main_repmlp.py

# --------------------------------------------------------
# RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality (https://arxiv.org/abs/2112.11081)
# CVPR 2022
# Github source: https://github.com/DingXiaoH/RepMLP
# Licensed under The MIT License [see LICENSE for details]
# The training script is based on the code of Swin Transformer (https://github.com/microsoft/Swin-Transformer)
# --------------------------------------------------------
import time
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import get_config
from data import build_loader
from lr_scheduler import build_scheduler
from logger import create_logger
from utils import load_checkpoint, save_checkpoint, get_grad_norm, auto_resume_helper, reduce_tensor, save_latest, update_model_ema, unwrap_model, load_weights
import copy
from optimizer import build_optimizer
from repmlpnet import get_RepMLPNet_model

try:
    # noinspection PyUnresolvedReferences
    from apex import amp
except ImportError:
    amp = None


def parse_option():
    parser = argparse.ArgumentParser('RepOpt-VGG training script built on the codebase of Swin Transformer', add_help=False)
    parser.add_argument(
        "--opts",
        help="Modify config options by adding 'KEY VALUE' pairs. ",
        default=None,
        nargs='+',
    )

    # easy config modification
    parser.add_argument('--arch', default=None, type=str, help='arch name')
    parser.add_argument('--batch-size', default=128, type=int, help="batch size for single GPU")
    parser.add_argument('--data-path', default='/path/to/imgnet/', 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('--accumulation-steps', type=int, help="gradient accumulation steps")
    parser.add_argument('--use-checkpoint', action='store_true',
                        help="whether to use gradient checkpointing to save memory")
    parser.add_argument('--amp-opt-level', type=str, default='O0', choices=['O0', 'O1', 'O2'],  #TODO Note: use amp if you have it
                        help='mixed precision opt level, if O0, no amp is used')
    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')

    # distributed training
    parser.add_argument("--local_rank", type=int, default=0, help='local rank for DistributedDataParallel')

    args, unparsed = parser.parse_known_args()

    config = get_config(args)

    return args, config




def main(config):
    dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn = build_loader(config)

    logger.info(f"Creating model:{config.MODEL.ARCH}")

    model = get_RepMLPNet_model(config.MODEL.ARCH, deploy=False)

    optimizer = build_optimizer(config, model)

    model.cuda()

    if torch.cuda.device_count() > 1:
        if config.AMP_OPT_LEVEL != "O0":
            model, optimizer = amp.initialize(model, optimizer, opt_level=config.AMP_OPT_LEVEL)
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK],
                                                          broadcast_buffers=False)
        model_without_ddp = model.module
    else:
        if config.AMP_OPT_LEVEL != "O0":
            model, optimizer = amp.initialize(model, optimizer, opt_level=config.AMP_OPT_LEVEL)
        model_without_ddp = model

    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    logger.info(f"number of params: {n_parameters}")
    if hasattr(model_without_ddp, 'flops'):
        flops = model_without_ddp.flops()
        logger.info(f"number of GFLOPs: {flops / 1e9}")

    if config.THROUGHPUT_MODE:
        throughput(data_loader_val, model, logger)
        return

    if config.EVAL_MODE:
        load_weights(model, config.MODEL.RESUME)
        acc1, acc5, loss = validate(config, data_loader_val, model)
        logger.info(f"Only eval. top-1 acc, top-5 acc, loss: {acc1:.3f}, {acc5:.3f}, {loss:.5f}")
        return


    lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))

    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 = torch.nn.CrossEntropyLoss()



    max_accuracy = 0.0
    max_ema_accuracy = 0.0

    if config.TRAIN.EMA_ALPHA > 0 and (not config.EVAL_MODE) and (not config.THROUGHPUT_MODE):
        model_ema = copy.deepcopy(model)
    else:
        model_ema = None

    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 (not config.THROUGHPUT_MODE) and config.MODEL.RESUME:
        max_accuracy = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler, logger, model_ema=model_ema)



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

        train_one_epoch(config, model, criterion, data_loader_train, optimizer, epoch, mixup_fn, lr_scheduler, model_ema=model_ema)
        if dist.get_rank() == 0:
            save_latest(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger, model_ema=model_ema)
            if epoch % config.SAVE_FREQ == 0:
                save_checkpoint(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger, model_ema=model_ema)

        if epoch % config.SAVE_FREQ == 0 or epoch >= (config.TRAIN.EPOCHS - 10):

            if data_loader_val is not None:
                acc1, acc5, loss = validate(config, data_loader_val, model)
                logger.info(f"Accuracy of the network at epoch {epoch}: {acc1:.3f}%")
                max_accuracy = max(max_accuracy, acc1)
                logger.info(f'Max accuracy: {max_accuracy:.2f}%')
                if max_accuracy == acc1 and dist.get_rank() == 0:
                    save_checkpoint(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, logger,
                                    is_best=True, model_ema=model_ema)

