def print_img(img, i):
img_url = FLAGS.image_url_prefix + img if not img.startswith(
"http://") else img
logging.info(
img_html.format(img_url, i, img, melt.epoch(), melt.step(),
melt.train_loss(), melt.eval_loss(), melt.duration(),
gezi.now_time()))
def print_img(img, i):
img_url = get_img_url(img)
logging.info(img_html.format(
img_url,
i,
img,
melt.epoch(),
melt.step(),
melt.train_loss(),
melt.eval_loss(),
melt.duration(),
gezi.now_time()))
def evaluate_scores(predictor, random=False):
timer = gezi.Timer('evaluate_scores')
init()
imgs, img_features = get_image_names_and_features()
num_metric_eval_examples = min(FLAGS.num_metric_eval_examples, len(imgs))
step = FLAGS.metric_eval_batch_size
if random:
index = np.random.choice(len(imgs),
num_metric_eval_examples,
replace=False)
imgs = imgs[index]
img_features = img_features[index]
text_max_words = all_distinct_texts.shape[1]
rank_metrics = gezi.rank_metrics.RecallMetrics()
print('text_max_words:', text_max_words)
start = 0
while start < num_metric_eval_examples:
end = start + step
if end > num_metric_eval_examples:
end = num_metric_eval_examples
print('predicts start:', start, 'end:', end, file=sys.stderr)
predicts(imgs[start:end], img_features[start:end], predictor,
rank_metrics)
start = end
melt.logging_results(
rank_metrics.get_metrics(),
rank_metrics.get_names(),
tag='evaluate: epoch:{} step:{} train:{} eval:{}'.format(
melt.epoch(), melt.step(), melt.train_loss(), melt.eval_loss()))
timer.print()
return rank_metrics.get_metrics(), rank_metrics.get_names()
def evaluate_scores(predictor, random=False):
timer = gezi.Timer('evaluate_scores')
init()
if FLAGS.eval_img2text:
imgs, img_features = get_image_names_and_features()
num_metric_eval_examples = min(FLAGS.num_metric_eval_examples,
len(imgs))
step = FLAGS.metric_eval_batch_size
if random:
index = np.random.choice(len(imgs),
num_metric_eval_examples,
replace=False)
imgs = imgs[index]
img_features = img_features[index]
rank_metrics = gezi.rank_metrics.RecallMetrics()
start = 0
while start < num_metric_eval_examples:
end = start + step
if end > num_metric_eval_examples:
end = num_metric_eval_examples
print('predicts image start:', start, 'end:', end, file=sys.stderr)
predicts(imgs[start:end], img_features[start:end], predictor,
rank_metrics)
start = end
melt.logging_results(
rank_metrics.get_metrics(),
rank_metrics.get_names(),
tag='evaluate: epoch:{} step:{} train:{} eval:{}'.format(
melt.epoch(), melt.step(), melt.train_loss(),
melt.eval_loss()))
if FLAGS.eval_text2img:
num_metric_eval_examples = min(FLAGS.num_metric_eval_examples,
len(all_distinct_texts))
if random:
index = np.random.choice(len(all_distinct_texts),
num_metric_eval_examples,
replace=False)
text_strs = all_distinct_text_strs[index]
texts = all_distinct_texts[index]
rank_metrics2 = gezi.rank_metrics.RecallMetrics()
start = 0
while start < num_metric_eval_examples:
end = start + step
if end > num_metric_eval_examples:
end = num_metric_eval_examples
print('predicts start:', start, 'end:', end, file=sys.stderr)
predicts_txt2im(text_strs[start:end], texts[start:end], predictor,
rank_metrics2)
start = end
melt.logging_results(rank_metrics2.get_metrics(),
['t2i' + x for x in rank_metrics2.get_names()],
tag='text2img')
timer.print()
if FLAGS.eval_img2text and FLAGS.eval_text2img:
return rank_metrics.get_metrics() + rank_metrics2.get_metrics(
), rank_metrics.get_names() + [
't2i' + x for x in rank_metrics2.get_names()
]
elif FLAGS.eval_img2text:
return rank_metrics.get_metrics(), rank_metrics.get_names()
else:
return rank_metrics2.get_metrics(), rank_metrics2.get_names()
def evaluate_translation(predictor, random=False, index=None):
timer = gezi.Timer('evaluate_translation')
refs = prepare_refs()
imgs, img_features = get_image_names_and_features()
num_metric_eval_examples = min(FLAGS.num_metric_eval_examples, len(imgs))
if num_metric_eval_examples <= 0:
num_metric_eval_examples = len(imgs)
if num_metric_eval_examples == len(imgs):
random = False
step = FLAGS.metric_eval_batch_size
if random:
if index is None:
index = np.random.choice(len(imgs), num_metric_eval_examples, replace=False)
imgs = imgs[index]
img_features = img_features[index]
else:
img_features = img_features[:num_metric_eval_examples]
results = {}
start = 0
while start < num_metric_eval_examples:
end = start + step
if end > num_metric_eval_examples:
end = num_metric_eval_examples
print('predicts image start:', start, 'end:', end, file=sys.stderr, end='\r')
translation_predicts(imgs[start: end], img_features[start: end], predictor, results)
start = end
scorers = [
(Bleu(4), ["bleu_1", "bleu_2", "bleu_3", "bleu_4"]),
(Meteor(),"meteor"),
(Rouge(), "rouge_l"),
(Cider(), "cider")
]
score_list = []
