def evaluate_score():
evaluator.init()
text_max_words = evaluator.all_distinct_texts.shape[1]
print('text_max_words:', text_max_words)
predictor = algos_factory.gen_predictor(FLAGS.algo)
predictor.init_predict(text_max_words)
predictor.load(FLAGS.model_dir)
evaluator.evaluate_scores(predictor)
def evaluate_score():
predictor = algos_factory.gen_predictor(FLAGS.algo)
score = predictor.init_predict(TEXT_MAX_WORDS)
tf.add_to_collection('score', score)
predictor.load(FLAGS.model_dir)
step = melt.get_model_step_from_dir(FLAGS.model_dir)
model_dir, _ = melt.get_model_dir_and_path(FLAGS.model_dir)
print('step', step, file=sys.stderr)
print('model_dir', model_dir)
#melt.save_model(melt.get_session(), FLAGS.model_dir, step + 1)
melt.save_model(melt.get_session(), model_dir, step + 1)
def predict():
predictor = algos_factory.gen_predictor(algo)
predictor.init_predict(TEXT_MAX_WORDS)
predictor.load('./model.ckpt-12000')
for line in sys.stdin:
l = line.strip().split('\t')
image_name = l[0]
image_feature = np.array([[float(x) for x in l[1:]]])
#image_feature = [[float(x) for x in l[1:]]]
scores = predictor.bulk_predict(image_feature, ids_list)[0]
for i, score in enumerate(scores):
print('{}\t{}\t{}'.format(image_name, score, text_list[i]))
def main(_):
text2ids.init()
global_scope = ''
if FLAGS.add_global_scope:
global_scope = FLAGS.global_scope if FLAGS.global_scope else FLAGS.algo
with tf.variable_scope(global_scope):
predictor = algos_factory.gen_predictor(FLAGS.algo)
with tf.variable_scope(FLAGS.main_scope) as scope:
beam_text, beam_score = predictor.init_predict_text(decode_method=SeqDecodeMethod.beam_search,
beam_size=FLAGS.beam_size,
convert_unk=False)
predictor.load(FLAGS.model_dir)
predict(predictor, "����̫����ô����")
predict(predictor, "���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ���������ڿ�Ů��-�Ա���")
predict(predictor, "����������ʵ��С��ô��,����������ʵ��С���δ�ʩ")
def main(_):
text2ids.init()
global_scope = ''
if FLAGS.add_global_scope:
global_scope = FLAGS.global_scope if FLAGS.global_scope else FLAGS.algo
global sess
sess = melt.get_session(log_device_placement=FLAGS.log_device_placement)
with tf.variable_scope(global_scope):
predictor = algos_factory.gen_predictor(FLAGS.algo)
with tf.variable_scope(FLAGS.main_scope) as scope:
text, score, beam_text, beam_score = gen_predict_graph(predictor, scope)
predictor.load(FLAGS.model_dir)
#input_text = "������������_��������ǰ��Ա���Ƭ"
input_texts = ['���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ���������ڿ�Ů��-�Ա���',
'����̫����ô����',
'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
'����ף�Ŀǰ4����1�굶']
for input_text in input_texts:
word_ids = _text2ids(input_text, INPUT_TEXT_MAX_WORDS)
print(word_ids)
print(text2ids.ids2text(word_ids))
timer = gezi.Timer()
text_, score_ = sess.run([text, score], {predictor.input_text_place : [word_ids]})
print(text_[0], text2ids.ids2text(text_[0]), score_[0], 'time(ms):', timer.elapsed_ms())
timer = gezi.Timer()
texts, scores = sess.run([beam_text, beam_score], {predictor.input_text_place : [word_ids]})
texts = texts[0]
scores = scores[0]
for text_, score_ in zip(texts, scores):
print(text_, text2ids.ids2text(text_), score_)
print('beam_search using time(ms):', timer.elapsed_ms())
def main(_):
text2ids.init()
global_scope = ''
if FLAGS.add_global_scope:
global_scope = FLAGS.global_scope if FLAGS.global_scope else FLAGS.algo
sess = melt.get_session(log_device_placement=FLAGS.log_device_placement)
