VerificationCode/chinese_order/inference_phrase.py

import os
import random
import sys
from itertools import product
import tensorflow as tf
import numpy as np
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
from chinese_order.model import lstm_phrase, text_cnn_phrase

package_dir = os.path.abspath(os.path.dirname(__file__))
vocabulary_len = 5792
sequence_len = 6
model_path = package_dir + "/models/phrase_f1_0.85.h5"
char_path = package_dir + "/char.txt"
with open(char_path, "r") as f:
    char_map_list = f.readlines()


def recognize(char_list, model=None, sess=None):
    if sess is None:
        sess = tf.compat.v1.Session(graph=tf.Graph())

    if model is None:
        with sess.as_default():
            with sess.graph.as_default():
                model = lstm_phrase((sequence_len, vocabulary_len))
                model.load_weights(model_path)

    if len(char_list) > sequence_len:
        return None

    # 生成所有可能组合的映射数据
    char_dict = {}
    char_dict_reverse = {}
    for i in range(len(char_map_list)):
        char_dict[char_map_list[i][:-1]] = i
        char_dict_reverse[i] = char_map_list[i][:-1]
    char_dict_reverse[vocabulary_len] = ""

    index_list = [char_dict[x] for x in char_list]

    products = list(product(index_list, repeat=len(index_list)))
    all_index_list = []
    for index_list in products:
        index_list = list(index_list)
        if len(set(index_list)) != len(index_list):
            continue
        index_list = index_list + [vocabulary_len]*(sequence_len-len(index_list))
        all_index_list.append(index_list)
    X = np.array(all_index_list)

    # 预测
    with sess.as_default():
        with sess.graph.as_default():
            pred = model.predict(X)
    # for i in range(len(pred)):
    #     p = all_index_list[i]
    #     print(pred[i], "".join([char_dict_reverse[x] for x in p]))
    decode = all_index_list[int(np.argmax(pred))]
    decode = [char_dict_reverse[x] for x in decode][:len(char_list)]
    # print("".join(char_list))
    # print("".join(decode))
    return decode


if __name__ == "__main__":
    text = "吃饭了"
    text = [x for x in text]
    random.shuffle(text)
    print("text", text)
    recognize(text)