def __init__(self, infer_config):
self.infer_config = infer_config
setup_logging(infer_config)
self.logger = setup_logger(self, infer_config["verbose"])
self.run_inference = infer_config.get("run_inference", False)
train_cfg = infer_config["training"]
self.randomiser = globals()[train_cfg["randomiser"]](train_cfg["template"])
def __init__(self, model, loss, metrics, optimizer, start_epoch, config, device):
self.logger = setup_logger(self, verbose=config["training"]["verbose"])
self.model = model
self.loss = loss
self.metrics = metrics
self.optimizer = optimizer
self.start_epoch = start_epoch
self.config = config
self.device = device
self._setup_monitoring(config["training"])
self.checkpoint_dir, writer_dir = get_trainer_paths(config)
self.writer = TensorboardWriter(writer_dir, config["training"]["tensorboard"])
# Save configuration file into checkpoint directory:
config_save_path = Path(self.checkpoint_dir) / "config.yml"
with open(config_save_path, "w") as handle:
yaml.dump(config, handle, default_flow_style=False)
def __init__(self, verbose=0):
super().__init__()
self.logger = setup_logger(self, verbose=verbose)
def __init__(self, data_dir, nworkers=4, verbose=2):
self.data_dir = Path(data_dir)
self.nworkers = nworkers
self.logger = setup_logger(self, verbose)
self.data_dir.mkdir(parents=True, exist_ok=True)
def __init__(self, verbose=0):
self.logger = setup_logger(self, verbose)
def __init__(self, config: dict):
setup_logging(config)
seed_everything(config["seed"])
self.logger = setup_logger(self, config["verbose"])
self.cfg = config
def __init__(self, n_imgs, verbose=2):
self.N = n_imgs
self.logger = setup_logger(self, verbose)