def main(_):
# 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)
start, stop, step = [int(x) for x in FLAGS.eval_stat.split()]
for i in range(start, stop, step):
model_path = os.path.join(FLAGS.model_root, FLAGS.model_name,
'variables', 'model_%d.ckpt' % i)
while not os.path.exists(model_path + ".meta"):
logger.warning('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 = {
'image_id': anno['id'],
'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)
logger = logging.getLogger('ExpMscoco')
logging.basicConfig(
format="[%(asctime)s - %(filename)s:line %(lineno)4s] %(message)s",
datefmt='%d %b %H:%M:%S')
logger.setLevel(logging.INFO)
if __name__ == '__main__':
# Hyperparameters
min_count = 3
vocab_path = './cache/dictionary/mscoco_mc%d_vocab' % min_count
mscoco_root = './datasets/ms_coco'
anno_file_names = ['anno_list_mscoco_trainModelVal_m_RNN.npy']
# Preparations
cu = CommonUtiler()
cu.create_dir_if_not_exists(os.path.dirname(vocab_path))
# Scan the anno files
vocab = {}
for anno_file_name in anno_file_names:
anno_path = os.path.join(mscoco_root, 'mscoco_anno_files', anno_file_name)
annos = np.load(anno_path).tolist()
for anno in annos:
for sentence in anno['sentences']:
for word in sentence:
word = word.strip().lower()
if word in vocab:
vocab[word] += 1
else:
vocab[word] = 1
if __name__ == '__main__':
flag_ignore_exists = True
# Path
model_path = './external/tf_cnn_models/inception_v3.pb'
mscoco_root = './datasets/ms_coco'
anno_file_names = [
'anno_list_mscoco_trainModelVal_m_RNN.npy',
'anno_list_mscoco_crVal_m_RNN.npy', 'anno_list_mscoco_test2014.npy'
]
feat_dir = './cache/mscoco_image_features/inception_v3'
# Preparations
cu = CommonUtiler()
ife = ImageFeatureExtractor(model_path)
cu.create_dir_if_not_exists(os.path.join(feat_dir, 'train2014'))
cu.create_dir_if_not_exists(os.path.join(feat_dir, 'test2014'))
cu.create_dir_if_not_exists(os.path.join(feat_dir, 'val2014'))
# Extract features
for anno_file_name in anno_file_names:
anno_path = os.path.join(mscoco_root, 'mscoco_anno_files',
anno_file_name)
annos = np.load(anno_path).tolist()
for (ind_a, anno) in enumerate(annos):
image_path = os.path.join(mscoco_root, 'images', anno['file_path'],
anno['file_name'])
feat_path = os.path.join(feat_dir, anno['file_path'],
anno['file_name'].split('.')[0] + '.txt')
if flag_ignore_exists and os.path.exists(feat_path):