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提交 59a91c4b authored 作者: Brandon T. Willard's avatar Brandon T. Willard
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Apply pyupgrade to theano.scalar

上级 e96f06da
隐藏空白字符变更
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正在显示 包含 46 行增加 和 50 行删除
theano/scalar/basic.py
浏览文件 @ 59a91c4b
......@@ -18,7 +18,6 @@ from itertools import chain
from textwrap import dedent
import numpy as np
import six
import theano
from theano import config, gof, printing
......@@ -41,8 +40,6 @@ class ComplexError(NotImplementedError):
"""
pass
class IntegerDivisionError(Exception):
"""
......@@ -50,8 +47,6 @@ class IntegerDivisionError(Exception):
"""
pass
def upcast(dtype, *dtypes):
# This tries to keep data in floatX or lower precision, unless we
......@@ -135,7 +130,7 @@ def as_scalar(x, name=None):
raise TypeError("Cannot convert %s to Scalar" % x, type(x))
class NumpyAutocaster(object):
class NumpyAutocaster:
"""
This class is used to cast python ints and floats to numpy arrays.
......@@ -171,7 +166,7 @@ class NumpyAutocaster(object):
def __call__(self, x):
# Make sure we only deal with scalars.
assert isinstance(x, (six.integer_types, builtin_float)) or (
assert isinstance(x, (int, builtin_float)) or (
isinstance(x, np.ndarray) and x.ndim == 0
)
......@@ -230,7 +225,7 @@ autocast_int = NumpyAutocaster(("int8", "int16", "int32", "int64"))
autocast_float = NumpyAutocaster(("float16", "float32", "float64"))
class autocast_float_as(object):
class autocast_float_as:
"""
Temporarily adjust autocasting behavior.
......@@ -280,7 +275,7 @@ def convert(x, dtype=None):
# In this case, this function should infer the dtype according to the
# autocasting rules. See autocasting above.
x_ = None
if isinstance(x, six.integer_types):
if isinstance(x, int):
try:
x_ = autocast_int(x)
except OverflowError:
......@@ -442,7 +437,9 @@ class Scalar(Type):
}[self.dtype]
except KeyError:
raise TypeError(
"Unsupported dtype for %s: %s" % (self.__class__.__name__, self.dtype)
"Unsupported dtype for {}: {}".format(
self.__class__.__name__, self.dtype
)
)
def upcast(self, *others):
......@@ -1194,9 +1191,9 @@ class ScalarOp(Op):
not in ["name", "_op_use_c_code", "bool", "output_types_preference"]
]
if param:
return "%s{%s}" % (
return "{}{{{}}}".format(
self.__class__.__name__,
", ".join("%s=%s" % (k, v) for k, v in param),
", ".join("{}={}".format(k, v) for k, v in param),
)
else:
return self.__class__.__name__
......@@ -1343,7 +1340,7 @@ class LogicalComparison(BinaryScalarOp):
]
def c_code_cache_version(self):
super_version = super(LogicalComparison, self).c_code_cache_version()
super_version = super().c_code_cache_version()
return super_version + (0,)
......@@ -1376,7 +1373,7 @@ class FixedLogicalComparison(UnaryScalarOp):
return [x.zeros_like().astype(theano.config.floatX)]
def c_code_cache_version(self):
super_version = super(FixedLogicalComparison, self).c_code_cache_version()
super_version = super().c_code_cache_version()
return super_version + (0,)
......@@ -1522,7 +1519,7 @@ class IsNan(FixedLogicalComparison):
return "%(z)s = abs(isnan(%(x)s));" % locals()
def c_code_cache_version(self):
scalarop_version = super(IsNan, self).c_code_cache_version()
scalarop_version = super().c_code_cache_version()
return tuple(scalarop_version) + (3,)
