train_imagenet.py

import sys
import os
import argparse
import time
import logging
from datetime import datetime

try:
    from apex import amp
    from apex.parallel import DistributedDataParallel as DDP
    from apex.parallel import convert_syncbn_model
    has_apex = True
except ImportError:
    from torch.nn.parallel import DistributedDataParallel as DDP
    has_apex = False

from timm.data import Dataset, create_loader, resolve_data_config, FastCollateMixup, mixup_target
from timm.models import create_model, resume_checkpoint

from model import *
from timm.utils import *
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.optim import create_optimizer
from timm.scheduler import create_scheduler

import torch
import torch.nn as nn
import torchvision.utils

from utils.dataset.folder2lmdb import ImageFolderLMDB

torch.backends.cudnn.benchmark = True

parser = argparse.ArgumentParser(description='Training')
# Dataset / Model parameters
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--lmdb', action='store_true', default=False,
                    help='Load lmdb dataset')
parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL',
                    help='Name of model to train (default: "countception"')
parser.add_argument(
    '--pretrained',
    action='store_true',
    default=False,
    help='Start with pretrained version of specified network (if avail)')
parser.add_argument(
    '--initial-checkpoint',
    default='',
    type=str,
    metavar='PATH',
    help='Initialize model from this checkpoint (default: none)')
parser.add_argument(
    '--resume',
    default='',
    type=str,
    metavar='PATH',
    help='Resume full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--num-classes', type=int, default=1000, metavar='N',
                    help='number of label classes (default: 1000)')
parser.add_argument(
    '--gp',
    default='avg',
    type=str,
    metavar='POOL',
    help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")')
parser.add_argument('--img-size', type=int, default=None, metavar='N',
                    help='Image patch size (default: None => model default)')
parser.add_argument(
    '--mean',
    type=float,
    nargs='+',
    default=None,
    metavar='MEAN',
    help='Override mean pixel value of dataset')
parser.add_argument(
    '--std',
    type=float,
    nargs='+',
    default=None,
    metavar='STD',
    help='Override std deviation of of dataset')
parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
                    help='Image resize interpolation type (overrides model)')
parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
                    help='input batch size for training (default: 32)')
parser.add_argument('--drop', type=float, default=0.0, metavar='DROP',
                    help='Dropout rate (default: 0.)')
# Optimizer parameters
parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                    help='Optimizer (default: "sgd"')
parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON',
                    help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='SGD momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=0.00001,
                    help='weight decay (default: 0.0001)')
# Learning rate schedule parameters
parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER',
                    help='LR scheduler (default: "step"')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                    help='learning rate (default: 0.01)')
parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
                    help='warmup learning rate (default: 0.0001)')
parser.add_argument(
    '--min-lr',
    type=float,
    default=1e-5,
    metavar='LR',
    help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--epochs', type=int, default=200, metavar='N',
                    help='number of epochs to train (default: 2)')
parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('--decay-epochs', type=int, default=30, metavar='N',
                    help='epoch interval to decay LR')
parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N',
                    help='epochs to warmup LR, if scheduler supports')
parser.add_argument(
    '--cooldown-epochs',
    type=int,
    default=10,
    metavar='N',
    help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
parser.add_argument(
    '--decay-rate',
    '--dr',
    type=float,
    default=0.1,
    metavar='RATE',
    help='LR decay rate (default: 0.1)')
# Augmentation parameters
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                    help='Color jitter factor (default: 0.4)')
parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                    help='Random erase prob (default: 0.)')
parser.add_argument('--remode', type=str, default='const',
                    help='Random erase mode (default: "const")')
parser.add_argument('--mixup', type=float, default=0.0,
                    help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument(
    '--mixup-off-epoch',
    default=0,
    type=int,
    metavar='N',
    help='turn off mixup after this epoch, disabled if 0 (default: 0)')
parser.add_argument('--smoothing', type=float, default=0.1,
                    help='label smoothing (default: 0.1)')
# Batch norm parameters (only works with gen_efficientnet based models
# currently)
parser.add_argument(
    '--bn-tf',
    action='store_true',
    default=False,
    help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
parser.add_argument('--se', type=bool, default=False,
                    help='Equiped SE module')
parser.add_argument('--activation', type=str, default="relu",
                    help='Activation function')
parser.add_argument('--bn-momentum', type=float, default=None,
                    help='BatchNorm momentum override (if not None)')
parser.add_argument('--bn-eps', type=float, default=None,
                    help='BatchNorm epsilon override (if not None)')
# Model Exponential Moving Average
parser.add_argument('--model-ema', action='store_true', default=False,
                    help='Enable tracking moving average of model weights')
parser.add_argument(
    '--model-ema-force-cpu',
    action='store_true',
    default=False,
    help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
parser.add_argument(
    '--model-ema-decay',
    type=float,
    default=0.99995,
    help='decay factor for model weights moving average (default: 0.9998)')
# Misc
parser.add_argument('--seed', type=int, default=42, metavar='S',
                    help='random seed (default: 42)')
parser.add_argument(
    '--log-interval',
    type=int,
    default=50,
    metavar='N',
    help='how many batches to wait before logging training status')
parser.add_argument(
    '--recovery-interval',
    type=int,
    default=0,
    metavar='N',
    help='how many batches to wait before writing recovery checkpoint')
parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
                    help='how many training processes to use (default: 1)')
parser.add_argument('--num-gpu', type=int, default=1,
                    help='Number of GPUS to use')
parser.add_argument(
    '--save-images',
    action='store_true',
    default=False,
    help='save images of input bathes every log interval for debugging')
parser.add_argument('--amp', action='store_true', default=False,
                    help='use NVIDIA amp for mixed precision training')
parser.add_argument('--sync-bn', action='store_true',
                    help='enabling apex sync BN.')
parser.add_argument('--no-prefetcher', action='store_true', default=False,
                    help='disable fast prefetcher')
parser.add_argument('--output', default='', type=str, metavar='PATH',
                    help='path to output folder (default: none, current dir)')
parser.add_argument(
    '--eval-metric',
    default='prec1',
    type=str,
    metavar='EVAL_METRIC',
    help='Best metric (default: "prec1"')
parser.add_argument(
    '--tta',
    type=int,
    default=0,
    metavar='N',
    help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
parser.add_argument("--local_rank", default=0, type=int)

