def main():
args, cfg = parse_config()
if args.launcher == 'none':
dist_train = False
else:
args.batch_size, cfg.LOCAL_RANK = getattr(
common_utils, 'init_dist_%s' % args.launcher)(args.batch_size,
args.tcp_port,
args.local_rank,
backend='nccl')
dist_train = True
if args.fix_random_seed:
common_utils.set_random_seed(666)
output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
ckpt_dir = output_dir / 'ckpt'
output_dir.mkdir(parents=True, exist_ok=True)
ckpt_dir.mkdir(parents=True, exist_ok=True)
log_file = output_dir / ('log_train_%s.txt' %
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
# log to file
logger.info('**********************Start logging**********************')
gpu_list = os.environ[
'CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys(
) else 'ALL'
logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
if dist_train:
total_gpus = dist.get_world_size()
logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
for key, val in vars(args).items():
logger.info('{:16} {}'.format(key, val))
log_config_to_file(cfg, logger=logger)
if cfg.LOCAL_RANK == 0:
os.system('cp %s %s' % (args.cfg_file, output_dir))
tb_log = SummaryWriter(
log_dir=str(output_dir /
'tensorboard')) if cfg.LOCAL_RANK == 0 else None
# -----------------------create dataloader & network & optimizer---------------------------
train_set, train_loader, train_sampler = build_dataloader(
dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
batch_size=args.batch_size,
dist=dist_train,
workers=args.workers,
logger=logger,
training=True,
merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch,
total_epochs=args.epochs)
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=train_set)
if args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model.cuda()
optimizer = build_optimizer(model, cfg.OPTIMIZATION)
# load checkpoint if it is possible
start_epoch = it = 0
last_epoch = -1
if args.pretrained_model is not None:
model.load_params_from_file(filename=args.pretrained_model,
to_cpu=dist,
logger=logger)
if args.ckpt is not None:
it, start_epoch = model.load_params_with_optimizer(args.ckpt,
to_cpu=dist,
optimizer=optimizer,
logger=logger)
last_epoch = start_epoch + 1
else:
ckpt_list = glob.glob(str(ckpt_dir / '*checkpoint_epoch_*.pth'))
if len(ckpt_list) > 0:
ckpt_list.sort(key=os.path.getmtime)
it, start_epoch = model.load_params_with_optimizer(
ckpt_list[-1], to_cpu=dist, optimizer=optimizer, logger=logger)
last_epoch = start_epoch + 1
model.train(
) # before wrap to DistributedDataParallel to support fixed some parameters
if dist_train:
model = nn.parallel.DistributedDataParallel(
model, device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()])
logger.info(model)
lr_scheduler, lr_warmup_scheduler = build_scheduler(
optimizer,
total_iters_each_epoch=len(train_loader),
total_epochs=args.epochs,
last_epoch=last_epoch,
optim_cfg=cfg.OPTIMIZATION)
# -----------------------start training---------------------------
logger.info(
'**********************Start training %s/%s(%s)**********************'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
train_model(model,
optimizer,
train_loader,
model_func=model_fn_decorator(),
lr_scheduler=lr_scheduler,
optim_cfg=cfg.OPTIMIZATION,
start_epoch=start_epoch,
total_epochs=args.epochs,
start_iter=it,
rank=cfg.LOCAL_RANK,
tb_log=tb_log,
ckpt_save_dir=ckpt_dir,
train_sampler=train_sampler,
lr_warmup_scheduler=lr_warmup_scheduler,
ckpt_save_interval=args.ckpt_save_interval,
max_ckpt_save_num=args.max_ckpt_save_num,
merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch)
logger.info(
'**********************End training %s/%s(%s)**********************\n\n\n'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
logger.info(
'**********************Start evaluation %s/%s(%s)**********************'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
test_set, test_loader, sampler = build_dataloader(
dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
batch_size=args.batch_size,
dist=dist_train,
workers=args.workers,
logger=logger,
training=False)
eval_output_dir = output_dir / 'eval' / 'eval_with_train'
eval_output_dir.mkdir(parents=True, exist_ok=True)
