jhqxxx
314 lines
13 KiB
Rust
314 lines
13 KiB
Rust
// use std::io::Cursor;
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// use std::fs::File;
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// use symphonia::core::io::MediaSourceStream;
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// use std::io::{Read, Seek};
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// use std::{io::Cursor, time::Instant};
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// use aha::utils::tensor_utils::repeat_interleave;
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// use aha_openai_dive::v1::resources::chat::ChatCompletionParameters;
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use anyhow::Result;
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// use byteorder::{LittleEndian, ReadBytesExt};
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// use candle_core::Tensor;
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use modelscope::{DownloadOptions, ModelScope};
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// use sentencepiece::SentencePieceProcessor;
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// use zip::ZipArchive;
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#[tokio::test]
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async fn download_test() -> Result<()> {
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// cargo test -F cuda --test messy_test download_test -r -- --nocapture
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let model_id = "unsloth/Qwen3.5-4B-GGUF";
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let model_name = "Qwen3.5-4B-IQ4_NL.gguf";
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let save_dir =
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aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
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let _ = ModelScope::download_with_options(
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model_id,
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save_dir,
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DownloadOptions {
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files: (vec![model_name.to_string()]).into(),
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},
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)
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.await;
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Ok(())
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}
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#[test]
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fn messy_test() -> Result<()> {
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// RUST_BACKTRACE=1 cargo test -F cuda --test messy_test messy_test -r -- --nocapture
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// let t1 = Tensor::randn(0.0, 1.0, (1, 2, 6), device)?;
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// println!(" t1: {}", t1);
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// let t2 = t1.pad_with_zeros(D::Minus1, -3, 0)?;
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// println!(" t2: {}", t);
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// let save_dir =
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// aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
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// let model_path = format!("{}/deepseek-ai/DeepSeek-OCR-2/", save_dir);
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// let stem = std::path::Path::new(&model_path)
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// .file_name()
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// .and_then(|s| s.to_str())
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// .unwrap_or("qwen3.5");
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// println!("stem: {:?}", stem);
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// let device = &candle_core::Device::Cpu;
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// let t1 = Tensor::randn(0.0, 1.0, (16, 9, 64, 128), device)?;
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// let t2 = Tensor::randn(0.0, 1.0, (16, 9, 128, 64), device)?;
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// let out = t1.matmul(&t2)?;
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// println!("out shape: {:?}", out);
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// let input = Tensor::arange(0.0f32, 25.0f32, device)?.reshape((5, 5))?;
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// println!("input: {}", input);
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// // let input = input.unsqueeze(D::Minus1)?;
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// // let input = input.repeat((1, 1, 2))?;
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// // let input = input.flatten(D::Minus2, D::Minus1)?;
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// let output = repeat_interleave(&input, 2, 1)?;
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// println!("output: {}", output);
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// let x_nearest = interpolate_nearest_2d(&input, (10, 10))?;
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// println!("x_nearest: {}", x_nearest);
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// let input = Tensor::arange(0.0f32, 25.0f32, device)?.reshape((1, 5, 5))?;
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// println!("input: {}", input);
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// let x_nearest = interpolate_nearest_1d(&input, 10)?;
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// println!("x_nearest: {}", x_nearest);
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// let save_dir: String =
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// aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
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// let model_path = format!("{}/IndexTeam/IndexTTS-2/", save_dir);
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// let emo_matrix_path = model_path.clone() + "/feat2.pt";
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// let t_emo = load_tensor_from_pt(
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// &emo_matrix_path,
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// "feat2/data/0",
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// Shape::from_dims(&[73, 1280]),
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// device,
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// )?;
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// println!("t_emo: {}", t_emo);
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// let skp_matrix_path = model_path + "/feat1.pt";
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// let t_skp = load_tensor_from_pt(
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// &skp_matrix_path,
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// "feat1/data/0",
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// Shape::from_dims(&[73, 192]),
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// device,
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// )?;
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// println!("t_skp: {}", t_skp);
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// let file = File::open(emo_matrix_path)?;
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// let mut archive = ZipArchive::new(file)?;
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// // 列出所有文件（调试用）
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// for i in 0..archive.len() {
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// let file = archive.by_index(i)?;
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// println!("File: {} ({} bytes)", file.name(), file.size());
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// }
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// // 读取原始字节数据
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// let mut data_file = archive.by_name("feat2/data/0")?;
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// let mut buffer = Vec::new();
