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提交 b5a05a87 authored 作者: Frederic's avatar Frederic
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theano/tensor/tests/test_elemwise.py
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import cPickle, time, unittest
from itertools import imap
import cPickle
from copy import copy
from itertools import imap
import time
import unittest
import numpy
from numpy.testing import dec
......@@ -12,7 +14,8 @@ from theano import gof, scalar, config
from theano import tensor
from theano.tensor import TensorType
from theano.compile.mode import get_default_mode
from theano.tensor.elemwise import CAReduce, Elemwise, DimShuffle, Prod, ProdWithoutZeros
from theano.tensor.elemwise import (CAReduce, Elemwise, DimShuffle,
Prod, ProdWithoutZeros)
from theano.tests import unittest_tools
......@@ -20,6 +23,7 @@ def Env(i, o):
e = gof.Env(i, o)
return e
class test_DimShuffle(unittest.TestCase):
def with_linker(self, linker):
......@@ -27,11 +31,12 @@ class test_DimShuffle(unittest.TestCase):
((1, 2, 3), (1, 2), (2, 3)),
((1, 2, 1, 3), (1, 3), (2, 3)),
((2, 3, 4), (2, 1, 0), (4, 3, 2)),
((2, 3, 4), ('x', 2, 1, 0, 'x'), (1, 4, 3, 2, 1)),
((2, 3, 4), ('x', 2, 1, 0, 'x'),
(1, 4, 3, 2, 1)),
((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
((1, 1, 4), (1, 2), (1, 4)),
((1, 1, 1), (), ()),
((1,), ('x', 'x'), (1, 1)),]:
((1,), ('x', 'x'), (1, 1))]:
ib = [(entry == 1) for entry in xsh]
x = TensorType('float64', ib)('x')
e = DimShuffle(ib, shuffle)(x)
......@@ -69,6 +74,7 @@ class test_DimShuffle(unittest.TestCase):
# But This will test DimShuffle c code
self.with_linker(gof.OpWiseCLinker())
class test_Broadcast(unittest.TestCase):
def setUp(self):
unittest_tools.seed_rng()
......@@ -95,12 +101,12 @@ class test_Broadcast(unittest.TestCase):
#test Elemwise.infer_shape
#the Shape op don't implement c_code!
if isinstance(linker,gof.PerformLinker):
if isinstance(linker, gof.PerformLinker):
x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
e = Elemwise(scalar.add)(x, y)
f = copy(linker).accept(Env([x, y], [e.shape])).make_function()
assert tuple(f(xv, yv))==tuple(zv.shape)
assert tuple(f(xv, yv)) == tuple(zv.shape)
def with_linker_inplace(self, linker):
for xsh, ysh in [((5, 5), (5, 5)),
......@@ -113,7 +119,7 @@ class test_Broadcast(unittest.TestCase):
((), ())]:
x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0:0})(x, y)
e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0: 0})(x, y)
f = copy(linker).accept(Env([x, y], [e])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
yv = numpy.asarray(numpy.random.rand(*ysh))
......@@ -124,10 +130,10 @@ class test_Broadcast(unittest.TestCase):
self.assertTrue((xv == zv).all())
#test Elemwise.infer_shape
#the Shape op don't implement c_code!
if isinstance(linker,gof.PerformLinker):
if isinstance(linker, gof.PerformLinker):
x = TensorType('float64', [(entry == 1) for entry in xsh])('x')
y = TensorType('float64', [(entry == 1) for entry in ysh])('y')
e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0:0})(x, y)
e = Elemwise(scalar.Add(scalar.transfer_type(0)), {0: 0})(x, y)
f = copy(linker).accept(Env([x, y], [e.shape])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
yv = numpy.asarray(numpy.random.rand(*ysh))
......@@ -135,7 +141,7 @@ class test_Broadcast(unittest.TestCase):
f(xv, yv)
assert xv.shape==zv.shape
assert xv.shape == zv.shape
def test_perform(self):
self.with_linker(gof.PerformLinker())
......@@ -152,7 +158,7 @@ class test_Broadcast(unittest.TestCase):
def test_fill(self):
x = TensorType('float64', [0, 0])('x')
y = TensorType('float64', [1, 1])('y')
e = Elemwise(scalar.Second(scalar.transfer_type(0)), {0:0})(x, y)
e = Elemwise(scalar.Second(scalar.transfer_type(0)), {0: 0})(x, y)
f = gof.CLinker().accept(Env([x, y], [e])).make_function()
xv = numpy.ones((5, 5))
yv = numpy.random.rand(1, 1)
......@@ -203,21 +209,23 @@ class test_CAReduce(unittest.TestCase):
dtype = theano.config.floatX
x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
if tensor_op is None:
e = CAReduce(scalar_op, axis = tosum)(x)
e = CAReduce(scalar_op, axis=tosum)(x)
else:
e = tensor_op(x, axis=tosum)
if tosum is None: tosum = range(len(xsh))
if tosum is None:
tosum = range(len(xsh))
f = copy(linker).accept(Env([x], [e])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
if not "int" in dtype:
xv = numpy.asarray(xv,dtype=dtype)
xv = numpy.asarray(xv, dtype=dtype)
else:
xv = numpy.asarray(xv<0.5,dtype=dtype)
xv = numpy.asarray(xv < 0.5, dtype=dtype)
if test_nan and xv.size > 0:
if len(xsh)>0:
if len(xsh) > 0:
xv = xv.flatten()
xv[0] = numpy.nan
xv = xv.reshape(*xsh)
......@@ -225,13 +233,16 @@ class test_CAReduce(unittest.TestCase):
xv = numpy.asarray(numpy.nan, dtype=dtype)
zv = xv
numpy_raised = False
if len(tosum)>1 and any([a<0 for a in tosum]):
#In that case, we need to use the good order of axis in the reduction.
