def _basic_metrics(self):
metrics_basic = {}
metrics_basic['logits'] = []
metrics_basic['probs'] = [tf.keras.metrics.SparseCategoricalAccuracy(name='acc'),
um.ExpectedCalibrationError(num_bins=self.FLAGS.ece_bins,name='ece'),
nll(name='nll'),
BrierScore(name='brier')]
metrics_basic['certs'] = [tf.keras.metrics.SparseCategoricalAccuracy(name='acc'),
um.ExpectedCalibrationError(num_bins=self.FLAGS.ece_bins,name='ece'),
nll(name='nll'),
BrierScore(name='brier')]
metrics_basic['logits_from_certs'] = []
return metrics_basic
def get_diabetic_retinopathy_base_metrics(use_tpu, num_bins):
"""Initialize base metrics for non-ensemble Diabetic Retinopathy predictors.
Should be called within the distribution strategy scope (e.g. see
deterministic.py script).
Note:
We disclude AUC in non-TPU case, which must be defined and added to this
dict outside the strategy scope.
We disclude ECE in TPU case, which currently throws an XLA error on TPU.
Args:
use_tpu: bool, is run using TPU.
num_bins: number of ECE bins.
Returns:
dict, metrics
"""
metrics = {
'train/negative_log_likelihood': tf.keras.metrics.Mean(),
'train/accuracy': tf.keras.metrics.BinaryAccuracy(),
'train/loss': tf.keras.metrics.Mean(), # NLL + L2
'validation/negative_log_likelihood': tf.keras.metrics.Mean(),
'validation/accuracy': tf.keras.metrics.BinaryAccuracy(),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.BinaryAccuracy(),
}
if use_tpu:
# AUC does not yet work within GPU strategy scope, but does for TPU
metrics.update({
'train/auc': tf.keras.metrics.AUC(),
'validation/auc': tf.keras.metrics.AUC(),
'test/auc': tf.keras.metrics.AUC(),
})
else:
# ECE does not yet work on TPU
metrics.update({
'train/ece': um.ExpectedCalibrationError(num_bins=num_bins),
'validation/ece': um.ExpectedCalibrationError(num_bins=num_bins),
'test/ece': um.ExpectedCalibrationError(num_bins=num_bins),
})
return metrics
def testECEBinaryClassification(self):
num_bins = 10
pred_probs = np.array([0.51, 0.45, 0.39, 0.66, 0.68, 0.29, 0.81, 0.85])
# max_pred_probs: [0.51, 0.55, 0.61, 0.66, 0.68, 0.71, 0.81, 0.85]
# pred_class: [1, 0, 0, 1, 1, 0, 1, 1]
labels = np.array([0., 0., 0., 1., 0., 1., 1., 1.])
n = len(pred_probs)
# Bins for the max predicted probabilities are (0, 0.1), [0.1, 0.2), ...,
# [0.9, 1) and are numbered starting at zero.
bin_counts = np.array([0, 0, 0, 0, 0, 2, 3, 1, 2, 0])
bin_correct_sums = np.array([0, 0, 0, 0, 0, 1, 2, 0, 2, 0])
bin_prob_sums = np.array([0, 0, 0, 0, 0, 0.51 + 0.55, 0.61 + 0.66 + 0.68,
0.71, 0.81 + 0.85, 0])
correct_ece = 0.
bin_accs = np.array([0.] * num_bins)
bin_confs = np.array([0.] * num_bins)
for i in range(num_bins):
if bin_counts[i] > 0:
bin_accs[i] = bin_correct_sums[i] / bin_counts[i]
bin_confs[i] = bin_prob_sums[i] / bin_counts[i]
correct_ece += bin_counts[i] / n * abs(bin_accs[i] - bin_confs[i])
metric = um.ExpectedCalibrationError(
num_bins, name='ECE', dtype=tf.float64)
self.assertLen(metric.variables, 3)
ece1 = metric(labels, pred_probs)
self.assertAllClose(ece1, correct_ece)
actual_bin_counts = tf.convert_to_tensor(metric.counts)
actual_bin_correct_sums = tf.convert_to_tensor(metric.correct_sums)
actual_bin_prob_sums = tf.convert_to_tensor(metric.prob_sums)
self.assertAllEqual(bin_counts, actual_bin_counts)
self.assertAllEqual(bin_correct_sums, actual_bin_correct_sums)
self.assertAllClose(bin_prob_sums, actual_bin_prob_sums)
# Test various types of input shapes.
metric.reset_states()
metric.update_state(labels[:2], pred_probs[:2])
metric.update_state(labels[2:6].reshape(2, 2),
pred_probs[2:6].reshape(2, 2))
metric.update_state(labels[6:7], pred_probs[6:7])
ece2 = metric(labels[7:, np.newaxis], pred_probs[7:, np.newaxis])
ece3 = metric.result()
self.assertAllClose(ece2, ece3)
self.assertAllClose(ece3, correct_ece)
actual_bin_counts = tf.convert_to_tensor(metric.counts)
actual_bin_correct_sums = tf.convert_to_tensor(metric.correct_sums)
actual_bin_prob_sums = tf.convert_to_tensor(metric.prob_sums)
self.assertAllEqual(bin_counts, actual_bin_counts)
self.assertAllEqual(bin_correct_sums, actual_bin_correct_sums)
self.assertAllClose(bin_prob_sums, actual_bin_prob_sums)
def testECEMulticlassClassification(self):
num_bins = 10
pred_probs = [
[0.31, 0.32, 0.27],
[0.37, 0.33, 0.30],
[0.30, 0.31, 0.39],
[0.61, 0.38, 0.01],
[0.10, 0.65, 0.25],
[0.91, 0.05, 0.04],
]
# max_pred_probs: [0.32, 0.37, 0.39, 0.61, 0.65, 0.91]
# pred_class: [1, 0, 2, 0, 1, 0]
labels = [1., 0, 0., 1., 0., 0.]
n = len(pred_probs)
# Bins for the max predicted probabilities are (0, 0.1), [0.1, 0.2), ...,
# [0.9, 1) and are numbered starting at zero.
bin_counts = [0, 0, 0, 3, 0, 0, 2, 0, 0, 1]
bin_correct_sums = [0, 0, 0, 2, 0, 0, 0, 0, 0, 1]
bin_prob_sums = [0, 0, 0, 0.32 + 0.37 + 0.39, 0, 0, 0.61 + 0.65, 0, 0, 0.91]
correct_ece = 0.
bin_accs = [0.] * num_bins
bin_confs = [0.] * num_bins
for i in range(num_bins):
if bin_counts[i] > 0:
bin_accs[i] = bin_correct_sums[i] / bin_counts[i]
bin_confs[i] = bin_prob_sums[i] / bin_counts[i]
correct_ece += bin_counts[i] / n * abs(bin_accs[i] - bin_confs[i])
metric = um.ExpectedCalibrationError(
num_bins, name='ECE', dtype=tf.float64)
self.assertLen(metric.variables, 3)
metric.update_state(labels[:4], pred_probs[:4])
ece1 = metric(labels[4:], pred_probs[4:])
ece2 = metric.result()
self.assertAllClose(ece1, ece2)
self.assertAllClose(ece2, correct_ece)
actual_bin_counts = tf.convert_to_tensor(metric.counts)
actual_bin_correct_sums = tf.convert_to_tensor(metric.correct_sums)
actual_bin_prob_sums = tf.convert_to_tensor(metric.prob_sums)
self.assertAllEqual(bin_counts, actual_bin_counts)
self.assertAllEqual(bin_correct_sums, actual_bin_correct_sums)
self.assertAllClose(bin_prob_sums, actual_bin_prob_sums)
def testECEBinaryClassificationKerasModel(self):
num_bins = 10
pred_probs = np.array([0.51, 0.45, 0.39, 0.66, 0.68, 0.29, 0.81, 0.85])
# max_pred_probs: [0.51, 0.55, 0.61, 0.66, 0.68, 0.71, 0.81, 0.85]
# pred_class: [1, 0, 0, 1, 1, 0, 1, 1]
labels = np.array([0., 0., 0., 1., 0., 1., 1., 1.])
n = len(pred_probs)
# Bins for the max predicted probabilities are (0, 0.1), [0.1, 0.2), ...,
# [0.9, 1) and are numbered starting at zero.
bin_counts = [0, 0, 0, 0, 0, 2, 3, 1, 2, 0]
bin_correct_sums = [0, 0, 0, 0, 0, 1, 2, 0, 2, 0]
bin_prob_sums = [0, 0, 0, 0, 0, 0.51 + 0.55, 0.61 + 0.66 + 0.68, 0.71,
0.81 + 0.85, 0]
correct_ece = 0.
bin_accs = [0.] * num_bins
bin_confs = [0.] * num_bins
for i in range(num_bins):
if bin_counts[i] > 0:
bin_accs[i] = bin_correct_sums[i] / bin_counts[i]
bin_confs[i] = bin_prob_sums[i] / bin_counts[i]
correct_ece += bin_counts[i] / n * abs(bin_accs[i] - bin_confs[i])
metric = um.ExpectedCalibrationError(num_bins, name='ECE')
self.assertLen(metric.variables, 3)
model = tf.keras.models.Sequential([tf.keras.layers.Lambda(lambda x: 1*x)])
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=[metric])
outputs = model.predict(pred_probs)
self.assertAllClose(pred_probs.reshape([n, 1]), outputs)
_, ece = model.evaluate(pred_probs, labels)
self.assertAllClose(ece, correct_ece)
actual_bin_counts = tf.convert_to_tensor(metric.counts)
actual_bin_correct_sums = tf.convert_to_tensor(metric.correct_sums)
actual_bin_prob_sums = tf.convert_to_tensor(metric.prob_sums)
self.assertAllEqual(bin_counts, actual_bin_counts)
self.assertAllEqual(bin_correct_sums, actual_bin_correct_sums)
self.assertAllClose(bin_prob_sums, actual_bin_prob_sums)
def main(argv):
del argv # unused arg
if not FLAGS.use_gpu:
raise ValueError('Only GPU is currently supported.')
if FLAGS.num_cores > 1:
raise ValueError('Only a single accelerator is currently supported.')
tf.random.set_seed(FLAGS.seed)
tf.io.gfile.makedirs(FLAGS.output_dir)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
steps_per_eval = IMAGENET_VALIDATION_IMAGES // batch_size
builder = utils.ImageNetInput(data_dir=FLAGS.data_dir,
use_bfloat16=False)
clean_test_dataset = builder.as_dataset(split=tfds.Split.TEST,
batch_size=batch_size)
test_datasets = {'clean': clean_test_dataset}
corruption_types, max_intensity = utils.load_corrupted_test_info()
for name in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset_name = '{0}_{1}'.format(name, intensity)
test_datasets[dataset_name] = utils.load_corrupted_test_dataset(
corruption_name=name,
corruption_intensity=intensity,
batch_size=batch_size,
drop_remainder=True,
use_bfloat16=False)
model = ub.models.resnet50_deterministic(input_shape=(224, 224, 3),
num_classes=NUM_CLASSES)
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())
# Search for checkpoints from their index file; then remove the index suffix.
ensemble_filenames = tf.io.gfile.glob(os.path.join(FLAGS.checkpoint_dir,
'**/*.index'))
ensemble_filenames = [filename[:-6] for filename in ensemble_filenames]
ensemble_size = len(ensemble_filenames)
logging.info('Ensemble size: %s', ensemble_size)
logging.info('Ensemble number of weights: %s',
ensemble_size * model.count_params())
logging.info('Ensemble filenames: %s', str(ensemble_filenames))
checkpoint = tf.train.Checkpoint(model=model)
# Write model predictions to files.
num_datasets = len(test_datasets)
for m, ensemble_filename in enumerate(ensemble_filenames):
checkpoint.restore(ensemble_filename)
for n, (name, test_dataset) in enumerate(test_datasets.items()):
filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
if not tf.io.gfile.exists(filename):
logits = []
test_iterator = iter(test_dataset)
for _ in range(steps_per_eval):
features, _ = next(test_iterator) # pytype: disable=attribute-error
logits.append(model(features, training=False))
logits = tf.concat(logits, axis=0)
with tf.io.gfile.GFile(filename, 'w') as f:
np.save(f, logits.numpy())
percent = (m * num_datasets + (n + 1)) / (ensemble_size * num_datasets)
message = ('{:.1%} completion for prediction: ensemble member {:d}/{:d}. '
'Dataset {:d}/{:d}'.format(percent,
m + 1,
ensemble_size,
n + 1,
num_datasets))
logging.info(message)
metrics = {
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/gibbs_cross_entropy': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
corrupt_metrics = {}
for name in test_datasets:
corrupt_metrics['test/nll_{}'.format(name)] = tf.keras.metrics.Mean()
corrupt_metrics['test/accuracy_{}'.format(name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(
name)] = um.ExpectedCalibrationError(num_bins=FLAGS.num_bins)
# Evaluate model predictions.
for n, (name, test_dataset) in enumerate(test_datasets.items()):
logits_dataset = []
for m in range(ensemble_size):
filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
with tf.io.gfile.GFile(filename, 'rb') as f:
logits_dataset.append(np.load(f))
logits_dataset = tf.convert_to_tensor(logits_dataset)
test_iterator = iter(test_dataset)
for step in range(steps_per_eval):
_, labels = next(test_iterator) # pytype: disable=attribute-error
logits = logits_dataset[:, (step*batch_size):((step+1)*batch_size)]
labels = tf.cast(tf.reshape(labels, [-1]), tf.int32)
negative_log_likelihood = um.ensemble_cross_entropy(labels, logits)
per_probs = tf.nn.softmax(logits)
probs = tf.reduce_mean(per_probs, axis=0)
if name == 'clean':
gibbs_ce = um.gibbs_cross_entropy(labels, logits)
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/gibbs_cross_entropy'].update_state(gibbs_ce)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
else:
corrupt_metrics['test/nll_{}'.format(name)].update_state(
negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(name)].update_state(
labels, probs)
corrupt_metrics['test/ece_{}'.format(name)].update_state(
labels, probs)
message = ('{:.1%} completion for evaluation: dataset {:d}/{:d}'.format(
(n + 1) / num_datasets, n + 1, num_datasets))
logging.info(message)
corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
corruption_types,
max_intensity,
FLAGS.alexnet_errors_path)
total_results = {name: metric.result() for name, metric in metrics.items()}
total_results.update(corrupt_results)
logging.info('Metrics: %s', total_results)
def main(argv):
del argv # unused arg
if not FLAGS.use_gpu:
raise ValueError('Only GPU is currently supported.')
