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提交 1844ad38 authored 作者: David Warde-Farley's avatar David Warde-Farley
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PEP8: Fix E225, E251, E261.

上级 bcb362c8
隐藏空白字符变更
内嵌 并排
正在显示 包含 80 行增加 和 70 行删除
theano/sparse/basic.py
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......@@ -123,8 +123,8 @@ def constant(x, name=None):
if not isinstance(x, scipy.sparse.spmatrix):
raise TypeError("sparse.constant must be called on a scipy.sparse.spmatrix")
try:
return SparseConstant(SparseType(format = x.format,
dtype = x.dtype), x.copy(), name=name)
return SparseConstant(SparseType(format=x.format,
dtype=x.dtype), x.copy(), name=name)
except TypeError:
raise TypeError("Could not convert %s to SparseType" % x, type(x))
......@@ -133,14 +133,15 @@ if 0:
if not isinstance(x, scipy.sparse.spmatrix):
raise TypeError("sparse.value must be called on a scipy.sparse.spmatrix")
try:
return SparseValue(SparseType(format = x.format,
dtype = x.dtype), x)
return SparseValue(SparseType(format=x.format,
dtype=x.dtype), x)
except TypeError:
raise TypeError("Could not convert %s to SparseType" % x, type(x))
def sp_ones_like(x):
data, indices, indptr, shape = csm_properties(x) #TODO: don't restrict to CSM formats
# TODO: don't restrict to CSM formats
data, indices, indptr, shape = csm_properties(x)
return CSM(format=x.format)(tensor.ones_like(data), indices, indptr, shape)
......@@ -153,7 +154,8 @@ def sp_zeros_like(x):
class _sparse_py_operators:
T = property(lambda self: transpose(self), doc = "Return aliased transpose of self (read-only)")
T = property(lambda self: transpose(self),
doc="Return aliased transpose of self (read-only)")
def __neg__(self): return neg(self)
......@@ -178,15 +180,16 @@ class _sparse_py_operators:
#N.B. THIS IS COMMENTED OUT ON PURPOSE!!!
# Discussion with Fred & James (at least, and maybe others before)
# we decided that casting from a sparse to dense should be explicit
# because it's usually something you just want to be pretty careful about,
# and not to do by accident.
# because it's usually something you just want to be pretty careful
# about, and not to do by accident.
#def _as_TensorVariable(self):
# return dense_from_sparse(self)
shape = property(lambda self: tensor.shape(dense_from_sparse(self))) # don't worry!
# ... the plan is that the ShapeFeature in tensor.opt will do shape propagation
# ... and remove the dense_from_sparse from the graph. This will *NOT* actually expand
# ... your sparse matrix just to get the shape.
shape = property(lambda self: tensor.shape(dense_from_sparse(self)))
# don't worry!
# the plan is that the ShapeFeature in tensor.opt will do shape propagation
# and remove the dense_from_sparse from the graph. This will *NOT*
# actually expand your sparse matrix just to get the shape.
ndim = property(lambda self: self.type.ndim)
dtype = property(lambda self: self.type.dtype)
......@@ -222,7 +225,7 @@ class SparseVariable(gof.Variable, _sparse_py_operators):
format = property(lambda self: self.type.format)
def __str__(self):
return '%s{%s,%s}'%(
return '%s{%s,%s}' % (
self.__class__.__name__,
self.format,
self.dtype)
......@@ -238,7 +241,7 @@ class SparseConstantSignature(tuple):
and (b.dtype == y.dtype)\
and (type(b) == type(y))\
and (b.shape == y.shape)\
and (abs(b-y).sum() < 1e-6 * b.nnz)
and (abs(b - y).sum() < 1e-6 * b.nnz)
def __hash__(self):
(a, b) = self
......@@ -254,7 +257,7 @@ class SparseConstant(gof.Constant, _sparse_py_operators):
return SparseConstantSignature((self.type, self.data))
def __str__(self):
return '%s{%s,%s,shape=%s,nnz=%s}'%(
return '%s{%s,%s,shape=%s,nnz=%s}' % (
self.__class__.__name__,
self.format,
self.dtype,
......@@ -278,11 +281,10 @@ class SparseType(gof.Type):
@note As far as I can tell, L{scipy.sparse} objects must be matrices, i.e. have dimension 2.
