train.py

import argparse
import os, sys
import warnings
import pandas as pd
import time
import numpy as np
import yaml, csv
import shutil

import torch
import torch.backends.cudnn as cudnn
import torch.distributed as distributed
import torch.nn as nn
import torch.optim as optim

import torchvision.datasets as dset
import torchvision.transforms as tforms
from torchvision.utils import save_image

import lib.layers as layers
import lib.utils as utils
import lib.odenvp as odenvp
from lib.datasets import CelebAHQ, Imagenet64

from train_misc import standard_normal_logprob
from train_misc import set_cnf_options, count_nfe, count_parameters, count_total_time
from train_misc import create_regularization_fns, get_regularization, append_regularization_to_log
from train_misc import append_regularization_keys_header, append_regularization_csv_dict

import dist_utils
from dist_utils import env_world_size, env_rank
from torch.utils.data.distributed import DistributedSampler

SOLVERS = ["dopri5", "bdf", "rk4", "midpoint", 'adams', 'explicit_adams', 'adaptive_heun', 'bosh3']

def get_parser():
    parser = argparse.ArgumentParser("Continuous Normalizing Flow")
    parser.add_argument("--datadir", default="./data/")
    parser.add_argument("--nworkers", type=int, default=4)
    parser.add_argument("--data", choices=["mnist", "svhn", "cifar10", 'lsun_church', 'celebahq', 'imagenet64'], 
            type=str, default="mnist")
    parser.add_argument("--dims", type=str, default="64,64,64")
    parser.add_argument("--strides", type=str, default="1,1,1,1")
    parser.add_argument("--num_blocks", type=int, default=2, help='Number of stacked CNFs.')

    parser.add_argument(
        "--layer_type", type=str, default="concat",
        choices=["ignore", "concat"]
    )
    parser.add_argument("--divergence_fn", type=str, default="approximate", choices=["brute_force", "approximate"])
    parser.add_argument(
        "--nonlinearity", type=str, default="softplus", choices=["tanh", "relu", "softplus", "elu"]
    )
    parser.add_argument('--solver', type=str, default='dopri5', choices=SOLVERS)
    parser.add_argument('--optimizer', type=str, default='adam', choices=['adam', 'sgd'])
    parser.add_argument('--atol', type=float, default=1e-5, help='only for adaptive solvers')
    parser.add_argument('--rtol', type=float, default=1e-5,  help='only for adaptive solvers')
    parser.add_argument('--step_size', type=float, default=0.25, help='only for fixed step size solvers')
    parser.add_argument('--first_step', type=float, default=0.166667, help='only for adaptive solvers')

    parser.add_argument('--test_solver', type=str, default=None, choices=SOLVERS + [None])
    parser.add_argument('--test_atol', type=float, default=None)
    parser.add_argument('--test_rtol', type=float, default=None)
    parser.add_argument('--test_step_size', type=float, default=None)
    parser.add_argument('--test_first_step', type=float, default=None)

    parser.add_argument("--imagesize", type=int, default=None)
    parser.add_argument("--alpha", type=float, default=1e-6)
    parser.add_argument('--time_length', type=float, default=1.0)
    parser.add_argument('--train_T', type=eval, default=False)

    parser.add_argument("--num_epochs", type=int, default=100)
    parser.add_argument("--batch_size", type=int, default=200)
    parser.add_argument(
        "--batch_size_schedule", type=str, default="", help="Increases the batchsize at every given epoch, dash separated."
    )
    parser.add_argument("--test_batch_size", type=int, default=200)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--warmup_iters", type=float, default=1000)
    parser.add_argument("--weight_decay", type=float, default=0.)

    parser.add_argument("--add_noise", type=eval, default=True, choices=[True, False])
    parser.add_argument('--nbits', type=int, default=8)
    parser.add_argument('--div_samples',type=int, default=1)
    parser.add_argument('--squeeze_first', type=eval, default=False, choices=[True, False])
    parser.add_argument('--zero_last', type=eval, default=True, choices=[True, False])
    parser.add_argument('--seed', type=int, default=42)

    parser.add_argument('--jacfree_experiment', type=bool, default=False)

