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from typing import Any, Callable, Sequence, Optional
from jax import lax, random, vmap, numpy as jnp
from jax.experimental.ode import odeint
import flax
from flax.training import train_state
from flax.core import freeze, unfreeze
from flax import linen as nn
from flax import serialization
import optax
import tensorflow_datasets as tfds
import numpy as np
# Define model
class CNN(nn.Module):
"""A simple CNN model."""
@nn.compact
def __call__(self, inputs):
x = inputs
x = nn.Conv(features=32, kernel_size=(3, 3))(x)
x = nn.relu(x)
x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
x = nn.Conv(features=64, kernel_size=(3, 3))(x)
x = nn.relu(x)
x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))
x = x.reshape((x.shape[0], -1)) # flatten
x = nn.Dense(features=256)(x)
x = nn.relu(x)
x = nn.Dense(features=10)(x)
x = nn.log_softmax(x)
return x
# Define Residual Block
class ResBlock(nn.Module):
"""Single Resblock w/o downsample"""
@nn.compact
def __call__(self, inputs):
x = inputs
f_x = nn.relu(nn.GroupNorm(64)(x))
f_x = nn.Conv(features=64, kernel_size=(3, 3))(f_x)
f_x = nn.relu(nn.GroupNorm(64)(f_x))
f_x = nn.Conv(features=64, kernel_size=(3, 3))(f_x)
x = f_x + x
return x
class ResDownBlock(nn.Module):
"""Single ResBlock w/ downsample"""
@nn.compact
def __call__(self, inputs):
x = inputs
f_x = nn.relu(nn.GroupNorm(64)(x))
x = nn.Conv(features=64, kernel_size=(1, 1), strides=(2, 2))(x)
f_x = nn.Conv(features=64, kernel_size=(3, 3), strides=(2, 2))(f_x)
f_x = nn.relu(nn.GroupNorm(64)(f_x))
f_x = nn.Conv(features=64, kernel_size=(3, 3))(f_x)
x = f_x + x
return x
# Define Model for Mnist example in Neural ODE
class SmallResNet(nn.Module):
res_down1: Callable = ResDownBlock()
res_down2: Callable = ResDownBlock()
resblock1: Callable = ResBlock()
resblock2: Callable = ResBlock()
resblock3: Callable = ResBlock()
resblock4: Callable = ResBlock()
resblock5: Callable = ResBlock()
resblock6: Callable = ResBlock()
@nn.compact
def __call__(self, inputs):
x = inputs
x = nn.Conv(features=64, kernel_size=(3, 3))(x)
x = self.res_down1(x)
x = self.res_down2(x)
x = self.resblock1(x)
x = self.resblock2(x)
x = self.resblock3(x)
x = self.resblock4(x)
x = self.resblock5(x)
x = self.resblock6(x)
x = nn.GroupNorm(64)(x)
x = nn.relu(x)
x = nn.avg_pool(x, (1, 1))
x = x.reshape((x.shape[0], -1)) # flatten
x = nn.Dense(features=10)(x)
x = nn.log_softmax(x)
return x
# Define loss
def cross_entropy_loss(*, logits, labels):
one_hot_labels = jax.nn.one_hot(labels, num_classes=10)
return -jnp.mean(jnp.sum(one_hot_labels * logits, axis=-1))
# Metric computation
def compute_metrics(*, logits, labels):
loss = cross_entropy_loss(logits=logits, labels=labels)
accuracy = jnp.mean(jnp.argmax(logits, -1) == labels)
metrics = {
'loss': loss,
'accuracy': accuracy,
}
return metrics
def get_datasets():
"""Load MNIST train and test datasets into memory."""
ds_builder = tfds.builder('mnist')
ds_builder.download_and_prepare()
train_ds = tfds.as_numpy(ds_builder.as_dataset(split='train', batch_size=-1))
test_ds = tfds.as_numpy(ds_builder.as_dataset(split='test', batch_size=-1))
train_ds['image'] = jnp.float32(train_ds['image']) / 255.
test_ds['image'] = jnp.float32(test_ds['image']) / 255.
return train_ds, test_ds
def create_train_state(rng, learning_rate):
"""Creates initial 'TrainState'."""
cnn = SmallResNet()
params = cnn.init(rng, jnp.ones([1, 28, 28, 1]))['params']
tx = optax.adam(learning_rate)
return train_state.TrainState.create(
apply_fn=cnn.apply, params=params, tx=tx
)
# Training step
@jax.jit
def train_step(state, batch):
"""Train for a single step."""
def loss_fn(params):
logits = SmallResNet().apply({'params': params}, batch['image'])
loss = cross_entropy_loss(logits=logits, labels=batch['label'])
return loss, logits
grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
(_, logits), grads = grad_fn(state.params)
state = state.apply_gradients(grads=grads)
metrics = compute_metrics(logits=logits, labels=batch['label'])
return state, metrics
# Evaluation step
@jax.jit
def eval_step(params, batch):
logits = SmallResNet().apply({'params': params}, batch['image'])
return compute_metrics(logits=logits, labels=batch['label'])
# Train function
def train_epoch(state, train_ds, batch_size, epoch, rng):
"""Train for a single epoch"""
train_ds_size = len(train_ds['image'])
steps_per_epoch = train_ds_size // batch_size
perms = jax.random.permutation(rng, len(train_ds['image']))
perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch
perms = perms.reshape((steps_per_epoch, batch_size))
batch_metrics = []
for perm in perms:
batch = {k: v[perm, ...] for k, v in train_ds.items()}
state, metrics = train_step(state, batch)
batch_metrics.append(metrics)
# compute mean of metrics across each batch in epoch.
batch_metrics_np = jax.device_get(batch_metrics)
epoch_metrics_np = {
k: np.mean([metrics[k] for metrics in batch_metrics_np])
for k in batch_metrics_np[0]
}
print('train epoch: %d, loss: %.4f, accuracy: %.2f' % (
epoch, epoch_metrics_np['loss'], epoch_metrics_np['accuracy'] * 100
))
return state
# Eval function
def eval_model(params, test_ds):
metrics = eval_step(params, test_ds)
metrics = jax.device_get(metrics)
summary = jax.tree_map(lambda x: x.item(), metrics)
return summary['loss'], summary['accuracy']
if __name__ == '__main__':
train_ds, test_ds = get_datasets()
rng = jax.random.PRNGKey(0)
rng, init_rng = jax.random.split(rng)
learning_rate = 0.0001
state = create_train_state(init_rng, learning_rate)
del init_rng # Must not be used anymore.
num_epochs = 40
batch_size = 128
for epoch in range(1, num_epochs + 1):
rng, input_rng = jax.random.split(rng)
state = train_epoch(state, train_ds, batch_size, epoch, input_rng)
test_loss, test_accuracy = eval_model(state.params, test_ds)
print(' test epoch: %d, loss: %.2f, accuracy: %.2f' % (
epoch, test_loss, test_accuracy * 100
))
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