Add variable autoencoder from tch-rs examples
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src/vae.rs
105
src/vae.rs
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@ -1,13 +1,96 @@
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use tch::nn::{Module, OptimizerConfig};
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use tch::{kind, nn, Device, Tensor};
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/* Variational Auto-Encoder on MNIST.
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The implementation is based on:
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https://github.com/pytorch/examples/blob/master/vae/main.py
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pub fn vae(vs: &nn::Path) -> impl Module {
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nn::seq()
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.add(nn::linear(vs, 100, 50, Default::default()))
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.add_fn(|xs| xs.relu())
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.add(nn::linear(vs, 50, 10, Default::default()))
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.add_fn(|xs| xs.relu())
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.add(nn::linear(vs, 10, 50, Default::default()))
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.add_fn(|xs| xs.relu())
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.add(nn::linear(vs, 50, 100, Default::default()))
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The 4 following dataset files can be downloaded from http://yann.lecun.com/exdb/mnist/
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These files should be extracted in the 'data' directory.
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train-images-idx3-ubyte.gz
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train-labels-idx1-ubyte.gz
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t10k-images-idx3-ubyte.gz
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t10k-labels-idx1-ubyte.gz
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*/
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use anyhow::Result;
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use tch::{nn, nn::Module, nn::OptimizerConfig, Kind, Reduction, Tensor};
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struct VAE {
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fc1: nn::Linear,
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fc21: nn::Linear,
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fc22: nn::Linear,
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fc3: nn::Linear,
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fc4: nn::Linear,
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}
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impl VAE {
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fn new(vs: &nn::Path) -> Self {
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VAE {
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fc1: nn::linear(vs / "fc1", 784, 400, Default::default()),
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fc21: nn::linear(vs / "fc21", 400, 20, Default::default()),
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fc22: nn::linear(vs / "fc22", 400, 20, Default::default()),
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fc3: nn::linear(vs / "fc3", 20, 400, Default::default()),
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fc4: nn::linear(vs / "fc4", 400, 784, Default::default()),
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}
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}
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fn encode(&self, xs: &Tensor) -> (Tensor, Tensor) {
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let h1 = xs.apply(&self.fc1).relu();
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(self.fc21.forward(&h1), self.fc22.forward(&h1))
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}
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fn decode(&self, zs: &Tensor) -> Tensor {
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zs.apply(&self.fc3).relu().apply(&self.fc4).sigmoid()
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}
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fn forward(&self, xs: &Tensor) -> (Tensor, Tensor, Tensor) {
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let (mu, logvar) = self.encode(&xs.view([-1, 784]));
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let std = (&logvar * 0.5).exp();
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let eps = std.randn_like();
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(self.decode(&(&mu + eps * std)), mu, logvar)
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}
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}
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// Reconstruction + KL divergence losses summed over all elements and batch dimension.
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fn loss(recon_x: &Tensor, x: &Tensor, mu: &Tensor, logvar: &Tensor) -> Tensor {
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let bce = recon_x.binary_cross_entropy::<Tensor>(&x.view([-1, 784]), None, Reduction::Sum);
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// See Appendix B from VAE paper:
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// Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
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// https://arxiv.org/abs/1312.6114
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// 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
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let kld = -0.5 * (1i64 + logvar - mu.pow_tensor_scalar(2) - logvar.exp()).sum(Kind::Float);
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bce + kld
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}
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// Generate a 2D matrix of images from a tensor with multiple images.
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fn image_matrix(imgs: &Tensor, sz: i64) -> Result<Tensor> {
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let imgs = (imgs * 256.).clamp(0., 255.).to_kind(Kind::Uint8);
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let mut ys: Vec<Tensor> = vec![];
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for i in 0..sz {
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ys.push(Tensor::cat(&(0..sz).map(|j| imgs.narrow(0, 4 * i + j, 1)).collect::<Vec<_>>(), 2))
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}
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Ok(Tensor::cat(&ys, 3).squeeze_dim(0))
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}
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pub fn main() -> Result<()> {
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let device = tch::Device::cuda_if_available();
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let m = tch::vision::mnist::load_dir("data")?;
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let vs = nn::VarStore::new(device);
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let vae = VAE::new(&vs.root());
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let mut opt = nn::Adam::default().build(&vs, 1e-3)?;
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for epoch in 1..21 {
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let mut train_loss = 0f64;
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let mut samples = 0f64;
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for (bimages, _) in m.train_iter(128).shuffle().to_device(vs.device()) {
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let (recon_batch, mu, logvar) = vae.forward(&bimages);
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let loss = loss(&recon_batch, &bimages, &mu, &logvar);
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opt.backward_step(&loss);
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train_loss += f64::from(&loss);
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samples += bimages.size()[0] as f64;
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}
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println!("Epoch: {}, loss: {}", epoch, train_loss / samples);
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let s = Tensor::randn(&[64, 20], tch::kind::FLOAT_CPU).to(device);
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let s = vae.decode(&s).to(tch::Device::Cpu).view([64, 1, 28, 28]);
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tch::vision::image::save(&image_matrix(&s, 8)?, format!("s_{}.png", epoch))?
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}
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Ok(())
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}
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