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], 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, 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 _get_named_tuple_properties(obj):
assert issubclass(obj, tuple) and hasattr(obj, "_fields")
if hasattr(obj, "_field_defaults"):
defaults = [
obj._field_defaults[field]
for field in obj._fields
if field in obj._field_defaults
]
else:
defaults = []
# In 3.10 recommended way to get annotations is to call `inspect.get_annotations` function
# Also, annotations from base class are not inherited so they need to be queried explicitly
if sys.version_info[:2] < (3, 10):
obj_annotations = getattr(obj, "__annotations__", {})
else:
obj_annotations = inspect.get_annotations(obj)
if len(obj_annotations) == 0 and hasattr(obj, "__base__"):
obj_annotations = inspect.get_annotations(obj.__base__)
annotations = []
for field in obj._fields:
if field in obj_annotations:
the_type = torch.jit.annotations.ann_to_type(
obj_annotations[field], fake_range()
)
annotations.append(the_type)
else:
annotations.append(torch._C.TensorType.getInferred())
return type(obj).__name__, obj._fields, annotations, defaults
def _check_no_signature(func):
signature = torch.jit.annotations.get_signature(func, None, 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')
if hasattr(obj, "_field_defaults"):
defaults = [obj._field_defaults[field]
for field in obj._fields
if field in obj._field_defaults]
else:
defaults = []
annotations = []
has_annotations = hasattr(obj, '__annotations__')
for field in obj._fields:
if has_annotations and field in obj.__annotations__:
the_type = torch.jit.annotations.ann_to_type(obj.__annotations__[field], fake_range())
annotations.append(the_type)
else:
annotations.append(torch._C.TensorType.getInferred())
return type(obj).__name__, obj._fields, annotations, defaults