    # Regularizations
    parser.add_argument('--kinetic-energy', type=float, default=None, help="int_t ||f||_2^2")
    parser.add_argument('--jacobian-norm2', type=float, default=None, help="int_t ||df/dx||_F^2")
    parser.add_argument('--total-deriv', type=float, default=None, help="int_t ||df/dt||^2")
    parser.add_argument('--directional-penalty', type=float, default=None, help="int_t ||(df/dx)^T f||^2")

    parser.add_argument(
        "--max_grad_norm", type=float, default=np.inf,
        help="Max norm of graidents"
    )

    parser.add_argument("--resume", type=str, default=None, help='path to saved check point')
    parser.add_argument("--save", type=str, default="experiments/cnf")
    parser.add_argument("--val_freq", type=int, default=1)
    parser.add_argument("--log_freq", type=int, default=10)
    parser.add_argument('--validate', type=eval, default=False, choices=[True, False])

    parser.add_argument('--distributed', action='store_true', help='Run distributed training. Default True')
    parser.add_argument('--dist-url', default='env://', type=str,
                        help='url used to set up distributed training')
    parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend')
    parser.add_argument('--local_rank', default=0, type=int,
                        help='Used for multi-process training. Can either be manually set ' +
                        'or automatically set by using \'python -m multiproc\'.')

    #parser.add_argument('--skip-auto-shutdown', action='store_true',
    #                    help='Shutdown instance at the end of training or failure')
    #parser.add_argument('--auto-shutdown-success-delay-mins', default=10, type=int,
    #                    help='how long to wait until shutting down on success')
    #parser.add_argument('--auto-shutdown-failure-delay-mins', default=60, type=int,
    #                    help='how long to wait before shutting down on error')

    return parser

cudnn.benchmark = True ##**
args = get_parser().parse_args()
torch.manual_seed(args.seed) 
nvals = 2**args.nbits ##**

##**
# Only want master rank logging
is_master = (not args.distributed) or (dist_utils.env_rank()==0)
is_rank0 = args.local_rank == 0
write_log = is_rank0 and is_master


def add_noise(x, nbits=8): ##** What datatype is x input?
    if nbits<8:
        x = x // (2**(8-nbits)) #Divide by powers of 2, higher powers if bits farther from 8
    if args.add_noise:
        noise = x.new().resize_as_(x).uniform_()
    else:
        noise = 1/2
    return x.add_(noise).div_(2**nbits) ##** Divide by 2^8 ?!?

def shift(x, nbits=8): ##**
    if nbits<8:
        x = x // (2**(8-nbits))

    return x.add_(1/2).div_(2**nbits)

def unshift(x, nbits=8):
    return x.add_(-1/(2**(nbits+1)))


def update_lr(optimizer, itr):
    iter_frac = min(float(itr + 1) / max(args.warmup_iters, 1), 1.0)
    lr = args.lr * iter_frac
    for param_group in optimizer.param_groups:
        param_group["lr"] = lr



def get_dataset(args):
    trans = lambda im_size: tforms.Compose([tforms.Resize(im_size)])

    if args.data == "mnist":
        im_dim = 1
        im_size = 28 if args.imagesize is None else args.imagesize
        train_set = dset.MNIST(root=args.datadir, train=True, transform=trans(im_size), download=True)
        test_set = dset.MNIST(root=args.datadir, train=False, transform=trans(im_size), download=True)
    elif args.data == "svhn":
        im_dim = 3
        im_size = 32 if args.imagesize is None else args.imagesize
        train_set = dset.SVHN(root=args.datadir, split="train", transform=trans(im_size), download=True)
        test_set = dset.SVHN(root=args.datadir, split="test", transform=trans(im_size), download=True)
    elif args.data == "cifar10":
        im_dim = 3
        im_size = 32 if args.imagesize is None else args.imagesize
        train_set = dset.CIFAR10(
            root=args.datadir, train=True, transform=tforms.Compose([
                tforms.Resize(im_size),
                tforms.RandomHorizontalFlip(),
            ]), download=True
        )
        test_set = dset.CIFAR10(root=args.datadir, train=False, transform=None, download=True)
    elif args.data == 'celebahq':
        im_dim = 3
        im_size = 256 if args.imagesize is None else args.imagesize
        train_set = CelebAHQ(
            train=True, root=args.datadir, transform=tforms.Compose([
                tforms.ToPILImage(),
                tforms.Resize(im_size),
                tforms.RandomHorizontalFlip(),
            ])
        )
        test_set = CelebAHQ(
            train=False, root=args.datadir,  transform=tforms.Compose([
                tforms.ToPILImage(),
                tforms.Resize(im_size),
            ])
        )
    elif args.data == 'imagenet64':
        im_dim = 3
        if args.imagesize != 64:
            args.imagesize = 64
        im_size = 64
        train_set = Imagenet64(train=True, root=args.datadir)
        test_set = Imagenet64(train=False, root=args.datadir)
    elif args.data == 'lsun_church':
        im_dim = 3
        im_size = 64 if args.imagesize is None else args.imagesize
        train_set = dset.LSUN(
            'data', ['church_outdoor_train'], transform=tforms.Compose([
                tforms.Resize(96),
                tforms.RandomCrop(64),
                tforms.Resize(im_size),
            ])
        )
        test_set = dset.LSUN(
            'data', ['church_outdoor_val'], transform=tforms.Compose([
                tforms.Resize(96),
                tforms.RandomCrop(64),
                tforms.Resize(im_size),
            ])
        )
    data_shape = (im_dim, im_size, im_size)

