Vincentqyw commited on
Commit
c7a0722
1 Parent(s): f448045

add app queue

Browse files
app.py CHANGED
@@ -278,7 +278,7 @@ def run(config):
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  matcher_info,
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  ]
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  button_reset.click(fn=ui_reset_state, inputs=inputs, outputs=reset_outputs)
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-
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  app.launch(share=False)
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  matcher_info,
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  ]
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  button_reset.click(fn=ui_reset_state, inputs=inputs, outputs=reset_outputs)
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+ app.queue()
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  app.launch(share=False)
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hloc/extractors/dedode.py CHANGED
@@ -64,8 +64,8 @@ class DeDoDe(BaseModel):
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  # load the model
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  weights_detector = torch.load(model_detector_path, map_location="cpu")
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  weights_descriptor = torch.load(model_descriptor_path, map_location="cpu")
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- self.detector = dedode_detector_L(weights=weights_detector, device = device)
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- self.descriptor = dedode_descriptor_B(weights=weights_descriptor, device = device)
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  logger.info(f"Load DeDoDe model done.")
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  # load the model
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  weights_detector = torch.load(model_detector_path, map_location="cpu")
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  weights_descriptor = torch.load(model_descriptor_path, map_location="cpu")
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+ self.detector = dedode_detector_L(weights=weights_detector, device=device)
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+ self.descriptor = dedode_descriptor_B(weights=weights_descriptor, device=device)
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  logger.info(f"Load DeDoDe model done.")
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third_party/ASpanFormer/src/ASpanFormer/aspan_module/attention.py CHANGED
@@ -6,6 +6,7 @@ from torch.nn import functional as F
6
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  class layernorm2d(nn.Module):
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  def __init__(self, dim):
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  super().__init__()
@@ -177,7 +178,8 @@ class HierachicalAttention(Module):
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  offset_sample = self.sample_offset[None, None] * span_scale
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  sample_pixel = offset[:, :, None] + offset_sample # B*G*r^2*2
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  sample_norm = (
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- sample_pixel / torch.tensor([wk / 2, hk / 2]).to(device)[None, None, None] - 1
 
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  )
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183
  q = (
 
6
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
8
 
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+
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  class layernorm2d(nn.Module):
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  def __init__(self, dim):
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  super().__init__()
 
178
  offset_sample = self.sample_offset[None, None] * span_scale
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  sample_pixel = offset[:, :, None] + offset_sample # B*G*r^2*2
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  sample_norm = (
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+ sample_pixel / torch.tensor([wk / 2, hk / 2]).to(device)[None, None, None]
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+ - 1
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  )
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  q = (
third_party/DeDoDe/DeDoDe/utils.py CHANGED
@@ -13,6 +13,7 @@ from time import perf_counter
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  def recover_pose(E, kpts0, kpts1, K0, K1, mask):
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  best_num_inliers = 0
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  K0inv = np.linalg.inv(K0[:2, :2])
 
13
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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  def recover_pose(E, kpts0, kpts1, K0, K1, mask):
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  best_num_inliers = 0
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  K0inv = np.linalg.inv(K0[:2, :2])
third_party/SGMNet/sgmnet/match_model.py CHANGED
@@ -5,6 +5,7 @@ eps = 1e-8
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  def sinkhorn(M, r, c, iteration):
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  p = torch.softmax(M, dim=-1)
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  u = torch.ones_like(r)
 
5
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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  def sinkhorn(M, r, c, iteration):
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  p = torch.softmax(M, dim=-1)
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  u = torch.ones_like(r)