train.py

import argparse, os, sys, time, gc, datetime

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from datasets import find_dataset_def
from models import *
from utils import *
import torch.distributed as dist
from datasets.data_io import read_pfm, save_pfm
import datetime
import matplotlib.pyplot as plt
import cv2
cudnn.benchmark = True

parser = argparse.ArgumentParser(description='A PyTorch Implementation of Cascade Cost Volume MVSNet')
parser.add_argument('--mode', help='train or test')
parser.add_argument('--model', default='mvsnet', help='select model')
parser.add_argument('--device', default='cuda', help='select model')

parser.add_argument('--dataset', default='dtu_yao', help='select dataset')
parser.add_argument('--trainpath', help='train datapath')
parser.add_argument('--testpath', help='test datapath')
parser.add_argument('--trainlist', help='train list')
parser.add_argument('--testlist', help='test list')

parser.add_argument('--epochs', type=int, default=16, help='number of epochs to train')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate')
parser.add_argument('--lrepochs', type=str, default="10,12,14:2", help='epoch ids to downscale lr and the downscale rate')
parser.add_argument('--wd', type=float, default=0.001, help='weight decay')

parser.add_argument('--batch_size', type=int, default=4, help='train batch size')
parser.add_argument('--numdepth', type=int, default=384, help='the number of depth values')
parser.add_argument('--interval_scale', type=float, default=1.06, help='the number of depth values')

parser.add_argument('--loadckpt', default=None, help='load a specific checkpoint')
parser.add_argument('--logdir', default='./checkpoints/debug/refine', help='the directory to save checkpoints/logs')
parser.add_argument('--resume', type=bool, default=False, help='continue to train the model')

parser.add_argument('--summary_freq', type=int, default=50, help='print and summary frequency')
parser.add_argument('--save_freq', type=int, default=1, help='save checkpoint frequency')
parser.add_argument('--eval_freq', type=int, default=1, help='eval freq')

parser.add_argument('--seed', type=int, default=10, metavar='S', help='random seed')
parser.add_argument('--pin_m', action='store_true', help='data loader pin memory')
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--train loss", type=str, default="0.25,0.5,1", help='last_stage_name')
parser.add_argument('--lossrate', type=float, default=0.9, help='the number of depth values')
parser.add_argument('--last_stage', type=str, default="stage4", help='last_stage_name')
parser.add_argument('--share_cr', action='store_true', help='whether share the cost volume regularization')
parser.add_argument('--ndepths', type=str, default="48,8,8", help='ndepths')
parser.add_argument('--depth_inter_r', type=str, default="1", help='depth_intervals_ratio')
# parser.add_argument('--dlossw', type=str, default="0.25,0.5,1.0,2.0", help='depth loss weight for different stage')
parser.add_argument('--dlossw', type=str, default="1,1,1,1,2,2,2,3,3,3,4", help='depth loss weight for different stage')
parser.add_argument('--cr_base_chs', type=str, default="4", help='cost regularization base channels')
parser.add_argument('--grad_method', type=str, default="detach", choices=["detach", "undetach"], help='grad method')
parser.add_argument('--GRUiters', type=str, default="3,3,3",  help='iters')
parser.add_argument('--iters', type=int, default=12,  help='iters')
parser.add_argument('--maskupmode', type=str, default="laststage",  help='iters')
parser.add_argument('--CostNum', type=int, default=1,  help='CostNum')
parser.add_argument('--initloss', type=str, default='initloss',  help='initloss')
parser.add_argument('--trainviews', type=int, default=3,  help='trainviews')
parser.add_argument('--testviews', type=int, default=3,  help='testviews')
parser.add_argument('--dispmaxfirst', type=str, default='last',  help='testviews')

parser.add_argument('--maskupsample', type=str, default="last",  help='maskupsample')
parser.add_argument('--hiddenstate', type=str, default="init",  help='hiddenstate')


parser.add_argument('--using_apex', action='store_true', help='using apex, need to install apex')
parser.add_argument('--sync_bn', action='store_true',help='enabling apex sync BN.')
parser.add_argument('--opt-level', type=str, default="O0")
parser.add_argument('--keep-batchnorm-fp32', type=str, default=None)
parser.add_argument('--loss-scale', type=str, default=None)
parser.add_argument('--usingrefine', action='store_true')

parser.add_argument('--logdirX', default='./checkpoints/from_old_retrain/log/', help='the directory to save checkpoints/logs')
parser.add_argument('--outdir', default='./eval_training_log', help='output dir for eval')
parser.add_argument('--evalpath', default="/home2/dataset/jack/Documents_home1/Database/DTU/DTU/dtu_testing/",  help='testing data path')
parser.add_argument('--evallist', default="lists/dtu/val.txt",  help='testing scan list')

