import gradio as gr import spaces import os import time import json import numpy as np import av import torch from PIL import Image import functools from transformers import AutoProcessor, AutoConfig from models.idefics2 import Idefics2ForSequenceClassification from models.conversation import conv_templates from typing import List processor = AutoProcessor.from_pretrained("Mantis-VL/mantis-8b-idefics2-video-eval-20k-mantis-2epoch_4096_regression") model = Idefics2ForSequenceClassification.from_pretrained("Mantis-VL/mantis-8b-idefics2-video-eval-20k-mantis-2epoch_4096_regression", torch_dtype=torch.bfloat16).eval() MAX_NUM_FRAMES = 24 conv_template = conv_templates["idefics_2"] with open("./examples/all_subsets.json", 'r') as f: examples = json.load(f) for item in examples: video_id = item['images'][0].split("_")[0] item['images'] = [os.path.join("./examples", video_id, x) for x in item['images']] item['video'] = os.path.join("./examples", item['video']) with open("./examples/hd.json", 'r') as f: hd_examples = json.load(f) for item in hd_examples: item['video'] = os.path.join("./examples", item['video']) examples = hd_examples + examples VIDEO_EVAL_PROMPT = """ Suppose you are an expert in judging and evaluating the quality of AI-generated videos, please watch the following frames of a given video and see the text prompt for generating the video, then give scores from 7 different dimensions: (1) visual quality: the quality of the video in terms of clearness, resolution, brightness, and color (2) object consistency, the consistency of objects or humans in video (3) dynamic degree, the degree of dynamic changes (4) motion smoothness, the smoothness of motion or movements (5) text-to-video alignment, the alignment between the text prompt and the video content (6) factual consistency, the consistency of the video content with the common-sense and factual knowledge for each dimension, output a float number from 1.0 to 4.0, the higher the number is, the better the video performs in that sub-score, the lowest 1.0 means Bad, the highest 4.0 means Perfect/Real (the video is like a real video) Here is an output example: visual quality: 3.2 object consistency: 2.7 dynamic degree: 4.0 motion smoothness: 1.6 text-to-video alignment: 2.3 factual consistency: 1.8 For this video, the text prompt is "{text_prompt}", all the frames of video are as follows: """ aspect_mapping={ 1:"visual quality", 2:"object consistency", 3:"dynamic degree", 4:"motion smoothness", 5:'text-to-video alignment', 6:'factual consistency', } @spaces.GPU(duration=60) def score(prompt:str, images:List[Image.Image]): if not prompt: raise gr.Error("Please provide a prompt") model.to("cuda") if not images: images = None flatten_images = [] for x in images: if isinstance(x, list): flatten_images.extend(x) else: flatten_images.append(x) flatten_images = [Image.open(x) if isinstance(x, str) else x for x in flatten_images] inputs = processor(text=prompt, images=flatten_images, return_tensors="pt") inputs = {k: v.to(model.device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits num_aspects = logits.shape[-1] aspects = [aspect_mapping[i+1] for i in range(num_aspects)] aspect_scores = {} for i, aspect in enumerate(aspects): aspect_scores[aspect] = logits[0, i].item() return aspect_scores def read_video_pyav(container, indices): ''' Decode the video with PyAV decoder. Args: container (av.container.input.InputContainer): PyAV container. indices (List[int]): List of frame indices to decode. Returns: np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3). ''' frames = [] container.seek(0) start_index = indices[0] end_index = indices[-1] for i, frame in enumerate(container.decode(video=0)): if i > end_index: break if i >= start_index and i in indices: frames.append(frame) return np.stack([x.to_ndarray(format="rgb24") for x in frames]) def eval_video(prompt, video:str): container = av.open(video) # sample uniformly 8 frames from the video total_frames = container.streams.video[0].frames if total_frames > MAX_NUM_FRAMES: indices = np.arange(0, total_frames, total_frames / MAX_NUM_FRAMES).astype(int) else: indices = np.arange(total_frames) video_frames = read_video_pyav(container, indices) frames = [Image.fromarray(x) for x in video_frames] eval_prompt = VIDEO_EVAL_PROMPT.format(text_prompt=prompt) num_image_token = eval_prompt.count("") if num_image_token < len(frames): eval_prompt += " " * (len(frames) - num_image_token) aspect_scores = score(eval_prompt, [frames]) return aspect_scores def build_demo(): with gr.Blocks() as demo: gr.Markdown(""" ## Video Evaluation upload a video along with a text prompt when generating the video, this model will evaluate the video's quality from 7 different dimensions. """) with gr.Row(): video = gr.Video(width=500, label="Video") with gr.Column(): eval_prompt_template = gr.Textbox(VIDEO_EVAL_PROMPT.strip(' \n'), label="Evaluation Prompt Template", interactive=False, max_lines=26) video_prompt = gr.Textbox(label="Text Prompt", lines=1) with gr.Row(): eval_button = gr.Button("Evaluate Video") clear_button = gr.ClearButton([video, video_prompt]) # eval_result = gr.Textbox(label="Evaluation result", interactive=False, lines=7) eval_result = gr.Json(label="Evaluation result") eval_button.click( eval_video, [video_prompt, video], [eval_result] ) dummy_id = gr.Textbox("id", label="id", visible=False, min_width=50) dummy_output = gr.Textbox("reference score", label="reference scores", visible=False, lines=7) gr.Examples( examples= [ [ item['id'], item['prompt'], item['video'], item['conversations'][1]['value'] ] for item in examples ], inputs=[dummy_id, video_prompt, video, dummy_output], ) # gr.Markdown(""" # ## Citation # ``` # @article{jiang2024mantis, # title={MANTIS: Interleaved Multi-Image Instruction Tuning}, # author={Jiang, Dongfu and He, Xuan and Zeng, Huaye and Wei, Con and Ku, Max and Liu, Qian and Chen, Wenhu}, # journal={arXiv preprint arXiv:2405.01483}, # year={2024} # } # ```""") return demo if __name__ == "__main__": demo = build_demo() demo.launch(share=True)