my_train.py

import os
# os.environ['{CUDA_VISIBLE_DEVICES}'] = '3'

import argparse
import pickle
from collections import defaultdict
from datetime import datetime

import numpy as np
#from mmcv.cnn import get_model_complexity_info
from torch.utils.tensorboard import SummaryWriter
from visdom import Visdom

from configs import cfg, update_config
from dataset.multi_label.coco import COCO14
from dataset.augmentation import get_transform
from metrics.ml_metrics import get_map_metrics, get_multilabel_metrics
from metrics.pedestrian_metrics import get_pedestrian_metrics, get_pedestrian_metrics_labelwise, save_label_wise_metrics_to_csv
from models.model_ema import ModelEmaV2
from optim.adamw import AdamW
from scheduler.cos_annealing_with_restart import CosineAnnealingLR_with_Restart
from scheduler.cosine_lr import CosineLRScheduler
from tools.distributed import distribute_bn
from tools.vis import tb_visualizer_pedes
import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau, MultiStepLR
from torch.utils.data import DataLoader

from batch_engine import valid_trainer, batch_trainer
from dataset.pedes_attr.pedes import PedesAttr
from models.base_block import FeatClassifier
from models.model_factory import build_loss, build_classifier, build_backbone

from tools.function import get_model_log_path, get_reload_weight, seperate_weight_decay
from tools.utils import time_str, save_ckpt, ReDirectSTD, set_seed, str2bool
from models.backbone import swin_transformer, resnet, bninception, vit
from models.backbone.tresnet import tresnet
from losses import bceloss, scaledbceloss
from models import base_block


# torch.backends.cudnn.benchmark = True
# torch.autograd.set_detect_anomaly(True)
torch.autograd.set_detect_anomaly(True)


def main(cfg, args):
    set_seed(args.seed)
    exp_dir = os.path.join('exp_result', cfg.DATASET.NAME)

    model_dir, log_dir = get_model_log_path(exp_dir, cfg.NAME)
    pkl_name = args.pkl.split('/')[-1].split('.')[0]    


    stdout_file = os.path.join(log_dir, f'stdout_{time_str()}_{pkl_name}.txt')

    save_model_path = os.path.join(model_dir, f'ckpt_max_{time_str()}_{pkl_name}.pth')

    visdom = None
    if cfg.VIS.VISDOM:
        visdom = Visdom(env=f'{cfg.DATASET.NAME}_' + cfg.NAME, port=8401)
        assert visdom.check_connection()

    writer = None
    if cfg.VIS.TENSORBOARD.ENABLE:
        current_time = datetime.now().strftime('%b%d_%H-%M-%S')
        writer_dir = os.path.join(exp_dir, cfg.NAME, 'runs', current_time)
        writer = SummaryWriter(log_dir=writer_dir)

    if cfg.REDIRECTOR:
        print('redirector stdout')
        ReDirectSTD(stdout_file, 'stdout', False)

    """
    the reason for args usage is CfgNode is immutable
    """
    if 'WORLD_SIZE' in os.environ:
        args.distributed = int(os.environ['WORLD_SIZE']) > 1
    else:
        args.distributed = None

    args.world_size = 1
    args.rank = 0  # global rankm

    if args.distributed:
        args.device = 'cuda:%d' % args.local_rank
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(backend='nccl', init_method='env://')
        args.world_size = torch.distributed.get_world_size()
        args.rank = torch.distributed.get_rank()
        print(f'use GPU{args.device} for trainming')
        print(args.world_size, args.rank)

    if args.local_rank == 0:
        print(cfg)

    train_tsfm, valid_tsfm = get_transform(cfg)
    if args.local_rank == 0:
        print(train_tsfm)

    if cfg.DATASET.TYPE == 'pedes':
        train_set = PedesAttr(cfg=cfg, 
                              split=cfg.DATASET.TRAIN_SPLIT, 
                              transform=train_tsfm,
                              target_transform=cfg.DATASET.TARGETTRANSFORM,
                              pkl_name=args.pkl)

