq_to_list(dp.getQuesStr(qid))
if mode == 'test-dev' or 'test':
ans_str = ''
ans_list = [''] * 10
else:
ans_str = dp.vec_to_answer(ans)
ans_list = [
dp.getAnsObj(qid)[i]['answer'] for i in range(10)
]
stat_list.append({\
'qid' : qid,
'q_list' : q_list,
'iid' : iid,
'answer': ans_str,
'ans_list': ans_list,
'pred' : pred })
percent = 100 * float(len(pred_list)) / total_questions
sys.stdout.write('\r' + ('%.2f' % percent) + '%')
sys.stdout.flush()
mean_testloss = np.array(testloss_list).mean()
if mode == 'val':
valFile = './result/val2015_resfile'
with open(valFile, 'w') as f:
json.dump(pred_list, f)
if visualize:
visualize_failures(stat_list, mode)
annFile = config.DATA_PATHS['val']['ans_file']
quesFile = config.DATA_PATHS['val']['ques_file']
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(valFile, quesFile)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
acc_overall = vqaEval.accuracy['overall']
acc_perQuestionType = vqaEval.accuracy['perQuestionType']
acc_perAnswerType = vqaEval.accuracy['perAnswerType']
return mean_testloss, acc_overall, acc_perQuestionType, acc_perAnswerType
elif mode == 'test-dev':
filename = './result/vqa_OpenEnded_mscoco_test-dev2015_v3t' + str(
it).zfill(8) + '_results'
with open(filename + '.json', 'w') as f:
json.dump(pred_list, f)
if visualize:
visualize_failures(stat_list, mode)
elif mode == 'test':
filename = './result/vqa_OpenEnded_mscoco_test2015_v3c' + str(
it).zfill(8) + '_results'
with open(filename + '.json', 'w') as f:
json.dump(pred_list, f)
if visualize:
visualize_failures(stat_list, mode)
def exec_validation(model, opt, mode, folder, it, visualize=False):
model.eval()
criterion = nn.NLLLoss()
dp = VQADataProvider(opt,
batchsize=opt.VAL_BATCH_SIZE,
mode='val',
folder=folder)
epoch = 0
pred_list = []
testloss_list = []
stat_list = []
total_questions = len(dp.getQuesIds())
print('Validating...')
while epoch == 0:
t_word, word_length, t_img_feature, t_answer, t_qid_list, t_iid_list, epoch = dp.get_batch_vec(
)
word_length = np.sum(word_length, axis=1)
data = Variable(torch.from_numpy(t_word)).cuda()
word_length = torch.from_numpy(word_length).cuda()
img_feature = Variable(torch.from_numpy(t_img_feature)).cuda()
label = Variable(torch.from_numpy(t_answer)).cuda()
pred = model(data, word_length, img_feature, 'val')
pred = (pred.data).cpu().numpy()
if mode == 'test-dev' or 'test':
pass
else:
loss = criterion(pred, label.long())
loss = (loss.data).cpu().numpy()
testloss_list.append(loss)
t_pred_list = np.argmax(pred, axis=1)
t_pred_str = [
dp.vec_to_answer(pred_symbol) for pred_symbol in t_pred_list
]
for qid, iid, ans, pred in zip(t_qid_list, t_iid_list,
t_answer.tolist(), t_pred_str):
pred_list.append((pred, int(dp.getStrippedQuesId(qid))))
if visualize:
q_list = dp.seq_to_list(dp.getQuesStr(qid))
if mode == 'test-dev' or 'test':
ans_str = ''
ans_list = [''] * 10
else:
ans_str = dp.vec_to_answer(ans)
ans_list = [
dp.getAnsObj(qid)[i]['answer'] for i in range(10)
]
stat_list.append({\
'qid' : qid,
'q_list' : q_list,
'iid' : iid,
'answer': ans_str,
'ans_list': ans_list,
'pred' : pred })
percent = 100 * float(len(pred_list)) / total_questions
sys.stdout.write('\r' + ('%.2f' % percent) + '%')
sys.stdout.flush()
print('Deduping arr of len', len(pred_list))
deduped = []
seen = set()
for ans, qid in pred_list:
if qid not in seen:
seen.add(qid)
deduped.append((ans, qid))
print('New len', len(deduped))
final_list = []
for ans, qid in deduped:
final_list.append({u'answer': ans, u'question_id': qid})
if mode == 'val':
mean_testloss = np.array(testloss_list).mean()
valFile = './%s/val2015_resfile' % folder
with open(valFile, 'w') as f:
json.dump(final_list, f)
if visualize:
visualize_failures(stat_list, mode)
annFile = config.DATA_PATHS['val']['ans_file']
quesFile = config.DATA_PATHS['val']['ques_file']
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(valFile, quesFile)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
acc_overall = vqaEval.accuracy['overall']
acc_perQuestionType = vqaEval.accuracy['perQuestionType']
acc_perAnswerType = vqaEval.accuracy['perAnswerType']
return mean_testloss, acc_overall, acc_perQuestionType, acc_perAnswerType
elif mode == 'test-dev':
filename = './%s/vqa_OpenEnded_mscoco_test-dev2015_%s-' % (
