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