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Basic test for LayerFeaturePermutation #1272

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Basic test for LayerFeaturePermutation #1272

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83 changes: 45 additions & 38 deletions tests/attr/layer/test_layer_ablation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,10 +21,10 @@ def test_simple_ablation_with_mask(self) -> None:
net = BasicModel_MultiLayer()
inp = torch.tensor([[20.0, 50.0, 30.0]], requires_grad=True)
self._ablation_test_assert(
net,
net.linear0,
inp,
([280.0, 280.0, 120.0],),
model=net,
layer=net.linear0,
test_input=inp,
expected_ablation=([280.0, 280.0, 120.0],),
layer_mask=torch.tensor([[0, 0, 1]]),
perturbations_per_eval=(1, 2, 3),
attribute_to_layer_input=True,
Expand All @@ -37,20 +37,20 @@ def test_multi_input_ablation(self) -> None:
inp3 = torch.tensor([[0.0, 100.0, 10.0], [2.0, 10.0, 3.0]])
baseline = torch.tensor([[1.0, 2.0, 3.0]])
self._ablation_test_assert(
net,
net.model.linear1,
(inp1, inp2, inp3),
[[168.0, 992.0, 148.0], [84.0, 632.0, 120.0]],
model=net,
layer=net.model.linear1,
test_input=(inp1, inp2, inp3),
expected_ablation=[[168.0, 992.0, 148.0], [84.0, 632.0, 120.0]],
additional_input=(1,),
baselines=baseline,
perturbations_per_eval=(1, 2, 3),
attribute_to_layer_input=True,
)
self._ablation_test_assert(
net,
net.model.linear0,
(inp1, inp2, inp3),
[[168.0, 992.0, 148.0], [84.0, 632.0, 120.0]],
model=net,
layer=net.model.linear0,
test_input=(inp1, inp2, inp3),
expected_ablation=[[168.0, 992.0, 148.0], [84.0, 632.0, 120.0]],
additional_input=(1,),
baselines=baseline,
perturbations_per_eval=(1, 2, 3),
Expand All @@ -65,21 +65,21 @@ def test_multi_input_ablation_with_layer_mask(self) -> None:
baseline = torch.tensor([[1.0, 2.0, 3.0]])
layer_mask = torch.tensor([[0, 1, 0], [0, 1, 2]])
self._ablation_test_assert(
net,
net.model.linear1,
(inp1, inp2, inp3),
[[316.0, 992.0, 316.0], [84.0, 632.0, 120.0]],
model=net,
layer=net.model.linear1,
test_input=(inp1, inp2, inp3),
expected_ablation=[[316.0, 992.0, 316.0], [84.0, 632.0, 120.0]],
additional_input=(1,),
baselines=baseline,
perturbations_per_eval=(1, 2, 3),
layer_mask=layer_mask,
attribute_to_layer_input=True,
)
self._ablation_test_assert(
net,
net.model.linear0,
(inp1, inp2, inp3),
[[316.0, 992.0, 316.0], [84.0, 632.0, 120.0]],
model=net,
layer=net.model.linear0,
test_input=(inp1, inp2, inp3),
expected_ablation=[[316.0, 992.0, 316.0], [84.0, 632.0, 120.0]],
additional_input=(1,),
baselines=baseline,
layer_mask=layer_mask,
Expand All @@ -91,28 +91,32 @@ def test_simple_multi_input_conv_intermediate(self) -> None:
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones((1, 1, 4, 4))
self._ablation_test_assert(
net,
net.relu1,
(inp, inp2),
[[[[4.0, 13.0], [40.0, 49.0]], [[0, 0], [-15.0, -24.0]]]],
model=net,
layer=net.relu1,
test_input=(inp, inp2),
expected_ablation=[[[[4.0, 13.0], [40.0, 49.0]], [[0, 0], [-15.0, -24.0]]]],
perturbations_per_eval=(1, 2, 4, 8, 12, 16),
)
self._ablation_test_assert(
net,
net.relu1,
(inp, inp2),
([[[4.0, 13.0], [40.0, 49.0]], [[0, 0], [-15.0, -24.0]]],),
model=net,
layer=net.relu1,
test_input=(inp, inp2),
expected_ablation=(
[[[4.0, 13.0], [40.0, 49.0]], [[0, 0], [-15.0, -24.0]]],
),
baselines=torch.tensor(
[[[-4.0, -13.0], [-2.0, -2.0]], [[0, 0], [0.0, 0.0]]]
),
perturbations_per_eval=(1, 2, 4, 8, 12, 16),
attribute_to_layer_input=True,
)
self._ablation_test_assert(
net,
net.relu1,
(inp, inp2),
[[[[17.0, 17.0], [67.0, 67.0]], [[0, 0], [-39.0, -39.0]]]],
model=net,
layer=net.relu1,
test_input=(inp, inp2),
expected_ablation=[
[[[17.0, 17.0], [67.0, 67.0]], [[0, 0], [-39.0, -39.0]]]
],
perturbations_per_eval=(1, 2, 4),
layer_mask=torch.tensor([[[[0, 0], [1, 1]], [[2, 2], [3, 3]]]]),
)
Expand All @@ -121,17 +125,20 @@ def test_simple_multi_output_ablation(self) -> None:
net = BasicModel_MultiLayer(multi_input_module=True)
inp = torch.tensor([[0.0, 6.0, 0.0]])
self._ablation_test_assert(
net, net.multi_relu, inp, ([[0.0, 7.0, 7.0, 7.0]], [[0.0, 7.0, 7.0, 7.0]])
model=net,
layer=net.multi_relu,
test_input=inp,
expected_ablation=([[0.0, 7.0, 7.0, 7.0]], [[0.0, 7.0, 7.0, 7.0]]),
)

