import os import argparse import json from tqdm import tqdm # from video_chatgpt.eval.model_utils import initialize_model, load_video # from video_chatgpt.inference import video_chatgpt_infer from llava.eval.video.run_inference_video_qa import get_model_output from llava.mm_utils import get_model_name_from_path from llava.model.builder import load_pretrained_model 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', help='Path to the ground truth file.', 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("--model-name", type=str, required=True) 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) # parser.add_argument("--conv-mode", type=str, required=False, default='video-chatgpt_v1') # parser.add_argument("--projection_path", type=str, required=True) return parser.parse_args() def run_inference(args): """ Run inference on a set of video files using the provided 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 the ground truth file with open(args.gt_file) as file: gt_contents = json.load(file) # 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 # conv_mode = args.conv_mode video_formats = ['.mp4', '.avi', '.mov', '.mkv'] # Iterate over each sample in the ground truth file for sample in tqdm(gt_contents): video_name = sample['video_name'] sample_set = sample question = sample['Q'] try: # Load the video file for fmt in video_formats: # Added this line temp_path = os.path.join(args.video_dir, f"{video_name}{fmt}") if os.path.exists(temp_path): video_path = temp_path output = get_model_output(model, processor['video'], tokenizer, video_path, question, args) sample_set['pred'] = output output_list.append(sample_set) break except Exception as e: print(f"Error processing video file '{video_name}': {e}") # 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)