Switch to kornia DISK (#291)

* Switch to kornia DISK
* Bump kornia minimum version

.gitmodules

@@ -10,6 +10,3 @@
 [submodule "third_party/r2d2"]
 	path = third_party/r2d2
 	url = https://github.com/naver/r2d2.git
-[submodule "third_party/disk"]
-	path = third_party/disk
-	url = https://github.com/cvlab-epfl/disk.git

hloc/extractors/disk.py

@@ -1,81 +1,32 @@
-import sys
-from pathlib import Path
-from functools import partial
-import torch
-import torch.nn.functional as F
+import kornia
 
 from ..utils.base_model import BaseModel
 
-disk_path = Path(__file__).parent / "../../third_party/disk"
-sys.path.append(str(disk_path))
-from disk import DISK as _DISK  # noqa E402
-
 
 class DISK(BaseModel):
     default_conf = {
-        'model_name': 'depth-save.pth',
+        'weights': 'depth',
         'max_keypoints': None,
-        'desc_dim': 128,
-        'mode': 'nms',
         'nms_window_size': 5,
+        'detection_threshold': 0.0,
+        'pad_if_not_divisible': True,
     }
     required_inputs = ['image']
 
     def _init(self, conf):
-        self.model = _DISK(window=8, desc_dim=conf['desc_dim'])
-
-        state_dict = torch.load(
-            disk_path / conf['model_name'], map_location='cpu')
-        if 'extractor' in state_dict:
-            weights = state_dict['extractor']
-        elif 'disk' in state_dict:
-            weights = state_dict['disk']
-        else:
-            raise KeyError('Incompatible weight file!')
-        self.model.load_state_dict(weights)
-
-        if conf['mode'] == 'nms':
-            self.extract = partial(
-                self.model.features,
-                kind='nms',
-                window_size=conf['nms_window_size'],
-                cutoff=0.,
-                n=conf['max_keypoints']
-            )
-        elif conf['mode'] == 'rng':
-            self.extract = partial(self.model.features, kind='rng')
-        else:
-            raise KeyError(
-                f'mode must be `nms` or `rng`, got `{conf["mode"]}`')
+        self.model = kornia.feature.DISK.from_pretrained(conf['weights'])
 
     def _forward(self, data):
         image = data['image']
-        # make sure that the dimensions of the image are multiple of 16
-        orig_h, orig_w = image.shape[-2:]
-        new_h = round(orig_h / 16) * 16
-        new_w = round(orig_w / 16) * 16
-        image = F.pad(image, (0, new_w - orig_w, 0, new_h - orig_h))
-
-        batched_features = self.extract(image)
-
-        assert(len(batched_features) == 1)
-        features = batched_features[0]
-
-        # filter points detected in the padded areas
-        kpts = features.kp
-        valid = torch.all(kpts <= kpts.new_tensor([orig_w, orig_h]) - 1, 1)
-        kpts = kpts[valid]
-        descriptors = features.desc[valid]
-        scores = features.kp_logp[valid]
-
-        # order the keypoints
-        indices = torch.argsort(scores, descending=True)
-        kpts = kpts[indices]
-        descriptors = descriptors[indices]
-        scores = scores[indices]
-
+        features = self.model(
+            image,
+            n=self.conf['max_keypoints'],
+            window_size=self.conf['nms_window_size'],
+            score_threshold=self.conf['detection_threshold'],
+            pad_if_not_divisible=self.conf['pad_if_not_divisible'],
+        )
         return {
-            'keypoints': kpts[None],
-            'descriptors': descriptors.t()[None],
-            'scores': scores[None],
+            'keypoints': [f.keypoints for f in features],
+            'keypoint_scores': [f.detection_scores for f in features],
+            'descriptors': [f.descriptors.t() for f in features],
         }

requirements.txt

@@ -8,6 +8,6 @@ plotly
 scipy
 h5py
 pycolmap>=0.3.0
-kornia>=0.6.7
+kornia>=0.6.11
 gdown
 lightglue @ git+https://github.com/cvg/LightGlue