def filter_inplace(self, data, old_data, strict=False, allow_downcast=None):
if strict or allow_downcast or isinstance(data, cuda.CudaNdarray):
return cuda.filter(data, self.broadcastable, strict, old_data)
else: # (not strict) and (not allow_downcast)
# Check if data.dtype can be accurately cast to self.dtype
if isinstance(data, numpy.ndarray):
up_dtype = scal.upcast(self.dtype, data.dtype)
if up_dtype == self.dtype:
return cuda.filter(data, self.broadcastable, strict, old_data)
else:
raise TypeError(
"%s, with dtype %s, cannot store a value of "
"dtype %s without risking loss of precision."
"If you do not mind, please cast your data to %s." % (self, self.dtype, data.dtype, self.dtype),
data,
)
else:
converted_data = theano._asarray(data, self.dtype)
if allow_downcast is None and type(data) is float and self.dtype == theano.config.floatX:
return cuda.filter(converted_data, self.broadcastable, strict, old_data)
elif numpy.all(data == converted_data):
return cuda.filter(converted_data, self.broadcastable, strict, old_data)
else:
raise TypeError(
"%s, with dtype %s, cannot store accurately value %s, "
"it would be represented as %s. If you do not mind, "
"you can cast your data to %s." % (self, self.dtype, data, converted_data, self.dtype),
data,
)
def filter_inplace(self,
data,
old_data,
strict=False,
allow_downcast=None):
if strict or allow_downcast or isinstance(data, cuda.CudaNdarray):
return cuda.filter(data, self.broadcastable, strict, old_data)
else: # (not strict) and (not allow_downcast)
# Check if data.dtype can be accurately cast to self.dtype
if isinstance(data, numpy.ndarray):
up_dtype = scal.upcast(self.dtype, data.dtype)
if up_dtype == self.dtype:
return cuda.filter(data, self.broadcastable, strict,
old_data)
else:
raise TypeError(
'%s, with dtype %s, cannot store a value of '
'dtype %s without risking loss of precision.'
'If you do not mind, please cast your data to %s.' %
(self, self.dtype, data.dtype, self.dtype), data)
else:
converted_data = theano._asarray(data, self.dtype)
if (allow_downcast is None and type(data) is float
and self.dtype == theano.config.floatX):
return cuda.filter(converted_data, self.broadcastable,
strict, old_data)
elif numpy.all(data == converted_data):
return cuda.filter(converted_data, self.broadcastable,
strict, old_data)
else:
raise TypeError(
'%s, with dtype %s, cannot store accurately value %s, '
'it would be represented as %s. If you do not mind, '
'you can cast your data to %s.' %
(self, self.dtype, data, converted_data, self.dtype),
data)
