jhqxxx
38 lines
1.6 KiB
Rust
38 lines
1.6 KiB
Rust
use aha::utils::tensor_utils::bitor_tensor;
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use anyhow::Result;
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use candle_core::{IndexOp, Tensor};
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#[test]
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fn messy_test() -> Result<()> {
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let device = &candle_core::Device::Cpu;
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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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