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leakgan_instructor.py
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leakgan_instructor.py
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# -*- coding: utf-8 -*-
# @Author : William
# @Project : TextGAN-william
# @FileName : leakgan_instructor.py
# @Time : Created at 2019-06-05
# @Blog : http://zhiweil.ml/
# @Description :
# Copyrights (C) 2018. All Rights Reserved.
import torch
import torch.optim as optim
import config as cfg
from instructor.real_data.instructor import BasicInstructor
from models.LeakGAN_D import LeakGAN_D
from models.LeakGAN_G import LeakGAN_G
from utils import rollout
from utils.data_loader import GenDataIter, DisDataIter
from utils.text_process import tensor_to_tokens, write_tokens
class LeakGANInstructor(BasicInstructor):
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,
cfg.padding_idx, cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
def _run(self):
for inter_num in range(cfg.inter_epoch):
self.log.info('>>> Interleaved Round %d...' % inter_num)
self.sig.update() # update signal
if self.sig.pre_sig:
# ===DISCRIMINATOR PRE-TRAINING===
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.train_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)
print('Save pre-trained discriminator: {}'.format(cfg.pretrained_dis_path))
# ===GENERATOR MLE TRAINING===
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(), cfg.pretrained_gen_path)
print('Save pre-trained generator: {}'.format(cfg.pretrained_gen_path))
else:
self.log.info('>>> Stop by pre_signal! Skip to adversarial training...')
break
# ===ADVERSARIAL TRAINING===
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' % (str(self.cal_metrics(fmt_str=True))))
for adv_epoch in range(cfg.ADV_train_epoch):
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step) # Generator
self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch, 'ADV') # Discriminator
if adv_epoch % cfg.adv_log_step == 0 or adv_epoch == cfg.ADV_train_epoch - 1:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info('>>> Stop by adv_signal! Finishing adversarial training...')
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pretraining for the gen
- gen_opt: [mana_opt, work_opt]
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
pre_mana_loss = 0
pre_work_loss = 0
# ===Train===
for i, data in enumerate(self.train_data.loader):
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
mana_loss, work_loss = self.gen.pretrain_loss(target, self.dis)
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
pre_mana_loss += mana_loss.data.item()
pre_work_loss += work_loss.data.item()
pre_mana_loss = pre_mana_loss / len(self.train_data.loader)
pre_work_loss = pre_work_loss / len(self.train_data.loader)
# ===Test===
if epoch % cfg.pre_log_step == 0 or epoch == epochs - 1:
self.log.info('[MLE-GEN] epoch %d : pre_mana_loss = %.4f, pre_work_loss = %.4f, %s' % (
epoch, pre_mana_loss, pre_work_loss, self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info('>>> Stop by pre signal, skip to adversarial training...')
break
def adv_train_generator(self, g_step, current_k=0):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
adv_mana_loss = 0
adv_work_loss = 0
for step in range(g_step):
with torch.no_grad():
gen_samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis,
train=True) # !!! train=True, the only place
inp, target = GenDataIter.prepare(gen_samples, gpu=cfg.CUDA)
# ===Train===
rewards = rollout_func.get_reward_leakgan(target, cfg.rollout_num, self.dis,
current_k).cpu() # reward with MC search
mana_loss, work_loss = self.gen.adversarial_loss(target, rewards, self.dis)
# update parameters
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
adv_mana_loss += mana_loss.data.item()
adv_work_loss += work_loss.data.item()
# ===Test===
self.log.info('[ADV-GEN] adv_mana_loss = %.4f, adv_work_loss = %.4f, %s' % (
adv_mana_loss / g_step, adv_work_loss / g_step, self.cal_metrics(fmt_str=True)))
def train_discriminator(self, d_step, d_epoch, phase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
d_loss, train_acc = 0, 0
for step in range(d_step):
# prepare loader for training
pos_samples = self.train_data.target
neg_samples = self.gen.sample(cfg.samples_num, cfg.batch_size, self.dis)
dis_data = DisDataIter(pos_samples, neg_samples)
for epoch in range(d_epoch):
# ===Train===
d_loss, train_acc = self.train_dis_epoch(self.dis, dis_data.loader, self.dis_criterion,
self.dis_opt)
# ===Test===
self.log.info('[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' % (
phase, step, d_loss, train_acc))
def cal_metrics(self, fmt_str=False):
with torch.no_grad():
# Prepare data for evaluation
eval_samples = self.gen.sample(cfg.samples_num, cfg.batch_size, self.dis)
gen_data = GenDataIter(eval_samples)
gen_tokens = tensor_to_tokens(eval_samples, self.idx2word_dict)
gen_tokens_s = tensor_to_tokens(self.gen.sample(200, cfg.batch_size, self.dis), self.idx2word_dict)
# Reset metrics
self.bleu.reset(test_text=gen_tokens, real_text=self.test_data.tokens)
self.nll_gen.reset(self.gen, self.train_data.loader, leak_dis=self.dis)
self.nll_div.reset(self.gen, gen_data.loader, leak_dis=self.dis)
self.self_bleu.reset(test_text=gen_tokens_s, real_text=gen_tokens)
self.ppl.reset(gen_tokens)
if fmt_str:
return ', '.join(['%s = %s' % (metric.get_name(), metric.get_score()) for metric in self.all_metrics])
else:
return [metric.get_score() for metric in self.all_metrics]
def _save(self, phase, epoch):
torch.save(self.gen.state_dict(), cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(phase, epoch)
samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)
write_tokens(save_sample_path, tensor_to_tokens(samples, self.idx2word_dict))