def _internal_influence_test_assert(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_activation: Union[
float,
List[List[float]],
Tuple[List[float], ...],
Tuple[List[List[float]], ...],
],
baseline: BaselineType = None,
additional_args: Any = None,
attribute_to_layer_input: bool = False,
):
for internal_batch_size in [None, 5, 20]:
int_inf = InternalInfluence(model, target_layer)
self.assertFalse(int_inf.multiplies_by_inputs)
attributions = int_inf.attribute(
test_input,
baselines=baseline,
target=0,
n_steps=500,
method="riemann_trapezoid",
additional_forward_args=additional_args,
internal_batch_size=internal_batch_size,
attribute_to_layer_input=attribute_to_layer_input,
)
assertTensorTuplesAlmostEqual(
self, attributions, expected_activation, delta=0.01, mode="max"
)
def _conductance_test_assert(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_conductance: Union[List[List[float]], Tuple[List[List[float]], ...]],
baselines: BaselineType = None,
additional_args: Any = None,
) -> None:
cond = LayerConductance(model, target_layer)
self.assertTrue(cond.multiplies_by_inputs)
for internal_batch_size in (None, 4, 20):
attributions, delta = cond.attribute(
test_input,
baselines=baselines,
target=0,
n_steps=500,
method="gausslegendre",
additional_forward_args=additional_args,
internal_batch_size=internal_batch_size,
return_convergence_delta=True,
)
delta_condition = all(abs(delta.numpy().flatten()) < 0.01)
self.assertTrue(
delta_condition,
"Sum of attributions does {}"
" not match the difference of endpoints.".format(delta),
)
assertTensorTuplesAlmostEqual(
self, attributions, expected_conductance, delta=0.1
)
def test_multiple_tensors_compare_with_exp_wo_mult_by_inputs(self) -> None:
net = BasicModel_MultiLayer(multi_input_module=True)
inp = torch.tensor([[0.0, 100.0, 0.0]])
base = torch.tensor([[0.0, 0.0, 0.0]])
target_layer = net.multi_relu
layer_ig = LayerIntegratedGradients(net, target_layer)
layer_ig_wo_mult_by_inputs = LayerIntegratedGradients(
net, target_layer, multiply_by_inputs=False
)
layer_act = LayerActivation(net, target_layer)
attributions = layer_ig.attribute(inp, target=0)
attributions_wo_mult_by_inputs = layer_ig_wo_mult_by_inputs.attribute(
inp, target=0
)
inp_minus_baseline_activ = tuple(
inp_act - base_act
for inp_act, base_act in zip(
layer_act.attribute(inp), layer_act.attribute(base)
)
)
assertTensorTuplesAlmostEqual(
self,
tuple(
attr_wo_mult * inp_min_base
for attr_wo_mult, inp_min_base in zip(
attributions_wo_mult_by_inputs, inp_minus_baseline_activ
)
),
attributions,
)
def _ig_input_test_assert(
self,
model: Module,
target_layer: Module,
test_input: TensorOrTupleOfTensorsGeneric,
test_neuron: Union[int, Tuple[Union[int, slice], ...], Callable],
expected_input_ig: Union[List[float], Tuple[List[List[float]], ...]],
additional_input: Any = None,
multiply_by_inputs: bool = True,
) -> None:
for internal_batch_size in [None, 5, 20]:
grad = NeuronIntegratedGradients(
model, target_layer, multiply_by_inputs=multiply_by_inputs)
self.assertEquals(grad.multiplies_by_inputs, multiply_by_inputs)
attributions = grad.attribute(
test_input,
test_neuron,
n_steps=200,
method="gausslegendre",
additional_forward_args=additional_input,
internal_batch_size=internal_batch_size,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_input_ig,
delta=0.1)
def test_neuron_index_deprecated_warning(self) -> None:
net = BasicModel_MultiLayer()
grad = NeuronGradient(net, net.linear2)
inp = torch.tensor([[0.0, 100.0, 0.0]], requires_grad=True)
with self.assertWarns(DeprecationWarning):
attributions = grad.attribute(
inp,
neuron_index=(0, ),
)
assertTensorTuplesAlmostEqual(self, attributions, [4.0, 4.0, 4.0])
