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# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# coding: utf-8
# pylint: disable=unnecessary-pass
"""Neural network parameter."""
__all__ = ['DeferredInitializationError', 'Parameter', 'Constant',
'ParameterDict', 'tensor_types']
from collections import OrderedDict
import warnings
import numpy as np
from ..base import mx_real_t, MXNetError
from .. import symbol, ndarray, initializer, context
from ..context import Context, cpu
from .. import autograd
from .utils import _indent, _brief_print_list, shape_is_known
from .. import is_np_shape
# pylint: disable= invalid-name
tensor_types = (symbol.Symbol, ndarray.NDArray)
# pylint: enable= invalid-name
[docs]class DeferredInitializationError(MXNetError):
"""Error for unfinished deferred initialization."""
pass
[docs]class Parameter(object):
"""A Container holding parameters (weights) of Blocks.
:py:class:`Parameter` holds a copy of the parameter on each :py:class:`Context` after
it is initialized with ``Parameter.initialize(...)``. If :py:attr:`grad_req` is
not ``'null'``, it will also hold a gradient array on each :py:class:`Context`::
ctx = mx.gpu(0)
x = mx.nd.zeros((16, 100), ctx=ctx)
w = mx.gluon.Parameter('fc_weight', shape=(64, 100), init=mx.init.Xavier())
b = mx.gluon.Parameter('fc_bias', shape=(64,), init=mx.init.Zero())
w.initialize(ctx=ctx)
b.initialize(ctx=ctx)
out = mx.nd.FullyConnected(x, w.data(ctx), b.data(ctx), num_hidden=64)
Parameters
----------
name : str
Name of this parameter.
grad_req : {'write', 'add', 'null'}, default 'write'
Specifies how to update gradient to grad arrays.
- ``'write'`` means everytime gradient is written to grad :py:class:`NDArray`.
- ``'add'`` means everytime gradient is added to the grad :py:class:`NDArray`. You need
to manually call ``zero_grad()`` to clear the gradient buffer before each
iteration when using this option.
- 'null' means gradient is not requested for this parameter. gradient arrays
will not be allocated.
shape : int or tuple of int, default None
Shape of this parameter. By default shape is not specified. Parameter with
unknown shape can be used for :py:class:`Symbol` API, but ``init`` will throw an error
when using :py:class:`NDArray` API.
dtype : numpy.dtype or str, default 'float32'
Data type of this parameter. For example, ``numpy.float32`` or ``'float32'``.
lr_mult : float, default 1.0
Learning rate multiplier. Learning rate will be multiplied by lr_mult
when updating this parameter with optimizer.
wd_mult : float, default 1.0
Weight decay multiplier (L2 regularizer coefficient). Works similar to lr_mult.
init : Initializer, default None
Initializer of this parameter. Will use the global initializer by default.
stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.
The storage type of the parameter.
grad_stype: {'default', 'row_sparse', 'csr'}, defaults to 'default'.
The storage type of the parameter's gradient.
Attributes
----------
grad_req : {'write', 'add', 'null'}
This can be set before or after initialization. Setting ``grad_req`` to ``'null'``
with ``x.grad_req = 'null'`` saves memory and computation when you don't
need gradient w.r.t x.
lr_mult : float
Local learning rate multiplier for this Parameter. The actual learning rate
is calculated with ``learning_rate * lr_mult``. You can set it with
``param.lr_mult = 2.0``
wd_mult : float
Local weight decay multiplier for this Parameter.
