Esempio n. 1
0
def main(args):

    assert os.path.isdir(args.data_path), 'invalid data-path : {:}'.format(
        args.data_path)
    assert os.path.isfile(args.checkpoint), 'invalid checkpoint : {:}'.format(
        args.checkpoint)

    checkpoint = torch.load(args.checkpoint)
    xargs = checkpoint['args']
    train_data, valid_data, xshape, class_num = get_datasets(
        xargs.dataset, args.data_path, xargs.cutout_length)
    valid_loader = torch.utils.data.DataLoader(valid_data,
                                               batch_size=xargs.batch_size,
                                               shuffle=False,
                                               num_workers=xargs.workers,
                                               pin_memory=True)

    logger = PrintLogger()
    model_config = dict2config(checkpoint['model-config'], logger)
    base_model = obtain_model(model_config)
    flop, param = get_model_infos(base_model, xshape)
    logger.log('model ====>>>>:\n{:}'.format(base_model))
    logger.log('model information : {:}'.format(base_model.get_message()))
    logger.log('-' * 50)
    logger.log('Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(
        param, flop, flop / 1e3))
    logger.log('-' * 50)
    logger.log('valid_data : {:}'.format(valid_data))
    optim_config = dict2config(checkpoint['optim-config'], logger)
    _, _, criterion = get_optim_scheduler(base_model.parameters(),
                                          optim_config)
    logger.log('criterion  : {:}'.format(criterion))
    base_model.load_state_dict(checkpoint['base-model'])
    _, valid_func = get_procedures(xargs.procedure)
    logger.log(
        'initialize the CNN done, evaluate it using {:}'.format(valid_func))
    network = torch.nn.DataParallel(base_model).cuda()

    try:
        valid_loss, valid_acc1, valid_acc5 = valid_func(
            valid_loader, network, criterion, optim_config, 'pure-evaluation',
            xargs.print_freq_eval, logger)
    except:
        _, valid_func = get_procedures('basic')
        valid_loss, valid_acc1, valid_acc5 = valid_func(
            valid_loader, network, criterion, optim_config, 'pure-evaluation',
            xargs.print_freq_eval, logger)

    num_bytes = torch.cuda.max_memory_cached(
        next(network.parameters()).device) * 1.0
    logger.log(
        '***{:s}*** EVALUATION loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f}, error@1 = {:.2f}, error@5 = {:.2f}'
        .format(time_string(), valid_loss, valid_acc1, valid_acc5,
                100 - valid_acc1, 100 - valid_acc5))
    logger.log(
        '[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]'
        .format(
            next(network.parameters()).device, int(num_bytes), num_bytes / 1e3,
            num_bytes / 1e6, num_bytes / 1e9))
    logger.close()
Esempio n. 2
0
def main(args):
    assert torch.cuda.is_available(), "CUDA is not available."
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True
    torch.set_num_threads(args.workers)

    prepare_seed(args.rand_seed)
    logger = prepare_logger(args)

    # prepare dataset
    train_data, valid_data, xshape, class_num = get_datasets(
        args.dataset, args.data_path, args.cutout_length)
    # train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
    valid_loader = torch.utils.data.DataLoader(
        valid_data,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.workers,
        pin_memory=True,
    )

    split_file_path = Path(args.split_path)
    assert split_file_path.exists(), "{:} does not exist".format(
        split_file_path)
    split_info = torch.load(split_file_path)

    train_split, valid_split = split_info["train"], split_info["valid"]
    assert (len(set(train_split).intersection(set(valid_split))) == 0
            ), "There should be 0 element that belongs to both train and valid"
    assert len(train_split) + len(valid_split) == len(
        train_data), "{:} + {:} vs {:}".format(len(train_split),
                                               len(valid_split),
                                               len(train_data))
    search_dataset = SearchDataset(args.dataset, train_data, train_split,
                                   valid_split)

    search_train_loader = torch.utils.data.DataLoader(
        train_data,
        batch_size=args.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
        pin_memory=True,
        num_workers=args.workers,
    )
    search_valid_loader = torch.utils.data.DataLoader(
        train_data,
        batch_size=args.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
        pin_memory=True,
        num_workers=args.workers,
    )
    search_loader = torch.utils.data.DataLoader(
        search_dataset,
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.workers,
        pin_memory=True,
        sampler=None,
    )
    # get configures
    model_config = load_config(
        args.model_config,
        {
            "class_num": class_num,
            "search_mode": args.search_shape
        },
        logger,
    )

