Add CVAE in Flax

docs/source/index.rst

@@ -73,6 +73,7 @@ NumPyro documentation
    examples/holt_winters
    examples/mortality
    examples/zero_inflated_poisson
+   examples/cvae
 
 .. nbgallery::
    :maxdepth: 1

examples/cvae-flax/README.md

@@ -0,0 +1,12 @@
+## Conditional Variational Autoencoder in Flax
+
+Trains a *Conditional Variational Autoencoder* (CVAE) on the MNIST data using Flax' neural network API. 
+
+The model first trains a baseline to predict an entire MNIST image from a single quadrant of it (i.e., input is one quadrant of an image, output is the entire image (not the other three quadrants)).
+Then, in a second model, the generation/prior/recognition nets of the CVAE are trained while keeping the model parameters of the baseline fixed/frozen.
+We use Optax' `multi_transform` to apply different gradient transformations to the trainable parameters and the frozen parameters.
+
+Running `main.py` trains the model(s) and plots a figure in the end comparing the baseline prediction with the CVAE prediction like this one:
+
+![CVAE prediction](https://github.com/pyro-ppl/numpyro/tree/master/docs/source/_static/img/examples/cvae.png) 
+

examples/cvae-flax/data.py

@@ -0,0 +1,46 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+
+from jax import lax
+
+from numpyro.examples.datasets import _load
+
+
+def load_dataset(
+    dset, batch_size=None, split="train", shuffle=True, num_datapoints=None, seed=23
+):
+    data = _load(dset, num_datapoints)
+    if isinstance(data, dict):
+        arrays = data[split]
+    num_records = len(arrays[0])
+    idxs = np.arange(num_records)
+    if not batch_size:
+        batch_size = num_records
+
+    Y = arrays[0]
+    X = Y.copy()
+    _, m, n = X.shape
+
+    X = X[:, (m // 2) :, : (n // 2)]
+    arrays = (X, Y)
+
+    def init():
+        np.random.seed(seed)
+        return (
+            num_records // batch_size,
+            np.random.permutation(idxs) if shuffle else idxs,
+        )
+
+    def get_batch(i=0, idxs=idxs):
+        ret_idx = lax.dynamic_slice_in_dim(idxs, i * batch_size, batch_size)
+        d = tuple(
+            np.take(a, ret_idx, axis=0)
+            if isinstance(a, list)
+            else lax.index_take(a, (ret_idx,), axes=(0,))
+            for a in arrays
+        )
+        return d
+
+    return init, get_batch

