train_tpu.py

#!/usr/bin/env python3
""" ImageNet Training Script

This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
training results with some of the latest networks and training techniques. It favours canonical PyTorch
and standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speed
and training result improvements over the usual PyTorch example scripts. Repurpose as you see fit.

This script was started from an early version of the PyTorch ImageNet example
(https://github.com/pytorch/examples/tree/master/imagenet)

NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
(https://github.com/NVIDIA/apex/tree/master/examples/imagenet)

Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
"""
import torch_xla
import argparse
import time
import yaml
import os
import logging
from collections import OrderedDict
from datetime import datetime
from dataclasses import replace
from typing import Tuple

import torch
import torch.nn as nn
import torch_xla.core.xla_model as xm
#import torchvision.utils
import models.efficientnet
import models.vgg
import models.mobilenet
import models.mobilenetV3
import models.nasnetmobile

from bits import initialize_device, setup_model_and_optimizer, DeviceEnv, Monitor, Tracker,\
    TrainState, TrainServices, TrainCfg, CheckpointManager, AccuracyTopK, AvgTensor, distribute_bn
from data import create_dataset, create_transform_v2, create_loader_v2, resolve_data_config,\
    PreprocessCfg, AugCfg, MixupCfg, AugMixDataset
from timm.models import create_model, safe_model_name, convert_splitbn_model
from timm.loss import *
from timm.optim import optimizer_kwargs
from timm.scheduler import create_scheduler
from timm.utils import setup_default_logging, random_seed, get_outdir, unwrap_model
#import os
#WANDB_API_KEY = "5a0c9d2c04e141e969e5f63e2c722fe7737846c2"
#os.environ["WANDB_API_KEY"] = WANDB_API_KEY



_logger = logging.getLogger('train')


# The first arg parser parses out only the --config argument, this argument is used to
# load a yaml file containing key-values that override the defaults for the main parser below
config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
                    help='YAML config file specifying default arguments')


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')

# Dataset parameters
parser.add_argument('data_dir', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--dataset', '-d', metavar='NAME', default='',
                    help='dataset type (default: ImageFolder/ImageTar if empty)')
parser.add_argument('--train-split', metavar='NAME', default='train',
                    help='dataset train split (default: train)')
parser.add_argument('--val-split', metavar='NAME', default='validation',
                    help='dataset validation split (default: validation)')
parser.add_argument('--dataset-download', action='store_true', default=False,
                    help='Allow download of dataset for torch/ and tfds/ datasets that support it.')
parser.add_argument('--class-map', default='', type=str, metavar='FILENAME',
                    help='path to class to idx mapping file (default: "")')

# Model parameters
parser.add_argument('--model', default='resnet50', type=str, metavar='MODEL',
                    help='Name of model to train (default: "resnet50"')
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('--run_id', default='', type=str,
                    help='Resume full model and optimizer state from checkpoint (default: none)')
parser.add_argument('--no-resume-opt', action='store_true', default=False,
                    help='prevent resume of optimizer state when resuming model')
parser.add_argument('--num-classes', type=int, default=None, metavar='N',
                    help='number of label classes (Model default if None)')
parser.add_argument('--gp', default=None, type=str, metavar='POOL',
                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
parser.add_argument('--img-size', type=int, default=None, metavar='N',
                    help='Image patch size (default: None => model default)')
parser.add_argument('--input-size', default=None, nargs=3, type=int,
                    metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
parser.add_argument('--crop-pct', default=None, type=float,
                    metavar='N', help='Input image center crop percent (for validation only)')
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 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=256, metavar='N',
                    help='input batch size for training (default: 32)')
parser.add_argument('-vb', '--validation-batch-size', type=int, default=None, metavar='N',
                    help='validation batch size override (default: None)')
parser.add_argument('--channels-last', action='store_true', default=False,
                    help='Use channels_last memory layout')
parser.add_argument('--torchscript', dest='torchscript', action='store_true',
                    help='torch.jit.script the full model')
parser.add_argument('--grad-checkpointing', action='store_true', default=False,
                    help='Enable gradient checkpointing through model blocks/stages')

