提交 4d98657a authored 作者: Arnaud Bergeron's avatar Arnaud Bergeron

Flake8 for compile/function.py

上级 3578c80c
......@@ -14,7 +14,6 @@ from theano.compile.function_module import orig_function
from theano.compile.pfunc import pfunc
from numpy import any
import warnings
from theano import gof
from theano import compat
......@@ -63,61 +62,74 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
:param inputs: function parameters, these are not allowed to be shared
variables
:type outputs: list or dict of Variables or Out instances. If it is a
dict, the keys must be strings
:type outputs: list or dict of Variables or Out instances. If it is a
dict, the keys must be strings
:param outputs: expressions to compute
:type mode: string or `Mode` instance.
:param mode: compilation mode
:type updates: iterable over pairs (shared_variable, new_expression). List, tuple or OrderedDict.
:param updates: update the values for SharedVariable inputs according to these expressions
:type updates: iterable over pairs (shared_variable, new_expression).
List, tuple or OrderedDict.
:param updates: update the values for SharedVariable inputs
according to these expressions
:type givens: iterable over pairs (Var1, Var2) of Variables. List, tuple or dict. The Var1
and Var2 in each pair must have the same Type.
:param givens: specific substitutions to make in the computation graph (Var2 replaces
Var1).
:type givens: iterable over pairs (Var1, Var2) of Variables. List,
tuple or dict. The Var1 and Var2 in each pair must
have the same Type.
:param givens: specific substitutions to make in the computation
graph (Var2 replaces Var1).
:type no_default_updates: either bool or list of Variables
:param no_default_updates: if True, do not perform any automatic update on Variables.
If False (default), perform them all. Else, perform automatic updates on all Variables
that are neither in "updates" nor in "no_default_updates".
:param name: an optional name for this function. The profile mode will print the time spent in this function.
:param rebuild_strict: True (Default) is the safer and better tested setting, in which case
`givens` must substitute new variables with the same Type as the variables they replace.
False is a you-better-know-what-you-are-doing setting, that permits `givens` to replace
variables with new variables of any Type. The consequence of changing a Type is that all
results depending on that variable may have a different Type too (the graph is rebuilt from
inputs to outputs). If one of the new types does not make sense for one of the Ops in the
graph, an Exception will be raised.
:param no_default_updates: if True, do not perform any automatic
update on Variables. If False (default), perform them
all. Else, perform automatic updates on all Variables that are
neither in "updates" nor in "no_default_updates".
:param name: an optional name for this function. The profile mode
will print the time spent in this function.
:param rebuild_strict: True (Default) is the safer and better
tested setting, in which case `givens` must substitute new
variables with the same Type as the variables they replace.
False is a you-better-know-what-you-are-doing setting, that
permits `givens` to replace variables with new variables of
any Type. The consequence of changing a Type is that all
results depending on that variable may have a different Type
too (the graph is rebuilt from inputs to outputs). If one of
the new types does not make sense for one of the Ops in the
graph, an Exception will be raised.
:type allow_input_downcast: Boolean or None
:param allow_input_downcast: True means that the values passed as
inputs when calling the function can be silently downcasted to fit
the dtype of the corresponding Variable, which may lose precision.
False means that it will only be cast to a more general, or
precise, type. None (default) is almost like False, but allows
downcasting of Python float scalars to floatX.
inputs when calling the function can be silently downcasted to
fit the dtype of the corresponding Variable, which may lose
precision. False means that it will only be cast to a more
general, or precise, type. None (default) is almost like
False, but allows downcasting of Python float scalars to
floatX.
:type profile: None, True, or ProfileStats instance
:param profile: accumulate profiling information into a given ProfileStats
instance. If argument is `True` then a new ProfileStats instance will be
used. This profiling object will be available via self.profile.
:param profile: accumulate profiling information into a given
ProfileStats instance. If argument is `True` then a new
ProfileStats instance will be used. This profiling object
will be available via self.profile.
:param on_unused_input: What to do if a variable in the 'inputs' list is
not used in the graph. Possible values are 'raise', 'warn', 'ignore' and None.
:param on_unused_input: What to do if a variable in the 'inputs'
list is not used in the graph. Possible values are 'raise',
'warn', 'ignore' and None.
