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 setUp(self):
self.tmpdir = tempfile.mkdtemp()
self.trainer = argparse.Namespace()
self.config = load_yaml(os.path.join("configs", "defaults.yaml"))
self.config = OmegaConf.merge(
self.config,
{
"model": "simple",
"model_config": {},
"training": {
"checkpoint_interval": 1,
"evaluation_interval": 10,
"early_stop": {
"criteria": "val/total_loss"
},
"batch_size": 16,
"log_interval": 10,
"logger_level": "info",
},
"env": {
"save_dir": self.tmpdir
},
},
)
# Keep original copy for testing purposes
self.trainer.config = deepcopy(self.config)
registry.register("config", self.trainer.config)
setup_logger()
self.report = Mock(spec=Report)
self.report.dataset_name = "abcd"
self.report.dataset_type = "test"
self.trainer.model = SimpleModule()
self.trainer.val_loader = torch.utils.data.DataLoader(
NumbersDataset(), batch_size=self.config.training.batch_size)
self.trainer.optimizer = torch.optim.Adam(
self.trainer.model.parameters(), lr=1e-01)
self.trainer.device = "cpu"
self.trainer.num_updates = 0
self.trainer.current_iteration = 0
self.trainer.current_epoch = 0
self.trainer.max_updates = 0
self.trainer.meter = Meter()
self.cb = LogisticsCallback(self.config, self.trainer)
def interactive(opts: typing.Optional[typing.List[str]] = None):
"""Inference runs inference on an image and text provided by the user.
You can optionally run inference programmatically by passing an optlist as opts.
Args:
opts (typing.Optional[typing.List[str]], optional): Optlist which can be used.
to override opts programmatically. For e.g. if you pass
opts = ["checkpoint_path=my/directory"], this will set the checkpoint.
"""
if opts is None:
parser = flags.get_parser()
args = parser.parse_args()
else:
args = argparse.Namespace(config_override=None)
args.opts = opts
setup_logger()
logger = logging.getLogger("mmf_cli.interactive")
config = construct_config(args.opts)
inference = Inference(checkpoint_path=config.checkpoint_path)
logger.info("Enter 'exit' at any point to terminate.")
logger.info("Enter an image URL:")
image_url = input()
logger.info("Got image URL.")
logger.info("Enter text:")
text = input()
logger.info("Got text input.")
while text != "exit":
logger.info("Running inference on image and text input.")
answer = inference.forward(image_url, {"text": text}, image_format="url")
logger.info("Model response: " + answer)
logger.info(
f"Enter another image URL or leave it blank to continue using {image_url}:"
)
new_image_url = input()
if new_image_url != "":
image_url = new_image_url
if new_image_url == "exit":
break
logger.info("Enter another text input:")
text = input()
def setUpClass(cls) -> None:
cls._tmpdir = tempfile.mkdtemp()
args = argparse.Namespace()
args.opts = [
f"env.save_dir={cls._tmpdir}", f"model=cnn_lstm", f"dataset=clevr"
]
args.config_override = None
configuration = Configuration(args)
configuration.freeze()
cls.config = configuration.get_config()
registry.register("config", cls.config)
cls.writer = setup_logger()
