import math import os import argparse import json import torch import transformers from tqdm import tqdm from llava.conversation import conv_templates, SeparatorStyle from llava.constants import DEFAULT_X_START_TOKEN, DEFAULT_X_TOKEN, DEFAULT_X_END_TOKEN, X_TOKEN_INDEX from llava.mm_utils import get_model_name_from_path, tokenizer_X_token, KeywordsStoppingCriteria from llava.model.builder import load_pretrained_model from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM from llava.train.train import smart_tokenizer_and_embedding_resize def split_list(lst, n): """Split a list into n (roughly) equal-sized chunks""" chunk_size = math.ceil(len(lst) / n) # integer division return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] def get_chunk(lst, n, k): chunks = split_list(lst, n) return chunks[k] def parse_args(): """ Parse command-line arguments. """ parser = argparse.ArgumentParser() # Define the command-line arguments parser.add_argument('--model_path', help='', required=True) parser.add_argument('--cache_dir', help='', required=True) parser.add_argument('--video_dir', help='Directory containing video files.', required=True) parser.add_argument('--gt_file_question', help='Path to the ground truth file containing question.', required=True) parser.add_argument('--gt_file_answers', help='Path to the ground truth file containing answers.', required=True) parser.add_argument('--output_dir', help='Directory to save the model results JSON.', required=True) parser.add_argument('--output_name', help='Name of the file for storing results JSON.', required=True) parser.add_argument("--num_chunks", type=int, default=1) parser.add_argument("--chunk_idx", type=int, default=0) parser.add_argument("--device", type=str, required=False, default='cuda:0') parser.add_argument('--model_base', help='', default=None, type=str, required=False) parser.add_argument("--model_max_length", type=int, required=False, default=2048) return parser.parse_args() def get_model_output(model, video_processor, tokenizer, video, qs, args): if model.config.mm_use_x_start_end: qs = DEFAULT_X_START_TOKEN['VIDEO'] + DEFAULT_X_TOKEN['VIDEO'] + DEFAULT_X_END_TOKEN['VIDEO'] + '\n' + qs else: qs = DEFAULT_X_TOKEN['VIDEO'] + '\n' + qs conv_mode = "llava_v1" args.conv_mode = conv_mode conv = conv_templates[args.conv_mode].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() video_tensor = video_processor.preprocess(video, return_tensors='pt')['pixel_values'][0].half().to(args.device) # print(video_tensor.shape) input_ids = tokenizer_X_token(prompt, tokenizer, X_TOKEN_INDEX['VIDEO'], return_tensors='pt').unsqueeze(0).to(args.device) stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) ''' images (X_modalities) [ [img_feature, img_feature, video_feature, audio_feature], ['image', 'image', 'video', 'audio'] ] ''' with torch.inference_mode(): output_ids = model.generate( input_ids, images=[[video_tensor], ['video']], do_sample=True, temperature=0.2, max_new_tokens=1024, use_cache=True, stopping_criteria=[stopping_criteria]) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() print(outputs) return outputs def run_inference(args): """ Run inference on ActivityNet QA DataSet using the Video-ChatGPT model. Args: args: Command-line arguments. """ # Initialize the model model_name = get_model_name_from_path(args.model_path) tokenizer, model, processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name) model = model.to(args.device) # Load both ground truth file containing questions and answers # with open(args.gt_file_question) as file: # gt_questions = json.load(file) # with open(args.gt_file_answers) as file: # gt_answers = json.load(file) gt_questions = json.load(open(args.gt_file_question, "r")) gt_questions = get_chunk(gt_questions, args.num_chunks, args.chunk_idx) gt_answers = json.load(open(args.gt_file_answers, "r")) # gt_answers = get_chunk(gt_answers, args.num_chunks, args.chunk_idx) answers_file = os.path.join(args.output_dir, f"{args.output_name}.json") os.makedirs(args.output_dir, exist_ok=True) ans_file = open(answers_file, "w") # Create the output directory if it doesn't exist if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) output_list = [] # List to store the output results video_formats = ['.mp4', '.avi', '.mov', '.mkv'] # Iterate over each sample in the ground truth file index = 0 for sample in tqdm(gt_questions): video_name = sample['video_name'] question = sample['question'] id = sample['question_id'] answer = gt_answers[index]['answer'] index += 1 sample_set = {'id': id, 'question': question, 'answer': answer} # Load the video file for fmt in tqdm(video_formats): # Added this line temp_path = os.path.join(args.video_dir, f"v_{video_name}{fmt}") if os.path.exists(temp_path): video_path = temp_path # try: # Run inference on the video and add the output to the list output = get_model_output(model, processor['video'], tokenizer, video_path, question, args) sample_set['pred'] = output output_list.append(sample_set) # except Exception as e: # print(f"Error processing video file '{video_name}': {e}") ans_file.write(json.dumps(sample_set) + "\n") break ans_file.close() # Save the output list to a JSON file # with open(os.path.join(args.output_dir, f"{args.output_name}.json"), 'w') as file: # json.dump(output_list, file) if __name__ == "__main__": args = parse_args() run_inference(args)