if FLAGS.num_cores > 1:
raise ValueError('Only a single accelerator is currently supported.')
tf.random.set_seed(FLAGS.seed)
logging.info('Model checkpoint will be saved at %s', FLAGS.output_dir)
tf.io.gfile.makedirs(FLAGS.output_dir)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
test_batch_size = batch_size
data_buffer_size = batch_size * 10
ind_dataset_builder = ds.WikipediaToxicityDataset(
split='test',
data_dir=FLAGS.in_dataset_dir,
shuffle_buffer_size=data_buffer_size)
ood_dataset_builder = ds.CivilCommentsDataset(
split='test',
data_dir=FLAGS.ood_dataset_dir,
shuffle_buffer_size=data_buffer_size)
ood_identity_dataset_builder = ds.CivilCommentsIdentitiesDataset(
split='test',
data_dir=FLAGS.identity_dataset_dir,
shuffle_buffer_size=data_buffer_size)
test_dataset_builders = {
'ind': ind_dataset_builder,
'ood': ood_dataset_builder,
'ood_identity': ood_identity_dataset_builder,
}
class_weight = utils.create_class_weight(
test_dataset_builders=test_dataset_builders)
logging.info('class_weight: %s', str(class_weight))
ds_info = ind_dataset_builder.tfds_info
# Positive and negative classes.
num_classes = ds_info.metadata['num_classes']
test_datasets = {}
steps_per_eval = {}
for dataset_name, dataset_builder in test_dataset_builders.items():
test_datasets[dataset_name] = dataset_builder.load(
batch_size=test_batch_size)
steps_per_eval[dataset_name] = (
dataset_builder.num_examples // test_batch_size)
logging.info('Building %s model', FLAGS.model_family)
bert_config_dir, _ = utils.resolve_bert_ckpt_and_config_dir(
FLAGS.bert_model_type, FLAGS.bert_dir, FLAGS.bert_config_dir,
FLAGS.bert_ckpt_dir)
bert_config = utils.create_config(bert_config_dir)
gp_layer_kwargs = dict(
num_inducing=FLAGS.gp_hidden_dim,
gp_kernel_scale=FLAGS.gp_scale,
gp_output_bias=FLAGS.gp_bias,
normalize_input=FLAGS.gp_input_normalization,
gp_cov_momentum=FLAGS.gp_cov_discount_factor,
gp_cov_ridge_penalty=FLAGS.gp_cov_ridge_penalty)
spec_norm_kwargs = dict(
iteration=FLAGS.spec_norm_iteration,
norm_multiplier=FLAGS.spec_norm_bound)
model, _ = ub.models.SngpBertBuilder(
num_classes=num_classes,
bert_config=bert_config,
gp_layer_kwargs=gp_layer_kwargs,
spec_norm_kwargs=spec_norm_kwargs,
use_gp_layer=FLAGS.use_gp_layer,
use_spec_norm_att=FLAGS.use_spec_norm_att,
use_spec_norm_ffn=FLAGS.use_spec_norm_ffn,
use_layer_norm_att=FLAGS.use_layer_norm_att,
use_layer_norm_ffn=FLAGS.use_layer_norm_ffn,
use_spec_norm_plr=FLAGS.use_spec_norm_plr)
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())
# Search for checkpoints from their index file; then remove the index suffix.
ensemble_filenames = tf.io.gfile.glob(
os.path.join(FLAGS.checkpoint_dir, '**/*.index'))
ensemble_filenames = [filename[:-6] for filename in ensemble_filenames]
if FLAGS.num_models > len(ensemble_filenames):
raise ValueError('Number of models to be included in the ensemble '
'should be less than total number of models in '
'the checkpoint_dir.')
ensemble_filenames = ensemble_filenames[:FLAGS.num_models]
ensemble_size = len(ensemble_filenames)
logging.info('Ensemble size: %s', ensemble_size)
logging.info('Ensemble number of weights: %s',
ensemble_size * model.count_params())
logging.info('Ensemble filenames: %s', str(ensemble_filenames))
checkpoint = tf.train.Checkpoint(model=model)
# Write model predictions to files.
num_datasets = len(test_datasets)
for m, ensemble_filename in enumerate(ensemble_filenames):
checkpoint.restore(ensemble_filename).assert_existing_objects_matched()
for n, (dataset_name, test_dataset) in enumerate(test_datasets.items()):
filename = '{dataset}_{member}.npy'.format(dataset=dataset_name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
if not tf.io.gfile.exists(filename):
logits_list = []
test_iterator = iter(test_dataset)
for step in range(steps_per_eval[dataset_name]):
try:
inputs = next(test_iterator)
except StopIteration:
continue
features, labels, _ = utils.create_feature_and_label(inputs)
logits = model(features, training=False)
if isinstance(logits, (list, tuple)):
# If model returns a tuple of (logits, covmat), extract both.
logits, covmat = logits
else:
covmat = tf.eye(test_batch_size)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
covmat = tf.cast(covmat, tf.float32)
logits = ed.layers.utils.mean_field_logits(
logits, covmat,
mean_field_factor=FLAGS.gp_mean_field_factor_ensemble)
logits_list.append(logits)
logits_all = tf.concat(logits_list, axis=0)
with tf.io.gfile.GFile(filename, 'w') as f:
np.save(f, logits_all.numpy())
percent = (m * num_datasets + (n + 1)) / (ensemble_size * num_datasets)
message = ('{:.1%} completion for prediction: ensemble member {:d}/{:d}. '
'Dataset {:d}/{:d}'.format(percent, m + 1, ensemble_size,
n + 1, num_datasets))
logging.info(message)
metrics = {
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/auroc': tf.keras.metrics.AUC(curve='ROC'),
'test/aupr': tf.keras.metrics.AUC(curve='PR'),
'test/brier': tf.keras.metrics.MeanSquaredError(),
'test/brier_weighted': tf.keras.metrics.MeanSquaredError(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/acc': tf.keras.metrics.Accuracy(),
'test/acc_weighted': tf.keras.metrics.Accuracy(),
'test/precision': tf.keras.metrics.Precision(),
'test/recall': tf.keras.metrics.Recall(),
'test/f1': tfa_metrics.F1Score(
num_classes=num_classes, average='micro',
threshold=FLAGS.ece_label_threshold)
}
for fraction in FLAGS.fractions:
metrics.update({
'test_collab_acc/collab_acc_{}'.format(fraction):
um.OracleCollaborativeAccuracy(
fraction=float(fraction), num_bins=FLAGS.num_bins)
})
for dataset_name, test_dataset in test_datasets.items():
if dataset_name != 'ind':
metrics.update({
'test/nll_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
'test/auroc_{}'.format(dataset_name):
tf.keras.metrics.AUC(curve='ROC'),
'test/aupr_{}'.format(dataset_name):
tf.keras.metrics.AUC(curve='PR'),
'test/brier_{}'.format(dataset_name):
tf.keras.metrics.MeanSquaredError(),
'test/brier_weighted_{}'.format(dataset_name):
tf.keras.metrics.MeanSquaredError(),
'test/ece_{}'.format(dataset_name):
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/acc_weighted_{}'.format(dataset_name):
tf.keras.metrics.Accuracy(),
'test/acc_{}'.format(dataset_name):
tf.keras.metrics.Accuracy(),
'test/precision_{}'.format(dataset_name):
tf.keras.metrics.Precision(),
'test/recall_{}'.format(dataset_name):
tf.keras.metrics.Recall(),
'test/f1_{}'.format(dataset_name):
tfa_metrics.F1Score(
num_classes=num_classes, average='micro',
threshold=FLAGS.ece_label_threshold)
})
for fraction in FLAGS.fractions:
metrics.update({
'test_collab_acc/collab_acc_{}_{}'.format(fraction, dataset_name):
um.OracleCollaborativeAccuracy(
fraction=float(fraction), num_bins=FLAGS.num_bins)
})
@tf.function
def generate_sample_weight(labels, class_weight, label_threshold=0.7):
"""Generate sample weight for weighted accuracy calculation."""
if label_threshold != 0.7:
logging.warning('The class weight was based on `label_threshold` = 0.7, '
'and weighted accuracy/brier will be meaningless if '
'`label_threshold` is not equal to this value, which is '
'recommended by Jigsaw Conversation AI team.')
labels_int = tf.cast(labels > label_threshold, tf.int32)
sample_weight = tf.gather(class_weight, labels_int)
return sample_weight
# Evaluate model predictions.
for n, (dataset_name, test_dataset) in enumerate(test_datasets.items()):
logits_dataset = []
for m in range(ensemble_size):
filename = '{dataset}_{member}.npy'.format(dataset=dataset_name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
with tf.io.gfile.GFile(filename, 'rb') as f:
logits_dataset.append(np.load(f))
logits_dataset = tf.convert_to_tensor(logits_dataset)
test_iterator = iter(test_dataset)
texts_list = []
logits_list = []
labels_list = []
# Use dict to collect additional labels specified by additional label names.
# Here we use `OrderedDict` to get consistent ordering for this dict so
# we can retrieve the predictions for each identity labels in Colab.
additional_labels_dict = collections.OrderedDict()
for step in range(steps_per_eval[dataset_name]):
try:
inputs = next(test_iterator) # type: Mapping[Text, tf.Tensor] # pytype: disable=annotation-type-mismatch
except StopIteration:
continue
features, labels, additional_labels = (
utils.create_feature_and_label(inputs))
logits = logits_dataset[:, (step * batch_size):((step + 1) * batch_size)]
loss_logits = tf.squeeze(logits, axis=-1)
negative_log_likelihood = um.ensemble_cross_entropy(
labels, loss_logits, binary=True)
per_probs = tf.nn.sigmoid(logits)
probs = tf.reduce_mean(per_probs, axis=0)
# Cast labels to discrete for ECE computation
ece_labels = tf.cast(labels > FLAGS.ece_label_threshold, tf.float32)
one_hot_labels = tf.one_hot(tf.cast(ece_labels, tf.int32),
depth=num_classes)
ece_probs = tf.concat([1. - probs, probs], axis=1)
pred_labels = tf.math.argmax(ece_probs, axis=-1)
auc_probs = tf.squeeze(probs, axis=1)
texts_list.append(inputs['input_ids'])
logits_list.append(logits)
labels_list.append(labels)
if 'identity' in dataset_name:
for identity_label_name in utils.IDENTITY_LABELS:
if identity_label_name not in additional_labels_dict:
additional_labels_dict[identity_label_name] = []
additional_labels_dict[identity_label_name].append(
additional_labels[identity_label_name].numpy())
sample_weight = generate_sample_weight(
labels, class_weight['test/{}'.format(dataset_name)],
FLAGS.ece_label_threshold)
if dataset_name == 'ind':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/auroc'].update_state(labels, auc_probs)
metrics['test/aupr'].update_state(labels, auc_probs)
metrics['test/brier'].update_state(labels, auc_probs)
metrics['test/brier_weighted'].update_state(
tf.expand_dims(labels, -1), probs, sample_weight=sample_weight)
metrics['test/ece'].add_batch(ece_probs, label=ece_labels)
metrics['test/acc'].update_state(ece_labels, pred_labels)
metrics['test/acc_weighted'].update_state(
ece_labels, pred_labels, sample_weight=sample_weight)
metrics['test/precision'].update_state(ece_labels, pred_labels)
metrics['test/recall'].update_state(ece_labels, pred_labels)
metrics['test/f1'].update_state(one_hot_labels, ece_probs)
for fraction in FLAGS.fractions:
metrics['test_collab_acc/collab_acc_{}'.format(
fraction)].update_state(ece_labels, ece_probs)
else:
metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
metrics['test/auroc_{}'.format(dataset_name)].update_state(
labels, auc_probs)
metrics['test/aupr_{}'.format(dataset_name)].update_state(
labels, auc_probs)
metrics['test/brier_{}'.format(dataset_name)].update_state(
labels, auc_probs)
metrics['test/brier_weighted_{}'.format(dataset_name)].update_state(
tf.expand_dims(labels, -1), probs, sample_weight=sample_weight)
metrics['test/ece_{}'.format(dataset_name)].add_batch(
ece_probs, label=ece_labels)
metrics['test/acc_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels)
metrics['test/acc_weighted_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels, sample_weight=sample_weight)
metrics['test/precision_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels)
metrics['test/recall_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels)
metrics['test/f1_{}'.format(dataset_name)].update_state(
one_hot_labels, ece_probs)
for fraction in FLAGS.fractions:
metrics['test_collab_acc/collab_acc_{}_{}'.format(
fraction, dataset_name)].update_state(ece_labels, ece_probs)
texts_all = tf.concat(texts_list, axis=0)
logits_all = tf.concat(logits_list, axis=1)
labels_all = tf.concat(labels_list, axis=0)
additional_labels_all = []
if additional_labels_dict:
additional_labels_all = list(additional_labels_dict.values())
utils.save_prediction(
texts_all.numpy(),
path=os.path.join(FLAGS.output_dir, 'texts_{}'.format(dataset_name)))
utils.save_prediction(
labels_all.numpy(),
path=os.path.join(FLAGS.output_dir, 'labels_{}'.format(dataset_name)))
utils.save_prediction(
logits_all.numpy(),
path=os.path.join(FLAGS.output_dir, 'logits_{}'.format(dataset_name)))
if 'identity' in dataset_name:
utils.save_prediction(
np.array(additional_labels_all),
path=os.path.join(FLAGS.output_dir,
'additional_labels_{}'.format(dataset_name)))
message = ('{:.1%} completion for evaluation: dataset {:d}/{:d}'.format(
(n + 1) / num_datasets, n + 1, num_datasets))
logging.info(message)
