[New Sample] Add 'squim_subjective' Model Computational Graph

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PR Category

New Sample

Description

Model: squim_subjective
Framework: Pytorch
Dependency: torchaudio
Content: 要把 graph_net.torch.extract 中 dynamic 设置为 False 才可以验证通

复现脚本，dynamic=False

import json
import torch
import torchaudio
import graph_net

if __name__ == "__main__":
    bundle = torchaudio.pipelines.SQUIM_SUBJECTIVE
    model = bundle.get_model()
    model.eval()

    batch_size = 1
    sample_rate = bundle.sample_rate
    num_seconds = 5
    num_samples = sample_rate * num_seconds
    dummy_waveform = torch.randn(batch_size, num_samples)
    dummy_reference = torch.randn(batch_size, num_samples)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    dummy_waveform = dummy_waveform.to(device)
    dummy_reference = dummy_reference.to(device)
    print(f"Using device: {device}")

    model = graph_net.torch.extract(name="squim_subjective", dynamic=False)(model)

    print("Running inference...")
    print("Input waveform shape:", dummy_waveform.shape)
    print("Input reference shape:", dummy_reference.shape)

    score = model(dummy_waveform, dummy_reference)
    
    print("Inference finished.")

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