VariableRole.__hash__() - Code Metrics - Inspection of "[WIP] Managing initilizations via a role scheme di..." - mila-udem/blocks - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#1030)

unknown

created 2016-06-01 18:17 UTC

VariableRole.hash() A

↳ Parent: VariableRole

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cc	1
dl	0
loc	2
rs	10
c	0
b	0
f	0

import re


def add_role(var, role):
    r"""Add a role to a given Theano variable.

    Parameters
    ----------
    var : :class:`~tensor.TensorVariable`
        The variable to assign the new role to.
    role : :class:`.VariableRole` instance

    Notes
    -----
    Some roles are subroles of others (e.g. :const:`WEIGHT` is a subrole
    of :const:`PARAMETER`). This function will not add a role if a more
    specific role has already been added. If you need to replace a role
    with a parent role (e.g. replace :const:`WEIGHT` with
    :const:`PARAMETER`) you must do so manually.

    Examples
    --------
    >>> from theano import tensor
    >>> W = tensor.matrix()
    >>> from blocks.roles import PARAMETER, WEIGHT
    >>> add_role(W, PARAMETER)
    >>> print(*W.tag.roles)
    PARAMETER
    >>> add_role(W, WEIGHT)
    >>> print(*W.tag.roles)
    WEIGHT
    >>> add_role(W, PARAMETER)
    >>> print(*W.tag.roles)
    WEIGHT

    """
    roles = getattr(var.tag, 'roles', [])
    roles = [old_role for old_role in roles
             if not isinstance(role, old_role.__class__)]
    if not any(isinstance(old_role, role.__class__) for old_role in roles):
        roles += [role]
    var.tag.roles = roles


def has_roles(var, roles, match_all=False):
    r"""Test if a variable has given roles taking subroles into account.

    Parameters
    ----------
    var : :class:`~tensor.TensorVariable`
        Variable being queried.
    roles : an iterable of :class:`.VariableRole` instances.
    match_all : bool, optional
        If ``True``, checks if the variable has all given roles.
        If ``False``, any of the roles is sufficient.
        ``False`` by default.

    """
    var_roles = getattr(var.tag, 'roles', [])
    matches = (any(isinstance(var_role, role.__class__) for
                   var_role in var_roles) for role in roles)
    return all(matches) if match_all else any(matches)


class VariableRole(object):
    """Base class for all variable roles."""
    def __eq__(self, other):
        return self.__class__ == other.__class__

    def __repr__(self):
        return re.sub(r'(?!^)([A-Z]+)', r'_\1',
                      self.__class__.__name__[:-4]).upper()

    def __hash__(self):
        return hash(str(self))


class InputRole(VariableRole):
    pass

#: The input of a :class:`.Brick`
INPUT = InputRole()


class OutputRole(VariableRole):
    pass

#: The output of a :class:`.Brick`
OUTPUT = OutputRole()


class CostRole(VariableRole):
    pass

#: A scalar cost that can be used to train or regularize
COST = CostRole()


class PersistentRole(VariableRole):
    pass

# Any persistent quantity that should be saved as part of the model
PERSISTENT = PersistentRole()


class ParameterRole(PersistentRole):
    pass

#: A parameter of the model
PARAMETER = ParameterRole()


class AuxiliaryRole(VariableRole):
    pass

#: Variables added to the graph as annotations
AUXILIARY = AuxiliaryRole()


class WeightRole(ParameterRole):
    pass

#: The weight matrices of linear transformations
WEIGHT = WeightRole()


class BiasRole(ParameterRole):
    pass

#: Biases of linear transformations
BIAS = BiasRole()


class InitialStateRole(ParameterRole):
    pass

#: Initial state of a recurrent network
INITIAL_STATE = InitialStateRole()


class FilterRole(WeightRole):
    pass

#: The filters (kernels) of a convolution operation
FILTER = FilterRole()


class DropoutRole(VariableRole):
    pass

#: Inputs with applied dropout
DROPOUT = DropoutRole()


class CollectedRole(VariableRole):
    pass

#: The replacement of a variable collected into a single shared variable
COLLECTED = CollectedRole()


class CollectorRole(ParameterRole):
    pass

#: A collection of parameters combined into a single shared variable
COLLECTOR = CollectorRole()


class AlgorithmStateRole(VariableRole):
    pass

#: Shared variables used in algorithms updates
ALGORITHM_STATE = AlgorithmStateRole()


class AlgorithmHyperparameterRole(AlgorithmStateRole):
    pass

#: hyperparameters accociated with algorithms
ALGORITHM_HYPERPARAMETER = AlgorithmHyperparameterRole()


class AlgorithmBufferRole(AlgorithmStateRole):
    pass

#: buffers accociated with algorithms
ALGORITHM_BUFFER = AlgorithmBufferRole()


class BatchNormPopulationStatisticsRole(PersistentRole):
    pass

#: base role for batch normalization population statistics
BATCH_NORM_POPULATION_STATISTICS = BatchNormPopulationStatisticsRole()