total_results = {name: metric.result() for name, metric in metrics.items()}
# Metrics from Robustness Metrics (like ECE) will return a dict with a
# single key/value, instead of a scalar.
total_results = {
k: (list(v.values())[0] if isinstance(v, dict) else v)
for k, v in total_results.items()
}
logging.info('Metrics: %s', total_results)
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)
ds_info = tfds.builder(FLAGS.dataset).info
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
test_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 // test_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=test_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=test_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 ResNet model')
model = ub.models.wide_resnet_condconv(
input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=FLAGS.resnet_width_multiplier,
num_classes=num_classes,
num_experts=FLAGS.num_experts,
per_core_batch_size=FLAGS.per_core_batch_size,
use_cond_dense=FLAGS.use_cond_dense,
reduce_dense_outputs=FLAGS.reduce_dense_outputs,
cond_placement=FLAGS.cond_placement,
routing_fn=FLAGS.routing_fn,
normalize_routing=FLAGS.normalize_routing,
normalize_dense_routing=FLAGS.normalize_dense_routing,
top_k=FLAGS.top_k,
routing_pooling=FLAGS.routing_pooling,
l2=FLAGS.l2)
# reuse_routing=FLAGS.reuse_routing,
# shared_routing_type=FLAGS.shared_routing_type)
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)
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 not FLAGS.reduce_dense_outputs and FLAGS.use_cond_dense:
metrics.update({
'test/nll_poe':
tf.keras.metrics.Mean(),
'test/nll_moe':
tf.keras.metrics.Mean(),
'test/nll_unweighted_poe':
tf.keras.metrics.Mean(),
'test/nll_unweighted_moe':
tf.keras.metrics.Mean(),
'test/unweighted_gibbs_ce':
tf.keras.metrics.Mean(),
'test/ece_unweighted_moe':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/accuracy_unweighted_moe':
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece_poe':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/accuracy_poe':
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece_unweighted_poe':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/accuracy_unweighted_poe':
tf.keras.metrics.SparseCategoricalAccuracy(),
})
for idx in range(FLAGS.num_experts):
metrics['test/dense_routing_weight_{}'.format(
idx)] = tf.keras.metrics.Mean()
metrics['test/dense_routing_weight_normalized_{}'.format(
idx)] = 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/nll_weighted_moe_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
corrupt_metrics['test/accuracy_weighted_moe_{}'.format(
dataset_name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_weighted_moe_{}'.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 _process_3d_logits(logits, routing_weights, labels):
routing_weights_3d = tf.expand_dims(routing_weights, axis=-1)
weighted_logits = tf.math.reduce_mean(routing_weights_3d * logits, axis=1)
unweighted_logits = tf.math.reduce_mean(logits, axis=1)
probs = tf.nn.softmax(logits)
unweighted_probs = tf.math.reduce_mean(probs, axis=1)
weighted_probs = tf.math.reduce_sum(routing_weights_3d * probs, axis=1)
labels_broadcasted = tf.tile(
tf.reshape(labels, (-1, 1)), (1, FLAGS.num_experts))
neg_log_likelihoods = tf.keras.losses.sparse_categorical_crossentropy(
labels_broadcasted, logits, from_logits=True)
unweighted_gibbs_ce = tf.math.reduce_mean(neg_log_likelihoods)
weighted_gibbs_ce = tf.math.reduce_mean(
tf.math.reduce_sum(routing_weights * neg_log_likelihoods, axis=1))
return {
'weighted_logits': weighted_logits,
'unweighted_logits': unweighted_logits,
'unweighted_probs': unweighted_probs,
'weighted_probs': weighted_probs,
'neg_log_likelihoods': neg_log_likelihoods,
'unweighted_gibbs_ce': unweighted_gibbs_ce,
'weighted_gibbs_ce': weighted_gibbs_ce
}
def _process_3d_logits_train(logits, routing_weights, labels):
processing_results = _process_3d_logits(logits, routing_weights, labels)
if FLAGS.loss == 'gibbs_ce':
probs = processing_results['weighted_probs']
negative_log_likelihood = processing_results['weighted_gibbs_ce']
elif FLAGS.loss == 'unweighted_gibbs_ce':
probs = processing_results['unweighted_probs']
negative_log_likelihood = processing_results['unweighted_gibbs_ce']
elif FLAGS.loss == 'moe':
probs = processing_results['weighted_probs']
negative_log_likelihood = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, probs, from_logits=False))
elif FLAGS.loss == 'unweighted_moe':
probs = processing_results['unweighted_probs']
negative_log_likelihood = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, probs, from_logits=False))
elif FLAGS.loss == 'poe':
probs = tf.softmax(processing_results['weighted_logits'])
negative_log_likelihood = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, processing_results['weighted_logits'], from_logits=True))
elif FLAGS.loss == 'unweighted_poe':
probs = tf.softmax(processing_results['unweighted_logits'])
negative_log_likelihood = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, processing_results['unweighted_logits'],
from_logits=True))
return probs, negative_log_likelihood
def _process_3d_logits_test(routing_weights, logits, labels):
processing_results = _process_3d_logits(logits, routing_weights, labels)
nll_poe = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, processing_results['weighted_logits'], from_logits=True))
nll_unweighted_poe = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, processing_results['unweighted_logits'], from_logits=True))
nll_moe = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, processing_results['weighted_probs'], from_logits=False))
nll_unweighted_moe = tf.math.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, processing_results['unweighted_probs'], from_logits=False))
return {
'nll_poe': nll_poe,
'nll_moe': nll_moe,
'nll_unweighted_poe': nll_unweighted_poe,
'nll_unweighted_moe': nll_unweighted_moe,
'unweighted_gibbs_ce': processing_results['unweighted_gibbs_ce'],
'weighted_gibbs_ce': processing_results['weighted_gibbs_ce'],
'weighted_probs': processing_results['weighted_probs'],
'unweighted_probs': processing_results['unweighted_probs'],
'weighted_logits': processing_results['weighted_logits'],
'unweighted_logits': processing_results['unweighted_logits']
}
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
with tf.GradientTape() as tape:
logits = model(images, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
# if not FLAGS.reduce_dense_outputs and FLAGS.use_cond_dense:
if not isinstance(logits, tuple):
raise ValueError('Logits are not a tuple.')
# logits is a `Tensor` of shape [batch_size, num_experts, num_classes]
logits, all_routing_weights = logits
# routing_weights is a `Tensor` of shape [batch_size, num_experts]
routing_weights = all_routing_weights[-1]
if not FLAGS.reduce_dense_outputs and FLAGS.use_cond_dense:
probs, negative_log_likelihood = _process_3d_logits_train(
logits, routing_weights, labels)
else:
probs = tf.nn.softmax(logits)
# Prior to reduce_mean the NLLs are of the shape [batch, num_experts].
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))
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)
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 = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
if not isinstance(logits, tuple):
raise ValueError('Logits not a tuple')
# logits is a `Tensor` of shape [batch_size, num_experts, num_classes]
# routing_weights is a `Tensor` of shape [batch_size, num_experts]
logits, all_routing_weights = logits
routing_weights = all_routing_weights[-1]
if not FLAGS.reduce_dense_outputs and FLAGS.use_cond_dense:
results = _process_3d_logits_test(routing_weights, logits, labels)
else:
probs = tf.nn.softmax(logits)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
if dataset_name == 'clean':
if not FLAGS.reduce_dense_outputs and FLAGS.use_cond_dense:
metrics['test/nll_poe'].update_state(results['nll_poe'])
metrics['test/nll_moe'].update_state(results['nll_moe'])
metrics['test/nll_unweighted_poe'].update_state(
results['nll_unweighted_poe'])
metrics['test/nll_unweighted_moe'].update_state(
results['nll_unweighted_moe'])
metrics['test/unweighted_gibbs_ce'].update_state(
results['unweighted_gibbs_ce'])
metrics['test/negative_log_likelihood'].update_state(
results['weighted_gibbs_ce'])
metrics['test/ece'].update_state(labels, results['weighted_probs'])
metrics['test/accuracy'].update_state(labels,
results['weighted_probs'])
metrics['test/ece_unweighted_moe'].update_state(
labels, results['unweighted_probs'])
metrics['test/accuracy_unweighted_moe'].update_state(
labels, results['unweighted_probs'])
metrics['test/ece_poe'].update_state(labels,
results['weighted_logits'])
metrics['test/accuracy_poe'].update_state(labels,
results['weighted_logits'])
metrics['test/ece_unweighted_poe'].update_state(
labels, results['unweighted_logits'])
metrics['test/accuracy_unweighted_poe'].update_state(
labels, results['unweighted_logits'])
# TODO(ghassen): summarize all routing weights not only last layer's.
average_routing_weights = tf.math.reduce_mean(routing_weights, axis=0)
routing_weights_sum = tf.math.reduce_sum(average_routing_weights)
for idx in range(FLAGS.num_experts):
metrics['test/dense_routing_weight_{}'.format(idx)].update_state(
average_routing_weights[idx])
metrics['test/dense_routing_weight_normalized_{}'.format(
idx)].update_state(average_routing_weights[idx] /
routing_weights_sum)
# TODO(ghassen): add more metrics for expert utilization,
# load loss and importance/balance loss.
else:
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:
# TODO(ghassen): figure out how to aggregate probs for the OOD case.
if not FLAGS.reduce_dense_outputs and FLAGS.use_cond_dense:
corrupt_metrics['test/nll_{}'.format(dataset_name)].update_state(
results['unweighted_gibbs_ce'])
corrupt_metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, results['unweighted_probs'])
corrupt_metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, results['unweighted_probs'])
corrupt_metrics['test/nll_weighted_moe{}'.format(
dataset_name)].update_state(results['weighted_gibbs_ce'])
corrupt_metrics['test/accuracy_weighted_moe_{}'.format(
dataset_name)].update_state(labels, results['weighted_probs'])
corrupt_metrics['test/ece_weighted_moe{}'.format(
dataset_name)].update_state(labels, results['weighted_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 / 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)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
steps_per_epoch = APPROX_IMAGENET_TRAIN_IMAGES // batch_size
steps_per_eval = IMAGENET_VALIDATION_IMAGES // batch_size
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)
imagenet_train = utils.ImageNetInput(is_training=True,
data_dir=FLAGS.data_dir,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
imagenet_eval = utils.ImageNetInput(is_training=False,
data_dir=FLAGS.data_dir,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets = {
'clean':
strategy.experimental_distribute_datasets_from_function(
imagenet_eval.input_fn)
}
if FLAGS.corruptions_interval > 0:
corruption_types, max_intensity = utils.load_corrupted_test_info()
for name in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset_name = '{0}_{1}'.format(name, intensity)
corrupt_input_fn = utils.corrupt_test_input_fn(
batch_size=FLAGS.per_core_batch_size,
corruption_name=name,
corruption_intensity=intensity,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets[dataset_name] = (
strategy.experimental_distribute_datasets_from_function(
corrupt_input_fn))
train_dataset = strategy.experimental_distribute_datasets_from_function(
imagenet_train.input_fn)
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
with strategy.scope():
logging.info('Building Keras ResNet-50 model')
model = ub.models.resnet50_sngp(
input_shape=(224, 224, 3),
batch_size=None,
num_classes=NUM_CLASSES,
use_mc_dropout=FLAGS.use_mc_dropout,
dropout_rate=FLAGS.dropout_rate,
filterwise_dropout=FLAGS.filterwise_dropout,
use_gp_layer=FLAGS.use_gp_layer,
gp_hidden_dim=FLAGS.gp_hidden_dim,
gp_scale=FLAGS.gp_scale,
gp_bias=FLAGS.gp_bias,
gp_input_normalization=FLAGS.gp_input_normalization,
gp_cov_discount_factor=FLAGS.gp_cov_discount_factor,
gp_cov_ridge_penalty=FLAGS.gp_cov_ridge_penalty,
gp_output_imagenet_initializer=FLAGS.