folder, folder) + str(it).zfill(8) + '_results'
with open(filename + '.json', 'w') as f:
json.dump(final_list, f)
if visualize:
visualize_failures(stat_list, mode)
elif mode == 'test':
filename = './%s/vqa_OpenEnded_mscoco_test2015_%s-' % (
folder, folder) + str(it).zfill(8) + '_results'
with open(filename + '.json', 'w') as f:
json.dump(final_list, f)
if visualize:
visualize_failures(stat_list, mode)
def exec_validation(self, sess, mode, folder, it=0, visualize=False):
dp = VQADataLoader(mode=mode,
batchsize=config.VAL_BATCH_SIZE,
folder=folder)
total_questions = len(dp.getQuesIds())
epoch = 0
pred_list = []
testloss_list = []
stat_list = []
while epoch == 0:
q_strs, q_word_vec_list, q_len_list, ans_vectors, img_features, a_word_vec, ans_score, ans_space_score, t_qid_list, img_ids, epoch = dp.next_batch(
config.BATCH_SIZE)
feed_dict = {
self.model.q_input: q_word_vec_list,
self.model.ans1: ans_vectors,
self.model.seqlen: q_len_list,
self.model.img_vec: img_features,
self.lr: config.VQA_LR,
self.model.keep_prob: 1.0,
self.model.is_training: False
}
t_predict_list, predict_loss = sess.run(
[self.model.predict1, self.model.softmax_cross_entrophy1],
feed_dict=feed_dict)
t_pred_str = [
dp.vec_to_answer(pred_symbol) for pred_symbol in t_predict_list
]
testloss_list.append(predict_loss)
ans_vectors = np.asarray(ans_vectors).argmax(1)
for qid, iid, ans, pred in zip(t_qid_list, img_ids, ans_vectors,
t_pred_str):
# pred_list.append({u'answer':pred, u'question_id': int(dp.getStrippedQuesId(qid))})
pred_list.append((pred, int(dp.getStrippedQuesId(qid))))
if visualize:
q_list = dp.seq_to_list(dp.getQuesStr(qid))
if mode == 'test-dev' or 'test':
ans_str = ''
ans_list = [''] * 10
else:
ans_str = dp.vec_to_answer(ans)
ans_list = [
dp.getAnsObj(qid)[i]['answer'] for i in xrange(10)
]
stat_list.append({ \
'qid': qid,
'q_list': q_list,
'iid': iid,
'answer': ans_str,
'ans_list': ans_list,
'pred': pred})
percent = 100 * float(len(pred_list)) / total_questions
sys.stdout.write('\r' + ('%.2f' % percent) + '%')
sys.stdout.flush()
print 'Deduping arr of len', len(pred_list)
deduped = []
seen = set()
for ans, qid in pred_list:
if qid not in seen:
seen.add(qid)
deduped.append((ans, qid))
print 'New len', len(deduped)
final_list = []
for ans, qid in deduped:
final_list.append({u'answer': ans, u'question_id': qid})
mean_testloss = np.array(testloss_list).mean()
if mode == 'val':
valFile = './%s/val2015_resfile_%d' % (folder, it)
with open(valFile, 'w') as f:
json.dump(final_list, f)
if visualize:
visualize_failures(stat_list, mode)
annFile = config.DATA_PATHS['val']['ans_file']
quesFile = config.DATA_PATHS['val']['ques_file']
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(valFile, quesFile)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
acc_overall = vqaEval.accuracy['overall']
acc_perQuestionType = vqaEval.accuracy['perQuestionType']
acc_perAnswerType = vqaEval.accuracy['perAnswerType']
return mean_testloss, acc_overall, acc_perQuestionType, acc_perAnswerType
elif mode == 'test-dev':
filename = './%s/vqa_OpenEnded_mscoco_test-dev2015_%s-%d-' % (
folder, folder, it) + str(it).zfill(8) + '_results'
with open(filename + '.json', 'w') as f:
json.dump(final_list, f)
if visualize:
visualize_failures(stat_list, mode)
elif mode == 'test':
filename = './%s/vqa_OpenEnded_mscoco_test2015_%s-%d-' % (
folder, folder, it) + str(it).zfill(8) + '_results'
with open(filename + '.json', 'w') as f:
json.dump(final_list, f)
if visualize:
visualize_failures(stat_list, mode)
def exec_validation(model, opt, mode, folder, it, logger, visualize=False, dp=None):
"""
execute validation and save predictions as json file for visualization
avg_loss: average loss on given validation dataset split
acc_overall: overall accuracy
"""
if opt.LATE_FUSION:
criterion = nn.BCELoss()
model_prob = model[1]
model = model[0]
else:
criterion = nn.NLLLoss()
check_mkdir(folder)
model.eval()
# criterion = nn.KLDivLoss(reduction='batchmean')
if opt.BINARY:
criterion2 = nn.BCELoss()
acc_counter = 0
all_counter = 0
if not dp:
dp = VQADataProvider(opt, batchsize=opt.VAL_BATCH_SIZE, mode=mode, logger=logger)
epoch = 0
pred_list = []
loss_list = []
stat_list = []
total_questions = len(dp.getQuesIds())
percent_counter = 0
logger.info('Validating...')