    # obtain the model
    search_model = obtain_search_model(model_config)
    MAX_FLOP, param = get_model_infos(search_model, xshape)
    optim_config = load_config(args.optim_config, {
        "class_num": class_num,
        "FLOP": MAX_FLOP
    }, logger)
    logger.log("Model Information : {:}".format(search_model.get_message()))
    logger.log("MAX_FLOP = {:} M".format(MAX_FLOP))
    logger.log("Params   = {:} M".format(param))
    logger.log("train_data : {:}".format(train_data))
    logger.log("search-data: {:}".format(search_dataset))
    logger.log("search_train_loader : {:} samples".format(len(train_split)))
    logger.log("search_valid_loader : {:} samples".format(len(valid_split)))
    base_optimizer, scheduler, criterion = get_optim_scheduler(
        search_model.base_parameters(), optim_config)
    arch_optimizer = torch.optim.Adam(
        search_model.arch_parameters(),
        lr=optim_config.arch_LR,
        betas=(0.5, 0.999),
        weight_decay=optim_config.arch_decay,
    )
    logger.log("base-optimizer : {:}".format(base_optimizer))
    logger.log("arch-optimizer : {:}".format(arch_optimizer))
    logger.log("scheduler      : {:}".format(scheduler))
    logger.log("criterion      : {:}".format(criterion))

    last_info, model_base_path, model_best_path = (
        logger.path("info"),
        logger.path("model"),
        logger.path("best"),
    )
    network, criterion = torch.nn.DataParallel(
        search_model).cuda(), criterion.cuda()

    # load checkpoint
    if last_info.exists() or (args.resume is not None and osp.isfile(
            args.resume)):  # automatically resume from previous checkpoint
        if args.resume is not None and osp.isfile(args.resume):
            resume_path = Path(args.resume)
        elif last_info.exists():
            resume_path = last_info
        else:
            raise ValueError("Something is wrong.")
        logger.log("=> loading checkpoint of the last-info '{:}' start".format(
            resume_path))
        checkpoint = torch.load(resume_path)
        if "last_checkpoint" in checkpoint:
            last_checkpoint_path = checkpoint["last_checkpoint"]
            if not last_checkpoint_path.exists():
                logger.log("Does not find {:}, try another path".format(
                    last_checkpoint_path))
                last_checkpoint_path = (resume_path.parent /
                                        last_checkpoint_path.parent.name /
                                        last_checkpoint_path.name)
            assert (last_checkpoint_path.exists()
                    ), "can not find the checkpoint from {:}".format(
                        last_checkpoint_path)
            checkpoint = torch.load(last_checkpoint_path)
        start_epoch = checkpoint["epoch"] + 1
        search_model.load_state_dict(checkpoint["search_model"])
        scheduler.load_state_dict(checkpoint["scheduler"])
        base_optimizer.load_state_dict(checkpoint["base_optimizer"])
        arch_optimizer.load_state_dict(checkpoint["arch_optimizer"])
        valid_accuracies = checkpoint["valid_accuracies"]
        arch_genotypes = checkpoint["arch_genotypes"]
        discrepancies = checkpoint["discrepancies"]
        logger.log(
            "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
            .format(resume_path, start_epoch))
    else:
        logger.log(
            "=> do not find the last-info file : {:} or resume : {:}".format(
                last_info, args.resume))
        start_epoch, valid_accuracies, arch_genotypes, discrepancies = (
            0,
            {
                "best": -1
            },
            {},
            {},
        )

    # main procedure
    train_func, valid_func = get_procedures(args.procedure)
    total_epoch = optim_config.epochs + optim_config.warmup
    start_time, epoch_time = time.time(), AverageMeter()
    for epoch in range(start_epoch, total_epoch):
        scheduler.update(epoch, 0.0)
        search_model.set_tau(args.gumbel_tau_max, args.gumbel_tau_min,
                             epoch * 1.0 / total_epoch)
        need_time = "Time Left: {:}".format(
            convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
        epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
        LRs = scheduler.get_lr()
        find_best = False

        logger.log(
            "\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}"
            .format(
                time_string(),
                epoch_str,
                need_time,
                min(LRs),
                max(LRs),
                scheduler,
                search_model.tau,
                MAX_FLOP,
            ))

        # train for one epoch
        train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(
            search_loader,
            network,
            criterion,
            scheduler,
            base_optimizer,
            arch_optimizer,
            optim_config,
            {
                "epoch-str": epoch_str,
                "FLOP-exp": MAX_FLOP * args.FLOP_ratio,
                "FLOP-weight": args.FLOP_weight,
                "FLOP-tolerant": MAX_FLOP * args.FLOP_tolerant,
            },
            args.print_freq,
            logger,
        )
        # log the results
        logger.log(
            "***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}"
            .format(
                time_string(),
                epoch_str,
                train_base_loss,
                train_arch_loss,
                train_acc1,
                train_acc5,
            ))
        cur_FLOP, genotype = search_model.get_flop("genotype",
                                                   model_config._asdict(),
                                                   None)
        arch_genotypes[epoch] = genotype
        arch_genotypes["last"] = genotype
        logger.log("[{:}] genotype : {:}".format(epoch_str, genotype))
        arch_info, discrepancy = search_model.get_arch_info()
        logger.log(arch_info)
        discrepancies[epoch] = discrepancy
        logger.log(
            "[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}"
            .format(
                epoch_str,
                cur_FLOP,
                cur_FLOP / MAX_FLOP,
                args.FLOP_ratio,
                np.mean(discrepancy),
            ))

        # if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
        #  init_flop_weight = init_flop_weight * args.FLOP_decay
        # else:
        #  init_flop_weight = init_flop_weight / args.FLOP_decay

