train.py

# -*- coding: utf-8 -*-
# ------------------
# @Author: BinLiang
# @Mail: bin.liang@stu.hit.edu.cn
# ------------------

import os
import math
import argparse
import random
import numpy
import torch
import torch.nn as nn
from bucket_iterator import BucketIterator
from sklearn import metrics
from data_utils import ABSADatesetReader
from models import CAER
import torch.nn.functional as F


class Instructor:
    def __init__(self, opt):
        self.opt = opt

        absa_dataset = ABSADatesetReader(dataset=opt.dataset, embed_dim=opt.embed_dim)
        
        self.train_data_loader = BucketIterator(data=absa_dataset.train_data, batch_size=opt.batch_size, shuffle=True)
        self.test_data_loader = BucketIterator(data=absa_dataset.test_data, batch_size=opt.batch_size, shuffle=False)

        self.model = opt.model_class(absa_dataset.embedding_matrix, opt).to(opt.device)
        self._print_args()
        self.global_f1 = 0.

        if torch.cuda.is_available():
            print('cuda memory allocated:', torch.cuda.memory_allocated(device=opt.device.index))

    def _print_args(self):
        n_trainable_params, n_nontrainable_params = 0, 0
        for p in self.model.parameters():
            n_params = torch.prod(torch.tensor(p.shape)).item()
            if p.requires_grad:
                n_trainable_params += n_params
            else:
                n_nontrainable_params += n_params
        print('n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params))
        print('> training arguments:')
        for arg in vars(self.opt):
            print('>>> {0}: {1}'.format(arg, getattr(self.opt, arg)))

    def _reset_params(self):
        for p in self.model.parameters():
            if p.requires_grad:
                if len(p.shape) > 1:
                    self.opt.initializer(p)
                else:
                    stdv = 1. / math.sqrt(p.shape[0])
                    torch.nn.init.uniform_(p, a=-stdv, b=stdv)

    def _train(self, criterion, optimizer):
        min_test_loss = 100000
        global_step = 0
        continue_not_increase = 0
        for epoch in range(self.opt.num_epoch):
            print('>' * 100)
            print('epoch: ', epoch)
            n_correct, n_total = 0, 0
            increase_flag = False
            for i_batch, sample_batched in enumerate(self.train_data_loader):
                global_step += 1

                self.model.train()
                optimizer.zero_grad()

                inputs = [sample_batched[col].to(self.opt.device) for col in self.opt.inputs_cols]

                outputs, targets = self.model(inputs)
                targets = torch.tensor(targets).to(self.opt.device)

                loss = criterion(outputs, targets)
                loss.backward()
                optimizer.step()

                if global_step % self.opt.log_step == 0:
                    print('loss: {:.6f}'.format(loss.item()))
            test_loss = self._evaluate_loss()
            print('test_loss: {:.6f}'.format(test_loss))
            if test_loss < min_test_loss:
                increase_flag = True
                min_test_loss = test_loss
                if self.opt.save and test_f1 > self.global_f1:
                    self.global_f1 = test_f1
                    torch.save(self.model.state_dict(), 'state_dict/'+self.opt.model_name+'_'+self.opt.dataset+'.pkl')
                    print('>>> best model saved.')
            if increase_flag == False:
                continue_not_increase += 1
                if continue_not_increase >= 3:
                    print('Early stopping.')
                    break
            else:
                continue_not_increase = 0
        return min_test_loss

    def _evaluate_loss(self):
        self.model.eval()
        n_test_loss, n_test_total = 0, 0
        t_targets_all, t_outputs_all = None, None
        with torch.no_grad():
            for t_batch, t_sample_batched in enumerate(self.test_data_loader):
                t_inputs = [t_sample_batched[col].to(opt.device) for col in self.opt.inputs_cols]
                t_outputs, t_targets = self.model(t_inputs)
                t_targets = torch.tensor(t_targets).to(self.opt.device)

                sub_loss = ((t_outputs - t_targets) ** 2) / opt.embed_dim
                n_test_loss += F.mse_loss(t_outputs, t_targets)
                n_test_total += len(t_outputs)

        test_loss = n_test_loss / n_test_total
        return test_loss

    def run(self):
        criterion = nn.MSELoss()
        _params = filter(lambda p: p.requires_grad, self.model.parameters())
        optimizer = self.opt.optimizer(_params, lr=self.opt.learning_rate, weight_decay=self.opt.l2reg)
        
        if not os.path.exists('log/'):
            os.mkdir('log/')

        f_out = open('log/'+self.opt.model_name+'_'+self.opt.dataset+'_val.txt', 'w', encoding='utf-8')

        self._reset_params()
        min_test_loss = self._train(criterion, optimizer)
        print('min_test_loss: {0}'.format(min_test_loss))
        f_out.write('min_test_loss: {0}'.format(min_test_loss))

        f_out.close()


if __name__ == '__main__':
    # Hyper Parameters
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_name', default='caer', type=str)
    parser.add_argument('--dataset', default='rest15', type=str, help='sentihood, rest15')
    parser.add_argument('--optimizer', default='adam', type=str)
    parser.add_argument('--initializer', default='xavier_uniform_', type=str)
    parser.add_argument('--learning_rate', default=0.001, type=float)
    parser.add_argument('--l2reg', default=0.00001, type=float)
    parser.add_argument('--num_epoch', default=100, type=int)
    parser.add_argument('--batch_size', default=32, type=int)
    parser.add_argument('--log_step', default=5, type=int)
    parser.add_argument('--embed_dim', default=300, type=int)
    parser.add_argument('--hidden_dim', default=300, type=int)
    parser.add_argument('--polarities_dim', default=3, type=int)
    parser.add_argument('--save', default=False, type=bool)
    parser.add_argument('--seed', default=776, type=int)
    parser.add_argument('--device', default=None, type=str)
    parser.add_argument('--valset_ratio', default=0, type=float, help='set ratio between 0 and 1 for validation support')
    opt = parser.parse_args()

    model_classes = {
        'caer': CAER,
    }
    input_colses = {
        'caer': ['text_indices', 'aspect_indices', 'target_indices'],

    }
    initializers = {
        'xavier_uniform_': torch.nn.init.xavier_uniform_,
        'xavier_normal_': torch.nn.init.xavier_normal,
        'orthogonal_': torch.nn.init.orthogonal_,
    }
    optimizers = {
        'adadelta': torch.optim.Adadelta,  # default lr=1.0
        'adagrad': torch.optim.Adagrad,  # default lr=0.01
        'adam': torch.optim.Adam,  # default lr=0.001
        'adamax': torch.optim.Adamax,  # default lr=0.002
        'asgd': torch.optim.ASGD,  # default lr=0.01
        'rmsprop': torch.optim.RMSprop,  # default lr=0.01
        'sgd': torch.optim.SGD,
    }
    opt.model_class = model_classes[opt.model_name]
    opt.inputs_cols = input_colses[opt.model_name]
    opt.initializer = initializers[opt.initializer]
    opt.optimizer = optimizers[opt.optimizer]
    opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') \
        if opt.device is None else torch.device(opt.device)

    if opt.seed is not None:
        random.seed(opt.seed)
        numpy.random.seed(opt.seed)
        torch.manual_seed(opt.seed)
        torch.cuda.manual_seed(opt.seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

    ins = Instructor(opt)
    ins.run()