def test_saliency_test_basic_multivar_sg_n_samples_batch_size_3(self) -> None:
attributions_batch_size = self._saliency_base_assert(
*_get_multiargs_basic_config_large(),
nt_type="smoothgrad_sq",
n_samples_batch_size=3,
)
attributions = self._saliency_base_assert(
*_get_multiargs_basic_config_large(),
nt_type="smoothgrad_sq",
)
assertTensorTuplesAlmostEqual(self, attributions_batch_size, attributions)
def _kernel_shap_test_assert(
self,
model: Callable,
test_input: TensorOrTupleOfTensorsGeneric,
expected_attr,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
additional_input: Any = None,
perturbations_per_eval: Tuple[int, ...] = (1, ),
baselines: BaselineType = None,
target: Union[None, int] = 0,
n_samples: int = 100,
delta: float = 1.0,
expected_coefs: Union[None, List[float], List[List[float]]] = None,
show_progress: bool = False,
) -> None:
for batch_size in perturbations_per_eval:
kernel_shap = KernelShap(model)
attributions = kernel_shap.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
show_progress=show_progress,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_attr,
delta=delta,
mode="max")
if expected_coefs is not None:
# Test with return_input_shape = False
attributions = kernel_shap.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
return_input_shape=False,
show_progress=show_progress,
)
assertTensorAlmostEqual(self,
attributions,
expected_coefs,
delta=delta,
mode="max")
def _assert_compare_with_expected(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_ig: Tuple[List[List[float]], ...],
additional_input: Any = None,
):
layer_ig = LayerIntegratedGradients(model, target_layer)
attributions = layer_ig.attribute(
test_input, target=0, additional_forward_args=additional_input
)
assertTensorTuplesAlmostEqual(self, attributions, expected_ig, delta=0.01)
def _shapley_test_assert(
self,
model: Callable,
test_input: TensorOrTupleOfTensorsGeneric,
expected_attr,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
additional_input: Any = None,
perturbations_per_eval: Tuple[int, ...] = (1, ),
baselines: BaselineType = None,
target: Union[None, int] = 0,
n_samples: int = 100,
delta: float = 1.0,
test_true_shapley: bool = True,
show_progress: bool = False,
) -> None:
for batch_size in perturbations_per_eval:
shapley_samp = ShapleyValueSampling(model)
attributions = shapley_samp.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_samples=n_samples,
show_progress=show_progress,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_attr,
delta=delta,
mode="max")
if test_true_shapley:
shapley_val = ShapleyValues(model)
attributions = shapley_val.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
show_progress=show_progress,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_attr,
mode="max",
delta=0.001)
def test_multiple_layers_multiple_inputs_shared_input(self) -> None:
input1 = torch.randn(5, 3)
input2 = torch.randn(5, 3)
input3 = torch.randn(5, 3)
inputs = (input1, input2, input3)
baseline = tuple(torch.zeros_like(inp) for inp in inputs)
net = BasicModel_MultiLayer_TrueMultiInput()
lig = LayerIntegratedGradients(net, layer=[net.m1, net.m234])
ig = IntegratedGradients(net)
# test layer inputs
attribs_inputs = lig.attribute(inputs,
baseline,
target=0,
attribute_to_layer_input=True)
attribs_inputs_regular_ig = ig.attribute(inputs, baseline, target=0)
self.assertIsInstance(attribs_inputs, list)
self.assertEqual(len(attribs_inputs), 2)
self.assertIsInstance(attribs_inputs[0], Tensor)
self.assertIsInstance(attribs_inputs[1], tuple)
self.assertEqual(len(attribs_inputs[1]), 3)
assertTensorTuplesAlmostEqual(
self,
# last input for second layer is first input =>
# add the attributions
(
attribs_inputs[0] + attribs_inputs[1][-1], ) +