        # evaluate the performance
        if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
            logger.log("-" * 150)
            valid_loss, valid_acc1, valid_acc5 = valid_func(
                search_valid_loader,
                network,
                criterion,
                epoch_str,
                args.print_freq_eval,
                logger,
            )
            valid_accuracies[epoch] = valid_acc1
            logger.log(
                "***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}"
                .format(
                    time_string(),
                    epoch_str,
                    valid_loss,
                    valid_acc1,
                    valid_acc5,
                    valid_accuracies["best"],
                    100 - valid_accuracies["best"],
                ))
            if valid_acc1 > valid_accuracies["best"]:
                valid_accuracies["best"] = valid_acc1
                arch_genotypes["best"] = genotype
                find_best = True
                logger.log(
                    "Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}."
                    .format(
                        epoch,
                        valid_acc1,
                        valid_acc5,
                        100 - valid_acc1,
                        100 - valid_acc5,
                        model_best_path,
                    ))

        # save checkpoint
        save_path = save_checkpoint(
            {
                "epoch": epoch,
                "args": deepcopy(args),
                "valid_accuracies": deepcopy(valid_accuracies),
                "model-config": model_config._asdict(),
                "optim-config": optim_config._asdict(),
                "search_model": search_model.state_dict(),
                "scheduler": scheduler.state_dict(),
                "base_optimizer": base_optimizer.state_dict(),
                "arch_optimizer": arch_optimizer.state_dict(),
                "arch_genotypes": arch_genotypes,
                "discrepancies": discrepancies,
            },
            model_base_path,
            logger,
        )
        if find_best:
            copy_checkpoint(model_base_path, model_best_path, logger)
        last_info = save_checkpoint(
            {
                "epoch": epoch,
                "args": deepcopy(args),
                "last_checkpoint": save_path,
            },
            logger.path("info"),
            logger,
        )

        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()

    logger.log("")
    logger.log("-" * 100)
    last_config_path = logger.path("log") / "seed-{:}-last.config".format(
        args.rand_seed)
    configure2str(arch_genotypes["last"], str(last_config_path))
    logger.log("save the last config int {:} :\n{:}".format(
        last_config_path, arch_genotypes["last"]))

    best_arch, valid_acc = arch_genotypes["best"], valid_accuracies["best"]
    for key, config in arch_genotypes.items():
        if key == "last":
            continue
        FLOP_ratio = config["estimated_FLOP"] / MAX_FLOP
        if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
            if valid_acc < valid_accuracies[key]:
                best_arch, valid_acc = config, valid_accuracies[key]
    print("Best-Arch : {:}\nRatio={:}, Valid-ACC={:}".format(
        best_arch, best_arch["estimated_FLOP"] / MAX_FLOP, valid_acc))
    best_config_path = logger.path("log") / "seed-{:}-best.config".format(
        args.rand_seed)
    configure2str(best_arch, str(best_config_path))
    logger.log("save the last config int {:} :\n{:}".format(
        best_config_path, best_arch))
    logger.log("\n" + "-" * 200)
    logger.log(
        "Finish training/validation in {:}, and save final checkpoint into {:}"
        .format(convert_secs2time(epoch_time.sum, True), logger.path("info")))
    logger.close()
Esempio n. 3
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def main(args):
    assert torch.cuda.is_available(), 'CUDA is not available.'
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True
    #torch.backends.cudnn.deterministic = True
    torch.set_num_threads(args.workers)

    prepare_seed(args.rand_seed)
    logger = prepare_logger(args)

    train_data, valid_data, xshape, class_num = get_datasets(
        args.dataset, args.data_path, args.cutout_length)
    train_loader = torch.utils.data.DataLoader(train_data,
                                               batch_size=args.batch_size,
                                               shuffle=True,
                                               num_workers=args.workers,
                                               pin_memory=True)
    valid_loader = torch.utils.data.DataLoader(valid_data,
                                               batch_size=args.batch_size,
                                               shuffle=False,
                                               num_workers=args.workers,
                                               pin_memory=True)
    # get configures
    model_config = load_config(args.model_config, {'class_num': class_num},
                               logger)
    optim_config = load_config(
        args.optim_config, {
            'class_num': class_num,
            'KD_alpha': args.KD_alpha,
            'KD_temperature': args.KD_temperature
        }, logger)

    # load checkpoint
    teacher_base = load_net_from_checkpoint(args.KD_checkpoint)
    teacher = torch.nn.DataParallel(teacher_base).cuda()

    base_model = obtain_model(model_config)
    flop, param = get_model_infos(base_model, xshape)
    logger.log('Student ====>>>>:\n{:}'.format(base_model))
    logger.log('Teacher ====>>>>:\n{:}'.format(teacher_base))
    logger.log('model information : {:}'.format(base_model.get_message()))
    logger.log('-' * 50)
    logger.log('Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(
        param, flop, flop / 1e3))
    logger.log('-' * 50)
    logger.log('train_data : {:}'.format(train_data))
    logger.log('valid_data : {:}'.format(valid_data))
    optimizer, scheduler, criterion = get_optim_scheduler(
        base_model.parameters(), optim_config)
    logger.log('optimizer  : {:}'.format(optimizer))
    logger.log('scheduler  : {:}'.format(scheduler))
    logger.log('criterion  : {:}'.format(criterion))