class BatchNormPopulationMeanRole(BatchNormPopulationStatisticsRole):
    pass

#: mean activations accumulated over the dataset
BATCH_NORM_POPULATION_MEAN = BatchNormPopulationMeanRole()


class BatchNormPopulationStdevRole(BatchNormPopulationStatisticsRole):
    pass

#: standard deviations of activations accumulated over the dataset
BATCH_NORM_POPULATION_STDEV = BatchNormPopulationStdevRole()


class BatchNormGraphVariableRole(VariableRole):
    pass

#: base for roles used for within-graph batch normalization replacement
BATCH_NORM_GRAPH_VARIABLE = BatchNormGraphVariableRole()


class BatchNormOffsetRole(BatchNormGraphVariableRole):
    pass

#: offset applied in a BatchNormalization application (or its
#  batch-normalized replacement)
BATCH_NORM_OFFSET = BatchNormOffsetRole()


class BatchNormDivisorRole(BatchNormGraphVariableRole):
    pass

#: divisor applied in a BatchNormalization application (or its
#  batch-normalized replacement)
BATCH_NORM_DIVISOR = BatchNormDivisorRole()


class BatchNormMinibatchEstimateRole(BatchNormGraphVariableRole):
    pass

#: role added to variables that are the result of a batch normalization
#  replacement, rather than the original population statistics variables.
BATCH_NORM_MINIBATCH_ESTIMATE = BatchNormMinibatchEstimateRole()


class BatchNormScaleParameterRole(ParameterRole):
    pass

#: role given to the scale parameter, referred to as "scale" in the
# batch normalization manuscript, applied after normalizing.
BATCH_NORM_SCALE_PARAMETER = BatchNormScaleParameterRole()


class BatchNormShiftParameterRole(BiasRole):
    pass

#: role given to the shift parameter, referred to as "beta" in the
# batch normalization manuscript, applied after normalizing and scaling.
# Inherits from BIAS, because there really is no functional difference
# with a normal bias, and indeed these are the only biases present
# inside a BatchNormalizedMLP.
BATCH_NORM_SHIFT_PARAMETER = BatchNormShiftParameterRole()