# Optimizer parameters
parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
                    help='Optimizer (default: "sgd"')
parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
                    help='Optimizer Epsilon (default: None, use opt default)')
parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                    help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='Optimizer momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=0.00002,
                    help='weight decay (default: 0.0001)')
parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                    help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--clip-mode', type=str, default='norm',
                    help='Gradient clipping mode. One of ("norm", "value", "agc")')
parser.add_argument('--layer-decay', type=float, default=None,
                    help='layer-wise learning rate decay (default: None)')

# Learning rate schedule parameters
parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                    help='LR scheduler (default: "cosine"')
parser.add_argument('--lr', type=float, default=None, metavar='LR',
                    help='learning rate (default: None => --lr-base')
parser.add_argument('--lr-base', type=float, default=0.1, metavar='LR',
                    help='base learning rate: lr = lr_base * global_batch_size / base_size')
parser.add_argument('--lr-base-size', type=int, default=256, metavar='DIV',
                    help='base learning rate batch size (divisor, default: 256).')
parser.add_argument('--lr-base-scale', type=str, default='', metavar='SCALE',
                    help='base learning rate vs batch_size scaling ("linear", "sqrt", based on opt if empty)')
parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                    help='learning rate noise on/off epoch percentages')
parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                    help='learning rate noise limit percent (default: 0.67)')
parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                    help='learning rate noise std-dev (default: 1.0)')
parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
                    help='learning rate cycle len multiplier (default: 1.0)')
parser.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT',
                    help='amount to decay each learning rate cycle (default: 0.5)')
parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
                    help='learning rate cycle limit, cycles enabled if > 1')
parser.add_argument('--lr-k-decay', type=float, default=1.0,
                    help='learning rate k-decay for cosine/poly (default: 1.0)')
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-6, metavar='LR',
                    help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
parser.add_argument('--epochs', type=int, default=300, metavar='N',
                    help='number of epochs to train (default: 300)')
parser.add_argument('--epoch-repeats', type=float, default=0., metavar='N',
                    help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).')
parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('--decay-epochs', type=float, default=100, 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('--patience-epochs', type=int, default=10, metavar='N',
                    help='patience epochs for Plateau LR scheduler (default: 10')
parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                    help='LR decay rate (default: 0.1)')

# Augmentation & regularization parameters
parser.add_argument('--no-aug', action='store_true', default=False,
                    help='Disable all training augmentation, override other train aug args')
parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
                    help='Random resize scale (default: 0.08 1.0)')
parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
                    help='Random resize aspect ratio (default: 0.75 1.33)')
parser.add_argument('--hflip', type=float, default=0.5,
                    help='Horizontal flip training aug probability')
parser.add_argument('--vflip', type=float, default=0.,
                    help='Vertical flip training aug probability')
parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
                    help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default="original", metavar='NAME',
                    help='Use AutoAugment policy. "v0" or "original". (default: None)'),
parser.add_argument('--aug-splits', type=int, default=0,
                    help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
parser.add_argument('--jsd-loss', action='store_true', default=False,
                    help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
parser.add_argument('--bce-loss', action='store_true', default=False,
                    help='Enable BCE loss w/ Mixup/CutMix use.')
parser.add_argument('--bce-target-thresh', type=float, default=None,
                    help='Threshold for binarizing softened BCE targets (default: None, disabled)')
parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
                    help='Random erase prob (default: 0.)')
parser.add_argument('--remode', type=str, default='pixel',
                    help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,
                    help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
                    help='Do not random erase first (clean) augmentation split')
parser.add_argument('--mixup', type=float, default=0.0,
                    help='mixup alpha, mixup enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix', type=float, default=0.0,
                    help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
                    help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup-prob', type=float, default=1.0,
                    help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
                    help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup-mode', type=str, default='batch',
                    help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
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)')
parser.add_argument('--train-interpolation', type=str, default='random',
                    help='Training interpolation (random, bilinear, bicubic default: "random")')
parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                    help='Dropout rate (default: 0.)')
parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
                    help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
                    help='Drop path rate (default: None)')
parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
                    help='Drop block rate (default: None)')