    last_info, model_base_path, model_best_path = logger.path(
        'info'), logger.path('model'), logger.path('best')
    network, criterion = torch.nn.DataParallel(
        base_model).cuda(), criterion.cuda()

    if last_info.exists():  # automatically resume from previous checkpoint
        logger.log("=> loading checkpoint of the last-info '{:}' start".format(
            last_info))
        last_info = torch.load(last_info)
        start_epoch = last_info['epoch'] + 1
        checkpoint = torch.load(last_info['last_checkpoint'])
        base_model.load_state_dict(checkpoint['base-model'])
        scheduler.load_state_dict(checkpoint['scheduler'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        valid_accuracies = checkpoint['valid_accuracies']
        max_bytes = checkpoint['max_bytes']
        logger.log(
            "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
            .format(last_info, start_epoch))
    elif args.resume is not None:
        assert Path(
            args.resume).exists(), 'Can not find the resume file : {:}'.format(
                args.resume)
        checkpoint = torch.load(args.resume)
        start_epoch = checkpoint['epoch'] + 1
        base_model.load_state_dict(checkpoint['base-model'])
        scheduler.load_state_dict(checkpoint['scheduler'])
        optimizer.load_state_dict(checkpoint['optimizer'])
        valid_accuracies = checkpoint['valid_accuracies']
        max_bytes = checkpoint['max_bytes']
        logger.log(
            "=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
                args.resume, start_epoch))
    elif args.init_model is not None:
        assert Path(args.init_model).exists(
        ), 'Can not find the initialization file : {:}'.format(args.init_model)
        checkpoint = torch.load(args.init_model)
        base_model.load_state_dict(checkpoint['base-model'])
        start_epoch, valid_accuracies, max_bytes = 0, {'best': -1}, {}
        logger.log('=> initialize the model from {:}'.format(args.init_model))
    else:
        logger.log("=> do not find the last-info file : {:}".format(last_info))
        start_epoch, valid_accuracies, max_bytes = 0, {'best': -1}, {}

    train_func, valid_func = get_procedures(args.procedure)

    total_epoch = optim_config.epochs + optim_config.warmup
    # Main Training and Evaluation Loop
    start_time = time.time()
    epoch_time = AverageMeter()
    for epoch in range(start_epoch, total_epoch):
        scheduler.update(epoch, 0.0)
        need_time = 'Time Left: {:}'.format(
            convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
        epoch_str = 'epoch={:03d}/{:03d}'.format(epoch, total_epoch)
        LRs = scheduler.get_lr()
        find_best = False

        logger.log(
            '\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}'
            .format(time_string(), epoch_str, need_time, min(LRs), max(LRs),
                    scheduler))

        # train for one epoch
        train_loss, train_acc1, train_acc5 = train_func(
            train_loader, teacher, network, criterion, scheduler, optimizer,
            optim_config, epoch_str, args.print_freq, logger)
        # log the results
        logger.log(
            '***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}'
            .format(time_string(), epoch_str, train_loss, train_acc1,
                    train_acc5))

        # evaluate the performance
        if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
            logger.log('-' * 150)
            valid_loss, valid_acc1, valid_acc5 = valid_func(
                valid_loader, teacher, network, criterion, optim_config,
                epoch_str, args.print_freq_eval, logger)
            valid_accuracies[epoch] = valid_acc1
            logger.log(
                '***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}'
                .format(time_string(), epoch_str, valid_loss, valid_acc1,
                        valid_acc5, valid_accuracies['best'],
                        100 - valid_accuracies['best']))
            if valid_acc1 > valid_accuracies['best']:
                valid_accuracies['best'] = valid_acc1
                find_best = True
                logger.log(
                    'Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.'
                    .format(epoch, valid_acc1, valid_acc5, 100 - valid_acc1,
                            100 - valid_acc5, model_best_path))
            num_bytes = torch.cuda.max_memory_cached(
                next(network.parameters()).device) * 1.0
            logger.log(
                '[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]'
                .format(
                    next(network.parameters()).device, int(num_bytes),
                    num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9))
            max_bytes[epoch] = num_bytes
        if epoch % 10 == 0: torch.cuda.empty_cache()

        # save checkpoint
        save_path = save_checkpoint(
            {
                'epoch': epoch,
                'args': deepcopy(args),
                'max_bytes': deepcopy(max_bytes),
                'FLOP': flop,
                'PARAM': param,
                'valid_accuracies': deepcopy(valid_accuracies),
                'model-config': model_config._asdict(),
                'optim-config': optim_config._asdict(),
                'base-model': base_model.state_dict(),
                'scheduler': scheduler.state_dict(),
                'optimizer': optimizer.state_dict(),
            }, model_base_path, logger)
        if find_best: copy_checkpoint(model_base_path, model_best_path, logger)
        last_info = save_checkpoint(
            {
                'epoch': epoch,
                'args': deepcopy(args),
                'last_checkpoint': save_path,
            }, logger.path('info'), logger)

