Vincentqyw commited on
Commit
7bff0e4
1 Parent(s): 74659e4
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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  def _forward(self, data):
 
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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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  def _forward(self, data):
third_party/ASpanFormer/src/ASpanFormer/aspan_module/transformer.py CHANGED
@@ -6,6 +6,7 @@ from .attention import FullAttention, HierachicalAttention, layernorm2d
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  class messageLayer_ini(nn.Module):
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  def __init__(self, d_model, d_flow, d_value, nhead):
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  super().__init__()
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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  class messageLayer_ini(nn.Module):
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  def __init__(self, d_model, d_flow, d_value, nhead):
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  super().__init__()
third_party/ASpanFormer/src/ASpanFormer/aspanformer.py CHANGED
@@ -15,6 +15,7 @@ from .utils.fine_matching import FineMatching
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  class ASpanFormer(nn.Module):
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  def __init__(self, config):
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  super().__init__()
@@ -160,7 +161,7 @@ class ASpanFormer(nn.Module):
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  train_res_h / data["image1"].shape[2],
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  train_res_w / data["image1"].shape[3],
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  ]
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-
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  data["online_resize_scale0"], data["online_resize_scale1"] = (
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  torch.tensor([w0 / data["image0"].shape[3], h0 / data["image0"].shape[2]])[
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  None
 
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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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  class ASpanFormer(nn.Module):
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  def __init__(self, config):
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  super().__init__()
 
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  train_res_h / data["image1"].shape[2],
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  train_res_w / data["image1"].shape[3],
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  ]
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+
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  data["online_resize_scale0"], data["online_resize_scale1"] = (
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  torch.tensor([w0 / data["image0"].shape[3], h0 / data["image0"].shape[2]])[
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  None
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)
 
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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)