def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
train_input_fn = utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size // FLAGS.batch_repetitions,
use_bfloat16=FLAGS.use_bfloat16)
clean_test_input_fn = utils.load_input_fn(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_datasets_from_function(
train_input_fn)
test_datasets = {
'clean':
strategy.experimental_distribute_datasets_from_function(
clean_test_input_fn),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_input_fn = utils.load_cifar10_c_input_fn
else:
load_c_input_fn = functools.partial(
utils.load_cifar100_c_input_fn, path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
input_fn = load_c_input_fn(
corruption_name=corruption,
corruption_intensity=intensity,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_datasets_from_function(input_fn))
ds_info = tfds.builder(FLAGS.dataset).info
train_batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores // FLAGS.batch_repetitions
test_batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
train_dataset_size = ds_info.splits['train'].num_examples
steps_per_epoch = train_dataset_size // train_batch_size
steps_per_eval = ds_info.splits['test'].num_examples // test_batch_size
num_classes = ds_info.features['label'].num_classes
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building Keras model')
model = cifar_model_reg_path.wide_resnet(
input_shape=[FLAGS.ensemble_size] +
list(ds_info.features['image'].shape),
depth=28,
width_multiplier=FLAGS.width_multiplier,
num_classes=num_classes,
ensemble_size=FLAGS.ensemble_size,
l2=FLAGS.l2, l1=FLAGS.l1)
logging.info('Model input shape: %s', model.input_shape)
logging.info('Model output shape: %s', model.output_shape)
logging.info('Model number of weights: %s', model.count_params())
# Linearly scale learning rate and the decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * train_batch_size / 128
lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
for start_epoch_str in FLAGS.lr_decay_epochs]
lr_schedule = utils.LearningRateSchedule(steps_per_epoch, base_lr,
FLAGS.lr_decay_ratio,
lr_decay_epochs,
FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(
lr_schedule, momentum=0.9, nesterov=True)
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'train/loss': tf.keras.metrics.Mean(),
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
for log10_threshold in NNZ_LOG10_THRESHOLDS:
metrics['train/nnz{}'.format(log10_threshold)] = tf.keras.metrics.Mean()
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
for i in range(FLAGS.ensemble_size):
metrics['test/nll_member_{}'.format(i)] = tf.keras.metrics.Mean()
metrics['test/accuracy_member_{}'.format(i)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
test_diversity = {
'test/disagreement': tf.keras.metrics.Mean(),
'test/average_kl': tf.keras.metrics.Mean(),
'test/cosine_similarity': tf.keras.metrics.Mean(),
}
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
batch_size = tf.shape(images)[0]
main_shuffle = tf.random.shuffle(tf.tile(
tf.range(batch_size), [FLAGS.batch_repetitions]))
to_shuffle = tf.cast(tf.cast(tf.shape(main_shuffle)[0], tf.float32)
* (1. - FLAGS.input_repetition_probability),
tf.int32)
shuffle_indices = [
tf.concat([tf.random.shuffle(main_shuffle[:to_shuffle]),
main_shuffle[to_shuffle:]], axis=0)
for _ in range(FLAGS.ensemble_size)]
images = tf.stack([tf.gather(images, indices, axis=0)
for indices in shuffle_indices], axis=1)
labels = tf.stack([tf.gather(labels, indices, axis=0)
for indices in shuffle_indices], axis=1)
with tf.GradientTape() as tape:
logits = model(images, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
negative_log_likelihood = tf.reduce_mean(tf.reduce_sum(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True), axis=1))
regularization = sum(model.losses)
# Scale the loss given the TPUStrategy will reduce sum all gradients.
loss = negative_log_likelihood + regularization
scaled_loss = loss / strategy.num_replicas_in_sync
grads = tape.gradient(scaled_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
probs = tf.nn.softmax(tf.reshape(logits, [-1, num_classes]))
flat_labels = tf.reshape(labels, [-1])
metrics['train/ece'].update_state(flat_labels, probs)
metrics['train/loss'].update_state(loss)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(flat_labels, probs)
for log10_threshold in NNZ_LOG10_THRESHOLDS:
nnz = compute_nnz(model, threshold=10.**log10_threshold)
metrics['train/nnz{}'.format(log10_threshold)].update_state(nnz)
strategy.run(step_fn, args=(next(iterator),))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
images = tf.tile(
tf.expand_dims(images, 1), [1, FLAGS.ensemble_size, 1, 1, 1])
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
probs = tf.nn.softmax(logits)
if dataset_name == 'clean':
per_probs = tf.transpose(probs, perm=[1, 0, 2])
diversity_results = um.average_pairwise_diversity(
per_probs, FLAGS.ensemble_size)
for k, v in diversity_results.items():
test_diversity['test/' + k].update_state(v)
for i in range(FLAGS.ensemble_size):
member_probs = probs[:, i]
member_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, member_probs)
metrics['test/nll_member_{}'.format(i)].update_state(member_loss)
metrics['test/accuracy_member_{}'.format(i)].update_state(
labels, member_probs)
# Negative log marginal likelihood computed in a numerically-stable way.
labels_tiled = tf.tile(
tf.expand_dims(labels, 1), [1, FLAGS.ensemble_size])
log_likelihoods = -tf.keras.losses.sparse_categorical_crossentropy(
labels_tiled, logits, from_logits=True)
negative_log_likelihood = tf.reduce_mean(
-tf.reduce_logsumexp(log_likelihoods, axis=[1]) +
tf.math.log(float(FLAGS.ensemble_size)))
probs = tf.math.reduce_mean(probs, axis=1) # marginalize
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
else:
corrupt_metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, probs)
corrupt_metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, probs)
strategy.run(step_fn, args=(next(iterator),))
metrics.update({'test/ms_per_example': tf.keras.metrics.Mean()})
train_iterator = iter(train_dataset)
start_time = time.time()
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
for step in range(steps_per_epoch):
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * (FLAGS.train_epochs)
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps, epoch + 1, FLAGS.train_epochs,
steps_per_sec, eta_seconds / 60, time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s', step, epoch)
test_start_time = time.time()
test_step(test_iterator, dataset_name)
ms_per_example = (time.time() - test_start_time) * 1e6 / test_batch_size
metrics['test/ms_per_example'].update_state(ms_per_example)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
corruption_types,
max_intensity)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
for i in range(FLAGS.ensemble_size):
logging.info(
'Member %d Test Loss: %.4f, Accuracy: %.2f%%', i,
metrics['test/nll_member_{}'.format(i)].result(),
metrics['test/accuracy_member_{}'.format(i)].result() * 100)
metrics.update(test_diversity)
total_results = {name: metric.result() for name, metric in metrics.items()}
total_results.update(corrupt_results)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for metric in metrics.values():
metric.reset_states()
if (FLAGS.checkpoint_interval > 0 and
(epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
ds_info = tfds.builder(FLAGS.dataset).info
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
steps_per_epoch = ds_info.splits['train'].num_examples // batch_size
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
num_classes = ds_info.features['label'].num_classes
if FLAGS.cifar_class is not None:
num_classes = 2
aug_params = {
'augmix': FLAGS.augmix,
'aug_count': FLAGS.aug_count,
'augmix_depth': FLAGS.augmix_depth,
'augmix_prob_coeff': FLAGS.augmix_prob_coeff,
'augmix_width': FLAGS.augmix_width,
'ensemble_size': FLAGS.ensemble_size,
'random_augment': FLAGS.random_augment,
}
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
aug_params=aug_params)
clean_test_input_fn = data_utils.load_input_fn(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_datasets_from_function(
train_input_fn)
test_datasets = {
'clean': strategy.experimental_distribute_datasets_from_function(
clean_test_input_fn),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(
corruption_name=corruption,
corruption_intensity=intensity,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
if FLAGS.use_bfloat16:
tf.keras.mixed_precision.set_global_policy('mixed_bfloat16')
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building ResNet model')
model = wide_resnet(input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
l2=FLAGS.l2,
version=2,
distance_logits=FLAGS.distance_logits)
logging.info('Model input shape: %s', model.input_shape)
logging.info('Model output shape: %s', model.output_shape)
logging.info('Model number of weights: %s', model.count_params())
# Linearly scale learning rate and the decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 128
lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
for start_epoch_str in FLAGS.lr_decay_epochs]
lr_schedule = schedules.WarmUpPiecewiseConstantSchedule(
steps_per_epoch,
base_lr,
decay_ratio=FLAGS.lr_decay_ratio,
decay_epochs=lr_decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(lr_schedule,
momentum=0.9,
nesterov=True)
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'train/loss': tf.keras.metrics.Mean(),
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.augmix and FLAGS.aug_count >= 1:
# Index 0 at augmix preprocessing is the unperturbed image.
images = images[:, 1, ...]
elif FLAGS.random_augment and FLAGS.aug_count >= 1:
images = images[:, 0, ...]
