Skip to content

P
pytensor
提交 034bb5a3 authored 作者: Pascal Lamblin's avatar Pascal Lamblin
浏览文件
操作

PEP 8 fixes on files I've recently worked on.

上级 41a4a100
全部展开 隐藏空白字符变更
内嵌 并排
正在显示 包含 93 行增加 和 89 行删除
theano/compile/tests/test_shared.py
浏览文件 @ 034bb5a3
import numpy
import unittest
import copy
import theano
from theano.tensor import Tensor, TensorType
from theano.compile.sharedvalue import *
class Test_SharedVariable(unittest.TestCase):
def test_ctors(self):
if 0: #when using an implementation that handles scalars with Scalar type
if 0:
# when using an implementation that handles scalars with
# Scalar type
assert shared(7).type == Scalar('int64')
assert shared(7.0).type == Scalar('float64')
assert shared(7, dtype='float64').type == Scalar('float64')
......@@ -24,14 +26,16 @@ class Test_SharedVariable(unittest.TestCase):
assert shared(numpy.float32(7)).type == theano.tensor.fscalar
# test tensor constructor
b = shared(numpy.zeros((5,5), dtype='int32'))
assert b.type == TensorType('int32', broadcastable=[False,False])
b = shared(numpy.random.rand(4,5))
assert b.type == TensorType('float64', broadcastable=[False,False])
b = shared(numpy.random.rand(5,1,2))
assert b.type == TensorType('float64', broadcastable=[False,False,False])
b = shared(numpy.zeros((5, 5), dtype='int32'))
assert b.type == TensorType('int32', broadcastable=[False, False])
b = shared(numpy.random.rand(4, 5))
assert b.type == TensorType('float64', broadcastable=[False, False])
b = shared(numpy.random.rand(5, 1, 2))
assert b.type == TensorType('float64',
broadcastable=[False, False, False])
assert shared([]).type == generic
def badfunc():
shared(7, bad_kw=False)
self.assertRaises(TypeError, badfunc)
......@@ -70,7 +74,7 @@ class Test_SharedVariable(unittest.TestCase):
SharedVariable(
name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=[1, 2], #different dtype and not a numpy array
value=[1, 2], # different dtype and not a numpy array
strict=False)
# here the value is not castable, and we're not strict about it,
......@@ -79,7 +83,7 @@ class Test_SharedVariable(unittest.TestCase):
SharedVariable(
name='u',
type=Tensor(broadcastable=[False], dtype='float64'),
value=dict(), #not an array by any stretch
value=dict(), # not an array by any stretch
strict=False)
assert 0
except TypeError:
......@@ -96,10 +100,10 @@ class Test_SharedVariable(unittest.TestCase):
strict=False)
# check that assignments to value are cast properly
u.set_value([3,4])
u.set_value([3, 4])
assert type(u.get_value()) is numpy.ndarray
assert str(u.get_value(borrow=True).dtype) == 'float64'
assert numpy.all(u.get_value() == [3,4])
assert numpy.all(u.get_value() == [3, 4])
# check that assignments of nonsense fail
try:
......@@ -109,7 +113,7 @@ class Test_SharedVariable(unittest.TestCase):
pass
# check that an assignment of a perfect value results in no copying
uval = theano._asarray([5,6,7,8], dtype='float64')
uval = theano._asarray([5, 6, 7, 8], dtype='float64')
u.set_value(uval, borrow=True)
assert u.get_value(borrow=True) is uval
......@@ -149,10 +153,8 @@ class Test_SharedVariable(unittest.TestCase):
assert b.type == theano.tensor.dscalar
self.assertRaises(TypeError, f, b, 8)
c = shared(numpy.zeros((5,5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5,5))
b = shared(numpy.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
def test_tensor_strict(self):
def f(var, val):
......@@ -192,19 +194,16 @@ class Test_SharedVariable(unittest.TestCase):
# assert b.type == theano.tensor.dvector
# self.assertRaises(TypeError, f, b, 8)
c = shared(numpy.zeros((5,5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5,5))
b = shared(numpy.zeros((5, 5), dtype='float32'))
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
def test_scalar_floatX(self):