        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()

    logger.log('\n' + '-' * 200)
    logger.log('||| Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(
        param, flop, flop / 1e3))
    logger.log(
        'Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}'
        .format(convert_secs2time(epoch_time.sum, True),
                max(v for k, v in max_bytes.items()) / 1e6,
                logger.path('info')))
    logger.log('-' * 200 + '\n')
    logger.close()
Esempio n. 4
0
def main(args):
    assert torch.cuda.is_available(), 'CUDA is not available.'
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True
    #torch.backends.cudnn.deterministic = True
    torch.set_num_threads(args.workers)

    prepare_seed(args.rand_seed)
    logger = prepare_logger(args)

    # prepare dataset
    train_data, valid_data, xshape, class_num = get_datasets(
        args.dataset, args.data_path, args.cutout_length)
    #train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
    valid_loader = torch.utils.data.DataLoader(valid_data,
                                               batch_size=args.batch_size,
                                               shuffle=False,
                                               num_workers=args.workers,
                                               pin_memory=True)

    split_file_path = Path(args.split_path)
    assert split_file_path.exists(), '{:} does not exist'.format(
        split_file_path)
    split_info = torch.load(split_file_path)

    train_split, valid_split = split_info['train'], split_info['valid']
    assert len(
        set(train_split).intersection(set(valid_split))
    ) == 0, 'There should be 0 element that belongs to both train and valid'
    assert len(train_split) + len(valid_split) == len(
        train_data), '{:} + {:} vs {:}'.format(len(train_split),
                                               len(valid_split),
                                               len(train_data))
    search_dataset = SearchDataset(args.dataset, train_data, train_split,
                                   valid_split)

    search_train_loader = torch.utils.data.DataLoader(
        train_data,
        batch_size=args.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
        pin_memory=True,
        num_workers=args.workers)
    search_valid_loader = torch.utils.data.DataLoader(
        train_data,
        batch_size=args.batch_size,
        sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
        pin_memory=True,
        num_workers=args.workers)
    search_loader = torch.utils.data.DataLoader(search_dataset,
                                                batch_size=args.batch_size,
                                                shuffle=True,
                                                num_workers=args.workers,
                                                pin_memory=True,
                                                sampler=None)
    # get configures
    if args.ablation_num_select is None or args.ablation_num_select <= 0:
        model_config = load_config(args.model_config, {
            'class_num': class_num,
            'search_mode': 'shape'
        }, logger)
    else:
        model_config = load_config(
            args.model_config, {
                'class_num': class_num,
                'search_mode': 'ablation',
                'num_random_select': args.ablation_num_select
            }, logger)

    # obtain the model
    search_model = obtain_search_model(model_config)
    MAX_FLOP, param = get_model_infos(search_model, xshape)
    optim_config = load_config(args.optim_config, {
        'class_num': class_num,
        'FLOP': MAX_FLOP
    }, logger)
    logger.log('Model Information : {:}'.format(search_model.get_message()))
    logger.log('MAX_FLOP = {:} M'.format(MAX_FLOP))
    logger.log('Params   = {:} M'.format(param))
    logger.log('train_data : {:}'.format(train_data))
    logger.log('search-data: {:}'.format(search_dataset))
    logger.log('search_train_loader : {:} samples'.format(len(train_split)))
    logger.log('search_valid_loader : {:} samples'.format(len(valid_split)))
    base_optimizer, scheduler, criterion = get_optim_scheduler(
        search_model.base_parameters(), optim_config)
    arch_optimizer = torch.optim.Adam(search_model.arch_parameters(
        optim_config.arch_LR),
                                      lr=optim_config.arch_LR,
                                      betas=(0.5, 0.999),
                                      weight_decay=optim_config.arch_decay)
    logger.log('base-optimizer : {:}'.format(base_optimizer))
    logger.log('arch-optimizer : {:}'.format(arch_optimizer))
    logger.log('scheduler      : {:}'.format(scheduler))
    logger.log('criterion      : {:}'.format(criterion))

    last_info, model_base_path, model_best_path = logger.path(
        'info'), logger.path('model'), logger.path('best')
    network, criterion = torch.nn.DataParallel(
        search_model).cuda(), criterion.cuda()