attribs_inputs[1][0:-1],
attribs_inputs_regular_ig,
delta=1e-5,
)
# test layer outputs
attribs = lig.attribute(inputs, baseline, target=0)
ig = IntegratedGradients(lambda x, y: x + y)
attribs_ig = ig.attribute(
(net.m1(input1), net.m234(input2, input3, input1, 1)),
(net.m1(baseline[0]),
net.m234(baseline[1], baseline[2], baseline[1], 1)),
target=0,
)
assertTensorTuplesAlmostEqual(self, attribs, attribs_ig, delta=1e-5)
def test_relu_layer_deeplift_multiple_output(self) -> None:
model = BasicModel_MultiLayer(multi_input_module=True)
inputs, baselines = _create_inps_and_base_for_deeplift_neuron_layer_testing(
)
layer_dl = LayerDeepLift(model, model.multi_relu)
attributions, delta = layer_dl.attribute(
inputs[0],
baselines[0],
target=0,
attribute_to_layer_input=False,
return_convergence_delta=True,
)
assertTensorTuplesAlmostEqual(
self, attributions,
([[0.0, -1.0, -1.0, -1.0]], [[0.0, -1.0, -1.0, -1.0]]))
assert_delta(self, delta)
def test_multiple_layers_multiple_input_outputs(self) -> None:
# test with multiple layers, where one layer accepts multiple inputs
input1 = torch.randn(5, 3)
input2 = torch.randn(5, 3)
input3 = torch.randn(5, 3)
input4 = torch.randn(5, 3)
inputs = (input1, input2, input3, input4)
baseline = tuple(torch.zeros_like(inp) for inp in inputs)
net = BasicModel_MultiLayer_TrueMultiInput()
lig = LayerIntegratedGradients(net, layer=[net.m1, net.m234])
ig = IntegratedGradients(net)
# test layer inputs
attribs_inputs = lig.attribute(inputs,
baseline,
target=0,
attribute_to_layer_input=True)
attribs_inputs_regular_ig = ig.attribute(inputs, baseline, target=0)
self.assertIsInstance(attribs_inputs, list)
self.assertEqual(len(attribs_inputs), 2)
self.assertIsInstance(attribs_inputs[0], Tensor)
self.assertIsInstance(attribs_inputs[1], tuple)
self.assertEqual(len(attribs_inputs[1]), 3)
assertTensorTuplesAlmostEqual(
self,
(attribs_inputs[0], ) + attribs_inputs[1],
attribs_inputs_regular_ig,
delta=1e-7,
)
# test layer outputs
attribs = lig.attribute(inputs, baseline, target=0)
ig = IntegratedGradients(lambda x, y: x + y)
attribs_ig = ig.attribute(
(net.m1(input1), net.m234(input2, input3, input4, 1)),
(net.m1(baseline[0]),
net.m234(baseline[1], baseline[2], baseline[3], 1)),
target=0,
)
assertTensorTuplesAlmostEqual(self, attribs, attribs_ig, delta=1e-7)
def layer_method_with_input_layer_patches(
self,
layer_method_class: Callable,
equiv_method_class: Callable,
multi_layer: bool,
) -> None:
model = BasicModel_MultiLayer_TrueMultiInput(
) if multi_layer else BasicModel()
input_names = ["x1", "x2", "x3", "x4"] if multi_layer else ["input"]
model = ModelInputWrapper(model)
layers = [model.input_maps[inp] for inp in input_names]
layer_method = layer_method_class(
model, layer=layers if multi_layer else layers[0])
equivalent_method = equiv_method_class(model)
inputs = tuple(torch.rand(5, 3) for _ in input_names)
baseline = tuple(torch.zeros(5, 3) for _ in input_names)
args = inspect.getfullargspec(
equivalent_method.attribute.__wrapped__).args
args_to_use = [inputs]
if "baselines" in args:
args_to_use += [baseline]
a1 = layer_method.attribute(*args_to_use, target=0)
a2 = layer_method.attribute(*args_to_use,
target=0,
attribute_to_layer_input=True)
real_attributions = equivalent_method.attribute(*args_to_use, target=0)
if not isinstance(a1, tuple):
a1 = (a1, )
a2 = (a2, )
if not isinstance(real_attributions, tuple):
real_attributions = (real_attributions, )
assertTensorTuplesAlmostEqual(self, a1, a2)
assertTensorTuplesAlmostEqual(self, a1, real_attributions)
def _layer_activation_test_assert(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_activation: Union[List[float], Tuple[List[float], ...]],
additional_input: Any = None,
