Skip to content

P
pytensor
提交 b82123d4 authored 作者: Iban Harlouchet's avatar Iban Harlouchet
浏览文件
操作

__props__ to theano/sparse/basic.py

上级 2207c24f
隐藏空白字符变更
内嵌 并排
正在显示 包含 46 行增加 和 210 行删除
theano/sparse/basic.py
浏览文件 @ b82123d4
......@@ -578,6 +578,7 @@ class CSM(gof.Op):
_hashval = None
"""Pre-computed hash value, defined by __init__"""
__props__ = ()
def __init__(self, format, kmap=None):
if format not in ('csr', 'csc'):
......@@ -598,13 +599,6 @@ class CSM(gof.Op):
self._hashval = (hash(type(self)) ^ hash(self.format) ^
_kmap_hash(self.kmap))
def __eq__(self, other):
return (type(other) is CSM and other.format == self.format and
_kmap_eq(self.kmap, other.kmap))
def __hash__(self):
return self._hashval
def __str__(self):
if self.kmap is not None:
return "%s{%s}" % (self.__class__.__name__, str(self.kmap))
......@@ -758,6 +752,7 @@ class CSMGrad(gof.op.Op):
# 2. The elements in the sparse dimension are not guaranteed to be sorted.
# Therefore, the input data vector may have a different order than the
# gradient data vector.
__props__ = ("kmap")
def __init__(self, kmap=None):
self.kmap = kmap
......@@ -766,12 +761,6 @@ class CSMGrad(gof.op.Op):
# if self.kmap is None:
# self.view_map = {0: [1]}
def __eq__(self, other):
return type(self) == type(other) and _kmap_eq(self.kmap, other.kmap)
def __hash__(self):
return 82345 ^ hash(type(self)) ^ _kmap_hash(self.kmap)
def __str__(self):
return "%s{%s}" % (
self.__class__.__name__,
......@@ -822,15 +811,11 @@ csm_grad = CSMGrad
class Cast(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ("out_type")
def __init__(self, out_type):
self.out_type = out_type
def __eq__(self, other):
return (type(self) == type(other)) and self.out_type == other.out_type
def __hash__(self):
return hash(type(self)) ^ hash(self.out_type)
def make_node(self, x):
x = as_sparse_variable(x)
assert x.format in ["csr", "csc"]
......@@ -900,16 +885,11 @@ def cast(variable, dtype):
class DenseFromSparse(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ()
def __init__(self, structured=True):
self.sparse_grad = structured
def __eq__(self, other):
return ((type(self) == type(other)) and
(self.sparse_grad == other.sparse_grad))
def __hash__(self):
return hash(type(self)) ^ hash(self.sparse_grad)
def __str__(self):
return "%s{structured_grad=%s}" % (
self.__class__.__name__,
......@@ -973,14 +953,11 @@ dense_from_sparse = DenseFromSparse()
class SparseFromDense(gof.op.Op):
def __init__(self, format):
self.format = format
def __eq__(self, other):
return type(self) == type(other) and self.format == other.format
__props__ = ()
def __hash__(self):
return 982374 ^ hash(self.format) ^ hash(DenseFromSparse)
def __init__(self, format):
self.format = format
def __str__(self):
return "%s{%s}" % (
......@@ -1036,11 +1013,7 @@ csc_from_dense = SparseFromDense('csc')
# Indexing
class GetItemList(gof.op.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def infer_shape(self, node, shapes):
return [(shapes[1][0], shapes[0][1])]
......@@ -1084,11 +1057,7 @@ returning them as a new sparse matrix.