gp_output_imagenet_initializer,
use_spec_norm=FLAGS.use_spec_norm,
spec_norm_iteration=FLAGS.spec_norm_iteration,
spec_norm_bound=FLAGS.spec_norm_bound)
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())
# Scale learning rate and decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 256
learning_rate = utils.LearningRateSchedule(steps_per_epoch, base_lr,
FLAGS.train_epochs,
_LR_SCHEDULE)
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
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/stddev': 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/stddev_{}'.format(dataset_name)] = (
tf.keras.metrics.Mean())
logging.info('Finished building Keras ResNet-50 model')
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
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
with tf.GradientTape() as tape:
logits = model(images, training=True)
if isinstance(logits, tuple):
# If model returns a tuple of (logits, covmat), extract logits
logits, _ = logits
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))
filtered_variables = []
for var in model.trainable_variables:
# Apply l2 on the weights. This excludes BN parameters and biases, 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))
# Scale the loss given the TPUStrategy will reduce sum all gradients.
loss = negative_log_likelihood + l2_loss
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
logits_list = []
stddev_list = []
for _ in range(FLAGS.num_dropout_samples):
logits = model(images, training=False)
if isinstance(logits, tuple):
# If model returns a tuple of (logits, covmat), extract both
logits, covmat = logits
else:
covmat = tf.eye(FLAGS.per_core_batch_size)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
logits = ed.layers.utils.mean_field_logits(
logits,
covmat,
mean_field_factor=FLAGS.gp_mean_field_factor)
stddev = tf.sqrt(tf.linalg.diag_part(covmat))
stddev_list.append(stddev)
logits_list.append(logits)
# Logits dimension is (num_samples, batch_size, num_classes).
logits_list = tf.stack(logits_list, axis=0)
stddev_list = tf.stack(stddev_list, axis=0)
stddev = tf.reduce_mean(stddev_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)
metrics['test/stddev'].update_state(stddev)
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)
corrupt_metrics['test/stddev_{}'.format(
dataset_name)].update_state(stddev)
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 / 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,
FLAGS.alexnet_errors_path)
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)
# Save final checkpoint.
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
# Export final model as SavedModel.
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 __init__(self, dataset_info):
metric = um.ExpectedCalibrationError()
super().__init__(
dataset_info, metric, "ece", take_argmax=False, one_hot=False)
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)
ds_info = tfds.builder(FLAGS.dataset).info
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 // batch_size
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
num_classes = ds_info.features['label'].num_classes
train_dataset = ub.datasets.get(
FLAGS.dataset, split=tfds.Split.TRAIN).load(batch_size=batch_size)
clean_test_dataset = ub.datasets.get(
FLAGS.dataset, split=tfds.Split.TEST).load(batch_size=batch_size)
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
test_datasets = {
'clean': strategy.experimental_distribute_dataset(clean_test_dataset),
}
if FLAGS.corruptions_interval > 0:
extra_kwargs = {}
if FLAGS.dataset == 'cifar100':
extra_kwargs['data_dir'] = FLAGS.cifar100_c_path
corruption_types, _ = utils.load_corrupted_test_info(FLAGS.dataset)
for corruption_type in corruption_types:
for severity in range(1, 6):
dataset = ub.datasets.get(
f'{FLAGS.dataset}_corrupted',
corruption_type=corruption_type,
severity=severity,
split=tfds.Split.TEST,
**extra_kwargs).load(batch_size=batch_size)
test_datasets[f'{corruption_type}_{severity}'] = (
strategy.experimental_distribute_dataset(dataset))
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_variational(
input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
prior_stddev=FLAGS.prior_stddev,
dataset_size=train_dataset_size,
stddev_init=FLAGS.stddev_init)
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)
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),
'train/kl': tf.keras.metrics.Mean(),
'train/kl_scale': tf.keras.metrics.Mean(),
'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, 6):
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 = inputs['features']
labels = inputs['labels']
with tf.GradientTape() as tape:
logits = model(images, training=True)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True))
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 '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))
kl = sum(model.losses)
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
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/kl'].update_state(kl)
metrics['train/kl_scale'].update_state(kl_scale)
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 = inputs['features']
labels = inputs['labels']
# TODO(trandustin): Use more eval samples only on corrupted predictions;
# it's expensive but a one-time compute if scheduled post-training.
if FLAGS.num_eval_samples > 1 and dataset_name != 'clean':
logits = tf.stack([
model(images, training=False)
for _ in range(FLAGS.num_eval_samples)
],
axis=0)
else:
logits = model(images, training=False)
probs = tf.nn.softmax(logits)
if FLAGS.num_eval_samples > 1 and dataset_name != 'clean':
probs = tf.reduce_mean(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)
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 / 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)
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.experimental.TPUStrategy(resolver)
per_core_batch_size = FLAGS.per_core_batch_size // FLAGS.ensemble_size
check_bool = FLAGS.train_proportion > 0 and FLAGS.train_proportion <= 1
assert check_bool, 'Proportion of train set has to meet 0 < prop <= 1.'
drop_remainder_validation = True
if not FLAGS.use_gpu:
# This has to be True for TPU traing, otherwise the batchsize of images in
# the validation set can't be determined by TPU compile.
assert drop_remainder_validation, 'drop_remainder must be True in TPU mode.'
train_input_fn = utils.load_input_fn(split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
repeat=True,
proportion=FLAGS.train_proportion)
validation_proportion = 1 - FLAGS.train_proportion
validation_input_fn = utils.load_input_fn(
split=tfds.Split.VALIDATION,
name=FLAGS.dataset,
batch_size=per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16,
repeat=True,
proportion=validation_proportion,
drop_remainder=drop_remainder_validation)
clean_test_input_fn = utils.load_input_fn(split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = strategy.experimental_distribute_datasets_from_function(
train_input_fn)
validation_dataset = strategy.experimental_distribute_datasets_from_function(
validation_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=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
batch_size = per_core_batch_size * FLAGS.num_cores
train_sample_size = ds_info.splits[
'train'].num_examples * FLAGS.train_proportion
steps_per_epoch = int(train_sample_size / batch_size)
train_sample_size = int(train_sample_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'))
logging.info('Building Keras model.')
depth = 28
width = 10
dict_ranges = {'min': FLAGS.min_l2_range, 'max': FLAGS.max_l2_range}
ranges = [dict_ranges for _ in range(6)] # 6 independent l2 parameters
model_config = {
'key_to_index': {
'input_conv_l2_kernel': 0,
'group_l2_kernel': 1,
'group_1_l2_kernel': 2,
'group_2_l2_kernel': 3,
'dense_l2_kernel': 4,
'dense_l2_bias': 5,
},
'ranges': ranges,
'test': None
}
lambdas_config = LambdaConfig(model_config['ranges'],
model_config['key_to_index'])
if FLAGS.e_body_hidden_units > 0:
e_body_arch = '({},)'.format(FLAGS.e_body_hidden_units)
else:
e_body_arch = '()'
e_shared_arch = '()'
e_activation = 'tanh'
filters_resnet = [16]
for i in range(0, 3): # 3 groups of blocks
filters_resnet.extend([16 * width * 2**i] *
9) # 9 layers in each block
# e_head dim for conv2d is just the number of filters (only
# kernel) and twice num of classes for the last dense layer (kernel + bias)
e_head_dims = [x for x in filters_resnet] + [2 * num_classes]
with strategy.scope():
e_models = e_factory(
lambdas_config.input_shape,
e_head_dims=e_head_dims,
e_body_arch=eval(e_body_arch), # pylint: disable=eval-used
e_shared_arch=eval(e_shared_arch), # pylint: disable=eval-used
activation=e_activation,
use_bias=FLAGS.e_model_use_bias,
e_head_init=FLAGS.init_emodels_stddev)
model = wide_resnet_hyperbatchensemble(
input_shape=ds_info.features['image'].shape,
depth=depth,
width_multiplier=width,
num_classes=num_classes,
ensemble_size=FLAGS.ensemble_size,
random_sign_init=FLAGS.random_sign_init,
config=lambdas_config,
e_models=e_models,
l2_batchnorm_layer=FLAGS.l2_batchnorm,
regularize_fast_weights=FLAGS.regularize_fast_weights,
fast_weights_eq_contraint=FLAGS.fast_weights_eq_contraint,
version=2)
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())
# build hyper-batchensemble complete -------------------------
# Initialize Lambda distributions for tuning
lambdas_mean = tf.reduce_mean(
log_uniform_mean([lambdas_config.log_min, lambdas_config.log_max]))
lambdas0 = tf.random.normal((FLAGS.ensemble_size, lambdas_config.dim),
lambdas_mean,
0.1 * FLAGS.ens_init_delta_bounds)
lower0 = lambdas0 - tf.constant(FLAGS.ens_init_delta_bounds)
lower0 = tf.maximum(lower0, 1e-8)
upper0 = lambdas0 + tf.constant(FLAGS.ens_init_delta_bounds)
log_lower = tf.Variable(tf.math.log(lower0))
log_upper = tf.Variable(tf.math.log(upper0))
lambda_parameters = [log_lower, log_upper] # these variables are tuned
clip_lambda_parameters(lambda_parameters, lambdas_config)
# Optimizer settings to train model weights
# Linearly scale learning rate and the decay epochs by vanilla settings.
# Note: Here, we don't divide the epochs by 200 as for the other uncertainty
# baselines.
base_lr = FLAGS.base_learning_rate * batch_size / 128
lr_decay_epochs = [int(l) for l 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)
# tuner used for optimizing lambda_parameters
tuner = tf.keras.optimizers.Adam(FLAGS.lr_tuning)
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),
'train/disagreement': tf.keras.metrics.Mean(),
'train/average_kl': tf.keras.metrics.Mean(),
'train/cosine_similarity': tf.keras.metrics.Mean(),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/gibbs_nll': tf.keras.metrics.Mean(),
'test/gibbs_accuracy':
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/disagreement': tf.keras.metrics.Mean(),
'test/average_kl': tf.keras.metrics.Mean(),
'test/cosine_similarity': tf.keras.metrics.Mean(),
'validation/loss': tf.keras.metrics.Mean(),
'validation/loss_entropy': tf.keras.metrics.Mean(),
'validation/loss_ce': tf.keras.metrics.Mean()
}
corrupt_metrics = {}
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:
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,
lambda_parameters=lambda_parameters,
optimizer=optimizer)
latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
initial_epoch = 0
if latest_checkpoint and FLAGS.restore_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
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
# generate lambdas
lambdas = log_uniform_sample(per_core_batch_size,
lambda_parameters)
lambdas = tf.reshape(lambdas,
(FLAGS.ensemble_size * per_core_batch_size,
lambdas_config.dim))
with tf.GradientTape() as tape:
logits = model([images, lambdas], training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
if FLAGS.use_gibbs_ce:
# Average of single model CEs
# tiling of labels should be only done for Gibbs CE loss
labels = tf.tile(labels, [FLAGS.ensemble_size])
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True))
else:
# Ensemble CE uses no tiling of the labels
negative_log_likelihood = ensemble_crossentropy(
labels, logits, FLAGS.ensemble_size)
# Note: Divide l2_loss by sample_size (this differs from uncertainty_
# baselines implementation.)
l2_loss = sum(model.losses) / train_sample_size
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 for fast weights.
grads_and_vars = []
for grad, var in zip(grads, model.trainable_variables):
if (('alpha' in var.name or 'gamma' in var.name)
and 'batch_norm' 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)
probs = tf.nn.softmax(logits)
per_probs = tf.split(probs,
num_or_size_splits=FLAGS.ensemble_size,
axis=0)
per_probs_stacked = tf.stack(per_probs, axis=0)
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)
diversity_results = um.average_pairwise_diversity(
per_probs_stacked, FLAGS.ensemble_size)
for k, v in diversity_results.items():
metrics['train/' + k].update_state(v)
if grads_and_vars:
grads, _ = zip(*grads_and_vars)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def tuning_step(iterator):
"""Tuning StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
with tf.GradientTape(watch_accessed_variables=False) as tape:
tape.watch(lambda_parameters)
# sample lambdas
if FLAGS.sample_and_tune:
lambdas = log_uniform_sample(per_core_batch_size,
lambda_parameters)
else:
lambdas = log_uniform_mean(lambda_parameters)
lambdas = tf.repeat(lambdas, per_core_batch_size, axis=0)
lambdas = tf.reshape(lambdas,
(FLAGS.ensemble_size *
per_core_batch_size, lambdas_config.dim))
# ensemble CE
logits = model([images, lambdas], training=False)
ce = ensemble_crossentropy(labels, logits, FLAGS.ensemble_size)
# entropy penalty for lambda distribution
entropy = FLAGS.tau * log_uniform_entropy(lambda_parameters)
loss = ce - entropy
scaled_loss = loss / strategy.num_replicas_in_sync
gradients = tape.gradient(loss, lambda_parameters)
tuner.apply_gradients(zip(gradients, lambda_parameters))
metrics['validation/loss_ce'].update_state(
ce / strategy.num_replicas_in_sync)
metrics['validation/loss_entropy'].update_state(
entropy / strategy.num_replicas_in_sync)
metrics['validation/loss'].update_state(scaled_loss)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
# Note that we don't use tf.tile for labels here
images, labels = inputs
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
# get lambdas
lambdas = log_uniform_mean(lambda_parameters)
rep_lambdas = tf.repeat(lambdas, per_core_batch_size, axis=0)
# eval on testsets
logits = model([images, rep_lambdas], 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)
# per member performance and gibbs performance (average per member perf)
if dataset_name == 'clean':
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)
labels_tile = tf.tile(labels, [FLAGS.ensemble_size])
metrics['test/gibbs_nll'].update_state(
tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels_tile, logits, from_logits=True)))
metrics['test/gibbs_accuracy'].update_state(labels_tile, probs)
# ensemble performance
negative_log_likelihood = ensemble_crossentropy(
labels, logits, FLAGS.ensemble_size)
probs = tf.reduce_mean(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)
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)
if dataset_name == 'clean':
per_probs_stacked = tf.stack(per_probs, axis=0)
diversity_results = um.average_pairwise_diversity(
per_probs_stacked, FLAGS.ensemble_size)
for k, v in diversity_results.items():
metrics['test/' + k].update_state(v)
strategy.run(step_fn, args=(next(iterator), ))
logging.info('--- Starting training using %d examples. ---',
train_sample_size)
train_iterator = iter(train_dataset)
validation_iterator = iter(validation_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)
do_tuning = (epoch >= FLAGS.tuning_warmup_epochs)
if do_tuning and ((step + 1) % FLAGS.tuning_every_x_step == 0):
tuning_step(validation_iterator)
# clip lambda parameters if outside of range
clip_lambda_parameters(lambda_parameters, lambdas_config)
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)
# evaluate on test data
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('Validation Loss: %.4f, CE: %.4f, Entropy: %.4f',
metrics['validation/loss'].result(),
metrics['validation/loss_ce'].result(),
metrics['validation/loss_entropy'].result())
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_results = {
name: metric.result()
for name, metric in metrics.items()
}
total_results.update({
name: metric.result()
for name, metric in corrupt_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()
# save checkpoint and lambdas config
if (FLAGS.checkpoint_interval > 0
and (epoch + 1) % FLAGS.checkpoint_interval == 0):
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
lambdas_cf = lambdas_config.get_config()
filepath = os.path.join(FLAGS.output_dir, 'lambdas_config.p')
with tf.io.gfile.GFile(filepath, 'wb') as fp:
pickle.dump(lambdas_cf, fp, protocol=pickle.HIGHEST_PROTOCOL)
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': 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
if not FLAGS.use_gpu:
raise ValueError('Only GPU is currently supported.')
if FLAGS.num_cores > 1:
raise ValueError('Only a single accelerator is currently supported.')
