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pytensor
提交 0ab95847 authored 作者: Frederic's avatar Frederic
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some pep8 fix.

上级 6ddf8bec
隐藏空白字符变更
内嵌 并排
正在显示 包含 72 行增加 和 45 行删除
theano/tensor/tests/test_basic.py
浏览文件 @ 0ab95847
......@@ -40,12 +40,13 @@ try:
import scipy.special
imported_scipy_special = True
except ImportError:
if config.mode=="FAST_COMPILE":
if config.mode == "FAST_COMPILE":
mode_no_scipy = "FAST_RUN"
### seed random number generator so that unittests are deterministic ###
utt.seed_rng()
def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False):
if mode is None:
mode = get_default_mode()
......@@ -54,12 +55,14 @@ def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False):
allow_input_downcast=allow_input_downcast,
accept_inplace=True)
def eval_outputs(outputs):
variables = inplace_func([], outputs)()
if isinstance(variables,(tuple, list)) and len(variables) == 1:
if isinstance(variables, (tuple, list)) and len(variables) == 1:
return variables[0]
return variables
def get_numeric_subclasses(cls=numpy.number, ignore=None):
"""
Return subclasses of `cls` in the numpy scalar hierarchy.
......@@ -110,6 +113,7 @@ def get_numeric_types(with_int=True, with_float=True, with_complex=False,
if only_theano_types:
theano_types = [d.dtype for d in theano.scalar.all_types]
rval = []
def is_within(cls1, cls2):
# Return True if scalars defined from `cls1` are within the hierarchy
# starting from `cls2`.
......@@ -120,6 +124,7 @@ def get_numeric_types(with_int=True, with_float=True, with_complex=False,
return (cls1 is cls2 or
issubclass(cls1, cls2) or
isinstance(numpy.array([0], dtype=cls1)[0], cls2))
for cls in get_numeric_subclasses():
dtype = numpy.dtype(cls)
if ((not with_complex and is_within(cls, numpy.complexfloating)) or
......@@ -132,30 +137,39 @@ def get_numeric_types(with_int=True, with_float=True, with_complex=False,
# We sort it to be deterministic, then remove the string and num elements.
return [x[1] for x in sorted(rval, key=str)]
def _numpy_checker(x, y):
"""
Checks if x.data and y.data have the same contents.
Used in DualLinker to compare C version with Python version.
"""
x, y = x[0], y[0]
if x.dtype != y.dtype or x.shape != y.shape or numpy.any(numpy.abs(x - y) > 1e-10):
if (x.dtype != y.dtype or x.shape != y.shape
or numpy.any(numpy.abs(x - y) > 1e-10)):
raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y})
def safe_make_node(op, *inputs):
"""Emulate the behaviour of make_node when op is a function instead of an Op instance."""
""" Emulate the behaviour of make_node when op is a function.
Normally op in an instead of the Op class.
"""
node = op(*inputs)
if isinstance(node, list):
return node[0].owner
else:
return node.owner
def makeTester(name, op, expected, checks = {}, good = {}, bad_build = {},
bad_runtime = {}, grad = {}, mode = None, grad_rtol=None,
eps = 1e-10, skip = False):
def makeTester(name, op, expected, checks={}, good={}, bad_build={},
bad_runtime={}, grad={}, mode=None, grad_rtol=None,
eps=1e-10, skip=False):
if grad is True:
grad = good
_op, _expected, _checks, _good, _bad_build, _bad_runtime, _grad, _mode, _grad_rtol, _eps, skip_ = op, expected, checks, good, bad_build, bad_runtime, grad, mode, grad_rtol, eps, skip
_op, _expected, _checks, _good = op, expected, checks, good
_bad_build, _bad_runtime, _grad = bad_build, bad_runtime, grad
_mode, _grad_rtol, _eps, skip_ = mode, grad_rtol, eps, skip
class Checker(unittest.TestCase):
......@@ -186,14 +200,14 @@ def makeTester(name, op, expected, checks = {}, good = {}, bad_build = {},
raise type, exc_value, traceback
try:
f = inplace_func(inputrs, node.outputs, mode = mode)
f = inplace_func(inputrs, node.outputs, mode=mode)
except Exception:
type, exc_value, traceback = sys.exc_info()
err_msg = "Test %s::%s: Error occurred while trying to make a Function" \
% (self.op, testname)
exc_value.args = exc_value.args + (err_msg, )
raise type, exc_value, traceback
if isinstance(self.expected,dict) and testname in self.expected:
if isinstance(self.expected, dict) and testname in self.expected:
expecteds = self.expected[testname]
#with numpy version, when we print a number and read it back, we don't get exactly the same result
#So we accept rounding error in that case.
