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import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from ChatUniVi.constants import *
from ChatUniVi.conversation import conv_templates, SeparatorStyle
from ChatUniVi.model.builder import load_pretrained_model
from ChatUniVi.utils import disable_torch_init
from ChatUniVi.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import math
from decord import VideoReader, cpu
import numpy as np
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 _get_rawvideo_dec(video_path, image_processor, max_frames=MAX_IMAGE_LENGTH, image_resolution=224, video_framerate=1, s=None, e=None):
# speed up video decode via decord.
video_mask = np.zeros(max_frames, dtype=np.int64)
max_video_length = 0
# T x 3 x H x W
video = np.zeros((max_frames, 3, image_resolution, image_resolution), dtype=np.float64)
if s is None:
start_time, end_time = None, None
else:
start_time = int(s)
end_time = int(e)
start_time = start_time if start_time >= 0. else 0.
end_time = end_time if end_time >= 0. else 0.
if start_time > end_time:
start_time, end_time = end_time, start_time
elif start_time == end_time:
end_time = start_time + 1
if os.path.exists(video_path):
vreader = VideoReader(video_path, ctx=cpu(0))
else:
print(video_path)
raise FileNotFoundError
fps = vreader.get_avg_fps()
f_start = 0 if start_time is None else int(start_time * fps)
f_end = int(min(1000000000 if end_time is None else end_time * fps, len(vreader) - 1))
num_frames = f_end - f_start + 1
if num_frames > 0:
# T x 3 x H x W
sample_fps = int(video_framerate)
t_stride = int(round(float(fps) / sample_fps))
all_pos = list(range(f_start, f_end + 1, t_stride))
if len(all_pos) > max_frames:
sample_pos = [all_pos[_] for _ in np.linspace(0, len(all_pos) - 1, num=max_frames, dtype=int)]
else:
sample_pos = all_pos
patch_images = [Image.fromarray(f) for f in vreader.get_batch(sample_pos).asnumpy()]
patch_images = torch.stack([image_processor.preprocess(img, return_tensors='pt')['pixel_values'][0] for img in patch_images])
slice_len = patch_images.shape[0]
max_video_length = max_video_length if max_video_length > slice_len else slice_len
if slice_len < 1:
pass
else:
video[:slice_len, ...] = patch_images
return patch_images, video_mask
else:
print("video path: {} error.".format(video_path))
video_mask[:max_video_length] = [1] * max_video_length
return torch.from_numpy(video), video_mask
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = "ChatUniVi"
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
image_processor = vision_tower.image_processor
if model.config.config["use_cluster"]:
for n, m in model.named_modules():
m = m.to(dtype=torch.bfloat16)
# Load the ground truth file
with open(args.question_file) as file:
gt_contents = json.load(file)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
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
qs = sample['Q']
# Load the video file
for fmt in video_formats: # Added this line
temp_path = os.path.join(args.video_folder, f"{video_name}{fmt}")
if os.path.exists(temp_path):
video_path = temp_path
break
# Check if the video exists
if video_path is not None: # Modified this line
video_frames, _ = _get_rawvideo_dec(video_path, image_processor, max_frames=MAX_IMAGE_LENGTH)
try:
cur_prompt = qs
if model.config.mm_use_im_start_end:
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN * MAX_IMAGE_LENGTH + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN * MAX_IMAGE_LENGTH + '\n' + qs
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()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=video_frames.half().cuda(),
do_sample=True,
temperature=args.temperature,
top_p=args.top_p,
num_beams=args.num_beams,
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()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({'video_name': sample['video_name'],
"prompt": cur_prompt,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"answer": sample['A'],
"metadata": {}}) + "\n")
ans_file.flush()
except Exception as e:
print(f"Error processing video file '{video_name}': {e}")
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--video-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="v1")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--model_use", type=str, default="BASE")
args = parser.parse_args()
eval_model(args)
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