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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_1 = sample['Q1']
question_2 = sample['Q2']
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
# Run inference on the video for the first question and add the output to the list
output_1 = get_model_output(model, processor['video'], tokenizer, video_path, question_1, args)
sample_set['pred1'] = output_1
# Run inference on the video for the second question and add the output to the list
output_2 = get_model_output(model, processor['video'], tokenizer, video_path, question_2, args)
sample_set['pred2'] = output_2
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)
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