is_distributed = False
# main function

def adjust_learning_rate(optimizer, lr):
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

def train(model, model_loss, optimizer, TrainImgLoader, TestImgLoader, EvalImgLoader, lr_scheduler, start_epoch, args):
    milestones = [len(TrainImgLoader) * int(epoch_idx) for epoch_idx in args.lrepochs.split(':')[0].split(',')]
    lr_gamma = 1 / float(args.lrepochs.split(':')[1])
    # lr_scheduler = WarmupMultiStepLR(optimizer, milestones, gamma=lr_gamma, warmup_factor=1.0/3, warmup_iters=500,
    #                                                     last_epoch=len(TrainImgLoader) * start_epoch - 1)
    count_len = 0
    for epoch_idx in range(start_epoch, args.epochs):

        lr = 0.0002
        print('Epoch {}:'.format(epoch_idx))
        global_step = len(TrainImgLoader) * epoch_idx
        gru_loss = {}
        gru_loss_refine = {}
        for i in range(args.iters + 1):
            gru_loss["l{}".format(i)] = 0
        for i in range(args.iters - 3):
            gru_loss_refine["l{}".format(i)] = 0
        # training
        print_loss = 2000 // args.batch_size



        for batch_idx, sample in enumerate(TrainImgLoader):

            start_time = time.time()
            global_step = len(TrainImgLoader) * epoch_idx + batch_idx
            do_summary = global_step % args.summary_freq == 0
            loss, scalar_outputs, image_outputs = train_sample(model, model_loss, optimizer, sample, args)
            for i in range(args.iters + 1):
                gru_loss["l{}".format(i)] += scalar_outputs["l{}".format(i)]
            if args.usingrefine:
                for i in range(args.iters - 3):
                    gru_loss_refine["l{}".format(i)] += scalar_outputs["l_refine{}".format(i)]
            # print(gru_loss)
            lr_scheduler.step()


            if batch_idx % print_loss == 0 and batch_idx > 0:

                print(
                   "Epoch {}/{}, Iter {}/{}, lr {:.6f}, train loss = {:.3f}, depth loss = {:.3f}, time = {:.3f}".format(
                       epoch_idx, args.epochs, batch_idx, len(TrainImgLoader),
                       optimizer.param_groups[0]["lr"], loss,
                       scalar_outputs['depth_loss'],
                       time.time() - start_time))
                print(optimizer.state_dict()['param_groups'][0]['lr'])
                print(optimizer.param_groups[0]["lr"])
                print(['{}:{}'.format(key,gru_loss[key]/batch_idx) for key in gru_loss.keys()])
                if args.usingrefine:
                    print(['{}:{}'.format(key, gru_loss_refine[key] / batch_idx) for key in gru_loss_refine.keys()])


            del scalar_outputs


        # checkpoint
        if (not is_distributed) or (dist.get_rank() == 0):
            if (epoch_idx + 1) % args.save_freq == 0:
                torch.save({
                    'epoch': epoch_idx,
                    'model': model.module.state_dict(),
                    'optimizer': optimizer.state_dict()},
                    "{}/model_{:0>6}.ckpt".format(args.logdir, epoch_idx))
        # gc.collect()

        # testing
        if (epoch_idx % args.eval_freq == 0) or (epoch_idx == args.epochs - 1):
            avg_test_scalars = DictAverageMeter()
            for batch_idx, sample in enumerate(TestImgLoader):

                start_time = time.time()
                global_step = len(TrainImgLoader) * epoch_idx + batch_idx
                do_summary = global_step % args.summary_freq == 0
                loss, scalar_outputs_test, image_outputs = test_sample_depth(model, model_loss, sample, args)
                scalar_outputs_test['time'] = time.time() - start_time
                avg_test_scalars.update(scalar_outputs_test)
            del scalar_outputs_test
            # del scalar_outputs, image_outputs
            print("final", avg_test_scalars.mean())