"""
format_cls = {
'csr' : scipy.sparse.csr_matrix,
'csc' : scipy.sparse.csc_matrix
}
dtype_set = set(['int', 'int8', 'int16', 'int32', 'int64', 'float32', 'float64', 'complex64', 'complex128'])
format_cls = {'csr': scipy.sparse.csr_matrix,
'csc': scipy.sparse.csc_matrix}
dtype_set = set(['int', 'int8', 'int16', 'int32', 'int64', 'float32',
'float64', 'complex64', 'complex128'])
ndim = 2
Variable = SparseVariable
......@@ -320,7 +322,7 @@ class SparseType(gof.Type):
else:
sp = self.format_cls[self.format](value)
if str(sp.dtype) != self.dtype:
raise NotImplementedError("Expected %s dtype but got %s"%(self.dtype, str(sp.dtype)))
raise NotImplementedError("Expected %s dtype but got %s" % (self.dtype, str(sp.dtype)))
if sp.format != self.format:
raise NotImplementedError()
return sp
......@@ -341,9 +343,8 @@ class SparseType(gof.Type):
return True
return False
def make_variable(self, name = None):
return SparseVariable(self, name = name)
def make_variable(self, name=None):
return SparseVariable(self, name=name)
def __eq__(self, other):
return type(self) == type(other) and other.dtype == self.dtype and other.format == self.format
......@@ -363,10 +364,10 @@ class SparseType(gof.Type):
# a FAST_RUN computation..
if not scipy.sparse.issparse(a) or not scipy.sparse.issparse(b):
return False
diff = abs(a-b)
diff = abs(a - b)
if diff.nnz == 0:
return True
return max(diff)<eps
return max(diff) < eps
def values_eq(self, a, b):
#WARNING: equality comparison of sparse matrices is not fast or easy
......@@ -374,7 +375,7 @@ class SparseType(gof.Type):
# a FAST_RUN computation..
return scipy.sparse.issparse(a) \
and scipy.sparse.issparse(b) \
and abs(a-b).sum() == 0.0
and abs(a - b).sum() == 0.0
def is_valid_value(self, a):
return scipy.sparse.issparse(a) and (a.format == self.format)
......@@ -430,7 +431,8 @@ class CSMProperties(gof.Op):
def make_node(self, csm):
csm = as_sparse_variable(csm)
data = tensor.TensorType(dtype=csm.type.dtype, broadcastable = (False,)).make_variable()
data = tensor.TensorType(dtype=csm.type.dtype,
broadcastable=(False,)).make_variable()
return gof.Apply(self, [csm],
[data, tensor.ivector(), tensor.ivector(), tensor.ivector()])
......@@ -455,8 +457,8 @@ class CSMProperties(gof.Op):
return [CSM('csc')(g_data, indices, indptr, shape)]
else:
return [CSR('csm')(g_data, indices, indptr, shape)]
csm_properties = CSMProperties() #don't make this a function or it breaks some optimizations below
# don't make this a function or it breaks some optimizations below
csm_properties = CSMProperties()
def csm_data(csm): return csm_properties(csm)[0]
......@@ -491,7 +493,7 @@ class CSM(gof.Op):
self.format = format
# for efficiency, if remap does nothing, then do not apply it
if kmap is not None and all(kmap==numpy.arange(numpy.size(kmap))):
if kmap is not None and all(kmap == numpy.arange(numpy.size(kmap))):
kmap = None
self.kmap = kmap
......@@ -539,8 +541,8 @@ class CSM(gof.Op):
return gof.Apply(self,
[data, indices, indptr, shape],
[SparseType(dtype = data.type.dtype,
format = self.format).make_variable()])
[SparseType(dtype=data.type.dtype,
format=self.format).make_variable()])
def perform(self, node, (data, indices, indptr, shape), (out,)):
"""Build a csc_matrix"""
......@@ -551,19 +553,19 @@ class CSM(gof.Op):
if len(shape) != 2:
raise ValueError('Shape should be an array of length 2')
if data.shape != indices.shape and numpy.size(data) != numpy.size(self.kmap):
errmsg = 'Data (shape '+`data.shape`+' must have the same number of elements '+\
'as indices (shape'+`indices.shape`+') or elements as kmap ('+`numpy.size(self.kmap)`+')'
errmsg = 'Data (shape ' + `data.shape` + ' must have the same number of elements ' + \
'as indices (shape' + `indices.shape` + ') or elements as kmap (' + `numpy.size(self.kmap)` + ')'
raise ValueError(errmsg)
if self.format == 'csc':
out[0] = scipy.sparse.csc_matrix((data, indices.copy(), indptr.copy()),
numpy.asarray(shape),
copy = False #1000*len(data.flatten())
copy=False # 1000*len(data.flatten())
)
else:
assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(), indptr.copy()),
shape.copy(),
copy = False #1000*len(data.flatten())
copy=False # 1000*len(data.flatten())
)
def grad(self, (data, indices, indptr, shape), (g_out,)):
......@@ -639,8 +641,9 @@ class DenseFromSparse(gof.op.Op):
x = as_sparse_variable(x)
return gof.Apply(self,
[x],
[tensor.TensorType(dtype = x.type.dtype,
broadcastable = (False, False)).make_variable()])
[tensor.TensorType(dtype=x.type.dtype,
broadcastable=(False, False)
).make_variable()])
def perform(self, node, (x, ), (out, )):
if _is_dense(x):
print >> sys.stderr, "WARNING: You just called DenseFromSparse on a dense matrix."