"""
def __init__(self, name, grad_req='write', shape=None, dtype=mx_real_t,
lr_mult=1.0, wd_mult=1.0, init=None, allow_deferred_init=False,
differentiable=True, stype='default', grad_stype='default'):
self._var = None
self._data = None
self._grad = None
self._ctx_list = None
self._ctx_map = None
self._trainer = None
self._deferred_init = ()
self._differentiable = differentiable
self._allow_deferred_init = allow_deferred_init
self._grad_req = None
if isinstance(shape, int):
shape = (shape,)
self._shape = shape
self.name = name
self._dtype = dtype
self.lr_mult = lr_mult
self.wd_mult = wd_mult
self.grad_req = grad_req
self.init = init
# sparse related storage type information
valid_stypes = ['default', 'row_sparse', 'csr']
assert grad_stype in valid_stypes, "grad_stype for Parameter '%s' must be " \
"one of 'default', 'row_sparse', or 'csr', but got '%s'" % (name, grad_stype)
assert stype in valid_stypes, "stype for Parameter '%s' must be " \
"one of 'default', 'row_sparse', or 'csr', but got '%s'" % (name, stype)
self._grad_stype = grad_stype
self._stype = stype
def __repr__(self):
s = 'Parameter {name} (shape={shape}, dtype={dtype})'
return s.format(name=self.name, shape=self.shape, dtype=self.dtype)
@property
def grad_req(self):
return self._grad_req
@grad_req.setter
def grad_req(self, req):
assert req in ['write', 'add', 'null'], \
"grad_req must be one of 'write', 'add', or 'null', but got '%s'"%req
if not self._differentiable:
req = 'null'
if self._grad_req == req:
return
self._grad_req = req
if req == 'null' and self._grad is not None:
self._grad = None
self._data = [i.detach() for i in self._data]
elif self._data is not None:
self._init_grad()
@property
def dtype(self):
"""The type of the parameter.
Setting the dtype value is equivalent to casting the value of the parameter
"""
return self._dtype
@dtype.setter
def dtype(self, dtype):
self.cast(dtype)
@property
def shape(self):
"""The shape of the parameter.
By default, an unknown dimension size is 0. However, when the NumPy semantic
is turned on, unknown dimension size is -1.
"""
if self._shape is None:
return None
elif is_np_shape():
# Parameters shouldn't be zero-size. If one of its dimension is 0,
# it means the parameter isn't initialized. In the NumPy semantics,
# the unknown dimension should be marked with -1.
return tuple(i if i != 0 else -1 for i in self._shape)
else:
return self._shape
@shape.setter
def shape(self, new_shape):
if self._shape is None:
self._shape = new_shape
return
assert len(self._shape) == len(new_shape) and \
all(j in (0, i) for i, j in zip(new_shape, self._shape)), \
"Expected shape %s is incompatible with given shape %s."%(
str(new_shape), str(self._shape))
self._shape = new_shape
def _set_trainer(self, trainer):
""" Set the trainer this parameter is associated with. """
# trainer cannot be replaced for sparse params
if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer:
raise RuntimeError(
"Failed to set the trainer for Parameter '%s' because it was already set. " \
"More than one trainers for a %s Parameter is not supported." \
%(self.name, self._stype))
self._trainer = trainer
def _check_and_get(self, arr_list, ctx):
if arr_list is not None:
if ctx is list:
return arr_list
if ctx is None:
if len(arr_list) == 1:
return arr_list[0]
else:
ctx = context.current_context()
ctx_list = self._ctx_map[ctx.device_typeid&1]
if ctx.device_id < len(ctx_list):
idx = ctx_list[ctx.device_id]
if idx is not None:
return arr_list[idx]
raise RuntimeError(
"Parameter '%s' was not initialized on context %s. "
"It was only initialized on %s."%(
self.name, str(ctx), str(self._ctx_list)))
if self._deferred_init:
raise DeferredInitializationError(
"Parameter '%s' has not been initialized yet because initialization was " \
"deferred. Actual initialization happens during the first forward pass. " \
"Please pass one batch of data through the network before accessing Parameters. " \
"You can also avoid deferred initialization by specifying in_units, " \
"num_features, etc., for network layers."%(self.name))
raise RuntimeError(
"Parameter '%s' has not been initialized. Note that " \
"you should initialize parameters and create Trainer " \
"with Block.collect_params() instead of Block.params " \
"because the later does not include Parameters of " \
"nested child Blocks"%(self.name))
def _get_row_sparse(self, arr_list, ctx, row_id):
""" Get row_sparse data from row_sparse parameters based on row_id. """
# get row sparse params based on row ids
if not isinstance(row_id, ndarray.NDArray):
raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id)))
if not self._trainer:
raise RuntimeError("Cannot get row_sparse data for Parameter '%s' when no " \
"Trainer is created with it."%self.name)
results = self._check_and_get(arr_list, ctx)
# fetch row sparse params from the trainer
self._trainer._row_sparse_pull(self, results, row_id)
return results
def _load_init(self, data, ctx, cast_dtype=False, dtype_source='current'):
"""
(Re)initializes by loading from data.