if FLAGS.cifar_class is not None:
labels = tf.cast(labels == FLAGS.cifar_class, tf.float32)
with tf.GradientTape() as tape:
logits = model(images, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels,
logits,
from_logits=True))
l2_loss = sum(model.losses)
if FLAGS.distance_logits and FLAGS.one_vs_all:
distance_loss = one_vs_rest_dm_loss(labels, logits)
loss = distance_loss + l2_loss
else:
loss = negative_log_likelihood + l2_loss
# Scale the loss given the TPUStrategy will reduce sum all gradients.
scaled_loss = loss / strategy.num_replicas_in_sync
grads = tape.gradient(scaled_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
probs = tf.nn.softmax(logits)
metrics['train/ece'].update_state(labels, probs)
metrics['train/loss'].update_state(loss)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(labels, logits)
strategy.run(step_fn, args=(next(iterator),))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.cifar_class is not None:
labels = tf.cast(labels == FLAGS.cifar_class, tf.float32)
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
probs = tf.nn.softmax(logits)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
else:
corrupt_metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, probs)
corrupt_metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, probs)
strategy.run(step_fn, args=(next(iterator),))
train_iterator = iter(train_dataset)
start_time = time.time()
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
for step in range(steps_per_epoch):
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * FLAGS.train_epochs
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps,
epoch + 1,
FLAGS.train_epochs,
steps_per_sec,
eta_seconds / 60,
time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s', step,
epoch)
test_step(test_iterator, dataset_name)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
corruption_types,
max_intensity)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
total_results = {name: metric.result() for name, metric in metrics.items()}
total_results.update(corrupt_results)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for metric in metrics.values():
metric.reset_states()
if (FLAGS.checkpoint_interval > 0 and
(epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
aug_params = {
'augmix': FLAGS.augmix,
'aug_count': FLAGS.aug_count,
'augmix_depth': FLAGS.augmix_depth,
'augmix_prob_coeff': FLAGS.augmix_prob_coeff,
'augmix_width': FLAGS.augmix_width,
'label_smoothing': FLAGS.label_smoothing,
'ensemble_size': FLAGS.ensemble_size,
'mixup_alpha': FLAGS.mixup_alpha,
'random_augment': FLAGS.random_augment,
'adaptive_mixup': FLAGS.adaptive_mixup,
'forget_mixup': FLAGS.forget_mixup,
'num_cores': FLAGS.num_cores,
'threshold': FLAGS.forget_threshold,
'cutmix': FLAGS.cutmix,
}
batch_size = ((FLAGS.per_core_batch_size // FLAGS.ensemble_size) *
FLAGS.num_cores)
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16,
proportion=FLAGS.train_proportion,
validation_set=FLAGS.validation,
aug_params=aug_params)
if FLAGS.validation:
validation_input_fn = data_utils.load_input_fn(
split=tfds.Split.VALIDATION,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=True)
val_dataset = strategy.experimental_distribute_datasets_from_function(
validation_input_fn)
clean_test_input_fn = data_utils.load_input_fn(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size // FLAGS.ensemble_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_dataset(
train_input_fn())
test_datasets = {
'clean': strategy.experimental_distribute_datasets_from_function(
clean_test_input_fn),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(
corruption_name=corruption,
corruption_intensity=intensity,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
ds_info = tfds.builder(FLAGS.dataset).info
num_train_examples = ds_info.splits['train'].num_examples
# Train_proportion is a float so need to convert steps_per_epoch to int.
if FLAGS.validation:
# TODO(ywenxu): Remove hard-coding validation images.
steps_per_epoch = int((num_train_examples *
FLAGS.train_proportion - 2500) // batch_size)
steps_per_val = 2500 // (FLAGS.per_core_batch_size * FLAGS.num_cores)
else:
steps_per_epoch = int(
num_train_examples * FLAGS.train_proportion) // batch_size
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
num_classes = ds_info.features['label'].num_classes
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building Keras model')
model = batchensemble_model.wide_resnet(
input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
ensemble_size=FLAGS.ensemble_size,
random_sign_init=FLAGS.random_sign_init,
l2=FLAGS.l2,
use_ensemble_bn=FLAGS.use_ensemble_bn)
logging.info('Model input shape: %s', model.input_shape)
logging.info('Model output shape: %s', model.output_shape)
logging.info('Model number of weights: %s', model.count_params())
# Linearly scale learning rate and the decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 128
lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
for start_epoch_str in FLAGS.lr_decay_epochs]
lr_schedule = utils.LearningRateSchedule(
steps_per_epoch,
base_lr,
decay_ratio=FLAGS.lr_decay_ratio,
decay_epochs=lr_decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(lr_schedule,
momentum=0.9,
nesterov=True)
diversity_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
FLAGS.diversity_coeff, FLAGS.diversity_decay_epoch * steps_per_epoch,
decay_rate=0.97, staircase=True)
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'train/loss': tf.keras.metrics.Mean(),
'train/similarity': tf.keras.metrics.Mean(),
'train/l2': tf.keras.metrics.Mean(),
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/member_accuracy_mean': (
tf.keras.metrics.SparseCategoricalAccuracy()),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/member_ece_mean': um.ExpectedCalibrationError(
num_bins=FLAGS.num_bins)
}
for i in range(FLAGS.ensemble_size):
metrics['test/nll_member_{}'.format(i)] = tf.keras.metrics.Mean()
metrics['test/accuracy_member_{}'.format(i)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
metrics['test/ece_member_{}'.format(i)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
test_diversity = {}
training_diversity = {}
corrupt_diversity = {}
if FLAGS.ensemble_size > 1:
test_diversity = {
'test/disagreement': tf.keras.metrics.Mean(),
'test/average_kl': tf.keras.metrics.Mean(),
'test/cosine_similarity': tf.keras.metrics.Mean(),
}
training_diversity = {
'train/disagreement': tf.keras.metrics.Mean(),
'train/average_kl': tf.keras.metrics.Mean(),
'train/cosine_similarity': tf.keras.metrics.Mean(),
}
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
corrupt_metrics['test/member_acc_mean_{}'.format(dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/member_ece_mean_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
corrupt_diversity['corrupt_diversity/average_kl_{}'.format(
dataset_name)] = tf.keras.metrics.Mean()
corrupt_diversity['corrupt_diversity/cosine_similarity_{}'.format(
dataset_name)] = tf.keras.metrics.Mean()
corrupt_diversity['corrupt_diversity/disagreement_{}'.format(
dataset_name)] = tf.keras.metrics.Mean()
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
if FLAGS.forget_mixup:
images, labels, idx = inputs
else:
images, labels = inputs
if FLAGS.adaptive_mixup or FLAGS.forget_mixup:
images = tf.identity(images)
elif FLAGS.augmix or FLAGS.random_augment:
images_shape = tf.shape(images)
images = tf.reshape(tf.transpose(
images, [1, 0, 2, 3, 4]), [-1, images_shape[2],
images_shape[3], images_shape[4]])
else:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
# Augmix, adaptive mixup, forget mixup preprocessing gives tiled labels.
if FLAGS.mixup_alpha > 0 or FLAGS.label_smoothing > 0 or FLAGS.cutmix:
if FLAGS.augmix or FLAGS.adaptive_mixup or FLAGS.forget_mixup:
labels = tf.identity(labels)
else:
labels = tf.tile(labels, [FLAGS.ensemble_size, 1])
else:
labels = tf.tile(labels, [FLAGS.ensemble_size])
def _is_batch_norm(v):
"""Decide whether a variable belongs to `batch_norm`."""
keywords = ['batchnorm', 'batch_norm', 'bn']
return any([k in v.name.lower() for k in keywords])
def _normalize(x):
"""Normalize an input with l2 norm."""
l2 = tf.norm(x, ord=2, axis=-1)
return x / tf.expand_dims(l2, axis=-1)
# Taking the sum of upper triangular of XX^T and divided by ensemble size.
def pairwise_cosine_distance(x):
"""Compute the pairwise distance in a matrix."""
normalized_x = _normalize(x)
return (tf.reduce_sum(
tf.matmul(normalized_x, normalized_x, transpose_b=True)) -
FLAGS.ensemble_size) / (2.0 * FLAGS.ensemble_size)
with tf.GradientTape() as tape:
logits = model(images, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
if FLAGS.mixup_alpha > 0 or FLAGS.label_smoothing > 0 or FLAGS.cutmix:
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.categorical_crossentropy(labels,
logits,
from_logits=True))
else:
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels,
logits,
from_logits=True))
l2_loss = sum(model.losses)
fast_weights = [var for var in model.trainable_variables if
not _is_batch_norm(var) and (
'alpha' in var.name or 'gamma' in var.name)]
pairwise_distance_loss = tf.add_n(
[pairwise_cosine_distance(var) for var in fast_weights])
diversity_start_iter = steps_per_epoch * FLAGS.diversity_start_epoch
diversity_iterations = optimizer.iterations - diversity_start_iter
if diversity_iterations > 0:
diversity_coeff = diversity_schedule(diversity_iterations)
diversity_loss = diversity_coeff * pairwise_distance_loss
loss = negative_log_likelihood + l2_loss + diversity_loss
else:
loss = negative_log_likelihood + l2_loss
# Scale the loss given the TPUStrategy will reduce sum all gradients.
scaled_loss = loss / strategy.num_replicas_in_sync
grads = tape.gradient(scaled_loss, model.trainable_variables)
# Separate learning rate implementation.
if FLAGS.fast_weight_lr_multiplier != 1.0:
grads_and_vars = []
for grad, var in zip(grads, model.trainable_variables):
# Apply different learning rate on the fast weight approximate
# posterior/prior parameters. This is excludes BN and slow weights,
# but pay caution to the naming scheme.
if (not _is_batch_norm(var) and 'kernel' not in var.name):
grads_and_vars.append((grad * FLAGS.fast_weight_lr_multiplier, var))
else:
grads_and_vars.append((grad, var))
optimizer.apply_gradients(grads_and_vars)
else:
optimizer.apply_gradients(zip(grads, model.trainable_variables))
probs = tf.nn.softmax(logits)
if FLAGS.ensemble_size > 1:
per_probs = tf.reshape(
probs, tf.concat([[FLAGS.ensemble_size, -1], probs.shape[1:]], 0))
diversity_results = um.average_pairwise_diversity(
per_probs, FLAGS.ensemble_size)
for k, v in diversity_results.items():
training_diversity['train/' + k].update_state(v)
if FLAGS.mixup_alpha > 0 or FLAGS.label_smoothing > 0 or FLAGS.cutmix:
labels = tf.argmax(labels, axis=-1)
metrics['train/ece'].update_state(labels, probs)
metrics['train/similarity'].update_state(pairwise_distance_loss)
metrics['train/l2'].update_state(l2_loss)
metrics['train/loss'].update_state(loss)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(labels, logits)
if FLAGS.forget_mixup:
train_predictions = tf.argmax(probs, -1)
labels = tf.cast(labels, train_predictions.dtype)
