| import av |
| import torch |
| import decord |
| from decord import VideoReader, cpu |
|
|
| import numpy as np |
| from PIL import Image |
| from huggingface_hub import hf_hub_download |
| from transformers import LlavaNextVideoProcessor, LlavaNextVideoForConditionalGeneration, SiglipImageProcessor |
|
|
| model_id = "/raid/raushan/llava-next-video-qwen-7b" |
|
|
| model = LlavaNextVideoForConditionalGeneration.from_pretrained( |
| model_id, |
| torch_dtype=torch.bfloat16, |
| low_cpu_mem_usage=True, |
| ).to(0) |
|
|
| processor = LlavaNextVideoProcessor.from_pretrained(model_id, torch_dtype=torch.bfloat16) |
| img_proc = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384") |
|
|
| image = Image.open("/raid/raushan/image.png") |
|
|
|
|
| def load_video(video_path, max_frames_num,fps=1,force_sample=False): |
|
|
| vr = VideoReader(video_path) |
| total_frame_num = len(vr) |
| video_time = total_frame_num / vr.get_avg_fps() |
| fps = round(vr.get_avg_fps()/fps) |
| frame_idx = [i for i in range(0, len(vr), fps)] |
| frame_time = [i/fps for i in frame_idx] |
| if len(frame_idx) > max_frames_num or force_sample: |
| sample_fps = max_frames_num |
| uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int) |
| frame_idx = uniform_sampled_frames.tolist() |
| frame_time = [i/vr.get_avg_fps() for i in frame_idx] |
| frame_time = ",".join([f"{i:.2f}s" for i in frame_time]) |
| spare_frames = vr.get_batch(frame_idx).asnumpy() |
| print(spare_frames.shape) |
| return spare_frames,frame_time,video_time |
|
|
|
|
| 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: |
| result (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]) |
|
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| |
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| |
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| |
| |
|
|
| conversation = [ |
| { |
|
|
| "role": "system", |
| "content": [ |
| {"type": "text", "text": "You are a helpful assistant."}, |
| ], |
| }, |
| { |
|
|
| "role": "user", |
| "content": [ |
| {"type": "text", "text": "The video lasts for 19.97 seconds, and 64 frames are uniformly sampled from it. These frames are located at 0.00s,0.30s,0.60s,0.93s,1.23s,1.57s,1.87s,2.20s,2.50s,2.83s,3.13s,3.47s,3.77s,4.10s,4.40s,4.73s,5.03s,5.37s,5.67s,6.00s,6.30s,6.63s,6.93s,7.27s,7.57s,7.90s,8.20s,8.53s,8.83s,9.17s,9.47s,9.80s,10.10s,10.43s,10.73s,11.07s,11.37s,11.70s,12.00s,12.33s,12.63s,12.97s,13.27s,13.60s,13.90s,14.23s,14.53s,14.87s,15.17s,15.50s,15.80s,16.13s,16.43s,16.77s,17.07s,17.40s,17.70s,18.03s,18.33s,18.67s,18.97s,19.30s,19.60s,19.93s.Please answer the following questions related to this video.\nPlease describe this video in detail."}, |
| {"type": "video"}, |
| ], |
| }, |
| ] |
|
|
| prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) |
| prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<video>The video lasts for 19.97 seconds, and 64 frames are uniformly sampled from it. These frames are located at 0.00s,0.30s,0.60s,0.93s,1.23s,1.57s,1.87s,2.20s,2.50s,2.83s,3.13s,3.47s,3.77s,4.10s,4.40s,4.73s,5.03s,5.37s,5.67s,6.00s,6.30s,6.63s,6.93s,7.27s,7.57s,7.90s,8.20s,8.53s,8.83s,9.17s,9.47s,9.80s,10.10s,10.43s,10.73s,11.07s,11.37s,11.70s,12.00s,12.33s,12.63s,12.97s,13.27s,13.60s,13.90s,14.23s,14.53s,14.87s,15.17s,15.50s,15.80s,16.13s,16.43s,16.77s,17.07s,17.40s,17.70s,18.03s,18.33s,18.67s,18.97s,19.30s,19.60s,19.93s.Please answer the following questions related to this video.\nPlease describe this video in detail.<|im_end|>\n<|im_start|>assistant" |
|
|
| video_path = "/raid/raushan/karate.mp4" |
| container = av.open(video_path) |
|
|
| |
| total_frames = container.streams.video[0].frames |
| indices = np.arange(0, total_frames, total_frames / 64).astype(int) |
| clip = read_video_pyav(container, indices) |
|
|
| clip, frame_time,video_time = load_video(video_path, max_frames_num=64, force_sample=True) |
| inputs_video = processor(text=prompt, videos=clip, return_tensors="pt").to(device=model.device, dtype=torch.bfloat16) |
|
|
| output = model.generate(**inputs_video, max_new_tokens=100, do_sample=False) |
| print(processor.decode(output[0][2:], skip_special_tokens=True)) |
|
|
