Replace cdist in Patchcore (#1267)

src/anomalib/models/patchcore/torch_model.py

@@ -142,6 +142,28 @@ def subsample_embedding(self, embedding: Tensor, sampling_ratio: float) -> None:
         coreset = sampler.sample_coreset()
         self.memory_bank = coreset
 
+    @staticmethod
+    def euclidean_dist(x: Tensor, y: Tensor) -> Tensor:
+        """
+        Calculates pair-wise distance between row vectors in x and those in y.
+
+        Replaces torch cdist with p=2, as cdist is not properly exported to onnx and openvino format.
+        Resulting matrix is indexed by x vectors in rows and y vectors in columns.
+
+        Args:
+            x: input tensor 1
+            y: input tensor 2
+
+        Returns:
+            Matrix of distances between row vectors in x and y.
+        """
+        x_norm = x.pow(2).sum(dim=-1, keepdim=True)  # |x|
+        y_norm = y.pow(2).sum(dim=-1, keepdim=True)  # |y|
+        # row distance can be rewritten as sqrt(|x| - 2 * x @ y.T + |y|.T)
+        res = x_norm - 2 * torch.matmul(x, y.transpose(-2, -1)) + y_norm.transpose(-2, -1)
+        res = res.clamp_min_(0).sqrt_()
+        return res
+
     def nearest_neighbors(self, embedding: Tensor, n_neighbors: int) -> tuple[Tensor, Tensor]:
         """Nearest Neighbours using brute force method and euclidean norm.
 
@@ -153,7 +175,7 @@ def nearest_neighbors(self, embedding: Tensor, n_neighbors: int) -> tuple[Tensor
             Tensor: Patch scores.
             Tensor: Locations of the nearest neighbor(s).
         """
-        distances = torch.cdist(embedding, self.memory_bank, p=2.0)  # euclidean norm
+        distances = self.euclidean_dist(embedding, self.memory_bank)
         if n_neighbors == 1:
             # when n_neighbors is 1, speed up computation by using min instead of topk
             patch_scores, locations = distances.min(1)
@@ -188,7 +210,7 @@ def compute_anomaly_score(self, patch_scores: Tensor, locations: Tensor, embeddi
         # indices of N_b(m^*) in the paper
         _, support_samples = self.nearest_neighbors(nn_sample, n_neighbors=self.num_neighbors)
         # 4. Find the distance of the patch features to each of the support samples
-        distances = torch.cdist(max_patches_features.unsqueeze(1), self.memory_bank[support_samples], p=2.0)
+        distances = self.euclidean_dist(max_patches_features.unsqueeze(1), self.memory_bank[support_samples])
         # 5. Apply softmax to find the weights
         weights = (1 - F.softmax(distances.squeeze(1), 1))[..., 0]
         # 6. Apply the weight factor to the score