[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

quaterion/loss/fast_ap_loss.py

@@ -38,11 +38,7 @@ def get_config_dict(self) -> Dict[str, Any]:
             Dict[str, Any]: JSON-serializable dict of params
         """
         config = super().get_config_dict()
-        config.update(
-            {
-                "num_bins": self.num_bins
-            }
-        )
+        config.update({"num_bins": self.num_bins})
 
         return config
 
@@ -68,26 +64,40 @@ def forward(
         device = embeddings.device  # get the device of the embeddings tensor
 
         # 1. get positive and negative masks
-        pos_mask = get_anchor_positive_mask(groups).to(device)  # (batch_size, batch_size)
-        neg_mask = get_anchor_negative_mask(groups).to(device)  # (batch_size, batch_size)
+        pos_mask = get_anchor_positive_mask(groups).to(
+            device
+        )  # (batch_size, batch_size)
+        neg_mask = get_anchor_negative_mask(groups).to(
+            device
+        )  # (batch_size, batch_size)
         n_pos = torch.sum(pos_mask, dim=1)  # Sum over all columns (for each row)
 
         # 2. compute distances from embeddings squared Euclidean distance matrix
-        embeddings = F.normalize(embeddings, p=2, dim=1).to(device)  # normalize embeddings
+        embeddings = F.normalize(embeddings, p=2, dim=1).to(
+            device
+        )  # normalize embeddings
         dist_matrix = (
             self.distance_metric.distance_matrix(embeddings).to(device) ** 2
         )  # (batch_size, batch_size)
 
         # 3. estimate discrete histograms
         histogram_delta = torch.tensor(4.0 / self.num_bins, device=device)
-        mid_points = torch.linspace(0.0, 4.0, steps=self.num_bins + 1, device=device).view(-1, 1, 1)
+        mid_points = torch.linspace(
+            0.0, 4.0, steps=self.num_bins + 1, device=device
+        ).view(-1, 1, 1)
 
         pulse = F.relu(
             input=1 - torch.abs(dist_matrix - mid_points) / histogram_delta
-        ).to(device)  # max(0, input)
-
-        pos_hist = torch.t(torch.sum(pulse * pos_mask, dim=2)).to(device)  # positive histograms
-        neg_hist = torch.t(torch.sum(pulse * neg_mask, dim=2)).to(device)  # negative histograms
+        ).to(
+            device
+        )  # max(0, input)
+
+        pos_hist = torch.t(torch.sum(pulse * pos_mask, dim=2)).to(
+            device
+        )  # positive histograms
+        neg_hist = torch.t(torch.sum(pulse * neg_mask, dim=2)).to(
+            device
+        )  # negative histograms
 
         total_pos_hist = torch.cumsum(pos_hist, dim=1).to(device)
         total_hist = torch.cumsum(pos_hist + neg_hist, dim=1).to(device)