# Batch norm parameters (only works with gen_efficientnet based models currently)
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)')
parser.add_argument('--sync-bn', action='store_true',
                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
parser.add_argument('--dist-bn', type=str, default='reduce',
                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
parser.add_argument('--split-bn', action='store_true',
                    help='Enable separate BN layers per augmentation split.')

# 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-decay', type=float, default=0.9998,
                    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('--checkpoint-hist', type=int, default=10, metavar='N',
                    help='number of checkpoints to keep (default: 10)')
parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
                    help='how many training processes to use (default: 1)')
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 Apex AMP or Native AMP for mixed precision training')

parser.add_argument('--pin-mem', action='store_true', default=True,
                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--output', default='', type=str, metavar='PATH',
                    help='path to output folder (default: none, current dir)')
parser.add_argument('--experiment', default='', type=str, metavar='NAME',
                    help='name of train experiment, name of sub-folder for output')
parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
                    help='Best metric (default: "top1"')
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)
parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
                    help='use the multi-epochs-loader to save time at the beginning of every epoch')
parser.add_argument('--force-cpu', action='store_true', default=False,
                    help='Force CPU to be used even if HW accelerator exists.')
parser.add_argument('--log-wandb', action='store_true', default=False,
                    help='log training and validation metrics to wandb')
parser.add_argument('--update_freq', default=1, type=int,
                        help='gradient accumulation steps')


def _parse_args():
    # Do we have a config file to parse?
    args_config, remaining = config_parser.parse_known_args()
    if args_config.config:
        with open(args_config.config, 'r') as f:
            cfg = yaml.safe_load(f)
            parser.set_defaults(**cfg)

    # The main arg parser parses the rest of the args, the usual
    # defaults will have been overridden if config file specified.
    args = parser.parse_args(remaining)

    # Cache the args as a text string to save them in the output dir later
    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
    return args, args_text


def main():
    setup_default_logging()
    args, args_text = _parse_args()

    dev_env = initialize_device(force_cpu=args.force_cpu, amp=args.amp, channels_last=args.channels_last)
    if dev_env.distributed:
        _logger.info('Training in distributed mode with multiple processes, 1 device per process. Process %d, total %d.'
                     % (dev_env.global_rank, dev_env.world_size))
    else:
        _logger.info('Training with a single process on 1 device.')

    random_seed(args.seed, 0)  # Set all random seeds the same for model/state init (mandatory for XLA)

    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    assert args.aug_splits == 0 or args.aug_splits > 1, 'A split of 1 makes no sense'

    train_state = setup_train_task(args, dev_env, mixup_active)
    train_cfg = train_state.train_cfg

    # Set random seeds across ranks differently for train
    # FIXME perhaps keep the same and just set diff seeds for dataloader worker process? what about TFDS?
    random_seed(args.seed, dev_env.global_rank)

    data_config, loader_eval, loader_train = setup_data(
        args,
        unwrap_model(train_state.model).default_cfg,
        dev_env,
        mixup_active)

    # setup checkpoint manager
    eval_metric = args.eval_metric
    best_metric = None
    best_epoch = None
    checkpoint_manager = None
    output_dir = None
    if dev_env.primary:
        if args.experiment:
            exp_name = args.experiment
        else:
            exp_name = '-'.join([
                datetime.now().strftime("%Y%m%d-%H%M%S"),
                safe_model_name(args.model),
                str(data_config['input_size'][-1])
            ])
        output_dir = get_outdir(args.output if args.output else './output/train', exp_name)
        checkpoint_manager = CheckpointManager(
            hparams=vars(args),
            checkpoint_dir=output_dir,
            recovery_dir=output_dir,
            metric_name=eval_metric,
            metric_decreasing=True if eval_metric == 'loss' else False,
            max_history=args.checkpoint_hist)
        with open(os.path.join(output_dir, 'args.yaml'), 'w') as f:
            f.write(args_text)

