main_finetune.py

# --------------------------------------------------------
# Feaure Distillation
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# Modified by Zhenda Xie
# Modified by Yixuan Wei
# --------------------------------------------------------

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

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.cuda.amp as amp

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

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 import load_checkpoint, load_pretrained, save_checkpoint, get_grad_norm, auto_resume_helper, reduce_tensor

import warnings

warnings.filterwarnings('ignore',
                        'Argument interpolation should be of type InterpolationMode instead of int',
                        UserWarning)

import wandb
global no_wandb 
no_wandb = True

def wandb_log(*args, **kwargs):
    if dist.get_rank() == 0 and not no_wandb:
        wandb.log(*args, **kwargs)


def parse_option():
    global no_wandb 
    parser = argparse.ArgumentParser('Feature Distillation fine-tuning 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('--pretrained', type=str, help='path to pre-trained model')
    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('--enable-amp', action='store_true')
    parser.add_argument('--disable-amp', action='store_false', dest='enable_amp')
    parser.set_defaults(enable_amp=True)
    parser.add_argument('--output', default='output', type=str, metavar='PATH',
                        help='root of output folder, the full path is <output>/<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, required=True, help='local rank for DistributedDataParallel')

    parser.add_argument('--with-wandb', action='store_true')
    args = parser.parse_args()

    config = get_config(args)    
    if args.with_wandb:
        no_wandb = False
        print(" warning you're using wandb ! ")

    return args, config


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

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

    optimizer = build_optimizer(config, model, logger, is_pretrain=False)
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
    model_without_ddp = model.module

    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}")

    lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
    scaler = amp.GradScaler()

    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

    if config.TRAIN.AUTO_RESUME:
        resume_file = auto_resume_helper(config.OUTPUT, logger)
        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 = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler, scaler,  logger)
        acc1, acc5, loss = validate(config, data_loader_val, model)
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%")
        if config.EVAL_MODE:
            return
    elif config.PRETRAINED:
        load_pretrained(config, model_without_ddp, logger)

    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, 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, scaler)
        if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0 or epoch == (config.TRAIN.EPOCHS - 1)):
            save_checkpoint(config, epoch, model_without_ddp, max_accuracy, optimizer, lr_scheduler, scaler, logger)

        acc1, acc5, loss = validate(config, data_loader_val, model)
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {acc1:.1f}%")
        max_accuracy = max(max_accuracy, acc1)
        logger.info(f'Max accuracy: {max_accuracy:.2f}%')
        wandb_log(
            data=dict(acc1=acc1, acc5=acc5, val_loss=loss, max_acc=max_accuracy),
            step=(epoch + 1) * len(data_loader_train),
        )

    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, scaler):
    model.train()
    optimizer.zero_grad()

    logger.info(f'Current learning rate for different parameter groups: {[it["lr"] for it in optimizer.param_groups]}')

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

        with amp.autocast(enabled=config.ENABLE_AMP):
            outputs = model(samples)

        if config.TRAIN.ACCUMULATION_STEPS > 1:
            loss = criterion(outputs, targets)
            loss = loss / config.TRAIN.ACCUMULATION_STEPS
            scaler.scale(loss).backward()
            if (idx + 1) % config.TRAIN.ACCUMULATION_STEPS == 0:
                if config.TRAIN.CLIP_GRAD:
                    scaler.unscale_(optimizer)
                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
                else:
                    grad_norm = get_grad_norm(model.parameters())
                scaler.step(optimizer)
                optimizer.zero_grad()
                scaler.update()
                lr_scheduler.step_update(epoch * num_steps + idx)
            else:
                grad_norm = get_grad_norm(model.parameters())
        else:
            loss = criterion(outputs, targets)
            optimizer.zero_grad()
            scaler.scale(loss).backward()
            if config.TRAIN.CLIP_GRAD and (config.TRAIN.OPTIMIZER.NAME.lower() != 'fusedlamb'):
                scaler.unscale_(optimizer)
                grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
            else:
                grad_norm = get_grad_norm(model.parameters())
            scaler.step(optimizer)
            scaler.update()
            lr_scheduler.step_update(epoch * num_steps + idx)

        torch.cuda.synchronize()

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

        if idx % config.PRINT_FREQ == 0:
            lr = optimizer.param_groups[-1]['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'loss_scale {loss_scale_meter.val:.4f} ({loss_scale_meter.avg:.4f})\t'
                f'mem {memory_used:.0f}MB')
        if (epoch * num_steps + idx) % config.PRINT_FREQ == 0:
            wandb_log(
                data=dict(lr=lr, time=batch_time.val, epoch=epoch, iter=idx, loss=loss_meter.val, loss_ma=loss_meter.avg, grad_norm=norm_meter.val, loss_scale=loss_scale_meter.val),
                step=epoch * num_steps + idx,
            )
    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)

        # 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()
        logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}")
        return


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

    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        rank = int(os.environ["RANK"])
        world_size = int(os.environ['WORLD_SIZE'])
        print(f"RANK and WORLD_SIZE in environ: {rank}/{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

    # 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
    # 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()

    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}")

        # setup wandb
        if not no_wandb:
            raise NotImplementedError(" using yourself wandb ")

    # print config
    logger.info(config.dump())

    main(config)