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main.py
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main.py
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import argparse
import os
import time
import datetime
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from loguru import logger
from utils import *
from datasets import transforms, find_dataset_def, collate_fn
from models import PlanarRecon
from config import cfg, update_config
from datasets.sampler import DistributedSampler
from ops.comm import *
torch.multiprocessing.set_sharing_strategy('file_system')
def args():
parser = argparse.ArgumentParser(description='A PyTorch Implementation of NeuralRecon')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=True,
type=str)
parser.add_argument('opts',
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
# distributed training
parser.add_argument('--gpu',
help='gpu id for multiprocessing training',
type=str)
parser.add_argument('--world-size',
default=1,
type=int,
help='number of nodes for distributed training')
parser.add_argument('--dist-url',
default='tcp://127.0.0.1:23456',
type=str,
help='url used to set up distributed training')
parser.add_argument('--local_rank',
default=0,
type=int,
help='node rank for distributed training')
# parse arguments and check
args = parser.parse_args()
return args
args = args()
update_config(cfg, args)
cfg.defrost()
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
print('number of gpus: {}'.format(num_gpus))
cfg.DISTRIBUTED = num_gpus > 1
if cfg.DISTRIBUTED:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
synchronize()
cfg.LOCAL_RANK = args.local_rank
cfg.freeze()
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed(cfg.SEED)
# create logger
if is_main_process():
if not os.path.isdir(cfg.LOGDIR):
os.makedirs(cfg.LOGDIR)
current_time_str = str(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))
logfile_path = os.path.join(cfg.LOGDIR, f'{current_time_str}_{cfg.MODE}.log')
print('creating log file', logfile_path)
logger.add(logfile_path, format="{time} {level} {message}", level="INFO")
tb_writer = SummaryWriter(cfg.LOGDIR)
# Augmentation
if cfg.MODE == 'train':
n_views = cfg.TRAIN.N_VIEWS
random_rotation = cfg.TRAIN.RANDOM_ROTATION_3D
random_translation = cfg.TRAIN.RANDOM_TRANSLATION_3D
paddingXY = cfg.TRAIN.PAD_XY_3D
paddingZ = cfg.TRAIN.PAD_Z_3D
else:
n_views = cfg.TEST.N_VIEWS
random_rotation = False
random_translation = False
paddingXY = 0
paddingZ = 0
transform = []
transform += [transforms.ResizeImage((640, 480)),
transforms.ToTensor(),
transforms.RandomTransformSpace(
cfg.MODEL.N_VOX, cfg.MODEL.VOXEL_SIZE, random_rotation, random_translation,
paddingXY, paddingZ, max_epoch=cfg.TRAIN.EPOCHS),
transforms.IntrinsicsPoseToProjection(n_views, 4),
transforms.GeneratePlaneGT(cfg.MODEL.NORMAL_ANCHOR_PATH)
]
transforms = transforms.Compose(transform)
# dataset, dataloader
MVSDataset = find_dataset_def(cfg.DATASET)
train_dataset = MVSDataset(cfg.TRAIN.PATH, "train", transforms, cfg.TRAIN.N_VIEWS, len(cfg.MODEL.THRESHOLDS) - 1)
test_dataset = MVSDataset(cfg.TEST.PATH, "val", transforms, cfg.TEST.N_VIEWS, len(cfg.MODEL.THRESHOLDS) - 1)
if cfg.DISTRIBUTED:
train_sampler = DistributedSampler(train_dataset, shuffle=False)
TrainImgLoader = torch.utils.data.DataLoader(
train_dataset,
batch_size=cfg.BATCH_SIZE,
sampler=train_sampler,
collate_fn=collate_fn,
num_workers=cfg.TRAIN.N_WORKERS,
pin_memory=True,
drop_last=True
)
test_sampler = DistributedSampler(test_dataset, shuffle=False)
TestImgLoader = torch.utils.data.DataLoader(
test_dataset,
batch_size=cfg.BATCH_SIZE,
sampler=test_sampler,
collate_fn=collate_fn,
num_workers=cfg.TEST.N_WORKERS,
pin_memory=True,
drop_last=False
)
else:
TrainImgLoader = DataLoader(train_dataset, cfg.BATCH_SIZE, shuffle=False, num_workers=cfg.TRAIN.N_WORKERS,
collate_fn=collate_fn, drop_last=True)
TestImgLoader = DataLoader(test_dataset, cfg.BATCH_SIZE, shuffle=False, num_workers=cfg.TEST.N_WORKERS,
collate_fn=collate_fn, drop_last=False)
# model, optimizer
model = PlanarRecon(cfg)
if cfg.DISTRIBUTED:
model.cuda()
# model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# model.track_fuse = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model.track_fuse)
# model.backbone2d = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model.backbone2d)
model = DistributedDataParallel(
model, device_ids=[cfg.LOCAL_RANK], output_device=cfg.LOCAL_RANK,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
find_unused_parameters=True
)
else:
model = torch.nn.DataParallel(model, device_ids=[0])
model.cuda()
optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.TRAIN.LR, betas=(0.9, 0.999), weight_decay=cfg.TRAIN.WD)
# main function
def train():