:rtype: Function instance
:returns: a callable object that will compute the outputs (given the inputs)
and update the implicit function arguments according to the `updates`.
:returns: a callable object that will compute the outputs (given
the inputs) and update the implicit function arguments
according to the `updates`.
:note: Regarding givens: Be careful to make sure that these substitutions are
independent--behaviour when Var1 of one pair appears in the graph leading to Var2 in
another expression is undefined. Replacements specified with givens are different from
optimizations in that Var2 is not expected to be equivalent to Var1.
:note: Regarding givens: Be careful to make sure that these
substitutions are independent--behaviour when Var1 of one pair
appears in the graph leading to Var2 in another expression is
undefined. Replacements specified with givens are different
from optimizations in that Var2 is not expected to be
equivalent to Var1.
Internal documentation:
......@@ -195,26 +207,21 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
was easier to develop the VM in Python then translate it to C instead
of just writing it in C from scratch.
CVM stands for C Virtual Machine.
"""
if isinstance(outputs, dict):
output_items = outputs.items()
for item_pair in output_items:
for item_pair in output_items:
assert isinstance(item_pair[0], basestring)
output_items_sorted = sorted(output_items)
output_keys = []
outputs = []
for pair in output_items_sorted:
for pair in output_items_sorted:
output_keys.append(pair[0])
outputs.append(pair[1])
else:
output_keys = None
......@@ -256,12 +263,13 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
if givens is None:
givens = []
if not isinstance(inputs, (list, tuple)):
raise Exception("Input variables of a Theano function should be"
" contained in a list, even when there is a single input.")
raise Exception("Input variables of a Theano function should be "
"contained in a list, even when there is a single "
"input.")
# compute some features of the arguments:
uses_In = any([isinstance(i, In) for i in inputs]) # N.B. the square brackets are ncessary
uses_tuple = any([isinstance(i, (list, tuple)) for i in inputs]) # N.B. the square brackets are ncessary
uses_In = any([isinstance(i, In) for i in inputs])
uses_tuple = any([isinstance(i, (list, tuple)) for i in inputs])
uses_updates = bool(updates)
uses_givens = bool(givens)
......@@ -275,29 +283,30 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
if uses_In or uses_tuple:
# we must use old semantics in this case.
if profile:
raise NotImplementedError('profiling not supported in old-style function')
raise NotImplementedError("profiling not supported in old-style "
"function")
if uses_updates or uses_givens:
raise NotImplementedError(
"In() instances and tuple inputs trigger the old "
"semantics, which disallow using updates and givens")
"In() instances and tuple inputs trigger the old "
"semantics, which disallow using updates and givens")
fn = orig_function(inputs, outputs,
mode=mode,
accept_inplace=accept_inplace, name=name)
else:
# note: pfunc will also call orig_function-- orig_function is a choke point
# that all compilation must pass through
# note: pfunc will also call orig_function-- orig_function is
# a choke point that all compilation must pass through
fn = pfunc(params=inputs,
outputs=outputs,
mode=mode,
updates=updates,
givens=givens,
no_default_updates=no_default_updates,
accept_inplace=accept_inplace, name=name,
rebuild_strict=rebuild_strict,
allow_input_downcast=allow_input_downcast,
on_unused_input=on_unused_input,
profile=profile,
output_keys=output_keys)
outputs=outputs,
mode=mode,
updates=updates,
givens=givens,
no_default_updates=no_default_updates,
accept_inplace=accept_inplace, name=name,
rebuild_strict=rebuild_strict,
allow_input_downcast=allow_input_downcast,
on_unused_input=on_unused_input,
profile=profile,
output_keys=output_keys)
# We need to add the flag check_aliased inputs if we have any mutable or
# borrowed used defined inputs
fn._check_for_aliased_inputs = check_for_aliased_inputs
......
......@@ -38,7 +38,6 @@ whitelist_flake8 = [
"tests/test_tutorial.py",
"tests/disturb_mem.py",
"tests/unittest_tools.py",
"compile/function.py",
"compile/pfunc.py",
"compile/mode.py",
"compile/profilemode.py",
......
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