while epoch == 0:
data, word_length, img_feature, answer, embed_matrix, ocr_length, ocr_embedding, ocr_tokens, ocr_answer_flags, qid_list, iid_list, epoch = dp.get_batch_vec()
data = cuda_wrapper(Variable(torch.from_numpy(data))).long()
word_length = cuda_wrapper(torch.from_numpy(word_length))
img_feature = cuda_wrapper(Variable(torch.from_numpy(img_feature))).float()
label = cuda_wrapper(Variable(torch.from_numpy(answer)))
ocr_answer_flags = cuda_wrapper(torch.from_numpy(ocr_answer_flags))
if opt.OCR:
embed_matrix = cuda_wrapper(Variable(torch.from_numpy(embed_matrix))).float()
ocr_length = cuda_wrapper(torch.from_numpy(ocr_length))
ocr_embedding= cuda_wrapper(Variable(torch.from_numpy(ocr_embedding))).float()
if opt.BINARY:
ocr_answer_flags = cuda_wrapper(ocr_answer_flags)
if opt.LATE_FUSION:
binary = model(data, img_feature, embed_matrix, ocr_length, ocr_embedding, mode)
pred = model_prob(data, img_feature, embed_matrix, ocr_length, ocr_embedding, mode)
pred1 = pred[:, 0:opt.MAX_ANSWER_VOCAB_SIZE]
pred2 = pred[:, opt.MAX_ANSWER_VOCAB_SIZE:]
else:
binary, pred1, pred2 = model(data, img_feature, embed_matrix, ocr_length, ocr_embedding, mode)
else:
pred = model(data, img_feature, embed_matrix, ocr_length, ocr_embedding, mode)
elif opt.EMBED:
embed_matrix = cuda_wrapper(Variable(torch.from_numpy(embed_matrix))).float()
pred = model(data, img_feature, embed_matrix, mode)
else:
pred = model(data, word_length, img_feature, mode)
if mode == 'test-dev' or mode == 'test':
pass
else:
if opt.BINARY:
if opt.LATE_FUSION:
loss = criterion(binary, ocr_answer_flags.float())
else:
loss = criterion2(binary, ocr_answer_flags.float()) * opt.BIN_LOSS_RATE
loss += criterion(pred1[label < opt.MAX_ANSWER_VOCAB_SIZE], label[label < opt.MAX_ANSWER_VOCAB_SIZE].long())
loss += criterion(pred2[label >= opt.MAX_ANSWER_VOCAB_SIZE], label[label >= opt.MAX_ANSWER_VOCAB_SIZE].long() - opt.MAX_ANSWER_VOCAB_SIZE)
all_counter += binary.size()[0]
acc_counter += torch.sum((binary <= 0.5) * (ocr_answer_flags == 0) + (binary > 0.5) * (ocr_answer_flags == 1))
#print(all_counter, acc_counter)
else:
loss = criterion(pred, label.long())
loss = (loss.data).cpu().numpy()
loss_list.append(loss)
if opt.BINARY:
binary = (binary.data).cpu().numpy()
pred1 = (pred1.data).cpu().numpy()
pred2 = (pred2.data).cpu().numpy()
pred = np.hstack([pred1, pred2])
else:
pred = (pred.data).cpu().numpy()
if opt.OCR:
# select the largest index within the ocr length boundary
ocr_mask = np.fromfunction(lambda i, j: j >= (ocr_length[i].cpu().numpy() + opt.MAX_ANSWER_VOCAB_SIZE), pred.shape, dtype=int)
if opt.BINARY:
#ocr_mask += np.fromfunction(lambda i, j: np.logical_or(np.logical_and(binary[i] <= 0.5, j >= opt.MAX_ANSWER_VOCAB_SIZE), np.logical_and(binary[i] > 0.5, j < opt.MAX_ANSWER_VOCAB_SIZE)), pred.shape, dtype=int)
#ocr_mask += np.fromfunction(lambda i, j: np.logical_or(np.logical_and(ocr_answer_flags[i] == 0, j >= opt.MAX_ANSWER_VOCAB_SIZE), np.logical_and(ocr_answer_flags[i] == 1, j < opt.MAX_ANSWER_VOCAB_SIZE)), pred.shape, dtype=int)
