Towards-Realtime-MOT/train.py

import argparse
import json
import time

import test  # Import test.py to get mAP after each epoch
from models import *
from utils.datasets import JointDataset, collate_fn
from utils.utils import *
from torchvision.transforms import transforms as T


def train(
        cfg,
        data_cfg,
        img_size=(1088,608),
        resume=False,
        epochs=100,
        batch_size=16,
        accumulated_batches=1,
        freeze_backbone=False,
        var=0,
        opt=None,
):
    weights = 'weights' + os.sep
    latest = weights + 'latest.pt'
    best = weights + 'best.pt'
    device = torch_utils.select_device()

    torch.backends.cudnn.benchmark = True  # unsuitable for multiscale

    # Configure run
    f = open(data_cfg)
    data_config = json.load(f)
    trainset_paths = data_config['train']
    dataset_root = data_config['root']
    f.close()


    transforms = T.Compose([T.ToTensor()])
    # Get dataloader
    dataset = JointDataset(dataset_root, trainset_paths, img_size, augment=True, transforms=transforms)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
                                             num_workers=8, pin_memory=True, drop_last=True, collate_fn=collate_fn) 
    
    # Initialize model
    model = Darknet(cfg, img_size, dataset.nID)

    lr0 = opt.lr
    cutoff = -1  # backbone reaches to cutoff layer
    start_epoch = 0
    best_loss = float('inf')
    if resume:
        checkpoint = torch.load(latest, map_location='cpu')

        # Load weights to resume from
        model.load_state_dict(checkpoint['model'])
        model.to(device).train()

        # Set optimizer
        optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9)

        start_epoch = checkpoint['epoch'] + 1
        if checkpoint['optimizer'] is not None:
            optimizer.load_state_dict(checkpoint['optimizer'])
            best_loss = checkpoint['best_loss']

        del checkpoint  # current, saved

    else:
        # Initialize model with backbone (optional)
        if cfg.endswith('yolov3.cfg'):
            load_darknet_weights(model, weights + 'darknet53.conv.74')
            cutoff = 75
        elif cfg.endswith('yolov3-tiny.cfg'):
            load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
            cutoff = 15

        model.to(device).train()

        # Set optimizer
        optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=lr0, momentum=.9, weight_decay=1e-4)

    model = torch.nn.DataParallel(model)
    # Set scheduler
    scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, 
            milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
    
    # An important trick for detection: freeze bn during fine-tuning 
    if not opt.unfreeze_bn:
        for i, (name, p) in enumerate(model.named_parameters()):
            p.requires_grad = False if 'batch_norm' in name else True

    model_info(model)
    t0 = time.time()
    for epoch in range(epochs):
        epoch += start_epoch

        print(('%8s%12s' + '%10s' * 6) % (
            'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))

        # Update scheduler (automatic)
        scheduler.step()

        
        # Freeze darknet53.conv.74 for first epoch
        if freeze_backbone and (epoch < 2):
            for i, (name, p) in enumerate(model.named_parameters()):
                if int(name.split('.')[2]) < cutoff:  # if layer < 75
                    p.requires_grad = False if (epoch == 0) else True

        ui = -1
        rloss = defaultdict(float)  # running loss
        optimizer.zero_grad()
        for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
            if sum([len(x) for x in targets]) < 1:  # if no targets continue
                continue

            # SGD burn-in
            burnin = min(1000, len(dataloader))
            if (epoch == 0) & (i <= burnin):
                lr = lr0 * (i / burnin) **4 
                for g in optimizer.param_groups:
                    g['lr'] = lr

            # Compute loss, compute gradient, update parameters
            loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
            components = torch.mean(components.view(-1, 5),dim=0)

            loss = torch.mean(loss)
            loss.backward()

            # accumulate gradient for x batches before optimizing
            if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
                optimizer.step()
                optimizer.zero_grad()

            # Running epoch-means of tracked metrics
            ui += 1
            
            for ii, key in enumerate(model.module.loss_names):
                rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)

            s = ('%8s%12s' + '%10.3g' * 6) % (
                '%g/%g' % (epoch, epochs - 1),
                '%g/%g' % (i, len(dataloader) - 1),
                rloss['box'], rloss['conf'],
                rloss['id'],rloss['loss'],
                rloss['nT'], time.time() - t0)
            t0 = time.time()
            if i % opt.print_interval == 0:
                print(s)


        # Save latest checkpoint
        checkpoint = {'epoch': epoch,
        #              'best_loss': best_loss,
                      'model': model.module.state_dict(),
                      'optimizer': optimizer.state_dict()}
        torch.save(checkpoint, latest)


        # Calculate mAP
        if epoch % opt.test_interval ==0:
            with torch.no_grad():
                mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
                test.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)



if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--epochs', type=int, default=30, help='number of epochs')
    parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
    parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
    parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
    parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='coco.data file path')
    parser.add_argument('--img-size', type=int, default=(1088, 608), help='pixels')
    parser.add_argument('--resume', action='store_true', help='resume training flag')
    parser.add_argument('--var', type=float, default=0, help='test variable')
    parser.add_argument('--print-interval', type=int, default=40, help='print interval')
    parser.add_argument('--test-interval', type=int, default=9, help='test interval')
    parser.add_argument('--lr', type=float, default=1e-2, help='init lr')
    parser.add_argument('--idw', type=float, default=0.1, help='loss id weight')
    parser.add_argument('--unfreeze-bn', action='store_true', help='unfreeze bn')
    opt = parser.parse_args()
    print(opt, end='\n\n')

    init_seeds()

    train(
        opt.cfg,
        opt.data_cfg,
        img_size=opt.img_size,
        resume=opt.resume,
        epochs=opt.epochs,
        batch_size=opt.batch_size,
        accumulated_batches=opt.accumulated_batches,
        var=opt.var,
        opt=opt,
    )