#
# the test should assure that floatX is not used in the shared constructor for scalars
# Shared values can change, and since we don't know the range they might take, we
# should keep the same bit width / precision as the original value used to create the
# the test should assure that floatX is not used in the shared
# constructor for scalars Shared values can change, and since we don't
# know the range they might take, we should keep the same
# bit width / precision as the original value used to create the
# shared variable.
#
# Since downcasting of a value now raises an Exception,
......@@ -213,48 +212,46 @@ class Test_SharedVariable(unittest.TestCase):
b = shared(numpy.int64(7), allow_downcast=True)
assert b.type == theano.tensor.lscalar
f(b,8.23)
assert b.get_value()==8
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.int32(7), allow_downcast=True)
assert b.type == theano.tensor.iscalar
f(b,8.23)
assert b.get_value()==8
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.int16(7), allow_downcast=True)
assert b.type == theano.tensor.wscalar
f(b,8.23)
assert b.get_value()==8
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.int8(7), allow_downcast=True)
assert b.type == theano.tensor.bscalar
f(b,8.23)
assert b.get_value()==8
f(b, 8.23)
assert b.get_value() == 8
b = shared(numpy.float64(7.234), allow_downcast=True)
assert b.type == theano.tensor.dscalar
f(b,8)
assert b.get_value()==8
f(b, 8)
assert b.get_value() == 8
b = shared(numpy.float32(7.234), allow_downcast=True)
assert b.type == theano.tensor.fscalar
f(b,8)
assert b.get_value()==8
f(b, 8)
assert b.get_value() == 8
b = shared(numpy.float(7.234), allow_downcast=True)
assert b.type == theano.tensor.dscalar
f(b,8)
assert b.get_value()==8
f(b, 8)
assert b.get_value() == 8
b = shared(7.234, allow_downcast=True)
assert b.type == theano.tensor.dscalar
f(b,8)
assert b.get_value()==8
c = shared(numpy.zeros((5,5), dtype='float32'), allow_downcast=True)
self.assertRaises(TypeError, f, b, numpy.random.rand(5,5))
f(b, 8)
assert b.get_value() == 8
b = shared(numpy.zeros((5, 5), dtype='float32'), allow_downcast=True)
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
def test_tensor_floatX(self):
def f(var, val):
......@@ -262,32 +259,32 @@ class Test_SharedVariable(unittest.TestCase):
b = shared(numpy.int64([7]), allow_downcast=True)
assert b.type == theano.tensor.lvector
f(b,[8.23])
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.int32([7]), allow_downcast=True)
assert b.type == theano.tensor.ivector
f(b,[8.23])
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.int16([7]), allow_downcast=True)
assert b.type == theano.tensor.wvector
f(b,[8.23])
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.int8([7]), allow_downcast=True)
assert b.type == theano.tensor.bvector
f(b,[8.23])
f(b, [8.23])
assert b.get_value() == 8
b = shared(numpy.float64([7.234]), allow_downcast=True)
assert b.type == theano.tensor.dvector
f(b,[8])
f(b, [8])
assert b.get_value() == 8
b = shared(numpy.float32([7.234]), allow_downcast=True)
assert b.type == theano.tensor.fvector
f(b,[8])
f(b, [8])
assert b.get_value() == 8
#numpy.float([7.234]) don't work
......@@ -300,10 +297,12 @@ class Test_SharedVariable(unittest.TestCase):
# assert b.type == theano.tensor.dvector
# f(b,[8])
b = shared(numpy.asarray([7.234],dtype=theano.config.floatX), allow_downcast=True)
b = shared(numpy.asarray([7.234], dtype=theano.config.floatX),
allow_downcast=True)
assert b.dtype == theano.config.floatX
f(b,[8])
f(b, [8])
assert b.get_value() == 8
c = shared(numpy.zeros((5,5), dtype='float32'), allow_downcast=True)
self.assertRaises(TypeError, f, b, numpy.random.rand(5,5))
b = shared(numpy.zeros((5, 5), dtype='float32'),
allow_downcast=True)
self.assertRaises(TypeError, f, b, numpy.random.rand(5, 5))
theano/gof/cmodule.py
浏览文件 @ 034bb5a3
差异被折叠。
theano/gof/python25.py
浏览文件 @ 034bb5a3
"""
Helper functions to make gof backwards compatible (tested on python 2.4 and 2.5)
Helper functions to make gof backwards compatible
(tested on python 2.4 and 2.5)
"""
import collections
import sys
if sys.version_info[:2] < (2,5):
if sys.version_info[:2] < (2, 5):
def all(iterable):
for element in iterable:
......@@ -55,16 +57,19 @@ if sys.version_info[:2] < (2,5):
raise TypeError('first argument must be callable')
dict.__init__(self, *a, **kw)
self.default_factory = default_factory
def __getitem__(self, key):
try:
return dict.__getitem__(self, key)
except KeyError:
return self.__missing__(key)
def __missing__(self, key):
if self.default_factory is None:
raise KeyError(key)
self[key] = value = self.default_factory()
return value
def __reduce__(self):
if self.default_factory is None:
args = tuple()
......@@ -72,14 +77,18 @@ if sys.version_info[:2] < (2,5):
args = self.default_factory,
# consider replacing items() with iteritems()
return type(self), args, None, None, self.items()
def copy(self):
return self.__copy__()
def __copy__(self):
return type(self)(self.default_factory, self)
def __deepcopy__(self, memo):
import copy
return type(self)(self.default_factory,
copy.deepcopy(self.items()))
def __repr__(self):
return 'defaultdict(%s, %s)' % (self.default_factory,
dict.__repr__(self))
......@@ -90,14 +99,15 @@ else:
import __builtin__
all = __builtin__.all
any = __builtin__.any
import functools, collections
import collections
import functools
partial = functools.partial
defaultdict = collections.defaultdict
deque = collections.deque
__all__ = ['all', 'any']
if sys.version_info[:2] < (2,6):
if sys.version_info[:2] < (2, 6):
# Borrowed from Python docs
def combinations(iterable, r):
# combinations('ABCD', 2) --> AB AC AD BC BD CD
......@@ -115,18 +125,17 @@ if sys.version_info[:2] < (2,6):
else:
return
indices[i] += 1
for j in range(i+1, r):
indices[j] = indices[j-1] + 1
for j in range(i + 1, r):
indices[j] = indices[j - 1] + 1
yield tuple(pool[i] for i in indices)
def product(*args, **kwds):
# product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
# product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
pools = map(tuple, args) * kwds.get('repeat', 1)
result = [[]]
for pool in pools:
result = [x+[y] for x in result for y in pool]
result = [x + [y] for x in result for y in pool]
for prod in result:
yield tuple(prod)
......
theano/scan_module/scan_opt.py
浏览文件 @ 034bb5a3
......@@ -21,7 +21,6 @@ from theano.tensor import opt, get_constant_value
from theano import gof
from theano.gof.python25 import maxsize
from theano.compile import optdb
from theano import config
from theano.compile.function_module import deep_copy_op
import scan_op
......@@ -97,7 +96,6 @@ def remove_constants_and_unused_inputs_scan(node):
try:
# This works if input is a constant that has all entries
# equal
val = tensor.get_constant_value(node.inputs[idx + 1])
givens[op_ins[idx]] = node.inputs[idx + 1].clone()[0]
except TypeError:
pass
......@@ -729,7 +727,6 @@ class ScanSaveMem(gof.Optimizer):
nw_slice = (fslice,) + tuple(old_slices[1:])
nw_pos = inv_compress_map[idx]
nw_out = new_outs[nw_pos]
subtens = tensor.basic.Subtensor(nw_slice)
# slice inputs
......@@ -748,7 +745,6 @@ class ScanSaveMem(gof.Optimizer):
for pos, old_outs in old_outputs:
if len(old_outs) > 0:
nw_pos = compress_map[pos]
nw_out = new_outs[nw_pos]
for k, old in enumerate(old_outs):
# Get the correct slice
cnf_slice, old_slices = slices[pos][k]
......@@ -1066,7 +1062,6 @@ def scan_merge_inouts(node):
else:
a_inner_outs = a.inner_outputs
inner_outputs = scan_utils.clone(a_inner_outs, replace=inp_equiv)
orig_outputs = a.outer_outputs
op = scan_op.Scan(inner_inputs, inner_outputs, info)
outputs = op(*outer_inputs)
......
theano/tensor/basic.py
浏览文件 @ 034bb5a3
差异被折叠。
theano/tensor/opt.py
浏览文件 @ 034bb5a3
......@@ -721,14 +721,14 @@ class ShapeFeature(object):
def shape_ir(self, i, r):
"""Return symbolic r.shape[i] for tensor variable r, int i"""
if hasattr(r.type,"broadcastable") and r.type.broadcastable[i]:
if hasattr(r.type, "broadcastable") and r.type.broadcastable[i]:
return self.lscalar_one
else:
return Shape_i(i).make_node(r).outputs[0]
def shape_tuple(self, r):
"""Return a tuple of symbolic shape vars for tensor variable r"""
return tuple([self.shape_ir(i,r) for i in xrange(r.ndim)])
return tuple([self.shape_ir(i, r) for i in xrange(r.ndim)])
def default_infer_shape(self, node, i_shapes):
"""Return a list of shape tuple or None for the outputs of node.