    def fast_collate(batch): ##** 

        imgs = [img[0] for img in batch]
        targets = torch.tensor([target[1] for target in batch], dtype=torch.int64)
        w = imgs[0].size[0]
        h = imgs[0].size[1]

        tensor = torch.zeros( (len(imgs), im_dim, im_size, im_size), dtype=torch.uint8 )
        for i, img in enumerate(imgs):
            nump_array = np.asarray(img, dtype=np.uint8)
            tens = torch.from_numpy(nump_array)
            if(nump_array.ndim < 3):
                nump_array = np.expand_dims(nump_array, axis=-1)
            nump_array = np.rollaxis(nump_array, 2)
            tensor[i] += torch.from_numpy(nump_array)

        return tensor, targets

    train_sampler = (DistributedSampler(train_set,
        num_replicas=env_world_size(), rank=env_rank()) if args.distributed
        else None)

    train_loader = torch.utils.data.DataLoader(
        dataset=train_set, batch_size=args.batch_size, #shuffle=True,
        num_workers=args.nworkers, pin_memory=True, sampler=train_sampler, collate_fn=fast_collate
    )

    test_sampler = (DistributedSampler(test_set,
        num_replicas=env_world_size(), rank=env_rank(), shuffle=False) if args.distributed
        else None)

    test_loader = torch.utils.data.DataLoader(
        dataset=test_set, batch_size=args.test_batch_size, #shuffle=False,
        num_workers=args.nworkers, pin_memory=True, sampler=test_sampler, collate_fn=fast_collate
    )

    return train_loader, test_loader, data_shape


def compute_bits_per_dim(x, model):
    zero = torch.zeros(x.shape[0], 1).to(x)

    z, delta_logp, reg_states = model(x, zero)  # run model forward

    reg_states = tuple(torch.mean(rs) for rs in reg_states)

    logpz = standard_normal_logprob(z).view(z.shape[0], -1).sum(1, keepdim=True)  # logp(z)
    logpx = logpz - delta_logp

    logpx_per_dim = torch.sum(logpx) / x.nelement()  # averaged over batches
    bits_per_dim = -(logpx_per_dim - np.log(nvals)) / np.log(2)

    return bits_per_dim, (x, z), reg_states


def create_model(args, data_shape, regularization_fns):
    hidden_dims = tuple(map(int, args.dims.split(",")))
    strides = tuple(map(int, args.strides.split(",")))

    model = odenvp.ODENVP(
        (args.batch_size, *data_shape),
        n_blocks=args.num_blocks,
        intermediate_dims=hidden_dims,
        div_samples=args.div_samples,
        strides=strides,
        squeeze_first=args.squeeze_first,
        nonlinearity=args.nonlinearity,
        layer_type=args.layer_type,
        zero_last=args.zero_last,
        alpha=args.alpha,
        cnf_kwargs={"T": args.time_length, "train_T": args.train_T, "regularization_fns": regularization_fns},
    )

    return model


#if __name__ == "__main__":
def main():
    #os.system('shutdown -c')  # cancel previous shutdown command

    if write_log:
        utils.makedirs(args.save)
        logger = utils.get_logger(logpath=os.path.join(args.save, 'logs'), filepath=os.path.abspath(__file__))

        logger.info(args)

        args_file_path = os.path.join(args.save, 'args.yaml')
        with open(args_file_path, 'w') as f:
            yaml.dump(vars(args), f, default_flow_style=False)

    if args.distributed:
        if write_log: logger.info('Distributed initializing process group')
        torch.cuda.set_device(args.local_rank)
        distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=dist_utils.env_world_size(), rank=env_rank())
        assert(dist_utils.env_world_size() == distributed.get_world_size())
        if write_log: logger.info("Distributed: success (%d/%d)"%(args.local_rank, distributed.get_world_size()))

