def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Training")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.deprecated.init_process_group(backend="nccl",
init_method="env://")
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
model = train(cfg, args.local_rank, args.distributed)
if not args.skip_test:
test(cfg, model, args.distributed)
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default="./configs/seq.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
distributed = num_gpus > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.deprecated.init_process_group(
backend="nccl", init_method="env://"
)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("maskrcnn_benchmark", save_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(cfg)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
checkpointer = DetectronCheckpointer(cfg, model)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
iou_types = ("bbox",)
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm",)
output_folders = [None] * len(cfg.DATASETS.TEST)
if cfg.OUTPUT_DIR:
dataset_names = cfg.DATASETS.TEST
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
model_name = cfg.MODEL.WEIGHT.split('/')[-1]
for output_folder, data_loader_val in zip(output_folders, data_loaders_val):
inference(
model,
data_loader_val,
iou_types=iou_types,
box_only=cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
model_name=model_name,
cfg=cfg,
)
synchronize()
def main():
parser = argparse.ArgumentParser(description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default="/home/guli/Desktop/VOS_ICCV2019/maskrcnn-benchmark/configs/davis/e2e_mask_rcnn_R_50_FPN_1x_davis.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
distributed = num_gpus > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.deprecated.init_process_group(
backend="nccl", init_method="env://"
)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("DAVIS_MaskRCNN_baseline_test", save_dir, args.local_rank)
logger.info("Using {} GPUs".format(num_gpus))
logger.info(cfg)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
checkpointer = Checkpointer(model)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
iou_types = ("bbox",)
# if cfg.MODEL.MASK_ON:
# iou_types = iou_types + ("segm",)
output_folders = [None] * len(cfg.DATASETS.TEST)
if cfg.OUTPUT_DIR:
dataset_names = cfg.DATASETS.TEST
exp_name = cfg.EXP.NAME
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name + "_" + exp_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
for output_folder, data_loader_val in zip(output_folders, data_loaders_val):
inference_davis(
model,
data_loader_val,
iou_types=iou_types,
box_only=False,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
debug=cfg.TEST.DEBUG,
generate_annotation=cfg.TEST.GENERATE_ANNOTATION,
overlay_box=cfg.TEST.OVERLAY_BOX,
matching=cfg.TEST.MATCHING,
skip_computation_network=cfg.TEST.SKIP_NETWORK,
select_top_predictions_flag=cfg.TEST.SELECT_TOP_PREDICTIONS,
cfg=cfg
)
synchronize()
def main(args):
# parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
# parser.add_argument(
# "--config-file",
# default="",
# metavar="FILE",
# help="path to config file",
# type=str,
# )
# parser.add_argument("--local-rank", type=int, default=0)
# parser.add_argument(
# "--skip-test",
# dest="skip_test",
# help="Do not test the final model",
# action="store_true",
# )
# parser.add_argument(
# "opts",
# help="Modify config options using the command-line",
# default=None,
# nargs=argparse.REMAINDER,
# )
# parser.add_argument(
# "--eval-only", action="store_true", help="perform evaluation only"
# )
# parser.add_argument(
# "--no-color", action="store_true", help="disable colorful logging"
# )
# parser.add_argument(
# "--num-gpus", type=int, default=1, help="number of gpus per machine"
# )
# parser.add_argument("--num-machines", type=int, default=1)
# parser.add_argument(
# "--machine-rank",
# type=int,
# default=0,
# help="the rank of this machine (unique per machine)",
# )
# port = 2 ** 15 + 2 ** 14 + hash(os.getuid()) % 2 ** 14
# parser.add_argument("--dist-url", default="tcp://127.0.0.1:{}".format(port))
# parser.add_argument(
# "opts",
# help="Modify config options using the command-line",
# default=None,
# nargs=argparse.REMAINDER,
# )
#
# args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
# num_gpus = args.num_gpus
args.distributed = num_gpus > 1
# args.distributed = get_world_size() > 1
args.local_rank = get_rank() % args.num_gpus
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
# distributed = get_world_size() > 1
# args.distributed = distributed
# if distributed:
# args.local_rank = get_rank() % args.num_gpus
print(args.config_file)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
logger.info("Using {} GPUs".format(args.num_gpus))
logger.info(args)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
tb_logger = Logger(cfg.OUTPUT_DIR, get_rank())
train(cfg, args.local_rank, args.distributed, tb_logger)