    # load checkpoint
    if last_info.exists() or (args.resume is not None and osp.isfile(
            args.resume)):  # automatically resume from previous checkpoint
        if args.resume is not None and osp.isfile(args.resume):
            resume_path = Path(args.resume)
        elif last_info.exists():
            resume_path = last_info
        else:
            raise ValueError('Something is wrong.')
        logger.log("=> loading checkpoint of the last-info '{:}' start".format(
            resume_path))
        checkpoint = torch.load(resume_path)
        if 'last_checkpoint' in checkpoint:
            last_checkpoint_path = checkpoint['last_checkpoint']
            if not last_checkpoint_path.exists():
                logger.log('Does not find {:}, try another path'.format(
                    last_checkpoint_path))
                last_checkpoint_path = resume_path.parent / last_checkpoint_path.parent.name / last_checkpoint_path.name
            assert last_checkpoint_path.exists(
            ), 'can not find the checkpoint from {:}'.format(
                last_checkpoint_path)
            checkpoint = torch.load(last_checkpoint_path)
        start_epoch = checkpoint['epoch'] + 1
        #for key, value in checkpoint['search_model'].items():
        #  print('K {:} = Shape={:}'.format(key, value.shape))
        search_model.load_state_dict(checkpoint['search_model'])
        scheduler.load_state_dict(checkpoint['scheduler'])
        base_optimizer.load_state_dict(checkpoint['base_optimizer'])
        arch_optimizer.load_state_dict(checkpoint['arch_optimizer'])
        valid_accuracies = checkpoint['valid_accuracies']
        arch_genotypes = checkpoint['arch_genotypes']
        discrepancies = checkpoint['discrepancies']
        max_bytes = checkpoint['max_bytes']
        logger.log(
            "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch."
            .format(resume_path, start_epoch))
    else:
        logger.log(
            "=> do not find the last-info file : {:} or resume : {:}".format(
                last_info, args.resume))
        start_epoch, valid_accuracies, arch_genotypes, discrepancies, max_bytes = 0, {
            'best': -1
        }, {}, {}, {}

    # main procedure
    train_func, valid_func = get_procedures(args.procedure)
    total_epoch = optim_config.epochs + optim_config.warmup
    start_time, epoch_time = time.time(), AverageMeter()
    for epoch in range(start_epoch, total_epoch):
        scheduler.update(epoch, 0.0)
        search_model.set_tau(args.gumbel_tau_max, args.gumbel_tau_min,
                             epoch * 1.0 / total_epoch)
        need_time = 'Time Left: {:}'.format(
            convert_secs2time(epoch_time.avg * (total_epoch - epoch), True))
        epoch_str = 'epoch={:03d}/{:03d}'.format(epoch, total_epoch)
        LRs = scheduler.get_lr()
        find_best = False

        logger.log(
            '\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}'
            .format(time_string(), epoch_str, need_time, min(LRs), max(LRs),
                    scheduler, search_model.tau, MAX_FLOP))

        # train for one epoch
        train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(search_loader, network, criterion, scheduler, base_optimizer, arch_optimizer, optim_config, \
                                                                                    {'epoch-str'  : epoch_str,        'FLOP-exp': MAX_FLOP * args.FLOP_ratio,
                                                                                     'FLOP-weight': args.FLOP_weight, 'FLOP-tolerant': MAX_FLOP * args.FLOP_tolerant}, args.print_freq, logger)
        # log the results
        logger.log(
            '***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}'
            .format(time_string(), epoch_str, train_base_loss, train_arch_loss,
                    train_acc1, train_acc5))
        cur_FLOP, genotype = search_model.get_flop('genotype',
                                                   model_config._asdict(),
                                                   None)
        arch_genotypes[epoch] = genotype
        arch_genotypes['last'] = genotype
        logger.log('[{:}] genotype : {:}'.format(epoch_str, genotype))
        # save the configuration
        configure2str(
            genotype,
            str(
                logger.path('log') /
                'seed-{:}-temp.config'.format(args.rand_seed)))
        arch_info, discrepancy = search_model.get_arch_info()
        logger.log(arch_info)
        discrepancies[epoch] = discrepancy
        logger.log(
            '[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}'
            .format(epoch_str, cur_FLOP, cur_FLOP / MAX_FLOP, args.FLOP_ratio,
                    np.mean(discrepancy)))

        #if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
        #  init_flop_weight = init_flop_weight * args.FLOP_decay
        #else:
        #  init_flop_weight = init_flop_weight / args.FLOP_decay

        # evaluate the performance
        if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
            logger.log('-' * 150)
            valid_loss, valid_acc1, valid_acc5 = valid_func(
                search_valid_loader, network, criterion, epoch_str,
                args.print_freq_eval, logger)
            valid_accuracies[epoch] = valid_acc1
            logger.log(
                '***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}'
                .format(time_string(), epoch_str, valid_loss, valid_acc1,
                        valid_acc5, valid_accuracies['best'],
                        100 - valid_accuracies['best']))
            if valid_acc1 > valid_accuracies['best']:
                valid_accuracies['best'] = valid_acc1
                arch_genotypes['best'] = genotype
                find_best = True
                logger.log(
                    'Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.'
                    .format(epoch, valid_acc1, valid_acc5, 100 - valid_acc1,
                            100 - valid_acc5, model_best_path))
            # log the GPU memory usage
            #num_bytes = torch.cuda.max_memory_allocated( next(network.parameters()).device ) * 1.0
            num_bytes = torch.cuda.max_memory_cached(
                next(network.parameters()).device) * 1.0
            logger.log(
                '[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]'
                .format(
                    next(network.parameters()).device, int(num_bytes),
                    num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9))
            max_bytes[epoch] = num_bytes