tf.random.set_seed(FLAGS.seed)
tf.io.gfile.makedirs(FLAGS.output_dir)
ind_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='test', data_dir=FLAGS.data_dir, data_mode='ind')
ood_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='test', data_dir=FLAGS.data_dir, data_mode='ood')
all_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='test', data_dir=FLAGS.data_dir, data_mode='all')
dataset_builders = {
'clean': ind_dataset_builder,
'ood': ood_dataset_builder,
'all': all_dataset_builder
}
ds_info = ind_dataset_builder.tfds_info
feature_size = ds_info.metadata['feature_size']
# num_classes is number of valid intents plus out-of-scope intent
num_classes = ds_info.features['intent_label'].num_classes + 1
# vocab_size is total number of valid tokens plus the out-of-vocabulary token.
vocab_size = ind_dataset_builder.tokenizer.num_words + 1
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
test_datasets = {}
steps_per_eval = {}
for dataset_name, dataset_builder in dataset_builders.items():
test_datasets[dataset_name] = dataset_builder.load(
batch_size=batch_size)
steps_per_eval[
dataset_name] = dataset_builder.num_examples // batch_size
bert_config_dir, _ = sngp.resolve_bert_ckpt_and_config_dir(
FLAGS.bert_dir, FLAGS.bert_config_dir, FLAGS.bert_ckpt_dir)
bert_config = bert_utils.create_config(bert_config_dir)
gp_layer_kwargs = dict(num_inducing=FLAGS.gp_hidden_dim,
gp_kernel_scale=FLAGS.gp_scale,
gp_output_bias=FLAGS.gp_bias,
normalize_input=FLAGS.gp_input_normalization,
gp_cov_momentum=FLAGS.gp_cov_discount_factor,
gp_cov_ridge_penalty=FLAGS.gp_cov_ridge_penalty)
spec_norm_kwargs = dict(iteration=FLAGS.spec_norm_iteration,
norm_multiplier=FLAGS.spec_norm_bound)
model, bert_encoder = ub.models.SngpBertBuilder(
num_classes=num_classes,
bert_config=bert_config,
gp_layer_kwargs=gp_layer_kwargs,
spec_norm_kwargs=spec_norm_kwargs,
use_gp_layer=FLAGS.use_gp_layer,
use_spec_norm_att=FLAGS.use_spec_norm_att,
use_spec_norm_ffn=FLAGS.use_spec_norm_ffn,
use_layer_norm_att=FLAGS.use_layer_norm_att,
use_layer_norm_ffn=FLAGS.use_layer_norm_ffn,
use_spec_norm_plr=FLAGS.use_spec_norm_plr)
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())
# Search for checkpoints from their index file; then remove the index suffix.
ensemble_filenames = tf.io.gfile.glob(
os.path.join(FLAGS.checkpoint_dir, '**/*.index'))
ensemble_filenames = [filename[:-6] for filename in ensemble_filenames]
ensemble_size = len(ensemble_filenames)
logging.info('Ensemble size: %s', ensemble_size)
logging.info('Ensemble number of weights: %s',
ensemble_size * model.count_params())
logging.info('Ensemble filenames: %s', str(ensemble_filenames))
checkpoint = tf.train.Checkpoint(model=model)
# Write model predictions to files.
num_datasets = len(test_datasets)
for m, ensemble_filename in enumerate(ensemble_filenames):
checkpoint.restore(ensemble_filename)
for n, (name, test_dataset) in enumerate(test_datasets.items()):
filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
if not tf.io.gfile.exists(filename):
logits_list = []
test_iterator = iter(test_dataset)
for _ in range(steps_per_eval[name]):
inputs = next(test_iterator)
features, _ = bert_utils.create_feature_and_label(
inputs, feature_size)
logits, covmat = model(features, training=False)
logits = ed.layers.utils.mean_field_logits(
logits,
covmat,
mean_field_factor=FLAGS.gp_mean_field_factor)
logits_list.append(logits)
logits_list = tf.concat(logits_list, axis=0)
with tf.io.gfile.GFile(filename, 'w') as f:
np.save(f, logits_list.numpy())
percent = (m * num_datasets +
(n + 1)) / (ensemble_size * num_datasets)
message = (
'{:.1%} completion for prediction: ensemble member {:d}/{:d}. '
'Dataset {:d}/{:d}'.format(percent, m + 1, ensemble_size,
n + 1, num_datasets))
logging.info(message)
metrics = {
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/gibbs_cross_entropy': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
for dataset_name, test_dataset in test_datasets.items():
if dataset_name != 'clean':
metrics.update({
'test/nll_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
'test/accuracy_{}'.format(dataset_name):
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece_{}'.format(dataset_name):
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins)
})
# Finally, define OOD metrics for the combined IND and OOD dataset.
metrics.update({
'test/auroc_all': tf.keras.metrics.AUC(curve='ROC'),
'test/auprc_all': tf.keras.metrics.AUC(curve='PR')
})
# Evaluate model predictions.
for n, (name, test_dataset) in enumerate(test_datasets.items()):
logits_dataset = []
for m in range(ensemble_size):
filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
with tf.io.gfile.GFile(filename, 'rb') as f:
logits_dataset.append(np.load(f))
logits_dataset = tf.convert_to_tensor(logits_dataset)
test_iterator = iter(test_dataset)
for step in range(steps_per_eval[name]):
inputs = next(test_iterator)
_, labels = bert_utils.create_feature_and_label(
inputs, feature_size)
logits = logits_dataset[:, (step * batch_size):((step + 1) *
batch_size)]
labels = tf.cast(labels, tf.int32)
negative_log_likelihood = um.ensemble_cross_entropy(labels, logits)
per_probs = tf.nn.softmax(logits)
probs = tf.reduce_mean(per_probs, axis=0)
if name == 'clean':
gibbs_ce = um.gibbs_cross_entropy(labels, logits)
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/gibbs_cross_entropy'].update_state(gibbs_ce)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
else:
metrics['test/nll_{}'.format(name)].update_state(
negative_log_likelihood)
metrics['test/accuracy_{}'.format(name)].update_state(
labels, probs)
metrics['test/ece_{}'.format(name)].update_state(labels, probs)
if dataset_name == 'all':
ood_labels = tf.cast(labels == 150, labels.dtype)
ood_probs = 1. - tf.reduce_max(probs, axis=-1)
metrics['test/auroc_{}'.format(dataset_name)].update_state(
ood_labels, ood_probs)
metrics['test/auprc_{}'.format(dataset_name)].update_state(
ood_labels, ood_probs)
message = (
'{:.1%} completion for evaluation: dataset {:d}/{:d}'.format(
(n + 1) / num_datasets, n + 1, num_datasets))
logging.info(message)
total_results = {name: metric.result() for name, metric in metrics.items()}
logging.info('Metrics: %s', total_results)
def load_pivot_df_dict_googlefig(
hdf5file='metrics_googlefig/cifar_model_predictions.hdf5',
clean_key='test',
instance=0):
'''
load pivoted df_dict from google paper
'''
file = h5py.File(hdf5file, 'r')
corruptions = [
'corrupt-static-brightness-1', 'corrupt-static-brightness-2',
'corrupt-static-brightness-3', 'corrupt-static-brightness-4',
'corrupt-static-brightness-5', 'corrupt-static-contrast-1',
'corrupt-static-contrast-2', 'corrupt-static-contrast-3',
'corrupt-static-contrast-4', 'corrupt-static-contrast-5',
'corrupt-static-defocus_blur-1', 'corrupt-static-defocus_blur-2',
'corrupt-static-defocus_blur-3', 'corrupt-static-defocus_blur-4',
'corrupt-static-defocus_blur-5', 'corrupt-static-elastic_transform-1',
'corrupt-static-elastic_transform-2',
'corrupt-static-elastic_transform-3',
'corrupt-static-elastic_transform-4',
'corrupt-static-elastic_transform-5', 'corrupt-static-fog-1',
'corrupt-static-fog-2', 'corrupt-static-fog-3', 'corrupt-static-fog-4',
'corrupt-static-fog-5', 'corrupt-static-frost-1',
'corrupt-static-frost-2', 'corrupt-static-frost-3',
'corrupt-static-frost-4', 'corrupt-static-frost-5',
'corrupt-static-gaussian_blur-1', 'corrupt-static-gaussian_blur-2',
'corrupt-static-gaussian_blur-3', 'corrupt-static-gaussian_blur-4',
'corrupt-static-gaussian_blur-5', 'corrupt-static-gaussian_noise-1',
'corrupt-static-gaussian_noise-2', 'corrupt-static-gaussian_noise-3',
'corrupt-static-gaussian_noise-4', 'corrupt-static-gaussian_noise-5',
'corrupt-static-glass_blur-1', 'corrupt-static-glass_blur-2',
'corrupt-static-glass_blur-3', 'corrupt-static-glass_blur-4',
'corrupt-static-glass_blur-5', 'corrupt-static-impulse_noise-1',
'corrupt-static-impulse_noise-2', 'corrupt-static-impulse_noise-3',
'corrupt-static-impulse_noise-4', 'corrupt-static-impulse_noise-5',
'corrupt-static-pixelate-1', 'corrupt-static-pixelate-2',
'corrupt-static-pixelate-3', 'corrupt-static-pixelate-4',
'corrupt-static-pixelate-5', 'corrupt-static-saturate-1',
'corrupt-static-saturate-2', 'corrupt-static-saturate-3',
'corrupt-static-saturate-4', 'corrupt-static-saturate-5',
'corrupt-static-shot_noise-1', 'corrupt-static-shot_noise-2',
'corrupt-static-shot_noise-3', 'corrupt-static-shot_noise-4',
'corrupt-static-shot_noise-5', 'corrupt-static-spatter-1',
'corrupt-static-spatter-2', 'corrupt-static-spatter-3',
'corrupt-static-spatter-4', 'corrupt-static-spatter-5',
'corrupt-static-speckle_noise-1', 'corrupt-static-speckle_noise-2',
'corrupt-static-speckle_noise-3', 'corrupt-static-speckle_noise-4',
'corrupt-static-speckle_noise-5', 'corrupt-static-zoom_blur-1',
'corrupt-static-zoom_blur-2', 'corrupt-static-zoom_blur-3',
'corrupt-static-zoom_blur-4', 'corrupt-static-zoom_blur-5'
]
clean = [clean_key]
corruptions += clean
gfig_dict = dict()
metrics = [
tf.keras.metrics.SparseCategoricalAccuracy(name='acc'),
um.ExpectedCalibrationError(num_bins=15, name='ece'),
nll(name='nll'),
BrierScore(name='brier')
]
for model_name, model_data in file.items():
#print(model_name)
df = pd.DataFrame(columns=['acc', 'brier', 'ece', 'nll'])
df.columns.name = 'metrics'
df.index.name = 'dataset'
for key in model_data.keys():
if key in corruptions:
#model_data[key]
y = model_data[key]['labels']
p = model_data[key]['probs']
if 'roll' in key:
key = 'roll'
for metric in metrics:
metric.update_state(y[instance, :].astype(np.int32),
p[instance, :].astype(np.float32))
d0 = {
'acc': metrics[0].result().numpy(),
'ece': metrics[1].result().numpy(),
'nll': metrics[2].result().numpy(),
'brier': metrics[3].result().numpy()
}
record = pd.Series(d0, index=df.columns, name=key)
df = df.append(record)
for metric in metrics:
metric.reset_states()
gfig_dict[model_name] = df.copy()
return gfig_dict
def main(argv):
del argv # unused arg
if not FLAGS.use_gpu:
raise ValueError('Only GPU is currently supported.')
if FLAGS.num_cores > 1:
raise ValueError('Only a single accelerator is currently supported.')
tf.random.set_seed(FLAGS.seed)
tf.io.gfile.makedirs(FLAGS.output_dir)
ds_info = tfds.builder(FLAGS.dataset).info
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
num_classes = ds_info.features['label'].num_classes
dataset_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)
test_datasets = {'clean': dataset_input_fn()}
corruption_types, max_intensity = utils.load_corrupted_test_info(
FLAGS.dataset)
for name in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset_name = '{0}_{1}'.format(name, intensity)
if FLAGS.dataset == 'cifar10':
load_c_dataset = utils.load_cifar10_c_input_fn
else:
load_c_dataset = functools.partial(
utils.load_cifar100_c_input_fn, path=FLAGS.cifar100_c_path)
corrupted_input_fn = load_c_dataset(
corruption_name=name,
corruption_intensity=intensity,
batch_size=FLAGS.per_core_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets[dataset_name] = corrupted_input_fn()
model = ub.models.wide_resnet(
input_shape=ds_info.features['image'].shape,
depth=28,
width_multiplier=10,
num_classes=num_classes,
l2=0.,
version=2)
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())
# Search for checkpoints from their index file; then remove the index suffix.
ensemble_filenames = tf.io.gfile.glob(os.path.join(FLAGS.checkpoint_dir,
'**/*.index'))
ensemble_filenames = [filename[:-6] for filename in ensemble_filenames]
ensemble_size = len(ensemble_filenames)
logging.info('Ensemble size: %s', ensemble_size)
logging.info('Ensemble number of weights: %s',
ensemble_size * model.count_params())
logging.info('Ensemble filenames: %s', str(ensemble_filenames))
checkpoint = tf.train.Checkpoint(model=model)
