train_gpu.py

""" 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 argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import json
import os

from pathlib import Path

from timm.data import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.scheduler import create_scheduler
from timm.optim import create_optimizer
from timm.utils import NativeScaler, get_state_dict, ModelEma

from util.samplers import RASampler
from util import utils as utils
from util.optimizer import SophiaG
from util.engine import train_one_epoch, evaluate
from util.losses import DistillationLoss
from models import *

from datasets import build_dataset
from datasets.threeaugment import new_data_aug_generator

from estimate_model import Predictor, Plot_ROC


def get_args_parser():
    parser = argparse.ArgumentParser(
        'Agent-Attention-Models training and evaluation script', add_help=False)
    parser.add_argument('--batch-size', default=8, type=int)
    parser.add_argument('--epochs', default=5, type=int)
    parser.add_argument('--predict', default=True, type=bool, help='plot ROC curve and confusion matrix')

    # Model selections
    parser.add_argument('--model', default='Agent_CSWin_64_24181_tiny_224', type=str, metavar='MODEL',
                        choices=['Agent_CSWin_64_24181_tiny_224', 'Agent_CSWin_64_36292_small_224', 'Agent_CSWin_96_36292_base_224', 'Agent_CSWin_96_36292_base_384',
                                 'agent_pvt_tiny', 'agent_pvt_small', 'agent_pvt_medium', 'agent_pvt_large',
                                 'agent_deit_tiny', 'agent_deit_small', 'agent_deit_small_448', 'agent_deit_base',
                                 'agent_swin_tiny', 'agent_swin_samll', 'agent_swin_samll_288', 'agent_swin_base', 'agent_swin_base_384',
                                 ],
                        help='Name of model to train')
    parser.add_argument('--input-size', default=224, type=int, choices=[224, 288, 384, 448], help='images input size, specify this parameter based on selected model name')
    parser.add_argument('--model-ema', action='store_true')
    parser.add_argument('--no-model-ema', action='store_false', dest='model_ema')
    parser.set_defaults(model_ema=True)
    parser.add_argument('--model-ema-decay', type=float, default=0.99996, help='')
    parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, help='')

    # Optimizer parameters
    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('--clip-grad', type=float, default=0.02, metavar='NORM',
                        help='Clip gradient norm (default: None, no clipping)')
    parser.add_argument('--clip-mode', type=str, default='agc',
                        help='Gradient clipping mode. One of ("norm", "value", "agc")')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                        help='SGD momentum (default: 0.9)')
    parser.add_argument('--weight-decay', type=float, default=0.025,
                        help='weight decay (default: 0.025)')

    # 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=1e-3, metavar='LR',
                        help='learning rate (default: 1e-3)')
    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('--warmup-lr', type=float, default=1e-6, metavar='LR',
                        help='warmup learning rate (default: 1e-6)')
    parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
    parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                        help='epoch interval to decay LR')
    parser.add_argument('--warmup-epochs', type=int, default=5, 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 parameters
    parser.add_argument('--ThreeAugment', action='store_true')
    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='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")')
    parser.add_argument('--repeated-aug', action='store_true')
    parser.add_argument('--no-repeated-aug',
                        action='store_false', dest='repeated_aug')
    parser.set_defaults(repeated_aug=True)

    # 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', action='store_true', default=False,
                        help='Do not random erase first (clean) augmentation split')

    # Mixup params
    parser.add_argument('--mixup', type=float, default=0.8,
                        help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
    parser.add_argument('--cutmix', type=float, default=1.0,
                        help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
    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"')

    # Distillation parameters
    parser.add_argument('--teacher-model', default='regnety_160', type=str, metavar='MODEL',
                        help='Name of teacher model to train (default: "regnety_160"')
    parser.add_argument('--teacher-path', type=str,
                        default='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth')
    parser.add_argument('--distillation-type', default='none',
                        choices=['none', 'soft', 'hard'], type=str, help="")
    parser.add_argument('--distillation-alpha',
                        default=0.5, type=float, help="")
    parser.add_argument('--distillation-tau', default=1.0, type=float, help="")

    # Finetuning params
    parser.add_argument('--finetune', default='',
                        help='finetune from checkpoint')
    parser.add_argument('--set_bn_eval', action='store_true', default=False,
                        help='set BN layers to eval mode during finetuning.')

