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提交 ae9825c5 authored 作者: Razvan Pascanu's avatar Razvan Pascanu
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Removed trailing spaces

上级 af0fbb52
隐藏空白字符变更
内嵌 并排
正在显示 包含 65 行增加 和 65 行删除
theano/compile/io.py
浏览文件 @ ae9825c5
......@@ -8,11 +8,11 @@ class SymbolicInput(object):
"""
Represents a symbolic input for use with function or FunctionMaker.
variable: a Variable instance.
variable: a Variable instance.
This will be assigned a value before running the function,
not computed from its owner.
name: Any type. (If autoname=True, defaults to variable.name).
name: Any type. (If autoname=True, defaults to variable.name).
If name is a valid Python identifier, this input can be set by kwarg, and its value
can be accessed by self.<name>.
......@@ -41,9 +41,9 @@ class SymbolicInput(object):
assert implicit is not None # Safety check.
self.variable = variable
if (autoname and name is None):
self.name = variable.name
self.name = variable.name
else:
self.name = name
self.name = name
#backport
#self.name = variable.name if (autoname and name is None) else name
......@@ -131,11 +131,11 @@ class In(SymbolicInput):
"""
Represents a symbolic input for use with function or FunctionMaker.
variable: a Variable instance.
variable: a Variable instance.
This will be assigned a value before running the function,
not computed from its owner.
name: Any type. (If autoname=True, defaults to variable.name).
name: Any type. (If autoname=True, defaults to variable.name).
If name is a valid Python identifier, this input can be set by kwarg, and its value
can be accessed by self.<name>.
......@@ -194,7 +194,7 @@ class SymbolicOutput(object):
returned for this output might be clobbered by running
the function again, but the function might be faster.
"""
def __init__(self, variable, borrow=False):
self.variable = variable
self.borrow = borrow
......
theano/sparse/basic.py
浏览文件 @ ae9825c5
......@@ -99,6 +99,7 @@ def as_sparse_variable(x, name=None):
except TypeError:
raise TypeError("Cannot convert %s to SparseType" % x, type(x))
as_sparse = as_sparse_variable
def constant(x, name=None):
......@@ -147,13 +148,12 @@ class SparseType(gof.Type):
@param format: The sparse storage strategy.
@return An empty SparseVariable instance.
"""
dtype = str(dtype)
if dtype in self.dtype_set:
self.dtype = dtype
else:
raise NotImplementedError('unsupported dtype "%s" not in list' % dtype, list(self.dtype_set))
assert isinstance(format, str)
if format in self.format_cls:
self.format = format
......@@ -259,7 +259,7 @@ class SparseValue(gof.Value, _sparse_py_operators):
dtype = property(lambda self: self.type.dtype)
format = property(lambda self: self.type.format)
# CONSTRUCTION
class CSMProperties(gof.Op):
"""Extract all of .data .indices and .indptr"""
......@@ -276,14 +276,14 @@ class CSMProperties(gof.Op):
return type(self) == type(other) and _kmap_eq(self.kmap, other.kmap)
def __ne__(self, other): return not (self == other)
def __hash__(self):
return 8234 ^ hash(type(self)) ^ _kmap_hash(self.kmap)
def make_node(self, csm):
csm = as_sparse_variable(csm)
data = tensor.TensorType(dtype=csm.type.dtype, broadcastable = (False,)).make_variable()
return gof.Apply(self, [csm],
return gof.Apply(self, [csm],
[data, tensor.ivector(), tensor.ivector(), tensor.ivector()])
def perform(self, node, (csm,), out):
......@@ -347,10 +347,10 @@ class CSM(gof.Op):
def __hash__(self):
return self._hashval
def make_node(self, data, indices, indptr, shape):
def make_node(self, data, indices, indptr, shape):
"""Build a SparseVariable from the internal parametrization
:param data:
:param data:
:param indices:
:param indptr:
:type data: 1-d tensor
......@@ -397,13 +397,13 @@ class CSM(gof.Op):
'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()),
out[0] = scipy.sparse.csc_matrix((data, indices.copy(), indptr.copy()),
numpy.asarray(shape),
copy = False #1000*len(data.flatten())
)
else:
assert self.format == 'csr'
out[0] = scipy.sparse.csr_matrix((data, indices.copy(), indptr.copy()),
out[0] = scipy.sparse.csr_matrix((data, indices.copy(), indptr.copy()),
shape.copy(),
copy = False #1000*len(data.flatten())
)
......@@ -427,7 +427,7 @@ class CSMGrad(gof.op.Op):
return type(self) == type(other) and _kmap_eq(self.kmap, other.kmap)
def __ne__(self, other): return not (self == other)
def __hash__(self):
return 82345 ^ hash(type(self)) ^ _kmap_hash(self.kmap)
......@@ -456,7 +456,7 @@ def skip_pack_csc01(node):
register_specialize(skip_pack_csc01)
#
# Conversion
#
......@@ -479,7 +479,7 @@ class DenseFromSparse(gof.op.Op):
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."