# For each ensemble member, we accumulate the accuracy counts.
accuracy_counts = tf.cast(tf.reshape(
(train_predictions == labels), [FLAGS.ensemble_size, -1]),
tf.float32)
return accuracy_counts, idx
if FLAGS.forget_mixup:
return strategy.run(step_fn, args=(next(iterator),))
else:
strategy.run(step_fn, args=(next(iterator),))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
probs = tf.nn.softmax(logits)
per_probs = tf.split(probs,
num_or_size_splits=FLAGS.ensemble_size,
axis=0)
for i in range(FLAGS.ensemble_size):
member_probs = per_probs[i]
if dataset_name == 'clean':
member_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, member_probs)
metrics['test/nll_member_{}'.format(i)].update_state(member_loss)
metrics['test/accuracy_member_{}'.format(i)].update_state(
labels, member_probs)
metrics['test/member_accuracy_mean'].update_state(
labels, member_probs)
metrics['test/ece_member_{}'.format(i)].update_state(labels,
member_probs)
metrics['test/member_ece_mean'].update_state(labels, member_probs)
elif dataset_name != 'validation':
corrupt_metrics['test/member_acc_mean_{}'.format(
dataset_name)].update_state(labels, member_probs)
corrupt_metrics['test/member_ece_mean_{}'.format(
dataset_name)].update_state(labels, member_probs)
if FLAGS.ensemble_size > 1:
per_probs_tensor = tf.reshape(
probs, tf.concat([[FLAGS.ensemble_size, -1], probs.shape[1:]], 0))
diversity_results = um.average_pairwise_diversity(
per_probs_tensor, FLAGS.ensemble_size)
if dataset_name == 'clean':
for k, v in diversity_results.items():
test_diversity['test/' + k].update_state(v)
elif dataset_name != 'validation':
for k, v in diversity_results.items():
corrupt_diversity['corrupt_diversity/{}_{}'.format(
k, dataset_name)].update_state(v)
probs = tf.reduce_mean(per_probs, axis=0)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
elif dataset_name != 'validation':
corrupt_metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, probs)
corrupt_metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, probs)
if dataset_name == 'validation':
return per_probs_tensor, labels
if dataset_name == 'validation':
return strategy.run(step_fn, args=(next(iterator),))
else:
strategy.run(step_fn, args=(next(iterator),))
train_iterator = iter(train_dataset)
start_time = time.time()
forget_counts_history = []
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
acc_counts_list = []
idx_list = []
for step in range(steps_per_epoch):
if FLAGS.forget_mixup:
temp_accuracy_counts, temp_idx = train_step(train_iterator)
acc_counts_list.append(temp_accuracy_counts)
idx_list.append(temp_idx)
else:
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * FLAGS.train_epochs
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps,
epoch + 1,
FLAGS.train_epochs,
steps_per_sec,
eta_seconds / 60,
time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
# Only one of the forget_mixup and adaptive_mixup can be true.
if FLAGS.forget_mixup:
current_acc = [tf.concat(list(acc_counts_list[i].values), axis=1)
for i in range(len(acc_counts_list))]
total_idx = [tf.concat(list(idx_list[i].values), axis=0)
for i in range(len(idx_list))]
current_acc = tf.cast(tf.concat(current_acc, axis=1), tf.int32)
total_idx = tf.concat(total_idx, axis=0)
current_forget_path = os.path.join(FLAGS.output_dir,
'forget_counts.npy')
last_acc_path = os.path.join(FLAGS.output_dir, 'last_acc.npy')
if epoch == 0:
forget_counts = tf.zeros(
[FLAGS.ensemble_size, num_train_examples], dtype=tf.int32)
last_acc = tf.zeros(
[FLAGS.ensemble_size, num_train_examples], dtype=tf.int32)
else:
if 'last_acc' not in locals():
with tf.io.gfile.GFile(last_acc_path, 'rb') as f:
last_acc = np.load(f)
last_acc = tf.cast(tf.convert_to_tensor(last_acc), tf.int32)
if 'forget_counts' not in locals():
with tf.io.gfile.GFile(current_forget_path, 'rb') as f:
forget_counts = np.load(f)
forget_counts = tf.cast(tf.convert_to_tensor(forget_counts), tf.int32)
selected_last_acc = tf.gather(last_acc, total_idx, axis=1)
forget_this_epoch = tf.cast(current_acc < selected_last_acc, tf.int32)
forget_this_epoch = tf.transpose(forget_this_epoch)
target_shape = tf.constant([num_train_examples, FLAGS.ensemble_size])
current_forget_counts = tf.scatter_nd(tf.reshape(total_idx, [-1, 1]),
forget_this_epoch, target_shape)
current_forget_counts = tf.transpose(current_forget_counts)
acc_this_epoch = tf.transpose(current_acc)
last_acc = tf.scatter_nd(tf.reshape(total_idx, [-1, 1]),
acc_this_epoch, target_shape)
# This is lower bound of true acc.
last_acc = tf.transpose(last_acc)
# TODO(ywenxu): We count the dropped examples as forget. Fix this later.
forget_counts += current_forget_counts
forget_counts_history.append(forget_counts)
logging.info('forgetting counts')
logging.info(tf.stack(forget_counts_history, 0))
with tf.io.gfile.GFile(os.path.join(
FLAGS.output_dir, 'forget_counts_history.npy'), 'wb') as f:
np.save(f, tf.stack(forget_counts_history, 0).numpy())
with tf.io.gfile.GFile(current_forget_path, 'wb') as f:
np.save(f, forget_counts.numpy())
with tf.io.gfile.GFile(last_acc_path, 'wb') as f:
np.save(f, last_acc.numpy())
aug_params['forget_counts_dir'] = current_forget_path
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=FLAGS.num_cores * (
FLAGS.per_core_batch_size // FLAGS.ensemble_size),
use_bfloat16=FLAGS.use_bfloat16,
validation_set=FLAGS.validation,
aug_params=aug_params)
train_dataset = strategy.experimental_distribute_dataset(
train_input_fn())
train_iterator = iter(train_dataset)
if FLAGS.adaptive_mixup:
val_iterator = iter(val_dataset)
logging.info('Testing on validation dataset')
predictions_list = []
labels_list = []
for step in range(steps_per_val):
temp_predictions, temp_labels = test_step(val_iterator, 'validation')
predictions_list.append(temp_predictions)
labels_list.append(temp_labels)
predictions = [tf.concat(list(predictions_list[i].values), axis=1)
for i in range(len(predictions_list))]
labels = [tf.concat(list(labels_list[i].values), axis=0)
for i in range(len(labels_list))]
predictions = tf.concat(predictions, axis=1)
labels = tf.cast(tf.concat(labels, axis=0), tf.int64)
def compute_acc_conf(preds, label, focus_class):
class_preds = tf.boolean_mask(preds, label == focus_class, axis=1)
class_pred_labels = tf.argmax(class_preds, axis=-1)
confidence = tf.reduce_mean(tf.reduce_max(class_preds, axis=-1), -1)
accuracy = tf.reduce_mean(tf.cast(
class_pred_labels == focus_class, tf.float32), axis=-1)
return accuracy - confidence
calibration_per_class = [compute_acc_conf(
predictions, labels, i) for i in range(num_classes)]
calibration_per_class = tf.stack(calibration_per_class, axis=1)
logging.info('calibration per class')
logging.info(calibration_per_class)
mixup_coeff = tf.where(calibration_per_class > 0, 1.0, FLAGS.mixup_alpha)
mixup_coeff = tf.clip_by_value(mixup_coeff, 0, 1)
logging.info('mixup coeff')
logging.info(mixup_coeff)
aug_params['mixup_coeff'] = mixup_coeff
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size // FLAGS.ensemble_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=True,
aug_params=aug_params)
train_dataset = strategy.experimental_distribute_datasets_from_function(
train_input_fn)
train_iterator = iter(train_dataset)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s', step,
epoch)
test_step(test_iterator, dataset_name)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
# This includes corrupt_diversity whose disagreement normalized by its
# corrupt mean error rate.
corrupt_results = utils.aggregate_corrupt_metrics(
corrupt_metrics,
corruption_types,
max_intensity,
corrupt_diversity=corrupt_diversity,
output_dir=FLAGS.output_dir)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
for i in range(FLAGS.ensemble_size):
logging.info('Member %d Test Loss: %.4f, Accuracy: %.2f%%',
i, metrics['test/nll_member_{}'.format(i)].result(),
metrics['test/accuracy_member_{}'.format(i)].result() * 100)
total_metrics = metrics.copy()
total_metrics.update(training_diversity)
total_metrics.update(test_diversity)
total_results = {name: metric.result()
for name, metric in total_metrics.items()}
total_results.update(corrupt_results)