......@@ -861,7 +861,7 @@ class ShapeFeature(object):
if r not in self.shape_of:
try:
self.set_shape(r, self.shape_tuple(r))
except AttributeError: #XXX: where would this come from?
except AttributeError: # XXX: where would this come from?
self.set_shape(r, None)
def make_vector_shape(self, r):
......@@ -949,17 +949,18 @@ class ShapeFeature(object):
if sh is None:
continue
for i, d in enumerate(sh):
# Note: we ignore any shape element that is not typed (i.e. does
# not have a 'dtype' attribute). This means there may still
# remain int elements that are int32 on 32-bit platforms, but
# this works with `local_useless_subtensor`, so for now we
# Note: we ignore any shape element that is not typed (i.e.,
# does not have a 'dtype' attribute). This means there may
# still remain int elements that are int32 on 32-bit platforms,
# but this works with `local_useless_subtensor`, so for now we
# keep it this way. See #266 for a better long-term fix.
if getattr(d, 'dtype', 'int64') != 'int64':
assert d.dtype in theano.tensor.int_dtypes
new_shape += sh[len(new_shape):i + 1]
new_shape[i] = theano.tensor.cast(d, 'int64')
if new_shape:
# We replace the shape with wrong dtype by the one with 'int64'.
# We replace the shape with wrong dtype by the one with
# 'int64'.
new_shape += sh[len(new_shape):]
o_shapes[sh_idx] = tuple(new_shape)
new_shape = []
......@@ -990,8 +991,8 @@ class ShapeFeature(object):
for (shpnode, idx) in (r.clients + [(node, i)]):
if isinstance(getattr(shpnode, 'op', None), Shape_i):
self.scheduled[shpnode] = new_r
# In case 2, if r is a variable that we've scheduled for shape update, then we
# should cancel it.
# In case 2, if r is a variable that we've scheduled for shape update,
# then we should cancel it.
unscheduled = [k for k, v in self.scheduled.items() if v == r]
for k in unscheduled:
del self.scheduled[k]
......@@ -1212,9 +1213,10 @@ def local_alloc_unary(node):
class Assert(T.Op):
"""
Implements assertion in a computational graph.
Notes:
This Op can be removed from the graph because of optimizations, and can hide
some possible optimizations to the optimizer.
This Op can be removed from the graph because of optimizations, and can
hide some possible optimizations to the optimizer.
Also, the output of the Op must be returned by the function computing the
graph, otherwise it will not be used.
"""
......@@ -2773,7 +2775,6 @@ class Canonizer(gof.LocalOptimizer):
if op not in [self.main, self.inverse, self.reciprocal]:
return False
inputs = node.inputs
out = node.outputs[0]
assert len(node.outputs) == 1
......@@ -2934,7 +2935,6 @@ def local_sum_div_dimshuffle(node):
axis = range(node.inputs[0].ndim)
#print 'axis =', axis
thing_summed = node.inputs[0]
dimshuffled = None
if thing_summed.owner and thing_summed.owner.op == T.true_div:
numerator, denominator = thing_summed.owner.inputs
......@@ -3035,11 +3035,13 @@ def local_sum_sum(node):
if summed.owner.op.axis is None:
# special case of local_cut_useless_reduce
return [T.Sum(None, dtype=out_dtype)(summed.owner.inputs[0])]
return [T.Sum(None, dtype=out_dtype)(
summed.owner.inputs[0])]
if node.op.axis is None:
# we're summing up everything anyway so lets
# do it all at once
return [T.Sum(None, dtype=out_dtype)(summed.owner.inputs[0])]
return [T.Sum(None, dtype=out_dtype)(
summed.owner.inputs[0])]
newaxis = list(tuple(summed.owner.op.axis))
# figure out which dimensions of the original input
......@@ -3113,7 +3115,7 @@ def local_sum_alloc(node):
assert val.size == 1
val = val.reshape(1)[0] * T.mul(*shapes)
return [T.cast(val, dtype=node.outputs[0].dtype)]
except TypeError, e:
except TypeError:
pass
else:
try:
......@@ -3127,7 +3129,7 @@ def local_sum_alloc(node):
return [T.alloc(T.cast(val, dtype=node.outputs[0].dtype),
*[shapes[i] for i in xrange(len(shapes))
if i not in node.op.axis])]
except TypeError, e:
except TypeError:
pass
......@@ -4433,7 +4435,6 @@ def local_elemwise_fusion_op(OP, max_input_fct=lambda node: 1024):
fusion optimization. We skip this optimization. You can ignore this message,
your code will run correctly, but may be slower.""")
otype = node.outputs[0].type
s_new_out = node.op.scalar_op(*s_g)
try:
s_new_out.owner.op.c_code(s_new_out.owner,
......@@ -4509,7 +4510,7 @@ class FusionOptimizer(Optimizer):
zip(node.outputs, new_outputs),
reason=self.__class__.__name__)
did_something = True
except InconsistencyError, e:
except InconsistencyError:
pass
if config.tensor.local_elemwise_fusion:
......
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