# SGNAS
parser.add_argument('--target_flops', type=float, required=True,
                    help='Target flops to generate')
parser.add_argument('--config_path', type=str,
                    help='Config file path')


def get_logger(log_dir="./logs"):
    logger = logging.getLogger()
    for handler in logger.handlers:
        handler.close()
    logger.handlers.clear()

    log_format = "%(asctime)s | %(message)s"

    logging.basicConfig(stream=sys.stdout,
                        level=logging.INFO,
                        format=log_format,
                        datefmt="%m/%d %I:%M:%S %p")

    if log_dir:
        os.makedirs(log_dir, exist_ok=True)
        file_handler = logging.FileHandler(os.path.join(log_dir, "logger"))
        file_handler.setFormatter(logging.Formatter(log_format))

    logging.getLogger().addHandler(file_handler)


def main():
    get_logger("./")
    args = parser.parse_args()
    args.prefetcher = not args.no_prefetcher
    args.distributed = False
    if 'WORLD_SIZE' in os.environ:
        args.distributed = int(os.environ['WORLD_SIZE']) > 1
        if args.distributed and args.num_gpu > 1:
            logging.warning(
                'Using more than one GPU per process in distributed mode is not allowed. Setting num_gpu to 1.')
            args.num_gpu = 1

    args.device = 'cuda:0'
    args.world_size = 1
    args.rank = 0  # global rank
    if args.distributed:
        args.num_gpu = 1
        args.device = 'cuda:%d' % args.local_rank
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend='nccl', init_method='env://')
        args.world_size = torch.distributed.get_world_size()
        args.rank = torch.distributed.get_rank()
    assert args.rank >= 0

    if args.distributed:
        logging.info(
            'Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.' %
            (args.rank, args.world_size))
    else:
        logging.info(
            'Training with a single process on %d GPUs.' %
            args.num_gpu)
    logging.info("Exponential : {}".format(args.model_ema_decay))
    logging.info("Color Jitter : {}".format(args.color_jitter))
    logging.info("Model EMA Decay : {}".format(args.model_ema_decay))

    torch.manual_seed(args.seed + args.rank)
    model = eval(args.model)(
        config_path=args.config_path,
        target_flops=args.target_flops,
        num_classes=args.num_classes,
        bn_momentum=args.bn_momentum,
        activation=args.activation,
        se=args.se)

    if os.path.exists(args.initial_checkpoint):
        load_checkpoint(model, args.initial_checkpoint)

    if args.local_rank == 0:
        logging.info('Model %s created, param count: %d' %
                     (args.model, sum([m.numel() for m in model.parameters()])))

    data_config = resolve_data_config(
        vars(args), model=model, verbose=args.local_rank == 0)