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// data_file.read_to_end(&mut buffer)?;
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// // 将字节转换为 f32 (little endian)
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// let mut cursor = Cursor::new(buffer);
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// let num_elements = 73 * 1280; // 93,440
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// let mut data = Vec::with_capacity(num_elements);
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// for _ in 0..num_elements {
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// let val = cursor.read_f32::<LittleEndian>()?;
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// data.push(val);
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// }
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// let t = Tensor::from_vec(data, (73, 1280), device)?;
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// println!("t: {}", t);
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// let message = r#"
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// {
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// "model": "index-tts2",
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// "messages": [
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// {
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// "role": "user",
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// "content": [
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// {
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// "type": "audio",
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// "audio_url":
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// {
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// "url": "file:///home/jhq/Videos/voice_01.wav"
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// }
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// },
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// {
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// "type": "text",
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// "text": "你好啊"
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// }
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// ]
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// }
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// ],
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// "metadata": {"emo_vector": "[0, 0, 0, 0, 0, 0, 0.45, 0]"}
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// }
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// "#;
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// let mes: ChatCompletionParameters = serde_json::from_str(message)?;
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// if let Some(map) = &mes.metadata
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// && let Some(emo_vector_str) = map.get("emo_vector")
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// {
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// match serde_json::from_str::<Vec<f32>>(emo_vector_str) {
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// Ok(emo_vector) => {
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// println!("Parsed emo_vector: {:?}", emo_vector);
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// // 现在 emo_vector 是 Vec<f32>: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.45, 0.0]
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// }
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// Err(e) => {
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// eprintln!("Failed to parse emo_vector: {}", e);
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// }
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// }
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// }
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// let save_dir =
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// aha::utils::get_default_save_dir().ok_or(anyhow::anyhow!("Failed to get save dir"))?;
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// let model_path = format!("{}/IndexTeam/IndexTTS-2", save_dir);
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// let bpe_path = model_path.to_string() + "/bpe.model";
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// let tokenizer = SentencePieceProcessor::open(bpe_path)
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// .map_err(|e| anyhow!(format!("load bpe.model file error:{}", e)))?;
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// let tokens = tokenizer
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// .encode("你好啊")
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// .map_err(|e| anyhow!(format!("tokenizer encode error:{}", e)))?;
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// println!("tokens: {:?}", tokens);
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// let t = Tensor::arange(0.0f32, 40.0, device)?.broadcast_as((1, 40, 40))?;
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// println!("t: {}", t);
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// let i_start = Instant::now();
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// let t_inter = interpolate_nearest_1d(&t, 20)?;
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// let i_duration = i_start.elapsed();
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// println!("Time elapsed in interpolate_nearest_1d is: {:?}", i_duration);
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// println!("t_inter: {}", t_inter);
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// let url = "https://sis-sample-audio.obs.cn-north-1.myhuaweicloud.com/16k16bit.mp3";
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// let client = reqwest::blocking::Client::new();
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// let response = client.get(url).send()?;
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// let vec_u8 = response.bytes()?.to_vec();
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// let mut content = Cursor::new(vec_u8);
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// let mss = MediaSourceStream::new(Box::new(content), Default::default());
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// let window = create_hann_window(400, DType::F32, device)?;
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// println!("window: {}", window);
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// let audio_path = "file:///home/jhq/Videos/voice_01.wav";
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// let audio_path = "/home/jhq/Videos/zh.mp3";
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// let audio_path = "/home/jhq/Videos/zh.mp3";
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// // let audio_tensor = load_and_resample_audio_rubato(audio_path, 16000, device)?;
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// // let audio_tensor = load_audio_with_resample(audio_path, device, Some(16000))?;
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// // println!("audio_tensor: {}", audio_tensor);
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// #[cfg(feature = "ffmpeg")]
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// {
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// use aha::utils::audio_utils::load_and_resample_audio_ffmpeg;
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// let audio_tensor = load_and_resample_audio_ffmpeg(audio_path, Some(16000), device)?;
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// println!("audio_tensor: {}", audio_tensor);
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// }
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// // let path = get_default_save_dir();
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// // let x = Tensor::new(array, device)
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// let x = Tensor::arange(0.0, 9.0, device)?;
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// println!("x: {}", x);
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// let x = x
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// .unsqueeze(0)?
