src/fine_tune.py [282:302]:
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    pretrained_dict = {k.replace('module.', ''): v for k, v in checkpoint['encoder'].items()}
    for k, v in encoder.state_dict().items():
        if k not in pretrained_dict:
            logger.info(f'key "{k}" could not be found in loaded state dict')
        elif pretrained_dict[k].shape != v.shape:
            logger.info(f'key "{k}" is of different shape in model and loaded state dict')
            pretrained_dict[k] = v
    msg = encoder.load_state_dict(pretrained_dict, strict=False)
    logger.info(f'loaded pretrained model with msg: {msg}')
    logger.info(f'loaded pretrained encoder from epoch: {checkpoint["epoch"]} '
                f'path: {r_path}')
    del checkpoint
    return encoder


def load_from_path(
    r_path,
    encoder,
    opt,
    sched,
    scaler,
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src/snn_fine_tune.py [273:293]:
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        pretrained_dict = {k.replace('module.', ''): v for k, v in checkpoint['encoder'].items()}
    for k, v in encoder.state_dict().items():
        if k not in pretrained_dict:
            logger.info(f'key "{k}" could not be found in loaded state dict')
        elif pretrained_dict[k].shape != v.shape:
            logger.info(f'key "{k}" is of different shape in model and loaded state dict')
            pretrained_dict[k] = v
    msg = encoder.load_state_dict(pretrained_dict, strict=False)
    logger.info(f'loaded pretrained model with msg: {msg}')
    logger.info(f'loaded pretrained encoder from epoch: {checkpoint["epoch"]} '
                f'path: {r_path}')
    del checkpoint
    return encoder


def load_from_path(
    r_path,
    encoder,
    opt,
    sched,
    scaler,
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