mainbaseline.py

import warnings
warnings.simplefilter("ignore", UserWarning)
from tqdm import tqdm

import torch
import argparse
import numpy as np
from modules.tokenizers import Tokenizer
from modules.dataloaders import LADataLoader
from modules.metrics import compute_scores
from modules.optimizers import build_optimizer, build_lr_scheduler
from modules.trainer import BaseTrainer
from modules.loss import compute_loss
from models.baseline import LAMRGModel
from config import opts


class Trainer(BaseTrainer):
    def __init__(self, model, criterion, metric_ftns, optimizer, args, lr_scheduler, train_dataloader, val_dataloader,
                 test_dataloader):
        super(Trainer, self).__init__(model, criterion, metric_ftns, optimizer, args)
        self.lr_scheduler = lr_scheduler
        self.train_dataloader = train_dataloader
        self.val_dataloader = val_dataloader
        self.test_dataloader = test_dataloader

    def _train_epoch(self, epoch):

        train_loss = 0
        self.model.train()
        for batch_idx, (images_id, images, reports_ids, reports_masks, labels) in tqdm(enumerate(self.train_dataloader)):
            images, reports_ids, reports_masks, labels = images.to(self.device), reports_ids.to(self.device), \
                                                         reports_masks.to(self.device), labels.to(self.device)
            output, outlabels = self.model(images, reports_ids, labels, mode='train')
            loss = self.criterion(output, reports_ids, reports_masks)
            train_loss += loss.item()
            self.optimizer.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_value_(self.model.parameters(), 0.1)
            self.optimizer.step()
        log = {'train_loss': train_loss / len(self.train_dataloader)}

        self.model.eval()
        with torch.no_grad():
            val_gts, val_res, val_idxs = [], [], []
            for batch_idx, (images_id, images, reports_ids, reports_masks, labels) in tqdm(enumerate(self.val_dataloader)):
                images, reports_ids, reports_masks, labels = images.to(self.device), reports_ids.to(self.device), \
                                                             reports_masks.to(self.device), labels.to(self.device)
                output, outlabels = self.model(images, labels=labels, mode='sample')
                reports = self.model.tokenizer.decode_batch(output.cpu().numpy())
                ground_truths = self.model.tokenizer.decode_batch(reports_ids[:, 1:].cpu().numpy())
                val_res.extend(reports)
                val_gts.extend(ground_truths)
                val_idxs.extend(images_id)
            val_met = self.metric_ftns({i: [gt] for i, gt in enumerate(val_gts)},
                                       {i: [re] for i, re in enumerate(val_res)})

            log.update(**{'val_' + k: v for k, v in val_met.items()})
            self._output_generation(val_res, val_gts, val_idxs, epoch, log, 'val')

        self.model.eval()
        with torch.no_grad():
            test_gts, test_res, test_idxs = [], [], []
            for batch_idx, (images_id, images, reports_ids, reports_masks, labels) in tqdm(enumerate(self.test_dataloader)):
                images, reports_ids, reports_masks, labels = images.to(self.device), reports_ids.to(self.device), \
                                                             reports_masks.to(self.device), labels.to(self.device)
                output, outlabels = self.model(images, labels=labels, mode='sample')
                reports = self.model.tokenizer.decode_batch(output.cpu().numpy())
                ground_truths = self.model.tokenizer.decode_batch(reports_ids[:, 1:].cpu().numpy())
                test_res.extend(reports)
                test_gts.extend(ground_truths)
                test_idxs.extend(images_id)
            test_met = self.metric_ftns({i: [gt] for i, gt in enumerate(test_gts)},
                                        {i: [re] for i, re in enumerate(test_res)})
            log.update(**{'test_' + k: v for k, v in test_met.items()})
            self._output_generation(test_res, test_gts, test_idxs, epoch, log, 'test')

        self.lr_scheduler.step()

        return log


def main():
    # parse arguments
    # args = parse_agrs()
    args = opts.parse_opt()
    args.version = 900
    args.save_dir = args.save_dir + f'V{args.version}'
    print(args)

    # fix random seeds
    torch.manual_seed(args.seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    np.random.seed(args.seed)

    # create tokenizer
    tokenizer = Tokenizer(args)

    # create data loader
    train_dataloader = LADataLoader(args, tokenizer, split='train', shuffle=True)
    val_dataloader = LADataLoader(args, tokenizer, split='val', shuffle=False)
    test_dataloader = LADataLoader(args, tokenizer, split='test', shuffle=False)

    # build model architecture
    model = LAMRGModel(args, tokenizer)

    # get function handles of loss and metrics
    criterion = compute_loss
    metrics = compute_scores

    # build optimizer, learning rate scheduler
    optimizer = build_optimizer(args, model)
    lr_scheduler = build_lr_scheduler(args, optimizer)

    # build trainer and start to train
    trainer = Trainer(model, criterion, metrics, optimizer, args, lr_scheduler, train_dataloader, val_dataloader, test_dataloader)
    trainer.train()


if __name__ == '__main__':
    main()