def make_meters():
meters_dict = {
'loss': logger.AvgMeter(),
'acc1': logger.AvgMeter(),
'acc5': logger.AvgMeter(),
'batch_time': logger.AvgMeter(),
'data_time': logger.AvgMeter(),
'epoch_time': logger.SumMeter()
}
return meters_dict
def construct_logger(name: str) -> logger.Experiment:
exp_logger = logger.Experiment(name)
exp_logger.add_meters(
"test",
{
"acc1": logger.AvgMeter(),
"acc5": logger.AvgMeter(),
"acc10": logger.AvgMeter(),
"bleu_score": logger.AvgMeter(),
"batch_time": logger.AvgMeter(),
},
)
return exp_logger
def make_meters():
meters_dict = {
'loss': logger.AvgMeter(),
'loss_a': logger.AvgMeter(),
'loss_q': logger.AvgMeter(),
'batch_time': logger.AvgMeter(),
'data_time': logger.AvgMeter(),
'epoch_time': logger.SumMeter(),
'bleu_score': logger.AvgMeter(),
'acc1': logger.AvgMeter(),
'acc5': logger.AvgMeter(),
'acc10': logger.AvgMeter(),
'dual_loss': logger.AvgMeter(),
}
return meters_dict
def make_meters():
meters_dict = {
"loss": logger.AvgMeter(),
"loss_a": logger.AvgMeter(),
"loss_q": logger.AvgMeter(),
"batch_time": logger.AvgMeter(),
"data_time": logger.AvgMeter(),
"epoch_time": logger.SumMeter(),
"bleu_score": logger.AvgMeter(),
"acc1": logger.AvgMeter(),
"acc5": logger.AvgMeter(),
"acc10": logger.AvgMeter(),
"dual_loss": logger.AvgMeter(),
}
return meters_dict
def evaluate(loader, model, logger, print_freq=10, sampling_num=5):
model.eval()
model.module.set_testing(True, sample_num=sampling_num)
meters = logger.reset_meters('test')
meters['Bleu_1'] = logger2.AvgMeter()
meters['Bleu_2'] = logger2.AvgMeter()
meters['Bleu_3'] = logger2.AvgMeter()
meters['Bleu_4'] = logger2.AvgMeter()
meters['METEOR'] = logger2.AvgMeter()
meters['ROUGE_L'] = logger2.AvgMeter()
meters['CIDEr'] = logger2.AvgMeter()
results = []
end = time.time()
blue_score_all = 0
for i, sample in enumerate(loader):
batch_size = sample['visual'].size(0)
# measure data loading time
input_visual = Variable(sample['visual'].cuda(async=True),
volatile=True)
input_answer = Variable(sample['answer'].cuda(async=True),
volatile=True)
target_answer = sample['answer']
input_question = Variable(sample['question'].cuda(async=True),
volatile=True)
nlg_metrics = calculate_nlg_score(generated_q.cpu().data,
sample['question'],
loader.dataset.wid_to_word)
output_answer, g_answers, g_answers_score, generated_q = model(
input_visual, input_question, input_answer)
bleu_score = calculate_bleu_score(generated_q.cpu().data,
sample['question'],
loader.dataset.wid_to_word)
acc1, acc5, acc10 = utils.accuracy(output_answer.cpu().data,
target_answer,
topk=(1, 5, 10))
meters['acc1'].update(acc1[0], n=batch_size)
meters['acc5'].update(acc5[0], n=batch_size)
meters['acc10'].update(acc10[0], n=batch_size)
meters['bleu_score'].update(bleu_score, n=batch_size)
meters['Bleu_1'].update(nlg_metrics['Bleu_1'], n=batch_size)
meters['Bleu_2'].update(nlg_metrics['Bleu_2'], n=batch_size)
meters['Bleu_3'].update(nlg_metrics['Bleu_3'], n=batch_size)
meters['Bleu_4'].update(nlg_metrics['Bleu_4'], n=batch_size)
meters['METEOR'].update(nlg_metrics['METEOR'], n=batch_size)
meters['ROUGE_L'].update(nlg_metrics['ROUGE_L'], n=batch_size)
meters['CIDEr'].update(nlg_metrics['CIDEr'], n=batch_size)
g_answers = g_answers.cpu().data
g_answers_score = g_answers_score.cpu().data
for j in range(batch_size):
new_question = generated_q.cpu().data[j].tolist()
new_answer = g_answers[j]
new_answer_score = g_answers_score[j]
sampled_aqa = [
[new_question, new_answer, new_answer_score],
]
num_result = {
'gt_question': sample['question'][j]
[1:].tolist(), #sample['question'][j].numpy(),
'gt_answer': sample['answer'][j],
'augmented_qa': sampled_aqa,
}
readable_result = {
'gt_question':
translate_tokens(sample['question'][j][1:],
loader.dataset.wid_to_word),
'gt_answer':
loader.dataset.aid_to_ans[sample['answer'][j]],
'augmented_qa': [
[
translate_tokens(
item[0],
loader.dataset.wid_to_word), # translate question
loader.dataset.aid_to_ans[item[1]], # translate answer
] for item in sampled_aqa
],
}
results.append(
{
'image': sample['image'][j],
'numeric_result': num_result,
'readable_result': readable_result
}, )
# measure elapsed time
meters['batch_time'].update(time.time() - end, n=batch_size)
end = time.time()
print('* [Evaluation] Result: Acc@1:{acc1.avg:.3f}\t'
'Acc@5:{acc5.avg:.3f}\tAcc@10:{acc10.avg:.3f}\t'
'Time: {batch_time.avg:.3f}\t'
'Bleu_1: {Bleu_1.avg:.5f}\t'
'Bleu_2: {Bleu_2.avg:.5f}\t'
'Bleu_3: {Bleu_3.avg:.5f}\t'
'Bleu_4: {Bleu_4.avg:.5f}\t'
'METEOR: {METEOR.avg:.5f}\t'
'ROUGE_L: {ROUGE_L.avg:.5f}\t '
'CIDEr: {CIDEr.avg:.5f}\t'
'BLEU: {bleu_score.avg:.5f}'.format(acc1=meters['acc1'],
acc5=meters['acc5'],
acc10=meters['acc10'],
batch_time=meters['batch_time'],
Bleu_1=meters['Bleu_1'],
Bleu_2=meters['Bleu_2'],
Bleu_3=meters['Bleu_3'],
Bleu_4=meters['Bleu_4'],
METEOR=meters['METEOR'],
ROUGE_L=meters['ROUGE_L'],
CIDEr=meters['CIDEr'],
bleu_score=meters['bleu_score']))
model.module.set_testing(False)
return results