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Calculate feature map shape patchcore #148

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merged 9 commits into from
Mar 22, 2022
Merged

Calculate feature map shape patchcore #148

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3323faa
calculate feature map shape patchcore
alexriedel1 Mar 16, 2022
6a44b8a
passed to wrong function
alexriedel1 Mar 16, 2022
c141767
unpack values from torch tensor directly
alexriedel1 Mar 17, 2022
49a6d67
get feature map shape before reshaping
alexriedel1 Mar 17, 2022
16d0b39
updating docstrings in patchcore
alexriedel1 Mar 18, 2022
0292b4f
fix line length
alexriedel1 Mar 18, 2022
fe1fb0f
w, h (too short) -> width, height
alexriedel1 Mar 18, 2022
01b9b6a
typing torch.Size of feature_map_shape
alexriedel1 Mar 18, 2022
0e8a10d
removing trailing whitespaces
alexriedel1 Mar 18, 2022
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18 changes: 11 additions & 7 deletions anomalib/models/patchcore/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,18 +46,18 @@ def __init__(
self.input_size = input_size
self.sigma = sigma

def compute_anomaly_map(self, patch_scores: torch.Tensor) -> torch.Tensor:
def compute_anomaly_map(self, patch_scores: torch.Tensor, feature_map_shape: torch.Tensor) -> torch.Tensor:
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"""Pixel Level Anomaly Heatmap.

Args:
patch_scores (torch.Tensor): Patch-level anomaly scores
Returns:
torch.Tensor: Map of the pixel-level anomaly scores
"""
# TODO: https://github.com/openvinotoolkit/anomalib/issues/40
batch_size = len(patch_scores) // (28 * 28)
w, h = feature_map_shape
batch_size = len(patch_scores) // (w * h)

anomaly_map = patch_scores[:, 0].reshape((batch_size, 1, 28, 28))
anomaly_map = patch_scores[:, 0].reshape((batch_size, 1, w, h))
anomaly_map = F.interpolate(anomaly_map, size=(self.input_size[0], self.input_size[1]))

kernel_size = 2 * int(4.0 * self.sigma + 0.5) + 1
Expand Down Expand Up @@ -99,7 +99,9 @@ def __call__(self, **kwargs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
raise ValueError(f"Expected key `patch_scores`. Found {kwargs.keys()}")

patch_scores = kwargs["patch_scores"]
anomaly_map = self.compute_anomaly_map(patch_scores)
feature_map_shape = kwargs["feature_map_shape"]

anomaly_map = self.compute_anomaly_map(patch_scores, feature_map_shape)
anomaly_score = self.compute_anomaly_score(patch_scores)
return anomaly_map, anomaly_score

Expand Down Expand Up @@ -161,14 +163,16 @@ def forward(self, input_tensor: Tensor) -> Union[torch.Tensor, Tuple[torch.Tenso

if self.apply_tiling:
embedding = self.tiler.untile(embedding)


feature_map_shape = embedding.shape[-2:]
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embedding = self.reshape_embedding(embedding)


if self.training:
output = embedding
else:
patch_scores = self.nearest_neighbors(embedding=embedding, n_neighbors=9)
anomaly_map, anomaly_score = self.anomaly_map_generator(patch_scores=patch_scores)
anomaly_map, anomaly_score = self.anomaly_map_generator(patch_scores=patch_scores, feature_map_shape=feature_map_shape)
output = (anomaly_map, anomaly_score)

return output
Expand Down