metric_list = []
selected_refs = {}
selected_results = {}
#by doing this can force same .keys()
for key in results:
selected_refs[key] = refs[key]
selected_results[key] = results[key]
assert len(selected_results[key]) == 1, selected_results[key]
assert selected_results.keys() == selected_refs.keys(), '%d %d'%(len(selected_results.keys()), len(selected_refs.keys()))
if FLAGS.eval_translation_reseg:
print('tokenization...', file=sys.stderr)
global tokenizer
if tokenizer is None:
tokenizer = PTBTokenizer()
selected_refs = tokenizer.tokenize(selected_refs)
selected_results = tokenizer.tokenize(selected_results)
logging.info('predict&label:{}{}{}'.format('|'.join(selected_results.items()[0][1]), '---', '|'.join(selected_refs.items()[0][1])))
for scorer, method in scorers:
print('computing %s score...'%(scorer.method()), file=sys.stderr)
score, scores = scorer.compute_score(selected_refs, selected_results)
if type(method) == list:
for sc, scs, m in zip(score, scores, method):
score_list.append(sc)
metric_list.append(m)
if FLAGS.eval_result_dir:
out = open(os.path.join(FLAGS.eval_result_dir, m+'.txt'), 'w')
for i, sc in enumerate(scs):
key = selected_results.keys()[i]
result = selected_results[key]
refs = '\x01'.join(selected_refs[key])
print(key, result, refs, sc, sep='\t', file=out)
else:
score_list.append(score)
metric_list.append(method)
if FLAGS.eval_result_dir:
out = open(os.path.join(FLAGS.eval_result_dir, m+'.txt'), 'w')
for i, sc in enumerate(scores):
key = selected_results.keys()[i]
result = selected_results[key]
refs = '\x01'.join(selected_refs[key])
print(key, result, refs, sc, sep='\t', file=out)
#exclude "bleu_1", "bleu_2", "bleu_3"
score_list, metric_list = score_list[3:], metric_list[3:]
assert(len(score_list) == 4)
avg_score = sum(score_list) / len(score_list)
score_list.append(avg_score)
metric_list.append('avg')
metric_list = ['trans_' + x for x in metric_list]
melt.logging_results(
score_list,
metric_list,
tag='evaluate: epoch:{} step:{} train:{} eval:{}'.format(
melt.epoch(),
melt.step(),
melt.train_loss(),
melt.eval_loss()))
timer.print()
return score_list, metric_list
def evaluate_scores(predictor, random=False, index=None, exact_predictor=None, exact_ratio=1.):
"""
actually this is rank metrics evaluation, by default recall@1,2,5,10,50
"""
timer = gezi.Timer('evaluate_scores')
init()
if FLAGS.eval_img2text:
imgs, img_features = get_image_names_and_features()
num_metric_eval_examples = min(FLAGS.num_metric_eval_examples, len(imgs))
if num_metric_eval_examples <= 0:
num_metric_eval_examples = len(imgs)
if num_metric_eval_examples == len(imgs):
random = False
step = FLAGS.metric_eval_batch_size
if random:
if index is None:
index = np.random.choice(len(imgs), num_metric_eval_examples, replace=False)
imgs = imgs[index]
img_features = img_features[index]
else:
img_features = img_features[:num_metric_eval_examples]
rank_metrics = gezi.rank_metrics.RecallMetrics()
start = 0
while start < num_metric_eval_examples:
end = start + step
if end > num_metric_eval_examples:
end = num_metric_eval_examples
print('predicts image start:', start, 'end:', end, file=sys.stderr, end='\r')
predicts(imgs[start: end], img_features[start: end], predictor, rank_metrics,
exact_predictor=exact_predictor, exact_ratio=exact_ratio)
start = end
melt.logging_results(
rank_metrics.get_metrics(),
rank_metrics.get_names(),
tag='evaluate: epoch:{} step:{} train:{} eval:{}'.format(
melt.epoch(),
melt.step(),
melt.train_loss(),
melt.eval_loss()))
if FLAGS.eval_text2img:
num_metric_eval_examples = min(FLAGS.num_metric_eval_examples, len(all_distinct_texts))
if random:
index = np.random.choice(len(all_distinct_texts), num_metric_eval_examples, replace=False)
text_strs = all_distinct_text_strs[index]
texts = all_distinct_texts[index]
else:
text_strs = all_distinct_text_strs
texts = all_distinct_texts
rank_metrics2 = gezi.rank_metrics.RecallMetrics()
start = 0
while start < num_metric_eval_examples:
end = start + step
if end > num_metric_eval_examples:
end = num_metric_eval_examples
print('predicts start:', start, 'end:', end, file=sys.stderr, end='\r')
predicts_txt2im(text_strs[start: end], texts[start: end], predictor, rank_metrics2, exact_predictor=exact_predictor)
start = end
melt.logging_results(
rank_metrics2.get_metrics(),
['t2i' + x for x in rank_metrics2.get_names()],
tag='text2img')
timer.print()
if FLAGS.eval_img2text and FLAGS.eval_text2img:
return rank_metrics.get_metrics() + rank_metrics2.get_metrics(), rank_metrics.get_names() + ['t2i' + x for x in rank_metrics2.get_names()]
elif FLAGS.eval_img2text:
return rank_metrics.get_metrics(), rank_metrics.get_names()
else:
return rank_metrics2.get_metrics(), rank_metrics2.get_names()