......@@ -1550,7 +1547,7 @@ class IsInf(FixedLogicalComparison):
return "%(z)s = abs(isinf(%(x)s));" % locals()
def c_code_cache_version(self):
scalarop_version = super(IsInf, self).c_code_cache_version()
scalarop_version = super().c_code_cache_version()
return tuple(scalarop_version) + (3,)
......@@ -1737,7 +1734,7 @@ class AND(BinaryBitOp):
return "%(z)s = (%(x)s & %(y)s);" % locals()
def c_code_cache_version(self):
super_version = super(AND, self).c_code_cache_version()
super_version = super().c_code_cache_version()
return super_version + (3,)
......@@ -2060,7 +2057,7 @@ class TrueDiv(BinaryScalarOp):
if all(t in discrete_types for t in types):
return [get_scalar_type(config.floatX)]
else:
return super(TrueDiv, self).output_types(types)
return super().output_types(types)
def impl(self, x, y):
x = np.asarray(x)
......@@ -2544,12 +2541,12 @@ class Cast(UnaryScalarOp):
def __init__(self, o_type, name=None):
if not isinstance(o_type, Scalar):
raise TypeError(o_type)
super(Cast, self).__init__(specific_out(o_type), name=name)
super().__init__(specific_out(o_type), name=name)
self.o_type = o_type
self.ctor = getattr(np, o_type.dtype)
def __str__(self):
return "%s{%s}" % (self.__class__.__name__, self.o_type.dtype)
return "{}{{{}}}".format(self.__class__.__name__, self.o_type.dtype)
def clone_float32(self):
if self.o_type == float16:
......@@ -2575,8 +2572,8 @@ class Cast(UnaryScalarOp):
(x,) = inputs
(z,) = outputs
if node.outputs[0].type == bool:
return "%s = (%s) ? 1 : 0;" % (z, x)
return "%s = (%s)%s;" % (z, node.outputs[0].type.dtype_specs()[1], x)
return "{} = ({}) ? 1 : 0;".format(z, x)
return "{} = ({}){};".format(z, node.outputs[0].type.dtype_specs()[1], x)
def grad(self, inputs, gout):
(x,) = inputs
......@@ -2587,7 +2584,7 @@ class Cast(UnaryScalarOp):
return [x.zeros_like().astype(theano.config.floatX)]
def c_code_cache_version(self):
s = super(Cast, self).c_code_cache_version()
s = super().c_code_cache_version()
if s:
return (4,) + s
else:
......@@ -2738,7 +2735,7 @@ class Sgn(UnaryScalarOp):
raise ComplexError("complex has no sgn")
def c_code_cache_version(self):
s = super(Sgn, self).c_code_cache_version()
s = super().c_code_cache_version()
if s:
return (4,) + s
else: # if parent is unversioned, we are too
......@@ -4108,7 +4105,7 @@ class Composite(ScalarOp):
This fct allow fix patch this.
"""
d = dict([(k, getattr(self, k)) for k in self.init_param])
d = {k: getattr(self, k) for k in self.init_param}
out = self.__class__(**d)
if name:
out.name = name
......@@ -4163,7 +4160,7 @@ class Composite(ScalarOp):
i += 1
name = "V%%(id)s_tmp%i" % i
subd[output] = name
_c_code += "%s %s;\n" % (output.type.dtype_specs()[1], name)
_c_code += "{} {};\n".format(output.type.dtype_specs()[1], name)
s = node.op.c_code(
node,
self.nodenames[j],
......@@ -4338,7 +4335,7 @@ class Composite(ScalarOp):
def make_node(self, *inputs):
if tuple([i.type for i in self.inputs]) == tuple([i.type for i in inputs]):
return super(Composite, self).make_node(*inputs)
return super().make_node(*inputs)
else:
# Make a new op with the right input type.
assert len(inputs) == self.nin
......@@ -4470,7 +4467,7 @@ class Composite(ScalarOp):
self.init_py_impls()
class Compositef32(object):
class Compositef32:
# This is a dict of scalar op classes that need special handling
special = {}
......