print >> sys.stderr, "WARNING: You just called DenseFromSparse on a dense matrix."
out[0] = x
else:
out[0] = x.toarray()
......@@ -572,7 +572,7 @@ class AddSS(gof.op.Op):
[x, y],
[SparseType(dtype = x.type.dtype,
format = x.type.format).make_variable()])
def perform(self, node, (x, y), (out, )):
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and _is_sparse(y)
assert x.shape == y.shape
out[0] = x + y
......@@ -598,7 +598,7 @@ class AddSD(gof.op.Op):
[x, y],
[tensor.TensorType(dtype = y.type.dtype,
broadcastable = y.type.broadcastable).make_variable()])
def perform(self, node, (x, y), (out, )):
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and _is_dense(y)
out[0] = x + y
def grad(self, (x, y), (gz,)):
......@@ -612,7 +612,7 @@ def add(x,y):
"""
if hasattr(x, 'getnnz'): x = as_sparse_variable(x)
if hasattr(y, 'getnnz'): y = as_sparse_variable(y)
x_is_sparse_variable = _is_sparse_variable(x)
y_is_sparse_variable = _is_sparse_variable(y)
......@@ -637,7 +637,7 @@ class MulSS(gof.op.Op):
if x.type != y.type:
raise NotImplementedError()
return gof.Apply(self, [x, y], [x.type()])
def perform(self, node, (x, y), (out, )):
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and _is_sparse(y)
assert len(x.shape) == 2
assert y.shape == x.shape
......@@ -664,7 +664,7 @@ class MulSD(gof.op.Op):
# Broadcasting of the sparse matrix is not supported.
assert y.type.ndim <= 2
return gof.Apply(self, [x, y], [x.type()])
def perform(self, node, (x, y), (out, )):
def perform(self, node, (x, y), (out, )):
assert _is_sparse(x) and _is_dense(y)
if len(y.shape) == 0:
out[0] = x.copy()
......@@ -716,7 +716,7 @@ def mul(x,y):
"""
if hasattr(x, 'getnnz'): x = as_sparse_variable(x)
if hasattr(y, 'getnnz'): y = as_sparse_variable(y)
x_is_sparse_variable = _is_sparse_variable(x)
y_is_sparse_variable = _is_sparse_variable(y)
......@@ -770,7 +770,7 @@ class StructuredDot(gof.Op):
else:
raise Exception("a.shape=%s, b.shape=%s, variable.shape=%s ??? I have no idea why")
#The cast is needed as otherwise we hit the bug mentioned into
#The cast is needed as otherwise we hit the bug mentioned into
#theano._asarray function documentation.
out[0] = theano._asarray(variable, str(variable.dtype))
......@@ -809,12 +809,12 @@ class StructuredDotCSC(gof.Op):
return hash(type(self))
def make_node(self, a_val, a_ind, a_ptr, a_nrows, b):
dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
r = gof.Apply(self, [a_val, a_ind, a_ptr, a_nrows, b],
r = gof.Apply(self, [a_val, a_ind, a_ptr, a_nrows, b],
[tensor.tensor(dtype_out, (False, b.type.broadcastable[1]))])
return r
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 = scipy.sparse.csc_matrix((a_val, a_ind, a_ptr),
(a_nrows, b.shape[0]),
copy = False)
#out[0] = a.dot(b)
......@@ -852,7 +852,7 @@ class StructuredDotCSC(gof.Op):
if (%(a_val)s->descr->type_num != %(typenum_a_val)s) {
PyErr_SetString(PyExc_NotImplementedError, "Invalid type for a_val"); %(fail)s;}
if (%(b)s->descr->type_num != %(typenum_b)s) {
PyErr_SetString(PyExc_NotImplementedError, "Invalid type for b"); %(fail)s;}
......@@ -908,7 +908,7 @@ class StructuredDotCSC(gof.Op):
//clear the output array
memset(Dz, 0, M*N*sizeof(dtype_%(z)s));
//iterate over the sparse array, making the most of an entry wherever we find it.