if len(tosum) > 1 and any([a < 0 for a in tosum]):
#In that case, we need to use the good order of axis
#in the reduction.
axis2 = []
for a in tosum:
if a<0: axis2.append(a+len(xsh))
else: axis2.append(a)
assert len(axis2)==len(tosum)
if a < 0:
axis2.append(a + len(xsh))
else:
axis2.append(a)
assert len(axis2) == len(tosum)
tosum = tuple(axis2)
if tensor_op == tensor.all:
for axis in reversed(sorted(tosum)):
......@@ -254,13 +265,13 @@ class test_CAReduce(unittest.TestCase):
for axis in reversed(sorted(tosum)):
zv = numpy.maximum.reduce(zv, axis)
except ValueError:
numpy_raised=True
numpy_raised = True
elif scalar_op == scalar.minimum:
try:
for axis in reversed(sorted(tosum)):
zv = numpy.minimum.reduce(zv, axis)
except ValueError:
numpy_raised=True
numpy_raised = True
elif scalar_op == scalar.or_:
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_or.reduce(zv, axis)
......@@ -270,13 +281,15 @@ class test_CAReduce(unittest.TestCase):
elif scalar_op == scalar.xor:
# There is no identity value for the xor function
# So we can't support shape of dimensions 0.
if numpy.prod(zv.shape)==0:
if numpy.prod(zv.shape) == 0:
continue
for axis in reversed(sorted(tosum)):
zv = numpy.bitwise_xor.reduce(zv, axis)
else:
raise Exception("Test for CAReduce with scalar_op %s not implemented"%str(scalar_op))
if scalar_op in [scalar.maximum,scalar.minimum] and numpy_raised:
raise Exception(
"Test for CAReduce with scalar_op %s not implemented" %
str(scalar_op))
if scalar_op in [scalar.maximum, scalar.minimum] and numpy_raised:
try:
out = f(xv)
assert out.dtype == dtype
......@@ -289,22 +302,25 @@ class test_CAReduce(unittest.TestCase):
if scalar_op in [scalar.and_, scalar.or_]:
zv = numpy.asarray(zv, dtype=dtype)
if test_nan:
self.assertTrue(theano.tensor.TensorType.values_eq(f(xv), zv), (f(xv), zv))
self.assertTrue(theano.tensor.TensorType.values_eq(f(xv),
zv),
(f(xv), zv))
else:
self.assertTrue(numpy.allclose(f(xv), zv), (f(xv), zv))
#test CAReduce.infer_shape
#the Shape op don't implement c_code!
if isinstance(linker,gof.PerformLinker):
if isinstance(linker, gof.PerformLinker):
x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
if tensor_op is None:
e = CAReduce(scalar_op, axis = tosum)(x)
e = CAReduce(scalar_op, axis=tosum)(x)
else:
e = tensor_op(x, axis=tosum)
if tosum is None: tosum = range(len(xsh))
if tosum is None:
tosum = range(len(xsh))
f = copy(linker).accept(Env([x], [e.shape])).make_function()
if not(scalar_op in [scalar.maximum,scalar.minimum] and ((xsh==() or numpy.prod(xsh)==0))):
if not(scalar_op in [scalar.maximum, scalar.minimum] and
((xsh == () or numpy.prod(xsh) == 0))):
assert all(f(xv) == zv.shape)
def test_perform(self):
......@@ -324,7 +340,8 @@ class test_CAReduce(unittest.TestCase):
@dec.knownfailureif(
True,
("When there is nan in the input of CAReduce, we don't have a good output. "))
("When there is nan in the input of CAReduce,"
" we don't have a good output. "))
def test_perform_nan(self):
for dtype in ["floatX", "complex64", "complex128"]:
self.with_linker(gof.PerformLinker(), scalar.add, dtype=dtype,
......@@ -362,7 +379,8 @@ class test_CAReduce(unittest.TestCase):
@dec.knownfailureif(
True,
("When there is nan in the input of CAReduce, we don't have a good output. "))
("When there is nan in the input of CAReduce,"
" we don't have a good output. "))
def test_c_nan(self):
for dtype in ["floatX", "complex64", "complex128"]:
self.with_linker(gof.CLinker(), scalar.add, dtype=dtype,
......@@ -380,7 +398,8 @@ class test_Prod(unittest.TestCase):
def setUp(self):
unittest_tools.seed_rng()
# we want to allow nans in the matrices, so we disable this DEBUG_MODE check
# we want to allow nans in the matrices, so we disable this
# DEBUG_MODE check
mode = theano.compile.mode.get_default_mode()
mode = copy(mode)
mode.check_isfinite = False
......
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