main_finetune.py

# Copyright (c) Meta Platforms, Inc. and affiliates.

# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.


import argparse
import datetime
import numpy as np
import time
import json
import os
from pathlib import Path

import torch
import torch.backends.cudnn as cudnn

from timm.models.layers import trunc_normal_
from timm.data.mixup import Mixup
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import ModelEma
from optim_factory import create_optimizer, LayerDecayValueAssigner

from datasets import build_dataset
from engine_finetune import train_one_epoch, evaluate

import utils
from utils import NativeScalerWithGradNormCount as NativeScaler
from utils import str2bool, remap_checkpoint_keys
import models.convnextv2 as convnextv2

def get_args_parser():
    parser = argparse.ArgumentParser('FCMAE fine-tuning', add_help=False)
    parser.add_argument('--batch_size', default=64, type=int,
                        help='Per GPU batch size')
    parser.add_argument('--epochs', default=100, type=int)
    parser.add_argument('--update_freq', default=1, type=int,
                        help='gradient accumulation steps')

    # Model parameters
    parser.add_argument('--model', default='convnextv2_base', type=str, metavar='MODEL',
                        help='Name of model to train')
    parser.add_argument('--input_size', default=224, type=int,
                        help='image input size')
    parser.add_argument('--drop_path', type=float, default=0., metavar='PCT',
                        help='Drop path rate (default: 0.1)')
    parser.add_argument('--layer_decay_type', type=str, choices=['single', 'group'], default='single',
                        help="""Layer decay strategies. The single strategy assigns a distinct decaying value for each layer,
                        whereas the group strategy assigns the same decaying value for three consecutive layers""")
    
    # EMA related parameters
    parser.add_argument('--model_ema', type=str2bool, default=False)
    parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
    parser.add_argument('--model_ema_force_cpu', type=str2bool, default=False, help='')
    parser.add_argument('--model_ema_eval', type=str2bool, default=False, help='Using ema to eval during training.')

    # Optimization parameters
    parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
                        help='Clip gradient norm (default: None, no clipping)')
    parser.add_argument('--weight_decay', type=float, default=0.05,
                        help='weight decay (default: 0.05)')
    parser.add_argument('--lr', type=float, default=None, metavar='LR',
                        help='learning rate (absolute lr)')
    parser.add_argument('--blr', type=float, default=5e-4, metavar='LR',
                        help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
    parser.add_argument('--layer_decay', type=float, default=1.0)
    parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
    parser.add_argument('--warmup_epochs', type=int, default=20, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    
    parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
                        help='num of steps to warmup LR, will overload warmup_epochs if set > 0')    
    parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                        help='Optimizer (default: "adamw"')
    parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
                        help='Optimizer Epsilon (default: 1e-8)')
    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='SGD momentum (default: 0.9)')
    parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
        weight decay. We use a cosine schedule for WD and using a larger decay by
        the end of training improves performance for ViTs.""")

    # Augmentation parameters
    parser.add_argument('--color_jitter', type=float, default=None, metavar='PCT',
                       help='Color jitter factor (enabled only when not using Auto/RandAug)')
    parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
                        help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)')
    parser.add_argument('--smoothing', type=float, default=0.1,
                        help='Label smoothing (default: 0.1)')
    
    parser.add_argument('--train_interpolation', type=str, default='bicubic',
                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')

    # * Random Erase params
    parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                        help='Random erase prob (default: 0.25)')
    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', type=str2bool, default=False,
                        help='Do not random erase first (clean) augmentation split')

    # * Mixup params
    parser.add_argument('--mixup', type=float, default=0.,
                        help='mixup alpha, mixup enabled if > 0.')
    parser.add_argument('--cutmix', type=float, default=0.,
                        help='cutmix alpha, cutmix enabled if > 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"')

    # * Finetuning params
    parser.add_argument('--finetune', default='',
                        help='finetune from checkpoint')
    parser.add_argument('--head_init_scale', default=0.001, type=float,
                        help='classifier head initial scale, typically adjusted in fine-tuning')
    parser.add_argument('--model_key', default='model|module', type=str,
                        help='which key to load from saved state dict, usually model or model_ema')
    parser.add_argument('--model_prefix', default='', type=str)

    # Dataset parameters
    parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
                        help='dataset path')
    parser.add_argument('--nb_classes', default=1000, type=int,
                        help='number of the classification types')
    parser.add_argument('--output_dir', default='',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default=None,
                        help='path where to tensorboard log')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=0, type=int)
    parser.add_argument('--resume', default='',
                        help='resume from checkpoint')
    
    parser.add_argument('--eval_data_path', default=None, type=str,
                        help='dataset path for evaluation')
    parser.add_argument('--imagenet_default_mean_and_std', type=str2bool, default=True)
    parser.add_argument('--data_set', default='IMNET', choices=['CIFAR', 'IMNET', 'image_folder'],
                        type=str, help='ImageNet dataset path')
    parser.add_argument('--auto_resume', type=str2bool, default=True)
    parser.add_argument('--save_ckpt', type=str2bool, default=True)
    parser.add_argument('--save_ckpt_freq', default=1, type=int)
    parser.add_argument('--save_ckpt_num', default=3, type=int)

