Black formatting

gluefactory/datasets/eth3d.py

@@ -1,6 +1,7 @@
 """
 ETH3D multi-view benchmark, used for line matching evaluation.
 """
+
 import logging
 import os
 import shutil

gluefactory/datasets/hpatches.py

@@ -1,6 +1,7 @@
 """
 Simply load images from a folder or nested folders (does not have any split).
 """
+
 import argparse
 import logging
 import tarfile

gluefactory/geometry/gt_generation.py

@@ -375,9 +375,9 @@ def gt_line_matches_from_pose_depth(
     all_in_batch = (
         torch.arange(b_size)[:, None].repeat(1, assignation.shape[-1]).flatten()
     )
-    positive[
-        all_in_batch, assignation[:, 0].flatten(), assignation[:, 1].flatten()
-    ] = True
+    positive[all_in_batch, assignation[:, 0].flatten(), assignation[:, 1].flatten()] = (
+        True
+    )
 
     m0 = assignation.new_full((b_size, n_lines0), unmatched, dtype=torch.long)
     m0.scatter_(-1, assignation[:, 0], assignation[:, 1])

gluefactory/models/cache_loader.py

@@ -47,9 +47,11 @@ def pad_line_features(pred, seq_l: int = None):
 
 def recursive_load(grp, pkeys):
     return {
-        k: torch.from_numpy(grp[k].__array__())
-        if isinstance(grp[k], h5py.Dataset)
-        else recursive_load(grp[k], list(grp.keys()))
+        k: (
+            torch.from_numpy(grp[k].__array__())
+            if isinstance(grp[k], h5py.Dataset)
+            else recursive_load(grp[k], list(grp.keys()))
+        )
         for k in pkeys
     }
 
@@ -108,9 +110,12 @@ def _forward(self, data):
             pred = recursive_load(grp, pkeys)
             if self.numeric_dtype is not None:
                 pred = {
-                    k: v
-                    if not isinstance(v, torch.Tensor) or not torch.is_floating_point(v)
-                    else v.to(dtype=self.numeric_dtype)
+                    k: (
+                        v
+                        if not isinstance(v, torch.Tensor)
+                        or not torch.is_floating_point(v)
+                        else v.to(dtype=self.numeric_dtype)
+                    )
                     for k, v in pred.items()
                 }
             pred = batch_to_device(pred, device)

gluefactory/models/extractors/aliked.py

@@ -717,9 +717,11 @@ def _init(self, conf):
             radius=conf.nms_radius,
             top_k=-1 if conf.detection_threshold > 0 else conf.max_num_keypoints,
             scores_th=conf.detection_threshold,
-            n_limit=conf.max_num_keypoints
-            if conf.max_num_keypoints > 0
-            else self.n_limit_max,
+            n_limit=(
+                conf.max_num_keypoints
+                if conf.max_num_keypoints > 0
+                else self.n_limit_max
+            ),
         )
 
         # load pretrained

gluefactory/models/extractors/superpoint_open.py

@@ -5,6 +5,7 @@
    The implementation of this model and its trained weights are made
    available under the MIT license.
 """
+
 from collections import OrderedDict
 from types import SimpleNamespace
 

gluefactory/models/lines/wireframe.py

@@ -256,9 +256,9 @@ def _forward(self, data):
                 associativity = torch.eye(
                     len(all_points[-1]), dtype=torch.bool, device=device
                 )
-                associativity[
-                    : n_true_junctions[bs], : n_true_junctions[bs]
-                ] = line_association[bs][: n_true_junctions[bs], : n_true_junctions[bs]]
+                associativity[: n_true_junctions[bs], : n_true_junctions[bs]] = (
+                    line_association[bs][: n_true_junctions[bs], : n_true_junctions[bs]]
+                )
                 pl_associativity.append(associativity)
 
             all_points = torch.stack(all_points, dim=0)

tests/test_integration.py

@@ -35,9 +35,11 @@ def create_input_data(cv_img0, cv_img1, device):
     data = {"view0": ip(img0), "view1": ip(img1)}
     data = map_tensor(
         data,
-        lambda t: t[None].to(device)
-        if isinstance(t, Tensor)
-        else torch.from_numpy(t)[None].to(device),
+        lambda t: (
+            t[None].to(device)
+            if isinstance(t, Tensor)
+            else torch.from_numpy(t)[None].to(device)
+        ),
     )
     return data