Source code for mindspore.common.parameter

# Copyright 2020 Huawei Technologies Co., Ltd
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

"""Parameter for cell."""
from copy import copy
import numpy as np
from .initializer import initializer
from .tensor import Tensor
from .._checkparam import _check_str_by_regular
from ..parallel._utils import _set_clone_info, _CloneInfo

__all__ = ['Parameter', 'ParameterTuple']


def _check_type(x):
    """Check input data type"""
    if not isinstance(x, Parameter):
        raise ValueError("Should be `Parameter` collection.")
    return True

[docs]class Parameter: """ Parameter types of cell models. Note: Each parameter of Cell is represented by Parameter class. Args: default_input (Tensor): A parameter tensor. name (str): Name of the child parameter. requires_grad (bool): True if the parameter requires gradient. Default: True. layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in paralle mode, broadcast and gradients communication would not be applied on parameters. Default: False. """ def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False): self.set_parameter_data(default_input) = name self.requires_grad = requires_grad self.layerwise_parallel = layerwise_parallel self._is_init = False self.clone_info = _CloneInfo() def __repr__(self): format_str = 'Parameter (name={name})' return format_str.format(name=self._name) def __parameter__(self): """For parse check.""" @property def name(self): """Get the name of the parameter.""" return self._name @name.setter def name(self, name_): """ Define a name for the parameter. Args: name_ (`str` or `None`): The name of the parameter. When the parameter is None or an empty string, the default value `PARAMETER_NAME_DEFAULT` is used. """ if name_ is None: name_ = PARAMETER_NAME_DEFAULT elif isinstance(name_, str): name_ = name_.strip() if name_ == '': name_ = PARAMETER_NAME_DEFAULT if len(name_) > PARAMETER_NAME_PREFIX_MAX_LEN: raise ValueError("The length of the '{}' name should be less than {}.". format(name_, PARAMETER_NAME_PREFIX_MAX_LEN)) else: raise ValueError("The type of the name should be `str` or `None`.") self._name = name_ @property def is_init(self): """Get init status of the parameter.""" return self._is_init @is_init.setter def is_init(self, is_init_): """ Set init status of the parameter. Args: is_init_ (bool): The init status of the parameter. """ self._is_init = is_init_
[docs] def clone(self, prefix, init='same'): """ Clone the parameter. Args: prefix (str): Namespace of parameter. init (str): Initialize the shape of the parameter. Default: 'same'. Returns: Parameter, a new parameter. """ _check_str_by_regular(prefix) x = copy(self) = prefix + '.' + x.is_init = False if init != 'same': shape = self.default_input.shape() dtype = self.default_input.dtype() x.default_input = initializer(init, shape=shape, dtype=dtype) x.clone_info = copy(self.clone_info) _set_clone_info(self.clone_info, x.clone_info) return x
@property def layerwise_parallel(self): return self._layerwise_parallel @layerwise_parallel.setter def layerwise_parallel(self, value=True): if not isinstance(value, bool): raise TypeError("`layerwise_parallel` parameter must be bool type") self._layerwise_parallel = value @property def requires_grad(self): """Return whether the parameter requires gradient.""" return self._requires_grad @requires_grad.setter def requires_grad(self, value=True): if not isinstance(value, bool): raise TypeError("`requires_grad` parameter must be bool type") self._requires_grad = value @property def data(self): return self.default_input def set_parameter_data(self, data): if isinstance(data, (Tensor, list, int, float, np.float16, np.float32, np.int32, np.int16, np.ndarray)) and not isinstance(data, bool): if isinstance(data, Tensor): # make a copy of Tensor to init the parameter data = Tensor(data.asnumpy().copy()) self.default_input = data else: raise ValueError("Parameter data must be tensor or number.")
[docs]class ParameterTuple(tuple): """ Class for storing tuple of parameters. Note: Used to store the parameters of the network into the parameter tuple collection. """ def __new__(cls, iterable): """Create instance object of ParameterTuple.""" g = (x for x in iterable if _check_type(x)) return tuple.__new__(ParameterTuple, g)
[docs] def clone(self, prefix, init='same'): """ Clone the parameter. Args: prefix (str): Namespace of parameter. init (str): Initialize the shape of the parameter. Default: 'same'. Returns: Tuple, the new Parameter tuple. """ _check_str_by_regular(prefix) new = [] for x in self: x1 = x.clone(prefix, init) new.append(x1) return ParameterTuple(new)
def __parameter_tuple__(self): """For parse check."""