main_ema.py

import os
import time
import argparse
import datetime
import numpy as np

import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.cuda.amp import autocast, GradScaler

from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter, ModelEma

from config import get_config
from models import build_model
from data import build_loader
from lr_scheduler import build_scheduler
from optimizer import build_optimizer
from logger import create_logger
from utils_ema import load_checkpoint, save_checkpoint, get_grad_norm, auto_resume_helper, reduce_tensor, load_pretrained, save_checkpoint_ema, save_checkpoint_ema_new

import warnings
warnings.filterwarnings('ignore')

def parse_option():
    parser = argparse.ArgumentParser('FLatten Transformer training and evaluation script', add_help=False)
    parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', )
    parser.add_argument(
        "--opts",
        help="Modify config options by adding 'KEY VALUE' pairs. ",
        default=None,
        nargs='+',
    )

    # easy config modification
    parser.add_argument('--batch-size', type=int, help="batch size for single GPU")
    parser.add_argument('--data-path', type=str, help='path to dataset')
    parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
    parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'],
                        help='no: no cache, '
                             'full: cache all data, '
                             'part: sharding the dataset into nonoverlapping pieces and only cache one piece')
    parser.add_argument('--resume', help='resume from checkpoint')
    parser.add_argument('--use-checkpoint', action='store_true',
                        help="whether to use gradient checkpointing to save memory")
    parser.add_argument('--amp', action='store_true', default=False)
    parser.add_argument('--output', default='output', type=str, metavar='PATH',
                        help='root of output folder, the full path is <output>/<model_name>/<tag> (default: output)')
    parser.add_argument('--tag', help='tag of experiment')
    parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
    parser.add_argument('--throughput', action='store_true', help='Test throughput only')
    parser.add_argument('--pretrained', type=str, help='Finetune 384 initial checkpoint.', default='')
    parser.add_argument('--find-unused-params', action='store_true', default=False)

    # Model Exponential Moving Average
    parser.add_argument('--model-ema', action='store_true', default=True,
                        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=0.9999,
                        help='decay factor for model weights moving average (default: 0.9998)')
    
    args, unparsed = parser.parse_known_args()

    config = get_config(args)

    return args, config


def main():
    os.environ["NCCL_BLOCKING_WAIT"] = "1"
    args, config = parse_option()

    rank = int(os.environ["RANK"])
    world_size = int(os.environ['WORLD_SIZE'])
    local_rank = int(os.environ['LOCAL_RANK'])
    torch.cuda.set_device(local_rank)
    dist.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)

    seed = config.SEED + dist.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    cudnn.enabled = True
    cudnn.benchmark = True

    # linear scale the learning rate according to total batch size, may not be optimal
    linear_scaled_lr = config.TRAIN.BASE_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
    linear_scaled_warmup_lr = config.TRAIN.WARMUP_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
    linear_scaled_min_lr = config.TRAIN.MIN_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
    
    config.defrost()
    config.TRAIN.BASE_LR = linear_scaled_lr
    config.TRAIN.WARMUP_LR = linear_scaled_warmup_lr
    config.TRAIN.MIN_LR = linear_scaled_min_lr
    config.LOCAL_RANK = local_rank
    config.freeze()

    os.makedirs(config.OUTPUT, exist_ok=True)
    logger = create_logger(output_dir=config.OUTPUT, dist_rank=dist.get_rank(), name=f"{config.MODEL.NAME}")

    if dist.get_rank() == 0:
        path = os.path.join(config.OUTPUT, "config.json")
        with open(path, "w") as f:
            f.write(config.dump())
        logger.info(f"Full config saved to {path}")

