Video-Text-to-Text
Transformers
Safetensors
English
llava
text-generation
multimodal
Eval Results
Inference Endpoints
ZhangYuanhan mfarre HF staff commited on
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1 Parent(s): 7bcec28

Update README.md (#3)

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- Update README.md (e0b7f7a7a63a5dd8ec988ef5ed2014cca7d4cfe4)


Co-authored-by: Miquel Farré <mfarre@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +3 -3
README.md CHANGED
@@ -171,7 +171,7 @@ import warnings
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  from decord import VideoReader, cpu
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  import numpy as np
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  warnings.filterwarnings("ignore")
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- def load_video(self, video_path, max_frames_num,fps=1,force_sample=False):
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  if max_frames_num == 0:
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  return np.zeros((1, 336, 336, 3))
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  vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
@@ -196,9 +196,9 @@ device_map = "auto"
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  tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args
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  model.eval()
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  video_path = "XXXX"
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- max_frames_num = "64"
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  video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
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- video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16()
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  video = [video]
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  conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
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  time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."
 
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  from decord import VideoReader, cpu
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  import numpy as np
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  warnings.filterwarnings("ignore")
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+ def load_video(video_path, max_frames_num,fps=1,force_sample=False):
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  if max_frames_num == 0:
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  return np.zeros((1, 336, 336, 3))
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  vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
 
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  tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args
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  model.eval()
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  video_path = "XXXX"
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+ max_frames_num = 64
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  video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
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+ video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().half()
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  video = [video]
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  conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
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  time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."