    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', type=str2bool, default=False,
                        help='Perform evaluation only')
    parser.add_argument('--dist_eval', type=str2bool, default=True,
                        help='Enabling distributed evaluation')
    parser.add_argument('--disable_eval', type=str2bool, default=False,
                        help='Disabling evaluation during training')
    parser.add_argument('--num_workers', default=10, type=int)
    parser.add_argument('--pin_mem', type=str2bool, default=True,
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')

    # Evaluation parameters
    parser.add_argument('--crop_pct', type=float, default=None)

    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--local_rank', default=-1, type=int)
    parser.add_argument('--dist_on_itp', type=str2bool, default=False)
    parser.add_argument('--dist_url', default='env://',
                        help='url used to set up distributed training')
    
    parser.add_argument('--use_amp', type=str2bool, default=False, 
                        help="Use apex AMP (Automatic Mixed Precision) or not")
    return parser

def main(args):
    utils.init_distributed_mode(args)
    print(args)
    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    cudnn.benchmark = True

    dataset_train, args.nb_classes = build_dataset(is_train=True, args=args)
    if args.disable_eval:
        args.dist_eval = False
        dataset_val = None
    else:
        dataset_val, _ = build_dataset(is_train=False, args=args)

    num_tasks = utils.get_world_size()
    global_rank = utils.get_rank()
    sampler_train = torch.utils.data.DistributedSampler(
        dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True, seed=args.seed,
    )
    print("Sampler_train = %s" % str(sampler_train))
    if args.dist_eval:
        if len(dataset_val) % num_tasks != 0:
            print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
                    'This will slightly alter validation results as extra duplicate entries are added to achieve '
                    'equal num of samples per-process.')
        sampler_val = torch.utils.data.DistributedSampler(
            dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=False)
    else:
        sampler_val = torch.utils.data.SequentialSampler(dataset_val)
    
    if global_rank == 0 and args.log_dir is not None:
        os.makedirs(args.log_dir, exist_ok=True)
        log_writer = utils.TensorboardLogger(log_dir=args.log_dir)
    else:
        log_writer = None

    data_loader_train = torch.utils.data.DataLoader(
        dataset_train, sampler=sampler_train,
        batch_size=args.batch_size,
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=True,
    )
    if dataset_val is not None:
        data_loader_val = torch.utils.data.DataLoader(
            dataset_val, sampler=sampler_val,
            batch_size=args.batch_size,
            num_workers=args.num_workers,
            pin_memory=args.pin_mem,
            drop_last=False
        )
    else:
        data_loader_val = None

    mixup_fn = None
    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    if mixup_active:
        print("Mixup is activated!")
        mixup_fn = Mixup(
            mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
            prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
            label_smoothing=args.smoothing, num_classes=args.nb_classes)
    
    model = convnextv2.__dict__[args.model](
        num_classes=args.nb_classes,
        drop_path_rate=args.drop_path,
        head_init_scale=args.head_init_scale,
    )
    
    if args.finetune:
        checkpoint = torch.load(args.finetune, map_location='cpu')

        print("Load pre-trained checkpoint from: %s" % args.finetune)
        checkpoint_model = checkpoint['model']
        state_dict = model.state_dict()
        for k in ['head.weight', 'head.bias']:
            if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
                print(f"Removing key {k} from pretrained checkpoint")
                del checkpoint_model[k]
        

        # remove decoder weights
        checkpoint_model_keys = list(checkpoint_model.keys())
        for k in checkpoint_model_keys:
            if 'decoder' in k or 'mask_token'in k or \
               'proj' in k or 'pred' in k:
                print(f"Removing key {k} from pretrained checkpoint")
                del checkpoint_model[k]
        
        checkpoint_model = remap_checkpoint_keys(checkpoint_model)
        utils.load_state_dict(model, checkpoint_model, prefix=args.model_prefix)
        
        # manually initialize fc layer
        trunc_normal_(model.head.weight, std=2e-5)
        torch.nn.init.constant_(model.head.bias, 0.)

    model.to(device)

