示例#1
0
def train(train_loader, model, criterion, optimizer, epoch):
    logger.info('Epoch {}'.format(epoch))
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        target = target.cuda(async=True)
        input_var = Variable(input)
        target_var = Variable(target)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % opt.TRAIN.PRINT_FREQ == 0:
            logger.info('[{1}/{2}]\t'
                        'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                        'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                        'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                        'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                            epoch,
                            i,
                            len(train_loader),
                            batch_time=batch_time,
                            data_time=data_time,
                            loss=losses,
                            top1=top1,
                            top5=top5))

    train_losses.append(losses.avg)
    train_top1s.append(top1.avg)
示例#2
0
def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    for i, (input, target) in enumerate(val_loader):
        target = target.cuda(async=True)
        input_var = Variable(input, volatile=True)
        target_var = Variable(target, volatile=True)

        # compute output
        output = model(input_var)
        loss = criterion(output, target_var)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
        losses.update(loss.data[0], input.size(0))
        top1.update(prec1[0], input.size(0))
        top5.update(prec5[0], input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % opt.TRAIN.PRINT_FREQ == 0:
            logger.info('Test: [{0}/{1}]\t'
                        'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                        'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                        'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                            i,
                            len(val_loader),
                            batch_time=batch_time,
                            loss=losses,
                            top1=top1,
                            top5=top5))

    logger.info(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'.format(
        top1=top1, top5=top5))

    test_losses.append(losses.avg)
    test_top1s.append(top1.avg)

    return top1.avg