def main(_):
attrs = conf.__dict__['__flags']
pp(attrs)
dataset, img_feature, train_data = get_data(conf.input_json,
conf.input_img_h5,
conf.input_ques_h5,
conf.img_norm)
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=calc_gpu_fraction(conf.gpu_fraction))
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
model = question_generator.Question_Generator(sess, conf, dataset,
img_feature, train_data)
if conf.is_train:
model.build_model()
model.train()
else:
model.build_generator()
model.test(test_image_path=conf.test_image_path,
model_path=conf.test_model_path,
maxlen=26)
def home():
image_path_array = request.json['image_path_array']
image_paths = [
each_path[1:-1].replace('////', '/')
for each_path in image_path_array[1:-1].split(', ')
]
questions = []
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=calc_gpu_fraction(conf.gpu_fraction))
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
model = question_generator.Question_Generator(sess, conf, dataset,
img_feature, train_data)
model.build_generator()
for image_path in image_paths:
question = model.test(test_image_path=image_path,
model_path=conf.test_model_path,
maxlen=26)
questions.append(question)
return jsonify({'result': questions}), 200
def main(_):
attrs = conf.__dict__['__flags']
pp(attrs)
train_data = pickle.load(open('data/prepro.pkl', 'rb'))
dataset = pickle.load(open('assets/data_prepro.json', 'rb'))
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=calc_gpu_fraction(conf.gpu_fraction))
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
model = question_generator.Question_Generator(sess, conf, dataset,
train_data)
if conf.is_train:
model.build_model()
model.train()
else:
model.build_generator()
model.test(test_image_path=conf.test_image_path,
model_path=conf.test_model_path,
maxlen=26)