attribute_to_layer_input: bool = False,
):
layer_act = LayerActivation(model, target_layer)
self.assertTrue(layer_act.multiplies_by_inputs)
attributions = layer_act.attribute(
test_input,
additional_forward_args=additional_input,
attribute_to_layer_input=attribute_to_layer_input,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_activation,
delta=0.01)
def _gradient_input_test_assert(
self,
model: Module,
target_layer: Module,
test_input: TensorOrTupleOfTensorsGeneric,
test_neuron_selector: Union[int, Tuple[Union[int, slice], ...],
Callable],
expected_input_gradient: Union[List[float], Tuple[List[float], ...]],
additional_input: Any = None,
attribute_to_neuron_input: bool = False,
) -> None:
grad = NeuronGradient(model, target_layer)
attributions = grad.attribute(
test_input,
test_neuron_selector,
additional_forward_args=additional_input,
attribute_to_neuron_input=attribute_to_neuron_input,
)
assertTensorTuplesAlmostEqual(self, attributions,
expected_input_gradient)
def _layer_activation_test_assert(
self,
model: Module,
target_layer: ModuleOrModuleList,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_activation: Union[List, Tuple[List[float], ...]],
additional_input: Any = None,
) -> None:
layer_act = LayerGradientXActivation(model, target_layer)
self.assertTrue(layer_act.multiplies_by_inputs)
attributions = layer_act.attribute(
test_input, target=0, additional_forward_args=additional_input)
if isinstance(target_layer, Module):
assertTensorTuplesAlmostEqual(self,
attributions,
expected_activation,
delta=0.01)
else:
for i in range(len(target_layer)):
assertTensorTuplesAlmostEqual(self,
attributions[i],
expected_activation[i],
delta=0.01)
# test Layer Gradient without multiplying with activations
layer_grads = LayerGradientXActivation(model,
target_layer,
multiply_by_inputs=False)
layer_act = LayerActivation(model, target_layer)
self.assertFalse(layer_grads.multiplies_by_inputs)
grads = layer_grads.attribute(test_input,
target=0,
additional_forward_args=additional_input)
acts = layer_act.attribute(test_input,
additional_forward_args=additional_input)
if isinstance(target_layer, Module):
assertTensorTuplesAlmostEqual(
self,
attributions,
tuple(act * grad for act, grad in zip(acts, grads)),
delta=0.01,
)
else:
for i in range(len(target_layer)):
assertTensorTuplesAlmostEqual(
self,
attributions[i],
tuple(act * grad for act, grad in zip(acts[i], grads[i])),
delta=0.01,
)
def _assert_attributions(
self,
model: Module,
layer: Module,
inputs: TensorOrTupleOfTensorsGeneric,
baselines: Union[TensorOrTupleOfTensorsGeneric, Callable],
target: TargetType,
expected: Union[Tensor, Tuple[Tensor, ...], List[float],
List[List[float]], Tuple[List[float], ...],
Tuple[List[List[float]], ...], ],
expected_delta: Tensor = None,
n_samples: int = 5,
attribute_to_layer_input: bool = False,
add_args: Any = None,
) -> None:
lgs = LayerGradientShap(model, layer)
attrs, delta = lgs.attribute(
inputs,
baselines,
target=target,
additional_forward_args=add_args,
n_samples=n_samples,
stdevs=0.0009,
return_convergence_delta=True,
attribute_to_layer_input=attribute_to_layer_input,
)
assertTensorTuplesAlmostEqual(self, attrs, expected, delta=0.005)
if expected_delta is None:
_assert_attribution_delta(self, inputs, attrs, n_samples, delta,
True)
else:
for delta_i, expected_delta_i in zip(delta, expected_delta):
assertTensorAlmostEqual(self,
delta_i,
expected_delta_i,
delta=0.01)
def _grad_cam_test_assert(
self,
model: Module,
target_layer: Module,
test_input: Union[Tensor, Tuple[Tensor, ...]],
expected_activation: Union[List[float], Tuple[List[float], ...],
List[List[float]], Tuple[Tensor, ...]],
additional_input: Any = None,
attribute_to_layer_input: bool = False,