Parameters
----------
data : NDArray
The data to load
ctx : Context or list of Context
Context(s) initialize loaded parameters on.
cast_dtype : bool, default False
Cast the data type of the parameter
dtype_source : str, default 'current'
must be in {'current', 'saved'}
Only valid if cast_dtype=True, specify the source of the dtype for casting
the parameters
"""
if cast_dtype:
assert dtype_source in ['current', 'saved']
if self.shape:
for self_dim, data_dim in zip(self.shape, data.shape):
assert self_dim in (0, data_dim), \
"Failed loading Parameter '%s' from saved params: " \
"shape incompatible expected %s vs saved %s"%(
self.name, str(self.shape), str(data.shape))
self.shape = tuple(i if i != 0 else j for i, j in zip(self.shape, data.shape))
if self.dtype:
if cast_dtype and np.dtype(self.dtype).type != data.dtype:
if dtype_source == 'current':
data = data.astype(self.dtype, copy=False)
elif dtype_source == 'saved':
self.dtype = data.dtype
else:
assert np.dtype(self.dtype).type == data.dtype, \
"Failed loading Parameter '%s' from saved params: " \
"dtype incompatible expected %s vs saved %s. " \
"Set cast_dtype=True to cast the dtype of saved params."%(
self.name, str(self.dtype), str(data.dtype))
if self._stype != data.stype:
data = data.tostype(self._stype)
if isinstance(ctx, Context):
ctx = [ctx]
if self._data is None:
if self._deferred_init:
assert ctx is None or set(ctx) == set(self._deferred_init[1]), \
"Failed to load Parameter '%s' on %s because it was " \
"previous initialized on %s."%(
self.name, str(ctx), str(self.list_ctx()))
ctx = self._deferred_init[1]
elif ctx is None:
ctx = [cpu()]
self._init_impl(data, ctx)
else:
assert ctx is None or set(ctx) == set(self.list_ctx()), \
"Failed to load Parameter '%s' on %s because it was " \
"previous initialized on %s."%(
self.name, str(ctx), str(self.list_ctx()))
self.set_data(data)
self._deferred_init = ()
def _finish_deferred_init(self):
"""Finishes deferred initialization."""
if not self._deferred_init:
return
init, ctx, default_init, data = self._deferred_init
self._deferred_init = ()
assert shape_is_known(self.shape), \
"Cannot initialize Parameter '%s' because it has " \
"invalid shape: %s. Please specify in_units, " \
"in_channels, etc for `Block`s."%(
self.name, str(self.shape))
with autograd.pause():
if data is None:
data = ndarray.zeros(shape=self.shape, dtype=self.dtype,
ctx=context.cpu(), stype=self._stype)
initializer.create(default_init)(
initializer.InitDesc(self.name, {'__init__': init}), data)
self._init_impl(data, ctx)
def _init_impl(self, data, ctx_list):
"""Sets data and grad."""
self._ctx_list = list(ctx_list)
self._ctx_map = [[], []]
for i, ctx in enumerate(self._ctx_list):
dev_list = self._ctx_map[ctx.device_typeid&1]
while len(dev_list) <= ctx.device_id:
dev_list.append(None)
dev_list[ctx.device_id] = i
self._data = [data.copyto(ctx) for ctx in self._ctx_list]
self._init_grad()
def _init_grad(self):
"""Initialize grad buffers."""
if self.grad_req == 'null':
self._grad = None
return
self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
stype=self._grad_stype) for i in self._data]
autograd.mark_variables(self._check_and_get(self._data, list),
self._grad, self.grad_req)
def _reduce(self):
"""Reduce data from multiple context to cpu."""
ctx = context.cpu()
if self._stype == 'default':
block = self.list_data()
data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block)
else:
# fetch all rows for 'row_sparse' param
all_row_ids = ndarray.arange(0, self.shape[0], dtype='int64', ctx=ctx)
data = ndarray.zeros(self.shape, stype='row_sparse', ctx=ctx)
self._trainer._row_sparse_pull(self, data, all_row_ids, full_idx=True)
return data
[docs] def initialize(self, init=None, ctx=None, default_init=initializer.Uniform(),
force_reinit=False):
"""Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.
