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use crate::dataloader::{DataLoader, DataSlice};
use auto_diff::{Var, AutoDiffError};
use std::path::{Path, };
use std::io;
use std::fs::File;
use std::io::Read;
pub struct Mnist {
train: Var,
test: Var,
train_label: Var,
test_label: Var,
}
impl Mnist {
pub fn new() -> Mnist {
unimplemented!()
}
pub fn load(path: &Path) -> Mnist {
let train_fn = path.join("train-images-idx3-ubyte");
let test_fn = path.join("t10k-images-idx3-ubyte");
let train_label_fn = path.join("train-labels-idx1-ubyte");
let test_label_fn = path.join("t10k-labels-idx1-ubyte");
let train_img;
let test_img;
let train_label;
let test_label;
if path.exists() {
train_img = Self::load_images(train_fn);
test_img = Self::load_images(test_fn);
train_label = Self::load_labels(train_label_fn);
test_label = Self::load_labels(test_label_fn);
} else {
unimplemented!()
}
Mnist {
train: train_img,
test: test_img,
train_label,
test_label,
}
}
fn load_images<P: AsRef<Path>>(path: P) -> Var {
let mut reader = io::BufReader::new(File::open(path).expect(""));
let magic = Self::read_as_u32(&mut reader);
if magic != 2051 {
panic!("Invalid magic number. expected 2051, got {}", magic)
}
let num_image = Self::read_as_u32(&mut reader) as usize;
let rows = Self::read_as_u32(&mut reader) as usize;
let cols = Self::read_as_u32(&mut reader) as usize;
assert!(rows == 28 && cols == 28);
let mut buf: Vec<u8> = vec![0u8; num_image * rows * cols];
let _ = reader.read_exact(buf.as_mut());
let ret: Vec<f64> = buf.into_iter().map(|x| (x as f64) / 255.).collect();
Var::new(&ret[..], &[num_image, rows, cols])
}
fn load_labels<P: AsRef<Path>>(path: P) -> Var {
let mut reader = io::BufReader::new(File::open(path).expect(""));
let magic = Self::read_as_u32(&mut reader);
if magic != 2049 {
panic!("Invalid magic number. Got expect 2049, got {}", magic);
}
let num_label = Self::read_as_u32(&mut reader) as usize;
let mut buf: Vec<u8> = vec![0u8; num_label];
let _ = reader.read_exact(buf.as_mut());
let ret: Vec<f64> = buf.into_iter().map(|x| x as f64).collect();
Var::new(&ret[..], &[num_label])
}
fn read_as_u32<T: Read>(reader: &mut T) -> u32 {
let mut buf: [u8; 4] = [0, 0, 0, 0];
let _ = reader.read_exact(&mut buf);
u32::from_be_bytes(buf)
}
}
impl DataLoader for Mnist {
fn get_size(&self, slice: Option<DataSlice>) -> Result<Vec<usize>, AutoDiffError> {
match slice {
Some(DataSlice::Train) => {Ok(self.train.size())},
Some(DataSlice::Test) => {Ok(self.test.size())},
None => {
let n = self.train.size()[0] + self.test.size()[1];
let mut new_size = self.train.size();
new_size[0] = n;
Ok(new_size)
},
_ => {Err(AutoDiffError::new("TODO"))}
}
}
fn get_item(&self, index: usize, slice: Option<DataSlice>) -> Result<(Var, Var), AutoDiffError> {
match slice {
Some(DataSlice::Train) => {
let dim = self.train.size().len();
let mut index_block = vec![(index, index+1)];
index_block.append(
&mut vec![0; dim-1].iter().zip(&self.train.size()[1..])
.map(|(x,y)| (*x, *y)).collect());
let data = self.train.get_patch(&index_block, None)?;
let label = self.train_label.get_patch(&[(index, index+1)], None)?;
self.train.reset_net();
self.train_label.reset_net();
Ok((data, label))
},
Some(DataSlice::Test) => {
let dim = self.test.size().len();
let mut index_block = vec![(index, index+1)];
index_block.append(
&mut vec![0; dim-1].iter().zip(&self.test.size()[1..])
.map(|(x,y)| (*x, *y)).collect());
let data = self.test.get_patch(&index_block, None)?;
let label = self.test_label.get_patch(&[(index, index+1)], None)?;
self.test.reset_net();
self.test_label.reset_net();
Ok((data, label))
},
_ => {Err(AutoDiffError::new("only train and test"))}
}
}
fn get_batch(&self, start: usize, end: usize, slice: Option<DataSlice>) -> Result<(Var, Var), AutoDiffError> {
match slice {
Some(DataSlice::Train) => {
let dim = self.train.size().len();
let mut index_block = vec![(start, end)];
index_block.append(
&mut vec![0; dim-1].iter().zip(&self.train.size()[1..])
.map(|(x,y)| (*x, *y)).collect());
let data = self.train.get_patch(&index_block, None)?;
let label = self.train_label.get_patch(&[(start, end)], None)?;
self.train.reset_net();
self.train_label.reset_net();
Ok((data, label))
},
Some(DataSlice::Test) => {
let dim = self.test.size().len();
let mut index_block = vec![(start, end)];
index_block.append(
&mut vec![0; dim-1].iter().zip(&self.test.size()[1..])
.map(|(x,y)| (*x, *y)).collect());
let data = self.test.get_patch(&index_block, None)?;
let label = self.test_label.get_patch(&[(start, end)], None)?;
self.test.reset_net();
self.test_label.reset_net();
Ok((data, label))
},
_ => {Err(AutoDiffError::new("only train and test"))}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn mnist() {
let mnist = Mnist::load(Path::new("../auto-diff/examples/data/mnist/"));
let (t0, l0) = mnist.get_item(0, Some(DataSlice::Test)).unwrap();
println!("{:?}", t0);
}
}