    services = TrainServices(
        monitor=Monitor(
            output_dir=output_dir,
            logger=_logger,
            hparams=vars(args),
            output_enabled=dev_env.primary,
            experiment_name=args.experiment,
            resume=args.resume != '',
            wand_experiment_id=args.run_id
        ),
        checkpoint=checkpoint_manager,
    )

    try:
        for epoch in range(train_state.epoch, train_cfg.num_epochs):
            if dev_env.distributed and hasattr(loader_train.sampler, 'set_epoch'):
                loader_train.sampler.set_epoch(epoch)
            if args.mixup_off_epoch and epoch >= args.mixup_off_epoch:
                if loader_train.mixup_enabled:
                    loader_train.mixup_enabled = False

            train_metrics = train_one_epoch(
                state=train_state,
                services=services,
                loader=loader_train,
                dev_env=dev_env,
                update_freq=args.update_freq
            )

            if dev_env.distributed and args.dist_bn in ('broadcast', 'reduce'):
                if dev_env.primary:
                    _logger.info("Distributing BatchNorm running means and vars")
                distribute_bn(train_state.model, args.dist_bn == 'reduce', dev_env)

            eval_metrics = evaluate(
                train_state.model,
                train_state.eval_loss,
                loader_eval,
                services.monitor,
                dev_env)

            if train_state.model_ema is not None:
                if dev_env.distributed and args.dist_bn in ('broadcast', 'reduce'):
                    distribute_bn(train_state.model_ema, args.dist_bn == 'reduce', dev_env)

                ema_eval_metrics = evaluate(
                    train_state.model_ema.module,
                    train_state.eval_loss,
                    loader_eval,
                    services.monitor,
                    dev_env,
                    phase_suffix='EMA')
                eval_metrics = ema_eval_metrics

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

            if services.monitor is not None:
                services.monitor.write_summary(
                    index=epoch,
                    results=dict(train=train_metrics, eval=eval_metrics))

            if checkpoint_manager is not None:
                # save proper checkpoint with eval metric
                best_checkpoint = checkpoint_manager.save_checkpoint(train_state, eval_metrics)
                best_metric, best_epoch = best_checkpoint.sort_key, best_checkpoint.epoch

            train_state = replace(train_state, epoch=epoch + 1)

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


def setup_train_task(args, dev_env: DeviceEnv, mixup_active: bool):

    model = create_model(
        args.model,
        pretrained=args.pretrained,
        num_classes=args.num_classes,
        drop_rate=args.drop,
        drop_connect_rate=args.drop_connect,  # DEPRECATED, use drop_path
        drop_path_rate=args.drop_path,
        drop_block_rate=args.drop_block,
        global_pool=args.gp,
        bn_momentum=args.bn_momentum,
        bn_eps=args.bn_eps,
        scriptable=args.torchscript,
        checkpoint_path=args.initial_checkpoint)
    if args.num_classes is None:
        assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.'
        args.num_classes = model.num_classes  # FIXME handle model default vs config num_classes more elegantly

    if args.grad_checkpointing:
        model.set_grad_checkpointing(enable=True)

    if dev_env.primary:
        _logger.info(
            f'Model {safe_model_name(args.model)} created, param count:{sum([m.numel() for m in model.parameters()])}')