    # Dataset parameters
    parser.add_argument('--data_root', default='D:/flower_data', type=str,
                        help='dataset path')
    parser.add_argument('--nb_classes', default=5, type=int,
                        help='number classes of your dataset')
    parser.add_argument('--data-set', default='IMNET', choices=['CIFAR', 'IMNET', 'INAT', 'INAT19'],
                        type=str, help='Image Net dataset path')
    parser.add_argument('--inat-category', default='name',
                        choices=['kingdom', 'phylum', 'class', 'order',
                                 'supercategory', 'family', 'genus', 'name'],
                        type=str, help='semantic granularity')
    parser.add_argument('--output_dir', default='./output',
                        help='path where to save, empty for no saving')
    parser.add_argument('--writer_output', default='./',
                        help='path where to save SummaryWriter, empty for no saving')
    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('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', action='store_true',
                        help='Perform evaluation only')
    parser.add_argument('--dist-eval', action='store_true',
                        default=False, help='Enabling distributed evaluation')
    parser.add_argument('--num_workers', default=0, type=int)
    parser.add_argument('--pin-mem', action='store_true',
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
                        help='')
    parser.set_defaults(pin_mem=True)

    # training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--local_rank', default=0, type=int)
    parser.add_argument('--dist_url', default='env://',
                        help='url used to set up distributed training')
    parser.add_argument('--save_freq', default=1, type=int,
                        help='frequency of model saving')
    return parser




def main(args):
    print(args)
    utils.init_distributed_mode(args)

    if args.local_rank == 0:
        writer = SummaryWriter(os.path.join(args.writer_output, 'runs'))

    if args.distillation_type != 'none' and args.finetune and not args.eval:
        raise NotImplementedError(
            "Finetuning with distillation not yet supported")

    device = torch.device(args.device)

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

    cudnn.benchmark = True

    dataset_train, dataset_val = build_dataset(args=args)

    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        if args.repeated_aug:
            sampler_train = RASampler(
                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
            )
        else:
            sampler_train = torch.utils.data.DistributedSampler(
                dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
            )
        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)
    else:
        sampler_train = torch.utils.data.RandomSampler(dataset_train)
        sampler_val = torch.utils.data.SequentialSampler(dataset_val)

    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 args.ThreeAugment:
        data_loader_train.dataset.transform = new_data_aug_generator(args)

    data_loader_val = torch.utils.data.DataLoader(
        dataset_val, sampler=sampler_val,
        batch_size=int(1.5 * args.batch_size),
        num_workers=args.num_workers,
        pin_memory=args.pin_mem,
        drop_last=False
    )

    mixup_fn = None
    mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
    if mixup_active:
        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)

    print(f"Creating model: {args.model}")

    model = create_model(
        args.model,
        num_classes=args.nb_classes,
        args=args
    )

    if args.finetune:
        if args.finetune.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.finetune, map_location='cpu', check_hash=True)
        else:
            checkpoint = utils.load_model(args.finetune, model)

        checkpoint_model = checkpoint['model']
        state_dict = model.state_dict()
        for k in ['classifier.weight', 'classifier.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]

        msg = model.load_state_dict(checkpoint_model, strict=False)
        print(msg)

    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='')

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[args.gpu])
        model_without_ddp = model.module
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print('number of params:', n_parameters)

    linear_scaled_lr = args.lr * args.batch_size * utils.get_world_size() / 512.0
    args.lr = linear_scaled_lr

    print('*****************')
    print('Initial LR is ', linear_scaled_lr)
    print('*****************')

    optimizer = create_optimizer(args, model_without_ddp)
    loss_scaler = NativeScaler()

    lr_scheduler, _ = create_scheduler(args, optimizer)

    criterion = LabelSmoothingCrossEntropy()

    if args.mixup > 0.:
        # smoothing is handled with mixup label transform
        criterion = SoftTargetCrossEntropy()
    elif args.smoothing:
        criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
    else:
        criterion = torch.nn.CrossEntropyLoss()

    teacher_model = None
    if args.distillation_type != 'none':
        assert args.teacher_path, 'need to specify teacher-path when using distillation'
        print(f"Creating teacher model: {args.teacher_model}")
        teacher_model = create_model(
            args.teacher_model,
            pretrained=False,
            num_classes=args.nb_classes,
            global_pool='avg',
        )
        if args.teacher_path.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.teacher_path, map_location='cpu', check_hash=True)
        else:
            checkpoint = torch.load(args.teacher_path, map_location='cpu')
        teacher_model.load_state_dict(checkpoint['model'])
        teacher_model.to(device)
        teacher_model.eval()

