Remove disk_model folder from git tracking and add to .gitignore

.gitignore

@@ -19,3 +19,6 @@ wheels/
 .installed.cfg
 *.egg
 MANIFEST
+
+# Ignore disk_model folder
+disk_model/

disk_model/configuration_disk.py

@@ -1,30 +0,0 @@
-from typing import Optional
-
-from transformers import PretrainedConfig
-
-
-class DiskConfig(PretrainedConfig):
-    model_type = "disk"
-
-    def __init__(
-        self,
-        weights: str = "depth",
-        max_num_keypoints: Optional[int] = None,
-        descriptor_decoder_dim: int = 128,
-        nms_window_size: int = 5,
-        detection_threshold: float = 0.0,
-        pad_if_not_divisible: bool = True,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.weights = weights
-        self.max_num_keypoints = max_num_keypoints
-        self.descriptor_decoder_dim = descriptor_decoder_dim
-        self.nms_window_size = nms_window_size
-        self.detection_threshold = detection_threshold
-        self.pad_if_not_divisible = pad_if_not_divisible
-
-
-if __name__ == "__main__":
-    config = DiskConfig()
-    config.save_pretrained("stevenbucaille/disk", push_to_hub=True)

disk_model/modeling_disk.py

@@ -1,76 +0,0 @@
-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,
-        )
-
-
-if __name__ == "__main__":
-    config = DiskConfig()
-    model = DiskForKeypointDetection(config)
-    model.save_pretrained("stevenbucaille/disk", push_to_hub=True)
-
-    AutoConfig.register("disk", DiskConfig)
-    AutoModelForKeypointDetection.register(DiskConfig, DiskForKeypointDetection)
-
-    DiskConfig.register_for_auto_class()
-    DiskForKeypointDetection.register_for_auto_class("AutoModelForKeypointDetection")