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app.py
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from decord import VideoReader, cpu
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import torch
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import numpy as np
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from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
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from huggingface_hub import hf_hub_download
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import gradio as gr
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np.random.seed(0)
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def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
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converted_len = int(clip_len * frame_sample_rate)
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end_idx = np.random.randint(converted_len, seg_len)
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start_idx = end_idx - converted_len
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indices = np.linspace(start_idx, end_idx, num=clip_len)
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indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
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return indices
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def inference(file_path):
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# video clip consists of 300 frames (10 seconds at 30 FPS)
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videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))
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# sample 16 frames
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videoreader.seek(0)
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indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader))
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video = videoreader.get_batch(indices).asnumpy()
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feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
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model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
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inputs = feature_extractor(list(video), return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits
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# model predicts one of the 400 Kinetics-400 classes
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predicted_label = logits.argmax(-1).item()
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return model.config.id2label[predicted_label]
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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video = gr.Video()
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btn = gr.Button(value="Run")
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with gr.Column():
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label = gr.Textbox(label="Predicted Label")
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translate_btn.click(inference, inputs=video, outputs=label)
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demo.launch()
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