1			import re
2
3
4			def add_role(var, role):
5			r"""Add a role to a given Theano variable.
6
7			Parameters
8			----------
9			var : :class:`~tensor.TensorVariable`
10			The variable to assign the new role to.
11			role : :class:`.VariableRole` instance
12
13			Notes
14			-----
15			Some roles are subroles of others (e.g. :const:`WEIGHT` is a subrole
16			of :const:`PARAMETER`). This function will not add a role if a more
17			specific role has already been added. If you need to replace a role
18			with a parent role (e.g. replace :const:`WEIGHT` with
19			:const:`PARAMETER`) you must do so manually.
20
21			Examples
22			--------
23			>>> from theano import tensor
24			>>> W = tensor.matrix()
25			>>> from blocks.roles import PARAMETER, WEIGHT
26			>>> add_role(W, PARAMETER)
27			>>> print(*W.tag.roles)
28			PARAMETER
29			>>> add_role(W, WEIGHT)
30			>>> print(*W.tag.roles)
31			WEIGHT
32			>>> add_role(W, PARAMETER)
33			>>> print(*W.tag.roles)
34			WEIGHT
35
36			"""
37			roles = getattr(var.tag, 'roles', [])
38			roles = [old_role for old_role in roles
39			if not isinstance(role, old_role.__class__)]
40			if not any(isinstance(old_role, role.__class__) for old_role in roles):
41			roles += [role]
42			var.tag.roles = roles
43
44
45			def has_roles(var, roles, match_all=False):
46			r"""Test if a variable has given roles taking subroles into account.
47
48			Parameters
49			----------
50			var : :class:`~tensor.TensorVariable`
51			Variable being queried.
52			roles : an iterable of :class:`.VariableRole` instances.
53			match_all : bool, optional
54			If ``True``, checks if the variable has all given roles.
55			If ``False``, any of the roles is sufficient.
56			``False`` by default.
57
58			"""
59			var_roles = getattr(var.tag, 'roles', [])
60			matches = (any(isinstance(var_role, role.__class__) for
61			var_role in var_roles) for role in roles)
62			return all(matches) if match_all else any(matches)
63
64
65			class VariableRole(object):
66			"""Base class for all variable roles."""
67			def __eq__(self, other):
68			return self.__class__ == other.__class__
69
70			def __repr__(self):
71			return re.sub(r'(?!^)([A-Z]+)', r'_\1',
72			self.__class__.__name__[:-4]).upper()
73
74			def __hash__(self):
75			return hash(str(self))
76
77
78			class InputRole(VariableRole):
79			pass
80
81			#: The input of a :class:`.Brick`
82			INPUT = InputRole()
83
84
85			class OutputRole(VariableRole):
86			pass
87
88			#: The output of a :class:`.Brick`
89			OUTPUT = OutputRole()
90
91
92			class CostRole(VariableRole):
93			pass
94
95			#: A scalar cost that can be used to train or regularize
96			COST = CostRole()
97
98
99			class PersistentRole(VariableRole):
100			pass
101
102			# Any persistent quantity that should be saved as part of the model
103			PERSISTENT = PersistentRole()
104
105
106			class ParameterRole(PersistentRole):
107			pass
108
109			#: A parameter of the model
110			PARAMETER = ParameterRole()
111
112
113			class AuxiliaryRole(VariableRole):
114			pass
115
116			#: Variables added to the graph as annotations
117			AUXILIARY = AuxiliaryRole()
118
119
120			class WeightRole(ParameterRole):
121			pass
122
123			#: The weight matrices of linear transformations
124			WEIGHT = WeightRole()
125
126
127			class BiasRole(ParameterRole):
128			pass
129
130			#: Biases of linear transformations
131			BIAS = BiasRole()
132
133
134			class InitialStateRole(ParameterRole):
135			pass
136
137			#: Initial state of a recurrent network
138			INITIAL_STATE = InitialStateRole()
139
140
141			class FilterRole(WeightRole):
142			pass
143
144			#: The filters (kernels) of a convolution operation
145			FILTER = FilterRole()
146
147
148			class DropoutRole(VariableRole):
149			pass
150
151			#: Inputs with applied dropout
152			DROPOUT = DropoutRole()
153
154
155			class CollectedRole(VariableRole):
156			pass
157
158			#: The replacement of a variable collected into a single shared variable
159			COLLECTED = CollectedRole()
160
161
162			class CollectorRole(ParameterRole):
163			pass
164
165			#: A collection of parameters combined into a single shared variable
166			COLLECTOR = CollectorRole()
167
168
169			class AlgorithmStateRole(VariableRole):
170			pass
171
172			#: Shared variables used in algorithms updates
173			ALGORITHM_STATE = AlgorithmStateRole()
174
175
176			class AlgorithmHyperparameterRole(AlgorithmStateRole):
177			pass
178
179			#: hyperparameters accociated with algorithms
180			ALGORITHM_HYPERPARAMETER = AlgorithmHyperparameterRole()
181
182
183			class AlgorithmBufferRole(AlgorithmStateRole):
184			pass
185
186			#: buffers accociated with algorithms
187			ALGORITHM_BUFFER = AlgorithmBufferRole()
188
189
190			class BatchNormPopulationStatisticsRole(PersistentRole):
191			pass
192
193			#: base role for batch normalization population statistics
194			BATCH_NORM_POPULATION_STATISTICS = BatchNormPopulationStatisticsRole()
195
196
197			class BatchNormPopulationMeanRole(BatchNormPopulationStatisticsRole):
198			pass
199
200			#: mean activations accumulated over the dataset
201			BATCH_NORM_POPULATION_MEAN = BatchNormPopulationMeanRole()
202
203
204			class BatchNormPopulationStdevRole(BatchNormPopulationStatisticsRole):
205			pass
206
207			#: standard deviations of activations accumulated over the dataset
208			BATCH_NORM_POPULATION_STDEV = BatchNormPopulationStdevRole()
209
210
211			class BatchNormGraphVariableRole(VariableRole):
212			pass
213
214			#: base for roles used for within-graph batch normalization replacement
215			BATCH_NORM_GRAPH_VARIABLE = BatchNormGraphVariableRole()
216
217
218			class BatchNormOffsetRole(BatchNormGraphVariableRole):
219			pass
220
221			#: offset applied in a BatchNormalization application (or its
222			# batch-normalized replacement)
223			BATCH_NORM_OFFSET = BatchNormOffsetRole()
224
225
226			class BatchNormDivisorRole(BatchNormGraphVariableRole):
227			pass
228
229			#: divisor applied in a BatchNormalization application (or its
230			# batch-normalized replacement)
231			BATCH_NORM_DIVISOR = BatchNormDivisorRole()
232
233
234			class BatchNormMinibatchEstimateRole(BatchNormGraphVariableRole):
235			pass
236
237			#: role added to variables that are the result of a batch normalization
238			# replacement, rather than the original population statistics variables.
239			BATCH_NORM_MINIBATCH_ESTIMATE = BatchNormMinibatchEstimateRole()
240
241
242			class BatchNormScaleParameterRole(ParameterRole):
243			pass
244
245			#: role given to the scale parameter, referred to as "scale" in the
246			# batch normalization manuscript, applied after normalizing.
247			BATCH_NORM_SCALE_PARAMETER = BatchNormScaleParameterRole()
248
249
250			class BatchNormShiftParameterRole(BiasRole):
251			pass
252
253			#: role given to the shift parameter, referred to as "beta" in the
254			# batch normalization manuscript, applied after normalizing and scaling.
255			# Inherits from BIAS, because there really is no functional difference
256			# with a normal bias, and indeed these are the only biases present
257			# inside a BatchNormalizedMLP.
258			BATCH_NORM_SHIFT_PARAMETER = BatchNormShiftParameterRole()
259

mila-udem / blocks

Pull Request — master (#1030)

VariableRole.__hash__() A

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VariableRole.hash() A