# Write model predictions to files.
num_datasets = len(test_datasets)
for m, ensemble_filename in enumerate(ensemble_filenames):
checkpoint.restore(ensemble_filename)
for n, (name, test_dataset) in enumerate(test_datasets.items()):
filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
if not tf.io.gfile.exists(filename):
logits = []
test_iterator = iter(test_dataset)
for _ in range(steps_per_eval):
features, _ = next(test_iterator) # pytype: disable=attribute-error
logits.append(model(features, training=False))
logits = tf.concat(logits, axis=0)
with tf.io.gfile.GFile(filename, 'w') as f:
np.save(f, logits.numpy())
percent = (m * num_datasets + (n + 1)) / (ensemble_size * num_datasets)
message = ('{:.1%} completion for prediction: ensemble member {:d}/{:d}. '
'Dataset {:d}/{:d}'.format(percent,
m + 1,
ensemble_size,
n + 1,
num_datasets))
logging.info(message)
metrics = {
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/gibbs_cross_entropy': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
}
corrupt_metrics = {}
for name in test_datasets:
corrupt_metrics['test/nll_{}'.format(name)] = tf.keras.metrics.Mean()
corrupt_metrics['test/accuracy_{}'.format(name)] = (
tf.keras.metrics.SparseCategoricalAccuracy())
corrupt_metrics['test/ece_{}'.format(name)] = (
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))
for i in range(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(),
}
metrics.update(test_diversity)
# Evaluate model predictions.
for n, (name, test_dataset) in enumerate(test_datasets.items()):
logits_dataset = []
for m in range(ensemble_size):
filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
filename = os.path.join(FLAGS.output_dir, filename)
with tf.io.gfile.GFile(filename, 'rb') as f:
logits_dataset.append(np.load(f))
logits_dataset = tf.convert_to_tensor(logits_dataset)
test_iterator = iter(test_dataset)
for step in range(steps_per_eval):
_, labels = next(test_iterator) # pytype: disable=attribute-error
logits = logits_dataset[:, (step*batch_size):((step+1)*batch_size)]
labels = tf.cast(labels, tf.int32)
negative_log_likelihood = um.ensemble_cross_entropy(labels, logits)
per_probs = tf.nn.softmax(logits)
probs = tf.reduce_mean(per_probs, axis=0)
if name == 'clean':
gibbs_ce = um.gibbs_cross_entropy(labels, logits)
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/gibbs_cross_entropy'].update_state(gibbs_ce)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
for i in range(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)
diversity_results = um.average_pairwise_diversity(
per_probs, ensemble_size)
for k, v in diversity_results.items():
test_diversity['test/' + k].update_state(v)
else:
corrupt_metrics['test/nll_{}'.format(name)].update_state(
negative_log_likelihood)
corrupt_metrics['test/accuracy_{}'.format(name)].update_state(
labels, probs)
corrupt_metrics['test/ece_{}'.format(name)].update_state(
labels, probs)
message = ('{:.1%} completion for evaluation: dataset {:d}/{:d}'.format(
(n + 1) / num_datasets, n + 1, num_datasets))
logging.info(message)
corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
corruption_types,
max_intensity)
total_results = {name: metric.result() for name, metric in metrics.items()}
total_results.update(corrupt_results)
logging.info('Metrics: %s', total_results)
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)
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
steps_per_epoch = APPROX_IMAGENET_TRAIN_IMAGES // train_batch_size
steps_per_eval = IMAGENET_VALIDATION_IMAGES // test_batch_size
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_builder = ub.datasets.ImageNetDataset(
split=tfds.Split.TRAIN,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = train_builder.load(
batch_size=train_batch_size, strategy=strategy)
test_builder = ub.datasets.ImageNetDataset(
split=tfds.Split.TEST,
use_bfloat16=FLAGS.use_bfloat16)
test_dataset = test_builder.load(
batch_size=test_batch_size, strategy=strategy)
if FLAGS.use_bfloat16:
tf.keras.mixed_precision.set_global_policy('mixed_bfloat16')
with strategy.scope():
logging.info('Building Keras ResNet-50 model')
model = imagenet_model.resnet50(
input_shape=(FLAGS.ensemble_size, 224, 224, 3),
num_classes=NUM_CLASSES,
ensemble_size=FLAGS.ensemble_size,
width_multiplier=FLAGS.width_multiplier)
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())
# Scale learning rate and decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * train_batch_size / 256
decay_epochs = [
(FLAGS.train_epochs * int(FLAGS.lr_decay_epochs[0])) // 90,
(FLAGS.train_epochs * int(FLAGS.lr_decay_epochs[1])) // 90,
(FLAGS.train_epochs * int(FLAGS.lr_decay_epochs[2])) // 90,
]
learning_rate = ub.schedules.WarmUpPiecewiseConstantSchedule(
steps_per_epoch=steps_per_epoch,
base_learning_rate=base_lr,
decay_ratio=0.1,
decay_epochs=decay_epochs,
warmup_epochs=FLAGS.lr_warmup_epochs)
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
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 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(),
}
logging.info('Finished building Keras ResNet-50 model')
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
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
@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))
filtered_variables = []
for var in model.trainable_variables:
# Apply l2 on the weights. This excludes BN parameters and biases, 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))
# Scale the loss given the TPUStrategy will reduce sum all gradients.
loss = negative_log_likelihood + l2_loss
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)
strategy.run(step_fn, args=(next(iterator),))
@tf.function
def test_step(iterator):
"""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)
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
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/ece'].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)
test_iterator = iter(test_dataset)
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)
ms_per_example = (time.time() - test_start_time) * 1e6 / test_batch_size
metrics['test/ms_per_example'].update_state(ms_per_example)
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(test_diversity)
total_results = {name: metric.result()
for name, metric in total_metrics.items()}
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for _, metric in total_metrics.items():
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_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)
ds_info = tfds.builder(FLAGS.dataset).info
batch_size = (FLAGS.per_core_batch_size * FLAGS.num_cores //
FLAGS.num_dropout_samples_training)
test_batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
num_classes = ds_info.features['label'].num_classes
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,
}
validation_proportion = 1. - FLAGS.train_proportion
use_validation_set = validation_proportion > 0.
train_dataset = data_utils.load_dataset(
split=tfds.Split.TRAIN,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16,
aug_params=aug_params,
validation_set=use_validation_set,
validation_proportion=validation_proportion)
train_sample_size = ds_info.splits[
'train'].num_examples * FLAGS.train_proportion
val_sample_size = ds_info.splits['train'].num_examples - train_sample_size
if use_validation_set:
validation_dataset = data_utils.load_dataset(
split=tfds.Split.VALIDATION,
name=FLAGS.dataset,
batch_size=batch_size,
use_bfloat16=FLAGS.use_bfloat16,
aug_params=aug_params,
validation_set=use_validation_set,
validation_proportion=validation_proportion)
validation_dataset = strategy.experimental_distribute_dataset(
validation_dataset)
steps_per_val = steps_per_epoch = int(val_sample_size / batch_size)
clean_test_dataset = utils.load_dataset(split=tfds.Split.TEST,
name=FLAGS.dataset,
batch_size=test_batch_size,
use_bfloat16=FLAGS.use_bfloat16)
train_sample_size = ds_info.splits[
'train'].num_examples * FLAGS.train_proportion
steps_per_epoch = int(train_sample_size / batch_size)
steps_per_epoch = ds_info.splits['train'].num_examples // batch_size
steps_per_eval = ds_info.splits['test'].num_examples // batch_size
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=test_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 ResNet model')
if FLAGS.use_spec_norm:
logging.info('Use Spectral Normalization with norm bound %.2f',
FLAGS.spec_norm_bound)
if FLAGS.use_gp_layer:
logging.info('Use GP layer with hidden units %d',
FLAGS.gp_hidden_dim)
model = ub.models.wide_resnet_sngp(
input_shape=ds_info.features['image'].shape,
batch_size=batch_size,
depth=28,
width_multiplier=10,
num_classes=num_classes,
l2=FLAGS.l2,
use_mc_dropout=FLAGS.use_mc_dropout,
use_filterwise_dropout=FLAGS.use_filterwise_dropout,
dropout_rate=FLAGS.dropout_rate,
use_gp_layer=FLAGS.use_gp_layer,
gp_input_dim=FLAGS.gp_input_dim,
gp_hidden_dim=FLAGS.gp_hidden_dim,
gp_scale=FLAGS.gp_scale,
gp_bias=FLAGS.gp_bias,
gp_input_normalization=FLAGS.gp_input_normalization,
gp_random_feature_type=FLAGS.gp_random_feature_type,
gp_cov_discount_factor=FLAGS.gp_cov_discount_factor,
gp_cov_ridge_penalty=FLAGS.gp_cov_ridge_penalty,
use_spec_norm=FLAGS.use_spec_norm,
spec_norm_iteration=FLAGS.spec_norm_iteration,
spec_norm_bound=FLAGS.spec_norm_bound)
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)
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/stddev': tf.keras.metrics.Mean(),
}
if use_validation_set:
metrics.update({
'val/negative_log_likelihood':
tf.keras.metrics.Mean(),
'val/accuracy':
tf.keras.metrics.SparseCategoricalAccuracy(),
'val/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'val/stddev':
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/stddev_{}'.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, step):
"""Training StepFn."""
def step_fn(inputs, step):
"""Per-Replica StepFn."""
images, labels = inputs
if tf.equal(step, 0) and FLAGS.gp_cov_discount_factor < 0:
# Resetting covaraince estimator at the begining of a new epoch.
model.layers[-1].reset_covariance_matrix()
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 isinstance(logits, tuple):
# If model returns a tuple of (logits, covmat), extract logits
logits, _ = logits
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)
strategy.run(step_fn, args=(next(iterator), step))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
logits_list = []
stddev_list = []
for _ in range(FLAGS.num_dropout_samples):
logits = model(images, training=False)
if isinstance(logits, tuple):
# If model returns a tuple of (logits, covmat), extract both
logits, covmat = logits
else:
covmat = tf.eye(FLAGS.per_core_batch_size)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
logits = ed.layers.utils.mean_field_logits(
logits,
covmat,
mean_field_factor=FLAGS.gp_mean_field_factor)
stddev = tf.sqrt(tf.linalg.diag_part(covmat))
stddev_list.append(stddev)
logits_list.append(logits)
# Logits dimension is (num_samples, batch_size, num_classes).
logits_list = tf.stack(logits_list, axis=0)
stddev_list = tf.stack(stddev_list, axis=0)
stddev = tf.reduce_mean(stddev_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)
metrics['test/stddev'].update_state(stddev)
elif dataset_name == 'val':
metrics['val/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['val/accuracy'].update_state(labels, probs)
metrics['val/ece'].update_state(labels, probs)
metrics['val/stddev'].update_state(stddev)
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)
corrupt_metrics['test/stddev_{}'.format(
dataset_name)].update_state(stddev)
strategy.run(step_fn, args=(next(iterator), ))
metrics.update({'test/ms_per_example': tf.keras.metrics.Mean()})
step_variable = tf.Variable(0, dtype=tf.int32)
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):
step_variable.assign(step)
# Pass `step` as a tf.Variable to train_step to prevent the tf.function
# train_step() re-compiling itself at each function call.
train_step(train_iterator, step_variable)
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 use_validation_set:
datasets_to_evaluate['val'] = validation_dataset
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)
steps_per_eval = steps_per_val if dataset_name == 'val' else steps_per_eval
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)
if use_validation_set:
logging.info('Val NLL: %.4f, Accuracy: %.2f%%',
metrics['val/negative_log_likelihood'].result(),
metrics['val/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)
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 run(trial_dir: str, flag_string: Optional[str]):
"""Run the experiment.
Args:
trial_dir: String to the dir to write checkpoints to and read them from.
flag_string: Optional string used to record what flags the job was run with.
"""
tf.random.set_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
if not FLAGS.eval_frequency:
FLAGS.eval_frequency = FLAGS.log_frequency
if FLAGS.eval_frequency % FLAGS.log_frequency != 0:
raise ValueError(
'log_frequency ({}) must evenly divide eval_frequency '
'({}).'.format(FLAGS.log_frequency, FLAGS.eval_frequency))
strategy = ub.strategy_utils.get_strategy(FLAGS.tpu,
use_tpu=not FLAGS.use_cpu
and not FLAGS.use_gpu)
with strategy.scope():
_maybe_setup_trial_dir(strategy, trial_dir, flag_string)
# TODO(znado): pass all dataset and model kwargs.
train_dataset_builder = ub.datasets.get(
dataset_name=FLAGS.dataset_name,
split='train',
validation_percent=FLAGS.validation_percent,
shuffle_buffer_size=FLAGS.shuffle_buffer_size)
if FLAGS.validation_percent > 0:
validation_dataset_builder = ub.datasets.get(
dataset_name=FLAGS.dataset_name,
split='validation',
validation_percent=FLAGS.validation_percent,
shuffle_buffer_size=FLAGS.shuffle_buffer_size)
else:
validation_dataset_builder = None
test_dataset_builder = ub.datasets.get(
dataset_name=FLAGS.dataset_name,
split='test',
validation_percent=FLAGS.validation_percent,
shuffle_buffer_size=FLAGS.shuffle_buffer_size)
if FLAGS.use_spec_norm:
logging.info('Use spectral normalization.')
spec_norm_hparams = {
'spec_norm_bound': FLAGS.spec_norm_bound,
'spec_norm_iteration': FLAGS.spec_norm_iteration
}
else:
spec_norm_hparams = None
if FLAGS.use_gp_layer:
logging.info('Use GP for output layer.')
