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提交 87850539 authored 作者: Iban Harlouchet's avatar Iban Harlouchet
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flake8 for neighbourhoods.py

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隐藏空白字符变更
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theano/sandbox/neighbourhoods.py
浏览文件 @ 87850539
#!/usr/bin/python
"""WARNING: This code is not recommanded. It is not finished, it is
slower then the version in sandbox/neighbours.py, and it do not work
on the GPU.
......@@ -8,17 +7,17 @@ it cover more cases. But thoses cases aren't needed frequently, so you
probably don't want to use this version, go see neighbours.py!!!!!!!
"""
import theano
from theano import gof, Op, tensor, Variable, Apply
import numpy
from six.moves import xrange
import six.moves.builtins as builtins
import theano
from theano import gof, Op
class NeighbourhoodsFromImages(Op):
def __init__(self, n_dims_before, dims_neighbourhoods,
strides=None, ignore_border=False, inverse=False):
strides=None, ignore_border=False, inverse=False):
"""
This extracts neighbourhoods from "images", but in a
dimension-generic manner.
......@@ -75,7 +74,7 @@ class NeighbourhoodsFromImages(Op):
"""
self.n_dims_before = n_dims_before
self.dims_neighbourhoods = dims_neighbourhoods
if not strides is None:
if strides is not None:
self.strides = strides
else:
self.strides = dims_neighbourhoods
......@@ -85,35 +84,26 @@ class NeighbourhoodsFromImages(Op):
self.code_string, self.code = self.make_py_code()
def _compute_neigh_strides(self):
neigh_strides = [1 for i in xrange(len(self.strides))]
cur_stride = 1
for i in xrange(len(self.strides)-1, -1, -1):
neigh_strides[i] = cur_stride
cur_stride *= self.dims_neighbourhoods[i]
return neigh_strides
def __eq__(self, other):
return type(self) == type(other) and \
self.n_dims_before == other.n_dims_before and \
self.dims_neighbourhoods == other.dims_neighbourhoods and \
self.strides == other.strides and \
self.ignore_border == other.ignore_border
self.n_dims_before == other.n_dims_before and \
self.dims_neighbourhoods == other.dims_neighbourhoods and \
self.strides == other.strides and \
self.ignore_border == other.ignore_border
def __hash__(self):
return hash(type(self)) ^ \
hash(self.n_dims_before) ^ \
hash(self.dims_neighbourhoods) ^ \
hash(self.strides) ^ \
hash(self.ignore_border)
hash(self.n_dims_before) ^ \
hash(self.dims_neighbourhoods) ^ \
hash(self.strides) ^ \
hash(self.ignore_border)
def __str__(self):
return '%s{%s,%s,%s,%s}' % \
(self.__class__.__name__,
self.n_dims_before,
self.dims_neighbourhoods,
self.strides,
self.ignore_border)
return '%s{%s,%s,%s,%s}' % (self.__class__.__name__,
self.n_dims_before,
self.dims_neighbourhoods,
self.strides,
self.ignore_border)
def out_shape(self, input_shape):
dims = list(input_shape[:self.n_dims_before])
......@@ -163,12 +153,12 @@ class NeighbourhoodsFromImages(Op):
x = theano.tensor.as_tensor_variable(x)
if self.inverse:
# +1 in the inverse case
if x.type.ndim != (self.n_dims_before + \
len(self.dims_neighbourhoods) + 1):
if x.type.ndim != (self.n_dims_before +
len(self.dims_neighbourhoods) + 1):
raise TypeError()
else:
if x.type.ndim != (self.n_dims_before + \
len(self.dims_neighbourhoods)):
if x.type.ndim != (self.n_dims_before +
len(self.dims_neighbourhoods)):
raise TypeError()
return gof.Apply(self, [x], [x.type()])
......@@ -177,22 +167,24 @@ class NeighbourhoodsFromImages(Op):
z, = out
if self.inverse:
# +1 in the inverse case
if len(x.shape) != (self.n_dims_before + \
len(self.dims_neighbourhoods) + 1):
raise ValueError("Images passed as input don't match the " +\
"dimensions passed when this (inversed) Apply node was created")
if len(x.shape) != (self.n_dims_before +
len(self.dims_neighbourhoods) + 1):
raise ValueError("Images passed as input don't match the "
"dimensions passed when this (inversed) "
"Apply node was created")
prod = 1
for dim in self.dims_neighbourhoods:
prod *= dim
if x.shape[-1] != prod:
raise ValueError(("Last dimension of neighbourhoods (%s) is not " +\
"the product of the neighbourhoods dimensions (%s)") % \
(str(x.shape[-1]), str(prod)))
raise ValueError("Last dimension of neighbourhoods (%s) is not"
" the product of the neighbourhoods dimensions"