def test_simple_multi_output_input_ablation(self) -> None:
net = BasicModel_MultiLayer(multi_input_module=True)
inp = torch.tensor([[0.0, 6.0, 0.0]])
self._ablation_test_assert(
net,
net.multi_relu,
inp,
([[0.0, 7.0, 7.0, 7.0]], [[0.0, 7.0, 7.0, 7.0]]),
model=net,
layer=net.multi_relu,
test_input=inp,
expected_ablation=([[0.0, 7.0, 7.0, 7.0]], [[0.0, 7.0, 7.0, 7.0]]),
attribute_to_layer_input=True,
)

Expand All @@ -151,7 +158,7 @@ def _ablation_test_assert(
for batch_size in perturbations_per_eval:
ablation = LayerFeatureAblation(model, layer)
attributions = ablation.attribute(
test_input,
inputs=test_input,
target=target,
layer_mask=layer_mask,
additional_forward_args=additional_input,
Expand Down
33 changes: 33 additions & 0 deletions tests/attr/layer/test_layer_feature_permutation.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,33 @@
# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.

import torch
from captum.attr._core.layer.layer_feature_permutation import LayerFeaturePermutation
from tests.helpers import BaseTest
from tests.helpers.basic import assertTensorAlmostEqual
from tests.helpers.basic_models import BasicModel_MultiLayer
from torch import Tensor


class TestLayerFeaturePermutation(BaseTest):
def test_single_input(self) -> None:
net = BasicModel_MultiLayer()
feature_importance = LayerFeaturePermutation(
forward_func=net,
layer=net.linear0,
)

batch_size = 2
input_size = (3,)
constant_value = 10000

inp = torch.randn((batch_size,) + input_size)
inp[:, 0] = constant_value

attribs = feature_importance.attribute(inputs=inp)

self.assertTrue(isinstance(attribs, Tensor))
self.assertEqual(len(attribs), 4)
self.assertEqual(attribs.squeeze(0).size(), (2 * batch_size,) + input_size)
zeros = torch.zeros(2 * batch_size)
assertTensorAlmostEqual(self, attribs[:, 0], zeros, delta=0, mode="max")
self.assertTrue((attribs[:, 1 : input_size[0]].abs() > 0).all())