Add the candle-datasets crate (#322)

* Move the vision datasets to a separate crate.

* Move the batcher bits.

* Update the readme.

* Move the tiny-stories bits.

---------

Co-authored-by: Jane Doe <jane.doe@example.org>

candle-datasets/src/vision/cifar.rs

@@ -0,0 +1,62 @@
+//! The CIFAR-10 dataset.
+//!
+//! The files can be downloaded from the following page:
+//! <https://www.cs.toronto.edu/~kriz/cifar.html>
+//! The binary version of the dataset is used.
+use crate::vision::Dataset;
+use candle::{DType, Device, Result, Tensor};
+use std::fs::File;
+use std::io::{BufReader, Read};
+
+const W: usize = 32;
+const H: usize = 32;
+const C: usize = 3;
+const BYTES_PER_IMAGE: usize = W * H * C + 1;
+const SAMPLES_PER_FILE: usize = 10000;
+
+fn read_file(filename: &std::path::Path) -> Result<(Tensor, Tensor)> {
+    let mut buf_reader = BufReader::new(File::open(filename)?);
+    let mut data = vec![0u8; SAMPLES_PER_FILE * BYTES_PER_IMAGE];
+    buf_reader.read_exact(&mut data)?;
+    let mut images = vec![];
+    let mut labels = vec![];
+    for index in 0..SAMPLES_PER_FILE {
+        let content_offset = BYTES_PER_IMAGE * index;
+        labels.push(data[content_offset]);
+        images.push(&data[1 + content_offset..content_offset + BYTES_PER_IMAGE]);
+    }
+    let images: Vec<u8> = images
+        .iter()
+        .copied()
+        .flatten()
+        .copied()
+        .collect::<Vec<_>>();
+    let labels = Tensor::from_vec(labels, SAMPLES_PER_FILE, &Device::Cpu)?;
+    let images = Tensor::from_vec(images, (SAMPLES_PER_FILE, C, H, W), &Device::Cpu)?;
+    let images = (images.to_dtype(DType::F32)? / 255.)?;
+    Ok((images, labels))
+}
+
+pub fn load_dir<T: AsRef<std::path::Path>>(dir: T) -> Result<Dataset> {
+    let dir = dir.as_ref();
+    let (test_images, test_labels) = read_file(&dir.join("test_batch.bin"))?;
+    let train_images_and_labels = [
+        "data_batch_1.bin",
+        "data_batch_2.bin",
+        "data_batch_3.bin",
+        "data_batch_4.bin",
+        "data_batch_5.bin",
+    ]
+    .iter()
+    .map(|x| read_file(&dir.join(x)))
+    .collect::<Result<Vec<_>>>()?;
+    let (train_images, train_labels): (Vec<_>, Vec<_>) =
+        train_images_and_labels.into_iter().unzip();
+    Ok(Dataset {
+        train_images: Tensor::cat(&train_images, 0)?,
+        train_labels: Tensor::cat(&train_labels, 0)?,
+        test_images,
+        test_labels,
+        labels: 10,
+    })
+}