class GetItemListGrad(gof.op.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def infer_shape(self, node, shapes):
return [(shapes[0])]
......@@ -1134,11 +1103,7 @@ get_item_list_grad = GetItemListGrad()
class GetItem2Lists(gof.op.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, ind1, ind2):
x = as_sparse_variable(x)
......@@ -1184,11 +1149,7 @@ get_item_2lists = GetItem2Lists()
class GetItem2ListsGrad(gof.op.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def infer_shape(self, node, shapes):
return [(shapes[0])]
......@@ -1232,11 +1193,8 @@ get_item_2lists_grad = GetItem2ListsGrad()
class GetItem2d(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
# Fred:Too complicated for now. If you need it, look at
# the Subtensor.infer_shape.
......@@ -1356,11 +1314,7 @@ when sparse vectors are supported.
class GetItemScalar(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def infer_shape(self, node, shapes):
return [()]
......@@ -1422,12 +1376,7 @@ class Transpose(gof.op.Op):
format_map = {'csr': 'csc',
'csc': 'csr'}
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return "Sparse" + self.__class__.__name__
......@@ -1471,11 +1420,8 @@ transpose = Transpose()
class Neg(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return "Sparse" + self.__class__.__name__
......@@ -1524,11 +1470,7 @@ class ColScaleCSC(gof.op.Op):
# :note: The grad implemented is structured.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, s):
if x.format != 'csc':
......@@ -1576,12 +1518,7 @@ class RowScaleCSC(gof.op.Op):
# :note: The grad implemented is structured.
view_map = {0: [0]}
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, s):
x = as_sparse_variable(x)
......@@ -1660,6 +1597,9 @@ def row_scale(x, s):
class SpSum(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ("axis", "sparse_grad")
def __init__(self, axis=None, sparse_grad=True):
super(SpSum, self).__init__()
self.axis = axis
......@@ -1667,22 +1607,6 @@ class SpSum(gof.op.Op):
if self.axis not in (None, 0, 1):
raise ValueError('Illegal value for self.axis.')
def __eq__(self, other):
# WARNING: judgement call...
# We are not using the structured in the comparison or hashing
# because it doesn't change the perform method therefore, we
# *do* want Sums with different structured values to be merged
# by the merge optimization and this requires them to compare equal.
return type(self) == type(other) and self.axis == other.axis
def __hash__(self):
# WARNING: judgement call...
# We are not using the structured in the comparison or hashing
# because it doesn't change the perform method therefore, we
# *do* want Sums with different structured values to be merged
# by the merge optimization and this requires them to compare equal.
return 76324 ^ hash(type(self)) ^ hash(self.axis)
def make_node(self, x):
x = as_sparse_variable(x)
assert x.format in ["csr", "csc"]
......@@ -1773,11 +1697,7 @@ def sp_sum(x, axis=None, sparse_grad=False):
class Diag(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x):
x = as_sparse_variable(x)
......@@ -1819,11 +1739,8 @@ diag = Diag()
class SquareDiagonal(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, diag):
diag = tensor.as_tensor_variable(diag)
......@@ -1868,17 +1785,13 @@ is given by the dense vector argument.
class EnsureSortedIndices(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ("inplace")
def __init__(self, inplace):
self.inplace = inplace
if self.inplace:
self.view_map = {0: [0]}
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
def make_node(self, x):
x = as_sparse_variable(x)
assert x.format in ["csr", "csc"]
......@@ -1941,11 +1854,7 @@ def clean(x):
class AddSS(gof.op.Op):
# add(sparse, sparse).
# see the doc of add() for more detail.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -1982,11 +1891,7 @@ add_s_s = AddSS()
class AddSSData(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, y):
x, y = map(as_sparse_variable, [x, y])
......@@ -2041,14 +1946,7 @@ pattern.