relu_attributions: bool = False,
):
layer_gc = LayerGradCam(model, target_layer)
self.assertFalse(layer_gc.multiplies_by_inputs)
attributions = layer_gc.attribute(
test_input,
target=0,
additional_forward_args=additional_input,
attribute_to_layer_input=attribute_to_layer_input,
relu_attributions=relu_attributions,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_activation,
delta=0.01)
def _lime_test_assert(
self,
model: Callable,
test_input: TensorOrTupleOfTensorsGeneric,
expected_attr,
expected_coefs_only=None,
feature_mask: Union[None, TensorOrTupleOfTensorsGeneric] = None,
additional_input: Any = None,
perturbations_per_eval: Tuple[int, ...] = (1, ),
baselines: BaselineType = None,
target: Union[None, int] = 0,
n_perturb_samples: int = 100,
alpha: float = 1.0,
delta: float = 1.0,
batch_attr: bool = False,
) -> None:
for batch_size in perturbations_per_eval:
lime = Lime(
model,
similarity_func=get_exp_kernel_similarity_function(
"cosine", 10.0),
)
attributions = lime.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_perturb_samples=n_perturb_samples,
alpha=alpha,
)
assertTensorTuplesAlmostEqual(self,
attributions,
expected_attr,
delta=delta,
mode="max")
if expected_coefs_only is not None:
# Test with return_input_shape = False
attributions = lime.attribute(
test_input,
target=target,
feature_mask=feature_mask,
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_perturb_samples=n_perturb_samples,
alpha=alpha,
return_input_shape=False,
)
assertTensorAlmostEqual(self,
attributions,
expected_coefs_only,
delta=delta,
mode="max")
lime_alt = LimeBase(
model,
lasso_interpretable_model_trainer,
get_exp_kernel_similarity_function("euclidean", 1000.0),
alt_perturb_func,
False,
None,
alt_to_interp_rep,
)
# Test with equivalent sampling in original input space
formatted_inputs, baselines = _format_input_baseline(
test_input, baselines)
if feature_mask is None:
(
formatted_feature_mask,
num_interp_features,
) = _construct_default_feature_mask(formatted_inputs)
else:
formatted_feature_mask = _format_input(feature_mask)
num_interp_features = int(
max(
torch.max(single_inp).item()
for single_inp in feature_mask) + 1)
if batch_attr:
attributions = lime_alt.attribute(
test_input,
target=target,
feature_mask=formatted_feature_mask if isinstance(
test_input, tuple) else formatted_feature_mask[0],
additional_forward_args=additional_input,
baselines=baselines,
perturbations_per_eval=batch_size,
n_perturb_samples=n_perturb_samples,
alpha=alpha,
num_interp_features=num_interp_features,
)
assertTensorAlmostEqual(self,
attributions,
expected_coefs_only,
delta=delta,
mode="max")
return
bsz = formatted_inputs[0].shape[0]
for (
curr_inps,
curr_target,
curr_additional_args,
curr_baselines,
curr_feature_mask,
expected_coef_single,
) in _batch_example_iterator(
bsz,
test_input,
target,
additional_input,
baselines
if isinstance(test_input, tuple) else baselines[0],
formatted_feature_mask if isinstance(
test_input, tuple) else formatted_feature_mask[0],
expected_coefs_only,
):
attributions = lime_alt.attribute(
curr_inps,
target=curr_target,
feature_mask=curr_feature_mask,
additional_forward_args=curr_additional_args,
baselines=curr_baselines,
perturbations_per_eval=batch_size,
n_perturb_samples=n_perturb_samples,
alpha=alpha,
num_interp_features=num_interp_features,
)
assertTensorAlmostEqual(
self,
attributions,
expected_coef_single,
delta=delta,
mode="max",
)
def jit_test_assert(self) -> None:
model_1 = model
attr_args = args
if (mode is JITCompareMode.data_parallel_jit_trace
or JITCompareMode.data_parallel_jit_script):
if not torch.cuda.is_available() or torch.cuda.device_count(
) == 0:
raise unittest.SkipTest(
"Skipping GPU test since CUDA not available.")
