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Minor fixes in tcav, visualization and test_av.py #804

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Minor fixes in tcav, visualization and test_av.py #804

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8 changes: 4 additions & 4 deletions captum/attr/_utils/visualization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -437,7 +437,7 @@ class VisualizationDataRecord:
"true_class",
"attr_class",
"attr_score",
"raw_input",
"raw_input_ids",
"convergence_score",
]

Expand All @@ -449,7 +449,7 @@ def __init__(
true_class,
attr_class,
attr_score,
raw_input,
raw_input_ids,
convergence_score,
) -> None:
self.word_attributions = word_attributions
Expand All @@ -458,7 +458,7 @@ def __init__(
self.true_class = true_class
self.attr_class = attr_class
self.attr_score = attr_score
self.raw_input = raw_input
self.raw_input_ids = raw_input_ids
self.convergence_score = convergence_score


Expand Down Expand Up @@ -541,7 +541,7 @@ def visualize_text(
format_classname(datarecord.attr_class),
format_classname("{0:.2f}".format(datarecord.attr_score)),
format_word_importances(
datarecord.raw_input, datarecord.word_attributions
datarecord.raw_input_ids, datarecord.word_attributions
),
"<tr>",
]
Expand Down
4 changes: 0 additions & 4 deletions captum/concept/_core/tcav.py
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Original file line number Diff line number Diff line change
Expand Up @@ -733,10 +733,6 @@ def _tcav_sub_computation(
sign_count_score = (
torch.sum(tcav_score > 0.0, dim=1).float() / tcav_score.shape[1]
)
# n_experiments x n_concepts
sign_count_score = (
torch.sum(tcav_score > 0.0, dim=1).float() / tcav_score.shape[1]
)

magnitude_score = torch.sum(
torch.abs(tcav_score * (tcav_score > 0.0).float()), dim=1
Expand Down
22 changes: 10 additions & 12 deletions tests/utils/test_av.py
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Original file line number Diff line number Diff line change
Expand Up @@ -389,10 +389,9 @@ def test_generate_activation(self) -> None:
low, high = 0, 16
mymodel = BasicLinearReLULinear(num_features)
mydata = RangeDataset(low, high, num_features)
layers: List[str] = []
for name, _module in mymodel.named_modules():
layers.append(name)
layers: List[str] = list(filter(None, layers))
layers: List[str] = [
value[0] for value in mymodel.named_modules() if value[0]
]

# First AV generation on last 2 layers
inputs = torch.stack((mydata[1], mydata[8], mydata[14]))
Expand Down Expand Up @@ -422,10 +421,9 @@ def test_generate_dataset_activations(self) -> None:
batch_size = high // 2
mymodel = BasicLinearReLULinear(num_features)
mydata = RangeDataset(low, high, num_features)
layers: List[str] = []
for name, _module in mymodel.named_modules():
layers.append(name)
layers: List[str] = list(filter(None, layers))
layers: List[str] = [
value[0] for value in mymodel.named_modules() if value[0]
]

# First AV generation on last 2 layers
AV.generate_dataset_activations(
Expand Down Expand Up @@ -466,10 +464,10 @@ def test_equal_activation(self) -> None:
low, high = 0, 16
mymodel = BasicLinearReLULinear(num_features)
mydata = RangeDataset(low, high, num_features)
layers: List[str] = []
for name, _module in mymodel.named_modules():
layers.append(name)
layers: List[str] = list(filter(None, layers))
layers: List[str] = [
value[0] for value in mymodel.named_modules() if value[0]
]

# First AV generation on last 2 layers
test_input = mydata[1].unsqueeze(0)
act = AV.generate_activation(
Expand Down