def __new__(cls, name: str, bases: Tuple, attrs: Dict):
for test_config in config:
(
algorithms,
model,
args,
layer,
noise_tunnel,
baseline_distr,
) = parse_test_config(test_config)
for algorithm in algorithms:
if not should_create_generated_test(algorithm):
continue
if algorithm in JIT_SUPPORTED:
for mode in JITCompareMode:
# Creates test case corresponding to each algorithm and
# JITCompareMode
test_method = cls.make_single_jit_test(
algorithm, model, args, noise_tunnel,
baseline_distr, mode)
test_name = gen_test_name(
"test_jit_" + mode.name,
cast(str, test_config["name"]),
algorithm,
noise_tunnel,
)
if test_name in attrs:
raise AssertionError(
"Trying to overwrite existing test with name: %r"
% test_name)
attrs[test_name] = test_method
return super(JITMeta, cls).__new__(cls, name, bases, attrs)
def __new__(cls, name: str, bases: Tuple, attrs: Dict):
for test_config in config:
(
algorithms,
model,
args,
layer,
noise_tunnel,
baseline_distr,
) = parse_test_config(test_config)
target_delta = (test_config["target_delta"]
if "target_delta" in test_config else 0.0001)
if "target" not in args or not isinstance(args["target"],
(list, Tensor)):
continue
for algorithm in algorithms:
# FeaturePermutation requires a batch of inputs
# so skipping tests
if issubclass(algorithm, FeaturePermutation
) or not should_create_generated_test(algorithm):
continue
test_method = cls.make_single_target_test(
algorithm,
model,
layer,
args,
target_delta,
noise_tunnel,
baseline_distr,
)
test_name = gen_test_name(
"test_target",
cast(str, test_config["name"]),
algorithm,
noise_tunnel,
)
if test_name in attrs:
raise AssertionError(
"Trying to overwrite existing test with name: %r" %
test_name)
attrs[test_name] = test_method
return super(TargetsMeta, cls).__new__(cls, name, bases, attrs)
def __new__(cls, name: str, bases: Tuple, attrs: Dict):
created_tests: Dict[Tuple[Type[Attribution], HookRemovalMode],
bool] = {}
for test_config in config:
(
algorithms,
model,
args,
layer,
noise_tunnel,
_,
) = parse_test_config(test_config)
for algorithm in algorithms:
if not should_create_generated_test(algorithm):
continue
for mode in HookRemovalMode:
if mode is HookRemovalMode.invalid_module and layer is None:
continue
# Only one test per algorithm and mode is necessary
if (algorithm, mode) in created_tests:
continue
test_method = cls.make_single_hook_removal_test(
algorithm,
model,
layer,
args,
noise_tunnel,
mode,
)
test_name = gen_test_name(
"test_hook_removal_" + mode.name,
cast(str, test_config["name"]),
algorithm,
noise_tunnel,
)
if test_name in attrs:
raise AssertionError(
"Trying to overwrite existing test with name: %r" %
test_name)
attrs[test_name] = test_method
created_tests[(algorithm, mode)] = True
return super(HookRemovalMeta, cls).__new__(cls, name, bases, attrs)
def __new__(cls, name: str, bases: Tuple, attrs: Dict):
for test_config in config:
(
algorithms,
model,
args,
layer,
noise_tunnel,
baseline_distr,
) = parse_test_config(test_config)
dp_delta = test_config[
"dp_delta"] if "dp_delta" in test_config else 0.0001
for algorithm in algorithms:
if not should_create_generated_test(algorithm):
continue
for mode in DataParallelCompareMode:
# Creates test case corresponding to each algorithm and
# DataParallelCompareMode
test_method = cls.make_single_dp_test(
algorithm,
model,
layer,
args,
dp_delta,
noise_tunnel,
baseline_distr,
mode,
)
test_name = gen_test_name(
"test_dp_" + mode.name,
cast(str, test_config["name"]),
algorithm,
noise_tunnel,
)
if test_name in attrs:
raise AssertionError(
"Trying to overwrite existing test with name: %r" %
test_name)
attrs[test_name] = test_method
return super(DataParallelMeta, cls).__new__(cls, name, bases, attrs)