# Construct cuda_args, moving all tensor inputs in args to CUDA device
cuda_args = {}
for key in args:
if isinstance(args[key], Tensor):
cuda_args[key] = args[key].cuda()
elif isinstance(args[key], tuple):
cuda_args[key] = tuple(
elem.cuda() if isinstance(elem, Tensor) else elem
for elem in args[key])
else:
cuda_args[key] = args[key]
attr_args = cuda_args
model_1 = model_1.cuda()
# Initialize models based on JITCompareMode
if (mode is JITCompareMode.cpu_jit_script
or JITCompareMode.data_parallel_jit_script):
model_2 = torch.jit.script(model_1) # type: ignore
elif (mode is JITCompareMode.cpu_jit_trace
or JITCompareMode.data_parallel_jit_trace):
all_inps = _format_input(args["inputs"]) + (
_format_additional_forward_args(
args["additional_forward_args"])
if "additional_forward_args" in args and
args["additional_forward_args"] is not None else tuple())
model_2 = torch.jit.trace(model_1, all_inps) # type: ignore
else:
raise AssertionError("JIT compare mode type is not valid.")
attr_method_1 = algorithm(model_1)
attr_method_2 = algorithm(model_2)
if noise_tunnel:
attr_method_1 = NoiseTunnel(attr_method_1)
attr_method_2 = NoiseTunnel(attr_method_2)
if attr_method_1.has_convergence_delta():
attributions_1, delta_1 = attr_method_1.attribute(
return_convergence_delta=True, **attr_args)
self.setUp()
attributions_2, delta_2 = attr_method_2.attribute(
return_convergence_delta=True, **attr_args)
assertTensorTuplesAlmostEqual(self,
attributions_1,
attributions_2,
mode="max")
assertTensorTuplesAlmostEqual(self,
delta_1,
delta_2,
mode="max")
else:
attributions_1 = attr_method_1.attribute(**attr_args)
self.setUp()
attributions_2 = attr_method_2.attribute(**attr_args)
assertTensorTuplesAlmostEqual(self,
attributions_1,
attributions_2,
mode="max")
def target_test_assert(self) -> None:
attr_method: Attribution
if target_layer:
internal_algorithm = cast(Type[InternalAttribution], algorithm)
attr_method = internal_algorithm(model, target_layer)
else:
attr_method = algorithm(model)
if noise_tunnel:
attr_method = NoiseTunnel(attr_method)
attributions_orig = attr_method.attribute(**args)
self.setUp()
for i in range(num_examples):
args["target"] = (original_targets[i] if len(original_targets)
== num_examples else original_targets)
args["inputs"] = (original_inputs[i:i + 1] if isinstance(
original_inputs, Tensor) else tuple(
original_inp[i:i + 1]
for original_inp in original_inputs))
if original_additional_forward_args is not None:
args["additional_forward_args"] = tuple(
single_add_arg[i:i + 1] if isinstance(
single_add_arg, Tensor) else single_add_arg
for single_add_arg in original_additional_forward_args)
if replace_baselines:
if isinstance(original_inputs, Tensor):
args["baselines"] = original_baselines[i:i + 1]
elif isinstance(original_baselines, tuple):
args["baselines"] = tuple(
single_baseline[i:i + 1] if isinstance(
single_baseline, Tensor) else single_baseline
for single_baseline in original_baselines)
# Since Lime methods compute attributions for a batch
# sequentially, random seed should not be reset after
# each example after the first.
if not issubclass(algorithm, Lime):
self.setUp()
single_attr = attr_method.attribute(**args)
current_orig_attributions = (
attributions_orig[i:i + 1] if isinstance(
attributions_orig, Tensor) else tuple(
single_attrib[i:i + 1]
for single_attrib in attributions_orig))
assertTensorTuplesAlmostEqual(
self,
current_orig_attributions,
single_attr,
delta=target_delta,
mode="max",
)
if (not issubclass(algorithm, Lime)
and len(original_targets) == num_examples):
