(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tmp_g = tf.get_default_graph().as_graph_def()
with tf.Session(config=config) as sess:
tf.logging.info('load parameters from checkpoint...')
sess.run(tf.global_variables_initializer())
tf.logging.info('freeze...')
tmp_g = tf.graph_util.convert_variables_to_constants(
sess, tmp_g, [n.name[:-2] for n in output_tensors])
tmp_file = os.path.join(params.ckpt_dir, 'export_model')
tf.logging.info('write graph to a tmp file: %s' % tmp_file)
with tf.gfile.GFile(tmp_file, 'wb') as f:
f.write(tmp_g.SerializeToString())
return tmp_file
if __name__ == "__main__":
if FLAGS.model_dir:
base_dir, dir_name = os.path.split(FLAGS.model_dir)
else:
base_dir, dir_name = None, None
params = Params()
params.assign_problem(FLAGS.problem, base_dir=base_dir, dir_name=dir_name)
optimize_graph(params)
params.to_json()
def main(_):
if not os.path.exists('tmp'):
os.mkdir('tmp')
if FLAGS.model_dir:
base_dir, dir_name = os.path.split(FLAGS.model_dir)
else:
base_dir, dir_name = None, None
params = Params()
params.assign_problem(FLAGS.problem,
gpu=int(FLAGS.gpu),
base_dir=base_dir,
dir_name=dir_name)
tf.logging.info('Checkpoint dir: %s' % params.ckpt_dir)
time.sleep(3)
model = BertMultiTask(params=params)
model_fn = model.get_model_fn(warm_start=False)
dist_trategy = tf.contrib.distribute.MirroredStrategy(
num_gpus=int(FLAGS.gpu),
cross_tower_ops=tf.contrib.distribute.AllReduceCrossDeviceOps(
'nccl', num_packs=int(FLAGS.gpu)))
run_config = tf.estimator.RunConfig(
train_distribute=dist_trategy,
eval_distribute=dist_trategy,
log_step_count_steps=params.log_every_n_steps)
# ws = make_warm_start_setting(params)
estimator = Estimator(model_fn,
model_dir=params.ckpt_dir,
params=params,
config=run_config)
if FLAGS.schedule == 'train':
train_hook = RestoreCheckpointHook(params)
def train_input_fn():
return train_eval_input_fn(params)
estimator.train(train_input_fn,
max_steps=params.train_steps,
hooks=[train_hook])
def input_fn():
return train_eval_input_fn(params, mode='eval')
estimator.evaluate(input_fn=input_fn)
params.to_json()
elif FLAGS.schedule == 'eval':
params.from_json()
evaluate_func = getattr(metrics, FLAGS.eval_scheme + '_evaluate')
print(evaluate_func(FLAGS.problem, estimator, params))
elif FLAGS.schedule == 'predict':
def input_fn():
return predict_input_fn([
'''兰心餐厅\n作为一个无辣不欢的妹子，对上海菜的偏清淡偏甜真的是各种吃不惯。
每次出门和闺蜜越饭局都是避开本帮菜。后来听很多朋友说上海有几家特别正宗味道做
的很好的餐厅于是这周末和闺蜜们准备一起去尝一尝正宗的本帮菜。\n进贤路是我在上
海比较喜欢的一条街啦，这家餐厅就开在这条路上。已经开了三十多年的老餐厅了，地
方很小，就五六张桌子。但是翻桌率比较快。二楼之前的居民间也改成了餐厅，但是在
上海的名气却非常大。烧的就是家常菜，普通到和家里烧的一样，生意非常好，外面排
队的比里面吃的人还要多。'''
],
params,
mode=PREDICT)
pred = estimator.predict(input_fn=input_fn)
for p in pred:
print(p)