    # get deivce
    device = torch.device("cuda:%d"%torch.cuda.current_device() if torch.cuda.is_available() else "cpu")
    cvt = lambda x: x.type(torch.float32).to(device, non_blocking=True)

    # load dataset
    train_loader, test_loader, data_shape = get_dataset(args)

    trainlog = os.path.join(args.save,'training.csv')
    testlog = os.path.join(args.save,'test.csv')

    traincolumns = ['itr','wall','itr_time','loss','bpd','fe','total_time','grad_norm'] ##lg
    testcolumns = ['wall','epoch','eval_time','bpd','fe', 'total_time', 'transport_cost']

    # build model
    regularization_fns, regularization_coeffs = create_regularization_fns(args)
    if not args.jacfree_experiment: model = create_model(args, data_shape, regularization_fns).cuda() ##** what does the .cuda() do?
    else: model = create_model(args, data_shape, None).cuda() 
    if args.distributed: model = dist_utils.DDP(model,
                                                device_ids=[args.local_rank], 
                                                output_device=args.local_rank)

    traincolumns = append_regularization_keys_header(traincolumns, regularization_fns)

    if not args.resume and write_log:
        with open(trainlog,'w') as f:
            csvlogger = csv.DictWriter(f, traincolumns)
            csvlogger.writeheader()
        with open(testlog,'w') as f:
            csvlogger = csv.DictWriter(f, testcolumns)
            csvlogger.writeheader()

    set_cnf_options(args, model)

    if write_log: logger.info(model)
    if write_log: logger.info("Number of trainable parameters: {}".format(count_parameters(model)))
    if write_log: logger.info('Iters per train epoch: {}'.format(len(train_loader)))
    if write_log: logger.info('Iters per test: {}'.format(len(test_loader)))

    # optimizer
    if args.optimizer=='adam':
        optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    elif args.optimizer=='sgd':
        optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=0.9,
                nesterov=False)

    # restore parameters
    if args.resume is not None:
        checkpt = torch.load(args.resume, map_location = lambda storage, loc: storage.cuda(args.local_rank))
        model.load_state_dict(checkpt["state_dict"])
        if "optim_state_dict" in checkpt.keys():
            optimizer.load_state_dict(checkpt["optim_state_dict"])
            # Manually move optimizer state to device.
            for state in optimizer.state.values():
                for k, v in state.items():
                    if torch.is_tensor(v):
                        state[k] = cvt(v)


    if args.jacfree_experiment:
        fixed_z = cvt(torch.randn(min(args.test_batch_size,100), *data_shape))
        with torch.no_grad():
            fig_filename = os.path.join(args.save, "jacfree_figs.jpg")
            utils.makedirs(os.path.dirname(fig_filename))
            generated_samples, _, _ = model(fixed_z, reverse=True,jacfree=True)
            generated_samples = generated_samples.view(-1, *data_shape)
            nb = int(np.ceil(np.sqrt(float(fixed_z.size(0)))))
            save_image(unshift(generated_samples, nbits=args.nbits), fig_filename, nrow=nb)
        return