examples/cvae-flax/main.py

@@ -0,0 +1,88 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+
+from data import load_dataset
+import matplotlib.pyplot as plt
+from models import BaselineNet, Decoder, Encoder, cvae_guide, cvae_model
+from train_baseline import train_baseline
+from train_cvae import train_cvae
+
+from numpyro.examples.datasets import MNIST
+
+
+def main(args):
+    train_init, train_fetch = load_dataset(
+        MNIST, batch_size=args.batch_size, split="train", seed=args.rng_seed
+    )
+    test_init, test_fetch = load_dataset(
+        MNIST, batch_size=args.batch_size, split="test", seed=args.rng_seed
+    )
+
+    num_train, train_idx = train_init()
+    num_test, test_idx = test_init()
+
+    baseline = BaselineNet()
+    baseline_params = train_baseline(
+        baseline,
+        num_train,
+        train_idx,
+        train_fetch,
+        num_test,
+        test_idx,
+        test_fetch,
+        n_epochs=args.num_epochs,
+    )
+
+    cvae_params = train_cvae(
+        cvae_model,
+        cvae_guide,
+        baseline_params,
+        num_train,
+        train_idx,
+        train_fetch,
+        num_test,
+        test_idx,
+        test_fetch,
+        n_epochs=args.num_epochs,
+    )
+
+    x_test, y_test = test_fetch(0, test_idx)
+
+    baseline = BaselineNet()
+    recognition_net = Encoder()
+    generation_net = Decoder()
+
+    y_hat_base = baseline.apply({"params": cvae_params["baseline$params"]}, x_test)
+    z_loc, z_scale = recognition_net.apply(
+        {"params": cvae_params["recognition_net$params"]}, x_test, y_hat_base
+    )
+    y_hat_vae = generation_net.apply(
+        {"params": cvae_params["generation_net$params"]}, z_loc
+    )
+
+    fig, axs = plt.subplots(4, 10, figsize=(15, 5))
+    for i in range(10):
+        axs[0][i].imshow(x_test[i])
+        axs[1][i].imshow(y_test[i])
+        axs[2][i].imshow(y_hat_base[i])
+        axs[3][i].imshow(y_hat_vae[i])
+        for j, label in enumerate(["Input", "Truth", "Baseline", "CVAE"]):
+            axs[j][i].set_xticks([])
+            axs[j][i].set_yticks([])
+            if i == 0:
+                axs[j][i].set_ylabel(label, rotation="horizontal", labelpad=40)
+    plt.show()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Conditional Variational Autoencoder on MNIST using Flax"
+    )
+    parser.add_argument("--batch-size", type=int, default=128)
+    parser.add_argument("--rng_seed", type=int, default=23)
+    parser.add_argument("--num-epochs", type=int, default=10)
+
+    args = parser.parse_args()
+    main(args)

examples/cvae-flax/models.py

@@ -0,0 +1,92 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+from flax import linen as nn
+from jax import numpy as jnp
+
+import numpyro
+from numpyro.contrib.module import flax_module
+import numpyro.distributions as dist
+
+
+def cross_entropy_loss(y_pred, y):
+    log_p = jnp.log(y_pred)
+    log_not_p = jnp.log1p(-y_pred)
+    return -y * log_p - (1.0 - y) * log_not_p
+
+
+class BaselineNet(nn.Module):
+    hidden_size: int = 512
+
+    @nn.compact
+    def __call__(self, x):
+        batch_size, _, _ = x.shape
+        y_hat = nn.relu(nn.Dense(self.hidden_size)(x.reshape(-1, 196)))
+        y_hat = nn.relu(nn.Dense(self.hidden_size)(y_hat))
+        y_hat = nn.Dense(784)(y_hat)
+        y_hat = nn.sigmoid(y_hat).reshape((-1, 28, 28))
+        return y_hat
+
+
+class Encoder(nn.Module):
+    hidden_size: int = 512
+    latent_dim: int = 256
+
+    @nn.compact
+    def __call__(self, x, y):
+        z = jnp.concatenate([x.reshape(-1, 196), y.reshape(-1, 784)], axis=-1)
+        hidden = nn.relu(nn.Dense(self.hidden_size)(z))
+        hidden = nn.relu(nn.Dense(self.hidden_size)(hidden))
+        z_loc = nn.Dense(self.latent_dim)(hidden)
+        z_scale = jnp.exp(nn.Dense(self.latent_dim)(hidden))
+        return z_loc, z_scale
+
+
+class Decoder(nn.Module):
+    hidden_size: int = 512
+    latent_dim: int = 256
+
+    @nn.compact
+    def __call__(self, z):
+        y_hat = nn.relu(nn.Dense(self.hidden_size)(z))
+        y_hat = nn.relu(nn.Dense(self.hidden_size)(y_hat))
+        y_hat = nn.Dense(784)(y_hat)
+        y_hat = nn.sigmoid(y_hat).reshape((-1, 28, 28))
+        return y_hat
+
+
+def cvae_model(x, y=None):
+    baseline_net = flax_module(
+        "baseline", BaselineNet(), x=jnp.ones((1, 14, 14), dtype=jnp.float32)
+    )
+    prior_net = flax_module(
+        "prior_net",
+        Encoder(),
+        x=jnp.ones((1, 14, 14), dtype=jnp.float32),
+        y=jnp.ones((1, 28, 28), dtype=jnp.float32),
+    )
+    generation_net = flax_module(
+        "generation_net", Decoder(), z=jnp.ones((1, 256), dtype=jnp.float32)
+    )
+
+    y_hat = baseline_net(x)
+    z_loc, z_scale = prior_net(x, y_hat)
+    z = numpyro.sample("z", dist.Normal(z_loc, z_scale))
+    loc = generation_net(z)
+
+    if y is None:
+        numpyro.deterministic("y", loc)
+    else:
+        numpyro.sample("y", dist.Bernoulli(loc), obs=y)
+    return loc
+
+
+def cvae_guide(x, y=None):
+    recognition_net = flax_module(
+        "recognition_net",
+        Encoder(),
+        x=jnp.ones((1, 14, 14), dtype=jnp.float32),
+        y=jnp.ones((1, 28, 28), dtype=jnp.float32),
+    )
+    loc, scale = recognition_net(x, y)
+    numpyro.sample("z", dist.Normal(loc, scale))

