def gen_eval_generated_texts_ops(input_app, input_results, predictor,
eval_scores):
#need distinct_texts.npy distinct_text_strs.npy
evaluator.init()
evaluate_image_name, evaluate_text, evaluate_text_str, \
evaluate_input_text, evaluate_input_text_str = input_results[input_app.fixed_input_valid_name]
num_evaluate_examples = input_app.num_evaluate_examples
pos_scores = eval_scores[:num_evaluate_examples, 0]
build_predict_text_graph = functools.partial(
predictor.build_predict_text_graph,
input_text=evaluate_input_text,
beam_size=FLAGS.beam_size,
convert_unk=False)
generated_texts, generated_texts_score = build_predict_text_graph(
decode_method=FLAGS.seq_decode_method)
#beam search(ingraph)
generated_texts_beam, generated_texts_score_beam = build_predict_text_graph(
decode_method=SeqDecodeMethod.beam)
eval_ops = [evaluate_image_name, evaluate_input_text_str, evaluate_input_text, \
evaluate_text_str, evaluate_text, \
generated_texts, generated_texts_beam, \
generated_texts_score, generated_texts_score_beam, \
pos_scores]
print('eval_ops:')
for eval_op in eval_ops:
print(eval_op)
return eval_ops
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 gen_eval_generated_texts_ops(input_app,
input_results,
predictor,
eval_scores,
eval_neg_text=None,
eval_neg_text_str=None):
#need distinct_texts.npy distinct_text_strs.npy
evaluator.init()
evaluate_image_name, evaluate_image_feature, evaluate_text, evaluate_text_str = input_results[
input_app.fixed_input_valid_name]
num_evaluate_examples = input_app.num_evaluate_examples
pos_scores = eval_scores[:num_evaluate_examples, 0]
if eval_neg_text is not None:
neg_scores = eval_scores[:num_evaluate_examples, 1]
#eval neg text strs
evaluate_neg_text_str = eval_neg_text_str[:num_evaluate_examples, 0]
#eval neg text ids
evaluate_neg_text = eval_neg_text[:num_evaluate_examples, 0, :]
generated_texts, generated_texts_score = predictor.build_predict_text_graph(
evaluate_image_feature,
decode_method=FLAGS.seq_decode_method,
beam_size=FLAGS.beam_size,
convert_unk=False)
generated_texts_beam, generated_texts_score_beam = predictor.build_predict_text_graph(
evaluate_image_feature,
decode_method=SeqDecodeMethod.beam, #beam search(ingraph)
beam_size=FLAGS.beam_size,
convert_unk=False)
eval_ops = [evaluate_image_name, evaluate_text_str, evaluate_text, \
generated_texts, generated_texts_beam,
generated_texts_score, generated_texts_score_beam, \
pos_scores]
if eval_neg_text is not None:
eval_ops += [neg_scores, evaluate_neg_text_str, evaluate_neg_text]
print('eval_ops:')
for eval_op in eval_ops:
print(eval_op)
return eval_ops
def gen_eval_show_ops(input_app, input_results, predictor, eval_scores,
eval_neg_text, eval_neg_text_str):
eval_ops = []
#need distinct_texts.npy distinct_text_strs.npy
evaluator.init()
evaluate_image_name, evaluate_image_feature, evaluate_text, evaluate_text_str = input_results[
input_app.fixed_input_valid_name]
num_evaluate_examples = input_app.num_evaluate_examples
all_distinct_texts = evaluator.all_distinct_texts
#print(all_distinct_texts[0], evaluator.all_distinct_text_strs[0], text2ids.ids2text(all_distinct_texts[0]))
predictor.init_evaluate_constant_text(all_distinct_texts)
pos_scores = eval_scores[:num_evaluate_examples, 0]
neg_scores = eval_scores[:num_evaluate_examples, 1]
#eval neg text strs
evaluate_neg_text_str = eval_neg_text_str[:num_evaluate_examples, 0]
#eval neg text ids
evaluate_neg_text = eval_neg_text[:num_evaluate_examples, 0, :]
eval_score = predictor.build_evaluate_fixed_text_graph(
evaluate_image_feature)
eval_max_score, eval_max_index = tf.nn.top_k(eval_score,
FLAGS.num_text_topn)
eval_word_score = predictor.build_evaluate_image_word_graph(
evaluate_image_feature)
eval_word_max_score, eval_word_max_index = tf.nn.top_k(
eval_word_score, FLAGS.num_word_topn)
eval_ops += [
eval_max_score, eval_max_index, eval_word_max_score,
eval_word_max_index
]
eval_ops += [evaluate_image_name, evaluate_text_str, evaluate_text]
#notice evaluate_neg_text_str will run here so every thing related must be placeholder to avoid running twice (if FLAGS.feed_dict=1 and FLAGS.show_eval=1)
#@TODO at some steps we might want to use eval_score
eval_ops += [
pos_scores, neg_scores, evaluate_neg_text_str, evaluate_neg_text
]
print('eval_ops:')
for eval_op in eval_ops:
print(eval_op)
return eval_ops
def main(_):
#-----------init global resource
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
InputApp.init()
vocabulary.init()
text2ids.init()
evaluator.init()
logging.info('algo:{}'.format(FLAGS.algo))
logging.info('monitor_level:{}'.format(FLAGS.monitor_level))
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):
train()