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


parser = argparse.ArgumentParser(description='A PyTorch Implementation of Efficient edge-preserving multi-view stereo network')
parser.add_argument('--mode', default='train', help='train or test', choices=['train', 'test', 'profile'])
parser.add_argument('--model', default='mvsnet', help='select model')
parser.add_argument('--device', default='cuda', help='select device')
parser.add_argument('--gpu_device', type=str, default='2', help='gpu no.')

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

parser.add_argument('--epochs', type=int, default=10, 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="6,8,9:2", help='epoch ids to downscale lr and the downscale rate')
parser.add_argument('--wd', type=float, default=0.0, help='weight decay')

parser.add_argument('--batch_size', type=int, default=4, help='train batch size')
parser.add_argument('--numdepth', type=int, default=128, help='the number of depth values')
parser.add_argument('--interval_scale', type=float, default=1.0, help='the number of depth values')
parser.add_argument('--num_views', type=int, default=5, help='view numbers')

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

parser.add_argument('--summary_freq', type=int, default=100, 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=1, 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('--ndepths', type=str, default="32,16,8", help='ndepths')
parser.add_argument('--depth_inter_r', type=str, default="4,2,1", help='depth_intervals_ratio')
parser.add_argument('--dlossw', type=str, default="0.5,1.0,2.0", help='depth loss weight for different stage')
parser.add_argument('--num_groups', type=str, default="8,8,8,8,8", help='num_groups')

parser.add_argument('--sync_bn', action='store_true',help='enabling apex sync BN.')

parser.add_argument('--robust_train', action='store_true')
parser.add_argument('--random_crop', action='store_true')
parser.add_argument('--resize_wh', type=str, default='')
parser.add_argument('--crop_wh', type=str, default='')
parser.add_argument('--offsetnet_only', action='store_true')
parser.add_argument('--backbone_only', action='store_true')
parser.add_argument('--ph_w', type=float, default='')

args = parser.parse_args()
cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_device
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
is_distributed = num_gpus > 1

# main function
def train(model, model_loss, optimizer, TrainImgLoader, TestImgLoader, 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)

    for epoch_idx in range(start_epoch, args.epochs):
        print('Epoch {}:'.format(epoch_idx))
        # global_step = len(TrainImgLoader) * epoch_idx

        # training
        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)
            lr_scheduler.step()
            if (not is_distributed) or (dist.get_rank() == 0):
                if do_summary:
                    save_scalars(logger, 'train', scalar_outputs, global_step)
                    save_images(logger, 'train', image_outputs, global_step)
                    print(
                       "Epoch {}/{}, Iter {}/{}, lr {:.6f}, train loss = {:.3f}, depth loss = {:.3f}, ph_loss = {:3f}, res_loss = {:3f}, less1 = {:3f}, less3 = {:3f}, time = {:3f}".format(
                           epoch_idx, args.epochs, batch_idx, len(TrainImgLoader),
                           optimizer.param_groups[0]["lr"], loss,
                           scalar_outputs['depth_loss'], scalar_outputs['ph_loss'], scalar_outputs['res_loss'], scalar_outputs["less1"], scalar_outputs["less3"],
                           time.time() - start_time))
                del scalar_outputs, image_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()

def test(model, model_loss, TestImgLoader, args):
    avg_test_scalars = DictAverageMeter()
    for batch_idx, sample in enumerate(TestImgLoader):
        start_time = time.time()
        loss, scalar_outputs, image_outputs = test_sample_depth(model, model_loss, sample, args)
        avg_test_scalars.update(scalar_outputs)
        del scalar_outputs, image_outputs
        if (not is_distributed) or (dist.get_rank() == 0):
            print('Iter {}/{}, test loss = {:.3f}, time = {:3f}'.format(batch_idx, len(TestImgLoader), loss,
                                                                        time.time() - start_time))
            if batch_idx % 100 == 0:
                print("Iter {}/{}, test results = {}".format(batch_idx, len(TestImgLoader), avg_test_scalars.mean()))
    if (not is_distributed) or (dist.get_rank() == 0):
        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["stage{}".format(str(num_stage-2))]
    mask = mask_ms["stage{}".format(str(num_stage-2))]

    outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_min"], sample_cuda["depth_max"], sample_cuda["depth_interval"])
    depth_est = outputs["depth"]

    loss, depth_loss, ph_loss, res_loss, less1, less3 = model_loss(outputs, depth_gt_ms, mask_ms, sample_cuda["proj_matrices"], sample_cuda["imgs"], sample_cuda["depth_interval"], args.offsetnet_only, args.backbone_only, args.ph_w, dlossw=[float(e) for e in args.dlossw.split(",") if e])

    loss.backward()
    optimizer.step()

    scalar_outputs = {"loss": loss,
                      "depth_loss": depth_loss,
                      "ph_loss": ph_loss,
                      "res_loss": res_loss,
                      "less1": less1*100,
                      "less3": less3*100,
                      "abs_depth_error": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5),
                      "thres2mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 2),
                      "thres4mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 4),
                      "thres8mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 8),}

    image_outputs = {"depth_est": depth_est * mask,
                     "depth_est_nomask": depth_est,
                     "depth_gt": sample["depth"]["stage1"],
                     "ref_img": sample["imgs"][:, 0],
                     "mask": sample["mask"]["stage1"],
                     "errormap": (depth_est - depth_gt).abs() * mask,
                     }

