def main():
args = parse_args()
cfg = load_config(args)
launch_job(
cfg=cfg, init_method=args.init_method, func=benchmark_data_loading
)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
if cfg.DEMO.ENABLE:
original_num_gpus = cfg.NUM_GPUS
# Set num_gpus to 1 for the demo
cfg.NUM_GPUS = 1
launch_job(cfg=cfg, init_method=args.init_method, func=run_demo)
# Set num gpus back to original
cfg.NUM_GPUS = original_num_gpus
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
if cfg.DEMO_ORIGINAL.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=demo_original)
if cfg.TENSORBOARD.ENABLE and cfg.TENSORBOARD.MODEL_VIS.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=visualize)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
# Perform training.
if cfg.TRAIN.ENABLE:
if cfg.KD.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train_KD)
else:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
# Perform model visualization.
if cfg.TENSORBOARD.ENABLE and (cfg.TENSORBOARD.MODEL_VIS.ENABLE
or cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE):
launch_job(cfg=cfg, init_method=args.init_method, func=visualize)
# Run demo.
if cfg.DEMO.ENABLE:
demo(cfg)
def main():
args = parse_args()
cfg = load_config(args)
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
# Perform feature extraction.
if cfg.MODEL.EXTRACTOR:
extract(cfg)
if cfg.MODEL.VIDEO_EXTRACTOR:
video_extract(cfg)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
print("config files: {}".format(args.cfg_files))
for path_to_config in args.cfg_files:
cfg = load_config(args, path_to_config)
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
# Perform model visualization.
if cfg.TENSORBOARD.ENABLE and (cfg.TENSORBOARD.MODEL_VIS.ENABLE or
cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE):
launch_job(cfg=cfg, init_method=args.init_method, func=visualize)
# Run demo.
if cfg.DEMO.ENABLE:
demo(cfg)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
#cfg.DEMO.WEBCAM = 0
cfg.DEMO.WEBCAM = -1
cfg.DEMO.INPUT_VIDEO = "demo_test/demo_in2.mp4"
cfg.NUM_GPUS = 1
cfg.TRAIN.ENABLE = False
cfg.TEST.ENABLE = False
cfg.DEMO.OUTPUT_FILE = "demo_test/demo_out2.mp4"
cfg.DEMO.ENABLE = True
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
# Perform model visualization.
if cfg.TENSORBOARD.ENABLE and cfg.TENSORBOARD.MODEL_VIS.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=visualize)
# Run demo.
if cfg.DEMO.ENABLE:
demo(cfg)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
uuid = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
cfg.OUTPUT_DIR = os.path.join(cfg.OUTPUT_DIR, uuid)
os.makedirs(cfg.OUTPUT_DIR)
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# # Perform multi-clip testing.
# if cfg.TEST.ENABLE:
# launch_job(cfg=cfg, init_method=args.init_method, func=test)
# if cfg.DEMO.ENABLE:
# launch_job(cfg=cfg, init_method=args.init_method, func=demo)
if cfg.TENSORBOARD.ENABLE and cfg.TENSORBOARD.MODEL_VIS.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=visualize)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
# Perform training.
if cfg.TRAIN.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
# Perform multi-clip testing.
if cfg.TEST.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=test)
if cfg.DEMO.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=demo)
if cfg.TENSORBOARD.ENABLE and cfg.TENSORBOARD.MODEL_VIS.ENABLE:
launch_job(cfg=cfg, init_method=args.init_method, func=visualize)
def main():
"""
Main function to spawn the train and test process.
"""
args = parse_args()
cfg = load_config(args)
if cfg.TRAIN.ENABLE:
if cfg.TRAIN.ONLY_DES:
launch_job(cfg=cfg, init_method=args.init_method, func=train_des)
else:
launch_job(cfg=cfg, init_method=args.init_method, func=train)
else:
if cfg.TRAIN.ONLY_DES:
launch_job(cfg=cfg,
init_method=args.init_method,
func=test_implementation_des)
else:
launch_job(cfg=cfg,
init_method=args.init_method,
func=test_implementation)
def main():
argsOrig = ParseArgs()
host_name = socket.gethostname()
host_ip = socket.gethostbyname(host_name)
print("Starting on host {} host_ip {}".format(host_name, host_ip))
for config_file in argsOrig.config_files:
args = copy.deepcopy(argsOrig)
args.config_file = config_file
with YamlConfig(args, now='') as config:
args = config.ApplyConfigFile(args)
args = updatePaths(args)
with TempDir(baseDir=args.output_dir, deleteOnExit=True) as tmp:
# Convert args to cfg format
tmpFile = createFullPathTree(tmp.tempDir, 'cfgTmp')
args.cfg_file = tmpFile
config.SaveConfig(file=tmpFile)
cfg = load_config(args)
with CreateLogger(args, logger_type=args.logger_type) as logger:
logger.log_value('title', args.log_title,
'Run Title entered when job started')
logger.info("Starting on host {} host_ip {}".format(
host_name, host_ip))
logger.info("cv2 version {}".format(cv2.__version__))
logger.info("torch version {}".format(torch.__version__))
logger.info("Cuda enabled {} num GPU {}".format(
torch.cuda.is_available(), torch.cuda.device_count()))
logger.info("Torchvision version {}".format(
torchvision.__version__))
logger.info(config.ReportConfig())
args.master_addr = host_ip if cfg.NUM_SHARDS <= 1 or cfg.SHARD_ID == 0 else args.master_addr
os.environ["MASTER_ADDR"] = args.master_addr
os.environ["MASTER_PORT"] = str(args.master_port)
os.environ["WORLD_SIZE"] = str(cfg.NUM_SHARDS * cfg.NUM_GPUS)
logger.info(
"MASTER_ADDR {} MASTER_PORT {} WORLD_SIZE {}".format(
os.environ["MASTER_ADDR"], os.environ["MASTER_PORT"],
os.environ["WORLD_SIZE"]))
logger.info("MASTER_ADDR {} MASTER_PORT {} ".format(
os.environ["MASTER_ADDR"],
os.environ["MASTER_PORT"],
))
logger.info("CFG")
logger.info(cfg)
for op in args.operations:
if op.lower() == 'train':
trainer = Trainer(cfg)
launch_job(cfg=cfg,
init_method=None,
func=trainer.train)
elif op.lower() == 'to_onnx':
onnx = OnnxUtils(cfg, logger)
onnx.saveOnnxModel()
elif op.lower() == 'eval_onnx':
onnx = OnnxUtils(cfg, logger)
onnx.evalOnnx()
else:
logger.info(
"Unrecognized option {} expect one of [train, to_onnx, eval_onnx]"
)
def run_exp(cfg):
init_method = 'tcp://localhost:9999'
with open(cfg.TRAIN.TRAIN_STATS_FILE, 'a') as f:
f.write(str(dict(cfg.items())))
f.write('\n')
launch_job(cfg=cfg, init_method=init_method, func=train_des)
