Inputs to the model about model_search HOT 3 OPEN

import numpy as np
import tensorflow.compat.v1 as tf
#To make tf 2.0 compatible with tf1.0 code, we disable the tf2.0 functionalities
tf.disable_eager_execution()

from tensorflow.python.client import session
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.summary import summary
from tensorflow.python.tools import saved_model_utils
from tensorflow.core.framework import graph_pb2 as gpb
from google.protobuf import text_format as pbtf

def extract_tensors(signature_def, graph):
    output = dict()

    for key in signature_def:
        value = signature_def[key]

        if isinstance(value, tf.TensorInfo):
            output[key] = graph.get_tensor_by_name(value.name)

    return output

def extract_input_name(signature_def, graph):
    input_tensors = extract_tensors(signature_def['serving_default'].inputs, graph)
    #Assuming one input in model.
    
    name_list = []
    for key in list(input_tensors.keys()):
        name_list.append(input_tensors.get(key).name)
   
    return name_list

def extract_output_name(signature_def, graph):
    output_tensors = extract_tensors(signature_def['serving_default'].outputs, graph)
    #Assuming one output in model.
    
    name_list = []
    for key in list(output_tensors.keys()):
        name_list.append(output_tensors.get(key).name)
        
    return name_list

def ass_input_dict(tensor_input_sample): 
    dict_input = {str(i+1)+":0" : [tensor_input_sample[i]] for i in range(len(tensor_input_sample))}
    return dict_input


checkpoint_path = "/tmp/run/tuner-1/160/saved_model/assets/"

with tf.Session(graph=tf.Graph()) as sess:
    serve = tf.saved_model.load(sess, tags=["serve"], export_dir=checkpoint_path)
    #print(type(model))  <class 'tensorflow.core.protobuf.meta_graph_pb2.MetaGraphDef'>
    
    #input_tensor_name = extract_input_name(serve.signature_def, sess.graph)
    output_tensor_name = extract_output_name(serve.signature_def, sess.graph)
    input_dict = ass_input_dict(sen_vec.detach().numpy())
    
    prediction = sess.run(output_tensor_name, feed_dict=input_dict)
    
print(prediction)