args.start_epoch = max(args.epochs - 10,
0) # Only evaluate the last 10 epochs
repeat_eval_ckpt(model.module if dist_train else model,
test_loader,
args,
eval_output_dir,
logger,
ckpt_dir,
dist_test=dist_train)
logger.info(
'**********************End evaluation %s/%s(%s)**********************'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
def main():
cfg = get_config()
logger = get_logger(cfg.log_dir, cfg.tag)
logger.info(pprint.pformat(cfg))
if cfg.launcher == 'none':
dist_train = False
else:
cfg.batch_size, cfg.local_rank = dist.init_dist_pytorch(
cfg.batch_size, cfg.local_rank, backend='nccl'
)
cfg.data.train.batch_size = cfg.batch_size
dist_train = True
set_random_seed(cfg['random_seed'])
if dist_train:
total_gpus = dist.get_world_size()
logger.info('total_batch_size: %d' % (total_gpus * cfg.batch_size))
tb_log = SummaryWriter(log_dir=str(cfg.log_dir)) if cfg.local_rank == 0 else None
train_set, train_loader, train_sampler = build_dataloader(
cfg.data.train, dist=dist_train, training=True, logger=logger)
#data = train_set[0]
model = build_model(cfg.model)
if cfg.get('sync_bn', False):
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model.cuda()
optimizer = build_optimizer(model, cfg.optimization)
start_epoch = it = 0
last_epoch = -1
if cfg.ckpt is not None:
it, start_epoch = model.load_params_with_optimizer(cfg.ckpt, to_cpu=dist, optimizer=optimizer, logger=logger)
last_epoch = start_epoch + 1
model.train()
if dist_train:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[cfg.local_rank % torch.cuda.device_count()])
logger.info(model)
total_iters_each_epoch = len(train_loader)
lr_scheduler, lr_warmup_scheduler = build_scheduler(
optimizer, total_iters_each_epoch=total_iters_each_epoch, total_epochs=cfg.epochs,
last_epoch=last_epoch, optim_cfg=cfg.optimization
)
# -----------------------start training---------------------------
logger.info('*'*20 + 'Start training' +'*'*20)
train_model(
model,
optimizer,
train_loader,
model_func=model_fn_decorator(),
lr_scheduler=lr_scheduler,
optim_cfg=cfg.optimization,
start_epoch=start_epoch,
total_epochs=cfg.epochs,
start_iter=it,
rank=cfg.local_rank,
tb_log=tb_log,
ckpt_save_dir=cfg.model_dir,
train_sampler=train_sampler,
lr_warmup_scheduler=lr_warmup_scheduler,
ckpt_save_interval=cfg.ckpt_save_interval,
max_ckpt_save_num=cfg.max_ckpt_save_num,
merge_all_iters_to_one_epoch=False
)
logger.info('*'*20 + 'End training' +'*'*20)
def main():
args, cfg = parge_config()
if args.launcher == 'none':
dist_train = False
else:
args.batch_size, cfg.LOCAL_RANK = getattr(
common_utils, 'init_dist_%s' % args.launcher)(args.batch_size,
args.tcp_port,
args.local_rank,
backend='nccl')
dist_train = True
if args.fix_random_seed:
common_utils.set_random_seed(666)
output_dir = cfg.ROOT_DIR / 'output' / cfg.TAG / args.extra_tag
output_dir.mkdir(parents=True, exist_ok=True)
ckpt_dir = output_dir / 'ckpt'
ckpt_dir.mkdir(parents=True, exist_ok=True)
log_file = output_dir / ('log_train_%s.txt' %
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
# log to file
logger.info('**********************Start logging**********************')
gpu_list = os.environ[
'CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys(
) else 'ALL'
logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
if dist_train:
total_gpus = dist.get_world_size()
logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
for key, val in vars(args).items():
logger.info('{:16} {}'.format(key, val))
log_config_to_file(cfg, logger=logger)
tb_log = SummaryWriter(
log_dir=str(output_dir /
'tensorboard')) if cfg.LOCAL_RANK == 0 else None
if args.local_rank == 0:
wandb.init(project='BEVSEG-PCDet',
sync_tensorboard=True,
name=args.extra_tag,
config={
**vars(args),
**cfg
})
# -----------------------create dataloader & network & optimizer---------------------------
train_set, train_loader, train_sampler = build_dataloader(
cfg.DATA_CONFIG.DATA_DIR,
args.batch_size,
dist_train,
workers=args.workers,
logger=logger,
training=True)
model = build_network(train_set)
if args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model.cuda()
optimizer = build_optimizer(model, cfg.MODEL.TRAIN.OPTIMIZATION)