        valid_set = PedesAttr(cfg=cfg, 
                              split=cfg.DATASET.VAL_SPLIT, 
                              transform=valid_tsfm,
                              target_transform=cfg.DATASET.TARGETTRANSFORM) 
                              #here we do not add the PKL file name, so the validation set will use the default one for sure

    elif cfg.DATASET.TYPE == 'multi_label':
        train_set = COCO14(cfg=cfg, split=cfg.DATASET.TRAIN_SPLIT, transform=train_tsfm,
                           target_transform=cfg.DATASET.TARGETTRANSFORM)

        valid_set = COCO14(cfg=cfg, split=cfg.DATASET.VAL_SPLIT, transform=valid_tsfm,
                           target_transform=cfg.DATASET.TARGETTRANSFORM)
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_set)
    else:
        train_sampler = None

    train_loader = DataLoader(
        dataset=train_set,
        batch_size=cfg.TRAIN.BATCH_SIZE,
        sampler=train_sampler,
        shuffle=train_sampler is None,
        num_workers=4,
        pin_memory=True,
        drop_last=True,
    )

    valid_loader = DataLoader(
        dataset=valid_set,
        batch_size=cfg.TRAIN.BATCH_SIZE,
        shuffle=False,
        num_workers=4,
        pin_memory=True,
    )

    if args.local_rank == 0:
        print('-' * 60)
        print(f'{cfg.DATASET.NAME} attr_num : {train_set.attr_num}, eval_attr_num : {train_set.eval_attr_num} '
              f'{cfg.DATASET.TRAIN_SPLIT} set: {len(train_loader.dataset)}, '
              f'{cfg.DATASET.TEST_SPLIT} set: {len(valid_loader.dataset)}, '
              )

    labels = train_set.label
    label_ratio = labels.mean(0) if cfg.LOSS.SAMPLE_WEIGHT else None

    backbone, c_output = build_backbone(cfg.BACKBONE.TYPE, cfg.BACKBONE.MULTISCALE)


    classifier = build_classifier(cfg.CLASSIFIER.NAME)(
        nattr=train_set.attr_num,
        c_in=c_output,
        bn=cfg.CLASSIFIER.BN,
        pool=cfg.CLASSIFIER.POOLING,
        scale =cfg.CLASSIFIER.SCALE
    )

    model = FeatClassifier(backbone, classifier, bn_wd=cfg.TRAIN.BN_WD)

    
    if args.local_rank == 0:
        print(f"backbone: {cfg.BACKBONE.TYPE}, classifier: {cfg.CLASSIFIER.NAME}")
        print(f"model_name: {cfg.NAME}")

    # flops, params = get_model_complexity_info(model, (3, 256, 128), print_per_layer_stat=True)
    # print('{:<30}  {:<8}'.format('Computational complexity: ', flops))
    # print('{:<30}  {:<8}'.format('Number of parameters: ', params))

    model = model.cuda()
    if args.distributed:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank])
    else:
        model = torch.nn.DataParallel(model)

    model_ema = None
    if cfg.TRAIN.EMA.ENABLE:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
        model_ema = ModelEmaV2(
            model, decay=cfg.TRAIN.EMA.DECAY, device='cpu' if cfg.TRAIN.EMA.FORCE_CPU else None)

    if cfg.RELOAD.TYPE:
        model = get_reload_weight(model_dir, model, pth=cfg.RELOAD.PTH)


    loss_weight = cfg.LOSS.LOSS_WEIGHT


    criterion = build_loss(cfg.LOSS.TYPE)(
        sample_weight=label_ratio, scale=cfg.CLASSIFIER.SCALE, size_sum=cfg.LOSS.SIZESUM, tb_writer=writer)
    criterion = criterion.cuda()

