def clear_note_fake_program():
note_pytorch_method_names.clear()
note_pytorch_args.clear()
note_pytorch_kwargs.clear()
call_tensor_id.clear()
vis_parameters.clear()
extra_input_tensor.clear()
flow.set_printoptions(profile="full")
parser = doctest.DocTestParser()
if raise_on_error:
runner = doctest.DebugRunner(verbose=verbose, optionflags=optionflags)
else:
runner = doctest.DocTestRunner(verbose=verbose,
optionflags=optionflags)
r = inspect.getmembers(module)
for (name, fun) in r:
if fun.__doc__ is not None:
test = parser.get_doctest(fun.__doc__, {}, __name__, __file__, 0)
runner.run(test)
@flow.unittest.skip_unless_1n1d()
@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
class TestFunctionalDocstrModule(flow.unittest.TestCase):
def test_functional_docstr(test_case):
arg_dict = OrderedDict()
arg_dict["module"] = [flow, flow.sbp, flow.env, flow.nn.functional]
for arg in GenArgList(arg_dict):
_run_functional_doctest(test_case,
raise_on_error=True,
verbose=True,
module=arg[0])
if __name__ == "__main__":
flow.set_printoptions(linewidth=80)
unittest.main()