    # enable split bn (separate bn stats per batch-portion)
    if args.split_bn:
        assert args.aug_splits > 1
        model = convert_splitbn_model(model, max(args.aug_splits, 2))

    if args.lr is None:
        global_batch_size = args.batch_size * dev_env.world_size
        batch_ratio = global_batch_size / args.lr_base_size
        if not args.lr_base_scale:
            on = args.opt.lower()
            args.lr_base_scale = 'sqrt' if any([o in on for o in ('adam', 'lamb', 'adabelief')]) else 'linear'
        if args.lr_base_scale == 'sqrt':
            batch_ratio = batch_ratio ** 0.5
        args.lr = args.lr_base * batch_ratio
        if dev_env.primary:
            _logger.info(f'Learning rate ({args.lr}) calculated from base learning rate ({args.lr_base}) '
                         f'and global batch size ({global_batch_size}) with {args.lr_base_scale} scaling.')

    train_state = setup_model_and_optimizer(
        dev_env=dev_env,
        model=model,
        optimizer=args.opt,
        optimizer_cfg=optimizer_kwargs(cfg=args),
        clip_fn=args.clip_mode if args.clip_grad is not None else None,
        clip_value=args.clip_grad,
        model_ema=args.model_ema,
        model_ema_decay=args.model_ema_decay,
        resume_path=args.resume,
        resume_opt=not args.no_resume_opt,
        use_syncbn=args.sync_bn,
    )

    # setup learning rate schedule and starting epoch
    # FIXME move into updater?
    lr_scheduler, num_epochs = create_scheduler(args, train_state.updater.optimizer)
    if lr_scheduler is not None and train_state.epoch > 0:
        lr_scheduler.step(train_state.epoch)

    # setup loss function
    if args.jsd_loss:
        assert args.aug_splits > 1  # JSD only valid with aug splits set
        train_loss_fn = JsdCrossEntropy(num_splits=args.aug_splits, smoothing=args.smoothing)
    elif mixup_active:
        # smoothing is handled with mixup target transform
        if args.bce_loss:
            train_loss_fn = BinaryCrossEntropy(target_threshold=args.bce_target_thresh)
        else:
            train_loss_fn = SoftTargetCrossEntropy()
    elif args.smoothing:
        if args.bce_loss:
            train_loss_fn = BinaryCrossEntropy(smoothing=args.smoothing, target_threshold=args.bce_target_thresh)
        else:
            train_loss_fn = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        train_loss_fn = nn.CrossEntropyLoss()
    eval_loss_fn = nn.CrossEntropyLoss()
    dev_env.to_device(train_loss_fn, eval_loss_fn)

    if dev_env.primary:
        _logger.info('Scheduled epochs: {}'.format(num_epochs))

    train_cfg = TrainCfg(
        num_epochs=num_epochs,
        log_interval=args.log_interval,
        recovery_interval=args.recovery_interval,
    )

    train_state = replace(
        train_state,
        lr_scheduler=lr_scheduler,
        train_loss=train_loss_fn,
        eval_loss=eval_loss_fn,
        train_cfg=train_cfg,
    )

    return train_state


def setup_data(args, default_cfg, dev_env: DeviceEnv, mixup_active: bool):
    data_config = resolve_data_config(vars(args), default_cfg=default_cfg, verbose=dev_env.primary)

    # create the train and eval datasets
    dataset_train = create_dataset(
        name=args.dataset, root=args.data_dir, split=args.train_split, is_training=True,
        class_map=args.class_map,
        download=args.dataset_download,
        batch_size=args.batch_size,
        repeats=args.epoch_repeats)

    dataset_eval = create_dataset(
        name=args.dataset, root=args.data_dir, split=args.val_split, is_training=False,
        class_map=args.class_map,
        download=args.dataset_download,
        batch_size=args.batch_size)

    # setup mixup / cutmix
    mixup_cfg = None
    if mixup_active:
        mixup_cfg = MixupCfg(
            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
            mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
            label_smoothing=args.smoothing, num_classes=args.num_classes)

    # wrap dataset in AugMix helper
    if args.aug_splits > 1:
        dataset_train = AugMixDataset(dataset_train, num_splits=args.aug_splits)

