Upload DiskForKeypointDetection

config.json

@@ -3,7 +3,8 @@
     "DiskForKeypointDetection"
   ],
   "auto_map": {
-    "AutoConfig": "configuration_disk.DiskConfig"
+    "AutoConfig": "configuration_disk.DiskConfig",
+    "AutoModelForKeypointDetection": "modeling_disk.DiskForKeypointDetection"
   },
   "descriptor_decoder_dim": 128,
   "detection_threshold": 0.0,

modeling_disk.py

@@ -0,0 +1,64 @@
+import kornia
+import torch
+
+from .configuration_disk import DiskConfig
+from transformers import AutoConfig, AutoModelForKeypointDetection, PreTrainedModel
+from transformers.models.superpoint.modeling_superpoint import (
+    SuperPointKeypointDescriptionOutput,
+)
+
+
+class DiskForKeypointDetection(PreTrainedModel):
+    config_class = DiskConfig
+
+    def __init__(self, config: DiskConfig):
+        super().__init__(config)
+
+        self.config = config
+        self.model = kornia.feature.DISK.from_pretrained(self.config.weights)
+
+    def forward(
+        self, pixel_values: torch.Tensor
+    ) -> SuperPointKeypointDescriptionOutput:
+        detections = self.model(
+            pixel_values,
+            n=self.config.max_num_keypoints,
+            window_size=self.config.nms_window_size,
+            score_threshold=self.config.detection_threshold,
+            pad_if_not_divisible=self.config.pad_if_not_divisible,
+        )
+        max_num_keypoints = max(
+            detection.keypoints.shape[0] for detection in detections
+        )
+        keypoints = torch.zeros(
+            len(detections), max_num_keypoints, 2, device=pixel_values.device
+        )
+        descriptors = torch.zeros(
+            len(detections),
+            max_num_keypoints,
+            self.config.descriptor_decoder_dim,
+            device=pixel_values.device,
+        )
+        scores = torch.zeros(
+            len(detections), max_num_keypoints, device=pixel_values.device
+        )
+        mask = torch.zeros(
+            len(detections), max_num_keypoints, device=pixel_values.device
+        )
+        for i, detection in enumerate(detections):
+            keypoints[i, : detection.keypoints.shape[0]] = detection.keypoints
+            descriptors[i, : detection.descriptors.shape[0]] = detection.descriptors
+            scores[i, : detection.detection_scores.shape[0]] = (
+                detection.detection_scores
+            )
+            mask[i, : detection.detection_scores.shape[0]] = 1
+        width, height = pixel_values.shape[-1], pixel_values.shape[-2]
+        keypoints[:, :, 0] = keypoints[:, :, 0] / width
+        keypoints[:, :, 1] = keypoints[:, :, 1] / height
+
+        return SuperPointKeypointDescriptionOutput(
+            keypoints=keypoints,
+            scores=scores,
+            descriptors=descriptors,
+            mask=mask,
+        )