# load checkpoint if it is possible
start_epoch = it = 0
last_epoch = -1
if args.pretrained_model is not None:
model.load_params_from_file(filename=args.pretrained_model,
to_cpu=dist,
logger=logger)
if args.ckpt is not None:
it, start_epoch = model.load_params_with_optimizer(args.ckpt,
to_cpu=dist,
optimizer=optimizer,
logger=logger)
last_epoch = start_epoch + 1
else:
ckpt_list = glob.glob(str(ckpt_dir / '*checkpoint_epoch_*.pth'))
if len(ckpt_list) > 0:
ckpt_list.sort(key=os.path.getmtime)
it, start_epoch = model.load_params_with_optimizer(
ckpt_list[-1], to_cpu=dist, optimizer=optimizer, logger=logger)
last_epoch = start_epoch + 1
model.train(
) # before wrap to DistributedDataParallel to support fixed some parameters
if dist_train:
model = nn.parallel.DistributedDataParallel(
model,
device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()],
find_unused_parameters=
True # uncomment this line to debug unused params
)
logger.info(model)
lr_scheduler, lr_warmup_scheduler = build_scheduler(
optimizer,
total_iters_each_epoch=len(train_loader),
total_epochs=args.epochs,
last_epoch=last_epoch,
optim_cfg=cfg.MODEL.TRAIN.OPTIMIZATION)
# -----------------------start training---------------------------
logger.info(
'**********************Start training %s(%s)**********************' %
(cfg.TAG, args.extra_tag))
train_model(model,
optimizer,
train_loader,
model_func=model_fn_decorator(),
lr_scheduler=lr_scheduler,
optim_cfg=cfg.MODEL.TRAIN.OPTIMIZATION,
start_epoch=start_epoch,
total_epochs=args.epochs,
start_iter=it,
rank=cfg.LOCAL_RANK,
tb_log=tb_log,
ckpt_save_dir=ckpt_dir,
train_sampler=train_sampler,
lr_warmup_scheduler=lr_warmup_scheduler,
ckpt_save_interval=args.ckpt_save_interval,
max_ckpt_save_num=args.max_ckpt_save_num)
logger.info('**********************End training**********************')
'''
def main():
args, cfg = parse_config()
if args.launcher == 'none':
dist_train = False
total_gpus = 1
else:
total_gpus, cfg.LOCAL_RANK = getattr(common_utils, 'init_dist_%s' %
args.launcher)(args.tcp_port,
args.local_rank,
backend='nccl')
dist_train = True
if args.batch_size is None:
args.batch_size = cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU
else:
assert args.batch_size % total_gpus == 0, 'Batch size should match the number of gpus'
args.batch_size = args.batch_size // total_gpus
args.epochs = cfg.OPTIMIZATION.NUM_EPOCHS if args.epochs is None else args.epochs
if args.fix_random_seed:
common_utils.set_random_seed(666)
output_dir = cfg.ROOT_DIR / 'output' / cfg.EXP_GROUP_PATH / cfg.TAG / args.extra_tag
ckpt_dir = output_dir / 'ckpt'
output_dir.mkdir(parents=True, exist_ok=True)
ckpt_dir.mkdir(parents=True, exist_ok=True)
log_file = output_dir / ('log_train_%s.txt' %
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))
logger = common_utils.create_logger(log_file, rank=cfg.LOCAL_RANK)
# log to file
logger.info('**********************Start logging**********************')
gpu_list = os.environ[
'CUDA_VISIBLE_DEVICES'] if 'CUDA_VISIBLE_DEVICES' in os.environ.keys(
) else 'ALL'
logger.info('CUDA_VISIBLE_DEVICES=%s' % gpu_list)
if dist_train:
logger.info('total_batch_size: %d' % (total_gpus * args.batch_size))
for key, val in vars(args).items():
logger.info('{:16} {}'.format(key, val))
log_config_to_file(cfg, logger=logger)
if cfg.LOCAL_RANK == 0:
os.system('cp %s %s' % (args.cfg_file, output_dir))
tb_log = SummaryWriter(
log_dir=str(output_dir /
'tensorboard')) if cfg.LOCAL_RANK == 0 else None
# -----------------------create dataloader & network & optimizer---------------------------
train_set, train_loader, train_sampler = build_dataloader(
dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
batch_size=args.batch_size,
dist=dist_train,
workers=args.workers,
logger=logger,
training=True,
merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch,
total_epochs=args.epochs)
logger.info(
'**********************Starting Inference on Pointpillars**********************'
)
# Load model to GPU and deactivate gradients
MODEL_PATH = '/home/triasamo/entire_model.pth'
model_point = torch.load(MODEL_PATH)
model_point.cuda()
model_point.eval()