            # gc.collect()
    for i in gru_loss.keys():
        gru_loss[i] = 0
    for i in gru_loss_refine.keys():
        gru_loss_refine[i] = 0

def eval_training(model, EvalImgLoader, args):
    # avg_test_scalars = DictAverageMeter()
    for batch_idx, sample in enumerate(EvalImgLoader):
        model_eval = model
        model_eval.eval()
        sample_cuda = tocuda(sample)
        outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_values"])
        outputs = tensor2numpy(outputs)
        del sample_cuda
        filenames = sample["filename"]
        # save depth maps and confidence maps
        for filename, depth_est, photometric_confidence in zip(filenames, outputs["depth"][-1],
                                                               outputs["photometric_confidence"]):
            depth_filename = os.path.join(args.outdir, filename.format('depth_est', '.pfm'))
            confidence_filename = os.path.join(args.outdir, filename.format('confidence', '.pfm'))
            os.makedirs(depth_filename.rsplit('/', 1)[0], exist_ok=True)
            os.makedirs(confidence_filename.rsplit('/', 1)[0], exist_ok=True)
            # save depth maps
            save_pfm(depth_filename, depth_est)
            # save confidence maps
            save_pfm(confidence_filename, photometric_confidence)

        with open(args.evallist) as f:
            scans = f.readlines()
            scans = [line.rstrip() for line in scans]

        for scan in scans:
            scan_id = int(scan[4:])
            scan_folder = os.path.join(args.testpath, scan)
            out_folder = os.path.join(args.outdir, scan)
            # step2. filter saved depth maps with photometric confidence maps and geometric constraints
            filter_depth(scan_folder, out_folder, os.path.join(args.outdir, 'mvsnet{:0>3}_l3.ply'.format(scan_id)))

def test(model, model_loss, TestImgLoader, args):
    avg_test_scalars = DictAverageMeter()
    i = 0
    print(len(TestImgLoader))
    for batch_idx, sample in enumerate(TestImgLoader):
        # print(batch_idx)
        start_time = time.time()
        loss, scalar_outputs, image_outputs = test_sample_depth(model, model_loss, sample, args)

        scalar_outputs['time'] = time.time() - start_time
        avg_test_scalars.update(scalar_outputs)

        del scalar_outputs, image_outputs
    print("final", avg_test_scalars.mean())


def train_sample(model, model_loss, optimizer, sample, args):
    model.train()
    optimizer.zero_grad()

    sample_cuda = tocuda(sample)
    depth_gt_ms = sample_cuda["depth"]
    mask_ms = sample_cuda["mask"]

    # num_stage = len([int(nd) for nd in args.ndepths.split(",") if nd])
    depth_gt = depth_gt_ms
    mask = mask_ms


    outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_values"])
    outputs_depth = outputs["depth"]
    iter_list = [int(e) for e in args.GRUiters.split(",")]

    dlossw_list = [1 for x in range(iter_list[0] + 1)] + [2 for x in range(iter_list[1] + 1)] + [3 for x in range(iter_list[2] + 1)] + [4]
    loss, depth_loss_dict = model_loss(outputs_depth, depth_gt_ms, mask_ms, dlossw_list, sample_cuda["depth_values"], loss_rate=args.lossrate)

    if args.usingrefine:
        loss = loss + loss_refine
    else:
        loss = loss
    depth_est = outputs_depth[-1]

    depth_loss = depth_loss_dict["l{}".format(args.iters)]

    if is_distributed and args.using_apex:
        with amp.scale_loss(loss, optimizer) as scaled_loss:
            scaled_loss.backward()
    else:
        loss.backward()

    optimizer.step()


    scalar_outputs = {"loss": loss,
                      "depth_loss": depth_loss,
                      "abs_depth_error": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5),
                      "thres2mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 2),
                      "thres4mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 4),
                      "thres8mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 8),}

    for i in range(args.iters + 1):
        scalar_outputs["l{}".format(i)] = depth_loss_dict["l{}".format(i)]
    if args.usingrefine:
        for i in range(args.iters - 3):
            scalar_outputs["l_refine{}".format(i)] = depth_loss_dict_refine["l{}".format(i)]
    image_outputs = {"depth_est": depth_est * mask[args.last_stage],
                     "depth_est_nomask": depth_est,
                     "depth_gt": sample["depth"],
                     "ref_img": sample["imgs"][:, 0],
                     "mask": sample["mask"],
                     "errormap": (depth_est - depth_gt[args.last_stage]).abs() * mask[args.last_stage],
                     }


    if is_distributed:
        scalar_outputs = reduce_scalar_outputs(scalar_outputs)

    return tensor2float(scalar_outputs["loss"]), tensor2float(scalar_outputs), tensor2numpy(image_outputs)


@make_nograd_func
def test_sample_depth(model, model_loss, sample, args):
    if is_distributed:
        model_eval = model.module
    else:
        model_eval = model
    model_eval.eval()

    sample_cuda = tocuda(sample)
    depth_gt_ms = sample_cuda["depth"]
    mask_ms = sample_cuda["mask"]