with tf.variable_scope(global_scope):
predictor = algos_factory.gen_predictor(FLAGS.algo)
with tf.variable_scope(FLAGS.main_scope) as scope:
##--notice if not add below len(tf.get_collection('encode_state') is 1, add below will be 2
## even though in init_predict_text(decode_method=SeqDecodeMethod.beam) will call generate_sequence_greedy
#text, score = predictor.init_predict_text(decode_method=SeqDecodeMethod.greedy,
# beam_size=FLAGS.beam_size,
# convert_unk=False)
#scope.reuse_variables()
beam_text, beam_score = predictor.init_predict_text(decode_method=SeqDecodeMethod.beam,
beam_size=FLAGS.beam_size,
convert_unk=False)
predictor.load(FLAGS.model_dir, sess=sess)
for item in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
print(item)
#input_text = "������������_��������ǰ��Ա���Ƭ"
input_texts = [
#'���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ���������ڿ�Ů��-�Ա���',
#'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
#'����̫����ô����',
'����ף�Ŀǰ4����1�굶',
'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
]
for input_text in input_texts:
word_ids = _text2ids(input_text, INPUT_TEXT_MAX_WORDS)
print(word_ids)
print(text2ids.ids2text(word_ids))
#timer = gezi.Timer()
#text_, score_ = sess.run([text, score], {predictor.input_text_feed : [word_ids]})
#print(text_[0], text2ids.ids2text(text_[0]), score_[0], 'time(ms):', timer.elapsed_ms())
timer = gezi.Timer()
texts, scores = sess.run([beam_text, beam_score],
{predictor.input_text_feed : [word_ids]})
texts = texts[0]
scores = scores[0]
for text_, score_ in zip(texts, scores):
print(text_, text2ids.ids2text(text_), score_, math.log(score_))
print('beam_search using time(ms):', timer.elapsed_ms())
input_texts = [
'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
'���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ���������ڿ�Ů��-�Ա���',
#'���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ����', #same length as lajiao sentence 15
#"����̫����ô����",
#'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
#'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
#'�콨�ǰ���˹��',
]
word_ids_list = [_text2ids(input_text, INPUT_TEXT_MAX_WORDS) for input_text in input_texts]
timer = gezi.Timer()
texts_list, scores_list = sess.run([beam_text, beam_score],
feed_dict={predictor.input_text_feed: word_ids_list})
for texts, scores in zip(texts_list, scores_list):
for text, score in zip(texts, scores):
print(text, text2ids.ids2text(text), score, math.log(score))
print('beam_search using time(ms):', timer.elapsed_ms())
import numpy as np
from libword_counter import Vocabulary
import gezi
from deepiu.image_caption.algos import algos_factory
WORDS_SEP = ' '
TEXT_MAX_WORDS = 80
NUM_RESERVED_IDS = 1
ENCODE_UNK = 0
IMAGE_FEATURE_LEN = 1000
vocabulary = Vocabulary(sys.argv[3], NUM_RESERVED_IDS)
algo = 'bow'
predictor = algos_factory.gen_predictor(algo)
predictor.init_predict(TEXT_MAX_WORDS)
predictor.load(sys.argv[2])
ids_list = []
for line in open(sys.argv[1]):
line = line.strip().split('\t')[-1]
words = line.split()
ids = [
vocabulary.id(word) for word in text.split(WORDS_SEP)
if vocabulary.has(word) or ENCODE_UNK
]
ids = gezi.pad(ids, TEXT_MAX_WORDS)
ids_list.append(ids)
ids_list = np.array(ids_list)
def main(_):
text2ids.init()
global_scope = ''
if FLAGS.add_global_scope:
global_scope = FLAGS.global_scope if FLAGS.global_scope else FLAGS.algo
sess = melt.get_session(log_device_placement=FLAGS.log_device_placement)