gp_layer_hparams = {
'gp_input_dim': FLAGS.gp_input_dim,
'gp_hidden_dim': FLAGS.gp_hidden_dim,
'gp_scale': FLAGS.gp_scale,
'gp_bias': FLAGS.gp_bias,
'gp_input_normalization': FLAGS.gp_input_normalization,
'gp_cov_discount_factor': FLAGS.gp_cov_discount_factor,
'gp_cov_ridge_penalty': FLAGS.gp_cov_ridge_penalty
}
else:
gp_layer_hparams = None
model = ub_smu_models.get(
FLAGS.model_name,
num_classes=FLAGS.num_classes,
batch_size=FLAGS.batch_size,
len_seqs=FLAGS.len_seqs,
num_motifs=FLAGS.num_motifs,
len_motifs=FLAGS.len_motifs,
num_denses=FLAGS.num_denses,
depth=FLAGS.wide_resnet_depth,
width=FLAGS.wide_resnet_depth,
l2_weight=FLAGS.l2_regularization,
depth_multiplier=FLAGS.wide_resnet_width_multiplier,
dropout_rate=FLAGS.dropout_rate,
before_conv_dropout=FLAGS.before_conv_dropout,
use_mc_dropout=FLAGS.use_mc_dropout,
spec_norm_hparams=spec_norm_hparams,
gp_layer_hparams=gp_layer_hparams)
metrics = {
'accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
'brier_score': BrierScore(name='brier_score'),
'ece': um.ExpectedCalibrationError(num_bins=10, name='ece'),
'loss': tf.keras.metrics.SparseCategoricalCrossentropy(),
}
# Record all non-default hparams in tensorboard.
hparams = _get_hparams()
ood_dataset_builder = None
ood_metrics = None
if FLAGS.run_ood:
if 'cifar' in FLAGS.dataset_name and FLAGS.ood_dataset_name == 'svhn':
svhn_normalize_by_cifar = True
else:
svhn_normalize_by_cifar = False
ood_dataset_builder_cls = ub.datasets.DATASETS[
FLAGS.ood_dataset_name]
ood_dataset_builder_cls = ub.datasets.make_ood_dataset(
ood_dataset_builder_cls)
ood_dataset_builder = ood_dataset_builder_cls(
in_distribution_dataset=test_dataset_builder,
name=FLAGS.ood_dataset_name,
split='test',
validation_percent=FLAGS.validation_percent,
normalize_by_cifar=svhn_normalize_by_cifar,
data_mode='ood')
_check_batch_replica_divisible(FLAGS.eval_batch_size, strategy)
ood_metrics = {
'auroc':
tf.keras.metrics.AUC(curve='ROC',
summation_method='interpolation'),
'auprc':
tf.keras.metrics.AUC(curve='PR',
summation_method='interpolation')
}
aux_metrics = [
('spec_at_sen', tf.keras.metrics.SpecificityAtSensitivity,
FLAGS.sensitivity_thresholds),
('sen_at_spec', tf.keras.metrics.SensitivityAtSpecificity,
FLAGS.specificity_thresholds),
('prec_at_rec', tf.keras.metrics.PrecisionAtRecall,
FLAGS.recall_thresholds),
('rec_at_prec', tf.keras.metrics.RecallAtPrecision,
FLAGS.precision_thresholds)
]
for metric_name, metric_fn, threshold_vals in aux_metrics:
vals = [float(x) for x in threshold_vals]
thresholds = np.linspace(vals[0], vals[1], int(vals[2]))
for thresh in thresholds:
name = f'{metric_name}_{thresh:.2f}'
ood_metrics[name] = metric_fn(thresh)
if FLAGS.mode == 'eval':
_check_batch_replica_divisible(FLAGS.eval_batch_size, strategy)
eval_lib.run_eval_loop(
validation_dataset_builder=validation_dataset_builder,
test_dataset_builder=test_dataset_builder,
batch_size=FLAGS.eval_batch_size,
model=model,
trial_dir=trial_dir,
train_steps=FLAGS.train_steps,
strategy=strategy,
metrics=metrics,
checkpoint_step=FLAGS.checkpoint_step,
hparams=hparams,
ood_dataset_builder=ood_dataset_builder,
ood_metrics=ood_metrics)
return
if FLAGS.mode == 'train_and_eval':
_check_batch_replica_divisible(FLAGS.eval_batch_size, strategy)
steps_per_epoch = train_dataset_builder.num_examples // FLAGS.batch_size
optimizer_kwargs = {
k[len('optimizer_hparams_'):]: FLAGS[k].value
for k in FLAGS if k.startswith('optimizer_hparams_')
}
optimizer_kwargs.update({
k[len('schedule_hparams_'):]: FLAGS[k].value
for k in FLAGS if k.startswith('schedule_hparams_')
})
optimizer = ub.optimizers.get(
optimizer_name=FLAGS.optimizer,
learning_rate_schedule=FLAGS.learning_rate_schedule,
learning_rate=FLAGS.learning_rate,
weight_decay=FLAGS.weight_decay,
steps_per_epoch=steps_per_epoch,
model=model,
**optimizer_kwargs)
train_lib.run_train_loop(
train_dataset_builder=train_dataset_builder,
validation_dataset_builder=validation_dataset_builder,
test_dataset_builder=test_dataset_builder,
batch_size=FLAGS.batch_size,
eval_batch_size=FLAGS.eval_batch_size,
model=model,
optimizer=optimizer,
eval_frequency=FLAGS.eval_frequency,
log_frequency=FLAGS.log_frequency,
trial_dir=trial_dir,
train_steps=FLAGS.train_steps,
mode=FLAGS.mode,
strategy=strategy,
metrics=metrics,
hparams=hparams,
ood_dataset_builder=ood_dataset_builder,
ood_metrics=ood_metrics,
focal_loss_gamma=FLAGS.focal_loss_gamma)
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)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
steps_per_epoch = APPROX_IMAGENET_TRAIN_IMAGES // batch_size
steps_per_eval = IMAGENET_VALIDATION_IMAGES // batch_size
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)
enable_mixup = (FLAGS.mixup_alpha > 0.0)
mixup_params = {
'mixup_alpha': FLAGS.mixup_alpha,
'adaptive_mixup': False,
'same_mix_weight_per_batch': FLAGS.same_mix_weight_per_batch,
'use_random_shuffling': FLAGS.use_random_shuffling,
'use_truncated_beta': FLAGS.use_truncated_beta
}
train_builder = utils.ImageNetInput(
data_dir=FLAGS.data_dir,
use_bfloat16=FLAGS.use_bfloat16,
one_hot=True,
mixup_params=mixup_params)
test_builder = utils.ImageNetInput(
data_dir=FLAGS.data_dir, use_bfloat16=FLAGS.use_bfloat16)
train_dataset = train_builder.as_dataset(
split=tfds.Split.TRAIN, batch_size=batch_size)
test_dataset = test_builder.as_dataset(
split=tfds.Split.TEST, batch_size=batch_size)
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
test_dataset = strategy.experimental_distribute_dataset(test_dataset)
if enable_mixup:
mean_theta = mean_truncated_beta_distribution(FLAGS.mixup_alpha)
# Train set to compute the means of the images and of the (one-hot) labels
imagenet_train_no_mixup = utils.ImageNetInput(
data_dir=FLAGS.data_dir, use_bfloat16=FLAGS.use_bfloat16, one_hot=True)
imagenet_train_no_mixup = imagenet_train_no_mixup.as_dataset(
split=tfds.Split.TRAIN, batch_size=batch_size)
tr_data_no_mixup = strategy.experimental_distribute_dataset(
imagenet_train_no_mixup)
if FLAGS.use_bfloat16:
policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
tf.keras.mixed_precision.experimental.set_policy(policy)
with strategy.scope():
if enable_mixup:
# Variables used to track the means of the images and the (one-hot) labels
count = tf.Variable(tf.zeros((1,), dtype=tf.float32))
mean_images = tf.Variable(tf.zeros(IMAGE_SHAPE, dtype=tf.float32))
mean_labels = tf.Variable(tf.zeros((NUM_CLASSES,), dtype=tf.float32))
logging.info('Building Keras ResNet-50 model')
model = ub.models.resnet50_deterministic(input_shape=IMAGE_SHAPE,
num_classes=NUM_CLASSES)
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())
# Scale learning rate and decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 256
learning_rate = utils.LearningRateSchedule(steps_per_epoch,
base_lr,
FLAGS.train_epochs,
_LR_SCHEDULE)
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
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),
}
logging.info('Finished building Keras ResNet-50 model')
if enable_mixup:
# With mixup enabled, we log the predictions with the rescaling from [2]
metrics['test/negative_log_likelihood+rescaling'] = (tf.keras.metrics
.Mean())
metrics['test/accuracy+rescaling'] = (tf.keras.metrics
.SparseCategoricalAccuracy())
metrics['test/ece+rescaling'] = 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
summary_writer = tf.summary.create_file_writer(
os.path.join(FLAGS.output_dir, 'summaries'))
@tf.function
def moving_average_step(iterator):
"""Training StepFn to compute the means of the images and labels."""
def step_fn_labels(labels):
return tf.reduce_mean(labels, axis=0)
def step_fn_images(images):
return tf.reduce_mean(tf.cast(images, tf.float32), axis=0)
new_count = count + 1.
count.assign(new_count)
images, labels = next(iterator)
per_replica_means = strategy.run(step_fn_labels, args=(labels,))
cr_replica_means = strategy.reduce('mean', per_replica_means, axis=0)
mean_labels.assign(cr_replica_means/count + (count-1.)/count * mean_labels)
per_replica_means = strategy.run(step_fn_images, args=(images,))
cr_replica_means = strategy.reduce('mean', per_replica_means, axis=0)
mean_images.assign(cr_replica_means/count + (count-1.)/count * mean_images)
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
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.categorical_crossentropy(
labels, logits, from_logits=True))
filtered_variables = []
for var in model.trainable_variables:
# Apply l2 on the weights. This excludes BN parameters and biases, 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))
# Scale the loss given the TPUStrategy will reduce sum all gradients.
loss = negative_log_likelihood + l2_loss
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)
# We go back from one-hot labels to integers
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 update_test_metrics(labels, logits, metric_suffix=''):
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
labels, logits, from_logits=True))
probs = tf.nn.softmax(logits)
metrics['test/negative_log_likelihood' + metric_suffix].update_state(
negative_log_likelihood)
metrics['test/accuracy' + metric_suffix].update_state(labels, probs)
metrics['test/ece' + metric_suffix].update_state(labels, probs)
@tf.function
def test_step(iterator):
"""Evaluation StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
images, labels = inputs
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
update_test_metrics(labels, logits)
# Rescaling logic in Eq.(15) from [2]
if enable_mixup:
images *= mean_theta
images += (1.-mean_theta) * tf.cast(mean_images, images.dtype)
scaled_logits = model(images, training=False)
if FLAGS.use_bfloat16:
scaled_logits = tf.cast(scaled_logits, tf.float32)
scaled_logits *= 1./mean_theta
scaled_logits += (1.-1./mean_theta) * tf.cast(mean_labels, logits.dtype)
update_test_metrics(labels, scaled_logits, '+rescaling')
strategy.run(step_fn, args=(next(iterator),))
metrics.update({'test/ms_per_example': tf.keras.metrics.Mean()})
if enable_mixup:
logging.info('Starting to compute the means of labels and images')
tr_iterator_no_mixup = iter(tr_data_no_mixup)
for step in range(steps_per_epoch):
moving_average_step(tr_iterator_no_mixup)
# Save stats required by the mixup rescaling [2] for subsequent predictions
mixup_rescaling_stats = {
'mean_labels': mean_labels.numpy(),
'mean_images': mean_images.numpy(),
'mean_theta': mean_theta
}
output_dir = os.path.join(FLAGS.output_dir, 'mixup_rescaling_stats.npz')
with tf.io.gfile.GFile(output_dir, 'wb') as f:
np.save(f, list(mixup_rescaling_stats.items()))
logging.info('Finished to compute the means of labels and images')
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)
test_iterator = iter(test_dataset)
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)
ms_per_example = (time.time() - test_start_time) * 1e6 / batch_size
metrics['test/ms_per_example'].update_state(ms_per_example)
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 enable_mixup:
logging.info(
'Test NLL (+ rescaling): %.4f, Accuracy (+ rescaling): %.2f%%',
metrics['test/negative_log_likelihood+rescaling'].result(),
metrics['test/accuracy+rescaling'].result() * 100)
total_results = {name: metric.result() for name, metric in metrics.items()}
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_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)
per_core_batch_size = FLAGS.per_core_batch_size // FLAGS.ensemble_size
batch_size = per_core_batch_size * FLAGS.num_cores
steps_per_epoch = APPROX_IMAGENET_TRAIN_IMAGES // batch_size
steps_per_eval = IMAGENET_VALIDATION_IMAGES // batch_size
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)
mixup_params = {
'ensemble_size': FLAGS.ensemble_size,
'mixup_alpha': FLAGS.mixup_alpha,
'adaptive_mixup': FLAGS.adaptive_mixup,
'num_classes': NUM_CLASSES,
}
train_builder = utils.ImageNetInput(data_dir=FLAGS.data_dir,
one_hot=(FLAGS.mixup_alpha > 0),
use_bfloat16=FLAGS.use_bfloat16,
mixup_params=mixup_params,
ensemble_size=FLAGS.ensemble_size)
test_builder = utils.ImageNetInput(data_dir=FLAGS.data_dir,
use_bfloat16=FLAGS.use_bfloat16)
train_dataset = train_builder.as_dataset(split=tfds.Split.TRAIN,
batch_size=batch_size)
clean_test_dataset = test_builder.as_dataset(split=tfds.Split.TEST,
batch_size=batch_size)
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
test_datasets = {
'clean': strategy.experimental_distribute_dataset(clean_test_dataset)
}
if FLAGS.adaptive_mixup:
imagenet_confidence_dataset = test_builder.as_dataset(
split=tfds.Split.VALIDATION,
batch_size=FLAGS.per_core_batch_size * FLAGS.num_cores)
imagenet_confidence_dataset = (
strategy.experimental_distribute_dataset(
imagenet_confidence_dataset))
if FLAGS.corruptions_interval > 0:
corruption_types, max_intensity = utils.load_corrupted_test_info()
for name in corruption_types:
for intensity in range(1, max_intensity + 1):
dataset_name = '{0}_{1}'.format(name, intensity)
dataset = utils.load_corrupted_test_dataset(
batch_size=batch_size,
corruption_name=name,
corruption_intensity=intensity,
use_bfloat16=FLAGS.use_bfloat16)
test_datasets[dataset_name] = (
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 ResNet-50 model')
model = ub.models.resnet_batchensemble(
input_shape=(224, 224, 3),
num_classes=NUM_CLASSES,
ensemble_size=FLAGS.ensemble_size,
random_sign_init=FLAGS.random_sign_init,
use_ensemble_bn=FLAGS.use_ensemble_bn,
depth=FLAGS.depth)
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())