examples/cvae-flax/train_baseline.py

@@ -0,0 +1,83 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+from models import cross_entropy_loss
+
+from flax.training.train_state import TrainState
+import jax
+from jax import lax, numpy as jnp, random
+import optax
+
+
+def create_train_state(model, x, learning_rate_fn):
+    params = model.init(random.PRNGKey(0), x)
+    tx = optax.adam(learning_rate_fn)
+    state = TrainState.create(apply_fn=model.apply, params=params, tx=tx)
+    return state
+
+
+def train_step(state, x_batched, y_batched):
+    def loss_fn(params):
+        y_pred = state.apply_fn(params, x_batched)
+        loss = cross_entropy_loss(y_pred, y_batched)
+        return jnp.sum(loss)
+
+    grad_fn = jax.value_and_grad(loss_fn)
+    loss, grads = grad_fn(state.params)
+
+    new_state = state.apply_gradients(grads=grads)
+    return new_state, loss
+
+
+def train_epoch(state, train_fetch, num_train, train_idx, epoch_rng):
+    def _fn(i, val):
+        state, loss_sum = val
+        x_batched, y_batched = train_fetch(i, train_idx)
+        state, loss = train_step(state, x_batched, y_batched)
+        loss_sum += loss
+        return state, loss_sum
+
+    return lax.fori_loop(0, num_train, _fn, (state, 0.0))
+
+
+def eval_epoch(state, test_fetch, num_test, test_idx, epoch_rng):
+    def _fn(i, loss_sum):
+        x_batched, y_batched = test_fetch(i, test_idx)
+        y_pred = state.apply_fn(state.params, x_batched)
+        loss = cross_entropy_loss(y_pred, y_batched)
+        loss_sum += jnp.sum(loss)
+        return loss_sum
+
+    loss = lax.fori_loop(0, num_test, _fn, 0.0)
+    loss = loss / num_test
+    return loss
+
+
+def train_baseline(
+    model,
+    num_train,
+    train_idx,
+    train_fetch,
+    num_test,
+    test_idx,
+    test_fetch,
+    n_epochs=100,
+):
+
+    state = create_train_state(model, train_fetch(0, train_idx)[0], 0.003)
+
+    rng = random.PRNGKey(0)
+    best_val_loss = jnp.inf
+    best_state = state
+    for i in range(n_epochs):
+        epoch_rng = jax.random.fold_in(rng, i)
+        state, train_loss = train_epoch(
+            state, train_fetch, num_train, train_idx, epoch_rng
+        )
+        val_loss = eval_epoch(state, test_fetch, num_test, test_idx, epoch_rng)
+        print(f"Epoch loss - train loss: {train_loss}, validation loss: {val_loss}")
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            best_state = state
+
+    return best_state.params