ocr_mask += np.fromfunction(lambda i, j: np.logical_or(np.logical_and(ocr_answer_flags[i].cpu().numpy() == 0, j >= opt.MAX_ANSWER_VOCAB_SIZE), np.logical_and(ocr_answer_flags[i].cpu().numpy() == 1, j < opt.MAX_ANSWER_VOCAB_SIZE)), pred.shape, dtype=int)
masked_pred = np.ma.array(pred, mask=ocr_mask)
pred_max = np.ma.argmax(masked_pred, axis=1)
pred_str = [dp.vec_to_answer_ocr(pred_symbol, ocr) for pred_symbol, ocr in zip(pred_max, ocr_tokens)]
else:
pred_max = np.argmax(pred, axis=1)
pred_str = [dp.vec_to_answer(pred_symbol) for pred_symbol in pred_max]
for qid, iid, ans, pred, ocr in zip(qid_list, iid_list, answer.tolist(), pred_str, ocr_tokens):
pred_list.append((pred, int(dp.getStrippedQuesId(qid))))
# prepare pred json file
if visualize:
q_list = dp.seq_to_list(dp.getQuesStr(qid), opt.MAX_QUESTION_LENGTH)
if mode == 'test-dev' or mode == 'test':
ans_str = ''
ans_list = ['']*10
else:
if opt.OCR:
ans_str = dp.vec_to_answer_ocr(int(ans), ocr)
else:
ans_str = dp.vec_to_answer(int(ans))
ans_list = [ dp.getAnsObj(qid)[i]['answer'] for i in range(10)]
stat_list.append({
'qid': qid,
'q_list': q_list,
'iid': iid,
'answer': ans_str,
'ans_list': ans_list,
'pred': pred,
'ocr_tokens': ocr
})
percent = 100 * float(len(pred_list)) / total_questions
if percent <= 100 and percent - percent_counter >= 5:
percent_counter = percent
sys.stdout.write('\r' + ('%.2f' % percent) + '%')
sys.stdout.flush()
if visualize:
with open(os.path.join(folder, 'visualize.json'), 'w') as f:
json.dump(stat_list, f, indent=4, sort_keys=True)
if opt.BINARY:
logger.info('Binary Acc: {},({}/{})'.format(acc_counter.item()/all_counter, acc_counter, all_counter))
logger.info('Deduping arr of len {}'.format(len(pred_list)))
deduped = []
seen = set()
for ans, qid in pred_list:
if qid not in seen:
seen.add(qid)
deduped.append((ans, qid))
logger.info('New len {}'.format(len(deduped)))
final_list=[]
for ans,qid in deduped:
final_list.append({u'answer': ans, u'question_id': qid})
if mode == 'val':
avg_loss = np.array(loss_list).mean()
valFile = os.path.join(folder, 'val2015_resfile')
with open(valFile, 'w') as f:
json.dump(final_list, f)
# if visualize:
# visualize_pred(stat_list,mode)
exp_type = opt.EXP_TYPE
annFile = config.DATA_PATHS[exp_type]['val']['ans_file']
quesFile = config.DATA_PATHS[exp_type]['val']['ques_file']
vqa = VQA(annFile, quesFile)
vqaRes = vqa.loadRes(valFile, quesFile)
vqaEval = VQAEval(vqa, vqaRes, n=2)
vqaEval.evaluate()
acc_overall = vqaEval.accuracy['overall']
acc_perQuestionType = vqaEval.accuracy['perQuestionType']
acc_perAnswerType = vqaEval.accuracy['perAnswerType']
elif mode == 'test-dev':
filename = os.path.join(folder, 'test-dev_results_' + str(it).zfill(8))
with open(filename+'.json', 'w') as f:
json.dump(final_list, f)
# if visualize:
# visualize_pred(stat_list,mode)
elif mode == 'test':
filename = os.path.join(folder, 'test_results_' + str(it).zfill(8))
with open(filename+'.json', 'w') as f:
json.dump(final_list, f)
# if visualize:
# visualize_pred(stat_list,mode)
return avg_loss, acc_overall, acc_perQuestionType, acc_perAnswerType
