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use candle::{Device, Result, Tensor};
use candle_nn::Module;
pub fn linspace(start: f64, stop: f64, steps: usize) -> Result<Tensor> {
if steps < 1 {
candle::bail!("cannot use linspace with steps {steps} <= 1")
}
let delta = (stop - start) / (steps - 1) as f64;
let vs = (0..steps)
.map(|step| start + step as f64 * delta)
.collect::<Vec<_>>();
Tensor::from_vec(vs, steps, &Device::Cpu)
}
/// Saves an image to disk using the image crate, this expects an input with shape
/// (c, width, height).
pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<()> {
let p = p.as_ref();
let (channel, width, height) = img.dims3()?;
if channel != 3 {
candle::bail!("save_image expects an input of shape (3, width, height)")
}
let img = img.transpose(0, 1)?.t()?.flatten_all()?;
let pixels = img.to_vec1::<u8>()?;
let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
Some(image) => image,
None => candle::bail!("error saving image {p:?}"),
};
image.save(p).map_err(candle::Error::wrap)?;
Ok(())
}
// Wrap the conv2d op to provide some tracing.
#[derive(Debug)]
pub struct Conv2d {
inner: candle_nn::Conv2d,
span: tracing::Span,
}
impl Conv2d {
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
self.inner.forward(x)
}
}
pub fn conv2d(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
cfg: candle_nn::Conv2dConfig,
vs: candle_nn::VarBuilder,
) -> Result<Conv2d> {
let span = tracing::span!(tracing::Level::TRACE, "conv2d");
let inner = candle_nn::conv2d(in_channels, out_channels, kernel_size, cfg, vs)?;
Ok(Conv2d { inner, span })
}
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