Add image retrieval with OpenIBL (#164)

Author: skydes

README.md

@@ -95,7 +95,7 @@ We show in [`pipeline_SfM.ipynb`](https://nbviewer.jupyter.org/github/cvg/Hierar
 
 - Supported local feature extractors: [SuperPoint](https://arxiv.org/abs/1712.07629), [D2-Net](https://arxiv.org/abs/1905.03561), [SIFT](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf), and [R2D2](https://arxiv.org/abs/1906.06195).
 - Supported feature matchers: [SuperGlue](https://arxiv.org/abs/1911.11763) and nearest neighbor search with ratio test, distance test, and/or mutual check.
-- Supported image retrieval: [NetVLAD](https://arxiv.org/abs/1511.07247) and [AP-GeM/DIR](https://github.com/naver/deep-image-retrieval).
+- Supported image retrieval: [NetVLAD](https://arxiv.org/abs/1511.07247), [AP-GeM/DIR](https://github.com/naver/deep-image-retrieval), and [OpenIBL](https://github.com/yxgeee/OpenIBL).
 
 Using NetVLAD for retrieval, we obtain the following best results:
 

hloc/extract_features.py

@@ -97,24 +97,22 @@
             'resize_max': 1600,
         },
     },
+    # Global descriptors
     'dir': {
         'output': 'global-feats-dir',
-        'model': {
-            'name': 'dir',
-        },
-        'preprocessing': {
-            'resize_max': 1024,
-        },
+        'model': {'name': 'dir'},
+        'preprocessing': {'resize_max': 1024},
     },
     'netvlad': {
         'output': 'global-feats-netvlad',
-        'model': {
-            'name': 'netvlad',
-        },
-        'preprocessing': {
-            'resize_max': 1024,
-        },
+        'model': {'name': 'netvlad'},
+        'preprocessing': {'resize_max': 1024},
     },
+    'openibl': {
+        'output': 'global-feats-openibl',
+        'model': {'name': 'openibl'},
+        'preprocessing': {'resize_max': 1024},
+    }
 }
 
 

hloc/extractors/openibl.py

@@ -0,0 +1,25 @@
+import torch
+import torchvision.transforms as tvf
+
+from ..utils.base_model import BaseModel
+
+
+class OpenIBL(BaseModel):
+    default_conf = {
+        'model_name': 'vgg16_netvlad',
+    }
+    required_inputs = ['image']
+
+    def _init(self, conf):
+        self.net = torch.hub.load(
+            'yxgeee/OpenIBL', conf['model_name'], pretrained=True).eval()
+        mean = [0.48501960784313836, 0.4579568627450961, 0.4076039215686255]
+        std = [0.00392156862745098, 0.00392156862745098, 0.00392156862745098]
+        self.norm_rgb = tvf.Normalize(mean=mean, std=std)
+
+    def _forward(self, data):
+        image = self.norm_rgb(data['image'])
+        desc = self.net(image)
+        return {
+            'global_descriptor': desc,
+        }