InternVideo2-Chat-8B

Cómo usar el modelo

import os
token = os.environ['HF_TOKEN']
import torch

tokenizer =  AutoTokenizer.from_pretrained('OpenGVLab/InternVideo2-Chat-8B', trust_remote_code=True, use_fast=False)

from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained(
'OpenGVLab/InternVideo2-Chat-8B',
torch_dtype=torch.bfloat16,
trust_remote_code=True).cuda()

from decord import VideoReader, cpu
from PIL import Image
import numpy as np
import numpy as np
import decord
from decord import VideoReader, cpu
import torch.nn.functional as F
import torchvision.transforms as T
from torchvision.transforms import PILToTensor
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
decord.bridge.set_bridge('torch')

def get_index(num_frames, num_segments):
    seg_size = float(num_frames - 1) / num_segments
    start = int(seg_size / 2)
    offsets = np.array([
        start + int(np.round(seg_size * idx)) for idx in range(num_segments)
    ])
    return offsets

def load_video(video_path, num_segments=8, return_msg=False, resolution=224, hd_num=4, padding=False):
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    num_frames = len(vr)
    frame_indices = get_index(num_frames, num_segments)
    mean = (0.485, 0.456, 0.406)
    std = (0.229, 0.224, 0.225)
    transform = transforms.Compose([
        transforms.Lambda(lambda x: x.float().div(255.0)),
        transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
        transforms.CenterCrop(224),
        transforms.Normalize(mean, std)
    ])
    frames = vr.get_batch(frame_indices)
    frames = frames.permute(0, 3, 1, 2)
    frames = transform(frames)
    T_, C, H, W = frames.shape
    if return_msg:
        fps = float(vr.get_avg_fps())
        sec = ', '.join([str(round(f / fps, 1)) for f in frame_indices])
        msg = f'El video contiene {len(frame_indices)} cuadros muestreados en {sec} segundos.'
        return frames, msg
    else:
        return frames

video_path = 'yoga.mp4'
# muestra uniformemente 8 cuadros del video
video_tensor = load_video(video_path, num_segments=8, return_msg=False)
video_tensor = video_tensor.to(model.device)

chat_history= []
response, chat_history = model.chat(tokenizer, '', 'describe the action step by step.', media_type='video', media_tensor=video_tensor, chat_history= chat_history, return_history=True,generation_config={'do_sample':False})
print(response)

response, chat_history = model.chat(tokenizer, '', 'What is she wearing?', media_type='video', media_tensor=video_tensor, chat_history= chat_history, return_history=True,generation_config={'do_sample':False})
print(response)