lint test_models

tests/test_models.py

@@ -213,13 +213,14 @@ def test_transformer_basic():
     for i in range(num_frames):
         instances = []
         for j in range(num_detected):
-            instances.append(Instance(bbox=torch.rand(size=(1, 4)),
-                                    features=torch.rand(size=(1, feats))))
-        frames.append(Frame(video_id = 0, frame_id=i,
-                            instances=instances))
-
+            instances.append(
+                Instance(
+                    bbox=torch.rand(size=(1, 4)), features=torch.rand(size=(1, feats))
+                )
+            )
+        frames.append(Frame(video_id=0, frame_id=i, instances=instances))
 
-    asso_preds,_ = transformer(frames)
+    asso_preds, _ = transformer(frames)
 
     assert asso_preds[0].size() == (num_detected * num_frames,) * 2
 
@@ -274,10 +275,12 @@ def test_transformer_embedding():
     for i in range(num_frames):
         instances = []
         for j in range(num_detected):
-            instances.append(Instance(bbox=torch.rand(size=(1, 4)),
-                                    features=torch.rand(size=(1, feats))))
-        frames.append(Frame(video_id = 0, frame_id=i,
-                            instances=instances))
+            instances.append(
+                Instance(
+                    bbox=torch.rand(size=(1, 4)), features=torch.rand(size=(1, feats))
+                )
+            )
+        frames.append(Frame(video_id=0, frame_id=i, instances=instances))
 
     embedding_meta = {
         "embedding_type": "learned_pos_temp",
@@ -316,13 +319,14 @@ def test_tracking_transformer():
     for i in range(num_frames):
         instances = []
         for j in range(num_detected):
-            instances.append(Instance(bbox=torch.rand(size=(1, 4)),
-                                      crop=torch.rand(size=(1, 1, 64, 64))
-                                      ))
-        frames.append(Frame(video_id=0, 
-                            frame_id=i,
-                            img_shape=img_shape,
-                            instances=instances))
+            instances.append(
+                Instance(
+                    bbox=torch.rand(size=(1, 4)), crop=torch.rand(size=(1, 1, 64, 64))
+                )
+            )
+        frames.append(
+            Frame(video_id=0, frame_id=i, img_shape=img_shape, instances=instances)
+        )
 
     embedding_meta = {
         "embedding_type": "fixed_pos",