# Normalize all disagreement metrics (training, testing) by test accuracy.
# Disagreement on corrupt dataset is normalized by their own error rate.
test_acc = total_metrics['test/accuracy'].result()
for name, metric in total_metrics.items():
if 'disagreement' in name:
total_results[name] = metric.result() / test_acc
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
if FLAGS.forget_mixup:
tf.summary.histogram('forget_counts', forget_counts, step=epoch + 1)
for metric in total_metrics.values():
metric.reset_states()
if (FLAGS.checkpoint_interval > 0 and
(epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
# Need to store the last but one checkpoint in adaptive mixup setup.
if FLAGS.adaptive_mixup and epoch == (FLAGS.train_epochs - 2):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'last_but_one_checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
final_save_name = os.path.join(FLAGS.output_dir, 'model')
model.save(final_save_name)
logging.info('Saved model to %s', final_save_name)
def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
aug_params = {
'augmix': FLAGS.augmix,
'aug_count': FLAGS.aug_count,
'augmix_depth': FLAGS.augmix_depth,
'augmix_prob_coeff': FLAGS.augmix_prob_coeff,
'augmix_width': FLAGS.augmix_width,
'ensemble_size': 1,
'mixup_alpha': FLAGS.mixup_alpha,
'adaptive_mixup': FLAGS.adaptive_mixup,
'random_augment': FLAGS.random_augment,
'forget_mixup': FLAGS.forget_mixup,
'num_cores': FLAGS.num_cores,
'threshold': FLAGS.forget_threshold,
}
batch_size = (FLAGS.per_core_batch_size * FLAGS.num_cores //
FLAGS.num_dropout_samples_training)
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16,
proportion=FLAGS.train_proportion,
validation_set=FLAGS.validation,
aug_params=aug_params)
if FLAGS.validation:
validation_input_fn = data_utils.load_input_fn(
split=tfds.Split.VALIDATION,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=True)
val_dataset = strategy.experimental_distribute_datasets_from_function(
validation_input_fn)
clean_test_input_fn = data_utils.load_input_fn(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_dataset(train_input_fn())
test_datasets = {
'clean':
strategy.experimental_distribute_datasets_from_function(
clean_test_input_fn),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(corruption_name=corruption,
corruption_intensity=intensity,
batch_size=FLAGS.per_core_batch_size *
FLAGS.num_cores,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
ds_info = tfds.builder(FLAGS.dataset).info
num_train_examples = ds_info.splits['train'].num_examples
# Train_proportion is a float so need to convert steps_per_epoch to int.
if FLAGS.validation:
# TODO(ywenxu): Remove hard-coding validation images.
steps_per_epoch = int(
(num_train_examples * FLAGS.train_proportion - 2500) // batch_size)
steps_per_val = 2500 // (FLAGS.per_core_batch_size * FLAGS.num_cores)
else:
steps_per_epoch = int(
num_train_examples * FLAGS.train_proportion) // batch_size
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
num_classes = ds_info.features['label'].num_classes
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building ResNet model')
model = ub.models.wide_resnet_dropout(
input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
l2=FLAGS.l2,
dropout_rate=FLAGS.dropout_rate,
residual_dropout=FLAGS.residual_dropout,
filterwise_dropout=FLAGS.filterwise_dropout)
logging.info('Model input shape: %s', model.input_shape)
logging.info('Model output shape: %s', model.output_shape)
logging.info('Model number of weights: %s', model.count_params())
# Linearly scale learning rate and the decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 128
lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
for start_epoch_str in FLAGS.lr_decay_epochs]
lr_schedule = schedules.WarmUpPiecewiseConstantSchedule(
steps_per_epoch,
base_lr,
decay_ratio=FLAGS.lr_decay_ratio,
decay_epochs=lr_decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(lr_schedule,
momentum=0.9,
nesterov=True)
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'train/loss': tf.keras.metrics.Mean(),
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
if FLAGS.forget_mixup:
images, labels, idx = inputs
else:
images, labels = inputs
if FLAGS.augmix and FLAGS.aug_count >= 1:
# Index 0 at augmix preprocessing is the unperturbed image.
images = images[:, 1, ...]
# This is for the case of combining AugMix and Mixup.
if FLAGS.mixup_alpha > 0:
labels = tf.split(labels, FLAGS.aug_count + 1, axis=0)[1]
images = tf.tile(images,
[FLAGS.num_dropout_samples_training, 1, 1, 1])
if FLAGS.mixup_alpha > 0:
labels = tf.tile(labels,
[FLAGS.num_dropout_samples_training, 1])
else:
labels = tf.tile(labels, [FLAGS.num_dropout_samples_training])
with tf.GradientTape() as tape:
logits = model(images, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
if FLAGS.mixup_alpha > 0:
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.categorical_crossentropy(
labels, logits, from_logits=True))
else:
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True))
l2_loss = sum(model.losses)
loss = negative_log_likelihood + l2_loss
# Scale the loss given the TPUStrategy will reduce sum all gradients.
scaled_loss = loss / strategy.num_replicas_in_sync
grads = tape.gradient(scaled_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
probs = tf.nn.softmax(logits)
if FLAGS.mixup_alpha > 0:
labels = tf.argmax(labels, axis=-1)
metrics['train/ece'].update_state(labels, probs)
metrics['train/loss'].update_state(loss)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(labels, logits)
if FLAGS.forget_mixup:
train_predictions = tf.argmax(probs, -1)
labels = tf.cast(labels, train_predictions.dtype)
# For each ensemble member (1 here), we accumulate the accuracy counts.
accuracy_counts = tf.cast(
tf.reshape((train_predictions == labels), [1, -1]),
tf.float32)
return accuracy_counts, idx
if FLAGS.forget_mixup:
return strategy.run(step_fn, args=(next(iterator), ))
else:
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
logits_list = []
for _ in range(FLAGS.num_dropout_samples):
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
logits_list.append(logits)
# Logits dimension is (num_samples, batch_size, num_classes).
logits_list = tf.stack(logits_list, axis=0)
probs_list = tf.nn.softmax(logits_list)
probs = tf.reduce_mean(probs_list, axis=0)
labels_broadcasted = tf.broadcast_to(
labels, [FLAGS.num_dropout_samples, labels.shape[0]])
log_likelihoods = -tf.keras.losses.sparse_categorical_crossentropy(
labels_broadcasted, logits_list, from_logits=True)
negative_log_likelihood = tf.reduce_mean(
-tf.reduce_logsumexp(log_likelihoods, axis=[0]) +
tf.math.log(float(FLAGS.num_dropout_samples)))
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
elif dataset_name != 'validation':
corrupt_metrics['test/nll_{}'.format(
dataset_name)].update_state(negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)].update_state(labels, probs)
corrupt_metrics['test/ece_{}'.format(
dataset_name)].update_state(labels, probs)
if dataset_name == 'validation':
return tf.reshape(probs, [1, -1, num_classes]), labels
if dataset_name == 'validation':
return strategy.run(step_fn, args=(next(iterator), ))
else:
strategy.run(step_fn, args=(next(iterator), ))
metrics.update({'test/ms_per_example': tf.keras.metrics.Mean()})
train_iterator = iter(train_dataset)
forget_counts_history = []
start_time = time.time()
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
acc_counts_list = []
idx_list = []
for step in range(steps_per_epoch):
if FLAGS.forget_mixup:
temp_accuracy_counts, temp_idx = train_step(train_iterator)
acc_counts_list.append(temp_accuracy_counts)
idx_list.append(temp_idx)
else:
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * FLAGS.train_epochs
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps, epoch + 1,
FLAGS.train_epochs, steps_per_sec, eta_seconds / 60,
time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
# Only one of the forget_mixup and adaptive_mixup can be true.
if FLAGS.forget_mixup:
current_acc = [
tf.concat(list(acc_counts_list[i].values), axis=1)
for i in range(len(acc_counts_list))
]
total_idx = [
tf.concat(list(idx_list[i].values), axis=0)
for i in range(len(idx_list))
]
current_acc = tf.cast(tf.concat(current_acc, axis=1), tf.int32)
total_idx = tf.concat(total_idx, axis=0)
current_forget_path = os.path.join(FLAGS.output_dir,
'forget_counts.npy')
last_acc_path = os.path.join(FLAGS.output_dir, 'last_acc.npy')
if epoch == 0:
forget_counts = tf.zeros([1, num_train_examples],
dtype=tf.int32)
last_acc = tf.zeros([1, num_train_examples], dtype=tf.int32)
else:
if 'last_acc' not in locals():
with tf.io.gfile.GFile(last_acc_path, 'rb') as f:
last_acc = np.load(f)
last_acc = tf.cast(tf.convert_to_tensor(last_acc),
tf.int32)
if 'forget_counts' not in locals():
with tf.io.gfile.GFile(current_forget_path, 'rb') as f:
forget_counts = np.load(f)
forget_counts = tf.cast(
tf.convert_to_tensor(forget_counts), tf.int32)
selected_last_acc = tf.gather(last_acc, total_idx, axis=1)
forget_this_epoch = tf.cast(current_acc < selected_last_acc,
tf.int32)
forget_this_epoch = tf.transpose(forget_this_epoch)
target_shape = tf.constant([num_train_examples, 1])
current_forget_counts = tf.scatter_nd(
tf.reshape(total_idx, [-1, 1]), forget_this_epoch,
target_shape)
current_forget_counts = tf.transpose(current_forget_counts)
acc_this_epoch = tf.transpose(current_acc)
last_acc = tf.scatter_nd(tf.reshape(total_idx, [-1, 1]),
acc_this_epoch, target_shape)
# This is lower bound of true acc.
last_acc = tf.transpose(last_acc)
