def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
train_split = dataset.Split.from_string(FLAGS.train_split)
eval_split = dataset.Split.from_string(FLAGS.eval_split)
# The total batch size is the batch size accross all hosts and devices. In a
# multi-host training setup each host will only see a batch size of
# `total_train_batch_size / jax.host_count()`.
total_train_batch_size = FLAGS.train_device_batch_size * jax.device_count()
num_train_steps = (train_split.num_examples * 90) // total_train_batch_size
local_device_count = jax.local_device_count()
train_dataset = dataset.load(
train_split,
batch_dims=[local_device_count, FLAGS.train_device_batch_size])
# For initialization we need the same random key on each device.
rng = jax.random.PRNGKey(FLAGS.train_init_random_seed)
rng = jnp.broadcast_to(rng, (local_device_count, ) + rng.shape)
# Initialization requires an example input.
batch = next(train_dataset)
params, state, opt_state = jax.pmap(make_initial_state)(rng, batch)
eval_every = FLAGS.train_eval_every
log_every = FLAGS.train_log_every
with time_activity('train'):
for step_num in range(num_train_steps):
# Take a single training step.
params, state, opt_state, train_scalars = (train_step(
params, state, opt_state, next(train_dataset)))
# By default we do not evaluate during training, but you can configure
# this with a flag.
if eval_every > 0 and step_num and step_num % eval_every == 0:
with time_activity('eval during train'):
eval_scalars = evaluate(eval_split, params, state)
logging.info(
f'[Eval {step_num}/{num_train_steps}] {eval_scalars}')
# Log progress at fixed intervals.
if step_num and step_num % log_every == 0:
train_scalars = jax.tree_map(lambda v: np.mean(v).item(),
jax.device_get(train_scalars))
logging.info(
f'[Train {step_num}/{num_train_steps}] {train_scalars}')
# Once training has finished we run eval one more time to get final results.
with time_activity('final eval'):
eval_scalars = evaluate(eval_split, params, state)
logging.info(f'[Eval FINAL]: {eval_scalars}')
def evaluate(
split: dataset.Split,
params: hk.Params,
state: hk.State,
) -> Scalars:
"""Evaluates the model at the given params/state."""
# Params/state are sharded per-device during training. We just need the copy
# from the first device (since we do not pmap evaluation at the moment).
params, state = jax.tree_map(lambda x: x[0], (params, state))
test_dataset = dataset.load(split, batch_dims=[FLAGS.eval_batch_size])
correct = total = 0
for batch in test_dataset:
correct += eval_batch(params, state, next(test_dataset))
total += batch['images'].shape[0]
return {'top_1_acc': correct.item() / total}