class AddSD(gof.op.Op):
# add(sparse, sparse).
# see the doc of add() for more detail.
def __init__(self, *args, **kwargs):
gof.Op.__init__(self, *args, **kwargs)
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -2090,11 +1988,8 @@ add_s_d = AddSD()
class StructuredAddSV(gof.op.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, y):
x = as_sparse_variable(x)
......@@ -2206,11 +2101,7 @@ def sub(x, y):
class MulSS(gof.op.Op):
# mul(sparse, sparse)
# See the doc of mul() for more detail
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -2249,11 +2140,7 @@ mul_s_s = MulSS()
class MulSD(gof.op.Op):
# mul(sparse, dense)
# See the doc of mul() for more detail
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -2344,11 +2231,8 @@ mul_s_d = MulSD()
class MulSV(gof.op.Op):
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, y):
x = as_sparse_variable(x)
......@@ -2459,17 +2343,12 @@ class __ComparisonOpSS(gof.op.Op):
:return: Comparison(x,y)
"""
__props__ = ()
# Function to override
def comparison(self, x, y):
raise NotImplementedError()
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, x, y):
x = as_sparse_variable(x)
y = as_sparse_variable(y)
......@@ -2505,17 +2384,12 @@ class __ComparisonOpSD(gof.op.Op):
:return: Comparison(x,y)
"""
__props__ = ()
# Function to override
def comparison(self, x, y):
raise NotImplementedError()
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
def make_node(self, x, y):
x, y = as_sparse_variable(x), tensor.as_tensor_variable(y)
......@@ -2735,6 +2609,8 @@ ge = __ComparisonSwitch(greater_equal_s_s, greater_equal_s_d,
class HStack(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ("format", "dtype")
def __init__(self, format=None, dtype=None):
if format is None:
self.format = 'csc'
......@@ -2745,14 +2621,6 @@ class HStack(gof.op.Op):
raise ValueError('The output dtype must be specified.')
self.dtype = dtype
def __eq__(self, other):
return (type(self) == type(other) and
self.format == other.format and
self.dtype == other.dtype)
def __hash__(self):
return hash(type(self)) ^ hash(self.format) ^ hash(self.dtype)
def make_node(self, *mat):
if not mat:
raise ValueError('Cannot join an empty list of sparses.')
......@@ -2901,8 +2769,7 @@ def vstack(blocks, format=None, dtype=None):
class Remove0(gof.Op):
# See doc in instance of this Op or a function after the class definition.
def __init__(self, inplace=False, *args, **kwargs):
gof.Op.__init__(self, *args, **kwargs)
def __init__(self, inplace=False):
self.inplace = inplace
if self.inplace:
self.destroy_map = {0: [0]}
......@@ -3317,11 +3184,7 @@ def true_dot(x, y, grad_preserves_dense=True):
# Dot
class StructuredDot(gof.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -3457,12 +3320,7 @@ class StructuredDotGradCSC(gof.Op):
# :note: The grad implemented is structured.
# :note: a_* are the corresponding properties of a sparse
# matrix in csc format.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -3595,12 +3453,7 @@ class StructuredDotGradCSR(gof.Op):
# :note: The grad implemented is structured.
# :note: a_* are the corresponding properties of a sparse
# matrix in csr format.
def __eq__(self, other):
return (type(self) == type(other))
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......@@ -3742,11 +3595,7 @@ def structured_dot_grad(sparse_A, dense_B, ga):
class SamplingDot(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
__props__ = ()
def make_node(self, x, y, p):
x = tensor.as_tensor_variable(x)
......@@ -3823,11 +3672,7 @@ than `dot` because SamplingDot requires `x` to be a `m`x`k` matrix while
class Dot(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return "Sparse" + self.__class__.__name__
......@@ -3958,12 +3803,7 @@ class Usmm(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
# We don't implement the infer_shape as it is
# inserted by optimization only.
def __eq__(self, other):
return type(self) == type(other)
def __hash__(self):
return hash(type(self))
__props__ = ()
def __str__(self):
return 'Usmm{no_inplace}'
......@@ -4033,11 +3873,7 @@ usmm = Usmm()
class ConstructSparseFromList(gof.Op):
# See doc in instance of this Op or function after this class definition.
def __hash__(self):
return hash((type(self)))
def __eq__(self, other):
return (type(self) == type(other))
__props__ = ()
def __str__(self):
return self.__class__.__name__
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论