    # print config
    logger.info(config.dump())
    
    _, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(config)

    logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
    model = build_model(config)
    model.cuda()
    logger.info(str(model))

    optimizer = build_optimizer(config, model)

    model = nn.parallel.DistributedDataParallel(model, device_ids=[local_rank], broadcast_buffers=True, find_unused_parameters=args.find_unused_params)
    model_without_ddp = model.module
    
    lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
    total_epochs = config.TRAIN.EPOCHS + config.TRAIN.COOLDOWN_EPOCHS

    if config.AUG.MIXUP > 0.:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif config.MODEL.LABEL_SMOOTHING > 0.:
        criterion = LabelSmoothingCrossEntropy(smoothing=config.MODEL.LABEL_SMOOTHING)
    else:
        criterion = nn.CrossEntropyLoss()

    max_accuracy = 0.0
    max_accuracy_e = 0.0

    if args.pretrained != '':
        load_pretrained(args.pretrained, model_without_ddp, logger)

    if config.TRAIN.AUTO_RESUME:
        resume_file = auto_resume_helper(config.OUTPUT)
        if resume_file:
            if config.MODEL.RESUME:
                logger.warning(f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}")
            config.defrost()
            config.MODEL.RESUME = resume_file
            config.freeze()
            logger.info(f'auto resuming from {resume_file}')
        else:
            logger.info(f'no checkpoint found in {config.OUTPUT}, ignoring auto resume')

    if config.MODEL.RESUME:
        max_accuracy, max_accuracy_e = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler, logger)
        acc1, acc5, loss = validate(config, data_loader_val, model, logger)
        max_accuracy = max(max_accuracy, acc1)
        torch.cuda.empty_cache()
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%")
        if config.EVAL_MODE:
            return

    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=config.MODEL.RESUME)
        acc1_e, acc5_e, loss_e = validate(config, data_loader_val, model_ema.ema, logger)
        torch.cuda.empty_cache()
        logger.info(f"Accuracy of the ema network on the {len(dataset_val)} test images: {acc1_e:.1f}%")
        
    if config.THROUGHPUT_MODE:
        throughput(data_loader_val, model, logger)
        return

    logger.info("Start training")
    start_time = time.time()
    for epoch in range(config.TRAIN.START_EPOCH, total_epochs):
        data_loader_train.sampler.set_epoch(epoch)

        train_one_epoch(config, model, model_ema, criterion, data_loader_train, optimizer, epoch, mixup_fn, lr_scheduler, logger, total_epochs)
        acc1, acc5, loss = validate(config, data_loader_val, model, logger)
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%")
        if model_ema is not None and not args.model_ema_force_cpu:
            acc1_e, acc5_e, loss_e = validate(config, data_loader_val, model_ema.ema, logger)
            logger.info(f"Accuracy of the ema network on the {len(dataset_val)} test images: {acc1_e:.1f}%")
        else:
            acc1_e, acc5_e, loss_e = 0, 0, 0

        if dist.get_rank() == 0 and ((epoch + 1) % config.SAVE_FREQ == 0 or (epoch + 1) == (total_epochs)):
            save_checkpoint_ema_new(config, epoch + 1, model_without_ddp, model_ema, max(max_accuracy, acc1), max(max_accuracy_e, acc1_e), optimizer, lr_scheduler, logger)

        if dist.get_rank() == 0 and ((epoch + 1) % config.SAVE_FREQ == 0 or (epoch + 1) == (total_epochs)) and acc1 >= max_accuracy:
            save_checkpoint_ema_new(config, epoch + 1, model_without_ddp, model_ema, max(max_accuracy, acc1), max(max_accuracy_e, acc1_e), optimizer, lr_scheduler, logger, name='max_acc')
        max_accuracy = max(max_accuracy, acc1)
        logger.info(f'Max accuracy: {max_accuracy:.2f}%')

        if model_ema is not None and not args.model_ema_force_cpu:
            if dist.get_rank() == 0 and ((epoch + 1) % config.SAVE_FREQ == 0 or (epoch + 1) == (total_epochs)) and acc1_e >= max_accuracy_e:
                save_checkpoint_ema_new(config, epoch + 1, model_without_ddp, model_ema, max(max_accuracy, acc1), max(max_accuracy_e, acc1_e), optimizer, lr_scheduler, logger, name='max_ema_acc')
            max_accuracy_e = max(max_accuracy_e, acc1_e)
            logger.info(f'Max ema accuracy: {max_accuracy_e:.2f}%')
        