    if cfg.TRAIN.BN_WD:
        param_groups = [{'params': model.module.finetune_params(),
                         'lr': cfg.TRAIN.LR_SCHEDULER.LR_FT,
                         'weight_decay': cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY},
                        {'params': model.module.fresh_params(),
                         'lr': cfg.TRAIN.LR_SCHEDULER.LR_NEW,
                         'weight_decay': cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY}]
    else:
        # bn parameters are not applied with weight decay
        ft_params = seperate_weight_decay(
            model.module.finetune_params(),
            lr=cfg.TRAIN.LR_SCHEDULER.LR_FT,
            weight_decay=cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY)

        fresh_params = seperate_weight_decay(
            model.module.fresh_params(),
            lr=cfg.TRAIN.LR_SCHEDULER.LR_NEW,
            weight_decay=cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY)

        param_groups = ft_params + fresh_params

    if cfg.TRAIN.OPTIMIZER.TYPE.lower() == 'sgd':
        optimizer = torch.optim.SGD(param_groups, momentum=cfg.TRAIN.OPTIMIZER.MOMENTUM)
    elif cfg.TRAIN.OPTIMIZER.TYPE.lower() == 'adam':
        optimizer = torch.optim.Adam(param_groups)
    elif cfg.TRAIN.OPTIMIZER.TYPE.lower() == 'adamw':
        optimizer = AdamW(param_groups)
    else:
        assert None, f'{cfg.TRAIN.OPTIMIZER.TYPE} is not implemented'

    if cfg.TRAIN.LR_SCHEDULER.TYPE == 'plateau':
        lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
        if cfg.CLASSIFIER.BN:
            assert False, 'BN can not compatible with ReduceLROnPlateau'
    elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'multistep':
        lr_scheduler = MultiStepLR(optimizer, milestones=cfg.TRAIN.LR_SCHEDULER.LR_STEP, gamma=0.1)
    elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'annealing_cosine':
        lr_scheduler = CosineAnnealingLR_with_Restart(
            optimizer,
            T_max=(cfg.TRAIN.MAX_EPOCH + 5) * len(train_loader),
            T_mult=1,
            eta_min=cfg.TRAIN.LR_SCHEDULER.LR_NEW * 0.001
    )
    elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'warmup_cosine':


        lr_scheduler = CosineLRScheduler(
            optimizer,
            t_initial=cfg.TRAIN.MAX_EPOCH,
            lr_min=1e-5,  # cosine lr 最终回落的位置
            warmup_lr_init=1e-4,
            warmup_t=cfg.TRAIN.MAX_EPOCH * cfg.TRAIN.LR_SCHEDULER.WMUP_COEF,
        )

    else:
        assert False, f'{cfg.LR_SCHEDULER.TYPE} has not been achieved yet'

    best_metric, epoch = trainer(cfg, args, epoch=cfg.TRAIN.MAX_EPOCH,
                                 model=model, model_ema=model_ema,
                                 train_loader=train_loader,
                                 valid_loader=valid_loader,
                                 criterion=criterion,
                                 optimizer=optimizer,
                                 lr_scheduler=lr_scheduler,
                                 path=save_model_path,
                                 loss_w=loss_weight,
                                 viz=visdom,
                                 tb_writer=writer)
    if args.local_rank == 0:
        print(f'{cfg.NAME},  best_metrc : {best_metric} in epoch{epoch}')


def trainer(cfg, args, epoch, model, model_ema, train_loader, valid_loader, criterion, optimizer, lr_scheduler,
            path, loss_w, viz, tb_writer):
    maximum = float(-np.inf)
    best_epoch = 0

    result_list = defaultdict()

    result_path = path
    result_path = result_path.replace('ckpt_max', 'metric')
    result_path = result_path.replace('pth', 'pkl')

    for e in range(epoch):

        if args.distributed:
            train_loader.sampler.set_epoch(epoch)

        lr = optimizer.param_groups[1]['lr']

