<reponame>kirubarajan/Dynamic-Memory-Network-Plus
import os

import numpy as np
import torch
from torch.autograd import Variable
from torch.utils.data.dataloader import DataLoader

from data_gen import BabiDataset, pad_collate
from models import DMNPlus
from utils import parse_args


def train_net(args):
    torch.manual_seed(7)
    np.random.seed(7)

    for run in range(1):
        for task_id in range(1, 21):
            dset = BabiDataset(task_id)
            vocab_size = len(dset.QA.VOCAB)

            model = DMNPlus(args.hidden_size, vocab_size, num_hop=3, qa=dset.QA)
            model.cuda()
            early_stopping_cnt = 0
            early_stopping_flag = False
            best_acc = 0
            optim = torch.optim.Adam(model.parameters())

            for epoch in range(256):
                dset.set_mode('train')
                train_loader = DataLoader(
                    dset, batch_size=args.batch_size, shuffle=True, collate_fn=pad_collate
                )

                model.train()
                if not early_stopping_flag:
                    total_acc = 0
                    cnt = 0
                    for batch_idx, data in enumerate(train_loader):
                        optim.zero_grad()
                        contexts, questions, answers = data
                        batch_size = contexts.size()[0]
                        contexts = Variable(contexts.long().cuda())
                        questions = Variable(questions.long().cuda())
                        answers = Variable(answers.cuda())

                        loss, acc = model.get_loss(contexts, questions, answers)
                        loss.backward()
                        total_acc += acc * batch_size
                        cnt += batch_size

                        if batch_idx % 20 == 0:
                            print(
                                '[Task {}, Epoch {}] [Training] loss : {}, acc : {:.4f}, batch_idx : {}'.format(task_id,
                                                                                                                epoch,
                                                                                                                loss.item(),
                                                                                                                total_acc / cnt,
                                                                                                                batch_idx))
                        optim.step()

                    dset.set_mode('valid')
                    valid_loader = DataLoader(dset, batch_size=args.batch_size, shuffle=False, collate_fn=pad_collate)

                    model.eval()
                    total_acc = 0
                    cnt = 0
                    for batch_idx, data in enumerate(valid_loader):
                        contexts, questions, answers = data
                        batch_size = contexts.size()[0]
                        contexts = Variable(contexts.long().cuda())
                        questions = Variable(questions.long().cuda())
                        answers = Variable(answers.cuda())

                        _, acc = model.get_loss(contexts, questions, answers)
                        total_acc += acc * batch_size
                        cnt += batch_size

                    total_acc = total_acc / cnt
                    if total_acc > best_acc:
                        best_acc = total_acc
                        best_state = model.state_dict()
                        early_stopping_cnt = 0
                    else:
                        early_stopping_cnt += 1
                        if early_stopping_cnt > 20:
                            early_stopping_flag = True

                    print('[Run {}, Task {}, Epoch {}] [Validate] Accuracy : {:.4f}'.format(run, task_id, epoch,
                                                                                            total_acc))
                    with open('log.txt', 'a') as fp:
                        fp.write('[Run {}, Task {}, Epoch {}] [Validate] Accuracy : {:.4f}\n'.format(
                            run,
                            task_id,
                            epoch,
                            total_acc))
                    if total_acc == 1.0:
                        break
                else:
                    print('[Run {}, Task {}] Early Stopping at Epoch {}, Valid Accuracy : {:.4f}'.format(run,
                                                                                                         task_id,
                                                                                                         epoch,
                                                                                                         best_acc))
                    break

            dset.set_mode('test')
            test_loader = DataLoader(dset, batch_size=args.batch_size, shuffle=False, collate_fn=pad_collate)
            test_acc = 0
            cnt = 0

            for batch_idx, data in enumerate(test_loader):
                contexts, questions, answers = data
                batch_size = contexts.size()[0]
                contexts = Variable(contexts.long().cuda())
                questions = Variable(questions.long().cuda())
                answers = Variable(answers.cuda())

                model.load_state_dict(best_state)
                _, acc = model.get_loss(contexts, questions, answers)
                test_acc += acc * batch_size
                cnt += batch_size
            print('[Run {}, Task {}, Epoch {}] [Test] Accuracy : {:.4f}'.format(run, task_id, epoch, test_acc / cnt))
            os.makedirs('models', exist_ok=True)
            with open('models/task{}_epoch{}_run{}_acc{:.4f}.pth'.format(task_id, epoch, run, test_acc / cnt), 'wb') as fp:
                torch.save(model.state_dict(), fp)
            with open('log.txt', 'a') as fp:
                fp.write(
                    '[Run {}, Task {}, Epoch {}] [Test] Accuracy : {:.4f}\n'.format(run, task_id, epoch, total_acc))


def main():
    global args
    args = parse_args()
    train_net(args)