        # save checkpoint
        save_path = save_checkpoint(
            {
                'epoch': epoch,
                'args': deepcopy(args),
                'max_bytes': deepcopy(max_bytes),
                'valid_accuracies': deepcopy(valid_accuracies),
                'model-config': model_config._asdict(),
                'optim-config': optim_config._asdict(),
                'search_model': search_model.state_dict(),
                'scheduler': scheduler.state_dict(),
                'base_optimizer': base_optimizer.state_dict(),
                'arch_optimizer': arch_optimizer.state_dict(),
                'arch_genotypes': arch_genotypes,
                'discrepancies': discrepancies,
            }, model_base_path, logger)
        if find_best: copy_checkpoint(model_base_path, model_best_path, logger)
        last_info = save_checkpoint(
            {
                'epoch': epoch,
                'args': deepcopy(args),
                'last_checkpoint': save_path,
            }, logger.path('info'), logger)

        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()

    logger.log('')
    logger.log('-' * 100)
    last_config_path = logger.path('log') / 'seed-{:}-last.config'.format(
        args.rand_seed)
    configure2str(arch_genotypes['last'], str(last_config_path))
    logger.log('save the last config int {:} :\n{:}'.format(
        last_config_path, arch_genotypes['last']))

    best_arch, valid_acc = arch_genotypes['best'], valid_accuracies['best']
    for key, config in arch_genotypes.items():
        if key == 'last': continue
        FLOP_ratio = config['estimated_FLOP'] / MAX_FLOP
        if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
            if valid_acc <= valid_accuracies[key]:
                best_arch, valid_acc = config, valid_accuracies[key]
    print('Best-Arch : {:}\nRatio={:}, Valid-ACC={:}'.format(
        best_arch, best_arch['estimated_FLOP'] / MAX_FLOP, valid_acc))
    best_config_path = logger.path('log') / 'seed-{:}-best.config'.format(
        args.rand_seed)
    configure2str(best_arch, str(best_config_path))
    logger.log('save the last config int {:} :\n{:}'.format(
        best_config_path, best_arch))
    logger.log('\n' + '-' * 200)
    logger.log(
        'Finish training/validation in {:} with Max-GPU-Memory of {:.2f} GB, and save final checkpoint into {:}'
        .format(convert_secs2time(epoch_time.sum, True),
                max(v for k, v in max_bytes.items()) / 1e9,
                logger.path('info')))
    logger.close()
Esempio n. 5
0
def main(args):
    assert torch.cuda.is_available(), "CUDA is not available."
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True
    torch.set_num_threads(args.workers)

    prepare_seed(args.rand_seed)
    logger = prepare_logger(args)

    train_data, valid_data, xshape, class_num = get_datasets(
        args.dataset, args.data_path, args.cutout_length
    )
    train_loader = torch.utils.data.DataLoader(
        train_data,
        batch_size=args.batch_size,
        shuffle=True,
        num_workers=args.workers,
        pin_memory=True,
    )
    valid_loader = torch.utils.data.DataLoader(
        valid_data,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.workers,
        pin_memory=True,
    )
    # get configures
    model_config = load_config(args.model_config, {"class_num": class_num}, logger)
    optim_config = load_config(args.optim_config, {"class_num": class_num}, logger)

    if args.model_source == "normal":
        base_model = obtain_model(model_config)
    elif args.model_source == "nas":
        base_model = obtain_nas_infer_model(model_config, args.extra_model_path)
    elif args.model_source == "autodl-searched":
        base_model = obtain_model(model_config, args.extra_model_path)
    else:
        raise ValueError("invalid model-source : {:}".format(args.model_source))
    flop, param = get_model_infos(base_model, xshape)
    logger.log("model ====>>>>:\n{:}".format(base_model))
    logger.log("model information : {:}".format(base_model.get_message()))
    logger.log("-" * 50)
    logger.log(
        "Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G".format(
            param, flop, flop / 1e3
        )
    )
    logger.log("-" * 50)
    logger.log("train_data : {:}".format(train_data))
    logger.log("valid_data : {:}".format(valid_data))
    optimizer, scheduler, criterion = get_optim_scheduler(
        base_model.parameters(), optim_config
    )
    logger.log("optimizer  : {:}".format(optimizer))
    logger.log("scheduler  : {:}".format(scheduler))
    logger.log("criterion  : {:}".format(criterion))

    last_info, model_base_path, model_best_path = (
        logger.path("info"),
        logger.path("model"),
        logger.path("best"),
    )
    network, criterion = torch.nn.DataParallel(base_model).cuda(), criterion.cuda()