    # optionally resume from a checkpoint
    optimizer_state = None
    resume_epoch = None
    if args.resume:
        optimizer_state, resume_epoch = resume_checkpoint(model, args.resume)

    if args.num_gpu > 1:
        if args.amp:
            logging.warning(
                'AMP does not work well with nn.DataParallel, disabling. Use distributed mode for multi-GPU AMP.')
            args.amp = False
        model = nn.DataParallel(
            model, device_ids=list(
                range(
                    args.num_gpu))).cuda()
    else:
        model.cuda()

    logging.info(args.weight_decay)
    optimizer = create_optimizer(args, model)
    if optimizer_state is not None:
        optimizer.load_state_dict(optimizer_state["optimizer"])

    use_amp = False
    if has_apex and args.amp:
        model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
        use_amp = True
    if args.local_rank == 0:
        logging.info(
            'NVIDIA APEX {}. AMP {}.'.format(
                'installed' if has_apex else 'not installed',
                'on' if use_amp else 'off'))

    model_ema = None
    if args.model_ema:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but
        # before SyncBN and DDP wrapper
        model_ema = ModelEma(
            model,
            decay=args.model_ema_decay,
            device='cpu' if args.model_ema_force_cpu else '',
            resume=args.resume)

    if args.distributed:
        if args.sync_bn:
            try:
                if has_apex:
                    model = convert_syncbn_model(model)
                else:
                    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
                        model)
                if args.local_rank == 0:
                    logging.info(
                        'Converted model to use Synchronized BatchNorm.')
            except Exception as e:
                logging.error(
                    'Failed to enable Synchronized BatchNorm. Install Apex or Torch >= 1.1')
        if has_apex:
            model = DDP(model, delay_allreduce=True)
        else:
            if args.local_rank == 0:
                logging.info(
                    "Using torch DistributedDataParallel. Install NVIDIA Apex for Apex DDP.")
            # can use device str in Torch >= 1.1
            model = DDP(model, device_ids=[args.local_rank])
        # NOTE: EMA model does not need to be wrapped by DDP

    lr_scheduler, num_epochs = create_scheduler(args, optimizer)
    start_epoch = 0
    if args.start_epoch is not None:
        # a specified start_epoch will always override the resume epoch
        start_epoch = args.start_epoch
    elif resume_epoch is not None:
        start_epoch = resume_epoch
    if start_epoch > 0:
        lr_scheduler.step(start_epoch)

    if args.local_rank == 0:
        logging.info('Scheduled epochs: {}'.format(num_epochs))

    if args.lmdb:
        train_dir = os.path.join(args.data, 'train_lmdb', 'train.lmdb')
        dataset_train = ImageFolderLMDB(train_dir, None, None)
    else:
        train_dir = os.path.join(args.data, 'train')
        dataset_train = Dataset(train_dir)

    collate_fn = None
    if args.prefetcher and args.mixup > 0:
        collate_fn = FastCollateMixup(
            args.mixup, args.smoothing, args.num_classes)

    loader_train = create_loader(
        dataset_train,
        input_size=data_config['input_size'],
        batch_size=args.batch_size,
        is_training=True,
        use_prefetcher=args.prefetcher,
        rand_erase_prob=args.reprob,
        rand_erase_mode=args.remode,
        color_jitter=args.color_jitter,
        interpolation='random',
        # FIXME cleanly resolve this? data_config['interpolation'],
        mean=data_config['mean'],
        std=data_config['std'],
        num_workers=args.workers,
        distributed=args.distributed,
        collate_fn=collate_fn,
    )

    if args.lmdb:
        eval_dir = os.path.join(args.data, 'test_lmdb', 'test.lmdb')
        dataset_eval = ImageFolderLMDB(eval_dir, None, None)
    else:
        eval_dir = os.path.join(args.data, 'val')
        dataset_eval = Dataset(eval_dir)