    # num_stage = len([int(nd) for nd in args.ndepths.split(",") if nd])
    depth_gt = depth_gt_ms
    mask = mask_ms

    outputs = model_eval(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_values"])
    outputs_depth = outputs["depth"]
    iter_list = [int(e) for e in args.GRUiters.split(",")]

    dlossw_list = [1 for x in range(iter_list[0] + 1)] + [2 for x in range(iter_list[1] + 1)] + [3 for x in range(iter_list[2] + 1)] + [4]
    loss, depth_loss_dict = model_loss(outputs_depth, depth_gt_ms, mask_ms, dlossw_list, sample_cuda["depth_values"], loss_rate=args.lossrate)

    depth_est = outputs_depth[-1]
    # print(depth_est.size())

    depth_loss = depth_loss_dict["l{}".format(args.iters)]


    scalar_outputs = {"loss": loss,
                      "depth_loss": depth_loss,
                      "abs_depth_error": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5),
                      "thres2mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 0.125),
                      "thres4mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 0.25),
                      "thres8mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 0.5),
                      "thres14mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 1),
                      "thres20mm_error": Thres_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, 20),

                      "thres2mm_abserror": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, [0, 2.0]),
                      "thres4mm_abserror": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, [2.0, 4.0]),
                      "thres8mm_abserror": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, [4.0, 8.0]),
                      "thres14mm_abserror": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, [8.0, 14.0]),
                      "thres20mm_abserror": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, [14.0, 20.0]),
                      "thres>20mm_abserror": AbsDepthError_metrics(depth_est, depth_gt[args.last_stage], mask[args.last_stage] > 0.5, [20.0, 1e5]),
                    }
    for i in range(args.iters + 1):
        scalar_outputs["l{}".format(i)] = depth_loss_dict["l{}".format(i)]
    if args.usingrefine:
        for i in range(args.iters - 3):
            scalar_outputs["l_refine{}".format(i)] = depth_loss_dict_refine["l{}".format(i)]

    image_outputs = {"depth_est": depth_est * mask[args.last_stage],
                     "depth_est_nomask": depth_est,
                     "depth_gt": sample["depth"],
                     "ref_img": sample["imgs"][:, 0],
                     "mask": sample["mask"],
                     "errormap": (depth_est - depth_gt[args.last_stage]).abs() * mask[args.last_stage]}
    # if is_distributed:
    #     scalar_outputs = reduce_scalar_outputs(scalar_outputs)

    return tensor2float(scalar_outputs["loss"]), tensor2float(scalar_outputs), tensor2numpy(image_outputs)

def profile():
    warmup_iter = 5
    iter_dataloader = iter(TestImgLoader)

    @make_nograd_func
    def do_iteration():
        torch.cuda.synchronize()
        torch.cuda.synchronize()
        start_time = time.perf_counter()
        test_sample(next(iter_dataloader), detailed_summary=True)
        torch.cuda.synchronize()
        end_time = time.perf_counter()
        return end_time - start_time

    for i in range(warmup_iter):
        t = do_iteration()
        print('WarpUp Iter {}, time = {:.4f}'.format(i, t))

    with torch.autograd.profiler.profile(enabled=True, use_cuda=True) as prof:
        for i in range(5):
            t = do_iteration()
            print('Profile Iter {}, time = {:.4f}'.format(i, t))
            time.sleep(0.02)

    if prof is not None:
        # print(prof)
        trace_fn = 'chrome-trace.bin'
        prof.export_chrome_trace(trace_fn)
        print("chrome trace file is written to: ", trace_fn)


if __name__ == '__main__':
    # parse arguments and check
    args = parser.parse_args()

    # using sync_bn by using nvidia-apex, need to install apex.
    if args.sync_bn:
        assert args.using_apex, "must set using apex and install nvidia-apex"
    if args.using_apex:
        try:
            from apex.parallel import DistributedDataParallel as DDP
            from apex.fp16_utils import *
            from apex import amp, optimizers
            from apex.multi_tensor_apply import multi_tensor_applier
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to run this example.")

    if args.resume:
        assert args.mode == "train"
        assert args.loadckpt is None
    if args.testpath is None:
        args.testpath = args.trainpath

    if is_distributed:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend="nccl", init_method="env://"
        )
        synchronize()

    set_random_seed(args.seed)
    device = torch.device(args.device)

    if (not is_distributed) or (dist.get_rank() == 0):
        # create logger for mode "train" and "testall"
        if args.mode == "train":
            if not os.path.isdir(args.logdir):
                os.makedirs(args.logdir)
            current_time_str = str(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))
            print("current time", current_time_str)
            print("creating new summary file")
            logger = SummaryWriter(args.logdir)
        print("argv:", sys.argv[1:])
        print_args(args)

