def main(unused_args):
# Load model configuration
cu = CommonUtiler()
config_path = os.path.join('./model_conf', FLAGS.model_name + '.py')
config = cu.load_config(config_path)
# Evaluate trained models on val
decoder = mRNNDecoder(config,
FLAGS.model_name,
FLAGS.vocab_path,
gpu_memory_fraction=FLAGS.gpu_memory_fraction)
for i in xrange(*[int(x) for x in FLAGS.eval_stat.split()]):
model_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'variables', 'model_%d.ckpt' % i)
while not os.path.exists(model_path):
logger.warn('Cannot load model file, sleep 1 hour to retry')
time.sleep(3600)
decoder.load_model(model_path)
num_decode = 0
pred_sentences = []
for anno_file_path in FLAGS.anno_files_path.split(':'):
annos = np.load(anno_file_path).tolist()
for anno in annos:
feat_path = os.path.join(
FLAGS.vf_dir, anno['file_path'],
anno['file_name'].split('.')[0] + '.txt')
visual_features = np.loadtxt(feat_path)
sentences = decoder.decode(visual_features, FLAGS.beam_size)
sentence_coco = {}
sentence_coco['image_id'] = anno['id']
sentence_coco['caption'] = ' '.join(sentences[0]['words'])
pred_sentences.append(sentence_coco)
num_decode += 1
if num_decode % 100 == 0:
logger.info('%d images are decoded' % num_decode)
pred_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'decode_val_result', 'generated_%d.json' % i)
result_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'decode_val_result', 'result_%d.txt' % i)
cu.create_dir_if_not_exists(os.path.dirname(pred_path))
with open(pred_path, 'w') as fout:
json.dump(pred_sentences, fout)
cu.coco_val_eval(pred_path, result_path)
def main(unused_args):
# Load model configuration
cu = CommonUtiler()
config_path = os.path.join('./model_conf', FLAGS.model_name + '.py')
config = cu.load_config(config_path)
# Evaluate trained models on val
decoder = mRNNDecoder(config, FLAGS.model_name, FLAGS.vocab_path,
gpu_memory_fraction=FLAGS.gpu_memory_fraction)
for i in xrange(*[int(x) for x in FLAGS.eval_stat.split()]):
model_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'variables', 'model_%d.ckpt' % i)
while not os.path.exists(model_path):
logger.warn('Cannot load model file, sleep 1 hour to retry')
time.sleep(3600)
decoder.load_model(model_path)
num_decode = 0
pred_sentences = []
for anno_file_path in FLAGS.anno_files_path.split(':'):
annos = np.load(anno_file_path).tolist()
for anno in annos:
feat_path = os.path.join(FLAGS.vf_dir, anno['file_path'],
anno['file_name'].split('.')[0] + '.txt')
visual_features = np.loadtxt(feat_path)
sentences = decoder.decode(visual_features, FLAGS.beam_size)
sentence_coco = {}
sentence_coco['image_id'] = anno['id']
sentence_coco['caption'] = ' '.join(sentences[0]['words'])
pred_sentences.append(sentence_coco)
num_decode += 1
if num_decode % 100 == 0:
logger.info('%d images are decoded' % num_decode)
pred_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'decode_val_result', 'generated_%d.json' % i)
result_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'decode_val_result', 'result_%d.txt' % i)
cu.create_dir_if_not_exists(os.path.dirname(pred_path))
with open(pred_path, 'w') as fout:
json.dump(pred_sentences, fout)
cu.coco_val_eval(pred_path, result_path)
