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import math | |
import os | |
import argparse | |
import json | |
from tqdm import tqdm | |
from dc.eval.model_utils import load_video | |
import shortuuid | |
from dc.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
from dc.conversation import conv_templates, SeparatorStyle | |
from dc.model.builder import load_pretrained_model | |
from dc.utils import disable_torch_init | |
from dc.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path | |
from PIL import Image | |
import math | |
import torch | |
import time | |
def llava_inference(video_frames, question, conv_mode, model, tokenizer, image_processor, image_sizes): | |
if model.config.mm_use_im_start_end: | |
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + question | |
else: | |
qs = DEFAULT_IMAGE_TOKEN + '\n' + question | |
conv = conv_templates[conv_mode].copy() | |
conv.append_message(conv.roles[0], qs) | |
conv.append_message(conv.roles[1], None) | |
prompt = conv.get_prompt() | |
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda() | |
image_tensor = process_images(video_frames, image_processor, model.config) | |
with torch.inference_mode(): | |
output_ids = model.generate( | |
input_ids, | |
images=image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True), | |
image_sizes=image_sizes, | |
do_sample=True if args.temperature > 0 else False, | |
temperature=args.temperature, | |
top_p=args.top_p, | |
num_beams=args.num_beams, | |
max_new_tokens=512, | |
use_cache=True) | |
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() | |
print(outputs) | |
return outputs | |
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('--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("--conv-mode", type=str, required=False, default='vicuna_v1') | |
parser.add_argument("--num_chunks", type=int, default=1) | |
parser.add_argument("--chunk_idx", type=int, default=0) | |
parser.add_argument("--num_frames", type=int, default=100) | |
parser.add_argument("--device", type=str, required=False, default='cuda:0') | |
parser.add_argument("--model-base", type=str, default=None) | |
parser.add_argument("--num_beams", type=int, default=1) | |
parser.add_argument("--temperature", type=float, default=0.2) | |
parser.add_argument("--top_p", type=float, default=None) | |
parser.add_argument("--use_pool", action='store_true') | |
return parser.parse_args() | |
def run_inference(args): | |
""" | |
Run inference on a set of video files using the Dense Connector model. | |
Args: | |
args: Command-line arguments. | |
""" | |
disable_torch_init() | |
model_path = os.path.expanduser(args.model_name) | |
model_name = get_model_name_from_path(model_path) | |
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, is_video=True, if_pool=args.use_pool) | |
gt_contents = json.load(open(args.gt_file, "r")) | |
gt_contents = get_chunk(gt_contents, 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 | |
conv_mode = args.conv_mode | |
video_formats = ['.mp4', '.avi', '.mov', '.mkv'] | |
# Iterate over each sample in the ground truth file | |
index = 0 | |
for sample in tqdm(gt_contents): | |
video_name = sample['video_name'] | |
sample_set = sample | |
question = sample['Q'] | |
# 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 | |
video_frames, sizes = load_video(video_path, num_frm=args.num_frames) | |
# Run inference on the video and add the output to the list | |
output = llava_inference(video_frames, question, conv_mode, model, | |
tokenizer, image_processor, sizes) | |
sample_set['pred'] = output | |
output_list.append(sample_set) | |
ans_file.write(json.dumps(sample_set) + "\n") | |
index += 1 | |
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) | |