File size: 2,918 Bytes
d2b26ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import json
import argparse
import pandas as pd
import gradio as gr
from vllm import LLM, SamplingParams
from vllm_struct_caption import VideoTextDataset
class StructCaptioner:
def __init__(self, model_path, tensor_parallel_size):
self.model = LLM(model=model_path,
gpu_memory_utilization=0.6,
max_model_len=31920,
tensor_parallel_size=tensor_parallel_size)
self.model_path = model_path
self.sampling_params = SamplingParams(temperature=0.05, max_tokens=2048)
def __call__(self, video_path):
meta = pd.DataFrame([video_path], columns=['path'])
dataset = VideoTextDataset(meta, self.model_path)
item = dataset[0]['input']
batch_user_inputs = [{
'prompt': item['prompt'],
'multi_modal_data':{'video': item['multi_modal_data']['video'][0]},
}]
outputs = self.model.generate(batch_user_inputs, self.sampling_params, use_tqdm=False)
caption = outputs[0].outputs[0].text
caption = json.loads(caption)
caption = json.dumps(caption, indent=4, ensure_ascii=False)
return caption
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--skycaptioner_model_path", required=True, type=str)
parser.add_argument("--tensor_parallel_size", type=int, default=2)
args = parser.parse_args()
struct_captioner = StructCaptioner(args.skycaptioner_model_path, args.tensor_parallel_size)
def generate_caption(video_path):
caption = struct_captioner(video_path)
return caption
with gr.Blocks() as demo:
gr.Markdown(
"""
<h1 style="text-align: center; font-size: 2em;">SkyCaptioner</h1>
""",
elem_id="header"
)
with gr.Row():
with gr.Column(visible=True, scale=0.5):
with gr.Row():
video_input = gr.Video(
label="Upload Video",
interactive=True,
format="mp4",
)
with gr.Column(visible=True):
json_output = gr.Code(
label="Caption",
language="json",
lines=25,
interactive=False
)
gr.Button("Generate").click(
fn=generate_caption,
inputs=video_input,
outputs=json_output
)
gr.Examples(
examples=[
["./examples/data/1.mp4"],
["./examples/data/2.mp4"],
],
inputs=video_input,
label="Example Videos"
)
demo.launch(
server_name="0.0.0.0",
server_port=7862,
share=False
)
if __name__ == '__main__':
main()
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