if __name__ == '__main__':
    main()

import os

import numpy as np
import torch
from torch.autograd import Variable
from torch.utils.data.dataloader import DataLoader

from data_gen import BabiDataset, pad_collate
from models import DMNPlus
from utils import parse_args


def train_net(args):
    torch.manual_seed(7)
    np.random.seed(7)

    for run in range(1):
        for task_id in range(1, 21):
            dset = BabiDataset(task_id)
            vocab_size = len(dset.QA.VOCAB)

            model = DMNPlus(args.hidden_size, vocab_size, num_hop=3, qa=dset.QA)
            model.cuda()
            early_stopping_cnt = 0
            early_stopping_flag = False
            best_acc = 0
            optim = torch.optim.Adam(model.parameters())

            for epoch in range(256):
                dset.set_mode('train')
                train_loader = DataLoader(
                    dset, batch_size=args.batch_size, shuffle=True, collate_fn=pad_collate
                )

                model.train()
                if not early_stopping_flag:
                    total_acc = 0
                    cnt = 0
                    for batch_idx, data in enumerate(train_loader):
                        optim.zero_grad()
                        contexts, questions, answers = data
                        batch_size = contexts.size()[0]
                        contexts = Variable(contexts.long().cuda())
                        questions = Variable(questions.long().cuda())
                        answers = Variable(answers.cuda())

                        loss, acc = model.get_loss(contexts, questions, answers)
                        loss.backward()
                        total_acc += acc * batch_size
                        cnt += batch_size

                        if batch_idx % 20 == 0:
                            print(
                                '[Task {}, Epoch {}] [Training] loss : {}, acc : {:.4f}, batch_idx : {}'.format(task_id,
                                                                                                                epoch,
                                                                                                                loss.item(),
                                                                                                                total_acc / cnt,
                                                                                                                batch_idx))
                        optim.step()

                    dset.set_mode('valid')
                    valid_loader = DataLoader(dset, batch_size=args.batch_size, shuffle=False, collate_fn=pad_collate)

                    model.eval()
                    total_acc = 0
                    cnt = 0
                    for batch_idx, data in enumerate(valid_loader):
                        contexts, questions, answers = data
                        batch_size = contexts.size()[0]
                        contexts = Variable(contexts.long().cuda())
                        questions = Variable(questions.long().cuda())
                        answers = Variable(answers.cuda())

                        _, acc = model.get_loss(contexts, questions, answers)
                        total_acc += acc * batch_size
                        cnt += batch_size

                    total_acc = total_acc / cnt
                    if total_acc > best_acc:
                        best_acc = total_acc
                        best_state = model.state_dict()
                        early_stopping_cnt = 0
                    else:
                        early_stopping_cnt += 1
                        if early_stopping_cnt > 20:
                            early_stopping_flag = True

                    print('[Run {}, Task {}, Epoch {}] [Validate] Accuracy : {:.4f}'.format(run, task_id, epoch,
                                                                                            total_acc))
                    with open('log.txt', 'a') as fp:
                        fp.write('[Run {}, Task {}, Epoch {}] [Validate] Accuracy : {:.4f}\n'.format(
                            run,
                            task_id,
                            epoch,
                            total_acc))
                    if total_acc == 1.0:
                        break
                else:
                    print('[Run {}, Task {}] Early Stopping at Epoch {}, Valid Accuracy : {:.4f}'.format(run,
                                                                                                         task_id,
                                                                                                         epoch,
                                                                                                         best_acc))
                    break

            dset.set_mode('test')
            test_loader = DataLoader(dset, batch_size=args.batch_size, shuffle=False, collate_fn=pad_collate)
            test_acc = 0
            cnt = 0

            for batch_idx, data in enumerate(test_loader):
                contexts, questions, answers = data
                batch_size = contexts.size()[0]
                contexts = Variable(contexts.long().cuda())
                questions = Variable(questions.long().cuda())
                answers = Variable(answers.cuda())

                model.load_state_dict(best_state)
                _, acc = model.get_loss(contexts, questions, answers)
                test_acc += acc * batch_size
                cnt += batch_size
            print('[Run {}, Task {}, Epoch {}] [Test] Accuracy : {:.4f}'.format(run, task_id, epoch, test_acc / cnt))
            os.makedirs('models', exist_ok=True)
            with open('models/task{}_epoch{}_run{}_acc{:.4f}.pth'.format(task_id, epoch, run, test_acc / cnt), 'wb') as fp:
                torch.save(model.state_dict(), fp)
            with open('log.txt', 'a') as fp:
                fp.write(
                    '[Run {}, Task {}, Epoch {}] [Test] Accuracy : {:.4f}\n'.format(run, task_id, epoch, total_acc))


def main():
    global args
    args = parse_args()
    train_net(args)


if __name__ == '__main__':
    main()