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-rw-r--r--candle-nn/src/vision/cifar.rs62
-rw-r--r--candle-nn/src/vision/mnist.rs65
-rw-r--r--candle-nn/src/vision/mod.rs12
3 files changed, 0 insertions, 139 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,
- })
-}
diff --git a/candle-nn/src/vision/mnist.rs b/candle-nn/src/vision/mnist.rs
deleted file mode 100644
index 2267f9a0..00000000
--- a/candle-nn/src/vision/mnist.rs
+++ /dev/null
@@ -1,65 +0,0 @@
-//! The MNIST hand-written digit dataset.
-//!
-//! The files can be obtained from the following link:
-//! <http://yann.lecun.com/exdb/mnist/>
-use candle::{DType, Device, Result, Tensor};
-use std::fs::File;
-use std::io::{self, BufReader, Read};
-
-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 {
- Err(io::Error::new(
- io::ErrorKind::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)?;
- let samples = data.len();
- Tensor::from_vec(data, samples, &Device::Cpu)
-}
-
-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)? as usize;
- let rows = read_u32(&mut buf_reader)? as usize;
- let cols = read_u32(&mut buf_reader)? as usize;
- let data_len = samples * rows * cols;
- let mut data = vec![0u8; data_len];
- buf_reader.read_exact(&mut data)?;
- let tensor = Tensor::from_vec(data, (samples, rows * cols), &Device::Cpu)?;
- tensor.to_dtype(DType::F32)? / 255.
-}
-
-pub fn load_dir<T: AsRef<std::path::Path>>(dir: T) -> Result<crate::vision::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(crate::vision::Dataset {
- train_images,
- train_labels,
- test_images,
- test_labels,
- labels: 10,
- })
-}
diff --git a/candle-nn/src/vision/mod.rs b/candle-nn/src/vision/mod.rs
deleted file mode 100644
index 6ce743eb..00000000
--- a/candle-nn/src/vision/mod.rs
+++ /dev/null
@@ -1,12 +0,0 @@
-use candle::Tensor;
-
-pub struct Dataset {
- pub train_images: Tensor,
- pub train_labels: Tensor,
- pub test_images: Tensor,
- pub test_labels: Tensor,
- pub labels: usize,
-}
-
-pub mod cifar;
-pub mod mnist;