add app queue

app.py

@@ -278,7 +278,7 @@ def run(config):
                 matcher_info,
             ]
             button_reset.click(fn=ui_reset_state, inputs=inputs, outputs=reset_outputs)
-
+    app.queue()
     app.launch(share=False)
 
 

hloc/extractors/dedode.py

@@ -64,8 +64,8 @@ class DeDoDe(BaseModel):
         # load the model
         weights_detector = torch.load(model_detector_path, map_location="cpu")
         weights_descriptor = torch.load(model_descriptor_path, map_location="cpu")
-        self.detector = dedode_detector_L(weights=weights_detector, device = device)
-        self.descriptor = dedode_descriptor_B(weights=weights_descriptor, device = device)
+        self.detector = dedode_detector_L(weights=weights_detector, device=device)
+        self.descriptor = dedode_descriptor_B(weights=weights_descriptor, device=device)
 
         logger.info(f"Load DeDoDe model done.")
 

third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py

@@ -6,6 +6,7 @@ from torch.nn import functional as F
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+
 class layernorm2d(nn.Module):
     def __init__(self, dim):
         super().__init__()
@@ -177,7 +178,8 @@ class HierachicalAttention(Module):
         offset_sample = self.sample_offset[None, None] * span_scale
         sample_pixel = offset[:, :, None] + offset_sample  # B*G*r^2*2
         sample_norm = (
-            sample_pixel / torch.tensor([wk / 2, hk / 2]).to(device)[None, None, None] - 1
+            sample_pixel / torch.tensor([wk / 2, hk / 2]).to(device)[None, None, None]
+            - 1
         )
 
         q = (

third_party/DeDoDe/DeDoDe/utils.py

@@ -13,6 +13,7 @@ from time import perf_counter
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+
 def recover_pose(E, kpts0, kpts1, K0, K1, mask):
     best_num_inliers = 0
     K0inv = np.linalg.inv(K0[:2, :2])

third_party/SGMNet/sgmnet/match_model.py

@@ -5,6 +5,7 @@ eps = 1e-8
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
+
 def sinkhorn(M, r, c, iteration):
     p = torch.softmax(M, dim=-1)
     u = torch.ones_like(r)