# TODO(ywenxu): We count the dropped examples as forget. Fix this later.
forget_counts += current_forget_counts
forget_counts_history.append(forget_counts)
logging.info('forgetting counts')
logging.info(tf.stack(forget_counts_history, 0))
with tf.io.gfile.GFile(
os.path.join(FLAGS.output_dir,
'forget_counts_history.npy'), 'wb') as f:
np.save(f, tf.stack(forget_counts_history, 0).numpy())
with tf.io.gfile.GFile(current_forget_path, 'wb') as f:
np.save(f, forget_counts.numpy())
with tf.io.gfile.GFile(last_acc_path, 'wb') as f:
np.save(f, last_acc.numpy())
aug_params['forget_counts_dir'] = current_forget_path
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=FLAGS.validation,
aug_params=aug_params)
train_dataset = strategy.experimental_distribute_dataset(
train_input_fn())
train_iterator = iter(train_dataset)
elif FLAGS.adaptive_mixup:
val_iterator = iter(val_dataset)
logging.info('Testing on validation dataset')
predictions_list = []
labels_list = []
for step in range(steps_per_val):
temp_predictions, temp_labels = test_step(
val_iterator, 'validation')
predictions_list.append(temp_predictions)
labels_list.append(temp_labels)
predictions = [
tf.concat(list(predictions_list[i].values), axis=1)
for i in range(len(predictions_list))
]
labels = [
tf.concat(list(labels_list[i].values), axis=0)
for i in range(len(labels_list))
]
predictions = tf.concat(predictions, axis=1)
labels = tf.cast(tf.concat(labels, axis=0), tf.int64)
def compute_acc_conf(preds, label, focus_class):
class_preds = tf.boolean_mask(preds,
label == focus_class,
axis=1)
class_pred_labels = tf.argmax(class_preds, axis=-1)
confidence = tf.reduce_mean(
tf.reduce_max(class_preds, axis=-1), -1)
accuracy = tf.reduce_mean(tf.cast(
class_pred_labels == focus_class, tf.float32),
axis=-1)
return accuracy - confidence
calibration_per_class = [
compute_acc_conf(predictions, labels, i)
for i in range(num_classes)
]
calibration_per_class = tf.stack(calibration_per_class, axis=1)
logging.info('calibration per class')
logging.info(calibration_per_class)
mixup_coeff = tf.where(calibration_per_class > 0, 1.0,
FLAGS.mixup_alpha)
mixup_coeff = tf.clip_by_value(mixup_coeff, 0, 1)
logging.info('mixup coeff')
logging.info(mixup_coeff)
aug_params['mixup_coeff'] = mixup_coeff
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=True,
aug_params=aug_params)
train_dataset = strategy.experimental_distribute_dataset(
train_input_fn())
train_iterator = iter(train_dataset)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0
and (epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s',
step, epoch)
test_start_time = time.time()
test_step(test_iterator, dataset_name)
ms_per_example = (time.time() -
test_start_time) * 1e6 / batch_size
metrics['test/ms_per_example'].update_state(ms_per_example)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0
and (epoch + 1) % FLAGS.corruptions_interval == 0):
corrupt_results = utils.aggregate_corrupt_metrics(
corrupt_metrics, corruption_types, max_intensity)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
total_results = {
name: metric.result()
for name, metric in metrics.items()
}
total_results.update(corrupt_results)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for metric in metrics.values():
metric.reset_states()
if (FLAGS.checkpoint_interval > 0
and (epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
ds_info = tfds.builder(FLAGS.dataset).info
batch_size = ((FLAGS.per_core_batch_size // FLAGS.ensemble_size) *
FLAGS.num_cores)
train_dataset_size = ds_info.splits['train'].num_examples
steps_per_epoch = train_dataset_size // batch_size
test_dataset_size = ds_info.splits['test'].num_examples
steps_per_eval = test_dataset_size // batch_size
num_classes = ds_info.features['label'].num_classes
train_dataset = utils.load_dataset(split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16)
clean_test_dataset = utils.load_dataset(split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
test_datasets = {
'clean': strategy.experimental_distribute_dataset(clean_test_dataset),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(corruption_name=corruption,
corruption_intensity=intensity,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building Keras model')
model = cifar_model.wide_resnet(
input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
alpha_initializer=FLAGS.alpha_initializer,
gamma_initializer=FLAGS.gamma_initializer,
alpha_regularizer=FLAGS.alpha_regularizer,
gamma_regularizer=FLAGS.gamma_regularizer,
use_additive_perturbation=FLAGS.use_additive_perturbation,
ensemble_size=FLAGS.ensemble_size,
random_sign_init=FLAGS.random_sign_init,
dropout_rate=FLAGS.dropout_rate,
prior_mean=FLAGS.prior_mean,
prior_stddev=FLAGS.prior_stddev)
logging.info('Model input shape: %s', model.input_shape)
logging.info('Model output shape: %s', model.output_shape)
logging.info('Model number of weights: %s', model.count_params())
# Linearly scale learning rate and the decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 128
lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
for start_epoch_str in FLAGS.lr_decay_epochs]
lr_schedule = refining.LearningRateScheduleWithRefining(
steps_per_epoch,
base_lr,
decay_ratio=FLAGS.lr_decay_ratio,
decay_epochs=lr_decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs,
train_epochs=FLAGS.train_epochs,
refining_learning_rate=FLAGS.refining_learning_rate)
optimizer = tf.keras.optimizers.SGD(lr_schedule,
momentum=0.9,
nesterov=True)
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/kl': tf.keras.metrics.Mean(),
'train/kl_scale': tf.keras.metrics.Mean(),
'train/elbo': tf.keras.metrics.Mean(),
'train/loss': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/kl': tf.keras.metrics.Mean(),
'test/elbo': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
if FLAGS.ensemble_size > 1:
for i in range(FLAGS.ensemble_size):
metrics['test/nll_member_{}'.format(
i)] = tf.keras.metrics.Mean()
metrics['test/accuracy_member_{}'.format(i)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/kl_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/elbo_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
def compute_l2_loss(model):
filtered_variables = []
for var in model.trainable_variables:
# Apply l2 on the BN parameters and bias terms. This
# excludes only fast weight approximate posterior/prior parameters,
# but pay caution to their naming scheme.
if ('kernel' in var.name or 'batch_norm' in var.name
or 'bias' in var.name):
filtered_variables.append(tf.reshape(var, (-1, )))
l2_loss = FLAGS.l2 * 2 * tf.nn.l2_loss(
tf.concat(filtered_variables, axis=0))
return l2_loss
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.ensemble_size > 1:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
labels = tf.tile(labels, [FLAGS.ensemble_size])
with tf.GradientTape() as tape:
logits = model(images, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True))
l2_loss = compute_l2_loss(model)
kl = sum(model.losses) / train_dataset_size
kl_scale = tf.cast(optimizer.iterations + 1, kl.dtype)
kl_scale /= steps_per_epoch * FLAGS.kl_annealing_epochs
kl_scale = tf.minimum(1., kl_scale)
kl_loss = kl_scale * kl
# Scale the loss given the TPUStrategy will reduce sum all gradients.
loss = negative_log_likelihood + l2_loss + kl_loss
scaled_loss = loss / strategy.num_replicas_in_sync
elbo = -(negative_log_likelihood + l2_loss + kl)
grads = tape.gradient(scaled_loss, model.trainable_variables)
# Separate learning rate implementation.
if FLAGS.fast_weight_lr_multiplier != 1.0:
grads_and_vars = []
for grad, var in zip(grads, model.trainable_variables):
# Apply different learning rate on the fast weight approximate
# posterior/prior parameters. This is excludes BN and slow weights,
# but pay caution to the naming scheme.
if ('kernel' not in var.name
and 'batch_norm' not in var.name
and 'bias' not in var.name):
grads_and_vars.append(
(grad * FLAGS.fast_weight_lr_multiplier, var))
else:
grads_and_vars.append((grad, var))
optimizer.apply_gradients(grads_and_vars)
else:
optimizer.apply_gradients(zip(grads,
model.trainable_variables))
probs = tf.nn.softmax(logits)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/kl'].update_state(kl)
metrics['train/kl_scale'].update_state(kl_scale)
metrics['train/elbo'].update_state(elbo)
metrics['train/loss'].update_state(loss)
metrics['train/accuracy'].update_state(labels, probs)
metrics['train/ece'].update_state(labels, probs)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.ensemble_size > 1:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
logits = tf.reshape([
model(images, training=False)
for _ in range(FLAGS.num_eval_samples)
], [FLAGS.num_eval_samples, FLAGS.ensemble_size, -1, num_classes])
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
probs = tf.nn.softmax(logits)
if FLAGS.ensemble_size > 1:
per_probs = tf.reduce_mean(probs,
axis=0) # marginalize samples
for i in range(FLAGS.ensemble_size):
member_probs = per_probs[i]
member_loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, member_probs)
metrics['test/nll_member_{}'.format(i)].update_state(
member_loss)
metrics['test/accuracy_member_{}'.format(i)].update_state(
labels, member_probs)
# Negative log marginal likelihood computed in a numerically-stable way.
labels_broadcasted = tf.broadcast_to(
labels,
[FLAGS.num_eval_samples, FLAGS.ensemble_size, labels.shape[0]])
log_likelihoods = -tf.keras.losses.sparse_categorical_crossentropy(
labels_broadcasted, logits, from_logits=True)
negative_log_likelihood = tf.reduce_mean(
-tf.reduce_logsumexp(log_likelihoods, axis=[0, 1]) +
tf.math.log(float(FLAGS.num_eval_samples *
FLAGS.ensemble_size)))
probs = tf.math.reduce_mean(probs, axis=[0, 1]) # marginalize
l2_loss = compute_l2_loss(model)
kl = sum(model.losses) / test_dataset_size
elbo = -(negative_log_likelihood + l2_loss + kl)
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/kl'].update_state(kl)
metrics['test/elbo'].update_state(elbo)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
else:
corrupt_metrics['test/nll_{}'.format(
dataset_name)].update_state(negative_log_likelihood)
corrupt_metrics['test/kl_{}'.format(
dataset_name)].update_state(kl)
corrupt_metrics['test/elbo_{}'.format(
dataset_name)].update_state(elbo)
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)].update_state(labels, probs)
corrupt_metrics['test/ece_{}'.format(
dataset_name)].update_state(labels, probs)
strategy.run(step_fn, args=(next(iterator), ))
train_iterator = iter(train_dataset)
start_time = time.time()
for epoch in range(initial_epoch,
FLAGS.train_epochs + FLAGS.refining_epochs):
logging.info('Starting to run epoch: %s', epoch)
if epoch in np.linspace(FLAGS.train_epochs,
FLAGS.train_epochs + FLAGS.refining_epochs,
FLAGS.num_auxiliary_variables,
dtype=int):
logging.info('Sampling auxiliary variables with ratio %f',
FLAGS.auxiliary_variance_ratio)
refining.sample_rank1_auxiliaries(model,
FLAGS.auxiliary_variance_ratio)
if FLAGS.freeze_weights_during_refining:
refining.freeze_rank1_weights(model)
for step in range(steps_per_epoch):
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * (FLAGS.train_epochs +
FLAGS.refining_epochs)
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps, epoch + 1,
FLAGS.train_epochs + FLAGS.refining_epochs,
steps_per_sec, eta_seconds / 60, time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0
and (epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s',
step, epoch)
test_step(test_iterator, dataset_name)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0
and (epoch + 1) % FLAGS.corruptions_interval == 0):
corrupt_results = utils.aggregate_corrupt_metrics(
corrupt_metrics, corruption_types, max_intensity)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
if FLAGS.ensemble_size > 1:
for i in range(FLAGS.ensemble_size):
logging.info(
'Member %d Test Loss: %.4f, Accuracy: %.2f%%', i,
metrics['test/nll_member_{}'.format(i)].result(),
metrics['test/accuracy_member_{}'.format(i)].result() *
100)
total_results = {
name: metric.result()
for name, metric in metrics.items()
}
total_results.update(corrupt_results)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for metric in metrics.values():
metric.reset_states()
if (FLAGS.checkpoint_interval > 0
and (epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
def main(argv):
del argv # Unused arg.