......@@ -202,9 +216,9 @@ def makeTester(name, op, expected, checks = {}, good = {}, bad_build = {},
expecteds = self.expected(*inputs)
eps = 1e-10
if any([i.dtype=='float32' for i in inputs]):
eps=8e-6#1e-6
eps = numpy.max([eps,_eps])
if any([i.dtype == 'float32' for i in inputs]):
eps = 8e-6 # 1e-6
eps = numpy.max([eps, _eps])
try:
variables = f(*inputs)
......@@ -222,7 +236,10 @@ def makeTester(name, op, expected, checks = {}, good = {}, bad_build = {},
if variable.dtype != expected.dtype or variable.shape != expected.shape or \
numpy.any(numpy.abs(variable - expected) > eps):
self.fail("Test %s::%s: Output %s gave the wrong value. With inputs %s, expected %s, got %s. numpy.allclose return %s %s"
% (self.op, testname, i, inputs, expected, variable, numpy.allclose(variable,expected,atol=eps), numpy.allclose(variable,expected)))
% (self.op, testname, i, inputs, expected,
variable,
numpy.allclose(variable, expected, atol=eps),
numpy.allclose(variable, expected)))
for description, check in self.checks.items():
if not check(inputs, variables):
......@@ -246,7 +263,7 @@ def makeTester(name, op, expected, checks = {}, good = {}, bad_build = {},
inputs = [copy(input) for input in inputs]
inputrs = [value(input) for input in inputs]
try:
node = safe_make_node(self.op,*inputrs)
node = safe_make_node(self.op, *inputrs)
except Exception:
type, exc_value, traceback = sys.exc_info()
err_msg = "Test %s::%s: Error occurred while trying to make a node with inputs %s" \
......@@ -297,18 +314,25 @@ rand = lambda *shape: 2 * numpy.asarray(numpy.random.rand(*shape), dtype=config.
randint = lambda *shape: numpy.random.random_integers(-5, 5, shape)
randcomplex = lambda *shape: numpy.complex128(2 * numpy.asarray(numpy.random.rand(*shape), dtype=config.floatX) - 1)
def randint_nonzero(*shape):
r = numpy.random.random_integers(-5, 4, shape)
return r + (r == 0) * 5
def rand_ranged(min, max, shape):
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min, dtype=config.floatX)
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min,
dtype=config.floatX)
def randint_ranged(min, max, shape):
return numpy.random.random_integers(min, max, shape)
def randc128_ranged(min, max, shape):
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min, dtype='complex128')
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min,
dtype='complex128')
def rand_of_dtype(shape, dtype):
if 'int' in dtype:
......@@ -320,7 +344,8 @@ def rand_of_dtype(shape, dtype):
else:
raise TypeError()
def makeBroadcastTester(op, expected, checks = {}, name=None, **kwargs):
def makeBroadcastTester(op, expected, checks={}, name=None, **kwargs):
name = str(op)
# Here we ensure the test name matches the name of the variable defined in
# this script. This is needed to properly identify the test e.g. with the
......@@ -340,48 +365,50 @@ def makeBroadcastTester(op, expected, checks = {}, name=None, **kwargs):
if kwargs.has_key('inplace'):
if kwargs['inplace']:
_expected = expected
if not isinstance(_expected,dict):
expected = lambda *inputs: numpy.array(_expected(*inputs), dtype = inputs[0].dtype)
if not isinstance(_expected, dict):
expected = lambda *inputs: numpy.array(_expected(*inputs),
dtype=inputs[0].dtype)
def inplace_check(inputs, outputs):
# this used to be inputs[0] is output[0]
# I changed it so that it was easier to satisfy by the DebugMode
return numpy.all(inputs[0] == outputs[0])
checks = dict(checks, inplace_check=inplace_check) #lambda inputs, outputs: numpy.all(inputs[0] == outputs[0]))
checks = dict(checks, inplace_check=inplace_check)
del kwargs['inplace']
return makeTester(name, op, expected, checks, **kwargs)
_good_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
not_same_dimensions = (rand(2, 2), rand(2)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
integers = (randint(2, 3), randint(2, 3)),
dtype_mixup_1 = (rand(2, 3), randint(2, 3)),
dtype_mixup_2 = (randint(2, 3), rand(2, 3)),
complex1 = (randcomplex(2,3),randcomplex(2,3)),
complex2 = (randcomplex(2,3),rand(2,3)),
_good_broadcast_binary_normal = dict(same_shapes=(rand(2, 3), rand(2, 3)),
not_same_dimensions=(rand(2, 2), rand(2)),
scalar=(rand(2, 3), rand(1, 1)),
row=(rand(2, 3), rand(1, 3)),
column=(rand(2, 3), rand(2, 1)),
integers=(randint(2, 3), randint(2, 3)),
dtype_mixup_1=(rand(2, 3), randint(2, 3)),
dtype_mixup_2=(randint(2, 3), rand(2, 3)),
complex1=(randcomplex(2, 3), randcomplex(2, 3)),
complex2=(randcomplex(2, 3), rand(2, 3)),
# Disabled as we test the case where we reuse the same output as the first inputs.
# complex3 = (rand(2,3),randcomplex(2,3)),
empty = (numpy.asarray([]),numpy.asarray([1])),
# complex3=(rand(2,3),randcomplex(2,3)),
empty=(numpy.asarray([]), numpy.asarray([1])),
)
_bad_build_broadcast_binary_normal = dict()#not_same_dimensions = (rand(2), rand(2, 2)))
_bad_build_broadcast_binary_normal = dict() # not_same_dimensions = (rand(2), rand(2, 2)))
_bad_runtime_broadcast_binary_normal = dict(bad_shapes = (rand(2, 3), rand(3, 2)),
bad_row = (rand(2, 3), rand(1, 2)))
_bad_runtime_broadcast_binary_normal = dict(bad_shapes=(rand(2, 3), rand(3, 2)),
bad_row=(rand(2, 3), rand(1, 2)))
_grad_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
_grad_broadcast_binary_normal = dict(same_shapes=(rand(2, 3), rand(2, 3)),
scalar=(rand(2, 3), rand(1, 1)),
row=(rand(2, 3), rand(1, 3)),
column=(rand(2, 3), rand(2, 1)),
#This don't work as verify grad don't support that
#empty = (numpy.asarray([]), numpy.asarray([1]))
#complex1 = (randcomplex(2,3),randcomplex(2,3)),
#complex2 = (randcomplex(2,3),rand(2,3)),
#empty=(numpy.asarray([]), numpy.asarray([1]))
#complex1=(randcomplex(2,3),randcomplex(2,3)),
#complex2=(randcomplex(2,3),rand(2,3)),
# Disabled as we test the case where we reuse the same output as the first inputs.
#complex3 = (rand(2,3),randcomplex(2,3)),
#complex3=(rand(2,3),randcomplex(2,3)),
)
......
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