Python model.load_checkpoint方法代码示例

# 需要导入模块: from mxnet import model [as 别名]
# 或者: from mxnet.model import load_checkpoint [as 别名]
def load(prefix, epoch, load_optimizer_states=False, symbol=None, **kwargs):
        """Creates a model from previously saved checkpoint.

        Parameters
        ----------
        prefix : str
            path prefix of saved model files. You should have
            "prefix-symbol.json", "prefix-xxxx.params", and
            optionally "prefix-xxxx.states", where xxxx is the
            epoch number.
        epoch : int
            epoch to load.
        load_optimizer_states : bool
            whether to load optimizer states. Checkpoint needs
            to have been made with save_optimizer_states=True.
        data_names : list of str
            Default is `('data')` for a typical model used in image classification.
        label_names : list of str
            Default is `('softmax_label')` for a typical model used in image
            classification.
        logger : Logger
            Default is `logging`.
        context : Context or list of Context
            Default is ``cpu()``.
        work_load_list : list of number
            Default ``None``, indicating uniform workload.
        fixed_param_names: list of str
            Default ``None``, indicating no network parameters are fixed.
        """
        sym, args, auxs = load_checkpoint(prefix, epoch)
        sym = sym if symbol is None else symbol
        mod = CustomModule(symbol=sym, **kwargs)
        mod._arg_params = args
        mod._aux_params = auxs
        mod.params_initialized = True
        if load_optimizer_states:
            mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
        return mod 

# 需要导入模块: from mxnet import model [as 别名]
# 或者: from mxnet.model import load_checkpoint [as 别名]
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
        """Create a model from previously saved checkpoint.

        Parameters
        ----------
        prefix : str
            path prefix of saved model files. You should have
            "prefix-symbol.json", "prefix-xxxx.params", and
            optionally "prefix-xxxx.states", where xxxx is the
            epoch number.
        epoch : int
            epoch to load.
        load_optimizer_states : bool
            whether to load optimizer states. Checkpoint needs
            to have been made with save_optimizer_states=True.
        data_names : list of str
            Default is `('data')` for a typical model used in image classification.
        label_names : list of str
            Default is `('softmax_label')` for a typical model used in image
            classification.
        logger : Logger
            Default is `logging`.
        context : Context or list of Context
            Default is `cpu()`.
        work_load_list : list of number
            Default `None`, indicating uniform workload.
        fixed_param_names: list of str
            Default `None`, indicating no network parameters are fixed.
        """
        sym, args, auxs = load_checkpoint(prefix, epoch)
        mod = Module(symbol=sym, **kwargs)
        mod._arg_params = args
        mod._aux_params = auxs
        mod.params_initialized = True
        if load_optimizer_states:
            mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
        return mod 

# 需要导入模块: from mxnet import model [as 别名]
# 或者: from mxnet.model import load_checkpoint [as 别名]
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
        """Assign parameter and aux state values.

        Parameters
        ----------
        arg_params : dict
            Dictionary of name to value (`NDArray`) mapping.
        aux_params : dict
            Dictionary of name to value (`NDArray`) mapping.
        allow_missing : bool
            If true, params could contain missing values, and the initializer will be
            called to fill those missing params.
        force_init : bool
            If true, will force re-initialize even if already initialized.

        Examples
        --------
        An example of setting module parameters::
            >>> sym, arg_params, aux_params = \
            >>>     mx.model.load_checkpoint(model_prefix, n_epoch_load)
            >>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
        """
        if not allow_missing:
            self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
                             allow_missing=allow_missing, force_init=force_init)
            return

        if self.params_initialized and not force_init:
            warnings.warn("Parameters already initialized and force_init=False. "
                          "set_params call ignored.", stacklevel=2)
            return

        self._exec_group.set_params(arg_params, aux_params)

        # because we didn't update self._arg_params, they are dirty now.
        self._params_dirty = True
        self.params_initialized = True 

# 需要导入模块: from mxnet import model [as 别名]
# 或者: from mxnet.model import load_checkpoint [as 别名]
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
        """Creates a model from previously saved checkpoint.

        Parameters
        ----------
        prefix : str
            path prefix of saved model files. You should have
            "prefix-symbol.json", "prefix-xxxx.params", and
            optionally "prefix-xxxx.states", where xxxx is the
            epoch number.
        epoch : int
            epoch to load.
        load_optimizer_states : bool
            whether to load optimizer states. Checkpoint needs
            to have been made with save_optimizer_states=True.
        data_names : list of str
            Default is `('data')` for a typical model used in image classification.
        label_names : list of str
            Default is `('softmax_label')` for a typical model used in image
            classification.
        logger : Logger
            Default is `logging`.
        context : Context or list of Context
            Default is ``cpu()``.
        work_load_list : list of number
            Default ``None``, indicating uniform workload.
        fixed_param_names: list of str
            Default ``None``, indicating no network parameters are fixed.
        """
        sym, args, auxs = load_checkpoint(prefix, epoch)
        mod = DetModule(symbol=sym, **kwargs)
        mod._arg_params = args
        mod._aux_params = auxs
        mod.params_initialized = True
        if load_optimizer_states:
            mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
        return mod 

# 需要导入模块: from mxnet import model [as 别名]
# 或者: from mxnet.model import load_checkpoint [as 别名]
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True,
                   allow_extra=False):
        """Assigns parameter and aux state values.

        Parameters
        ----------
        arg_params : dict
            Dictionary of name to `NDArray`.
        aux_params : dict
            Dictionary of name to `NDArray`.
        allow_missing : bool
            If ``True``, params could contain missing values, and the initializer will be
            called to fill those missing params.
        force_init : bool
            If ``True``, will force re-initialize even if already initialized.
        allow_extra : boolean, optional
            Whether allow extra parameters that are not needed by symbol.
            If this is True, no error will be thrown when arg_params or aux_params
            contain extra parameters that is not needed by the executor.
        Examples
        --------
        >>> # An example of setting module parameters.
        >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load)
        >>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
        """
        if not allow_missing:
            self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
                             allow_missing=allow_missing, force_init=force_init,
                             allow_extra=allow_extra)
            return

        if self.params_initialized and not force_init:
            warnings.warn("Parameters already initialized and force_init=False. "
                          "set_params call ignored.", stacklevel=2)
            return

        self._exec_group.set_params(arg_params, aux_params, allow_extra=allow_extra)

        # because we didn't update self._arg_params, they are dirty now.
        self._params_dirty = True
        self.params_initialized = True