update: gim

hloc/matchers/gim.py

@@ -72,21 +72,17 @@ class GIM(BaseModel):
         # )
 
         image0, image1 = data['image0'], data['image1']
-        print('1.', 'image0', image0.shape, 'image1', image1.shape)
         orig_width0 = image0.shape[3]
         orig_height0 = image0.shape[2]
         orig_width1 = image1.shape[3]
         orig_height1 = image1.shape[2]
         aspect_ratio = 896 / 672
-
         new_width0 = max(orig_width0, int(orig_height0 * aspect_ratio))
         new_height0 = max(orig_height0, int(orig_width0 / aspect_ratio))
         new_width1 = max(orig_width1, int(orig_height1 * aspect_ratio))
         new_height1 = max(orig_height1, int(orig_width1 / aspect_ratio))
-
         new_width = max(new_width0, new_width1)
         new_height = max(new_height0, new_height1)
-
         pad_height0 = new_height - orig_height0
         pad_width0 = new_width - orig_width0
         pad_height1 = new_height - orig_height1
@@ -101,7 +97,6 @@ class GIM(BaseModel):
         pad_right1 = pad_width1 - pad_left1
         image0 = torch.nn.functional.pad(image0, (pad_left0, pad_right0, pad_top0, pad_bottom0))
         image1 = torch.nn.functional.pad(image1, (pad_left1, pad_right1, pad_top1, pad_bottom1))
-        print('2.', 'image0', image0.shape, 'image1', image1.shape)
         dense_matches, dense_certainty = self.net.match(image0, image1)
         sparse_matches, mconf = self.net.sample(dense_matches, dense_certainty, self.conf["max_keypoints"])
         height0, width0 = image0.shape[-2:]