    if last_info.exists():  # automatically resume from previous checkpoint
        logger.log(
            "=> loading checkpoint of the last-info '{:}' start".format(last_info)
        )
        last_infox = torch.load(last_info)
        start_epoch = last_infox["epoch"] + 1
        last_checkpoint_path = last_infox["last_checkpoint"]
        if not last_checkpoint_path.exists():
            logger.log(
                "Does not find {:}, try another path".format(last_checkpoint_path)
            )
            last_checkpoint_path = (
                last_info.parent
                / last_checkpoint_path.parent.name
                / last_checkpoint_path.name
            )
        checkpoint = torch.load(last_checkpoint_path)
        base_model.load_state_dict(checkpoint["base-model"])
        scheduler.load_state_dict(checkpoint["scheduler"])
        optimizer.load_state_dict(checkpoint["optimizer"])
        valid_accuracies = checkpoint["valid_accuracies"]
        max_bytes = checkpoint["max_bytes"]
        logger.log(
            "=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
                last_info, start_epoch
            )
        )
    elif args.resume is not None:
        assert Path(args.resume).exists(), "Can not find the resume file : {:}".format(
            args.resume
        )
        checkpoint = torch.load(args.resume)
        start_epoch = checkpoint["epoch"] + 1
        base_model.load_state_dict(checkpoint["base-model"])
        scheduler.load_state_dict(checkpoint["scheduler"])
        optimizer.load_state_dict(checkpoint["optimizer"])
        valid_accuracies = checkpoint["valid_accuracies"]
        max_bytes = checkpoint["max_bytes"]
        logger.log(
            "=> loading checkpoint from '{:}' start with {:}-th epoch.".format(
                args.resume, start_epoch
            )
        )
    elif args.init_model is not None:
        assert Path(
            args.init_model
        ).exists(), "Can not find the initialization file : {:}".format(args.init_model)
        checkpoint = torch.load(args.init_model)
        base_model.load_state_dict(checkpoint["base-model"])
        start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}
        logger.log("=> initialize the model from {:}".format(args.init_model))
    else:
        logger.log("=> do not find the last-info file : {:}".format(last_info))
        start_epoch, valid_accuracies, max_bytes = 0, {"best": -1}, {}

    train_func, valid_func = get_procedures(args.procedure)

    total_epoch = optim_config.epochs + optim_config.warmup
    # Main Training and Evaluation Loop
    start_time = time.time()
    epoch_time = AverageMeter()
    for epoch in range(start_epoch, total_epoch):
        scheduler.update(epoch, 0.0)
        need_time = "Time Left: {:}".format(
            convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
        )
        epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
        LRs = scheduler.get_lr()
        find_best = False
        # set-up drop-out ratio
        if hasattr(base_model, "update_drop_path"):
            base_model.update_drop_path(
                model_config.drop_path_prob * epoch / total_epoch
            )
        logger.log(
            "\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}".format(
                time_string(), epoch_str, need_time, min(LRs), max(LRs), scheduler
            )
        )

        # train for one epoch
        train_loss, train_acc1, train_acc5 = train_func(
            train_loader,
            network,
            criterion,
            scheduler,
            optimizer,
            optim_config,
            epoch_str,
            args.print_freq,
            logger,
        )
        # log the results
        logger.log(
            "***{:s}*** TRAIN [{:}] loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
                time_string(), epoch_str, train_loss, train_acc1, train_acc5
            )
        )

        # evaluate the performance
        if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
            logger.log("-" * 150)
            valid_loss, valid_acc1, valid_acc5 = valid_func(
                valid_loader,
                network,
                criterion,
                optim_config,
                epoch_str,
                args.print_freq_eval,
                logger,
            )
            valid_accuracies[epoch] = valid_acc1
            logger.log(
                "***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
                    time_string(),
                    epoch_str,
                    valid_loss,
                    valid_acc1,
                    valid_acc5,
                    valid_accuracies["best"],
                    100 - valid_accuracies["best"],
                )
            )
            if valid_acc1 > valid_accuracies["best"]:
                valid_accuracies["best"] = valid_acc1
                find_best = True
                logger.log(
                    "Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
                        epoch,
                        valid_acc1,
                        valid_acc5,
                        100 - valid_acc1,
                        100 - valid_acc5,
                        model_best_path,
                    )
                )
            num_bytes = (
                torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
            )
            logger.log(
                "[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
                    next(network.parameters()).device,
                    int(num_bytes),
                    num_bytes / 1e3,
                    num_bytes / 1e6,
                    num_bytes / 1e9,
                )
            )
            max_bytes[epoch] = num_bytes
        if epoch % 10 == 0:
            torch.cuda.empty_cache()

        # save checkpoint
        save_path = save_checkpoint(
            {
                "epoch": epoch,
                "args": deepcopy(args),
                "max_bytes": deepcopy(max_bytes),
                "FLOP": flop,
                "PARAM": param,
                "valid_accuracies": deepcopy(valid_accuracies),
                "model-config": model_config._asdict(),
                "optim-config": optim_config._asdict(),
                "base-model": base_model.state_dict(),
                "scheduler": scheduler.state_dict(),
                "optimizer": optimizer.state_dict(),
            },
            model_base_path,
            logger,
        )
        if find_best:
            copy_checkpoint(model_base_path, model_best_path, logger)
        last_info = save_checkpoint(
            {
                "epoch": epoch,
                "args": deepcopy(args),
                "last_checkpoint": save_path,
            },
            logger.path("info"),
            logger,
        )

        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()

    logger.log("\n" + "-" * 200)
    logger.log(
        "Finish training/validation in {:} with Max-GPU-Memory of {:.2f} MB, and save final checkpoint into {:}".format(
            convert_secs2time(epoch_time.sum, True),
            max(v for k, v in max_bytes.items()) / 1e6,
            logger.path("info"),
        )
    )
    logger.log("-" * 200 + "\n")
    logger.close()