def test_simple_input_conv_no_grad(self) -> None:
net = BasicModel_ConvNet_One_Conv()
# this way we deactivate require_grad. Some models explicitly
# do that before interpreting the model.
for param in net.parameters():
param.requires_grad = False
inp = torch.arange(16).view(1, 1, 4, 4).float()
self._grad_cam_test_assert(net, net.conv1, inp,
[[11.25, 13.5], [20.25, 22.5]])
def test_improper_stride(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
occ = Occlusion(net)
# Check error when too few stride dimensions
with self.assertRaises(AssertionError):
_ = occ.attribute(inp,
sliding_window_shapes=(1, 2, 2),
strides=(1, 2),
target=0)
# Check error when too many stride dimensions
with self.assertRaises(AssertionError):
_ = occ.attribute(
(inp, inp),
sliding_window_shapes=((1, 1, 2), (1, 2, 2)),
strides=((1, 1, 2), (2, 1, 2, 2)),
target=0,
)
# Check error when too many stride tuples
with self.assertRaises(AssertionError):
_ = occ.attribute(
(inp, inp),
sliding_window_shapes=((1, 1, 2), (1, 2, 2)),
strides=((1, 1, 2), (1, 2, 2), (1, 2, 2)),
target=0,
)
def test_simple_multi_input_conv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones((1, 1, 4, 4))
self._occlusion_test_assert(
net,
(inp, inp2),
(67 * torch.ones_like(inp), 13 * torch.ones_like(inp2)),
perturbations_per_eval=(1, 2, 4, 8, 12, 16),
sliding_window_shapes=((1, 4, 4), (1, 4, 4)),
)
self._occlusion_test_assert(
net,
(inp, inp2),
(
[
[17.0, 17.0, 17.0, 17.0],
[17.0, 17.0, 17.0, 17.0],
[64.0, 65.5, 65.5, 67.0],
[64.0, 65.5, 65.5, 67.0],
],
[
[3.0, 3.0, 3.0, 3.0],
[3.0, 3.0, 3.0, 3.0],
[3.0, 3.0, 3.0, 3.0],
[0.0, 0.0, 0.0, 0.0],
],
),
perturbations_per_eval=(1, 3, 7, 14),
sliding_window_shapes=((1, 2, 3), (1, 1, 2)),
strides=((1, 2, 1), (1, 1, 2)),
)
def test_improper_method_multi_input_conv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones(1)
self._guided_grad_cam_test_assert(
net, net.conv1, (inp, inp2), ([], []), interpolate_mode="made_up_nonlinear"
)
def test_simple_multi_input_conv(self) -> None:
ablation_algo = FeatureAblation(BasicModel_ConvNet_One_Conv())
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones((1, 1, 4, 4))
self._ablation_test_assert(
ablation_algo,
(inp, inp2),
(67 * torch.ones_like(inp), 13 * torch.ones_like(inp2)),
feature_mask=(torch.tensor(0), torch.tensor(1)),
perturbations_per_eval=(1, 2, 4, 8, 12, 16),
)
self._ablation_test_assert(
ablation_algo,
(inp, inp2),
(
[[[
[0.0, 2.0, 4.0, 3.0],
[4.0, 9.0, 10.0, 7.0],
[4.0, 13.0, 14.0, 11.0],
[0.0, 0.0, 0.0, 0.0],
]]],
[[[
[1.0, 2.0, 2.0, 1.0],
[1.0, 2.0, 2.0, 1.0],
[1.0, 2.0, 2.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
]]],
),
perturbations_per_eval=(1, 3, 7, 14),
)
def test_convnet_multi_target_and_default_pert_func(self) -> None:
r"""
Similar to previous example but here we also test default
perturbation function.