Parameters
----------
init : Initializer
The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.
ctx : Context or list of Context, defaults to :py:meth:`context.current_context()`.
Initialize Parameter on given context. If ctx is a list of Context, a
copy will be made for each context.
.. note::
Copies are independent arrays. User is responsible for keeping
their values consistent when updating.
Normally :py:class:`gluon.Trainer` does this for you.
default_init : Initializer
Default initializer is used when both :py:func:`init`
and :py:meth:`Parameter.init` are ``None``.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
Examples
--------
>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(ctx=mx.cpu(0))
>>> weight.data()
[[-0.01068833 0.01729892]
[ 0.02042518 -0.01618656]]
>>> weight.grad()
[[ 0. 0.]
[ 0. 0.]]
>>> weight.initialize(ctx=[mx.gpu(0), mx.gpu(1)])
>>> weight.data(mx.gpu(0))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
>>> weight.data(mx.gpu(1))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
"""
if self._data is not None and not force_reinit:
warnings.warn("Parameter '%s' is already initialized, ignoring. " \
"Set force_reinit=True to re-initialize."%self.name,
stacklevel=2)
return
self._data = self._grad = None
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if init is None:
init = default_init if self.init is None else self.init
if not shape_is_known(self.shape):
if self._allow_deferred_init:
self._deferred_init = (init, ctx, default_init, None)
return
raise ValueError("Cannot initialize Parameter '%s' because it has " \
"invalid shape: %s."%(self.name, str(self.shape)))
self._deferred_init = (init, ctx, default_init, None)
self._finish_deferred_init()
[docs] def reset_ctx(self, ctx):
"""Re-assign Parameter to other contexts.
Parameters
----------
ctx : Context or list of Context, default ``context.current_context()``.
Assign Parameter to given context. If ctx is a list of Context, a
copy will be made for each context.
"""
if ctx is None:
ctx = [context.current_context()]
if isinstance(ctx, Context):
ctx = [ctx]
if self._data:
data = self._reduce()
with autograd.pause():
self._init_impl(data, ctx)
elif self._deferred_init:
init, _, default_init, data = self._deferred_init
self._deferred_init = (init, ctx, default_init, data)
else:
raise ValueError("Cannot reset context for Parameter '%s' because it "
"has not been initialized."%self.name)
[docs] def set_data(self, data):
"""Sets this parameter's value on all contexts."""
self.shape = data.shape
if self._data is None:
assert self._deferred_init, \
"Parameter '%s' has not been initialized"%self.name
self._deferred_init = self._deferred_init[:3] + (data,)
return
# if update_on_kvstore, we need to make sure the copy stored in kvstore is in sync
if self._trainer and self._trainer._kv_initialized and self._trainer._update_on_kvstore:
if self not in self._trainer._params_to_init:
self._trainer._reset_kvstore()
for arr in self._check_and_get(self._data, list):
arr[:] = data
[docs] def row_sparse_data(self, row_id):
"""Returns a copy of the 'row_sparse' parameter on the same context as row_id's.
The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized on this context before.
Parameters
----------
row_id: NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
NDArray on row_id's context
"""
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return a copy of Parameter %s via row_sparse_data() " \
"because its storage type is %s. Please use data() instead." \
%(self.name, self._stype))
return self._get_row_sparse(self._data, row_id.context, row_id)
[docs] def list_row_sparse_data(self, row_id):
"""Returns copies of the 'row_sparse' parameter on all contexts, in the same order
as creation. The copy only retains rows whose ids occur in provided row ids.
The parameter must have been initialized before.
Parameters
----------
row_id: NDArray
Row ids to retain for the 'row_sparse' parameter.
Returns
-------
list of NDArrays
"""
if self._stype != 'row_sparse':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_row_sparse_data() because its storage type is %s. Please " \
"use data() instead." % (self.name, self._stype))
return self._get_row_sparse(self._data, list, row_id)
[docs] def data(self, ctx=None):
"""Returns a copy of this parameter on one context. Must have been
initialized on this context before. For sparse parameters, use
:py:meth:`Parameter.row_sparse_data` instead.
Parameters
----------
ctx : Context
Desired context.
