Merge branch 'main' of https://huggingface.co/stevenbucaille/disk

.gitattributes

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+model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "DiskForKeypointDetection"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_disk.DiskConfig",
+    "AutoModelForKeypointDetection": "modeling_disk.DiskForKeypointDetection"
+  },
+  "descriptor_decoder_dim": 128,
+  "detection_threshold": 0.0,
+  "max_num_keypoints": null,
+  "model_type": "disk",
+  "nms_window_size": 5,
+  "pad_if_not_divisible": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.54.0.dev0",
+  "weights": "depth"
+}

configuration_disk.py

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+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)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e49f19c4eeb6b422abd969ec0f20a583bd5920ca00dd4dd4a4510dfbae541650
+size 4372068

modeling_disk.py

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+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,
+        )