merge and fix linting issues.

limap/line2d/dense/__init__.py

@@ -1,2 +1,4 @@
 from .extractor import DenseNaiveExtractor
-from .matcher import BaseDenseLineMatcherOptions, RoMaLineMatcher
+from .matcher import RoMaLineMatcher
+
+__all__ = ["DenseNaiveExtractor", "RoMaLineMatcher"]

limap/line2d/dense/dense_matcher/__init__.py

@@ -1,2 +1,4 @@
 from .base import BaseDenseMatcher
 from .roma import RoMa
+
+__all__ = ["BaseDenseMatcher", "RoMa"]

limap/line2d/dense/dense_matcher/base.py

@@ -1,5 +1,3 @@
-import os
-
 import torch
 
 

limap/line2d/dense/dense_matcher/roma.py

@@ -1,5 +1,3 @@
-import os
-
 import romatch
 from PIL import Image
 

limap/line2d/dense/extractor.py

@@ -4,11 +4,11 @@
 
 import limap.util.io as limapio
 
-from ..base_detector import BaseDetector, BaseDetectorOptions
+from ..base_detector import BaseDetector, DefaultDetectorOptions
 
 
 class DenseNaiveExtractor(BaseDetector):
-    def __init__(self, options=BaseDetectorOptions(), device=None):
+    def __init__(self, options=DefaultDetectorOptions, device=None):
         super().__init__(options)
 
     def get_module_name(self):

limap/line2d/dense/matcher.py

@@ -4,7 +4,7 @@
 import torch
 import torch.nn.functional as F
 
-from ..base_matcher import BaseMatcher, BaseMatcherOptions
+from ..base_matcher import BaseMatcher, DefaultMatcherOptions
 from .dense_matcher import BaseDenseMatcher
 
 
@@ -16,13 +16,16 @@ class BaseDenseLineMatcherOptions(NamedTuple):
     one_to_many: bool = False
 
 
+DefaultDenseLineMatcherOptions = BaseDenseLineMatcherOptions()
+
+
 class BaseDenseLineMatcher(BaseMatcher):
     def __init__(
         self,
         extractor,
         dense_matcher,
-        dense_options=BaseDenseLineMatcherOptions(),
-        options=BaseMatcherOptions(),
+        dense_options=DefaultDenseLineMatcherOptions,
+        options=DefaultMatcherOptions,
     ):
         super().__init__(extractor, options)
         assert self.extractor.get_module_name() == "dense_naive"
@@ -169,7 +172,8 @@ def match_segs_with_descinfo(self, descinfo1, descinfo2):
         dists = torch.where(
             overlap_1to2 > overlap_2to1.T, dists_1to2, dists_2to1.T
         )
-        # Both warps must have a minimum overlap and close distance (= equivalent of mutual nearest neighbor)
+        # Both warps must have a minimum overlap and close distance
+        # (= equivalent of mutual nearest neighbor)
         overlap = torch.minimum(overlap_1to2, overlap_2to1.T)
         dists[overlap < self.dense_options.segment_percentage_th] = 10000
         dists[
@@ -213,7 +217,8 @@ def match_segs_with_descinfo_topk(self, descinfo1, descinfo2, topk=10):
         dists = torch.where(
             overlap_1to2 > overlap_2to1.T, dists_1to2, dists_2to1.T
         )
-        # Both warps must have a minimum overlap and close distance (= equivalent of mutual nearest neighbor)
+        # Both warps must have a minimum overlap and close distance
+        # (= equivalent of mutual nearest neighbor)
         overlap = torch.minimum(overlap_1to2, overlap_2to1.T)
         dists[overlap < self.dense_options.segment_percentage_th] = 10000
         dists[
@@ -238,8 +243,8 @@ def __init__(
         self,
         extractor,
         mode="outdoor",
-        dense_options=BaseDenseLineMatcherOptions(),
-        options=BaseMatcherOptions(),
+        dense_options=DefaultDenseLineMatcherOptions,
+        options=DefaultMatcherOptions,
     ):
         from .dense_matcher import RoMa