    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    logger.info('Training time {}'.format(total_time_str))


def train_one_epoch(config, model, model_ema, criterion, data_loader, optimizer, epoch, mixup_fn, lr_scheduler, logger, total_epochs):
    model.train()
    optimizer.zero_grad()

    num_steps = len(data_loader)
    batch_time = AverageMeter()
    loss_meter = AverageMeter()
    norm_meter = AverageMeter()

    start = time.time()
    end = time.time()

    scaler = GradScaler()

    for idx, (samples, targets) in enumerate(data_loader):
        

        optimizer.zero_grad()
        samples = samples.cuda(non_blocking=True)
        targets = targets.cuda(non_blocking=True)

        if mixup_fn is not None:
            samples, targets = mixup_fn(samples, targets)
        
        if config.AMP: 
            with autocast():
                outputs = model(samples)
                loss = criterion(outputs, targets)
            scaler.scale(loss).backward()
            if config.TRAIN.CLIP_GRAD:
                scaler.unscale_(optimizer)
                grad_norm = nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
                scaler.step(optimizer)
                scaler.update()
            else:
                grad_norm = get_grad_norm(model.parameters())
                scaler.step(optimizer)
                scaler.update()
        else:
            outputs = model(samples)
            loss = criterion(outputs, targets)
            loss.backward()
            if config.TRAIN.CLIP_GRAD:
                grad_norm = nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
            else:
                grad_norm = get_grad_norm(model.parameters())
            optimizer.step()

        lr_scheduler.step_update(epoch * num_steps + idx)

        torch.cuda.synchronize()
        if model_ema is not None:
            model_ema.update(model)

        loss_meter.update(loss.item(), targets.size(0))
        norm_meter.update(grad_norm)
        batch_time.update(time.time() - end)
        end = time.time()

        if (idx + 1) % config.PRINT_FREQ == 0:
            lr = optimizer.param_groups[0]['lr']
            memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
            etas = batch_time.avg * (num_steps - idx)
            logger.info(
                f'Train: [{epoch + 1}/{total_epochs}][{idx + 1}/{num_steps}]\t'
                f'eta {datetime.timedelta(seconds=int(etas))} lr {lr:.6f}\t'
                f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t'
                f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
                f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
                f'mem {memory_used:.0f}MB')
    epoch_time = time.time() - start
    logger.info(f"EPOCH {epoch + 1} training takes {datetime.timedelta(seconds=int(epoch_time))}")

@torch.no_grad()
def validate(config, data_loader, model, logger):
    criterion = nn.CrossEntropyLoss()
    model.eval()

    batch_time = AverageMeter()
    loss_meter = AverageMeter()
    acc1_meter = AverageMeter()
    acc5_meter = AverageMeter()

    end = time.time()
    for idx, (images, target) in enumerate(data_loader):
        images = images.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)

        # compute output
        output = model(images)

        # measure accuracy and record loss
        loss = criterion(output, target)
        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        acc1 = reduce_tensor(acc1)
        acc5 = reduce_tensor(acc5)
        loss = reduce_tensor(loss)

        loss_meter.update(loss.item(), target.size(0))
        acc1_meter.update(acc1.item(), target.size(0))
        acc5_meter.update(acc5.item(), target.size(0))

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

        if (idx + 1) % config.PRINT_FREQ == 0:
            memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
            logger.info(
                f'Test: [{(idx + 1)}/{len(data_loader)}]\t'
                f'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                f'Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
                f'Acc@1 {acc1_meter.val:.3f} ({acc1_meter.avg:.3f})\t'
                f'Acc@5 {acc5_meter.val:.3f} ({acc5_meter.avg:.3f})\t'
                f'Mem {memory_used:.0f}MB')
    logger.info(f' * Acc@1 {acc1_meter.avg:.3f} Acc@5 {acc5_meter.avg:.3f}')
    return acc1_meter.avg, acc5_meter.avg, loss_meter.avg


@torch.no_grad()
def throughput(data_loader, model, logger):
    model.eval()

    for _, (images, _) in enumerate(data_loader):
        images = images.cuda(non_blocking=True)
        batch_size = images.shape[0]
        for i in range(50):
            model(images)
        torch.cuda.synchronize()
        logger.info(f"throughput averaged with 30 times")
        tic1 = time.time()
        for i in range(30):
            model(images)
        torch.cuda.synchronize()
        tic2 = time.time()
        logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}")
        return


if __name__ == '__main__':
    
    main()