def makemask(self, arg=0): # default: mask zero
if isfunction(arg):
self._mask = arg(self)
elif isnumber(arg):
self._mask = tensorops.eq(self, arg)
else:
raise Exception("wrong input argument type")
return self
def wrapply(self, *args, **kwargs): # is this multi-output compatible?
transform = None
oldtrainmode = None
olddebugmode = None
global _TRAINMODE
global _DEBUGMODE
#print "{} train mode (in base.py)".format(_TRAINMODE)
if "transform" in kwargs and kwargs["transform"] is not None:
transform = kwargs.pop("transform")
if "_trainmode" in kwargs: # changes global _TRAINMODE
oldtrainmode = _TRAINMODE
_TRAINMODE = kwargs.pop("_trainmode")
if "_debugmode" in kwargs: # changes global _DEBUGMODE
olddebugmode = _DEBUGMODE
_DEBUGMODE = kwargs.pop("_debugmode")
if "_batsize" in kwargs:
batsize = kwargs.pop("_batsize")
if "_trainmode" in inspect.getargspec(self.apply)[0]:
kwargs["_trainmode"] = _TRAINMODE
if "_debugmode" in inspect.getargspec(self.apply)[0]:
kwargs["_debugmode"] = _DEBUGMODE
# param pushing
paramstopush = set(
) # params to transfer from input vars to output vars
for var in recurfilter(lambda x: isinstance(x, Var),
kwargs) + recurfilter(
lambda x: isinstance(x, Var), args):
paramstopush.update(var._params)
updatestopush = _get_updates_from([args, kwargs])
# extra outs pushing
extraoutstopush = {}
for var in recurfilter(lambda x: isinstance(x, Var),
kwargs) + recurfilter(
lambda x: isinstance(x, Var), args):
extraoutstopush.update(var._extra_outs)
if transform is not None and isfunction(transform):
args, kwargs = transform(*args, **kwargs)
ret = self.apply(
*args, **kwargs
) # ret carries params of its own --> these params have been added in this block
possiblechildren = recurfilter(lambda x: isinstance(x, Var), ret)
for p in possiblechildren:
p.push_params(paramstopush)
p.push_params(self.ownparams)
p.push_updates(updatestopush)
p.push_extra_outs(extraoutstopush)
for k, v in self._param_settings_toprop.items():
for param in p.allparams.difference(paramstopush):
if hasattr(param, k):
setattr(param, k, v)
if oldtrainmode is not None: # this was where we changed the global _TRAINMODE
_TRAINMODE = oldtrainmode # put it back
if olddebugmode is not None:
_DEBUGMODE = olddebugmode
return ret
def wrapf(attr, root=None):
if isfunction(attr): # real function
innerwrap = prefwrap(attr, root) #lambda *args, **kwargs: fwrap(attr, root, *args, **kwargs)
elif isnumber(attr) or isstring(attr): # or other literals/non-syms/modules/properties/...
return attr
elif isinstance(attr, ModuleType):
innerwrap = pwrap(attr)
elif isinstance(attr, theano.Variable):
innerwrap = vwrap(attr, root)
else:
innerwrap = attr
return innerwrap
def wrapf(attr, root=None):
if isfunction(attr): # real function
innerwrap = OpBlock(attr, root)
elif isnumber(attr) or isstring(attr): # or other literals/non-syms/modules/properties/...
return attr
elif isinstance(attr, ModuleType):
innerwrap = WrappedAttr(attr)
elif isinstance(attr, theano.Variable):
innerwrap = Var(attr)
if isinstance(root, Var): # propagate params
innerwrap.push_params(root._params)
else:
innerwrap = attr
return innerwrap
def wrapf(attr, root=None):
if isfunction(attr): # real function
innerwrap = OpBlock(attr, root)
elif isnumber(attr) or isstring(attr): # or other literals/non-syms/modules/properties/...
return attr
elif isinstance(attr, ModuleType):
innerwrap = WrappedAttr(attr)
elif isinstance(attr, theano.Variable):
innerwrap = Var(attr)
if isinstance(root, Var): # propagate params
innerwrap.push_params(root._params)
else:
innerwrap = attr
return innerwrap
def wrapply(self, *args, **kwargs): # is this multi-output compatible?
transform = None
if "transform" in kwargs and kwargs["transform"] is not None:
transform = kwargs.pop("transform")
paramstopush = set() # params to transfer from input vars to output vars
for var in recurfilter(lambda x: isinstance(x, Var), kwargs) + recurfilter(lambda x: isinstance(x, Var), args):
paramstopush.update(var._params)
if transform is not None and isfunction(transform):
args, kwargs = transform(*args, **kwargs)
ret = self.apply(*args, **kwargs) # ret carries params of its own --> these params have been added in this block
possiblechildren = recurfilter(lambda x: isinstance(x, Var), ret)
for p in possiblechildren:
p.push_params(paramstopush)
p.push_params(self.ownparams)
return ret
def wrapply(self, *args, **kwargs): # is this multi-output compatible?
transform = None
if "transform" in kwargs and kwargs["transform"] is not None:
transform = kwargs.pop("transform")
paramstopush = set(
) # params to transfer from input vars to output vars
for var in recurfilter(lambda x: isinstance(x, Var),
kwargs) + recurfilter(
lambda x: isinstance(x, Var), args):
paramstopush.update(var._params)
if transform is not None and isfunction(transform):
args, kwargs = transform(*args, **kwargs)
ret = self.apply(
*args, **kwargs
) # ret carries params of its own --> these params have been added in this block
possiblechildren = recurfilter(lambda x: isinstance(x, Var), ret)
for p in possiblechildren:
p.push_params(paramstopush)
p.push_params(self.ownparams)
return ret
def transform(self, f):
assert (isfunction(f))
self.transf = f if f is not None and isfunction(
f) else self.transf
return self
def init(self, arg, *args, **kwargs):
if isstring(arg):
assert hasattr(self, arg)
return getattr(self, arg)(*args, **kwargs)
elif isfunction(arg):
return self._init_helper(arg)
def init(self, arg, *args, **kwargs):
if isstring(arg):
assert hasattr(self, arg)
return getattr(self, arg)(*args, **kwargs)
elif isfunction(arg):
return self._init_helper(arg)
def transform(self, f):
assert(isfunction(f))
self.transf = f if f is not None and isfunction(f) else self.transf
return self