......@@ -677,8 +680,9 @@ class SparseFromDense(gof.op.Op):
x = tensor.as_tensor_variable(x)
return gof.Apply(self,
[x],
[SparseType(dtype = x.type.dtype,
format = self.format).make_variable()])
[SparseType(dtype=x.type.dtype,
format=self.format
).make_variable()])
def perform(self, node, (x, ), (out, )):
out[0] = SparseType.format_cls[self.format](x)
......@@ -858,8 +862,9 @@ class Transpose(gof.op.Op):
x = as_sparse_variable(x)
return gof.Apply(self,
[x],
[SparseType(dtype = x.type.dtype,
format = self.format_map[x.type.format]).make_variable()])
[SparseType(dtype=x.type.dtype,
format=self.format_map[x.type.format]
).make_variable()])
def perform(self, node, (x, ), (out, )):
assert _is_sparse(x)
......@@ -909,8 +914,9 @@ class AddSS(gof.op.Op):
raise NotImplementedError()
return gof.Apply(self,
[x, y],
[SparseType(dtype = x.type.dtype,
format = x.type.format).make_variable()])
[SparseType(dtype=x.type.dtype,
format=x.type.format
).make_variable()])
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and _is_sparse(y)
......@@ -941,8 +947,9 @@ class AddSD(gof.op.Op):
assert y.type.ndim == 2
return gof.Apply(self,
[x, y],
[tensor.TensorType(dtype = y.type.dtype,
broadcastable = y.type.broadcastable).make_variable()])
[tensor.TensorType(dtype=y.type.dtype,
broadcastable=y.type.broadcastable
).make_variable()])
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and _is_dense(y)
......@@ -1000,7 +1007,7 @@ class MulSS(gof.op.Op):
out[0] = y.copy()
out[0].data *= x.data
else:
raise NotImplementedError() #RowScale / ColScale
raise NotImplementedError() # RowScale / ColScale
def grad(self, (x, y), (gz,)):
return y * gz, x * gz
......@@ -1037,7 +1044,7 @@ class MulSD(gof.op.Op):
out[0] = x.copy()
out[0].data *= y
elif len(y.shape) == 1:
raise NotImplementedError() #RowScale / ColScale
raise NotImplementedError() # RowScale / ColScale
elif len(y.shape) == 2:
#if we have enough memory to fit y, maybe we can fit x.asarray() too?
#TODO: change runtime from O(M*N) to O(nonzeros)
......@@ -1052,7 +1059,7 @@ class MulSD(gof.op.Op):
z_data = z.data
for j in xrange(0, N):
for i_idx in xrange(indptr[j], indptr[j+1]):
for i_idx in xrange(indptr[j], indptr[j + 1]):
i = indices[i_idx]
z_data[i_idx] *= y[i, j]
out[0] = z
......@@ -1064,7 +1071,7 @@ class MulSD(gof.op.Op):
z_data = z.data
for i in xrange(0, M):
for j_idx in xrange(indptr[i], indptr[i+1]):
for j_idx in xrange(indptr[i], indptr[i + 1]):
j = indices[j_idx]
z_data[j_idx] *= y[i, j]
out[0] = z
......@@ -1135,7 +1142,7 @@ class StructuredDot(gof.Op):
out[0] = variable
return
assert _is_dense(variable) # scipy 0.7 automatically converts to dense
assert _is_dense(variable) # scipy 0.7 automatically converts to dense
# dot of an NxM sparse matrix, with a Mx1 dense matrix, returns vector not matrix
if variable.ndim == 1:
......@@ -1201,10 +1208,10 @@ class StructuredDotCSC(gof.Op):
def perform(self, node, (a_val, a_ind, a_ptr, a_nrows, b), (out,)):
a = scipy.sparse.csc_matrix((a_val, a_ind, a_ptr),
(a_nrows, b.shape[0]),
copy = False)
copy=False)
#out[0] = a.dot(b)
out[0] = theano._asarray(a * b, dtype=node.outputs[0].type.dtype)
assert _is_dense(out[0]) # scipy 0.7 automatically converts to dense
assert _is_dense(out[0]) # scipy 0.7 automatically converts to dense
def c_code(self, node, name, (a_val, a_ind, a_ptr, a_nrows, b), (z,), sub):
"""
......@@ -1223,9 +1230,9 @@ class StructuredDotCSC(gof.Op):
