xautodl/exps/NAS-Bench-201/functions.py

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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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import time, torch
from procedures import prepare_seed, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from config_utils import dict2config
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net


__all__ = ["evaluate_for_seed", "pure_evaluate"]


def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
    data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
    losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
    latencies = []
    network.eval()
    with torch.no_grad():
        end = time.time()
        for i, (inputs, targets) in enumerate(xloader):
            targets = targets.cuda(non_blocking=True)
            inputs = inputs.cuda(non_blocking=True)
            data_time.update(time.time() - end)
            # forward
            features, logits = network(inputs)
            loss = criterion(logits, targets)
            batch_time.update(time.time() - end)
            if batch is None or batch == inputs.size(0):
                batch = inputs.size(0)
                latencies.append(batch_time.val - data_time.val)
            # record loss and accuracy
            prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
            losses.update(loss.item(), inputs.size(0))
            top1.update(prec1.item(), inputs.size(0))
            top5.update(prec5.item(), inputs.size(0))
            end = time.time()
    if len(latencies) > 2:
        latencies = latencies[1:]
    return losses.avg, top1.avg, top5.avg, latencies


def procedure(xloader, network, criterion, scheduler, optimizer, mode):
    losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
    if mode == "train":
        network.train()
    elif mode == "valid":
        network.eval()
    else:
        raise ValueError("The mode is not right : {:}".format(mode))

    data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
    for i, (inputs, targets) in enumerate(xloader):
        if mode == "train":
            scheduler.update(None, 1.0 * i / len(xloader))

        targets = targets.cuda(non_blocking=True)
        if mode == "train":
            optimizer.zero_grad()
        # forward
        features, logits = network(inputs)
        loss = criterion(logits, targets)
        # backward
        if mode == "train":
            loss.backward()
            optimizer.step()
        # record loss and accuracy
        prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
        losses.update(loss.item(), inputs.size(0))
        top1.update(prec1.item(), inputs.size(0))
        top5.update(prec5.item(), inputs.size(0))
        # count time
        batch_time.update(time.time() - end)
        end = time.time()
    return losses.avg, top1.avg, top5.avg, batch_time.sum


def evaluate_for_seed(
    arch_config, config, arch, train_loader, valid_loaders, seed, logger
):

    prepare_seed(seed)  # random seed
    net = get_cell_based_tiny_net(
        dict2config(
            {
                "name": "infer.tiny",
                "C": arch_config["channel"],
                "N": arch_config["num_cells"],
                "genotype": arch,
                "num_classes": config.class_num,
            },
            None,
        )
    )
    # net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
    flop, param = get_model_infos(net, config.xshape)
    logger.log("Network : {:}".format(net.get_message()), False)
    logger.log(
        "{:} Seed-------------------------- {:} --------------------------".format(
            time_string(), seed
        )
    )
    logger.log("FLOP = {:} MB, Param = {:} MB".format(flop, param))
    # train and valid
    optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), config)
    network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
    # start training
    start_time, epoch_time, total_epoch = (
        time.time(),
        AverageMeter(),
        config.epochs + config.warmup,
    )
    (
        train_losses,
        train_acc1es,
        train_acc5es,
        valid_losses,
        valid_acc1es,
        valid_acc5es,
    ) = ({}, {}, {}, {}, {}, {})
    train_times, valid_times = {}, {}
    for epoch in range(total_epoch):
        scheduler.update(epoch, 0.0)

        train_loss, train_acc1, train_acc5, train_tm = procedure(
            train_loader, network, criterion, scheduler, optimizer, "train"
        )
        train_losses[epoch] = train_loss
        train_acc1es[epoch] = train_acc1
        train_acc5es[epoch] = train_acc5
        train_times[epoch] = train_tm
        with torch.no_grad():
            for key, xloder in valid_loaders.items():
                valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(
                    xloder, network, criterion, None, None, "valid"
                )
                valid_losses["{:}@{:}".format(key, epoch)] = valid_loss
                valid_acc1es["{:}@{:}".format(key, epoch)] = valid_acc1
                valid_acc5es["{:}@{:}".format(key, epoch)] = valid_acc5
                valid_times["{:}@{:}".format(key, epoch)] = valid_tm

        # measure elapsed time
        epoch_time.update(time.time() - start_time)
        start_time = time.time()
        need_time = "Time Left: {:}".format(
            convert_secs2time(epoch_time.avg * (total_epoch - epoch - 1), True)
        )
        logger.log(
            "{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%]".format(
                time_string(),
                need_time,
                epoch,
                total_epoch,
                train_loss,
                train_acc1,
                train_acc5,
                valid_loss,
                valid_acc1,
                valid_acc5,
            )
        )
    info_seed = {
        "flop": flop,
        "param": param,
        "channel": arch_config["channel"],
        "num_cells": arch_config["num_cells"],
        "config": config._asdict(),
        "total_epoch": total_epoch,
        "train_losses": train_losses,
        "train_acc1es": train_acc1es,
        "train_acc5es": train_acc5es,
        "train_times": train_times,
        "valid_losses": valid_losses,
        "valid_acc1es": valid_acc1es,
        "valid_acc5es": valid_acc5es,
        "valid_times": valid_times,
        "net_state_dict": net.state_dict(),
        "net_string": "{:}".format(net),
        "finish-train": True,
    }
    return info_seed