# If original_targets contained multiple elements, then
# we also compare with setting targets to a list with
# a single element.
args["target"] = original_targets[i:i + 1]
self.setUp()
single_attr_target_list = attr_method.attribute(**args)
assertTensorTuplesAlmostEqual(
self,
current_orig_attributions,
single_attr_target_list,
delta=target_delta,
mode="max",
)
def test_saliency_grad_unchanged(self) -> None:
model, inp, grads, add_args = _get_basic_config()
inp.grad = torch.randn_like(inp)
grad = inp.grad.detach().clone()
self._saliency_base_assert(model, inp, grads, add_args)
assertTensorTuplesAlmostEqual(self, inp.grad, grad, delta=0.0)
def data_parallel_test_assert(self) -> None:
# Construct cuda_args, moving all tensor inputs in args to CUDA device
cuda_args = {}
for key in args:
if isinstance(args[key], Tensor):
cuda_args[key] = args[key].cuda()
elif isinstance(args[key], tuple):
cuda_args[key] = tuple(
elem.cuda() if isinstance(elem, Tensor) else elem
for elem in args[key])
else:
cuda_args[key] = args[key]
alt_device_ids = None
cuda_model = copy.deepcopy(model).cuda()
# Initialize models based on DataParallelCompareMode
if mode is DataParallelCompareMode.cpu_cuda:
model_1, model_2 = model, cuda_model
args_1, args_2 = args, cuda_args
elif mode is DataParallelCompareMode.data_parallel_default:
model_1, model_2 = (
cuda_model,
torch.nn.parallel.DataParallel(cuda_model),
)
args_1, args_2 = cuda_args, cuda_args
elif mode is DataParallelCompareMode.data_parallel_alt_dev_ids:
alt_device_ids = [0] + [
x for x in range(torch.cuda.device_count() - 1, 0, -1)
]
model_1, model_2 = (
cuda_model,
torch.nn.parallel.DataParallel(cuda_model,
device_ids=alt_device_ids),
)
args_1, args_2 = cuda_args, cuda_args
elif mode is DataParallelCompareMode.dist_data_parallel:
model_1, model_2 = (
cuda_model,
torch.nn.parallel.DistributedDataParallel(cuda_model,
device_ids=[0],
output_device=0),
)
args_1, args_2 = cuda_args, cuda_args
else:
raise AssertionError(
"DataParallel compare mode type is not valid.")
attr_method_1: Attribution
attr_method_2: Attribution
if target_layer:
internal_algorithm = cast(Type[InternalAttribution], algorithm)
attr_method_1 = internal_algorithm(
model_1, get_target_layer(model_1, target_layer))
# cuda_model is used to obtain target_layer since DataParallel
# adds additional wrapper.
# model_2 is always either the CUDA model itself or DataParallel
if alt_device_ids is None:
attr_method_2 = internal_algorithm(
model_2, get_target_layer(cuda_model, target_layer))
else:
# LayerDeepLift and LayerDeepLiftShap do not take device ids
# as a parameter, since they must always have the DataParallel
# model object directly.
# Some neuron methods and GuidedGradCAM also require the
# model and cannot take a forward function.
if issubclass(
internal_algorithm,
(
LayerDeepLift,
LayerDeepLiftShap,
NeuronDeepLift,
NeuronDeepLiftShap,
NeuronDeconvolution,
NeuronGuidedBackprop,
GuidedGradCam,
),
):
attr_method_2 = internal_algorithm(
model_2, get_target_layer(cuda_model,
target_layer))
else:
attr_method_2 = internal_algorithm(
model_2.forward,
get_target_layer(cuda_model, target_layer),
device_ids=alt_device_ids,
)
else:
attr_method_1 = algorithm(model_1)
attr_method_2 = algorithm(model_2)
if noise_tunnel:
attr_method_1 = NoiseTunnel(attr_method_1)
attr_method_2 = NoiseTunnel(attr_method_2)
if attr_method_1.has_convergence_delta():
attributions_1, delta_1 = attr_method_1.attribute(
return_convergence_delta=True, **args_1)
self.setUp()
attributions_2, delta_2 = attr_method_2.attribute(
return_convergence_delta=True, **args_2)
if isinstance(attributions_1, list):
for i in range(len(attributions_1)):
assertTensorTuplesAlmostEqual(
self,
attributions_1[i],
attributions_2[i],
mode="max",
delta=dp_delta,
)
else:
assertTensorTuplesAlmostEqual(self,
attributions_1,
attributions_2,
mode="max",
delta=dp_delta)
assertTensorTuplesAlmostEqual(self,
delta_1,
delta_2,
mode="max",
delta=dp_delta)
else:
attributions_1 = attr_method_1.attribute(**args_1)
self.setUp()
attributions_2 = attr_method_2.attribute(**args_2)
if isinstance(attributions_1, list):
for i in range(len(attributions_1)):
assertTensorTuplesAlmostEqual(
self,
attributions_1[i],
attributions_2[i],
mode="max",
delta=dp_delta,
)
else:
assertTensorTuplesAlmostEqual(self,
attributions_1,
attributions_2,
mode="max",
delta=dp_delta)