# load parameters
start_epoch = 0
if cfg.RESUME:
saved_models = [fn for fn in os.listdir(cfg.LOGDIR) if fn.endswith(".ckpt")]
saved_models = sorted(saved_models, key=lambda x: int(x.split('_')[-1].split('.')[0]))
if len(saved_models) != 0:
# use the latest checkpoint file
loadckpt = os.path.join(cfg.LOGDIR, saved_models[-1])
logger.info("resuming " + str(loadckpt))
map_location = {'cuda:%d' % 0: 'cuda:%d' % cfg.LOCAL_RANK}
state_dict = torch.load(loadckpt, map_location=map_location)
model.load_state_dict(state_dict['model'], strict=False)
optimizer.param_groups[0]['initial_lr'] = state_dict['optimizer']['param_groups'][0]['lr']
optimizer.param_groups[0]['lr'] = state_dict['optimizer']['param_groups'][0]['lr']
start_epoch = state_dict['epoch'] + 1
elif cfg.LOADCKPT != '':
# load checkpoint file specified by args.loadckpt
logger.info("loading model {}".format(cfg.LOADCKPT))
map_location = {'cuda:%d' % 0: 'cuda:%d' % cfg.LOCAL_RANK}
state_dict = torch.load(cfg.LOADCKPT, map_location=map_location)
model.load_state_dict(state_dict['model'])
optimizer.param_groups[0]['initial_lr'] = state_dict['optimizer']['param_groups'][0]['lr']
optimizer.param_groups[0]['lr'] = state_dict['optimizer']['param_groups'][0]['lr']
start_epoch = state_dict['epoch'] + 1
logger.info("start at epoch {}".format(start_epoch))
logger.info('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
milestones = [int(epoch_idx) for epoch_idx in cfg.TRAIN.LREPOCHS.split(':')[0].split(',')]
lr_gamma = 1 / float(cfg.TRAIN.LREPOCHS.split(':')[1])
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=lr_gamma,
last_epoch=start_epoch - 1)
for epoch_idx in range(start_epoch, cfg.TRAIN.EPOCHS):
logger.info('Epoch {}:'.format(epoch_idx))
TrainImgLoader.dataset.epoch = epoch_idx
TrainImgLoader.dataset.tsdf_cashe = {}
# training
for batch_idx, sample in enumerate(TrainImgLoader):
global_step = len(TrainImgLoader) * epoch_idx + batch_idx
do_summary = global_step % cfg.SUMMARY_FREQ == 0
start_time = time.time()
loss, scalar_outputs = train_sample(sample)
if is_main_process():
logger.info(
'Epoch {}/{}, Iter {}/{}, train loss = {:.3f}, time = {:.3f}'.format(epoch_idx, cfg.TRAIN.EPOCHS,
batch_idx,
len(TrainImgLoader), loss,
time.time() - start_time))
if do_summary and is_main_process():
save_scalars(tb_writer, 'train', scalar_outputs, global_step)
del scalar_outputs
# print('reserved {}'.format(torch.cuda.max_memory_reserved() / (1024 ** 3)))
# torch.cuda.empty_cache()
lr_scheduler.step()
# checkpoint
if (epoch_idx + 1) % cfg.SAVE_FREQ == 0 and is_main_process():
torch.save({
'epoch': epoch_idx,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()},
"{}/model_{:0>6}.ckpt".format(cfg.LOGDIR, epoch_idx))
def test(from_latest=True):
ckpt_list = []
while True:
saved_models = [fn for fn in os.listdir(cfg.LOGDIR) if fn.endswith(".ckpt")]
saved_models = sorted(saved_models, key=lambda x: int(x.split('_')[-1].split('.')[0]))
if from_latest:
saved_models = saved_models[-1:]
for ckpt in saved_models:
if ckpt not in ckpt_list:
# use the latest checkpoint file
loadckpt = os.path.join(cfg.LOGDIR, ckpt)
logger.info("resuming " + str(loadckpt))
state_dict = torch.load(loadckpt)
model.load_state_dict(state_dict['model'], strict=False)
epoch_idx = state_dict['epoch']
TestImgLoader.dataset.epoch = epoch_idx
TestImgLoader.dataset.tsdf_cashe = {}
gpu_mem_usage_reserved = []
avg_test_scalars = DictAverageMeter()
save_mesh_scene = SaveScene(cfg)
for batch_idx, sample in enumerate(TestImgLoader):
for n in sample['fragment']:
logger.info(n)
start_time = time.time()
loss, scalar_outputs, outputs = test_sample(sample)
logger.info('Epoch {}, Iter {}/{}, test loss = {:.3f}, time = {:3f}'.format(epoch_idx, batch_idx,
len(TestImgLoader),
loss,
time.time() - start_time))
avg_test_scalars.update(scalar_outputs)
del scalar_outputs
if batch_idx % 100 == 0:
logger.info("Iter {}/{}, test results = {}".format(batch_idx, len(TestImgLoader),
avg_test_scalars.mean()))
# save mesh
if cfg.SAVE_SCENE_MESH:
save_mesh_scene(outputs, sample, epoch_idx)
save_scalars(tb_writer, 'fulltest', avg_test_scalars.mean(), epoch_idx)
logger.info("epoch {} avg_test_scalars:".format(epoch_idx), avg_test_scalars.mean())
ckpt_list.append(ckpt)
time.sleep(10)
def train_sample(sample):
model.train()
optimizer.zero_grad()
outputs, loss_dict = model(sample)
loss = loss_dict['total_loss']
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
optimizer.step()
return tensor2float(loss), tensor2float(loss_dict)
@make_nograd_func
def test_sample(sample):
model.eval()
outputs, loss_dict = model(sample)
loss = loss_dict['total_loss']
return tensor2float(loss), tensor2float(loss_dict), outputs
if __name__ == '__main__':
if cfg.MODE == "train":
train()
elif cfg.MODE == "test":
test()