start_time = time.time()
all_predictions = []
with torch.no_grad():
for data_dict in tqdm(train_loader):
load_data_to_gpu(data_dict)
# feed point cloud into model
predictions, _ = model_point(
data_dict
) # returns a list of dictionaries (one for each frame fed into the model)
for index, pred_dict in enumerate(predictions):
# Sort out predictions into boxes, scores, labels and centers
frame_id = data_dict['frame_id'][index]
pred_boxes = pred_dict['pred_boxes'].cpu().numpy()
pred_scores = pred_dict['pred_scores'].cpu().numpy()
pred_labels = pred_dict['pred_labels'].cpu().numpy()
pred_centers = pred_boxes[:, :3]
frame_dict = {
'frame_id': frame_id,
'pred_centers': pred_centers,
'pred_scores': pred_scores,
'pred_labels': pred_labels
}
all_predictions.append(frame_dict)
logger.info("Inferece of dataset executed in: %.2f sec" %
(time.time() - start_time))
#for idx, data_dict in enumerate(dataloader_extra):
# ic(data_dict.keys())
# dict_keys(['points', 'frame_id', 'gt_boxes', 'use_lead_xyz', 'voxels', 'voxel_coords', 'voxel_num_points', 'image_shape', 'batch_size'])
logger.info(
'**********************Finished Inference on Pointpillars**********************'
)
model = build_network(model_cfg=cfg.MODEL,
num_class=len(cfg.CLASS_NAMES),
dataset=train_set)
if args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model.cuda()
optimizer = build_optimizer(model, cfg.OPTIMIZATION)
# load checkpoint if it is possible
start_epoch = it = 0
last_epoch = -1
if args.pretrained_model is not None:
model.load_params_from_file(filename=args.pretrained_model,
to_cpu=dist,
logger=logger)
if args.ckpt is not None:
it, start_epoch = model.load_params_with_optimizer(args.ckpt,
to_cpu=dist,
optimizer=optimizer,
logger=logger)
last_epoch = start_epoch + 1
else:
ckpt_list = glob.glob(str(ckpt_dir / '*checkpoint_epoch_*.pth'))
if len(ckpt_list) > 0:
ckpt_list.sort(key=os.path.getmtime)
it, start_epoch = model.load_params_with_optimizer(
ckpt_list[-1], to_cpu=dist, optimizer=optimizer, logger=logger)
last_epoch = start_epoch + 1
model.train(
) # before wrap to DistributedDataParallel to support fixed some parameters
if dist_train:
model = nn.parallel.DistributedDataParallel(
model, device_ids=[cfg.LOCAL_RANK % torch.cuda.device_count()])
logger.info(model)
lr_scheduler, lr_warmup_scheduler = build_scheduler(
optimizer,
total_iters_each_epoch=len(train_loader),
total_epochs=args.epochs,
last_epoch=last_epoch,
optim_cfg=cfg.OPTIMIZATION)
# -----------------------start training---------------------------
logger.info(
'**********************Start training %s/%s(%s)**********************'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
train_model(model,
optimizer,
train_loader,
model_func=model_fn_decorator(),
lr_scheduler=lr_scheduler,
optim_cfg=cfg.OPTIMIZATION,
start_epoch=start_epoch,
total_epochs=args.epochs,
start_iter=it,
rank=cfg.LOCAL_RANK,
tb_log=tb_log,
ckpt_save_dir=ckpt_dir,
train_sampler=train_sampler,
lr_warmup_scheduler=lr_warmup_scheduler,
ckpt_save_interval=args.ckpt_save_interval,
max_ckpt_save_num=args.max_ckpt_save_num,
merge_all_iters_to_one_epoch=args.merge_all_iters_to_one_epoch)
logger.info(
'**********************End training %s/%s(%s)**********************\n\n\n'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
logger.info('************** Saving Entire model **************\n\n\n')
torch.save(model, '/home/triasamo/entire_model.pth')
logger.info(
'************** Saved model at /home/triasamo/entire_model.pth **************\n\n\n'
)
test_set, test_loader, sampler = build_dataloader(
dataset_cfg=cfg.DATA_CONFIG,
class_names=cfg.CLASS_NAMES,
batch_size=args.batch_size,
dist=dist_train,
workers=args.workers,
logger=logger,
training=False)
eval_output_dir = output_dir / 'eval' / 'eval_with_train'
eval_output_dir.mkdir(parents=True, exist_ok=True)
args.start_epoch = max(args.epochs - 10,
0) # Only evaluate the last 10 epochs
repeat_eval_ckpt(model.module if dist_train else model,
test_loader,
args,
eval_output_dir,
logger,
ckpt_dir,
dist_test=dist_train)
logger.info(
'**********************End evaluation %s/%s(%s)**********************'
% (cfg.EXP_GROUP_PATH, cfg.TAG, args.extra_tag))
