tch 0.24.0

Rust wrappers for the PyTorch C++ api (libtorch).
Documentation 
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//! The MNIST hand-written digit dataset.
//!
//! The files can be obtained from the following link:
//! <http://yann.lecun.com/exdb/mnist/>
use super::dataset::Dataset;
use crate::{Kind, Tensor};
use std::fs::File;
use std::io::{self, BufReader, Read, Result};

fn read_u32<T: Read>(reader: &mut T) -> Result<u32> {
    let mut b = vec![0u8; 4];
    reader.read_exact(&mut b)?;
    let (result, _) =
        b.iter().rev().fold((0u64, 1u64), |(s, basis), &x| (s + basis * u64::from(x), basis * 256));
    Ok(result as u32)
}

fn check_magic_number<T: Read>(reader: &mut T, expected: u32) -> Result<()> {
    let magic_number = read_u32(reader)?;
    if magic_number != expected {
        return Err(io::Error::other(format!(
            "incorrect magic number {magic_number} != {expected}"
        )));
    }
    Ok(())
}

fn read_labels_(filename: &std::path::Path) -> Result<Tensor> {
    let mut buf_reader = BufReader::new(File::open(filename)?);
    check_magic_number(&mut buf_reader, 2049)?;
    let samples = read_u32(&mut buf_reader)?;
    let mut data = vec![0u8; samples as usize];
    buf_reader.read_exact(&mut data)?;
    Ok(Tensor::from_slice(&data).to_kind(Kind::Int64))
}

fn read_images_(filename: &std::path::Path) -> Result<Tensor> {
    let mut buf_reader = BufReader::new(File::open(filename)?);
    check_magic_number(&mut buf_reader, 2051)?;
    let samples = read_u32(&mut buf_reader)?;
    let rows = read_u32(&mut buf_reader)?;
    let cols = read_u32(&mut buf_reader)?;
    let data_len = samples * rows * cols;
    let mut data = vec![0u8; data_len as usize];
    buf_reader.read_exact(&mut data)?;
    let tensor = Tensor::from_slice(&data)
        .view((i64::from(samples), i64::from(rows * cols)))
        .to_kind(Kind::Float);
    Ok(tensor / 255.)
}

fn read_labels(filename: &std::path::Path) -> Result<Tensor> {
    read_labels_(filename)
        .map_err(|err| std::io::Error::new(err.kind(), format!("{filename:?} {err}")))
}

fn read_images(filename: &std::path::Path) -> Result<Tensor> {
    read_images_(filename)
        .map_err(|err| std::io::Error::new(err.kind(), format!("{filename:?} {err}")))
}

pub fn load_dir<T: AsRef<std::path::Path>>(dir: T) -> Result<Dataset> {
    let dir = dir.as_ref();
    let train_images = read_images(&dir.join("train-images-idx3-ubyte"))?;
    let train_labels = read_labels(&dir.join("train-labels-idx1-ubyte"))?;
    let test_images = read_images(&dir.join("t10k-images-idx3-ubyte"))?;
    let test_labels = read_labels(&dir.join("t10k-labels-idx1-ubyte"))?;
    Ok(Dataset { train_images, train_labels, test_images, test_labels, labels: 10 })
}