    # For visualization.
    if write_log: fixed_z = cvt(torch.randn(min(args.test_batch_size,100), *data_shape))

    if write_log:
        time_meter = utils.RunningAverageMeter(0.97)
        bpd_meter = utils.RunningAverageMeter(0.97)
        loss_meter = utils.RunningAverageMeter(0.97)
        steps_meter = utils.RunningAverageMeter(0.97)
        grad_meter = utils.RunningAverageMeter(0.97)
        tt_meter = utils.RunningAverageMeter(0.97)


    if not args.resume:
        best_loss = float("inf")
        itr = 0
        wall_clock = 0.
        begin_epoch = 1
    else:
        chkdir = os.path.dirname(args.resume)
        tedf = pd.read_csv(os.path.join(chkdir,'test.csv'))
        trdf = pd.read_csv(os.path.join(chkdir,'training.csv'))
        wall_clock = trdf['wall'].to_numpy()[-1]
        itr = trdf['itr'].to_numpy()[-1]
        best_loss = tedf['bpd'].min()
        begin_epoch = int(tedf['epoch'].to_numpy()[-1]+1) # not exactly correct

    if args.distributed:
        if write_log: logger.info('Syncing machines before training')
        dist_utils.sum_tensor(torch.tensor([1.0]).float().cuda())
    

    for epoch in range(begin_epoch, args.num_epochs + 1):
        if not args.validate:
            model.train()  # inheritated method from torch nn, activates 'train mode'

            with open(trainlog,'a') as f:
                if write_log: csvlogger = csv.DictWriter(f, traincolumns)

                for _, (x, y) in enumerate(train_loader):
                    start = time.time()
                    update_lr(optimizer, itr)
                    optimizer.zero_grad()

                    # cast data and move to device
                    x = add_noise(cvt(x), nbits=args.nbits)
                    #x = x.clamp_(min=0, max=1 )
                    
                    # compute loss
                    bpd, (x, z), reg_states = compute_bits_per_dim(x, model)
                    if np.isnan(bpd.data.item()):
                        raise ValueError('model returned nan during training')
                    elif np.isinf(bpd.data.item()):
                        raise ValueError('model returned inf during training')
                    
                    loss = bpd
                    if regularization_coeffs:
                        reg_loss = sum(
                            reg_state * coeff for reg_state, coeff in zip(reg_states, regularization_coeffs) if coeff != 0
                        )
                        loss = loss + reg_loss
                    total_time = count_total_time(model)

                    loss.backward()
                    nfe_opt = count_nfe(model)
                    if write_log: steps_meter.update(nfe_opt)
                    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

                    optimizer.step()


                    itr_time = time.time() - start
                    wall_clock += itr_time
                    
                    batch_size = x.size(0)
                    metrics = torch.tensor([1., batch_size,
                                            loss.item(),
                                            bpd.item(),
                                            nfe_opt,
                                            grad_norm,
                                            *reg_states]).float().cuda()

                    rv = tuple(torch.tensor(0.).cuda() for r in reg_states)

                    total_gpus, batch_total, r_loss, r_bpd, r_nfe, r_grad_norm, *rv = dist_utils.sum_tensor(metrics).cpu().numpy()


                    
                    if write_log:
                        time_meter.update(itr_time)
                        bpd_meter.update(r_bpd/total_gpus)
                        loss_meter.update(r_loss/total_gpus)
                        grad_meter.update(r_grad_norm/total_gpus)
                        tt_meter.update(total_time)

                        fmt = '{:.4f}'
                        logdict = {'itr':itr, 
                            'wall': fmt.format(wall_clock),
                            'itr_time': fmt.format(itr_time),
                            'loss': fmt.format(r_loss/total_gpus),
                            'bpd': fmt.format(r_bpd/total_gpus),
                            'total_time':fmt.format(total_time),
                            'fe': r_nfe/total_gpus,
                            'grad_norm': fmt.format(r_grad_norm/total_gpus),
                            }
                        if regularization_coeffs:
                            rv = tuple(v_/total_gpus for v_ in rv)
                            logdict = append_regularization_csv_dict(logdict,
                                    regularization_fns, rv)
                        csvlogger.writerow(logdict)

                        if itr % args.log_freq == 0:
                            log_message = (
                                    "Itr {:06d} | Wall {:.3e}({:.2f}) | "
                                    "Time/Itr {:.2f}({:.2f}) | BPD {:.2f}({:.2f}) | "
                                    "Loss {:.2f}({:.2f}) | "
                                    "FE {:.0f}({:.0f}) | Grad Norm {:.3e}({:.3e}) | "
                                    "TT {:.2f}({:.2f})".format(
                                    itr, wall_clock, wall_clock/(itr+1), 
                                    time_meter.val, time_meter.avg,
                                    bpd_meter.val, bpd_meter.avg,
                                    loss_meter.val, loss_meter.avg,
                                    steps_meter.val, steps_meter.avg,
                                    grad_meter.val, grad_meter.avg, 
                                    tt_meter.val, tt_meter.avg
                                    )
                                )
                            if regularization_coeffs:
                                log_message = append_regularization_to_log(log_message,
                                        regularization_fns, rv)
                            logger.info(log_message)