        train_loss, train_gt, train_probs, train_imgs, train_logits, train_loss_mtr, attr_names = batch_trainer(
            cfg,
            args=args,
            epoch=e,
            model=model,
            model_ema=model_ema,
            train_loader=train_loader,
            criterion=criterion,
            optimizer=optimizer,
            loss_w=loss_w,
            scheduler=lr_scheduler if cfg.TRAIN.LR_SCHEDULER.TYPE == 'annealing_cosine' else None,
        )

        if args.distributed:
            if args.local_rank == 0:
                print("Distributing BatchNorm running means and vars")
            distribute_bn(model, args.world_size, args.dist_bn == 'reduce')

        if model_ema is not None and not cfg.TRAIN.EMA.FORCE_CPU:

            if args.local_rank == 0:
                print('using model_ema to validate')

            if args.distributed:
                distribute_bn(model_ema, args.world_size, args.dist_bn == 'reduce')
            valid_loss, valid_gt, valid_probs, valid_imgs, valid_logits, valid_loss_mtr, attr_names = valid_trainer(
                cfg,
                args=args,
                epoch=e,
                model=model_ema.module,
                valid_loader=valid_loader,
                criterion=criterion,
                loss_w=loss_w
            )
        else:
            valid_loss, valid_gt, valid_probs, valid_imgs, valid_logits, valid_loss_mtr, attr_names = valid_trainer(
                cfg,
                args=args,
                epoch=e,
                model=model,
                valid_loader=valid_loader,
                criterion=criterion,
                loss_w=loss_w
            )

        if cfg.TRAIN.LR_SCHEDULER.TYPE == 'plateau':
            lr_scheduler.step(metrics=valid_loss)
        elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'warmup_cosine':
            lr_scheduler.step(epoch=e + 1)
        elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'multistep':
            lr_scheduler.step()

        if cfg.METRIC.TYPE == 'pedestrian':

            train_result = get_pedestrian_metrics(train_gt, train_probs, index=None, cfg=cfg)
            valid_result = get_pedestrian_metrics(valid_gt, valid_probs, index=None, cfg=cfg)

            if args.local_rank == 0:
                print(f'Evaluation on train set, train losses {train_loss}\n',
                      'ma: {:.4f}, label_f1: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
                          train_result.ma, np.mean(train_result.label_f1),
                          np.mean(train_result.label_pos_recall),
                          np.mean(train_result.label_neg_recall)),
                      'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
                          train_result.instance_acc, train_result.instance_prec, train_result.instance_recall,
                          train_result.instance_f1))

                print(f'Evaluation on test set, valid losses {valid_loss}\n',
                      'ma: {:.4f}, label_f1: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
                          valid_result.ma, np.mean(valid_result.label_f1),
                          np.mean(valid_result.label_pos_recall),
                          np.mean(valid_result.label_neg_recall)),
                      'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
                          valid_result.instance_acc, valid_result.instance_prec, valid_result.instance_recall,
                          valid_result.instance_f1))

                print(f'{time_str()}')
                print('-' * 60)

            if args.local_rank == 0:
                tb_visualizer_pedes(tb_writer, lr, e, train_loss, valid_loss, train_result, valid_result,
                                    train_gt, valid_gt, train_loss_mtr, valid_loss_mtr, model, train_loader.dataset.attr_id)

            cur_metric = valid_result.ma
            if cur_metric > maximum:
                maximum = cur_metric
                best_epoch = e
                save_ckpt(model, path, e, maximum, args.pkl)
                #get label wise results
            label_wise_results = get_pedestrian_metrics_labelwise(valid_gt, 
                                                                  valid_probs, 
                                                                  attr_names, 
                                                                  cfg=cfg)
            label_wise_results_train = get_pedestrian_metrics_labelwise(train_gt, 
                                                                        train_probs, 
                                                                        attr_names, 
                                                                        cfg=cfg)
            save_label_wise_metrics_to_csv(label_wise_results,
                                            cfg=cfg,
                                            epoch=e,
                                            pkl_name=args.pkl,
                                            seed= args.seed)
            save_label_wise_metrics_to_csv(label_wise_results_train,
                                            cfg=cfg,
                                            epoch=e,
                                            pkl_name=args.pkl,
                                            train=True, 
                                            seed= args.seed)
            

