def load_latent_vectors()

in train_deep_sdf.py [0:0]

• 21 lines of code
• 7 McCabe index (conditional complexity)

def load_latent_vectors(experiment_directory, filename, lat_vecs):

    full_filename = os.path.join(
        ws.get_latent_codes_dir(experiment_directory), filename
    )

    if not os.path.isfile(full_filename):
        raise Exception('latent state file "{}" does not exist'.format(full_filename))

    data = torch.load(full_filename)

    if isinstance(data["latent_codes"], torch.Tensor):

        # for backwards compatibility
        if not lat_vecs.num_embeddings == data["latent_codes"].size()[0]:
            raise Exception(
                "num latent codes mismatched: {} vs {}".format(
                    lat_vecs.num_embeddings, data["latent_codes"].size()[0]
                )
            )

        if not lat_vecs.embedding_dim == data["latent_codes"].size()[2]:
            raise Exception("latent code dimensionality mismatch")

        for i, lat_vec in enumerate(data["latent_codes"]):
            lat_vecs.weight.data[i, :] = lat_vec

    else:
        lat_vecs.load_state_dict(data["latent_codes"])

    return data["epoch"]