theano/scalar/basic_scipy.py
浏览文件 @ 59a91c4b
......@@ -40,7 +40,7 @@ class Erf(UnaryScalarOp):
if imported_scipy_special:
return scipy.special.erf(x)
else:
super(Erf, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -77,7 +77,7 @@ class Erfc(UnaryScalarOp):
if imported_scipy_special:
return scipy.special.erfc(x)
else:
super(Erfc, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -129,7 +129,7 @@ class Erfcx(UnaryScalarOp):
if imported_scipy_special:
return scipy.special.erfcx(x)
else:
super(Erfcx, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -169,7 +169,7 @@ class Erfinv(UnaryScalarOp):
if imported_scipy_special:
return scipy.special.erfinv(x)
else:
super(Erfinv, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -206,7 +206,7 @@ class Erfcinv(UnaryScalarOp):
if imported_scipy_special:
return scipy.special.erfcinv(x)
else:
super(Erfcinv, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -247,7 +247,7 @@ class Gamma(UnaryScalarOp):
if imported_scipy_special:
return Gamma.st_impl(x)
else:
super(Gamma, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, gout):
(x,) = inputs
......@@ -289,7 +289,7 @@ class GammaLn(UnaryScalarOp):
if imported_scipy_special:
return GammaLn.st_impl(x)
else:
super(GammaLn, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -337,7 +337,7 @@ class Psi(UnaryScalarOp):
if imported_scipy_special:
return Psi.st_impl(x)
else:
super(Psi, self).impl(x)
super().impl(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
......@@ -434,7 +434,7 @@ class TriGamma(UnaryScalarOp):
if imported_scipy_special:
return TriGamma.st_impl(x)
else:
super(TriGamma, self).impl(x)
super().impl(x)
def grad(self, inputs, outputs_gradients):
raise NotImplementedError()
......@@ -526,7 +526,7 @@ class Chi2SF(BinaryScalarOp):
if imported_scipy_special:
return Chi2SF.st_impl(x, k)
else:
super(Chi2SF, self).impl(x, k)
super().impl(x, k)
def c_support_code(self):
with open(os.path.join(os.path.dirname(__file__), "c_code", "gamma.c")) as f:
......@@ -570,7 +570,7 @@ class GammaInc(BinaryScalarOp):
if imported_scipy_special:
return GammaInc.st_impl(k, x)
else:
super(GammaInc, self).impl(k, x)
super().impl(k, x)
def c_support_code(self):
with open(os.path.join(os.path.dirname(__file__), "c_code", "gamma.c")) as f:
......@@ -614,7 +614,7 @@ class GammaIncC(BinaryScalarOp):
if imported_scipy_special:
return GammaIncC.st_impl(k, x)
else:
super(GammaIncC, self).impl(k, x)
super().impl(k, x)
def c_support_code(self):
with open(os.path.join(os.path.dirname(__file__), "c_code", "gamma.c")) as f:
......@@ -658,7 +658,7 @@ class GammaU(BinaryScalarOp):
if imported_scipy_special:
return GammaU.st_impl(k, x)
else:
super(GammaU, self).impl(k, x)
super().impl(k, x)
def c_support_code(self):
with open(os.path.join(os.path.dirname(__file__), "c_code", "gamma.c")) as f:
......@@ -702,7 +702,7 @@ class GammaL(BinaryScalarOp):
if imported_scipy_special:
return GammaL.st_impl(k, x)
else:
super(GammaL, self).impl(k, x)
super().impl(k, x)
def c_support_code(self):
with open(os.path.join(os.path.dirname(__file__), "c_code", "gamma.c")) as f:
......@@ -746,7 +746,7 @@ class Jv(BinaryScalarOp):
if imported_scipy_special:
return self.st_impl(v, x)
else:
super(Jv, self).impl(v, x)
super().impl(v, x)
def grad(self, inputs, grads):
v, x = inputs
......@@ -775,7 +775,7 @@ class J1(UnaryScalarOp):
if imported_scipy_special:
return self.st_impl(x)
else:
super(J1, self).impl(x)
super().impl(x)
def grad(self, inputs, grads):
(x,) = inputs
......@@ -812,7 +812,7 @@ class J0(UnaryScalarOp):
if imported_scipy_special:
return self.st_impl(x)
else:
super(J0, self).impl(x)
super().impl(x)
def grad(self, inp, grads):
(x,) = inp
......@@ -849,7 +849,7 @@ class Iv(BinaryScalarOp):
if imported_scipy_special:
return self.st_impl(v, x)
else:
super(Iv, self).impl(v, x)
super().impl(v, x)
def grad(self, inputs, grads):
v, x = inputs
......@@ -878,7 +878,7 @@ class I1(UnaryScalarOp):
if imported_scipy_special:
return self.st_impl(x)
else:
super(I1, self).impl(x)
super().impl(x)
def grad(self, inputs, grads):
(x,) = inputs
......@@ -904,7 +904,7 @@ class I0(UnaryScalarOp):
if imported_scipy_special:
return self.st_impl(x)
else:
super(I0, self).impl(x)
super().impl(x)
def grad(self, inp, grads):
(x,) = inp
......
theano/scalar/sharedvar.py
浏览文件 @ 59a91c4b
......@@ -17,7 +17,6 @@ way (as scan does) to create a shared variable of this kind.
"""
import numpy as np
from six import integer_types
from theano.compile import SharedVariable
......@@ -51,7 +50,7 @@ def shared(value, name=None, strict=False, allow_downcast=None):
We implement this using 0-d tensors for now.
"""
if not isinstance(value, (np.number, float, integer_types, complex)):
if not isinstance(value, (np.number, float, int, complex)):
raise TypeError()
try:
dtype = value.dtype
......
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