fix: Remove left-over code which is not needed for prediction (#35)

 Reduce the calls of opencv to the minimum that is actually used.

Signed-off-by: Nikos Livathinos <[email protected]>

Author: nikos-livathinos

docling_ibm_models/tableformer/common.py

@@ -48,32 +48,6 @@ def validate_config(config):
     return True
 
 
-def parse_arguments():
-    r"""
-    Parse the input arguments
-    A ValueError exception will be thrown in case the config file is invalid
-    """
-    parser = argparse.ArgumentParser(description="Train the TableModel")
-    parser.add_argument(
-        "-c", "--config", required=True, default=None, help="configuration file (JSON)"
-    )
-    args = parser.parse_args()
-    config_filename = args.config
-
-    assert os.path.isfile(config_filename), "FAILURE: Config file not found."
-    return read_config(config_filename)
-
-
-def read_config(config_filename):
-    with open(config_filename, "r") as fd:
-        config = json.load(fd)
-
-    # Validate the config file
-    validate_config(config)
-
-    return config
-
-
 def safe_get_parameter(input_dict, index_path, default=None, required=False):
     r"""
     Safe get parameter from a nested dictionary.
@@ -130,71 +104,3 @@ def get_prepared_data_filename(prepared_data_part, dataset_name):
     if "<POSTFIX>" in template:
         template = template.replace("<POSTFIX>", dataset_name)
     return template
-
-
-def create_dataset_and_model(config, purpose, fixed_padding=False):
-    r"""
-    Gets a model from configuration
-
-    Parameters
-    ---------
-    config : Dictionary
-        The configuration of the model
-    purpose : string
-        One of "train", "eval", "predict"
-    fixed_padding : bool
-        Parameter passed to the constructor of the DataLoader
-
-    Returns
-    -------
-    In case a Model cannot be initialized return None, None, None. Otherwise:
-
-    device : selected device
-    dataset : Instance of the DataLoader
-    model : Instance of the model
-    """
-    from docling_ibm_models.tableformer.data_management.tf_dataset import TFDataset
-
-    model_type = config["model"]["type"]
-    model = None
-
-    # Get env vars:
-    use_cpu_only = os.environ.get("USE_CPU_ONLY", False)
-    use_cuda_only = not use_cpu_only
-
-    # Use the cpu for the evaluation
-    device = "cpu"  # Default, run on CPU
-    num_gpus = torch.cuda.device_count()  # Check if GPU is available
-    if use_cuda_only:
-        device = "cuda:0" if num_gpus > 0 else "cpu"  # Run on first available GPU
-    else:
-        device = "cpu"
-
-    # Create the DataLoader
-    # loader = DataLoader(config, purpose, fixed_padding=fixed_padding)
-    dataset = TFDataset(config, purpose, fixed_padding=fixed_padding)
-    dataset.set_device(device)
-    dataset_val = None
-    if config["train"]["validation"] and purpose == "train":
-        dataset_val = TFDataset(config, "val", fixed_padding=fixed_padding)
-        dataset_val.set_device(device)
-    if model_type == "TableModel04_rs":
-        from docling_ibm_models.tableformer.models.table04_rs.tablemodel04_rs import (  # noqa: F401
-            TableModel04_rs,
-        )
-    # Find the model class and create an instance of it
-    for candidate in BaseModel.__subclasses__():
-        if candidate.__name__ == model_type:
-            init_data = dataset.get_init_data()
-            model = candidate(config, init_data, purpose, device)
-
-    if model is None:
-        logger.warn("Not found model: " + str(model_type))
-        return None, None, None
-
-    logger.info("Found model: " + str(model_type))
-
-    if purpose == s.PREDICT_PURPOSE:
-        return device, dataset, model
-    else:
-        return device, dataset, dataset_val, model