    loader_eval = create_loader(
        dataset_eval,
        input_size=data_config['input_size'],
        batch_size=4 * args.batch_size,
        is_training=False,
        use_prefetcher=args.prefetcher,
        interpolation=data_config['interpolation'],
        mean=data_config['mean'],
        std=data_config['std'],
        num_workers=args.workers,
        distributed=args.distributed,
    )

    if args.mixup > 0.:
        # smoothing is handled with mixup label transform
        train_loss_fn = SoftTargetCrossEntropy().cuda()
        validate_loss_fn = nn.CrossEntropyLoss().cuda()
    elif args.smoothing:
        train_loss_fn = LabelSmoothingCrossEntropy(
            smoothing=args.smoothing).cuda()
        validate_loss_fn = nn.CrossEntropyLoss().cuda()
    else:
        train_loss_fn = nn.CrossEntropyLoss().cuda()
        validate_loss_fn = train_loss_fn

    eval_metric = args.eval_metric
    best_metric = None
    best_epoch = None
    saver = None
    output_dir = ''
    if args.local_rank == 0:
        output_base = args.output if args.output else './output'
        exp_name = '-'.join([
            datetime.now().strftime("%Y%m%d-%H%M%S"),
            args.model,
            str(data_config['input_size'][-1])
        ])
        output_dir = get_outdir(output_base, 'train', exp_name)
        decreasing = True if eval_metric == 'loss' else False
        saver = CheckpointSaver(
            checkpoint_dir=output_dir,
            decreasing=decreasing)

    try:
        for epoch in range(start_epoch, num_epochs):
            if args.distributed:
                loader_train.sampler.set_epoch(epoch)

            train_metrics = train_epoch(
                epoch, model, loader_train, optimizer, train_loss_fn, args,
                lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
                use_amp=use_amp, model_ema=model_ema)

            eval_metrics = validate(model, loader_eval, validate_loss_fn, args)

            if model_ema is not None and not args.model_ema_force_cpu:
                ema_eval_metrics = validate(
                    model_ema.ema,
                    loader_eval,
                    validate_loss_fn,
                    args,
                    log_suffix=' (EMA)')
                eval_metrics = ema_eval_metrics

            if lr_scheduler is not None:
                # step LR for next epoch
                lr_scheduler.step(epoch + 1, eval_metrics[eval_metric])

            update_summary(epoch, train_metrics, eval_metrics, os.path.join(
                output_dir, 'summary.csv'), write_header=best_metric is None)

            if saver is not None:
                # save proper checkpoint with eval metric
                save_metric = eval_metrics[eval_metric]
                best_metric, best_epoch = saver.save_checkpoint(
                    model, optimizer, args,
                    epoch=epoch, model_ema=model_ema, metric=save_metric)

    except KeyboardInterrupt:
        pass
    if best_metric is not None:
        logging.info(
            '*** Best metric: {0} (epoch {1})'.format(best_metric, best_epoch))


def train_epoch(
        epoch,
        model,
        loader,
        optimizer,
        loss_fn,
        args,
        lr_scheduler=None,
        saver=None,
        output_dir='',
        use_amp=False,
        model_ema=None):

    if args.prefetcher and args.mixup > 0 and loader.mixup_enabled:
        if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
            loader.mixup_enabled = False

    batch_time_m = AverageMeter()
    data_time_m = AverageMeter()
    losses_m = AverageMeter()

    model.train()

    end = time.time()
    last_idx = len(loader) - 1
    num_updates = epoch * len(loader)
    for batch_idx, (input, target) in enumerate(loader):
        last_batch = batch_idx == last_idx
        data_time_m.update(time.time() - end)
        if not args.prefetcher:
            input = input.cuda()
            target = target.cuda()
            if args.mixup > 0.:
                lam = 1.
                if not args.mixup_off_epoch or epoch < args.mixup_off_epoch:
                    lam = np.random.beta(args.mixup, args.mixup)
                input.mul_(lam).add_(1 - lam, input.flip(0))
                target = mixup_target(
                    target, args.num_classes, lam, args.smoothing)

        output = model(input)

        loss = loss_fn(output, target)
        if not args.distributed:
            losses_m.update(loss.item(), input.size(0))

        optimizer.zero_grad()
        if use_amp:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
        else:
            loss.backward()

        optimizer.step()

        torch.cuda.synchronize()
        if model_ema is not None:
            model_ema.update(model)
        num_updates += 1

        batch_time_m.update(time.time() - end)
        if last_batch or batch_idx % args.log_interval == 0:
            lrl = [param_group['lr'] for param_group in optimizer.param_groups]
            lr = sum(lrl) / len(lrl)