"""
model = BasicModel_ConvNet_One_Conv()
gbp = GuidedBackprop(model)
input = torch.stack([torch.arange(1, 17).float()] * 20, dim=0).view(20, 1, 4, 4)
sens1 = self.sensitivity_max_assert(
gbp.attribute,
input,
torch.zeros(20),
perturb_func=default_perturb_func,
target=torch.tensor([1] * 20),
n_perturb_samples=10,
max_examples_per_batch=40,
)
sens2 = self.sensitivity_max_assert(
gbp.attribute,
input,
torch.zeros(20),
perturb_func=default_perturb_func,
target=torch.tensor([1] * 20),
n_perturb_samples=10,
max_examples_per_batch=5,
)
assertTensorAlmostEqual(self, sens1, sens2)
def test_simple_input_conv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = 1.0 * torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
ex = [
[0.0, 0.0, 4.0, 4.0],
[0.0, 0.0, 12.0, 8.0],
[28.0, 84.0, 97.5, 65.0],
[28.0, 56.0, 65.0, 32.5],
]
self._guided_grad_cam_test_assert(net, net.relu1, inp, ex)
def test_simple_input_conv_without_final_relu(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16).view(1, 1, 4, 4).float()
# Adding negative value to test final relu is not applied by default
inp[0, 0, 1, 1] = -4.0
inp.requires_grad_()
self._grad_cam_test_assert(net,
net.conv1,
inp, (0.5625 * inp, ),
attribute_to_layer_input=True)
def test_simple_input_conv_neuron_deconv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = 1.0 * torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
exp = [
[2.0, 3.0, 3.0, 1.0],
[3.0, 5.0, 5.0, 2.0],
[3.0, 5.0, 5.0, 2.0],
[1.0, 2.0, 2.0, 1.0],
]
self._neuron_deconv_test_assert(net, net.fc1, (0, ), (inp, ), (exp, ))
def test_simple_input_conv_gb(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = 1.0 * torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
exp = [
[0.0, 1.0, 1.0, 1.0],
[1.0, 3.0, 3.0, 2.0],
[1.0, 3.0, 3.0, 2.0],
[1.0, 2.0, 2.0, 1.0],
]
self._guided_backprop_test_assert(net, (inp, ), (exp, ))
def test_improper_dims_multi_input_conv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones(1)
ex = [
[14.5, 29.0, 38.0, 19.0],
[29.0, 58.0, 76.0, 38.0],
[65.0, 130.0, 148.0, 74.0],
[32.5, 65.0, 74.0, 37.0],
]
self._guided_grad_cam_test_assert(net, net.conv1, (inp, inp2), (ex, []))
def test_simple_multi_input_conv_deconv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones((1, 1, 4, 4))
ex_attr = [
[2.0, 3.0, 3.0, 1.0],
[3.0, 5.0, 5.0, 2.0],
[3.0, 5.0, 5.0, 2.0],
[1.0, 2.0, 2.0, 1.0],
]
self._deconv_test_assert(net, (inp, inp2), (ex_attr, ex_attr))
def test_simple_multi_input_conv_gb(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones((1, 1, 4, 4))
ex_attr = [[[
[1.0, 2.0, 2.0, 1.0],
[2.0, 4.0, 4.0, 2.0],
[2.0, 4.0, 4.0, 2.0],
[1.0, 2.0, 2.0, 1.0],
]]]
self._guided_backprop_test_assert(net, (inp, inp2), (ex_attr, ex_attr))
def test_simple_input_conv_neuron_gb_agg_neurons(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = 1.0 * torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
exp = [[[
[0.0, 1.0, 1.0, 1.0],
[1.0, 3.0, 3.0, 2.0],
[1.0, 3.0, 3.0, 2.0],
[1.0, 2.0, 2.0, 1.0],
]]]
self._neuron_guided_backprop_test_assert(net, net.fc1,
(slice(0, 1, 1), ), (inp, ),
(exp, ))
def test_simple_multi_input_relu_input_inplace(self) -> None:
net = BasicModel_ConvNet_One_Conv(inplace=True)
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
inp2 = torch.ones((1, 1, 4, 4))
ex = [
[14.5, 29.0, 38.0, 19.0],
[29.0, 58.0, 76.0, 38.0],
[65.0, 130.0, 148.0, 74.0],
[32.5, 65.0, 74.0, 37.0],
]
self._guided_grad_cam_test_assert(
net, net.relu1, (inp, inp2), (ex, ex), attribute_to_layer_input=True
)
def test_simple_input_conv_fc_with_final_relu(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16).view(1, 1, 4, 4).float()
# Adding negative value to test final relu is applied
inp[0, 0, 1, 1] = -4.0
inp.requires_grad_()
exp = 0.5625 * inp
exp[0, 0, 1, 1] = 0.0
self._grad_cam_test_assert(
net,
net.conv1,
inp,
(exp, ),
attribute_to_layer_input=True,
relu_attributions=True,
)