            result_list[e] = {
                'train_result': train_result,  # 'train_map': train_map,
                'valid_result': valid_result,  # 'valid_map': valid_map,
                'train_gt': train_gt, 'train_probs': train_probs,
                'valid_gt': valid_gt, 'valid_probs': valid_probs,
                'train_imgs': train_imgs, 'valid_imgs': valid_imgs
            }

        elif cfg.METRIC.TYPE == 'multi_label':

            train_metric = get_multilabel_metrics(train_gt, train_probs)
            valid_metric = get_multilabel_metrics(valid_gt, valid_probs)

            if args.local_rank == 0:
                print(
                    'Train Performance : mAP: {:.4f}, OP: {:.4f}, OR: {:.4f}, OF1: {:.4f} CP: {:.4f}, CR: {:.4f}, '
                    'CF1: {:.4f}'.format(train_metric.map, train_metric.OP, train_metric.OR, train_metric.OF1,
                                         train_metric.CP, train_metric.CR, train_metric.CF1))

                print(
                    'Test Performance : mAP: {:.4f}, OP: {:.4f}, OR: {:.4f}, OF1: {:.4f} CP: {:.4f}, CR: {:.4f}, '
                    'CF1: {:.4f}'.format(valid_metric.map, valid_metric.OP, valid_metric.OR, valid_metric.OF1,
                                         valid_metric.CP, valid_metric.CR, valid_metric.CF1))
                print(f'{time_str()}')
                print('-' * 60)

                tb_writer.add_scalars('train/lr', {'lr': lr}, e)

                tb_writer.add_scalars('train/losses', {'train': train_loss,
                                                     'test': valid_loss}, e)

                tb_writer.add_scalars('train/perf', {'mAP': train_metric.map,
                                                     'OP': train_metric.OP,
                                                     'OR': train_metric.OR,
                                                     'OF1': train_metric.OF1,
                                                     'CP': train_metric.CP,
                                                     'CR': train_metric.CR,
                                                     'CF1': train_metric.CF1}, e)

                tb_writer.add_scalars('test/perf', {'mAP': valid_metric.map,
                                                    'OP': valid_metric.OP,
                                                    'OR': valid_metric.OR,
                                                    'OF1': valid_metric.OF1,
                                                    'CP': valid_metric.CP,
                                                    'CR': valid_metric.CR,
                                                    'CF1': valid_metric.CF1}, e)

            cur_metric = valid_metric.map
            if cur_metric > maximum:
                maximum = cur_metric
                best_epoch = e
                save_ckpt(model, path, e, maximum)

            result_list[e] = {
                'train_result': train_metric, 'valid_result': valid_metric,
                'train_gt': train_gt, 'train_probs': train_probs,
                'valid_gt': valid_gt, 'valid_probs': valid_probs
            }
        else:
            assert False, f'{cfg.METRIC.TYPE} is unavailable'

        with open(result_path, 'wb') as f:
            pickle.dump(result_list, f)

    return maximum, best_epoch


def argument_parser():
    parser = argparse.ArgumentParser(description="attribute recognition",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    parser.add_argument(
        "--cfg", help="decide which cfg to use", type=str,
        default="./configs/pedes_baseline/rap_zs.yaml",

    )

    parser.add_argument("--debug", type=str2bool, default="true")
    parser.add_argument('--local_rank', help='node rank for distributed training', default=0,
                        type=int)
    parser.add_argument('--dist_bn', type=str, default='',
                        help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
    parser.add_argument('--pkl', type=str, default='./data/RAP2/bald_1000_w_zs.pkl', help='pickle file path, if empty use pkl from config file')
    
    parser.add_argument('--seed', type=int, default=605, help='random seed')

    args = parser.parse_args()
    print(f"args: {args}")
    return args


if __name__ == '__main__':
    args = argument_parser()

    update_config(cfg, args)
    main(cfg, args)