Upload VINE model - config

config.json

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+{
+  "_attn_implementation_autoset": true,
+  "_device": "cuda",
+  "alpha": 0.5,
+  "auto_map": {
+    "AutoConfig": "vine_config.VineConfig"
+  },
+  "bbox_min_dim": 5,
+  "box_threshold": 0.35,
+  "debug_visualizations": false,
+  "hidden_dim": 768,
+  "interested_object_pairs": [],
+  "max_video_length": 100,
+  "model_name": "openai/clip-vit-base-patch32",
+  "model_type": "vine",
+  "multi_class": false,
+  "num_top_pairs": 18,
+  "output_logit": false,
+  "pretrained_vine_path": null,
+  "return_flattened_segments": false,
+  "return_valid_pairs": false,
+  "segmentation_method": "grounding_dino_sam2",
+  "target_fps": 1,
+  "text_threshold": 0.25,
+  "topk_cate": 3,
+  "transformers_version": "4.46.2",
+  "visualization_dir": null,
+  "visualize": false,
+  "white_alpha": 0.8
+}

vine_config.py

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+import torch
+from transformers import PretrainedConfig
+from typing import List, Optional, Dict, Any, Tuple
+
+
+class VineConfig(PretrainedConfig):
+    """
+    Configuration class for VINE (Video Understanding with Natural Language) model.
+    
+    VINE is a video understanding model that processes categorical (object class names),
+    unary keywords (actions on one object), and binary keywords (relations between two objects),
+    and returns probability distributions over all of them when passed a video.
+    
+    Args:
+        model_name (str): The CLIP model name to use as backbone. Default: "openai/clip-vit-large-patch14-336"
+        hidden_dim (int): Hidden dimension size. Default: 768
+        num_top_pairs (int): Number of top object pairs to consider. Default: 10
+        segmentation_method (str): Segmentation method to use ("sam2" or "grounding_dino_sam2"). Default: "grounding_dino_sam2"
+        box_threshold (float): Box threshold for Grounding DINO. Default: 0.35
+        text_threshold (float): Text threshold for Grounding DINO. Default: 0.25
+        target_fps (int): Target FPS for video processing. Default: 1
+        alpha (float): Alpha value for object extraction. Default: 0.5
+        white_alpha (float): White alpha value for background blending. Default: 0.8
+        topk_cate (int): Top-k categories to return. Default: 3
+        multi_class (bool): Whether to use multi-class classification. Default: False
+        output_logit (bool): Whether to output logits instead of probabilities. Default: False
+        max_video_length (int): Maximum number of frames to process. Default: 100
+        bbox_min_dim (int): Minimum bounding box dimension. Default: 5
+        visualize (bool): Whether to visualize results. Default: False
+        visualization_dir (str, optional): Directory to save visualizations. Default: None
+        debug_visualizations (bool): Whether to save debug visualizations. Default: False
+        return_flattened_segments (bool): Whether to return flattened segments. Default: False
+        return_valid_pairs (bool): Whether to return valid object pairs. Default: False
+        interested_object_pairs (List[Tuple[int, int]], optional): List of interested object pairs
+    """
+    
+    model_type = "vine"
+    
+    def __init__(
+        self,
+        model_name: str = "openai/clip-vit-base-patch32",
+        hidden_dim = 768,
+        pretrained_vine_path: Optional[str] = None,
+        num_top_pairs: int = 18,
+        segmentation_method: str = "grounding_dino_sam2",
+        box_threshold: float = 0.35,
+        text_threshold: float = 0.25,
+        target_fps: int = 1,
+        alpha: float = 0.5,
+        white_alpha: float = 0.8,
+        topk_cate: int = 3,
+        multi_class: bool = False,
+        output_logit: bool = False,
+        max_video_length: int = 100,
+        bbox_min_dim: int = 5,
+        visualize: bool = False,
+        visualization_dir: Optional[str] = None,
+        return_flattened_segments: bool = False,
+        return_valid_pairs: bool = False,
+        interested_object_pairs: Optional[List[Tuple[int, int]]] = None,
+        debug_visualizations: bool = False,
+        device: Optional[str | int] = None,
+        **kwargs
+    ):
+        self.model_name = model_name
+        self.pretrained_vine_path = pretrained_vine_path
+        self.hidden_dim = hidden_dim
+        self.num_top_pairs = num_top_pairs
+        self.segmentation_method = segmentation_method
+        self.box_threshold = box_threshold
+        self.text_threshold = text_threshold
+        self.target_fps = target_fps
+        self.alpha = alpha
+        self.white_alpha = white_alpha
+        self.topk_cate = topk_cate
+        self.multi_class = multi_class
+        self.output_logit = output_logit
+        self.max_video_length = max_video_length
+        self.bbox_min_dim = bbox_min_dim
+        self.visualize = visualize
+        self.visualization_dir = visualization_dir
+        self.return_flattened_segments = return_flattened_segments
+        self.return_valid_pairs = return_valid_pairs
+        self.interested_object_pairs = interested_object_pairs or []
+        self.debug_visualizations = debug_visualizations
+        if device is int:
+            self._device = f"cuda:{device}" if torch.cuda.is_available() else "cpu"
+        else:
+            self._device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+
+        super().__init__(**kwargs)