vlmtest/models/InternVideo2_5.py

import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD)])
    return transform

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set((i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = ((i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size)
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_images(images_path, input_size=448, max_num=1):
    transform = build_transform(input_size=input_size)
    pixel_values_list, num_patches_list = [], []
    for image_path in images_path:
        img = Image.open(image_path).convert("RGB")
        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
        pixel_values = [transform(tile) for tile in img]
        pixel_values = torch.stack(pixel_values)
        num_patches_list.append(pixel_values.shape[0])
        pixel_values_list.append(pixel_values)
    pixel_values = torch.cat(pixel_values_list)
    return pixel_values, num_patches_list
    
        
class InternVideo2_5(object):
    def __init__(self, gpu=1, model_path='OpenGVLab/InternVideo2_5_Chat_8B'):
        self.model_path = model_path
        self.device = torch.device(f"cuda:{gpu}" if torch.cuda.is_available() else "cpu")
        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
        self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().to(self.device).to(torch.bfloat16)
        self.generation_config = dict(
            do_sample=False,
            temperature=0.0,
            max_new_tokens=1024,
            top_p=0.1,
            num_beams=1
        )

    def inference(self, images_path, qa):
        with torch.no_grad():
            pixel_values, num_patches_list = load_images(images_path)
            pixel_values = pixel_values.to(torch.bfloat16).to(self.model.device)
            video_prefix = "".join([f"Frame{i+1}: <image>\n" for i in range(len(num_patches_list))])
            
            question = f"{video_prefix} This question is about the main topic discussed in the video. Question: {qa['question']} Choices: A) {qa['choice_a']} B) {qa['choice_b']} C) {qa['choice_c']} D) {qa['choice_d']}. Respond with a single capital letter (A, B, C, or D) only. No explanation. No punctuation. Just the letter."
            output, chat_history = self.model.chat(self.tokenizer, pixel_values, question, self.generation_config, num_patches_list=num_patches_list, history=None, return_history=True)
            return output