//
// Normal matrix matrix multiply: A MxK, B KxN => Z = AB
......@@ -916,7 +916,7 @@ class StructuredDotCSC(gof.Op):
// for n
// for k
// z[m,n] += a[m,k] * b[k,n]
// Here instead: Z =
// Here instead: Z =
// for k
// for m (sparse)
// for n
......@@ -927,20 +927,20 @@ class StructuredDotCSC(gof.Op):
{
// get pointer to k-th row of dense matrix
const dtype_%(b)s* __restrict__ bk = (dtype_%(b)s*)(%(b)s->data + %(b)s->strides[0] * k);
// loop over sparse column indices through index pointer array
// loop over sparse column indices through index pointer array
// (amounts to looping over rows M of sparse matrix)
for (npy_int32 m_idx = Dptr[k * Sptr]; m_idx < Dptr[(k+1) * Sptr]; ++m_idx)
{
npy_int32 m = Dind[m_idx * Sind]; // row index of non-null value for column K
const dtype_%(a_val)s Amk = Dval[m_idx * Sval]; // actual value at that location
// pointer to m-th row of the output matrix Z
dtype_%(z)s* __restrict__ zm = (dtype_%(z)s*)(%(z)s->data + %(z)s->strides[0] * m);
//RESOLVE: a.shape[0] equals z.shape[0], why is this not an equality constraint?
if (m >= %(z)s->dimensions[0])
if (m >= %(z)s->dimensions[0])
{PyErr_SetString(PyExc_NotImplementedError, "illegal row index in a"); %(fail)s;}
// loop over final dimension (cols of dense matrix) and perform dot product
......@@ -975,12 +975,12 @@ class StructuredDotCSR(gof.Op):
return hash(type(self))
def make_node(self, a_val, a_ind, a_ptr, b):
self.dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
r = gof.Apply(self, [a_val, a_ind, a_ptr, b],
r = gof.Apply(self, [a_val, a_ind, a_ptr, b],
[tensor.tensor(self.dtype_out, (False, b.type.broadcastable[1]))])
return r
def perform(self, node, (a_val, a_ind, a_ptr, b), (out,)):
a = scipy.sparse.csr_matrix((a_val, a_ind, a_ptr),
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
#out[0] = a.dot(b)
......@@ -1056,7 +1056,7 @@ class StructuredDotCSR(gof.Op):
//clear the output array
memset(Dz, 0, M*N*sizeof(dtype_%(z)s));
//iterate over the sparse array, making the most of an entry wherever we find it.
// Normal matrix matrix multiply:
// for m
......@@ -1075,16 +1075,16 @@ class StructuredDotCSR(gof.Op):
// pointer to m-th row of the output matrix Z
dtype_%(z)s* __restrict__ zm = (dtype_%(z)s*)(%(z)s->data + %(z)s->strides[0] * m);
// loop over sparse rows indices through index pointer array
// loop over sparse rows indices through index pointer array
// (amounts to looping over cols k of sparse matrix)
for (npy_int32 k_idx = Dptr[m * Sptr]; k_idx < Dptr[(m+1) * Sptr]; ++k_idx)
{
npy_int32 k = Dind[k_idx * Sind]; // col index of non-null value for row m
const dtype_%(a_val)s Amk = Dval[k_idx * Sval]; // actual value at that location
// get pointer to k-th row of dense matrix
const dtype_%(b)s* __restrict__ bk = (dtype_%(b)s*)(%(b)s->data + %(b)s->strides[0] * k);
// loop over final dimension (cols of dense matrix) and perform dot product
for(npy_int32 n = 0; n < N; ++n)
{
......@@ -1093,7 +1093,7 @@ class StructuredDotCSR(gof.Op):
}
}
}
"""% dict(locals(), **sub)
def c_code_cache_version(self):
......@@ -1114,7 +1114,7 @@ def local_structured_dot(node):
return [sd_csr(a_val, a_ind, a_ptr, b)]
return False
# Commented out because
# Commented out because
# a) it is only slightly faster than scipy these days, and sometimes a little slower, and
# b) the resulting graphs make it very difficult for an op to do size checking on the matrices
# involved. dimension mismatches are hard to detect sensibly.
......@@ -1152,7 +1152,7 @@ class StructuredDotGradCSC(gof.Op):
def __hash__(self):
return hash(type(self))
def make_node(self, a_indices, a_indptr, b, g_ab):
return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
[tensor.tensor(g_ab.dtype, (False,))])
def perform(self, node, (a_indices, a_indptr, b, g_ab), (out,)):
g_a_data = numpy.zeros(a_indices.shape, dtype=g_ab.dtype)
......@@ -1195,7 +1195,7 @@ class StructuredDotGradCSC(gof.Op):
PyErr_SetString(PyExc_NotImplementedError, "somehow _zout got the wrong size.. and I don't know how to resize it.");
%(fail)s;
}
{ //makes it compile even though labels jump over variable definitions.
npy_intp nnz = %(_indices)s->dimensions[0];
npy_intp N = %(_indptr)s->dimensions[0]-1; //TODO: error checking with this
......@@ -1223,15 +1223,15 @@ class StructuredDotGradCSC(gof.Op):
{
// extract row index of non-null value
npy_int32 i = indices[i_idx * Sindices];
// extract corresponding row in gradient
const dtype_%(_g)s* __restrict__ g_row = (dtype_%(_g)s*)(%(_g)s->data + %(_g)s->strides[0] * i);
double ip = 0.0;
// make sure that row index is not bigger than actual number of rows
// Note: wouldn't the above operation fail if that were the case ?