Returns
-------
NDArray on ctx
"""
if self._stype != 'default':
raise RuntimeError("Cannot return a copy of Parameter '%s' on ctx %s via data() " \
"because its storage type is %s. Please use row_sparse_data() " \
"instead." % (self.name, str(ctx), self._stype))
return self._check_and_get(self._data, ctx)
[docs] def list_data(self):
"""Returns copies of this parameter on all contexts, in the same order
as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data`
instead.
Returns
-------
list of NDArrays
"""
if self._stype != 'default':
raise RuntimeError("Cannot return copies of Parameter '%s' on all contexts via " \
"list_data() because its storage type is %s. Please use " \
"row_sparse_data() instead." % (self.name, self._stype))
return self._check_and_get(self._data, list)
[docs] def grad(self, ctx=None):
"""Returns a gradient buffer for this parameter on one context.
Parameters
----------
ctx : Context
Desired context.
"""
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_and_get(self._grad, ctx)
[docs] def list_grad(self):
"""Returns gradient buffers on all contexts, in the same order
as :py:meth:`values`."""
if self._data is not None and self._grad is None:
raise RuntimeError(
"Cannot get gradient array for Parameter '%s' " \
"because grad_req='null'"%(self.name))
return self._check_and_get(self._grad, list)
[docs] def list_ctx(self):
"""Returns a list of contexts this parameter is initialized on."""
if self._data is None:
if self._deferred_init:
return self._deferred_init[1]
raise RuntimeError("Parameter '%s' has not been initialized"%self.name)
return self._ctx_list
[docs] def zero_grad(self):
"""Sets gradient buffer on all contexts to 0. No action is taken if
parameter is uninitialized or doesn't require gradient."""
if self._grad is None:
return
for i in self._grad:
ndarray.zeros_like(i, out=i)
[docs] def var(self):
"""Returns a symbol representing this parameter."""
if self._var is None:
self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype,
lr_mult=self.lr_mult, wd_mult=self.wd_mult,
init=self.init, stype=self._stype)
return self._var
[docs] def cast(self, dtype):
"""Cast data and gradient of this Parameter to a new data type.
Parameters
----------
dtype : str or numpy.dtype
The new data type.
"""
self._dtype = dtype
if self._data is None:
return
with autograd.pause():
self._data = [i.astype(dtype) for i in self._data]
if self._grad is None:
return
self._grad = [i.astype(dtype) for i in self._grad]
autograd.mark_variables(self._data, self._grad, self.grad_req)
[docs]class Constant(Parameter):
"""A constant parameter for holding immutable tensors.
`Constant`s are ignored by `autograd` and `Trainer`, thus their values
will not change during training. But you can still update their values
manually with the `set_data` method.
`Constant` s can be created with either::
const = mx.gluon.Constant('const', [[1,2],[3,4]])
or::
class Block(gluon.Block):
def __init__(self, **kwargs):
super(Block, self).__init__(**kwargs)
self.const = self.params.get_constant('const', [[1,2],[3,4]])
Parameters
----------
name : str
Name of the parameter.
value : array-like
Initial value for the constant.
"""
def __init__(self, name, value):
if not isinstance(value, ndarray.NDArray):
value = ndarray.array(value)
self.value = value
class Init(initializer.Initializer):
def _init_weight(self, _, arr):
value.copyto(arr)
init_name = 'Constant_{}_{}'.format(name, id(self))
initializer.alias(init_name)(Init)
super(Constant, self).__init__(
name, grad_req='null', shape=value.shape, dtype=value.dtype,
init=init_name)
def __repr__(self):
s = 'Constant {name} (shape={shape}, dtype={dtype})'
return s.format(name=self.name, shape=self.shape, dtype=self.dtype)
@property
def grad_req(self):
return 'null'
@grad_req.setter
def grad_req(self, req):
if req != 'null':
warnings.warn('Constant parameter "{}" does not support '
'grad_req other than "null", and new value "{}" '
'is ignored.'.format(self.name, req))
[docs]class ParameterDict(object):
"""A dictionary managing a set of parameters.
Parameters
----------
prefix : str, default ``''``
The prefix to be prepended to all Parameters' names created by this dict.
shared : ParameterDict or None
If not ``None``, when this dict's :py:meth:`get` method creates a new parameter, will
first try to retrieve it from "shared" dict. Usually used for sharing
parameters with another Block.