                    itr += 1

        # compute test loss
        model.eval()
        if args.local_rank==0:
            utils.makedirs(args.save)
            torch.save({
                "args": args,
                "state_dict": model.module.state_dict() if torch.cuda.is_available() else model.state_dict(),
                "optim_state_dict": optimizer.state_dict(), 
                "fixed_z": fixed_z.cpu()
            }, os.path.join(args.save, "checkpt.pth"))
        if epoch % args.val_freq == 0 or args.validate:
            with open(testlog,'a') as f:
                if write_log: csvlogger = csv.DictWriter(f, testcolumns)
                with torch.no_grad():
                    start = time.time()
                    if write_log: logger.info("validating...")


                    lossmean = 0.
                    meandist = 0.
                    steps = 0
                    tt = 0.
                    for i, (x, y) in enumerate(test_loader):
                        sh = x.shape
                        x = shift(cvt(x), nbits=args.nbits)
                        loss, (x,z), _ = compute_bits_per_dim(x, model)
                        dist = (x.view(x.size(0),-1)-z).pow(2).mean(dim=-1).mean()
                        meandist = i/(i+1)*dist + meandist/(i+1)
                        lossmean = i/(i+1)*lossmean + loss/(i+1) 

                        tt = i/(i+1)*tt + count_total_time(model)/(i+1)
                        steps = i/(i+1)*steps + count_nfe(model)/(i+1)



                    loss = lossmean.item()
                    metrics = torch.tensor([1., loss, meandist, steps]).float().cuda()

                    total_gpus, r_bpd, r_mdist, r_steps = dist_utils.sum_tensor(metrics).cpu().numpy()
                    eval_time = time.time()-start

                    if write_log:
                        fmt = '{:.4f}'
                        logdict = {'epoch':epoch,
                                   'eval_time':fmt.format(eval_time),
                                   'bpd':fmt.format(r_bpd/total_gpus),
                                   'wall': fmt.format(wall_clock),
                                   'total_time':fmt.format(tt),
                                   'transport_cost':fmt.format(r_mdist/total_gpus),
                                   'fe':'{:.2f}'.format(r_steps/total_gpus)}

                        csvlogger.writerow(logdict)

                        logger.info("Epoch {:04d} | Time {:.4f}, Bit/dim {:.4f}, Steps {:.4f}, TT {:.2f}, Transport Cost {:.2e}".format(epoch, eval_time, r_bpd/total_gpus, r_steps/total_gpus, tt, r_mdist/total_gpus))

                    loss = r_bpd/total_gpus


                    if loss < best_loss and args.local_rank==0: 
                        best_loss = loss
                        shutil.copyfile(os.path.join(args.save, "checkpt.pth"),
                                        os.path.join(args.save, "best.pth"))



            # visualize samples and density
            if write_log:
                with torch.no_grad():
                    fig_filename = os.path.join(args.save, "figs", "{:04d}.jpg".format(epoch))
                    utils.makedirs(os.path.dirname(fig_filename))
                    generated_samples, _, _ = model(fixed_z, reverse=True)
                    generated_samples = generated_samples.view(-1, *data_shape)
                    nb = int(np.ceil(np.sqrt(float(fixed_z.size(0)))))
                    save_image(unshift(generated_samples, nbits=args.nbits), fig_filename, nrow=nb)
            if args.validate:
                break

if __name__ == '__main__':
    try:
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", category=UserWarning)
            main()
        #if not args.skip_auto_shutdown: os.system(f'sudo shutdown -h -P +{args.auto_shutdown_success_delay_mins}')
    except Exception as e:
        exc_type, exc_value, exc_traceback = sys.exc_info()
        import traceback
        traceback.print_tb(exc_traceback, file=sys.stdout)
        traceback.print_exception(exc_type,exc_value,exc_traceback)
        # in case of exception, wait 2 hours before shutting down
        #if not args.skip_auto_shutdown: os.system(f'sudo shutdown -h -P +{args.auto_shutdown_failure_delay_mins}')