def test_classification_infidelity_convnet_multi_targets(self) -> None:
model = BasicModel_ConvNet_One_Conv()
dl = DeepLift(model)
input = torch.stack([torch.arange(1, 17).float()] * 20,
dim=0).view(20, 1, 4, 4)
self.infidelity_assert(
model,
dl.attribute(input, target=torch.tensor([1] * 20)) / input,
input,
torch.zeros(20),
target=torch.tensor([1] * 20),
multi_input=False,
n_perturb_samples=500,
max_batch_size=120,
)
def test_simple_multi_input_conv_intermediate(self) -> None:
net = BasicModel_ConvNet_One_Conv(inplace=True)
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),
(torch.zeros_like(inp), torch.zeros_like(inp2)),
feature_mask=(torch.tensor(0), torch.tensor(1)),
perturbations_per_eval=(1, 2, 4, 8, 12, 16),
neuron_selector=(1, 0, 0),
)
self._ablation_test_assert(
net,
net.relu1,
(inp, inp2),
(45 * torch.ones_like(inp), 9 * torch.ones_like(inp2)),
feature_mask=(torch.tensor(0), torch.tensor(1)),
perturbations_per_eval=(1, 2, 4, 8, 12, 16),
neuron_selector=(1, 0, 0),
attribute_to_neuron_input=True,
)
self._ablation_test_assert(
net,
net.relu1,
(inp, inp2),
(
[
[0.0, 1.0, 2.0, 0.0],
[4.0, 5.0, 6.0, 0.0],
[8.0, 9.0, 10.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
],
[
[1.0, 1.0, 1.0, 0.0],
[1.0, 1.0, 1.0, 0.0],
[1.0, 1.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
],
),
perturbations_per_eval=(1, 3, 7, 14),
neuron_selector=(1, 0, 0),
attribute_to_neuron_input=True,
)
def test_convnet_multi_target(self) -> None:
r"""
Another test with Saliency, local sensitivity and more
complex model with higher dimensional input.
"""
model = BasicModel_ConvNet_One_Conv()
sa = Saliency(model)
input = torch.stack([torch.arange(1, 17).float()] * 20, dim=0).view(20, 1, 4, 4)
self.sensitivity_max_assert(
sa.attribute,
input,
torch.zeros(20),
target=torch.tensor([1] * 20),
n_perturb_samples=10,
max_examples_per_batch=40,
)
def test_too_large_stride(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16, dtype=torch.float).view(1, 1, 4, 4)
occ = Occlusion(net)
with self.assertRaises(AssertionError):
_ = occ.attribute(inp,
sliding_window_shapes=((1, 1, 2), ),
strides=2,
target=0)
with self.assertRaises(AssertionError):
_ = occ.attribute(
(inp, inp),
sliding_window_shapes=((1, 1, 2), (1, 4, 2)),
strides=(2, (1, 2, 3)),
target=0,
)
with self.assertRaises(AssertionError):
_ = occ.attribute(inp,
sliding_window_shapes=((2, 1, 2), ),
strides=2,
target=0)
def _get_basic_config() -> Tuple[Module, Tensor]:
input = torch.arange(16).view(1, 1, 4, 4).float()
return BasicModel_ConvNet_One_Conv(), input
def test_gb_matching(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = 100.0 * torch.randn(1, 1, 4, 4)
self._guided_backprop_matching_assert(net, net.relu2, inp)
def test_simple_multi_input_conv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16).view(1, 1, 4, 4).float()
inp2 = torch.ones((1, 1, 4, 4))
self._grad_cam_test_assert(net, net.conv1, (inp, inp2),
[[14.5, 19.0], [32.5, 37.0]])
def test_deconv_matching(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = 100.0 * torch.randn(1, 1, 4, 4)
self._deconv_matching_assert(net, net.relu2, inp)
def test_simple_input_conv_relu(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16).view(1, 1, 4, 4).float()
self._grad_cam_test_assert(net, net.relu1, inp,
[[0.0, 4.0], [28.0, 32.5]])
def test_sample_grads_conv_sum(self) -> None:
model = BasicModel_ConvNet_One_Conv()
inp = (123 * torch.randn(6, 1, 4, 4), )
self._compare_sample_grads_per_sample(model, inp,
lambda x: torch.sum(x), "sum")
def test_simple_input_conv(self) -> None:
net = BasicModel_ConvNet_One_Conv()
inp = torch.arange(16).view(1, 1, 4, 4).float()
self._grad_cam_test_assert(net, net.conv1, inp,
[[11.25, 13.5], [20.25, 22.5]])
def test_sample_grads_conv_mean_multi_inp(self) -> None:
model = BasicModel_ConvNet_One_Conv()
inp = (20 * torch.randn(6, 1, 4, 4), 9 * torch.randn(6, 1, 4, 4))
self._compare_sample_grads_per_sample(model, inp,
lambda x: torch.mean(x))