output wouldn't show

–2 votes
asked Jul 14, 2019 by anonymous
# -*- coding: utf-8 -*-

import os
import argparse
import random
import spacy
import re
import codecs

from random import randint
from nltk.corpus import wordnet as wn

nlp = spacy.load('en_core_web_sm')

nlp = spacy.load('en')
from random import randint
import constants

def readlines(fname):
    lines = []
    with open(fname, encoding="utf-8", errors="ignore") as rf:
        for line in rf:
            line = line.strip()
            if line:
    return lines

class PrepHandler:

    def __init__(self):
        # option 1exit
        self.captions = {
            "coco": readlines('old.en'),
            "real": readlines('new.en')
        # option 2
        self.captions_coco = readlines('old.en')
        self.captions_real = readlines('new.en')

    def match(self, orig_text, pattern_text):
        for i_ in range(len(orig_text)):
            if orig_text[i_] != pattern_text[i_]:
                return False
        return True

    def is_ascii_word(self,word):
        for k_ in range(len(word)):
            if word[k_] != ' ' and not ('a' <= word[k_] <= 'z'):
                return False
        return True

    def DetectionOfNounChunks(self, text_info):
        noun_chunks_ = list(text_info.noun_chunks)
        r_ = [[False for _ in range(len(noun_chunks_))] for __ in range(len(noun_chunks_))]
        for j_ in range(len(noun_chunks_)):
            for k_ in range(j_+1, len(noun_chunks_)):
                if noun_chunks_[j_].end + 1 < noun_chunks_[k_].start:
                if (noun_chunks_[j_].end == noun_chunks_[k_].start) or (
                                text_info[noun_chunks_[j_].end].text in [',  ',  'with',  'and',  'or']):
                    r_[j_][k_] = True
                    r_[k_][j_] = True
        # compute transitive closure
        for p_ in range(len(noun_chunks_)):
            for j_ in range(len(noun_chunks_)):
                for k_ in range(len(noun_chunks_)):
                    if r_[j_][p_] and r_[p_][k_]:
                        r_[j_][k_] = True
        return noun_chunks_, r_

    def generate_prep_adversaries(self,captions,seed =1):
        prep_substitute_dict = dict()
        captions = self.captions_real
        for subset in constants.prep_set:
            for word in subset:
                if word in prep_substitute_dict:
                prep_substitute_dict[word] = set()
                for sub in constants.prep_set:
                    if word in sub:
                    for w in sub:

        for p, caption in enumerate(captions):
            print("p:", p)
            substitution_cnt= 0
            text = nlp(caption.lower())
            noun_chunks, no_swap = DetectionOfNounChunks(text)
            output = []
            new_word_list = [item.text for item in text]
            for q, word in enumerate(text):
        #                 print(word)
                if word.pos_ == 'ADP' and (word.text in prep_substitute_dict):
                    for substituted_word in prep_substitute_dict[word.text]:
                        new_word_list[q] = substituted_word
                        temp = ' '.join(new_word_list)
                        new_word_list[q] = word.text
                        substitution_cnt+= 1
            if output:

        return output

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