if node.inputs[4].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for b')
typenum_z = node.outputs[0].type.dtype_specs()[-1] # retrieve dtype number
typenum_a_val = node.inputs[0].type.dtype_specs()[-1] # retrieve dtype number
typenum_b = node.inputs[4].type.dtype_specs()[-1] # retrieve dtype number
typenum_z = node.outputs[0].type.dtype_specs()[-1] # retrieve dtype number
typenum_a_val = node.inputs[0].type.dtype_specs()[-1] # retrieve dtype number
typenum_b = node.inputs[4].type.dtype_specs()[-1] # retrieve dtype number
rval = """
......@@ -1345,7 +1352,7 @@ class StructuredDotCSC(gof.Op):
}
}
}
"""% dict(locals(), **sub)
""" % dict(locals(), **sub)
return rval
......@@ -1369,11 +1376,12 @@ class StructuredDotCSR(gof.Op):
def perform(self, node, (a_val, a_ind, a_ptr, b), (out,)):
a = scipy.sparse.csr_matrix((a_val, a_ind, a_ptr),
(len(a_ptr)-1, b.shape[0]),
copy = True) #use view_map before setting this to False
(len(a_ptr) - 1, b.shape[0]),
copy=True) # use view_map before setting this to False
#out[0] = a.dot(b)
out[0] = a * b
assert _is_dense(out[0]) # scipy 0.7 automatically converts to dense, but not .6 sometimes
# scipy 0.7 automatically converts to dense, but not .6 sometimes
assert _is_dense(out[0])
def c_code(self, node, name, (a_val, a_ind, a_ptr, b), (z,), sub):
"""
......@@ -1386,7 +1394,8 @@ class StructuredDotCSR(gof.Op):
@param z: return value
@param sub: TODO, not too sure, something to do with weave probably
"""
typenum_z = tensor.TensorType(self.dtype_out, []).dtype_specs()[-1] # retrieve dtype number
# retrieve dtype number
typenum_z = tensor.TensorType(self.dtype_out, []).dtype_specs()[-1]
if node.inputs[0].type.dtype in ('complex64', 'complex128'):
raise NotImplementedError('Complex types are not supported for a_val')
if node.inputs[3].type.dtype in ('complex64', 'complex128'):
......@@ -1482,7 +1491,7 @@ class StructuredDotCSR(gof.Op):
}
}
"""% dict(locals(), **sub)
""" % dict(locals(), **sub)
def c_code_cache_version(self):
return (1,)
......@@ -1549,9 +1558,9 @@ class StructuredDotGradCSC(gof.Op):
def perform(self, node, (a_indices, a_indptr, b, g_ab), (out,)):
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
for j in xrange(len(a_indptr)-1):
for j in xrange(len(a_indptr) - 1):
ind0 = a_indptr[j]
ind1 = a_indptr[j+1]
ind1 = a_indptr[j + 1]
for i_idx in xrange(ind0, ind1):
i = a_indices[i_idx]
g_a_data[i_idx] = numpy.dot(g_ab[i], b[j])
......@@ -1640,7 +1649,7 @@ class StructuredDotGradCSC(gof.Op):
}
}
"""% dict(locals(), **sub)
""" % dict(locals(), **sub)
sdg_csc = StructuredDotGradCSC()
......@@ -1656,10 +1665,11 @@ class StructuredDotGradCSR(gof.Op):
def perform(self, node, (a_indices, a_indptr, b, g_ab), (out,)):
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
for i in xrange(len(a_indptr)-1): # loop over rows
for i in xrange(len(a_indptr) - 1): # loop over rows
ind0 = a_indptr[i]
ind1 = a_indptr[i+1]
for j_idx in xrange(ind0, ind1): # loop over values in that row (columns)
ind1 = a_indptr[i + 1]
# loop over values in that row (columns)
for j_idx in xrange(ind0, ind1):
j = a_indices[j_idx]
# grad is dot product of i-th row of gradient with j-th row of b
g_a_data[j_idx] = numpy.dot(g_ab[i], b[j])
......@@ -1749,7 +1759,7 @@ class StructuredDotGradCSR(gof.Op):
}
}
"""% dict(locals(), **sub)
""" % dict(locals(), **sub)
sdg_csr = StructuredDotGradCSR()
......
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