            if model_ema is not None:
                if data_loader_val is not None:
                    acc1, acc5, loss = validate(config, data_loader_val, model_ema)
                    logger.info(f"EMAAccuracy of the network at epoch {epoch} test images: {acc1:.3f}%")
                    max_ema_accuracy = max(max_ema_accuracy, acc1)
                    logger.info(f'EMAMax accuracy: {max_ema_accuracy:.2f}%')
                    if max_ema_accuracy == acc1 and dist.get_rank() == 0:
                        best_ema_path = os.path.join(config.OUTPUT, 'best_ema.pth')
                        logger.info(f"{best_ema_path} best EMA saving......")
                        torch.save(unwrap_model(model_ema).state_dict(), best_ema_path)
                else:
                    latest_ema_path = os.path.join(config.OUTPUT, 'latest_ema.pth')
                    logger.info(f"{latest_ema_path} latest EMA saving......")
                    torch.save(unwrap_model(model_ema).state_dict(), latest_ema_path)

    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, criterion, data_loader, optimizer, epoch, mixup_fn, lr_scheduler, model_ema=None):
    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()
    for idx, (samples, targets) in enumerate(data_loader):
        samples = samples.cuda(non_blocking=True)
        targets = targets.cuda(non_blocking=True)

        if mixup_fn is not None:
            samples, targets = mixup_fn(samples, targets)

        outputs = model(samples)

        if config.TRAIN.ACCUMULATION_STEPS > 1:

            loss = criterion(outputs, targets)
            loss = loss / config.TRAIN.ACCUMULATION_STEPS
            if config.AMP_OPT_LEVEL != "O0":
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(amp.master_params(optimizer))
            else:
                loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(model.parameters())
            if (idx + 1) % config.TRAIN.ACCUMULATION_STEPS == 0:
                optimizer.step()
                optimizer.zero_grad()
                lr_scheduler.step_update(epoch * num_steps + idx)

        else:
            if type(outputs) is dict:
                loss = 0.0
                for name, pred in outputs.items():
                    if 'aux' in name:
                        loss += 0.1 * criterion(pred, targets)
                    else:
                        loss += criterion(pred, targets)
            else:
                loss = criterion(outputs, targets)

            optimizer.zero_grad()
            if config.AMP_OPT_LEVEL != "O0":
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(amp.master_params(optimizer))
            else:
                loss.backward()
                if config.TRAIN.CLIP_GRAD:
                    grad_norm = torch.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()

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

        if model_ema is not None:
            update_model_ema(config, dist.get_world_size(), model=model, model_ema=model_ema, cur_epoch=epoch, cur_iter=idx)

        end = time.time()

        if idx % 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}/{config.TRAIN.EPOCHS}][{idx}/{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} training takes {datetime.timedelta(seconds=int(epoch_time))}")


@torch.no_grad()
def validate(config, data_loader, model):
    criterion = torch.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)

        if type(output) is dict:
            output = output['main']

        # 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 % config.PRINT_FREQ == 0:
            memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
            logger.info(
                f'Test: [{idx}/{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 idx, (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()
        throughput = 30 * batch_size / (tic2 - tic1)
        logger.info(f"batch_size {batch_size} throughput {throughput}")
        return


import os

if __name__ == '__main__':
    args, config = parse_option()

    if config.AMP_OPT_LEVEL != "O0":
        assert amp is not None, "amp not installed!"

    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        rank = int(os.environ["RANK"])
        world_size = int(os.environ['WORLD_SIZE'])
    else:
        rank = -1
        world_size = -1
    torch.cuda.set_device(config.LOCAL_RANK)
    torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)
    torch.distributed.barrier()
    seed = config.SEED + dist.get_rank()

    torch.manual_seed(seed)
    np.random.seed(seed)
    cudnn.benchmark = True

    if not config.EVAL_MODE:
        # 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() / 256.0
        linear_scaled_warmup_lr = config.TRAIN.WARMUP_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 256.0
        linear_scaled_min_lr = config.TRAIN.MIN_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 256.0
        # gradient accumulation also need to scale the learning rate
        if config.TRAIN.ACCUMULATION_STEPS > 1:
            linear_scaled_lr = linear_scaled_lr * config.TRAIN.ACCUMULATION_STEPS
            linear_scaled_warmup_lr = linear_scaled_warmup_lr * config.TRAIN.ACCUMULATION_STEPS
            linear_scaled_min_lr = linear_scaled_min_lr * config.TRAIN.ACCUMULATION_STEPS
        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.freeze()

    print('==========================================')
    print('real base lr: ', config.TRAIN.BASE_LR)
    print('==========================================')

    os.makedirs(config.OUTPUT, exist_ok=True)

    logger = create_logger(output_dir=config.OUTPUT, dist_rank=0 if torch.cuda.device_count() == 1 else dist.get_rank(), name=f"{config.MODEL.ARCH}")

    if torch.cuda.device_count() == 1 or 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())

    main(config)