can't run on platform without cuda

[liuheng135]

i try to run xrloc on a platform without cuda, but it does not work!

below is the error:
[03/13/2023 17:42:56 INFO] ==================================================
[03/13/2023 17:42:56 INFO] ****************** Init Success ******************
[03/13/2023 17:42:56 INFO] ==================================================
[03/13/2023 17:42:56 INFO] ======================[1 / 3]======================
[03/13/2023 17:42:56 INFO] Image path: query/000001.jpg
[03/13/2023 17:43:03 INFO] Local feature number: 2443
[03/13/2023 17:43:04 INFO] Coarse location number: 1
[03/13/2023 17:43:06 INFO] 3d points size: 7540
Traceback (most recent call last):
File "run_benchmark.py", line 52, in
main(args.query_path, args.map_path, args.json)
File "run_benchmark.py", line 34, in main
ret = loc.refine_localize(image, camera, ref_image_ids)
File "/home/gavin/Develop/xrlocalization/xrloc/localizer.py", line 212, in refine_localize
feat, image_ids)
File "/home/gavin/Develop/xrlocalization/xrloc/localizer.py", line 168, in establish_correspondences_2d3d
feat['shape'][0])
File "/home/gavin/Develop/xrlocalization/xrloc/localizer.py", line 113, in feature_match_2d3d
pred = self.matcher.match(query_feat, train_feat)
File "/home/gavin/Develop/xrlocalization/xrloc/matchers/matcher.py", line 57, in match
output = self.matcher(data)
File "/home/gavin/Develop/xrlocalization/xrloc/matchers/gam.py", line 34, in call
self.config['dis_thres'])
File "/home/gavin/Develop/xrlocalization/xrloc/matchers/gam.py", line 104, in knn_ratio_match
mask = torch.ones((dists.shape[0], k), dtype=torch.bool).cuda()
File "/home/gavin/miniconda3/envs/xrloc/lib/python3.7/site-packages/torch/cuda/init.py", line 221, in _lazy_init
raise AssertionError("Torch not compiled with CUDA enabled")
AssertionError: Torch not compiled with CUDA enabled

[liuheng135]

I found a temporary solution to this issue:

index fd9081f..252f89c 100644
--- a/xrloc/matchers/bmnet.py
+++ b/xrloc/matchers/bmnet.py
@@ -63,8 +63,8 @@ class ConcatenationLayer(torch.nn.Module):
     def forward(self, in0, in1, matches):
         num_matches = matches.shape[1]
         num_channels0, num_channels1 = in0.shape[1], in1.shape[1]
-        edges_feature_0 = torch.zeros(1, num_channels0, num_matches).cuda()
-        edges_feature_1 = torch.zeros(1, num_channels1, num_matches).cuda()
+        edges_feature_0 = torch.zeros(1, num_channels0, num_matches)
+        edges_feature_1 = torch.zeros(1, num_channels1, num_matches)
         edges_feature_0[0] = torch.index_select(in0[0],
                                                 dim=1,
                                                 index=matches[0])
@@ -86,7 +86,7 @@ class HungarianPooling(torch.autograd.Function):
         htb = torch.zeros([rows, cols], dtype=torch.int64)
         htb[row_ind, col_ind] = 1
         assignments = htb[edges[0], edges[1]]
-        assignments = assignments.cuda()
+        assignments = assignments
         ctx.save_for_backward(assignments)
         return assignments
 
diff --git a/xrloc/matchers/gam.py b/xrloc/matchers/gam.py
index 094f814..b9e1b62 100644
--- a/xrloc/matchers/gam.py
+++ b/xrloc/matchers/gam.py
@@ -101,7 +101,7 @@ class GAM(object):
         dists = torch.sqrt(2 * (1 - sims.clamp(-1, 1)))
 
         # Filter by ratio and thres
-        mask = torch.ones((dists.shape[0], k), dtype=torch.bool).cuda()
+        mask = torch.ones((dists.shape[0], k), dtype=torch.bool)
         mask[:, 0] = (dists[:, 0] < 1)
         for i in range(1, k):
             mask[:,
@@ -111,7 +111,7 @@ class GAM(object):
 
         # Compose match
         inds0 = torch.arange(0, inds1.shape[0]).unsqueeze(1).expand(
-            [inds1.shape[0], k]).cuda()
+            [inds1.shape[0], k])
         inds0, inds1 = inds0.reshape(-1), inds1.reshape(-1)
         matches = torch.stack([inds0, inds1],
                               dim=1).to(torch.long).permute([1, 0])'

[HailinYu0414]

Currently, only such a temporary solution can be used first, we will fix this issue later.