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Fix anomaly map computation in CFlow when batch size is 1. #589

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merged 2 commits into from
Sep 27, 2022
Merged

Fix anomaly map computation in CFlow when batch size is 1. #589

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0c28c84
Fix CFlow anomaly map generator
samet-akcay Sep 26, 2022
563a884
Refactor anomaly_map
samet-akcay Sep 26, 2022
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31 changes: 14 additions & 17 deletions anomalib/models/cflow/anomaly_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,7 @@ def __init__(
self.image_size = image_size if isinstance(image_size, tuple) else tuple(image_size)
self.pool_layers: List[str] = pool_layers

def compute_anomaly_map(
self, distribution: Union[List[Tensor], List[List]], height: List[int], width: List[int]
) -> Tensor:
def compute_anomaly_map(self, distribution: List[Tensor], height: List[int], width: List[int]) -> Tensor:
"""Compute the layer map based on likelihood estimation.
Args:
Expand All @@ -38,26 +36,25 @@ def compute_anomaly_map(
Final Anomaly Map
"""

test_map: List[Tensor] = []
layer_maps: List[Tensor] = []
for layer_idx in range(len(self.pool_layers)):
test_norm = torch.tensor(distribution[layer_idx], dtype=torch.double) # pylint: disable=not-callable
test_norm -= torch.max(test_norm) # normalize likelihoods to (-Inf:0] by subtracting a constant
test_prob = torch.exp(test_norm) # convert to probs in range [0:1]
test_mask = test_prob.reshape(-1, height[layer_idx], width[layer_idx])
layer_distribution = distribution[layer_idx].clone().detach()
# Normalize the likelihoods to (-Inf:0] and convert to probs in range [0:1]
layer_probabilities = torch.exp(layer_distribution - layer_distribution.max())
layer_map = layer_probabilities.reshape(-1, height[layer_idx], width[layer_idx])
# upsample
test_map.append(
layer_maps.append(
F.interpolate(
test_mask.unsqueeze(1), size=self.image_size, mode="bilinear", align_corners=True
).squeeze()
layer_map.unsqueeze(1), size=self.image_size, mode="bilinear", align_corners=True
).squeeze(1)
)
# score aggregation
score_map = torch.zeros_like(test_map[0])
score_map = torch.zeros_like(layer_maps[0])
for layer_idx in range(len(self.pool_layers)):
score_map += test_map[layer_idx]
score_mask = score_map
# invert probs to anomaly scores
anomaly_map = score_mask.max() - score_mask
score_map += layer_maps[layer_idx]

# Invert probs to anomaly scores
anomaly_map = score_map.max() - score_map

return anomaly_map

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