from vision_encoders.builder import build_vision_tower_aux_list from transformers import Qwen2Config from typing import Optional, List, Tuple import torch import json from transformers import BaseImageProcessor class CambrianConfig(Qwen2Config): model_type = "cambrian_qwen" debug = "debug" def __init__( self, **kwargs ) -> None: super().__init__(**kwargs) for key, value in kwargs.items(): setattr(self, key, value) @classmethod def from_json_file(cls, json_file_path): """Load a config from a json file.""" with open(json_file_path, "r") as f: config_dict = json.load(f) return cls(**config_dict) class CambrianEncoders: def __init__( self, config: CambrianConfig ) -> None: self.config: CambrianConfig = config self.vision_tower_aux_list = build_vision_tower_aux_list(config, delay_load=True) def encode_images(self, image_aux_list, encode_type=None): vision_tower_aux_list = self.vision_tower_aux_list image_aux_features_list = [] chunk_size = 64 if encode_type == "dino": image_aux = image_aux_list[-1] vision_tower_aux = vision_tower_aux_list[-1] if image_aux.shape[0] > chunk_size: image_aux_features_chunks = [] for start_idx in range(0, image_aux.shape[0], chunk_size): end_idx = min(start_idx + chunk_size, image_aux.shape[0]) chunk = image_aux[start_idx:end_idx] image_aux_features_chunk = vision_tower_aux(chunk) image_aux_features_chunks.append(image_aux_features_chunk) image_aux_features = torch.cat(image_aux_features_chunks, dim=0) else: image_aux_features = vision_tower_aux(image_aux) return image_aux_features elif encode_type == "siglip": image_aux = image_aux_list[0] vision_tower_aux = vision_tower_aux_list[0] if image_aux.shape[0] > chunk_size: image_aux_features_chunks = [] for start_idx in range(0, image_aux.shape[0], chunk_size): end_idx = min(start_idx + chunk_size, image_aux.shape[0]) chunk = image_aux[start_idx:end_idx] image_aux_features_chunk = vision_tower_aux(chunk) image_aux_features_chunks.append(image_aux_features_chunk) image_aux_features = torch.cat(image_aux_features_chunks, dim=0) else: image_aux_features = vision_tower_aux(image_aux) return image_aux_features else: for image_aux, vision_tower_aux in zip( image_aux_list, vision_tower_aux_list ): if image_aux.shape[0] > chunk_size: image_aux_features_chunks = [] for start_idx in range(0, image_aux.shape[0], chunk_size): end_idx = min(start_idx + chunk_size, image_aux.shape[0]) chunk = image_aux[start_idx:end_idx] image_aux_features_chunk = vision_tower_aux(chunk) image_aux_features_chunks.append(image_aux_features_chunk) image_aux_features = torch.cat(image_aux_features_chunks, dim=0) else: image_aux_features = vision_tower_aux(image_aux) image_aux_features_list.append(image_aux_features) return image_aux_features_list def select_frame( self, feature_list, split_sizes, new_image_aux_list, image_sizes, window_size=16, threshold=0.83, ): dino_features_batch = torch.split(feature_list, split_sizes, dim=0) new_image_aux_batch_0 = torch.split(new_image_aux_list[0], split_sizes, dim=0) new_image_aux_batch_1 = torch.split(new_image_aux_list[1], split_sizes, dim=0) new_split_sizes = [] selected_frames_all_0 = [] selected_frames_all_1 = [] selected_frames_feature_all = [] selected_frame_indices_all = [] for i_batch, frame_features in enumerate(dino_features_batch): original_width, original_height = image_sizes[i_batch] if getattr(self.get_model().config, "highres", False): token_per_frame = self.config.lowres_token ** 2 else: token_per_frame = self.config.image_token_len max_num_frames = max( 1, ( self.config.tokenizer_model_max_length - getattr(self.config, "inference_max_length", 16) ) // token_per_frame, ) if len(frame_features) < max_num_frames: selected_frames_all_0.append(new_image_aux_batch_0[i_batch]) selected_frames_all_1.append(new_image_aux_batch_1[i_batch]) selected_frames_feature_all.append(frame_features) new_split_sizes.append(len(frame_features)) selected_frame_indices_all.append(torch.arange(len(frame_features))) continue num_segments = len(frame_features) // window_size if num_segments == 0: query_feature = frame_features.flatten(1, 2) query_feature = query_feature / torch.norm( (query_feature), dim=1, keepdim=True ) similarities = torch.mean(query_feature @ query_feature.T, dim=1) similarities[len(frame_features) // 2] = 0 indices = torch.where(similarities < threshold)[0] selected_frame_indices_all.append(indices) selected_frames_all_0.append(new_image_aux_batch_0[i_batch][indices]) selected_frames_all_1.append(new_image_aux_batch_1[i_batch][indices]) selected_frames_feature_all.append(frame_features[indices]) new_split_sizes.append(len(indices)) continue segments_frames_0 = [] segments_frames_1 = [] segments_features = [] for start_idx in range(0, len(frame_features), window_size): end_idx = min(start_idx + window_size, len(frame_features)) segments_frames_0.append( new_image_aux_batch_0[i_batch][start_idx:end_idx] ) segments_frames_1.append( new_image_aux_batch_1[i_batch][start_idx:end_idx] ) segments_features.append(frame_features[start_idx:end_idx]) selected_frames_0 = [] selected_frames_1 = [] selected_features = [] selected_frame_indices = [] for i, segment in enumerate(segments_features): query_feature = segment.flatten(1, 2) query_feature = query_feature / torch.norm( (query_feature), dim=1, keepdim=True ) similarities = torch.mean(query_feature @ query_feature.T, dim=1) similarities[len(segment) // 2] = 0 indices = torch.where(similarities < threshold)[0] selected_frames_0.append(segments_frames_0[i][indices]) selected_frames_1.append(segments_frames_1[i][indices]) selected_features.append(segment[indices]) selected_frame_indices.extend(indices + i * window_size) selected_frames_0 = torch.cat(selected_frames_0, dim=0) selected_frames_1 = torch.cat(selected_frames_1, dim=0) selected_features = torch.cat(selected_features, dim=0) selected_frame_indices = torch.tensor(selected_frame_indices) # ablation max_num_frames = 400 # in case of OOM if len(selected_frames_0) > max_num_frames: interval = len(selected_frames_0) / float(max_num_frames) indices = [int(interval * i) for i in range(max_num_frames)] new_split_sizes.append(len(indices)) selected_frames_all_0.append(selected_frames_0[indices]) selected_frames_all_1.append(selected_frames_1[indices]) selected_frames_feature_all.append(selected_features[indices]) selected_frame_indices = selected_frame_indices[indices] else: new_split_sizes.append(len(selected_frames_0)) selected_frames_all_0.append(selected_frames_0) selected_frames_all_1.append(selected_frames_1) selected_frames_feature_all.append(selected_features) selected_frame_indices_all.append(selected_frame_indices) selected_frames_all_0 = torch.cat(selected_frames_all_0, dim=0) selected_frames_all_1 = torch.cat(selected_frames_all_1, dim=0) selected_frames_feature_all = torch.cat(selected_frames_feature_all, dim=0) return ( selected_frames_feature_all, new_split_sizes, [selected_frames_all_0, selected_frames_all_1], selected_frame_indices_all, ) def prepare_mm_features( self, images: List[torch.Tensor], image_sizes: List[Tuple[int, int]], ): image_aux_list = images split_sizes_ori = [ 1 if image.ndim == 3 else image.shape[0] for image in image_aux_list[0] ] new_image_aux_list = [] for image_aux in image_aux_list: if type(image_aux) is list: image_aux = [ x.unsqueeze(0) if x.ndim == 3 else x for x in image_aux ] concat_image_aux = torch.cat([image for image in image_aux], dim=0) new_image_aux_list.append(concat_image_aux) image_aux_features_dino = self.encode_images( new_image_aux_list, encode_type="dino" ) ( image_aux_features_dino, split_sizes, new_image_aux_list, selected_frame_indices_all, ) = self.select_frame( image_aux_features_dino, split_sizes_ori, new_image_aux_list, image_sizes, threshold=getattr(self.config, "dino_threshold", 0.83), ) image_aux_features_siglip = self.encode_images( new_image_aux_list, encode_type="siglip" ) image_aux_features_list = [ image_aux_features_siglip, image_aux_features_dino, ] return image_aux_features_list