"""
def __init__(self, prefix='', shared=None):
self._prefix = prefix
self._params = OrderedDict()
self._shared = shared
def __repr__(self):
s = '{name}(\n{content}\n)'
name = self._prefix+' ' if self._prefix else ''
return s.format(name=name,
content='\n'.join([_indent(' {0}'.format(v), 2)
for v in self.values()]))
def __getitem__(self, key):
return self._params[key]
def __iter__(self):
return iter(self._params)
def items(self):
return self._params.items()
def keys(self):
return self._params.keys()
def values(self):
return self._params.values()
@property
def prefix(self):
"""Prefix of this dict. It will be prepended to :py:class:`Parameter`s' name created
with :py:func:`get`."""
return self._prefix
def _get_impl(self, name):
if name in self._params:
return self._params[name]
if self._shared is not None and name in self._shared._params:
self._params[name] = self._shared._params[name]
return self._shared._params[name]
return None
[docs] def get(self, name, **kwargs):
"""Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found,
:py:func:`get` will first try to retrieve it from "shared" dict. If still not
found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and
insert it to self.
Parameters
----------
name : str
Name of the desired Parameter. It will be prepended with this dictionary's
prefix.
**kwargs : dict
The rest of key-word arguments for the created :py:class:`Parameter`.
Returns
-------
Parameter
The created or retrieved :py:class:`Parameter`.
"""
name = self.prefix + name
param = self._get_impl(name)
if param is None: # pylint: disable=too-many-nested-blocks
param = Parameter(name, **kwargs)
self._params[name] = param
else:
for k, v in kwargs.items():
if hasattr(param, k) and getattr(param, k) is not None:
existing = getattr(param, k)
if k == 'shape' and len(v) == len(existing):
inferred_shape = []
matched = True
for dim1, dim2 in zip(v, existing):
if dim1 != dim2 and dim1 * dim2 != 0:
matched = False
break
elif dim1 == dim2:
inferred_shape.append(dim1)
elif dim1 == 0:
inferred_shape.append(dim2)
else:
inferred_shape.append(dim1)
if matched:
param._shape = tuple(inferred_shape)
continue
elif k == 'dtype' and np.dtype(v) == np.dtype(existing):
continue
assert v is None or v == existing, \
"Cannot retrieve Parameter '%s' because desired attribute " \
"does not match with stored for attribute '%s': " \
"desired '%s' vs stored '%s'."%(
name, k, str(v), str(getattr(param, k)))
else:
setattr(param, k, v)
return param
[docs] def get_constant(self, name, value=None):
"""Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found,
:py:func:`get` will first try to retrieve it from "shared" dict. If still not
found, :py:func:`get` will create a new :py:class:`.Constant` with key-word
arguments and insert it to self.
Parameters
----------
name : str
Name of the desired Constant. It will be prepended with this dictionary's
prefix.
value : array-like
Initial value of constant.
Returns
-------
:py:class:`.Constant`
The created or retrieved :py:class:`.Constant`.
"""
name = self.prefix + name
param = self._get_impl(name)
if param is None:
if value is None:
raise KeyError("No constant named '{}'. Please specify value " \
"if you want to create a new constant.".format(
name))
param = Constant(name, value)
self._params[name] = param
elif value is not None:
assert isinstance(param, Constant), \
"Parameter '{}' already exists but it is not a constant.".format(
name)
if isinstance(value, ndarray.NDArray):
value = value.asnumpy()
assert param.shape == value.shape and \
(param.value.asnumpy() == value).all(), \
"Constant '{}' already exists but it's value doesn't match new " \
"value".format(name)
return param
[docs] def update(self, other):
"""Copies all Parameters in ``other`` to self."""
for k, v in other.items():
if k in self._params:
assert self._params[k] is v, \
"Cannot update self with other because they have different " \
"Parameters with the same name '%s'"%k
for k, v in other.items():
self._params[k] = v
[docs] def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False,
force_reinit=False):
"""Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray`
API. It has no effect when using :py:class:`Symbol` API.
Parameters
----------
init : Initializer
Global default Initializer to be used when :py:meth:`Parameter.init` is ``None``.