    # wrap the criterion in our custom DistillationLoss, which
    # just dispatches to the original criterion if args.distillation_type is
    # 'none'
    criterion = DistillationLoss(
        criterion, teacher_model, args.distillation_type, args.distillation_alpha, args.distillation_tau
    )

    max_accuracy = 0.0

    output_dir = Path(args.output_dir)
    if args.output_dir and utils.is_main_process():
        with (output_dir / "model.txt").open("a") as f:
            f.write(str(model))
    if args.output_dir and utils.is_main_process():
        with (output_dir / "args.txt").open("a") as f:
            f.write(json.dumps(args.__dict__, indent=2) + "\n")
    if args.resume:
        if args.resume.startswith('https'):
            checkpoint = torch.hub.load_state_dict_from_url(
                args.resume, map_location='cpu', check_hash=True)
        else:
            print("Loading local checkpoint at {}".format(args.resume))
            checkpoint = torch.load(args.resume, map_location='cpu')
        msg = model_without_ddp.load_state_dict(checkpoint['model'])
        print(msg)
        if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:

            optimizer.load_state_dict(checkpoint['optimizer'])
            for state in optimizer.state.values():  # load parameters to cuda
                for k, v in state.items():
                    if isinstance(v, torch.Tensor):
                        state[k] = v.cuda()

            lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
            max_accuracy = checkpoint['best_score']
            args.start_epoch = checkpoint['epoch'] + 1
            if args.model_ema:
                utils._load_checkpoint_for_ema(
                    model_ema, checkpoint['model_ema'])
            if 'scaler' in checkpoint:
                loss_scaler.load_state_dict(checkpoint['scaler'])
    if args.eval:
        # utils.replace_batchnorm(model) # Users may choose whether to merge Conv-BN layers during eval
        print(f"Evaluating model: {args.model}")
        print(f'No Visualization')
        test_stats = evaluate(data_loader_val, model, device, None, None, args, visualization=False)
        print(
            f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%"
        )

    print(f"Start training for {args.epochs} epochs")
    start_time = time.time()

    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            data_loader_train.sampler.set_epoch(epoch)

        train_stats = train_one_epoch(
            model, criterion, data_loader_train,
            optimizer, device, epoch, loss_scaler,
            args.clip_grad, args.clip_mode, model_ema, mixup_fn,
            # set_training_mode=args.finetune == ''  # keep in eval mode during finetuning
            set_training_mode=True,
            set_bn_eval=args.set_bn_eval,  # set bn to eval if finetune
            writer=writer,
            args=args
        )

        lr_scheduler.step(epoch)

        test_stats = evaluate(data_loader_val, model, device, epoch, writer, args, visualization=True)
        print(
            f"Accuracy of the network 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:
                ckpt_path = os.path.join(output_dir, 'best_checkpoint.pth')
                checkpoint_paths = [ckpt_path]
                print("Saving checkpoint to {}".format(ckpt_path))
                for checkpoint_path in checkpoint_paths:
                    utils.save_on_master({
                        'model': model_without_ddp.state_dict(),
                        'optimizer': optimizer.state_dict(),
                        'lr_scheduler': lr_scheduler.state_dict(),
                        'epoch': epoch,
                        'best_score': max_accuracy,
                        'model_ema': get_state_dict(model_ema),
                        'scaler': loss_scaler.state_dict(),
                        'args': args,
                    }, checkpoint_path)

        print(f'Max accuracy: {max_accuracy:.2f}%')

        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}

        if args.output_dir and utils.is_main_process():
            with (output_dir / "log.txt").open("a") 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))

    # plot ROC curve and confusion matrix
    if args.predict:
        model_predict = create_model(
            args.model,
            num_classes=args.nb_classes,
            args=args
        )

        model_predict.to(device)
        print('*******************STARTING PREDICT*******************')
        Predictor(model_predict, data_loader_val, f'{args.output_dir}/best_checkpoint.pth', device)
        Plot_ROC(model_predict, data_loader_val, f'{args.output_dir}/best_checkpoint.pth', device)


if __name__ == '__main__':
    parser = argparse.ArgumentParser('Agent-Attention-Models training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)