tf.random.set_seed(FLAGS.seed)
if FLAGS.version2:
per_core_bs_train = FLAGS.per_core_batch_size // (FLAGS.ensemble_size *
FLAGS.num_train_samples)
per_core_bs_eval = FLAGS.per_core_batch_size // (FLAGS.ensemble_size *
FLAGS.num_eval_samples)
else:
per_core_bs_train = FLAGS.per_core_batch_size // FLAGS.num_train_samples
per_core_bs_eval = FLAGS.per_core_batch_size // FLAGS.num_eval_samples
batch_size_train = per_core_bs_train * FLAGS.num_cores
batch_size_eval = per_core_bs_eval * FLAGS.num_cores
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
train_dataset = utils.load_dataset(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size_train,
use_bfloat16=FLAGS.use_bfloat16,
normalize=False)
clean_test_dataset = utils.load_dataset(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=batch_size_eval,
use_bfloat16=FLAGS.use_bfloat16,
normalize=False)
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
test_datasets = {
'clean': strategy.experimental_distribute_dataset(clean_test_dataset),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(
corruption_name=corruption,
corruption_intensity=intensity,
batch_size=batch_size_eval,
use_bfloat16=FLAGS.use_bfloat16,
normalize=False)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
ds_info = tfds.builder(FLAGS.dataset).info
train_dataset_size = ds_info.splits['train'].num_examples
test_dataset_size = ds_info.splits['test'].num_examples
num_classes = ds_info.features['label'].num_classes
steps_per_epoch = train_dataset_size // batch_size_train
steps_per_eval = test_dataset_size // batch_size_eval
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building Keras ResNet-32 model')
model = resnet_cifar_model.rank1_resnet_v1(
input_shape=ds_info.features['image'].shape,
depth=32,
num_classes=num_classes,
width_multiplier=4,
alpha_initializer=FLAGS.alpha_initializer,
gamma_initializer=FLAGS.gamma_initializer,
alpha_regularizer=FLAGS.alpha_regularizer,
gamma_regularizer=FLAGS.gamma_regularizer,
use_additive_perturbation=FLAGS.use_additive_perturbation,
ensemble_size=FLAGS.ensemble_size,
random_sign_init=FLAGS.random_sign_init,
dropout_rate=FLAGS.dropout_rate)
logging.info(model.summary())
base_lr = FLAGS.base_learning_rate * batch_size_train / 128
lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
for start_epoch_str in FLAGS.lr_decay_epochs]
lr_schedule = utils.LearningRateSchedule(
steps_per_epoch,
base_lr,
decay_ratio=FLAGS.lr_decay_ratio,
decay_epochs=lr_decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(
lr_schedule, momentum=0.9, nesterov=True)
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'train/loss': tf.keras.metrics.Mean(),
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/loss': tf.keras.metrics.Mean(),
}
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
test_diversity = {}
training_diversity = {}
if FLAGS.ensemble_size > 1:
for i in range(FLAGS.ensemble_size):
metrics['test/nll_member_{}'.format(i)] = tf.keras.metrics.Mean()
metrics['test/accuracy_member_{}'.format(i)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
test_diversity = {
'test/disagreement': tf.keras.metrics.Mean(),
'test/average_kl': tf.keras.metrics.Mean(),
'test/cosine_similarity': tf.keras.metrics.Mean(),
}
training_diversity = {
'train/disagreement': tf.keras.metrics.Mean(),
'train/average_kl': tf.keras.metrics.Mean(),
'train/cosine_similarity': tf.keras.metrics.Mean(),
}
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.version2 and FLAGS.ensemble_size > 1:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
if not (FLAGS.member_sampling or FLAGS.expected_probs):
labels = tf.tile(labels, [FLAGS.ensemble_size])
if FLAGS.num_train_samples > 1:
images = tf.tile(images, [FLAGS.num_train_samples, 1, 1, 1])
with tf.GradientTape() as tape:
logits = model(images, training=True)
probs = tf.nn.softmax(logits)
# Diversity evaluation.
if FLAGS.version2 and FLAGS.ensemble_size > 1:
per_probs = tf.reshape(
probs, tf.concat([[FLAGS.ensemble_size, -1], probs.shape[1:]], 0))
diversity_results = um.average_pairwise_diversity(
per_probs, FLAGS.ensemble_size)
if FLAGS.num_train_samples > 1:
probs = tf.reshape(probs,
tf.concat([[FLAGS.num_train_samples, -1],
probs.shape[1:]], 0))
probs = tf.reduce_mean(probs, 0)
if FLAGS.member_sampling and FLAGS.version2 and FLAGS.ensemble_size > 1:
idx = tf.random.uniform([], maxval=FLAGS.ensemble_size,
dtype=tf.int64)
idx_one_hot = tf.expand_dims(tf.one_hot(idx, FLAGS.ensemble_size,
dtype=probs.dtype), 0)
probs_shape = probs.shape
probs = tf.reshape(probs, [FLAGS.ensemble_size, -1])
probs = tf.matmul(idx_one_hot, probs)
probs = tf.reshape(probs, tf.concat([[-1], probs_shape[1:]], 0))
elif FLAGS.expected_probs and FLAGS.version2 and FLAGS.ensemble_size > 1:
probs = tf.reshape(probs,
tf.concat([[FLAGS.ensemble_size, -1],
probs.shape[1:]], 0))
probs = tf.reduce_mean(probs, 0)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
filtered_variables = []
for var in model.trainable_variables:
# Apply l2 on the slow weights and bias terms. This excludes BN
# parameters and fast weight approximate posterior/prior parameters,
# but pay caution to their naming scheme.
if 'kernel' in var.name or 'bias' in var.name:
filtered_variables.append(tf.reshape(var, (-1,)))
l2_loss = FLAGS.l2 * 2 * tf.nn.l2_loss(
tf.concat(filtered_variables, axis=0))
kl = sum(model.losses) / train_dataset_size
kl_scale = tf.cast(optimizer.iterations + 1, kl.dtype)
kl_scale /= FLAGS.kl_annealing_steps
kl_scale = tf.minimum(1., kl_scale)
kl_loss = kl_scale * kl
# Scale the loss given the TPUStrategy will reduce sum all gradients.
loss = negative_log_likelihood + l2_loss + kl_loss
scaled_loss = loss / strategy.num_replicas_in_sync
grads = tape.gradient(scaled_loss, model.trainable_variables)
# Separate learning rate implementation.
grad_list = []
if FLAGS.fast_weight_lr_multiplier != 1.0:
grads_and_vars = list(zip(grads, model.trainable_variables))
for vec, var in grads_and_vars:
# Apply different learning rate on the fast weight approximate
# posterior/prior parameters. This is excludes BN and slow weights,
# but pay caution to the naming scheme.
if ('batch_norm' not in var.name and 'kernel' not in var.name):
grad_list.append((vec * FLAGS.fast_weight_lr_multiplier, var))
else:
grad_list.append((vec, var))
optimizer.apply_gradients(grad_list)
else:
optimizer.apply_gradients(zip(grads, model.trainable_variables))
metrics['train/ece'].update_state(labels, probs)
metrics['train/loss'].update_state(loss)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(labels, probs)
if FLAGS.version2 and FLAGS.ensemble_size > 1:
for k, v in diversity_results.items():
training_diversity['train/' + k].update_state(v)
strategy.run(step_fn, args=(next(iterator),))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.ensemble_size > 1:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
if FLAGS.num_eval_samples > 1:
images = tf.tile(images, [FLAGS.num_eval_samples, 1, 1, 1])
logits = model(images, training=False)
probs = tf.nn.softmax(logits)
if FLAGS.num_eval_samples > 1:
probs = tf.reshape(probs,
tf.concat([[FLAGS.num_eval_samples, -1],
probs.shape[1:]], 0))
probs = tf.reduce_mean(probs, 0)
if FLAGS.ensemble_size > 1:
per_probs = tf.split(probs,
num_or_size_splits=FLAGS.ensemble_size,
axis=0)
if dataset_name == 'clean':
per_probs_tensor = tf.reshape(
probs, tf.concat([[FLAGS.ensemble_size, -1], probs.shape[1:]], 0))
diversity_results = um.average_pairwise_diversity(
per_probs_tensor, FLAGS.ensemble_size)
for k, v in diversity_results.items():
test_diversity['test/' + k].update_state(v)
for i in range(FLAGS.ensemble_size):
member_probs = per_probs[i]
member_nll = tf.keras.losses.sparse_categorical_crossentropy(
labels, member_probs)
metrics['test/nll_member_{}'.format(i)].update_state(member_nll)
metrics['test/accuracy_member_{}'.format(i)].update_state(
labels, member_probs)
probs = tf.reduce_mean(per_probs, axis=0)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
filtered_variables = []
for var in model.trainable_variables:
if 'kernel' in var.name or 'bias' in var.name:
filtered_variables.append(tf.reshape(var, (-1,)))
kl = sum(model.losses) / test_dataset_size
l2_loss = kl + FLAGS.l2 * 2 * tf.nn.l2_loss(
tf.concat(filtered_variables, axis=0))
loss = negative_log_likelihood + l2_loss
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
metrics['test/loss'].update_state(loss)
else:
corrupt_metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, probs)
corrupt_metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, probs)
strategy.run(step_fn, args=(next(iterator),))
train_iterator = iter(train_dataset)
start_time = time.time()
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
for step in range(steps_per_epoch):
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * FLAGS.train_epochs
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps,
epoch + 1,
FLAGS.train_epochs,
steps_per_sec,
eta_seconds / 60,
time_elapsed / 60))
work_unit.set_notes(message)
if step % 20 == 0:
logging.info(message)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s', step,
epoch)
test_step(test_iterator, dataset_name)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0 and
(epoch + 1) % FLAGS.corruptions_interval == 0):
corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
corruption_types,
max_intensity)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
for i in range(FLAGS.ensemble_size):
logging.info('Member %d Test Loss: %.4f, Accuracy: %.2f%%',
i, metrics['test/nll_member_{}'.format(i)].result(),
metrics['test/accuracy_member_{}'.format(i)].result() * 100)
total_metrics = itertools.chain(metrics.items(),
training_diversity.items(),
test_diversity.items())
total_results = {name: metric.result() for name, metric in total_metrics}
total_results.update(corrupt_results)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for name, result in total_results.items():
name = name.replace('/', '_')
if 'negative_log_likelihood' in name:
# Plots sort WIDs from high-to-low so look at maximization objectives.
name = name.replace('negative_log_likelihood', 'log_likelihood')
result = -result
objective = work_unit.get_measurement_series(name)
objective.create_measurement(result, epoch + 1)
for _, metric in total_metrics:
metric.reset_states()
summary_writer.flush()
if (FLAGS.checkpoint_interval > 0 and
(epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving checkpoints at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
aug_params = {
'augmix': FLAGS.augmix,
'aug_count': FLAGS.aug_count,
'augmix_depth': FLAGS.augmix_depth,
'augmix_prob_coeff': FLAGS.augmix_prob_coeff,
'augmix_width': FLAGS.augmix_width,
'ensemble_size': FLAGS.ensemble_size,
'mixup_alpha': FLAGS.mixup_alpha,
'random_augment': FLAGS.random_augment,
'adaptive_mixup': FLAGS.adaptive_mixup,
}
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=FLAGS.validation,
aug_params=aug_params)
if FLAGS.adaptive_mixup:
validation_input_fn = data_utils.load_input_fn(
split='validation',
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=FLAGS.adaptive_mixup)
val_dataset = strategy.experimental_distribute_datasets_from_function(
validation_input_fn)
clean_test_input_fn = data_utils.load_input_fn(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_datasets_from_function(
train_input_fn)
test_datasets = {
'clean':
strategy.experimental_distribute_datasets_from_function(
clean_test_input_fn),
}
if FLAGS.corruptions_interval > 0:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(corruption_name=corruption,
corruption_intensity=intensity,
batch_size=FLAGS.per_core_batch_size *
FLAGS.num_cores,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
ds_info = tfds.builder(FLAGS.dataset).info
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
if FLAGS.adaptive_mixup:
# TODO(ywenxu): Remove hard-coded 0.95 in the future.
total_train_examples = ds_info.splits['train'].num_examples
num_val_examples = int(total_train_examples * 0.95)
steps_per_epoch = (total_train_examples -
num_val_examples) // batch_size
steps_per_val = num_val_examples // batch_size
else:
steps_per_epoch = ds_info.splits['train'].num_examples // batch_size
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
num_classes = ds_info.features['label'].num_classes
if FLAGS.cifar_class is not None:
num_classes = 2
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building ResNet model')
models = []
for _ in range(FLAGS.ensemble_size):
models.append(
wide_resnet(input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
l2=FLAGS.l2,
version=2,
distance_logits=FLAGS.distance_logits))
model = NaiveEnsemble(models)
logging.info('Model input shape: %s', models[0].input_shape)
logging.info('Model output shape: %s', models[0].output_shape)
logging.info('Model number of weights: %s', models[0].count_params())
# Linearly scale learning rate and the decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 128
lr_decay_epochs = [(start_epoch * FLAGS.train_epochs) // 200
for start_epoch in FLAGS.lr_decay_epochs]
lr_schedule = utils.LearningRateSchedule(
steps_per_epoch,
base_lr,
decay_ratio=FLAGS.lr_decay_ratio,
decay_epochs=lr_decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(lr_schedule,
momentum=0.9,
nesterov=True)
metrics = {
'train/negative_log_likelihood':
tf.keras.metrics.Mean(),
'train/accuracy':
tf.keras.metrics.SparseCategoricalAccuracy(),
'train/loss':
tf.keras.metrics.Mean(),
'train/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood':
tf.keras.metrics.Mean(),
'test/accuracy':
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/member_accuracy_mean':
(tf.keras.metrics.SparseCategoricalAccuracy()),
'test/member_ece_mean':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins)
}
test_diversity = {}
corrupt_diversity = {}
if FLAGS.ensemble_size > 1:
test_diversity = {
'test/disagreement': tf.keras.metrics.Mean(),
'test/average_kl': tf.keras.metrics.Mean(),
'test/cosine_similarity': tf.keras.metrics.Mean(),
}
if FLAGS.corruptions_interval > 0:
corrupt_metrics = {}
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
corrupt_metrics['test/member_acc_mean_{}'.format(
dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/member_ece_mean_{}'.format(
dataset_name)] = (um.ExpectedCalibrationError(
num_bins=FLAGS.num_bins))
corrupt_diversity['corrupt_diversity/average_kl_{}'.format(
dataset_name)] = tf.keras.metrics.Mean()
corrupt_diversity[
'corrupt_diversity/cosine_similarity_{}'.format(
dataset_name)] = tf.keras.metrics.Mean()
corrupt_diversity[
'corrupt_diversity/disagreement_{}'.format(
dataset_name)] = tf.keras.metrics.Mean()
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint:
# checkpoint.restore must be within a strategy.scope() so that optimizer
# slot variables are mirrored.
checkpoint.restore(latest_checkpoint)
logging.info('Loaded checkpoint %s', latest_checkpoint)
initial_epoch = optimizer.iterations.numpy() // steps_per_epoch
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.adaptive_mixup:
images = tf.identity(images)
elif FLAGS.augmix or FLAGS.random_augment:
images_shape = tf.shape(images)
images = tf.reshape(
tf.transpose(images, [1, 0, 2, 3, 4]),
[-1, images_shape[2], images_shape[3], images_shape[4]])
else:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
if FLAGS.cifar_class is not None:
labels = tf.cast(labels == FLAGS.cifar_class, tf.float32)
if FLAGS.mixup_alpha > 0:
split_inputs = True
if not FLAGS.augmix and not FLAGS.adaptive_mixup:
labels = tf.tile(labels, [FLAGS.ensemble_size, 1])
else:
labels = tf.identity(labels)
else:
labels = tf.tile(labels, [FLAGS.ensemble_size])
split_inputs = True if FLAGS.augmix else False
with tf.GradientTape() as tape:
logits = model(images, split_inputs, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
if FLAGS.mixup_alpha > 0:
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.categorical_crossentropy(
labels, logits, from_logits=True))
else:
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True))
l2_loss = sum(model.losses)
if FLAGS.distance_logits and FLAGS.one_vs_all:
distance_loss = one_vs_rest_dm_loss(labels, logits)
loss = distance_loss + l2_loss
else:
loss = negative_log_likelihood + l2_loss
# Scale the loss given the TPUStrategy will reduce sum all gradients.
scaled_loss = loss / strategy.num_replicas_in_sync
grads = tape.gradient(scaled_loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
probs = tf.nn.softmax(logits)
if FLAGS.mixup_alpha > 0:
labels = tf.argmax(labels, axis=-1)
metrics['train/ece'].update_state(labels, probs)
metrics['train/loss'].update_state(loss)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(labels, logits)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
if FLAGS.cifar_class is not None:
labels = tf.cast(labels == FLAGS.cifar_class, tf.float32)
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
probs = tf.nn.softmax(logits)
per_probs = tf.split(probs,
num_or_size_splits=FLAGS.ensemble_size,
axis=0)
if FLAGS.ensemble_size > 1:
per_probs_tensor = tf.reshape(
probs,
tf.concat([[FLAGS.ensemble_size, -1], probs.shape[1:]], 0))
diversity_results = um.average_pairwise_diversity(
per_probs_tensor, FLAGS.ensemble_size)
if dataset_name == 'clean':
for k, v in diversity_results.items():
test_diversity['test/' + k].update_state(v)
elif dataset_name != 'validation':
for k, v in diversity_results.items():
corrupt_diversity['corrupt_diversity/{}_{}'.format(
k, dataset_name)].update_state(v)
probs = tf.reduce_mean(per_probs, axis=0)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
tiled_labels = tf.tile(labels, [FLAGS.ensemble_size])
tiled_probs = tf.concat(per_probs, axis=0)
if dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
metrics['test/member_accuracy_mean'].update_state(
tiled_labels, tiled_probs)
metrics['test/member_ece_mean'].update_state(
tiled_labels, tiled_probs)
elif dataset_name != 'validation':
corrupt_metrics['test/nll_{}'.format(
dataset_name)].update_state(negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)].update_state(labels, probs)
corrupt_metrics['test/ece_{}'.format(
dataset_name)].update_state(labels, probs)
corrupt_metrics['test/member_acc_mean_{}'.format(
dataset_name)].update_state(tiled_labels, tiled_probs)
corrupt_metrics['test/member_ece_mean_{}'.format(
dataset_name)].update_state(tiled_labels, tiled_probs)
if dataset_name == 'validation':
return per_probs_tensor, labels
if dataset_name == 'validation':
return strategy.run(step_fn, args=(next(iterator), ))
else:
strategy.run(step_fn, args=(next(iterator), ))
train_iterator = iter(train_dataset)
start_time = time.time()
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
for step in range(steps_per_epoch):
train_step(train_iterator)
current_step = epoch * steps_per_epoch + (step + 1)
max_steps = steps_per_epoch * FLAGS.train_epochs
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps, epoch + 1,
FLAGS.train_epochs, steps_per_sec, eta_seconds / 60,
time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
if FLAGS.adaptive_mixup:
val_iterator = iter(val_dataset)
logging.info('Testing on validation dataset')
predictions_list = []
labels_list = []
for step in range(steps_per_val):
temp_predictions, temp_labels = test_step(
val_iterator, 'validation')
predictions_list.append(temp_predictions)
labels_list.append(temp_labels)
predictions = [
tf.concat(list(predictions_list[i].values), axis=1)
for i in range(len(predictions_list))
]
labels = [
tf.concat(list(labels_list[i].values), axis=0)
for i in range(len(labels_list))
]
predictions = tf.concat(predictions, axis=1)
labels = tf.cast(tf.concat(labels, axis=0), tf.int64)
def compute_acc_conf(preds, label, focus_class):
class_preds = tf.boolean_mask(preds,
label == focus_class,
axis=1)
class_pred_labels = tf.argmax(class_preds, axis=-1)
confidence = tf.reduce_mean(
tf.reduce_max(class_preds, axis=-1), -1)
accuracy = tf.reduce_mean(tf.cast(
class_pred_labels == focus_class, tf.float32),
axis=-1)
return accuracy - confidence
calibration_per_class = [
compute_acc_conf(predictions, labels, i)
for i in range(num_classes)
]
calibration_per_class = tf.stack(calibration_per_class, axis=1)
logging.info('calibration per class')
logging.info(calibration_per_class)
mixup_coeff = tf.where(calibration_per_class > 0, 1.0,
FLAGS.mixup_alpha)
mixup_coeff = tf.clip_by_value(mixup_coeff, 0, 1)
logging.info('mixup coeff')
logging.info(mixup_coeff)
aug_params['mixup_coeff'] = mixup_coeff
train_input_fn = data_utils.load_input_fn(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
aug_params=aug_params,
validation_set=True)
train_dataset = strategy.experimental_distribute_datasets_from_function(
train_input_fn)
train_iterator = iter(train_dataset)
datasets_to_evaluate = {'clean': test_datasets['clean']}
if (FLAGS.corruptions_interval > 0
and (epoch + 1) % FLAGS.corruptions_interval == 0):
datasets_to_evaluate = test_datasets
for dataset_name, test_dataset in datasets_to_evaluate.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s',
step, epoch)
test_step(test_iterator, dataset_name)
logging.info('Done with testing on %s', dataset_name)
corrupt_results = {}
if (FLAGS.corruptions_interval > 0
and (epoch + 1) % FLAGS.corruptions_interval == 0):
# This includes corrupt_diversity whose disagreement normalized by its
# corrupt mean error rate.
corrupt_results = utils.aggregate_corrupt_metrics(
corrupt_metrics,
corruption_types,
max_intensity,
corrupt_diversity=corrupt_diversity,
output_dir=FLAGS.output_dir)
logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100)
logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100)
total_metrics = metrics.copy()
total_metrics.update(test_diversity)
total_results = {
name: metric.result()
for name, metric in total_metrics.items()
}
total_results.update(corrupt_results)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for metric in metrics.values():
metric.reset_states()
if (FLAGS.checkpoint_interval > 0
and (epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
if FLAGS.adaptive_mixup and epoch == (FLAGS.train_epochs - 2):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'last_but_one_checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
def main(argv):
del argv # unused arg
tf.io.gfile.makedirs(FLAGS.output_dir)
logging.info('Saving summaries at %s', FLAGS.output_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.use_gpu:
logging.info('Use GPU')
strategy = tf.distribute.MirroredStrategy()
else:
logging.info('Use TPU at %s',
FLAGS.tpu if FLAGS.tpu is not None else 'local')
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu=FLAGS.tpu)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
validation_input_fn = data_utils.load_input_fn(
split=tfds.Split.VALIDATION,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
validation_set=True)
clean_test_input_fn = data_utils.load_input_fn(
split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets = {
'validation':
strategy.experimental_distribute_datasets_from_function(
validation_input_fn),
'clean':
strategy.experimental_distribute_datasets_from_function(
clean_test_input_fn),
}
if FLAGS.corruptions:
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c
else:
load_c_dataset = functools.partial(utils.load_cifar100_c,
path=FLAGS.cifar100_c_path)
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for corruption in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset = load_c_dataset(corruption_name=corruption,
corruption_intensity=intensity,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets['{0}_{1}'.format(corruption, intensity)] = (
strategy.experimental_distribute_dataset(dataset))
ds_info = tfds.builder(FLAGS.dataset).info
# TODO(ywenxu): Remove hard-coding validation images.
steps_per_val = 2500 // (FLAGS.per_core_batch_size * FLAGS.num_cores)
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
if FLAGS.use_bfloat16:
tf.keras.mixed_precision.set_global_policy('mixed_bfloat16')
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
with strategy.scope():
logging.info('Building Keras model')
model = tf.keras.models.load_model(FLAGS.model_dir)
logging.info('Model input shape: %s', model.input_shape)
logging.info('Model output shape: %s', model.output_shape)
logging.info('Model number of weights: %s', model.count_params())
# Compute grid search over [0.1, ..., 4.9, 5.0].
temperatures = [x * 0.1 for x in range(1, 51)]
temperature_metrics = []
temperature_corrupt_metrics = []
for _ in temperatures:
metrics = {
'val/negative_log_likelihood': tf.keras.metrics.Mean(),
'val/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'val/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
temperature_metrics.append(metrics)
corrupt_metrics = {}
if FLAGS.corruptions:
for intensity in range(1, max_intensity + 1):
for corruption in corruption_types:
dataset_name = '{0}_{1}'.format(corruption, intensity)
corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
um.ExpectedCalibrationError(
num_bins=FLAGS.num_bins))
temperature_corrupt_metrics.append(corrupt_metrics)
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
for (temperature, metrics,
corrupt_metrics) in zip(temperatures, temperature_metrics,
temperature_corrupt_metrics):
tempered_logits = logits
if not FLAGS.ensemble_then_calibrate:
tempered_logits = logits / temperature
probs = tf.nn.softmax(tempered_logits)
per_probs = tf.split(probs,
num_or_size_splits=FLAGS.ensemble_size,
axis=0)
probs = tf.reduce_mean(per_probs, axis=0)
if FLAGS.ensemble_then_calibrate:
probs = tf.nn.softmax(tf.math.log(probs) / temperature)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, probs))
if dataset_name == 'validation':
metrics['val/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['val/accuracy'].update_state(labels, probs)
metrics['val/ece'].update_state(labels, probs)
elif dataset_name == 'clean':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
else:
corrupt_metrics['test/nll_{}'.format(
dataset_name)].update_state(negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(
dataset_name)].update_state(labels, probs)
corrupt_metrics['test/ece_{}'.format(
dataset_name)].update_state(labels, probs)
return logits, labels
return strategy.run(step_fn, args=(next(iterator), ))
start_time = time.time()
for i, (dataset_name, test_dataset) in enumerate(test_datasets.items()):
logging.info('Testing on dataset %s', dataset_name)
test_iterator = iter(test_dataset)
if dataset_name == 'validation':
steps = steps_per_val
else:
steps = steps_per_eval
full_logits = []
full_labels = []
for step in range(steps):
logits, labels = test_step(test_iterator, dataset_name)
full_logits.append(logits)
full_labels.append(labels)
current_step = i * steps_per_eval + (step + 1)
max_steps = steps_per_eval * len(test_datasets)
time_elapsed = time.time() - start_time
steps_per_sec = float(current_step) / time_elapsed
eta_seconds = (max_steps - current_step) / steps_per_sec
message = ('{:.1%} completion: dataset {:d}/{:d}. {:.1f} steps/s. '
'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
current_step / max_steps, i + 1, len(test_datasets),
steps_per_sec, eta_seconds / 60, time_elapsed / 60))
if step % 20 == 0:
logging.info(message)
if FLAGS.save_predictions:
full_logits = tf.concat([
tf.concat(strategy.experimental_local_results(logits), axis=0)
for logits in full_logits
],
axis=0)
full_labels = tf.cast(
tf.concat([
tf.concat(strategy.experimental_local_results(labels),
axis=0) for labels in full_labels
],
axis=0), tf.int64)
with tf.io.gfile.GFile(
os.path.join(FLAGS.output_dir,
f'{dataset_name}_logits.npy'), 'wb') as f:
np.save(f, full_logits.numpy())
with tf.io.gfile.GFile(
os.path.join(FLAGS.output_dir,
f'{dataset_name}_labels.npy'), 'wb') as f:
np.save(f, full_labels.numpy())
logging.info('Done with testing on %s', dataset_name)
for i, metrics in enumerate(temperature_metrics):
total_results = {
name: metric.result()
for name, metric in metrics.items()
}
if FLAGS.corruptions:
corrupt_results = utils.aggregate_corrupt_metrics(
temperature_corrupt_metrics[i],
corruption_types,
max_intensity,
output_dir=FLAGS.output_dir)
total_results.update(corrupt_results)
logging.info('Temperature: %.2f, Test NLL: %.4f, Accuracy: %.2f%%',
temperatures[i],
total_results['test/negative_log_likelihood'],
total_results['test/accuracy'] * 100)
# Use step counter as an alternative to temperature on x-axis. Can't do the
# latter.
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=i)
logging.info('Completed script')