    # create data loaders w/ augmentation pipeiine
    train_interpolation = args.train_interpolation
    if args.no_aug or not train_interpolation:
        train_interpolation = data_config['interpolation']

    if args.no_aug:
        train_aug_cfg = None
    else:
        train_aug_cfg = AugCfg(
            re_prob=args.reprob,
            re_mode=args.remode,
            re_count=args.recount,
            ratio_range=args.ratio,
            scale_range=args.scale,
            hflip_prob=args.hflip,
            vflip_prob=args.vflip,
            color_jitter=args.color_jitter,
            auto_augment=args.aa,
            num_aug_splits=args.aug_splits,
        )

    train_pp_cfg = PreprocessCfg(
        input_size=data_config['input_size'],
        interpolation=train_interpolation,
        crop_pct=data_config['crop_pct'],
        mean=data_config['mean'],
        std=data_config['std'],
        aug=train_aug_cfg,
    )

    # if using PyTorch XLA and RandomErasing is enabled, we must normalize and do RE in transforms on CPU
    normalize_in_transform = dev_env.type_xla and args.reprob > 0
    loader_train = create_loader_v2(
        dataset_train,
        batch_size=args.batch_size,
        is_training=True,
        pp_cfg=train_pp_cfg,
        mix_cfg=mixup_cfg,
        normalize_in_transform=normalize_in_transform,
        num_workers=args.workers,
        pin_memory=args.pin_mem,
        use_multi_epochs_loader=args.use_multi_epochs_loader
    )

    eval_pp_cfg = PreprocessCfg(
        input_size=data_config['input_size'],
        interpolation=data_config['interpolation'],
        crop_pct=data_config['crop_pct'],
        mean=data_config['mean'],
        std=data_config['std'],
    )

    eval_workers = args.workers
    if 'tfds' in args.dataset or 'wds' in args.dataset:
        # FIXME reduces validation padding issues when using TFDS w/ workers and distributed training
        eval_workers = min(2, args.workers)

    loader_eval = create_loader_v2(
        dataset_eval,
        batch_size=args.validation_batch_size or args.batch_size,
        is_training=False,
        pp_cfg=eval_pp_cfg,
        normalize_in_transform=normalize_in_transform,
        num_workers=eval_workers,
        pin_memory=args.pin_mem,
    )
    return data_config, loader_eval, loader_train


def train_one_epoch(
        state: TrainState,
        services: TrainServices,
        loader,
        dev_env: DeviceEnv,
        update_freq: int = 1
):
    tracker = Tracker()
    loss_meter = AvgTensor()  # FIXME move loss meter into task specific TaskMetric

    state.model.train()
    state.updater.reset()  # zero-grad

    step_end_idx = len(loader) - 1
    tracker.mark_iter()
    for step_idx, (sample, target) in enumerate(loader):
        tracker.mark_iter_data_end()

        # FIXME move forward + loss into model 'task' wrapper
        with dev_env.autocast():
            output = state.model(sample)
            loss = state.train_loss(output, target)
        loss /= update_freq
        state.updater.apply(loss, accumulate=(step_idx+1) % update_freq != 0)

        tracker.mark_iter_step_end()

        state.updater.after_step(
            after_train_step,
            state,
            services,
            dev_env,
            step_idx,
            step_end_idx,
            tracker,
            loss_meter,
            (output, target, loss),
        )

        tracker.mark_iter()

        if update_freq > 1 and len(sample) > 1:
            xm.mark_step()
        # end for

    if hasattr(state.updater.optimizer, 'sync_lookahead'):
        state.updater.optimizer.sync_lookahead()

    return OrderedDict([('loss', loss_meter.compute().item())])


def after_train_step(
        state: TrainState,
        services: TrainServices,
        dev_env: DeviceEnv,
        step_idx: int,
        step_end_idx: int,
        tracker: Tracker,
        loss_meter: AvgTensor,
        tensors: Tuple[torch.Tensor, ...],
):
    """
    After the core loss / backward / gradient apply step, we perform all non-gradient related
    activities here including updating meters, metrics, performing logging, and writing checkpoints.