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// .unsqueeze(0)?
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// .broadcast_as((5, 5, 9))?
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// .reshape((5, 5, 3, 3))?;
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// println!("x: {}", x);
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// let x = x.permute((0, 2, 1, 3))?;
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// println!("x: {}", x);
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// let x = x.reshape((15, 15))?;
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// println!("x: {}", x);
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// let xs = Tensor::rand(0.0, 5.0, (1, 1, 3, 3), device)?;
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// println!("xs: {}", xs);
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// let xs = xs.pad_with_zeros(3, 2, 2)?
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// .pad_with_zeros(2, 2, 2)?;
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// println!("xs: {}", xs);
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// let xs = Tensor::arange(0.0, 25.0, device)?;
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// println!("xs: {}", xs);
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// let splits = split_tensor_with_size(&xs, 5, 0)?;
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// for v in splits {
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// println!("v: {}", v);
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// }
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// let xs = Tensor::arange(0.0, 25.0, device)?.broadcast_as((1, 1, 5, 5))?;
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// println!("xs: {}", xs);
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// let xs = xs.avg_pool2d(5)?;
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// println!("xs: {}", xs);
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// let xs = Tensor::rand(0.0, 1.0, (1, 4, 4, 2), device)?;
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// println!("xs: {}", xs);
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// let shape = Shape::from_dims(&[1, 2, 2, 2, 2, 2]);
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// let xs = xs.reshape(shape)?;
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// println!("xs: {}", xs);
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// let x0 = xs.i((.., .., 0, .., 0, ..))?;
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// let x1 = xs.i((.., .., 1, .., 0, ..))?;
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// let x2 = xs.i((.., .., 0, .., 1, ..))?;
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// let x3 = xs.i((.., .., 1, .., 1, ..))?;
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// let xs = Tensor::cat(&[x0, x1, x2, x3], D::Minus1)?;
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// println!("xs: {}", xs);
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// let xs = xs.reshape((1, (), 4 * 2))?;
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// println!("xs: {}", xs);
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// let path_str = "file://./assets/img/ocr_test1.png";
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// let path = url::Url::from_str(path_str)?;
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// let path = path.to_file_path();
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// let path = match path {
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// Ok(path) => path,
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// Err(_) => {
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// let mut path = path_str.to_owned();
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// path = path.split_off(7);
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// PathBuf::from(path)
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// }
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// };
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// println!("to file path: {:?}", path);
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// let device = &candle_core::Device::Cpu;
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// let t = Tensor::arange(0.0f32, 40.0, device)?.broadcast_as((1, 1, 40, 40))?;
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// println!("t: {}", t);
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// let i_start = Instant::now();
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// let t_inter = interpolate_bilinear(&t, (20, 20), Some(false))?;
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// let i_duration = i_start.elapsed();
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// println!("Time elapsed in interpolate_bilinear is: {:?}", i_duration);
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// println!("t_inter: {}", t_inter);
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// let x: Vec<u32> = (0..5).flat_map(|_| 0u32..10).collect();
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// let id: Vec<u32> = (0..5).flat_map(|h| vec![h; 10]).collect();
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// println!("x: {:?}", id);
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// let t = Tensor::randn(0.0f32, 1.0, (1, 768, 64, 64), device)?;
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// let t = Tensor::arange(0u32, 10, device)?.broadcast_as((1, 10))?;
