def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default=
"/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--ckpt",
help=
"The path to the checkpoint for test, default is the latest checkpoint.",
default=None,
)
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.init_process_group(backend="nccl",
init_method="env://")
synchronize()
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_face_trainer(cfg)
model.to(cfg.MODEL.DEVICE)
# Initialize mixed-precision if necessary
use_mixed_precision = cfg.DTYPE == 'float16'
amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
ckpt = cfg.MODEL.WEIGHT if args.ckpt is None else args.ckpt
_ = checkpointer.load(ckpt, use_latest=args.ckpt is None)
TEST_PATH_LIST = cfg.DATASETS.TEST_PATH_LIST
for TEST_PATH in TEST_PATH_LIST:
if cfg.OUTPUT_DIR:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference",
os.path.basename(TEST_PATH))
mkdir(output_folder)
total_time, acc = rank_1(model, TEST_PATH, True, cfg.MODEL.DEVICE,
cfg.INPUT.SIZE_TRAIN,
cfg.MODEL.BACKBONE.BACKBONE_OUT_CHANNELS,
get_transform(cfg), cfg.SOLVER.IMS_PER_BATCH,
'test')
TEST_PATH = os.path.basename(TEST_PATH)
logger.info(
"the {:s} rank-1 program consume time {:.4f} seconds and the accuracy is {:.4f}"
.format(TEST_PATH, total_time, acc))
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("--ngpu_shared_fc", type=list, default=1)
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
size = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
"MASTER_ADDR"
"MASTER_PORT"
"RANK"
"WORLD_SIZE"
if True:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl",
init_method="env://", #rank=args.local_rank,world_size=size
)
synchronize()
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))
proc_gpus = [int(i) for i in args.ngpu_shared_fc]
model = train(cfg, proc_gpus, args.distributed)
if not args.skip_test:
run_test(cfg, model, args.distributed)
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default=
"/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"--ckpt",
help=
"The path to the checkpoint for test, default is the latest checkpoint.",
default=None,
)
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.init_process_group(backend="nccl",
init_method="env://")
synchronize()
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_face_trainer(cfg)
model.to(cfg.MODEL.DEVICE)
# Initialize mixed-precision if necessary
use_mixed_precision = cfg.DTYPE == 'float16'
amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
#############============ face val ================##########
# ckpt = cfg.MODEL.WEIGHT if args.ckpt is None else args.ckpt
BACKBONE_PATH = os.path.join(cfg.OUTPUT_DIR, )
BACKBONE_ACC = {}
for BACKBONE in os.listdir(BACKBONE_PATH):
ckpt = os.path.join(BACKBONE_PATH, BACKBONE)
_ = checkpointer.load(ckpt, use_latest=False)
TEST_PATH_LIST = cfg.DATASETS.TEST_PATH_LIST
TESTS_ACC = {}
Q_IMG_PATH = "/data/hongwei/face_benchmark/10_11楼层人脸_align"
G_IMG_PATH = "/data/hongwei/face_benchmark/魔镜人脸抓拍_二号设备_all_align"
pic_save_path = "/data/hongwei/face_benchmark/tools/IMGLIST_ALL"
creat_face_json(model, Q_IMG_PATH, G_IMG_PATH, True, cfg.MODEL.DEVICE,
cfg.INPUT.SIZE_TRAIN,
cfg.MODEL.BACKBONE.BACKBONE_OUT_CHANNELS,
get_transform(cfg), pic_save_path,
cfg.SOLVER.IMS_PER_BATCH, 'test')