def test_one_feature(self):
batch_size = 2
classes = _get_classes()
dataset = list(
_labelled_img_data(num_labels=len(classes),
num_samples=batch_size))
# NOTE: using DataLoader to batch the inputs
# since AttributionVisualizer requires the input to be of size `B x ...`
data_loader = torch.utils.data.DataLoader(list(dataset),
batch_size=batch_size,
shuffle=False,
num_workers=0)
visualizer = AttributionVisualizer(
models=[_get_cnn()],
classes=classes,
features=[
ImageFeature(
"Photo",
input_transforms=[lambda x: x],
baseline_transforms=[lambda x: x * 0],
)
],
dataset=to_iter(data_loader),
score_func=None,
)
visualizer._config = FilterConfig(attribution_arguments={"n_steps": 2})
outputs = visualizer.visualize()
for output in outputs:
total_contrib = sum(
abs(f.contribution) for f in output.feature_outputs)
self.assertAlmostEqual(total_contrib, 1.0, places=6)
def test_multi_features(self):
batch_size = 2
classes = _get_classes()
img_dataset = list(
_labelled_img_data(num_labels=len(classes),
num_samples=batch_size))
misc_feature_size = 2
dataset = _multi_modal_data(img_dataset=img_dataset,
feature_size=misc_feature_size)
# NOTE: using DataLoader to batch the inputs since
# AttributionVisualizer requires the input to be of size `N x ...`
data_loader = torch.utils.data.DataLoader(list(dataset),
batch_size=batch_size,
shuffle=False,
num_workers=0)
visualizer = AttributionVisualizer(
models=[_get_multimodal(input_size=misc_feature_size)],
classes=classes,
features=[
ImageFeature(
"Photo",
input_transforms=[lambda x: x],
baseline_transforms=[lambda x: x * 0],
),
RealFeature(
"Random",
input_transforms=[lambda x: x],
baseline_transforms=[lambda x: x * 0],
),
],
dataset=to_iter(data_loader),
score_func=None,
)
visualizer._config = FilterConfig(steps=2)
outputs = visualizer.visualize()
for output in outputs:
total_contrib = sum(
abs(f.contribution) for f in output.feature_outputs)
self.assertAlmostEqual(total_contrib, 1.0, places=6)