pytorch-sbs/network/load_previous_weights.py

# %%
import torch
import glob
import numpy as np

from network.SbSLayer import SbSLayer
from network.NNMFLayer import NNMFLayer

from network.SplitOnOffLayer import SplitOnOffLayer
from network.Conv2dApproximation import Conv2dApproximation
import os


def load_previous_weights(
    network: torch.nn.Sequential,
    overload_path: str,
    logging,
    device: torch.device,
    default_dtype: torch.dtype,
    order_id: float | int | None = None,
) -> None:

    if order_id is None:
        post_fix: str = ""
    else:
        post_fix = f"_{order_id}"

    for id in range(0, len(network)):

        # #################################################
        # SbS
        # #################################################

        if isinstance(network[id], SbSLayer) is True:
            filename_wilcard = os.path.join(
                overload_path, f"Weight_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous weights files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id].weights = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Weights file used for layer {id} : {file_to_load[0]}")

        if isinstance(network[id], NNMFLayer) is True:
            filename_wilcard = os.path.join(
                overload_path, f"Weight_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous weights files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id].weights = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Weights file used for layer {id} : {file_to_load[0]}")

        if isinstance(network[id], torch.nn.modules.conv.Conv2d) is True:

            # #################################################
            # Conv2d weights
            # #################################################

            filename_wilcard = os.path.join(
                overload_path, f"Weight_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous weights files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id]._parameters["weight"].data = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Weights file used for layer {id} : {file_to_load[0]}")

            # #################################################
            # Conv2d bias
            # #################################################

            filename_wilcard = os.path.join(
                overload_path, f"Bias_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous weights files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id]._parameters["bias"].data = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Bias file used for layer {id} : {file_to_load[0]}")

        if isinstance(network[id], Conv2dApproximation) is True:

            # #################################################
            # Approximate Conv2d weights
            # #################################################

            filename_wilcard = os.path.join(
                overload_path, f"Weight_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous weights files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id].weights.data = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Weights file used for layer {id} : {file_to_load[0]}")

            # #################################################
            # Approximate Conv2d bias
            # #################################################

            filename_wilcard = os.path.join(
                overload_path, f"Bias_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous weights files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id].bias.data = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Bias file used for layer {id} : {file_to_load[0]}")

        # #################################################
        # SplitOnOffLayer
        # #################################################
        if isinstance(network[id], SplitOnOffLayer) is True:
            filename_wilcard = os.path.join(
                overload_path, f"Mean_L{id}_*{post_fix}.npy"
            )
            file_to_load = glob.glob(filename_wilcard)

            if len(file_to_load) > 1:
                raise Exception(f"Too many previous mean files {filename_wilcard}")

            if len(file_to_load) == 1:
                network[id].mean = torch.tensor(
                    np.load(file_to_load[0]),
                    dtype=default_dtype,
                    device=device,
                )
                logging.info(f"Meanfile used for layer {id} : {file_to_load[0]}")