    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["stage{}".format(num_stage)]
    mask = mask_ms["stage{}".format(num_stage)]

    outputs = model(sample_cuda["imgs"], sample_cuda["proj_matrices"], sample_cuda["depth_min"], sample_cuda["depth_max"], sample_cuda["depth_interval"])
    depth_est = outputs["depth"]

    loss, depth_loss, less1, less3 = model_loss(outputs, depth_gt_ms, mask_ms, sample_cuda["depth_interval"], dlossw=[float(e) for e in args.dlossw.split(",") if e])

    scalar_outputs = {"loss": loss,
                      "depth_loss": depth_loss,
                      "abs_depth_error": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5),
                      "thres2mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 2),
                      "thres4mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 4),
                      "thres8mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 8),
                      "thres14mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 14),
                      "thres20mm_error": Thres_metrics(depth_est, depth_gt, mask > 0.5, 20),

                      "thres2mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [0, 2.0]),
                      "thres4mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [2.0, 4.0]),
                      "thres8mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [4.0, 8.0]),
                      "thres14mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [8.0, 14.0]),
                      "thres20mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [14.0, 20.0]),
                      "thres>20mm_abserror": AbsDepthError_metrics(depth_est, depth_gt, mask > 0.5, [20.0, 1e5]),
                    }

    image_outputs = {"depth_est": depth_est * mask,
                     "depth_est_nomask": depth_est,
                     "depth_gt": sample["depth"]["stage1"],
                     "ref_img": sample["imgs"][:, 0],
                     "mask": sample["mask"]["stage1"],
                     "errormap": (depth_est - depth_gt).abs() * mask}

    if is_distributed:
        scalar_outputs = reduce_scalar_outputs(scalar_outputs)

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



if __name__ == '__main__':
    # parse arguments and check


    if args.sync_bn:
        assert is_distributed, "must be distributed"

    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 = MVSNet(ndepths=[int(nd) for nd in args.ndepths.split(",") if nd],
                   depth_interals_ratio=[float(d_i) for d_i in args.depth_inter_r.split(",") if d_i],
                   num_groups=[int(ng) for ng in args.num_groups.split(",") if ng], offsetnet_only=args.offsetnet_only, backbone_only=args.backbone_only)
    model.to(device)

    #model_loss = mvsnet_loss
    if args.dataset == "blendedmvs":
         model_loss = mvsnet_loss
    else:
         model_loss = mvsnet_loss_dtu

    if args.sync_bn:
        print("synced BN of model")
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

    optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.wd)

    # load parameters
    start_epoch = 0
    if args.resume:
        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
    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'])
    else:
        for m in model.modules():
            if any([isinstance(m, T) for T in [nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d, nn.ConvTranspose3d]]):
                if m.weight.requires_grad:
                    nn.init.xavier_uniform_(m.weight)
            elif any([isinstance(m, T) for T in [nn.BatchNorm2d, nn.BatchNorm3d]]):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    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 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.num_views, args.resize_wh, args.crop_wh, args.numdepth, args.interval_scale, args.robust_train, args.random_crop)
    test_dataset = MVSDataset(args.testpath, args.testlist, "test", args.num_views, args.resize_wh, args.crop_wh, args.numdepth, args.interval_scale, args.robust_train, args.random_crop)

    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=1, drop_last=True, pin_memory=args.pin_m)
        TestImgLoader = DataLoader(test_dataset, args.batch_size, sampler=test_sampler, num_workers=1, drop_last=False, pin_memory=args.pin_m)
    else:
        TrainImgLoader = DataLoader(train_dataset, args.batch_size, shuffle=True, num_workers=1, drop_last=True, pin_memory=args.pin_m)
        TestImgLoader = DataLoader(test_dataset, args.batch_size, shuffle=False, num_workers=1, drop_last=False, pin_memory=args.pin_m)


    if args.mode == "train":
        train(model, model_loss, optimizer, TrainImgLoader, TestImgLoader, start_epoch, args)
    elif args.mode == "test":
        test(model, model_loss, TestImgLoader, args)
    else:
        raise NotImplementedError