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generate_zhao.py
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import os
import math
import argparse
import torch
import torch.nn as nn
from torch import optim
import torch.nn.utils
from torch.autograd import Variable
from torch.nn.utils import clip_grad_norm
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
from torch.nn import functional as F
from models import CNNEncoder, Encoder, Decoder, Seq2Seq
from data_utils import *
from config import init_config
import sys
from vocab import *
import time
def word2id(sents, vocab):
if type(sents[0]) == list:
return [[vocab[w] for w in s] for s in sents]
else:
return [vocab[w] for w in sents]
def input_transpose(sents, pad_token):
max_len = max(len(s) for s in sents)
batch_size = len(sents)
sents_t = []
masks = []
for i in range(max_len):
sents_t.append([sents[k][i] if len(sents[k]) > i else pad_token for k in range(batch_size)])
masks.append([1 if len(sents[k]) > i else 0 for k in range(batch_size)])
return sents_t, masks
def to_input_variable(sents, vocab, cuda=False, is_test=False):
"""
return a tensor of shape (src_sent_len, batch_size)
"""
word_ids = word2id(sents, vocab)
sents_t, masks = input_transpose(word_ids, vocab['<pad>'])
sents_var = Variable(torch.LongTensor(sents_t), volatile=is_test, requires_grad=False)
if cuda:
sents_var = sents_var.cuda()
return sents_var
def input_transpose_src(sents, pad_token, max_len):
batch_size = len(sents)
sents_t = []
masks = []
for i in range(max_len):
sents_t.append([sents[k][i] if len(sents[k]) > i else pad_token for k in range(batch_size)])
masks.append([1 if len(sents[k]) > i else 0 for k in range(batch_size)])
return sents_t, masks
def to_input_variable_src(src_data, vocab, cuda=False, is_test=False):
"""
return a tensor of shape (src_sent_len, batch_size)
"""
# sys_sents = [turn[0] for turn in context for context in src_data]
# usr_sents = [turn[1] for turn in context for context in src_data]
# scores = [turn[2] for turn in context for context in src_data]
# word_ids = word2id(sys_sents, vocab)
# sys_sents_t, masks = input_transpose(word_ids, vocab['<pad>'])
# sys_sents_var = Variable(torch.LongTensor(sys_sents_t), volatile=is_test, requires_grad=False)
# if cuda:
# sys_sents_var = sys_sents_var.cuda()
# word_ids = word2id(usr_sents, vocab)
# usr_sents_t, masks = input_transpose(word_ids, vocab['<pad>'])
# usr_sents_var = Variable(torch.LongTensor(usr_sents_t), volatile=is_test, requires_grad=False)
# if cuda:
# usr_sents_var = usr_sents_var.cuda()
# score_var = Variable(torch.FloatTensor(scores), volatile=is_test, requires_grad=False)
# return sys_sents_var, usr_sents_var, score_var
"""
return a tensor of shape (src_sent_len, batch_size)
"""
ret = []
max_len = 30
for each in src_data:
word_ids = word2id(each, vocab)
sents_t, masks = input_transpose_src(word_ids, vocab['<pad>'], max_len)
ret.append(sents_t)
sents_var = Variable(torch.LongTensor(ret), volatile=is_test, requires_grad=False)
if cuda:
sents_var = sents_var.cuda()
#ret.append(sents_var)
return sents_var
def to_input_variable_conf(src_data, cuda=False, is_test=False):
ret = Variable(torch.FloatTensor(src_data), volatile=is_test, requires_grad=False)
if cuda:
ret = ret.cuda()
return ret
def init_model(args):
vocab = torch.load(args.vocab)
cnn_encoder = CNNEncoder(len(vocab.src), args.embed_size)
encoder = Encoder(cnn_encoder.out_size, args.hidden_size)
devoder = Decoder(args.embed_size, args.hidden_size, len(vocab.tgt))
model = Seq2Seq(cnn_encoder, encoder, devoder, args, vocab)
model.load_state_dict(torch.load(args.load_model_path))
model.eval()
return vocab, model
def generate():
args = init_config()
vocab = torch.load('./data/vocab.bin')
corpus = LetsGoCorpus('./data/union_data-1ab.p')
# train_loader = FakeLetsGoDataLoader(corpus.train)
# dev_loader = FakeLetsGoDataLoader(corpus.valid)
# test_loader = FakeLetsGoDataLoader(corpus.test)
#train_loader = LetsGoDataLoader(corpus.train)
#dev_loader = LetsGoDataLoader(corpus.valid)
test_loader = LetsGoDataLoader(corpus.test)
#train_data = list(zip(train_loader.get_src(), train_loader.get_tgt()))
#dev_data = list(zip(dev_loader.get_src(), dev_loader.get_tgt()))
test_data = list(zip(test_loader.get_src(), test_loader.get_tgt()))
vocab, model = init_model(args)
for sent, tgt in data_iter_test(test_data):
sys_utt = [[turn[0] for turn in dial] for dial in sent]
usr_utt = [[turn[1] for turn in dial] for dial in sent]
conf = [[turn[2] for turn in dial] for dial in sent]
src_sents_sys_vars = to_input_variable_src(sys_utt, vocab.src, cuda=False)
src_sents_usr_vars = to_input_variable_src(usr_utt, vocab.src, cuda=False)
src_sents_conf_vars = to_input_variable_conf(conf, cuda=False)
#print(src_sents_sys_vars.size())
#print(src_sents_usr_vars.size())
#exit(0)
src_sent_len = [len(s) for s in sent]
sampled_ids_all, scores_, attn_ = model.greedy(src_sents_sys_vars, src_sents_usr_vars, src_sents_conf_vars, src_sent_len)
sentences = []
for sampled_ids in sampled_ids_all[0]: # just a hack, todo
# Decode word_ids to words
sampled_words = []
for word_id in sampled_ids:
word = vocab.tgt.id2word[word_id]
sampled_words.append(word)
if word == '</s>':
break
sentence = ' '.join(sampled_words[:-1])
sentences.append(sentence)
# Print generated sequence
print(sentence)
def print_data():
args = init_config()
vocab = torch.load('./data/vocab.bin')
corpus = LetsGoCorpus('./data/union_data-1ab.p')
train_loader = LetsGoDataLoader(corpus.train)
dev_loader = LetsGoDataLoader(corpus.valid)
test_loader = LetsGoDataLoader(corpus.test)
train_data = list(zip(train_loader.get_src(), train_loader.get_tgt()))
dev_data = list(zip(dev_loader.get_src(), dev_loader.get_tgt()))
test_data = list(zip(test_loader.get_src(), test_loader.get_tgt()))
for each in test_data:
print(' '.join(each[1][1:-1]))
def main():
#print_data()
generate()
if __name__ == "__main__":
try:
main()
except KeyboardInterrupt as e:
print("[STOP]", e)