    # model, optimizer
    model = Effi_MVS_plus(args, refine=False)
    model.to(device)
    # model_loss = cas_raft_1to8_loss_smooth_dispmode
    model_loss = mvs_loss
    if args.sync_bn:
        import apex
        print("using apex synced BN")
        model = apex.parallel.convert_syncbn_model(model)


    optimizer = optim.AdamW(model.parameters(), lr=args.lr,
        weight_decay=args.wd, eps=1e-8)

    # load parameters
    start_epoch = 0
    if (args.mode == "train" and args.resume) or (args.mode == "test" and not args.loadckpt):
        saved_models = [fn for fn in os.listdir(args.logdir) if fn.endswith(".ckpt")]
        saved_models = sorted(saved_models, key=lambda x: int(x.split('_')[-1].split('.')[0]))
        # use the latest checkpoint file
        loadckpt = os.path.join(args.logdir, saved_models[-1])
        print("resuming", loadckpt)
        state_dict = torch.load(loadckpt, map_location=torch.device("cpu"))
        model.load_state_dict(state_dict['model'])
        optimizer.load_state_dict(state_dict['optimizer'])
        start_epoch = state_dict['epoch'] + 1
        # start_epoch = 0
    elif args.loadckpt:
        # load checkpoint file specified by args.loadckpt
        print("loading model {}".format(args.loadckpt))
        state_dict = torch.load(args.loadckpt, map_location=torch.device("cpu"))
        model.load_state_dict(state_dict['model'])

    if (not is_distributed) or (dist.get_rank() == 0):
        print("start at epoch {}".format(start_epoch))
        print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))

    if args.using_apex:
        # Initialize Amp
        model, optimizer = amp.initialize(model, optimizer,
                                          opt_level=args.opt_level,
                                          keep_batchnorm_fp32=args.keep_batchnorm_fp32,
                                          loss_scale=args.loss_scale
                                          )

    if is_distributed:
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[args.local_rank], output_device=args.local_rank,
            # find_unused_parameters=False,
            # this should be removed if we update BatchNorm stats
            # broadcast_buffers=False,
        )
    else:
        if torch.cuda.is_available():
            print("Let's use", torch.cuda.device_count(), "GPUs!")
            model = nn.DataParallel(model)

    # dataset, dataloader
    MVSDataset = find_dataset_def(args.dataset)
    train_dataset = MVSDataset(args.trainpath, args.trainlist, "train", args.trainviews, args.numdepth, args.interval_scale, args.dispmaxfirst)
    test_dataset = MVSDataset(args.testpath, args.testlist, "test", args.testviews, args.numdepth, args.interval_scale, args.dispmaxfirst)

    if is_distributed:
        train_sampler = torch.utils.data.DistributedSampler(train_dataset, num_replicas=dist.get_world_size(),
                                                            rank=dist.get_rank())
        test_sampler = torch.utils.data.DistributedSampler(test_dataset, num_replicas=dist.get_world_size(),
                                                           rank=dist.get_rank())

        TrainImgLoader = DataLoader(train_dataset, args.batch_size, sampler=train_sampler, num_workers=8,
                                    drop_last=True)
        TestImgLoader = DataLoader(test_dataset, args.batch_size, sampler=test_sampler, num_workers=8, drop_last=False)
        # EvalImgLoader = DataLoader(eval_dataset, 1, shuffle=False, num_workers=1, drop_last=False,
        #                            pin_memory=args.pin_m)
    else:
        TrainImgLoader = DataLoader(train_dataset, args.batch_size, shuffle=True, num_workers=8, drop_last=True)
        TestImgLoader = DataLoader(test_dataset, args.batch_size, shuffle=False, num_workers=8, drop_last=False)
        # EvalImgLoader = DataLoader(eval_dataset, 1, shuffle=False, num_workers=1, drop_last=False,
        #                            pin_memory=args.pin_m)
    EvalImgLoader = None
    lr_scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, len(TrainImgLoader) * args.epochs + 100,
        pct_start=0.05, cycle_momentum=False, anneal_strategy='linear')
    # lr_scheduler = None
    if args.mode == "train":
        train(model, model_loss, optimizer, TrainImgLoader, TestImgLoader, EvalImgLoader, lr_scheduler, start_epoch, args)
    elif args.mode == "finetune":
        train(model, model_loss, optimizer, TrainImgLoader, TestImgLoader, EvalImgLoader, lr_scheduler, start_epoch, args)
    elif args.mode == "test":
        test(model, model_loss, TestImgLoader, args)
    elif args.mode == "profile":
        profile()
    else:
        raise NotImplementedError