    Many / most of these operations require tensors to be moved to CPU, they shoud not be done
    every step and for XLA use they should be done via the optimizer step_closure. This function includes
    everything that should be executed within the step closure.

    Args:
        state:
        services:
        dev_env:
        step_idx:
        step_end_idx:
        tracker:
        loss_meter:
        tensors:

    Returns:

    """
    last_step = step_idx == step_end_idx

    with torch.no_grad():
        output, target, loss = tensors
        loss_meter.update(loss, output.shape[0])

        if state.model_ema is not None:
            # FIXME should ema update be included here or in train / updater step? does it matter?
            state.model_ema.update(state.model)

        state = replace(state, step_count_global=state.step_count_global + 1)
        cfg = state.train_cfg

        if services.monitor is not None and last_step or (step_idx + 1) % cfg.log_interval == 0:
            global_batch_size = dev_env.world_size * output.shape[0]
            loss_avg = loss_meter.compute()
            if services.monitor is not None:
                lr_avg = state.updater.get_average_lr()
                services.monitor.log_step(
                    'Train',
                    step_idx=step_idx,
                    step_end_idx=step_end_idx,
                    epoch=state.epoch,
                    loss=loss_avg.item(),
                    rate=(tracker.get_last_iter_rate(global_batch_size), tracker.get_avg_iter_rate(global_batch_size)),
                    lr=lr_avg,
                )

        if services.checkpoint is not None and cfg.recovery_interval and (
                last_step or (step_idx + 1) % cfg.recovery_interval == 0):
            services.checkpoint.save_recovery(state)

        if state.lr_scheduler is not None:
            # FIXME perform scheduler update here or via updater after_step call?
            state.lr_scheduler.step_update(num_updates=state.step_count_global)


def evaluate(
        model: nn.Module,
        loss_fn: nn.Module,
        loader,
        logger: Monitor,
        dev_env: DeviceEnv,
        phase_suffix: str = '',
        log_interval: int = 10,
):

    tracker = Tracker()
    losses_m = AvgTensor()
    accuracy_m = AccuracyTopK()  # FIXME move loss and accuracy modules into task specific TaskMetric obj

    model.eval()

    end_idx = len(loader) - 1
    tracker.mark_iter()
    with torch.no_grad():
        for step_idx, (sample, target) in enumerate(loader):
            tracker.mark_iter_data_end()
            last_step = step_idx == end_idx

            with dev_env.autocast():
                output = model(sample)
                if isinstance(output, (tuple, list)):
                    output = output[0]
                loss = loss_fn(output, target)

            # FIXME, explictly marking step for XLA use since I'm not using the parallel xm loader
            # need to investigate whether parallel loader wrapper is helpful on tpu-vm or only use for 2-vm setup.
            if dev_env.type_xla:
                dev_env.mark_step()
            elif dev_env.type_cuda:
                dev_env.synchronize()
            # FIXME uncommenting this fixes race btw model `output` / `loss` and loss_m / accuracy_m meter input
            # for PyTorch XLA GPU use.
            # This issue does not exist for normal PyTorch w/ GPU (CUDA) or PyTorch XLA w/ TPU.
            # loss.item()
            tracker.mark_iter_step_end()

            losses_m.update(loss, output.size(0))
            accuracy_m.update(output, target)
            if last_step or step_idx % log_interval == 0:
                top1, top5 = accuracy_m.compute().values()
                loss_avg = losses_m.compute()
                logger.log_step(
                    'Eval',
                    step_idx=step_idx,
                    step_end_idx=end_idx,
                    loss=loss_avg.item(),
                    top1=top1.item(),
                    top5=top5.item(),
                    phase_suffix=phase_suffix,
                )
            tracker.mark_iter()

    top1, top5 = accuracy_m.compute().values()
    results = OrderedDict([('loss', losses_m.compute().item()), ('top1', top1.item()), ('top5', top5.item())])
    return results


def _mp_entry(*args):
    main()


if __name__ == '__main__':
    main()