| import torch |
| from transformers import PretrainedConfig |
| from typing import List, Optional, Dict, Any, Tuple, Union |
| from pathlib import Path |
|
|
|
|
| class VineConfig(PretrainedConfig): |
| """ |
| Configuration class for VINE (Video Understanding with Natural Language) model. |
| """ |
|
|
| model_type = "vine" |
|
|
| def __init__( |
| self, |
| model_name: str = "openai/clip-vit-base-patch32", |
| hidden_dim: int = 768, |
| use_hf_repo: bool = True, |
| model_repo: Optional[str] = "KevinX-Penn28/testing", |
| model_file: Optional[str] = None, |
| local_dir: Optional[str] = str(Path(__file__).resolve().parent), |
| local_filename: Optional[str] = "laser_model_v1.pkl", |
| 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, |
| use_pretrained_cate_weights: bool = False, |
| categorical_pool: str = "mean", |
| max_video_length: int = 100, |
| bbox_min_dim: int = 1, |
| 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[Union[str, int]] = None, |
| auto_add_not_unary: bool = False, |
| **kwargs: Any, |
| ): |
| self.model_name = model_name |
| self.use_hf_repo = use_hf_repo |
| if use_hf_repo: |
| self.model_repo = model_repo |
| self.model_file = model_file |
| self.local_dir = None |
| self.local_filename = None |
| else: |
| self.model_repo = None |
| self.model_file = None |
| self.local_dir = local_dir |
| self.local_filename = local_filename |
|
|
| 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.use_pretrained_cate_weights = use_pretrained_cate_weights |
| self.categorical_pool = categorical_pool |
| 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 |
| self.auto_add_not_unary = auto_add_not_unary |
|
|
| if isinstance(device, 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) |
|
|
