def main(configuration, init_distributed=False, predict=False):
# A reload might be needed for imports
setup_imports()
configuration.import_user_dir()
config = configuration.get_config()
if torch.cuda.is_available():
torch.cuda.set_device(config.device_id)
torch.cuda.init()
if init_distributed:
distributed_init(config)
config.training.seed = set_seed(config.training.seed)
registry.register("seed", config.training.seed)
print(f"Using seed {config.training.seed}")
config = build_config(configuration)
# Logger should be registered after config is registered
registry.register("writer", Logger(config, name="mmf.train"))
trainer = build_trainer(config)
trainer.load()
if predict:
trainer.inference()
else:
trainer.train()
def get_model(device, opts):
from mmf.utils.build import build_config, build_trainer
from mmf.common.registry import registry
from mmf.utils.configuration import Configuration
from mmf.utils.env import set_seed, setup_imports
args = argparse.Namespace(config_override=None)
args.opts = opts
configuration = Configuration(args)
configuration.args = args
config = configuration.get_config()
config.start_rank = 0
config.device_id = 0
setup_imports()
configuration.import_user_dir()
config = configuration.get_config()
if torch.cuda.is_available():
torch.cuda.set_device(config.device_id)
torch.cuda.init()
config.training.seed = set_seed(config.training.seed)
registry.register("seed", config.training.seed)
config = build_config(configuration)
# Logger should be registered after config is registered
registry.register("writer", Logger(config, name="mmf.train"))
trainer = build_trainer(config)
# trainer.load()
ready_trainer(trainer)
trainer.model.to(device)
return trainer.model
def main(configuration, init_distributed=False, predict=False):
# A reload might be needed for imports
setup_imports()
configuration.import_user_dir()
config = configuration.get_config()
if torch.cuda.is_available():
torch.cuda.set_device(config.device_id)
torch.cuda.init()
if init_distributed:
distributed_init(config)
seed = config.training.seed
config.training.seed = set_seed(seed if seed == -1 else seed + get_rank())
registry.register("seed", config.training.seed)
config = build_config(configuration)
setup_logger(color=config.training.colored_logs,
disable=config.training.should_not_log)
logger = logging.getLogger("mmf_cli.run")
# Log args for debugging purposes
logger.info(configuration.args)
logger.info(f"Torch version: {torch.__version__}")
log_device_names()
logger.info(f"Using seed {config.training.seed}")
trainer = build_trainer(config)
trainer.load()
if predict:
trainer.inference()
else:
trainer.train()
def main(configuration, init_distributed=False):
# A reload might be needed for imports
setup_imports()
configuration.import_user_dir()
config = configuration.get_config()
if torch.cuda.is_available():
torch.cuda.set_device(config.device_id)
torch.cuda.init()
if init_distributed:
distributed_init(config)
config.training.seed = set_seed(config.training.seed)
registry.register("seed", config.training.seed)
print("Using seed {}".format(config.training.seed))
registry.register("writer", Logger(config, name="mmf.train"))
trainer = build_trainer(configuration)
trainer.load()
trainer.train()
