Dynamic-Memory-Network/Data_prep.py at master · sarthak96agarwal/Dynamic-Memory-Network · GitHub

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import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F
import nltk
import random
import numpy as np
from collections import Counter, OrderedDict
import nltk
from copy import deepcopy
import os
import re
import unicodedata
flatten = lambda l: [item for sublist in l for item in sublist]

from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence
random.seed(1024)

USE_CUDA = torch.cuda.is_available()
gpus = [0]
torch.cuda.set_device(gpus[0])

FloatTensor = torch.cuda.FloatTensor if USE_CUDA else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if USE_CUDA else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if USE_CUDA else torch.ByteTensor

def getBatch(batch_size, train_data):
    random.shuffle(train_data)
    sindex=0
    eindex=batch_size
    while eindex < len(train_data):
        batch = train_data[sindex: eindex]
        temp = eindex
        eindex = eindex + batch_size
        sindex = temp
        yield batch

    if eindex >= len(train_data):
        batch = train_data[sindex:]
        yield batch


def pad_to_batch(batch, w_to_ix):
    fact,q,a = list(zip(*batch))
    max_fact = max([len(f) for f in fact])
    max_len = max([f.size(1) for f in flatten(fact)])
    max_q = max([qq.size(1) for qq in q])
    max_a = max([aa.size(1) for aa in a])

    facts, fact_masks, q_p, a_p = [], [], [], []
    for i in range(len(batch)):
        fact_p_t = []
        for j in range(len(fact[i])):
            if fact[i][j].size(1) < max_len:
                fact_p_t.append(torch.cat([fact[i][j], Variable(LongTensor([w_to_ix['<PAD>']] * (max_len - fact[i][j].size(1)))).view(1, -1)], 1))
            else:
                fact_p_t.append(fact[i][j])

        while len(fact_p_t) < max_fact:
            fact_p_t.append(Variable(LongTensor([w_to_ix['<PAD>']] * max_len)).view(1, -1))

        fact_p_t = torch.cat(fact_p_t)
        facts.append(fact_p_t)
        fact_masks.append(torch.cat([Variable(ByteTensor(tuple(map(lambda s: s ==0, t.data))), volatile=False) for t in fact_p_t]).view(fact_p_t.size(0), -1))

        if q[i].size(1) < max_q:
            q_p.append(torch.cat([q[i], Variable(LongTensor([w_to_ix['<PAD>']] * (max_q - q[i].size(1)))).view(1, -1)], 1))
        else:
            q_p.append(q[i])

        if a[i].size(1) < max_a:
            a_p.append(torch.cat([a[i], Variable(LongTensor([w_to_ix['<PAD>']] * (max_a - a[i].size(1)))).view(1, -1)], 1))
        else:
            a_p.append(a[i])

    questions = torch.cat(q_p)
    answers = torch.cat(a_p)
    question_masks = torch.cat([Variable(ByteTensor(tuple(map(lambda s: s ==0, t.data))), volatile=False) for t in questions]).view(questions.size(0), -1)

    return facts, fact_masks, questions, question_masks, answers


def prepare_sequence(seq, to_index):
    idxs = list(map(lambda w: to_index[w] if to_index.get(w) is not None else to_index["<UNK>"], seq))
    return Variable(LongTensor(idxs))

## Load the Dataset

data = open('qa5_three-arg-relations_train.txt').readlines()
data = [d[:-1] for d in data]
train_data = []
fact=[]
qa=[]
for d in data:
    index=d.split(' ')[0]
    if(index=='1'):
        fact=[]
        qa=[]
    if('?' in d):
        temp = d.split('\t')
        ques = temp[0].strip().replace('?', '').split(' ')[1:] + ['?']
        ans=temp[1].split() + ['</s>']
        temp_s = deepcopy(fact)
        train_data.append([temp_s, ques, ans])
    else:
        fact.append(d.replace('.', '').split(' ')[1:] + ['</s>'])

fact,q,a = list(zip(*train_data))
vocab = list(set(flatten(flatten(fact)) + flatten(q) + flatten(a)))

word_to_index={'<PAD>': 0, '<UNK>': 1, '<s>': 2, '</s>': 3}
for vo in vocab:
    if word_to_index.get(vo) is None:
        word_to_index[vo] = len(word_to_index)
index_to_word = {v:k for k, v in word_to_index.items()}

for s in train_data:
    for i, fact in enumerate(s[0]):
        s[0][i] = prepare_sequence(fact, word_to_index).view(1, -1)
    s[1] = prepare_sequence(s[1], word_to_index).view(1, -1)
    s[2] = prepare_sequence(s[2], word_to_index).view(1, -1)