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- # -*- coding: utf-8 -*-
- # @Author : William
- # @Project : TextGAN-william
- # @FileName : maligan_instructor.py
- # @Time : Created at 2019/11/29
- # @Blog : http://zhiweil.ml/
- # @Description :
- # Copyrights (C) 2018. All Rights Reserved.
-
-
- import torch
- import torch.nn.functional as F
- import torch.optim as optim
-
- import config as cfg
- from instructor.real_data.instructor import BasicInstructor
- from models.MaliGAN_D import MaliGAN_D
- from models.MaliGAN_G import MaliGAN_G
- from utils.data_loader import GenDataIter, DisDataIter
-
-
- # noinspection PyUnresolvedReferences
- class MaliGANInstructor(BasicInstructor):
- def __init__(self, opt):
- super(MaliGANInstructor, self).__init__(opt)
-
- # generator, discriminator
- self.gen = MaliGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim, cfg.vocab_size, cfg.max_seq_len,
- cfg.padding_idx, gpu=cfg.CUDA)
- self.dis = MaliGAN_D(cfg.dis_embed_dim, cfg.vocab_size, cfg.padding_idx, gpu=cfg.CUDA)
- self.init_model()
-
- # Optimizer
- self.gen_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)
- self.gen_adv_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)
- self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
-
- def _run(self):
- # ===PRE-TRAINING===
- # TRAIN GENERATOR
- 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))
-
- # ===TRAIN DISCRIMINATOR====
- 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))
-
- # ===ADVERSARIAL TRAINING===
- self.log.info('Starting Adversarial Training...')
- self.log.info('Initial generator: %s' % (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 Pre-training for the generator
- """
- for epoch in range(epochs):
- self.sig.update()
- if self.sig.pre_sig:
- pre_loss = self.train_gen_epoch(self.gen, self.train_data.loader, self.mle_criterion, self.gen_opt)
-
- # ===Test===
- if epoch % cfg.pre_log_step == 0 or epoch == epochs - 1:
- self.log.info(
- '[MLE-GEN] epoch %d : pre_loss = %.4f, %s' % (epoch, pre_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):
- """
- The gen is trained by MLE-like objective.
- """
- total_g_loss = 0
- for step in range(g_step):
- inp, target = GenDataIter.prepare(self.gen.sample(cfg.batch_size, cfg.batch_size), gpu=cfg.CUDA)
-
- # ===Train===
- rewards = self.get_mali_reward(target)
- adv_loss = self.gen.adv_loss(inp, target, rewards)
- self.optimize(self.gen_adv_opt, adv_loss)
- total_g_loss += adv_loss.item()
-
- # ===Test===
- self.log.info('[ADV-GEN]: g_loss = %.4f, %s' % (total_g_loss, 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.
- """
- # prepare loader for validate
- global d_loss, train_acc
-
- for step in range(d_step):
- # prepare loader for training
- pos_samples = self.train_data.target # not re-sample the Oracle data
- neg_samples = self.gen.sample(cfg.samples_num, 4 * cfg.batch_size)
- 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))
-
- if cfg.if_save and not cfg.if_test:
- torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)
-
- def get_mali_reward(self, samples):
- rewards = []
- for _ in range(cfg.rollout_num):
- dis_out = F.softmax(self.dis(samples), dim=-1)[:, 1]
- rewards.append(dis_out)
-
- rewards = torch.mean(torch.stack(rewards, dim=0), dim=0) # batch_size
- rewards = torch.div(rewards, 1 - rewards)
- rewards = torch.div(rewards, torch.sum(rewards))
- rewards -= torch.mean(rewards)
- rewards = rewards.unsqueeze(1).expand(samples.size()) # batch_size * seq_len
-
- return rewards
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