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// let eq = t.broadcast_eq(&Tensor::new(5u32, device)?)?;
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// println!("eq: {}", eq);
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// let t = Tensor::arange(0.0f32, 10.0, device)?.broadcast_as((1, 1, 10, 10))?;
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// println!("t: {}", t);
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// let t_resized = interpolate_bicubic(&t, (5, 5), Some(true), Some(false))?;
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// println!("t_resized: {}", t_resized);
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// let t1 = Tensor::rand(0.0, 1.0, (1, 5, 5, 10), device)?;
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// let t2 = Tensor::rand(0.0, 1.0, (5, 8, 10), device)?;
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// let t2 = t2.t()?;
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// println!("t2: {:?}", t2);
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// let re = t1.broadcast_matmul(&t2)?;
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// println!("re: {:?}", re);
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// let index = Tensor::arange(0u32, 10u32, device)?;
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// let index_2d_vec = vec![index;5];
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// let index_2d = Tensor::stack(&index_2d_vec, 0)?;
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// println!("index_2d: {}", index_2d);
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// let t = Tensor::rand(0.0, 1.0, (20, 8), device)?;
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// println!("t: {}", t);
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// let res = index_select_2d(&t, &index_2d)?;
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// println!("res: {}", res);
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// let t = Tensor::arange(0.0, 10.0, device)?
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// .unsqueeze(0)?
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// .unsqueeze(0)?;
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// println!("t: {}", t);
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// let t_resized = interpolate_linear(&t, 20, None)?;
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// println!("t_resized: {}", t_resized);
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// let grid_thw = Tensor::new(vec![vec![3u32, 12, 20], vec![5, 30, 25]], device)?;
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// let cu_seqlens = grid_thw.i((.., 1))?.mul(&grid_thw.i((.., 2))?)?;
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// let grid_t = grid_thw.i((.., 0))?.to_vec1::<u32>()?;
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// println!("cu_seqlens: {}", cu_seqlens);
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// println!("cu_seqlens rank: {}", cu_seqlens.rank());
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// println!("grid_t: {:?}", grid_t);
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// let image_mask = Tensor::new(vec![0u32, 0, 0, 1, 0, 1], device)?;
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// let video_mask = Tensor::new(vec![0u32, 1, 0, 1, 0, 1], device)?;
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// let visual_mask = bitor_tensor(&image_mask, &video_mask)?;
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// println!("visual_mask: {}", visual_mask);
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// let x = Tensor::arange_step(0.0_f32, 5., 0.5, &device)?;
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// let x_int = x.to_dtype(candle_core::DType::U32)?;
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// println!("x: {}", x);
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// println!("x_int: {}", x_int);
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// let x_affine = x_int.affine(1.0, 1.0)?;
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// println!("x_affine: {}", x_affine);
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// let x_clamp = x_affine.clamp(0u32, 3u32)?;
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// println!("x_clamp: {}", x_clamp);
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// let wav_path = "./assets/audio/voice_01.wav";
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// let audio_tensor = load_audio_with_resample(wav_path, device, Some(16000))?;
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// println!("audio_tensor: {}", audio_tensor);
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// let string = "你好啊".to_string();
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// let vec_str: Vec<String>= string.chars().map(|c| c.to_string()).collect();
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// println!("vec_str: {:?}", vec_str);
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// let t = Tensor::rand(-1.0, 1.0, (2, 2), &device)?;
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// println!("t: {}", t);
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// let re_t = t.recip()?;
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// println!("re_t: {}", re_t);
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Ok(())
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}
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