1 files changed, 0 insertions, 62 deletions
diff --git a/candle-nn/src/vision/cifar.rs b/candle-nn/src/vision/cifar.rs
deleted file mode 100644
index 0683c4d2..00000000
--- a/candle-nn/src/vision/cifar.rs
+++ /dev/null
@@ -1,62 +0,0 @@
-//! 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,
-    })
-}