Add variable autoencoder from tch-rs examples

1 files changed, 95 insertions, 12 deletions

diff --git a/src/vae.rs b/src/vae.rs
index 2fc6ae8..3821cb1 100644
--- a/src/vae.rs
+++ b/src/vae.rs
@@ -1,13 +1,96 @@
-use tch::nn::{Module, OptimizerConfig};
-use tch::{kind, nn, Device, Tensor};
-
-pub fn vae(vs: &nn::Path) -> impl Module {
-    nn::seq()
-        .add(nn::linear(vs, 100, 50, Default::default()))
-        .add_fn(|xs| xs.relu())
-        .add(nn::linear(vs, 50, 10, Default::default()))
-        .add_fn(|xs| xs.relu())
-        .add(nn::linear(vs, 10, 50, Default::default()))
-        .add_fn(|xs| xs.relu())
-        .add(nn::linear(vs, 50, 100, Default::default()))
+/* Variational Auto-Encoder on MNIST.
+   The implementation is based on:
+     https://github.com/pytorch/examples/blob/master/vae/main.py
+
+   The 4 following dataset files can be downloaded from http://yann.lecun.com/exdb/mnist/
+   These files should be extracted in the 'data' directory.
+     train-images-idx3-ubyte.gz
+     train-labels-idx1-ubyte.gz
+     t10k-images-idx3-ubyte.gz
+     t10k-labels-idx1-ubyte.gz
+*/
+
+use anyhow::Result;
+use tch::{nn, nn::Module, nn::OptimizerConfig, Kind, Reduction, Tensor};
+
+struct VAE {
+    fc1: nn::Linear,
+    fc21: nn::Linear,
+    fc22: nn::Linear,
+    fc3: nn::Linear,
+    fc4: nn::Linear,
+}
+
+impl VAE {
+    fn new(vs: &nn::Path) -> Self {
+        VAE {
+            fc1: nn::linear(vs / "fc1", 784, 400, Default::default()),
+            fc21: nn::linear(vs / "fc21", 400, 20, Default::default()),
+            fc22: nn::linear(vs / "fc22", 400, 20, Default::default()),
+            fc3: nn::linear(vs / "fc3", 20, 400, Default::default()),
+            fc4: nn::linear(vs / "fc4", 400, 784, Default::default()),
+        }
+    }
+
+    fn encode(&self, xs: &Tensor) -> (Tensor, Tensor) {
+        let h1 = xs.apply(&self.fc1).relu();
+        (self.fc21.forward(&h1), self.fc22.forward(&h1))
+    }
+
+    fn decode(&self, zs: &Tensor) -> Tensor {
+        zs.apply(&self.fc3).relu().apply(&self.fc4).sigmoid()
+    }
+
+    fn forward(&self, xs: &Tensor) -> (Tensor, Tensor, Tensor) {
+        let (mu, logvar) = self.encode(&xs.view([-1, 784]));
+        let std = (&logvar * 0.5).exp();
+        let eps = std.randn_like();
+        (self.decode(&(&mu + eps * std)), mu, logvar)
+    }
 }
+
+// Reconstruction + KL divergence losses summed over all elements and batch dimension.
+fn loss(recon_x: &Tensor, x: &Tensor, mu: &Tensor, logvar: &Tensor) -> Tensor {
+    let bce = recon_x.binary_cross_entropy::<Tensor>(&x.view([-1, 784]), None, Reduction::Sum);
+    // See Appendix B from VAE paper:
+    //     Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+    // https://arxiv.org/abs/1312.6114
+    // 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+    let kld = -0.5 * (1i64 + logvar - mu.pow_tensor_scalar(2) - logvar.exp()).sum(Kind::Float);
+    bce + kld
+}
+
+// Generate a 2D matrix of images from a tensor with multiple images.
+fn image_matrix(imgs: &Tensor, sz: i64) -> Result<Tensor> {
+    let imgs = (imgs * 256.).clamp(0., 255.).to_kind(Kind::Uint8);
+    let mut ys: Vec<Tensor> = vec![];
+    for i in 0..sz {
+        ys.push(Tensor::cat(&(0..sz).map(|j| imgs.narrow(0, 4 * i + j, 1)).collect::<Vec<_>>(), 2))
+    }
+    Ok(Tensor::cat(&ys, 3).squeeze_dim(0))
+}
+
+pub fn main() -> Result<()> {
+    let device = tch::Device::cuda_if_available();
+    let m = tch::vision::mnist::load_dir("data")?;
+    let vs = nn::VarStore::new(device);
+    let vae = VAE::new(&vs.root());
+    let mut opt = nn::Adam::default().build(&vs, 1e-3)?;
+    for epoch in 1..21 {
+        let mut train_loss = 0f64;
+        let mut samples = 0f64;
+        for (bimages, _) in m.train_iter(128).shuffle().to_device(vs.device()) {
+            let (recon_batch, mu, logvar) = vae.forward(&bimages);
+            let loss = loss(&recon_batch, &bimages, &mu, &logvar);
+            opt.backward_step(&loss);
+            train_loss += f64::from(&loss);
+            samples += bimages.size()[0] as f64;
+        }
+        println!("Epoch: {}, loss: {}", epoch, train_loss / samples);
+        let s = Tensor::randn(&[64, 20], tch::kind::FLOAT_CPU).to(device);
+        let s = vae.decode(&s).to(tch::Device::Cpu).view([64, 1, 28, 28]);
+        tch::vision::image::save(&image_matrix(&s, 8)?, format!("s_{}.png", epoch))?
+    }
+    Ok(())
+}
+