    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='')
        print("Using EMA with decay = %.8f" % args.model_ema_decay)

    model_without_ddp = model
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)

    print("Model = %s" % str(model_without_ddp))
    print('number of params:', n_parameters)

    eff_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
    num_training_steps_per_epoch = len(dataset_train) // eff_batch_size
    
    if args.lr is None:
        args.lr = args.blr * eff_batch_size / 256

    print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
    print("actual lr: %.2e" % args.lr)

    print("accumulate grad iterations: %d" % args.update_freq)
    print("effective batch size: %d" % eff_batch_size)

    if args.layer_decay < 1.0 or args.layer_decay > 1.0:
        assert args.layer_decay_type in ['single', 'group']
        if args.layer_decay_type == 'group': # applies for Base and Large models
            num_layers = 12
        else:
            num_layers = sum(model_without_ddp.depths)
        assigner = LayerDecayValueAssigner(
            list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2)),
            depths=model_without_ddp.depths, layer_decay_type=args.layer_decay_type)
    else:
        assigner = None

    if assigner is not None:
        print("Assigned values = %s" % str(assigner.values))
    
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=False)
        model_without_ddp = model.module

    optimizer = create_optimizer(
        args, model_without_ddp, skip_list=None,
        get_num_layer=assigner.get_layer_id if assigner is not None else None, 
        get_layer_scale=assigner.get_scale if assigner is not None else None)
    loss_scaler = NativeScaler()

    if mixup_fn is not None:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif args.smoothing > 0.:
        criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        criterion = torch.nn.CrossEntropyLoss()
    
    print("criterion = %s" % str(criterion))

    utils.auto_load_model(
        args=args, model=model, model_without_ddp=model_without_ddp,
        optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)

    if args.eval:
        print(f"Eval only mode")
        test_stats = evaluate(data_loader_val, model, device)
        print(f"Accuracy of the network on {len(dataset_val)} test images: {test_stats['acc1']:.5f}%")
        return
    
    max_accuracy = 0.0
    if args.model_ema and args.model_ema_eval:
        max_accuracy_ema = 0.0

    print("Start training for %d epochs" % args.epochs)
    start_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            data_loader_train.sampler.set_epoch(epoch)
        if log_writer is not None:
            log_writer.set_step(epoch * num_training_steps_per_epoch * args.update_freq)
        train_stats = train_one_epoch(
            model, criterion, data_loader_train,
            optimizer, device, epoch, loss_scaler, 
            args.clip_grad, model_ema, mixup_fn,
            log_writer=log_writer,
            args=args
        )
        if args.output_dir and args.save_ckpt:
            if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
                utils.save_model(
                    args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                    loss_scaler=loss_scaler, epoch=epoch, model_ema=model_ema)
        if data_loader_val is not None:
            test_stats = evaluate(data_loader_val, model, device, use_amp=args.use_amp)
            print(f"Accuracy of the model on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
            if max_accuracy < test_stats["acc1"]:
                max_accuracy = test_stats["acc1"]
                if args.output_dir and args.save_ckpt:
                    utils.save_model(
                        args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                        loss_scaler=loss_scaler, epoch="best", model_ema=model_ema)
            print(f'Max accuracy: {max_accuracy:.2f}%')

            if log_writer is not None:
                log_writer.update(test_acc1=test_stats['acc1'], head="perf", step=epoch)
                log_writer.update(test_acc5=test_stats['acc5'], head="perf", step=epoch)
                log_writer.update(test_loss=test_stats['loss'], head="perf", step=epoch)

            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         **{f'test_{k}': v for k, v in test_stats.items()},
                         'epoch': epoch,
                         'n_parameters': n_parameters}
            
            # repeat testing routines for EMA, if ema eval is turned on
            if args.model_ema and args.model_ema_eval:
                test_stats_ema = evaluate(data_loader_val, model_ema.ema, device, use_amp=args.use_amp)
                print(f"Accuracy of the model EMA on {len(dataset_val)} test images: {test_stats_ema['acc1']:.1f}%")
                if max_accuracy_ema < test_stats_ema["acc1"]:
                    max_accuracy_ema = test_stats_ema["acc1"]
                    if args.output_dir and args.save_ckpt:
                        utils.save_model(
                            args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                            loss_scaler=loss_scaler, epoch="best-ema", model_ema=model_ema)
                    print(f'Max EMA accuracy: {max_accuracy_ema:.2f}%')
                if log_writer is not None:
                    log_writer.update(test_acc1_ema=test_stats_ema['acc1'], head="perf", step=epoch)
                log_stats.update({**{f'test_{k}_ema': v for k, v in test_stats_ema.items()}})
        else:
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                        'epoch': epoch,
                        'n_parameters': n_parameters}
        
        if args.output_dir and utils.is_main_process():
            if log_writer is not None:
                log_writer.flush()
            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                f.write(json.dumps(log_stats) + "\n")

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))

if __name__ == '__main__':
    parser = argparse.ArgumentParser('FCMAE fine-tuning', parents=[get_args_parser()])
    args = parser.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)