def infer_type(name, item):
# The forward function from Module is special; never use this annotations; we
# need to infer type directly using JIT. I originally wanted to write
# this test as isinstance(class_annotations[name], Callable) but
# isinstance on typing things doesn't seem to work: isinstance(list, Callable)
# is also true!
inferred = False
try:
if name in class_annotations and class_annotations[
name] != torch.nn.Module.__annotations__["forward"]:
ann_to_type = torch.jit.annotations.ann_to_type(
class_annotations[name], _jit_internal.fake_range())
attr_type = torch._C.InferredType(ann_to_type)
elif isinstance(item, torch.jit.Attribute):
ann_to_type = torch.jit.annotations.ann_to_type(
item.type, _jit_internal.fake_range())
attr_type = torch._C.InferredType(ann_to_type)
else:
attr_type = torch._C._jit_try_infer_type(item)
inferred = True
except RuntimeError as re:
raise RuntimeError(
"Error inferring type for {name}: {item}: {re}".format(
name=name, item=item, re=re))
return attr_type, inferred
def infer_type(name, item):
if name in class_annotations:
attr_type = torch.jit.annotations.ann_to_type(class_annotations[name], _jit_internal.fake_range())
elif isinstance(item, torch.jit.Attribute):
attr_type = torch.jit.annotations.ann_to_type(item.type, _jit_internal.fake_range())
else:
attr_type = torch._C._jit_try_infer_type(item)
return attr_type
def infer_type(name, item):
# The forward function from Module is special; never use this annotations; we
# need to infer type directly using JIT. I originally wanted to write
# this test as isinstance(class_annotations[name], Callable) but
# isinstance on typing things doesn't seem to work: isinstance(list, Callable)
# is also true!
if name in class_annotations and class_annotations[name] != torch.nn.Module.__annotations__["forward"]:
attr_type = torch.jit.annotations.ann_to_type(class_annotations[name], _jit_internal.fake_range())
elif isinstance(item, torch.jit.Attribute):
attr_type = torch.jit.annotations.ann_to_type(item.type, _jit_internal.fake_range())
else:
attr_type = torch._C._jit_try_infer_type(item)
return attr_type
def _check_no_signature(func):
signature = torch.jit.annotations.get_signature(func, None,
_jit_internal.fake_range(),
inspect.ismethod(func))
if signature is None:
qual_name = _jit_internal._qualified_name(func)
raise RuntimeError(
"Must explicitly add type annotations to overloaded functions: {}".
format(qual_name))
def _get_named_tuple_properties(obj):
assert issubclass(obj, tuple) and hasattr(obj, '_fields')
fields = list(obj._fields)
annotations = []
has_annotations = hasattr(obj, '__annotations__')
for field in fields:
if has_annotations and field in obj.__annotations__:
the_type = torch.jit.annotations.ann_to_type(obj.__annotations__[field], _jit_internal.fake_range())
annotations.append(the_type)
else:
annotations.append(torch._C.TensorType.get())
return type(obj).__name__, fields, annotations