" (%s)" % (str(x.shape[-1]), str(prod)))
else:
if len(x.shape) != (self.n_dims_before + \
len(self.dims_neighbourhoods)):
raise ValueError("Images passed as input don't match the " +\
"dimensions passed when this Apply node was created")
if len(x.shape) != (self.n_dims_before +
len(self.dims_neighbourhoods)):
raise ValueError("Images passed as input don't match the "
"dimensions passed when this Apply node "
"was created")
if self.inverse:
input_shape, num_strides = self.in_shape(x.shape)
......@@ -201,8 +193,6 @@ class NeighbourhoodsFromImages(Op):
input_shape = x.shape
out_shape, num_strides = self.out_shape(input_shape)
neigh_strides = self._compute_neigh_strides()
if z[0] is None:
if self.inverse:
z[0] = numpy.zeros(input_shape)
......@@ -228,45 +218,42 @@ class NeighbourhoodsFromImages(Op):
return code_before
def _py_innerloop(self, inner_dim_no):
base_indent = ('\t' * (self.n_dims_before + inner_dim_no*2))
base_indent = ('\t' * (self.n_dims_before + inner_dim_no * 2))
code_before = base_indent + \
"for stride_idx_%d in xrange(num_strides[%d]):\n" % \
(inner_dim_no, inner_dim_no)
"for stride_idx_%d in xrange(num_strides[%d]):\n" % \
(inner_dim_no, inner_dim_no)
base_indent += '\t'
code_before += base_indent + \
"dim_%d_offset = stride_idx_%d * self.strides[%d]\n" %\
(inner_dim_no, inner_dim_no, inner_dim_no)
"dim_%d_offset = stride_idx_%d * self.strides[%d]\n" %\
(inner_dim_no, inner_dim_no, inner_dim_no)
code_before += base_indent + \
"max_neigh_idx_%d = input_shape[%d] - dim_%d_offset\n" %\
(inner_dim_no,
self.n_dims_before+inner_dim_no, inner_dim_no)
"max_neigh_idx_%d = input_shape[%d] - dim_%d_offset\n" % \
(inner_dim_no, self.n_dims_before + inner_dim_no, inner_dim_no)
code_before += base_indent + \
("for neigh_idx_%d in xrange(min(max_neigh_idx_%d,"\
+ " self.dims_neighbourhoods[%d])):\n") % \
(inner_dim_no, inner_dim_no, inner_dim_no)
("for neigh_idx_%d in xrange(min(max_neigh_idx_%d,"
" self.dims_neighbourhoods[%d])):\n") %\
(inner_dim_no, inner_dim_no, inner_dim_no)
return code_before
def _py_flattened_idx(self):
return "+".join(["neigh_strides[%d]*neigh_idx_%d" % (i, i) \
for i in xrange(len(self.strides))])
return "+".join(["neigh_strides[%d]*neigh_idx_%d" % (i, i)
for i in xrange(len(self.strides))])
def _py_assignment(self):
input_idx = "".join(["outer_idx_%d," % (i,) \
for i in xrange(self.n_dims_before)])
input_idx += "".join(["dim_%d_offset+neigh_idx_%d," % \
(i, i) for i in xrange(len(self.strides))])
out_idx = "".join(\
["outer_idx_%d," % (i,) for i in \
xrange(self.n_dims_before)] + \
["stride_idx_%d," % (i,) for i in \
xrange(len(self.strides))])
input_idx = "".join(["outer_idx_%d," % (i,)
for i in xrange(self.n_dims_before)])
input_idx += "".join(["dim_%d_offset+neigh_idx_%d," %
(i, i) for i in xrange(len(self.strides))])
out_idx = "".join(
["outer_idx_%d," % (i,) for i in xrange(self.n_dims_before)] +
["stride_idx_%d," % (i,) for i in xrange(len(self.strides))])
out_idx += self._py_flattened_idx()
#return_val = '\t' * (self.n_dims_before + len(self.strides)*2)
#return_val += "print "+input_idx+"'\\n',"+out_idx+"\n"
# return_val = '\t' * (self.n_dims_before + len(self.strides)*2)
# return_val += "print "+input_idx+"'\\n',"+out_idx+"\n"
return_val = '\t' * (self.n_dims_before + len(self.strides)*2)
return_val = '\t' * (self.n_dims_before + len(self.strides) * 2)
if self.inverse:
# remember z and x are inversed:
......@@ -281,9 +268,10 @@ class NeighbourhoodsFromImages(Op):
class ImagesFromNeighbourhoods(NeighbourhoodsFromImages):
def __init__(self, n_dims_before, dims_neighbourhoods,
strides=None, ignore_border=False):
NeighbourhoodsFromImages.__init__(self, n_dims_before, dims_neighbourhoods,
strides=strides, ignore_border=ignore_border,
inverse=True)
strides=None, ignore_border=False):
NeighbourhoodsFromImages.__init__(self, n_dims_before,
dims_neighbourhoods,
strides=strides,
ignore_border=ignore_border,
inverse=True)
# and that's all there is to it
theano/tests/test_flake8.py
浏览文件 @ 87850539
......@@ -137,7 +137,6 @@ whitelist_flake8 = [
"sandbox/rng_mrg.py",
"sandbox/theano_object.py",
"sandbox/scan.py",
"sandbox/neighbourhoods.py",
"sandbox/fourier.py",
"sandbox/test_multinomial.py",
"sandbox/minimal.py",
......
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