// when would this ever be true anyway ?
if (i >= %(_g)s->dimensions[0])
// Note: wouldn't the above operation fail if that were the case ?
// when would this ever be true anyway ?
if (i >= %(_g)s->dimensions[0])
{PyErr_SetString(PyExc_NotImplementedError, "H"); %(fail)s;}
// perform dot product of dense and sparse rows
......@@ -1266,7 +1266,7 @@ class StructuredDotGradCSR(gof.Op):
ind1 = a_indptr[i+1]
for j_idx in xrange(ind0, ind1): # loop over values in that row (columns)
j = a_indices[j_idx]
# grad is dot product of i-th row of gradient with j-th row of b
# 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])
out[0] = g_a_data
......@@ -1302,7 +1302,7 @@ class StructuredDotGradCSR(gof.Op):
PyErr_SetString(PyExc_NotImplementedError, "somehow _zout got the wrong size.. and I don't know how to resize it.");
%(fail)s;
}
{ //makes it compile even though labels jump over variable definitions.
npy_intp nnz = %(_indices)s->dimensions[0];
// extract number of rows
......@@ -1327,7 +1327,7 @@ class StructuredDotGradCSR(gof.Op):
{
// extract column index of non-null value
npy_int32 j = indices[j_idx * Sindices];
// extract j-th row of dense matrix
const dtype_%(_d)s* __restrict__ d_row = (dtype_%(_d)s*)(%(_d)s->data + %(_d)s->strides[0] * j);
if(j >= %(_d)s->dimensions[0]) {PyErr_SetString(PyExc_NotImplementedError, "G"); %(fail)s;}
......@@ -1337,9 +1337,9 @@ class StructuredDotGradCSR(gof.Op):
double ip = 0.0;
// make sure that row index is not bigger than actual number of rows
// Note: wouldn't the above operation fail if that were the case ?
// when would this ever be true anyway ?
if (i >= %(_g)s->dimensions[0])
// Note: wouldn't the above operation fail if that were the case ?
// when would this ever be true anyway ?
if (i >= %(_g)s->dimensions[0])
{PyErr_SetString(PyExc_NotImplementedError, "H"); %(fail)s;}
// perform dot product of dense and sparse rows
......@@ -1353,7 +1353,7 @@ class StructuredDotGradCSR(gof.Op):
}
}
}
"""% dict(locals(), **sub)
sdg_csr = StructuredDotGradCSR()
theano/tensor/basic.py
浏览文件 @ ae9825c5
......@@ -898,24 +898,24 @@ class _tensor_py_operators:
#COMPARISONS
_is_nonzero = True
def __lt__(self,other):
def __lt__(self,other):
rval = lt(self, other)
rval._is_nonzero=False
return rval
def __le__(self,other):
def __le__(self,other):
rval = le(self, other)
rval._is_nonzero=False
return rval
def __gt__(self,other):
def __gt__(self,other):
rval = gt(self, other)
rval._is_nonzero=False
return rval
def __ge__(self,other):
def __ge__(self,other):
rval = ge(self, other)
rval._is_nonzero=False
return rval
def __nonzero__(self):
# This is meant to prohibit stuff like a < b < c, which is internally implemented as
# This is meant to prohibit stuff like a < b < c, which is internally implemented as
# (a < b) and (b < c). The trouble with this is the side-effect that checking for a
# non-NULL a by typing "if a: ..." uses the same __nonzero__ method. We want these
# both to work, but it seems impossible. Currently, all vars evaluate to nonzero
......@@ -3962,7 +3962,7 @@ def tensordot(x, y, axes=2):
raise ValueError('Cannot perform tensordot of 0-d inputs.')
axes = TensorDot.parse_axes(axes)
# check whether axes is valid given the dimensions of x and y
if numpy.isscalar(axes):
if axes >= x.ndim or axes >= y.ndim:
......@@ -3979,12 +3979,12 @@ def tensordot(x, y, axes=2):
if isinstance(axes[1],(list,tuple)) and \
(len(axes[1]) > y.ndim or (numpy.array(axes[1]) >= y.ndim).any()):
raise ValueError('axes[1] should be array_like, of length smaller'\
'than the dimension of y (y.ndim=%i, len(axes[1])=%i).' %
'than the dimension of y (y.ndim=%i, len(axes[1])=%i).' %
(y.ndim, len(axes[1])))
if not hasattr(tensordot, 'op'):
tensordot.op = {}
if axes not in tensordot.op:
tensordot.op[axes] = TensorDot(axes)
......
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