Otherwise, :py:meth:`Parameter.init` takes precedence.
ctx : Context or list of Context
Keeps a copy of Parameters on one or many context(s).
verbose : bool, default False
Whether to verbosely print out details on initialization.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
"""
if verbose:
init.set_verbosity(verbose=verbose)
for _, v in self.items():
v.initialize(None, ctx, init, force_reinit=force_reinit)
[docs] def zero_grad(self):
"""Sets all Parameters' gradient buffer to 0."""
for i in self.values():
i.zero_grad()
[docs] def reset_ctx(self, ctx):
"""Re-assign all Parameters to other contexts.
Parameters
----------
ctx : Context or list of Context, default :py:meth:`context.current_context()`.
Assign Parameter to given context. If ctx is a list of Context, a
copy will be made for each context.
"""
for i in self.values():
i.reset_ctx(ctx)
[docs] def setattr(self, name, value):
"""Set an attribute to a new value for all Parameters.
For example, set grad_req to null if you don't need gradient w.r.t a
model's Parameters::
model.collect_params().setattr('grad_req', 'null')
or change the learning rate multiplier::
model.collect_params().setattr('lr_mult', 0.5)
Parameters
----------
name : str
Name of the attribute.
value : valid type for attribute name
The new value for the attribute.
"""
for i in self.values():
setattr(i, name, value)
[docs] def save(self, filename, strip_prefix=''):
"""Save parameters to file.
Parameters
----------
filename : str
Path to parameter file.
strip_prefix : str, default ''
Strip prefix from parameter names before saving.
"""
arg_dict = {}
for param in self.values():
weight = param._reduce()
if not param.name.startswith(strip_prefix):
raise ValueError(
"Prefix '%s' is to be striped before saving, but Parameter's "
"name '%s' does not start with '%s'. "
"this may be due to your Block shares parameters from other "
"Blocks or you forgot to use 'with name_scope()' when creating "
"child blocks. For more info on naming, please see "
"/versions/1.5.0/tutorials/basic/naming.html"%(
strip_prefix, param.name, strip_prefix))
arg_dict[param.name[len(strip_prefix):]] = weight
ndarray.save(filename, arg_dict)
[docs] def load(self, filename, ctx=None, allow_missing=False,
ignore_extra=False, restore_prefix='', cast_dtype=False,
dtype_source="current"):
"""Load parameters from file.
Parameters
----------
filename : str
Path to parameter file.
ctx : Context or list of Context
Context(s) initialize loaded parameters on.
allow_missing : bool, default False
Whether to silently skip loading parameters not represents in the file.
ignore_extra : bool, default False
Whether to silently ignore parameters from the file that are not
present in this ParameterDict.
restore_prefix : str, default ''
prepend prefix to names of stored parameters before loading.
cast_dtype : bool, default False
Cast the data type of the parameter
dtype_source : str, default 'current'
must be in {'current', 'saved'}
Only valid if cast_dtype=True, specify the source of the dtype for casting
the parameters
"""
if restore_prefix:
for name in self.keys():
assert name.startswith(restore_prefix), \
"restore_prefix is '%s' but Parameters name '%s' does not start " \
"with '%s'"%(restore_prefix, name, restore_prefix)
lprefix = len(restore_prefix)
ndarray_load = ndarray.load(filename)
loaded = [(k[4:] if k.startswith('arg:') or k.startswith('aux:') else k, v) \
for k, v in ndarray_load.items()] if isinstance(ndarray_load, dict) else ndarray_load
arg_dict = {restore_prefix+k: v for k, v in loaded}
if not allow_missing:
for name in self.keys():
assert name in arg_dict, \
"Parameter '%s' is missing in file '%s', which contains parameters: %s. " \
"Please make sure source and target networks have the same prefix."%(
name[lprefix:], filename, _brief_print_list(arg_dict.keys()))
for name in arg_dict:
if name not in self._params:
assert ignore_extra, \
"Parameter '%s' loaded from file '%s' is not present in ParameterDict, " \
"choices are: %s. Set ignore_extra to True to ignore. " \
"Please make sure source and target networks have the same prefix."%(
name[lprefix:], filename, _brief_print_list(self._params.keys()))
continue
self[name]._load_init(arg_dict[name], ctx, cast_dtype=cast_dtype, dtype_source=dtype_source)
