mirror of
https://github.com/huggingface/candle.git
synced 2025-06-16 10:38:54 +00:00
a044907ffc
* Add the dilation parameter. * Restore the basic optimizer example. * Dilation support in cudnn. * Use the dilation parameter in the cpu backend. * More dilation support. * No support for dilation in transposed convolutions. * Add dilation to a test. * Remove a print. * Helper function.
30 lines
908 B
Rust
30 lines
908 B
Rust
#[cfg(feature = "accelerate")]
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extern crate accelerate_src;
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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use anyhow::Result;
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use candle_core::{Device, Tensor};
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fn main() -> Result<()> {
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let device = Device::new_cuda(0)?;
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let in_t = Tensor::rand(-1f32, 1f32, (1, 3, 12, 7), &device)?;
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let k_t = Tensor::rand(-1f32, 1f32, (6, 3, 1, 1), &device)?;
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let out_t = in_t.conv2d(&k_t, 0, 1, 1, 1)?;
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println!("{out_t}");
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let in_t = in_t.to_device(&Device::Cpu)?;
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let k_t = k_t.to_device(&Device::Cpu)?;
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let out_t2 = in_t.conv2d(&k_t, 0, 1, 1, 1)?;
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let diff = (out_t.to_device(&Device::Cpu)? - out_t2)?
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.sqr()?
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.sum_all()?;
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println!("{diff}");
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let t = Tensor::randn(0f32, 1f32, (2, 4, 96, 96), &device)?;
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let w = Tensor::randn(0f32, 1f32, (320, 4, 3, 3), &device)?;
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let res = t.conv2d(&w, 1, 1, 1, 1)?;
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println!("{res:?}");
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Ok(())
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}
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