with tf.variable_scope(global_scope):
predictor = algos_factory.gen_predictor(FLAGS.algo)
with tf.variable_scope(FLAGS.main_scope) as scope:
##--notice if not add below len(tf.get_collection('encode_state') is 1, add below will be 2
## even though in init_predict_text(decode_method=SeqDecodeMethod.beam) will call generate_sequence_greedy
#text, score = predictor.init_predict_text(decode_method=SeqDecodeMethod.greedy,
# beam_size=FLAGS.beam_size,
# convert_unk=False)
#scope.reuse_variables()
beam_text, beam_score = predictor.init_predict_text(decode_method=SeqDecodeMethod.beam,
beam_size=FLAGS.beam_size,
convert_unk=False)
predictor.load(FLAGS.model_dir, sess=sess)
for item in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
print(item)
#input_text = "������������_��������ǰ��Ա���Ƭ"
input_texts = [
#'���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ���������ڿ�Ů��-�Ա���',
#'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
#'����̫����ô����',
#'����ף�Ŀǰ4����1�굶',
'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
]
for input_text in input_texts:
word_ids = _text2ids(input_text, INPUT_TEXT_MAX_WORDS)
print(word_ids)
print(text2ids.ids2text(word_ids))
#timer = gezi.Timer()
#text_, score_ = sess.run([text, score], {predictor.input_text_feed : [word_ids]})
#print(text_[0], text2ids.ids2text(text_[0]), score_[0], 'time(ms):', timer.elapsed_ms())
timer = gezi.Timer()
print(tf.get_collection('encode_state'), len(tf.get_collection('encode_state')))
texts, scores, atkeys, atvals = sess.run([beam_text, beam_score,
tf.get_collection('attention_keys')[0],
tf.get_collection('attention_values')[0],
],
{predictor.input_text_feed : [word_ids]})
print(atkeys)
print(atvals)
print(np.shape(atkeys), np.shape(atvals))
texts = texts[0]
scores = scores[0]
for text_, score_ in zip(texts, scores):
print(text_, text2ids.ids2text(text_), score_)
print('beam_search using time(ms):', timer.elapsed_ms())
input_texts = [
'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
'���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ���������ڿ�Ů��-�Ա���',
#'���������һ�Ը�Ů�ڿ�����ջ�����˿¶�δ����', #same length as lajiao sentence 15
#"����̫����ô����",
#'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
#'����������ʵ��С��ô��,����������ʵ��С���δ�ʩ',
#'�콨�ǰ���˹��',
]
word_ids_list = [_text2ids(input_text, INPUT_TEXT_MAX_WORDS) for input_text in input_texts]
timer = gezi.Timer()
texts_list, scores_list, atkeys2, atvals2, weights = sess.run([beam_text, beam_score,
tf.get_collection('attention_keys')[0],
tf.get_collection('attention_values')[0],
'seq2seq/main/decode/attention_keys/weights:0'
],
feed_dict={predictor.input_text_feed: word_ids_list})
print(atkeys2)
print(atvals2[0])
print(np.shape(atkeys2), np.shape(atvals2))
print('beam_search using time(ms):', timer.elapsed_ms())
weights = tf.constant(weights)
values = tf.get_collection('attention_values')[0]
values = tf.squeeze(values[0])
zeros = tf.zeros_like(values[:5,:])
values2 = tf.concat([values, zeros], 0)
values = tf.cast(values, tf.float64)
values2 = tf.cast(values2, tf.float64)
weights = tf.cast(weights, tf.float64)
result1 = tf.matmul(values, weights)
result2 = tf.matmul(values2, weights)
result1_, result2_ = sess.run([result1, result2], feed_dict={predictor.input_text_feed: [word_ids]})
#result2 = sess.run(result, feed_dict={predictor.input_text_feed: word_ids_list})
print(result1_)
print(result2_)