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                losses_m.update(reduced_loss.item(), input.size(0))

            if args.local_rank == 0:
                logging.info(
                    'Train: {} [{:>4d}/{} ({:>3.0f}%)]  '
                    'Loss: {loss.val:>9.6f} ({loss.avg:>6.4f})  '
                    'Time: {batch_time.val:.3f}s, {rate:>7.2f}/s  '
                    '({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  '
                    'LR: {lr:.3e}  '
                    'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format(
                        epoch,
                        batch_idx,
                        len(loader),
                        100. *
                        batch_idx /
                        last_idx,
                        loss=losses_m,
                        batch_time=batch_time_m,
                        rate=input.size(0) *
                        args.world_size /
                        batch_time_m.val,
                        rate_avg=input.size(0) *
                        args.world_size /
                        batch_time_m.avg,
                        lr=lr,
                        data_time=data_time_m))

                if args.save_images and output_dir:
                    torchvision.utils.save_image(
                        input, os.path.join(
                            output_dir, 'train-batch-%d.jpg' %
                            batch_idx), padding=0, normalize=True)

        if saver is not None and args.recovery_interval and (
                last_batch or (batch_idx + 1) % args.recovery_interval == 0):
            saver.save_recovery(
                model,
                optimizer,
                args,
                epoch,
                model_ema=model_ema,
                batch_idx=batch_idx)

        if lr_scheduler is not None:
            lr_scheduler.step_update(
                num_updates=num_updates,
                metric=losses_m.avg)

        end = time.time()

    return OrderedDict([('loss', losses_m.avg)])


def validate(model, loader, loss_fn, args, log_suffix=''):
    batch_time_m = AverageMeter()
    losses_m = AverageMeter()
    prec1_m = AverageMeter()
    prec5_m = AverageMeter()

    model.eval()

    end = time.time()
    last_idx = len(loader) - 1
    with torch.no_grad():
        for batch_idx, (input, target) in enumerate(loader):
            last_batch = batch_idx == last_idx
            if not args.prefetcher:
                input = input.cuda()
                target = target.cuda()

            output = model(input)
            if isinstance(output, (tuple, list)):
                output = output[0]

            # augmentation reduction
            reduce_factor = args.tta
            if reduce_factor > 1:
                output = output.unfold(
                    0,
                    reduce_factor,
                    reduce_factor).mean(
                    dim=2)
                target = target[0:target.size(0):reduce_factor]

            loss = loss_fn(output, target)
            prec1, prec5 = accuracy(output, target, topk=(1, 5))

            if args.distributed:
                reduced_loss = reduce_tensor(loss.data, args.world_size)
                prec1 = reduce_tensor(prec1, args.world_size)
                prec5 = reduce_tensor(prec5, args.world_size)
            else:
                reduced_loss = loss.data

            torch.cuda.synchronize()

            losses_m.update(reduced_loss.item(), input.size(0))
            prec1_m.update(prec1.item(), output.size(0))
            prec5_m.update(prec5.item(), output.size(0))

            batch_time_m.update(time.time() - end)
            end = time.time()
            if args.local_rank == 0 and (
                    last_batch or batch_idx %
                    args.log_interval == 0):
                log_name = 'Test' + log_suffix
                logging.info(
                    '{0}: [{1:>4d}/{2}]  '
                    'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                    'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
                    'Prec@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
                    'Prec@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
                        log_name, batch_idx, last_idx,
                        batch_time=batch_time_m, loss=losses_m,
                        top1=prec1_m, top5=prec5_m))

    metrics = OrderedDict(
        [('loss', losses_m.avg), ('prec1', prec1_m.avg), ('prec5', prec5_m.avg)])

    return metrics


if __name__ == '__main__':
    main()