# Scale learning rate and decay epochs by vanilla settings.
base_lr = FLAGS.base_learning_rate * batch_size / 256
learning_rate = utils.LearningRateSchedule(steps_per_epoch, base_lr,
FLAGS.train_epochs,
_LR_SCHEDULE)
optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate,
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)
}
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))
test_diversity = {}
training_diversity = {}
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(),
}
logging.info('Finished building Keras ResNet-50 model')
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)
else:
images = tf.tile(images, [FLAGS.ensemble_size, 1, 1, 1])
if FLAGS.adaptive_mixup:
labels = tf.identity(labels)
elif FLAGS.mixup_alpha > 0:
labels = tf.tile(labels, [FLAGS.ensemble_size, 1])
else:
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)
probs = tf.nn.softmax(logits)
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.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))
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))
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 weights. This 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):
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))
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)
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
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)
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)
per_probs = tf.split(probs,
num_or_size_splits=FLAGS.ensemble_size,
axis=0)
probs = tf.reduce_mean(per_probs, axis=0)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(labels, probs))
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/member_ece_mean'].update_state(
labels, member_probs)
elif dataset_name != 'confidence_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 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 != 'confidence_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 == 'confidence_validation':
return tf.stack(per_probs, 0), labels
if dataset_name == 'confidence_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)
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:
confidence_set_iterator = iter(imagenet_confidence_dataset)
predictions_list = []
labels_list = []
for step in range(FLAGS.confidence_eval_iterations):
temp_predictions, temp_labels = test_step(
confidence_set_iterator, 'confidence_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)
mixup_params['mixup_coeff'] = mixup_coeff
builder = utils.ImageNetInput(data_dir=FLAGS.data_dir,
one_hot=(FLAGS.mixup_alpha > 0),
use_bfloat16=FLAGS.use_bfloat16,
mixup_params=mixup_params)
train_dataset = builder.as_dataset(split=tfds.Split.TRAIN,
batch_size=batch_size)
train_dataset = strategy.experimental_distribute_dataset(
train_dataset)
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,
FLAGS.alexnet_errors_path)
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)
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for _, metric in total_metrics.items():
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_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.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.ensemble_size,
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 // FLAGS.ensemble_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 //
FLAGS.ensemble_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
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
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 = ub.models.wide_resnet_rank1(
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 = 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/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):
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)
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.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)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
train_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
batch_size=FLAGS.per_core_batch_size,
eval_batch_size=FLAGS.per_core_batch_size,
data_dir=FLAGS.data_dir,
data_mode='ind')
ind_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
batch_size=batch_size,
eval_batch_size=FLAGS.eval_batch_size,
data_dir=FLAGS.data_dir,
data_mode='ind')
ood_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
batch_size=batch_size,
eval_batch_size=FLAGS.eval_batch_size,
data_dir=FLAGS.data_dir,
data_mode='ood')
all_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
batch_size=batch_size,
eval_batch_size=FLAGS.eval_batch_size,
data_dir=FLAGS.data_dir,
data_mode='all')
dataset_builders = {
'clean': ind_dataset_builder,
'ood': ood_dataset_builder,
'all': all_dataset_builder
}
train_dataset = train_dataset_builder.build(
split=ub.datasets.base.Split.TRAIN)
ds_info = train_dataset_builder.info
feature_size = ds_info['feature_size']
# num_classes is number of valid intents plus out-of-scope intent
num_classes = ds_info['num_classes'] + 1
steps_per_epoch = ds_info['num_train_examples'] // batch_size
test_datasets = {}
steps_per_eval = {}
for dataset_name, dataset_builder in dataset_builders.items():
test_datasets[dataset_name] = dataset_builder.build(
split=ub.datasets.base.Split.TEST)
steps_per_eval[dataset_name] = (
dataset_builder.info['num_test_examples'] // FLAGS.eval_batch_size)
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 BERT model')
logging.info('use_gp_layer=%s', FLAGS.use_gp_layer)
logging.info('use_spec_norm_att=%s', FLAGS.use_spec_norm_att)
logging.info('use_spec_norm_ffn=%s', FLAGS.use_spec_norm_ffn)
logging.info('use_layer_norm_att=%s', FLAGS.use_layer_norm_att)
logging.info('use_layer_norm_ffn=%s', FLAGS.use_layer_norm_ffn)
bert_config_dir, bert_ckpt_dir = resolve_bert_ckpt_and_config_dir(
FLAGS.bert_dir, FLAGS.bert_config_dir, FLAGS.bert_ckpt_dir)
bert_config = bert_utils.create_config(bert_config_dir)
gp_layer_kwargs = dict(num_inducing=FLAGS.gp_hidden_dim,
gp_kernel_scale=FLAGS.gp_scale,
gp_output_bias=FLAGS.gp_bias,
normalize_input=FLAGS.gp_input_normalization,
gp_cov_momentum=FLAGS.gp_cov_discount_factor,
gp_cov_ridge_penalty=FLAGS.gp_cov_ridge_penalty)
spec_norm_kwargs = dict(iteration=FLAGS.spec_norm_iteration,
norm_multiplier=FLAGS.spec_norm_bound)
model, bert_encoder = ub.models.SngpBertBuilder(
num_classes=num_classes,
bert_config=bert_config,
gp_layer_kwargs=gp_layer_kwargs,
spec_norm_kwargs=spec_norm_kwargs,
use_gp_layer=FLAGS.use_gp_layer,
use_spec_norm_att=FLAGS.use_spec_norm_att,
use_spec_norm_ffn=FLAGS.use_spec_norm_ffn,
use_layer_norm_att=FLAGS.use_layer_norm_att,
use_layer_norm_ffn=FLAGS.use_layer_norm_ffn,
use_spec_norm_plr=FLAGS.use_spec_norm_plr)
optimizer = bert_utils.create_optimizer(
FLAGS.base_learning_rate,
steps_per_epoch=steps_per_epoch,
epochs=FLAGS.train_epochs,
warmup_proportion=FLAGS.warmup_proportion)
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())
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),
}
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
else:
# load BERT from initial checkpoint
bert_encoder, _, _ = bert_utils.load_bert_weight_from_ckpt(
bert_model=bert_encoder,
bert_ckpt_dir=bert_ckpt_dir,
repl_patterns=ub.models.bert_sngp.CHECKPOINT_REPL_PATTERNS)
logging.info('Loaded BERT checkpoint %s', bert_ckpt_dir)
# Finally, define test metrics outside the accelerator scope for CPU eval.
metrics.update({
'test/negative_log_likelihood':
tf.keras.metrics.Mean(),
'test/accuracy':
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/stddev':
tf.keras.metrics.Mean(),
})
for dataset_name, test_dataset in test_datasets.items():
if dataset_name != 'clean':
metrics.update({
'test/nll_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
'test/accuracy_{}'.format(dataset_name):
tf.keras.metrics.SparseCategoricalAccuracy(),
'test/ece_{}'.format(dataset_name):
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/stddev_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
})
metrics.update({
'test/auroc_all': tf.keras.metrics.AUC(curve='ROC'),
'test/auprc_all': tf.keras.metrics.AUC(curve='PR')
})
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
features, labels = bert_utils.create_feature_and_label(
inputs, feature_size)
with tf.GradientTape() as tape:
# Set learning phase to enable dropout etc during training.
logits = model(features, training=True)
if isinstance(logits, tuple):
# If model returns a tuple of (logits, covmat), extract logits
logits, _ = logits
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)
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."""
features, labels = bert_utils.create_feature_and_label(
inputs, feature_size)
# Compute ensemble prediction over Monte Carlo forward-pass samples.
logits_list = []
stddev_list = []
for _ in range(FLAGS.num_mc_samples):
logits = model(features, training=False)
if isinstance(logits, tuple):
# If model returns a tuple of (logits, covmat), extract both.
logits, covmat = logits
else:
covmat = tf.eye(FLAGS.eval_batch_size)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
covmat = tf.cast(covmat, tf.float32)
logits = ed.layers.utils.mean_field_logits(
logits,
covmat,
mean_field_factor=FLAGS.gp_mean_field_factor)
stddev = tf.sqrt(tf.linalg.diag_part(covmat))
logits_list.append(logits)
stddev_list.append(stddev)
# Logits dimension is (num_samples, batch_size, num_classes).
logits_list = tf.stack(logits_list, axis=0)
stddev_list = tf.stack(stddev_list, axis=0)
stddev = tf.reduce_mean(stddev_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_mc_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_mc_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)
metrics['test/stddev'].update_state(stddev)
else:
metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, probs)
metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, probs)
metrics['test/stddev_{}'.format(dataset_name)].update_state(
stddev)
if dataset_name == 'all':
ood_labels = tf.cast(labels == 150, labels.dtype)
ood_probs = 1. - tf.reduce_max(probs, axis=-1)
metrics['test/auroc_{}'.format(dataset_name)].update_state(
ood_labels, ood_probs)
metrics['test/auprc_{}'.format(dataset_name)].update_state(
ood_labels, ood_probs)
step_fn(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 epoch % FLAGS.evaluation_interval == 0:
for dataset_name, test_dataset in test_datasets.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval[dataset_name]):
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)
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()
}
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)
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)
batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
train_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='train', data_dir=FLAGS.data_dir, data_mode='ind')
ind_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='test', data_dir=FLAGS.data_dir, data_mode='ind')
ood_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='test', data_dir=FLAGS.data_dir, data_mode='ood')
all_dataset_builder = ub.datasets.ClincIntentDetectionDataset(
split='test', data_dir=FLAGS.data_dir, data_mode='all')
dataset_builders = {
'clean': ind_dataset_builder,
'ood': ood_dataset_builder,
'all': all_dataset_builder
}
train_dataset = train_dataset_builder.load(batch_size=batch_size)
ds_info = train_dataset_builder.tfds_info
feature_size = ds_info.metadata['feature_size']
# num_classes is number of valid intents plus out-of-scope intent
num_classes = ds_info.features['intent_label'].num_classes + 1
# vocab_size is total number of valid tokens plus the out-of-vocabulary token.
vocab_size = ind_dataset_builder.tokenizer.num_words + 1
steps_per_epoch = train_dataset_builder.num_examples // batch_size
test_datasets = {}
steps_per_eval = {}
for dataset_name, dataset_builder in dataset_builders.items():
test_datasets[dataset_name] = dataset_builder.load(
batch_size=FLAGS.eval_batch_size)
steps_per_eval[dataset_name] = (dataset_builder.num_examples //
FLAGS.eval_batch_size)
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'))
premade_embedding_array = None
if FLAGS.word_embedding_dir:
with tf.io.gfile.GFile(FLAGS.word_embedding_dir,
'rb') as embedding_file:
premade_embedding_array = np.load(embedding_file)
with strategy.scope():
logging.info('Building %s model', FLAGS.model_family)
if FLAGS.model_family.lower() == 'textcnn':
model = cnn_model.textcnn(
filter_sizes=[int(x) for x in FLAGS.filter_sizes],
num_filters=FLAGS.num_filters,
num_classes=num_classes,
feature_size=feature_size,
vocab_size=vocab_size,
embed_size=FLAGS.embedding_size,
dropout_rate=FLAGS.dropout_rate,
l2=FLAGS.l2,
premade_embedding_arr=premade_embedding_array)
optimizer = tf.keras.optimizers.Adam(FLAGS.base_learning_rate)
elif FLAGS.model_family.lower() == 'bert':
bert_config_dir, bert_ckpt_dir = resolve_bert_ckpt_and_config_dir(
FLAGS.bert_dir, FLAGS.bert_config_dir, FLAGS.bert_ckpt_dir)
bert_config = bert_utils.create_config(bert_config_dir)
model, bert_encoder = ub.models.BertBuilder(
num_classes=num_classes,
max_seq_length=feature_size,
bert_config=bert_config)
optimizer = bert_utils.create_optimizer(
FLAGS.base_learning_rate,
steps_per_epoch=steps_per_epoch,
epochs=FLAGS.train_epochs,
warmup_proportion=FLAGS.warmup_proportion)
else:
raise ValueError(
'model_family ({}) can only be TextCNN or BERT.'.format(
FLAGS.model_family))
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())
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 dataset_name, test_dataset in test_datasets.items():
if dataset_name != 'clean':
metrics.update({
'test/nll_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
'test/accuracy_{}'.format(dataset_name):
tf.keras.metrics.SparseCategoricalAccuracy(),
'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
elif FLAGS.model_family.lower() == 'bert':
# load BERT from initial checkpoint
bert_checkpoint = tf.train.Checkpoint(model=bert_encoder)
bert_checkpoint.restore(
bert_ckpt_dir).assert_existing_objects_matched()
logging.info('Loaded BERT checkpoint %s', bert_ckpt_dir)
# Finally, define OOD metrics outside the accelerator scope for CPU eval.
metrics.update({
'test/auroc_all': tf.keras.metrics.AUC(curve='ROC'),
'test/auprc_all': tf.keras.metrics.AUC(curve='PR')
})
@tf.function
def train_step(iterator):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
features, labels = create_feature_and_label(
inputs, feature_size, model_family=FLAGS.model_family)
with tf.GradientTape() as tape:
# Set learning phase to enable dropout etc during training.
logits = model(features, 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)
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."""
features, labels = create_feature_and_label(
inputs, feature_size, model_family=FLAGS.model_family)
# Set learning phase to disable dropout etc during eval.
logits = model(features, 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:
metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
metrics['test/accuracy_{}'.format(dataset_name)].update_state(
labels, probs)
metrics['test/ece_{}'.format(dataset_name)].update_state(
labels, probs)
if dataset_name == 'all':
ood_labels = tf.cast(labels == 150, labels.dtype)
ood_probs = 1. - tf.reduce_max(probs, axis=-1)
metrics['test/auroc_{}'.format(dataset_name)].update_state(
ood_labels, ood_probs)
metrics['test/auprc_{}'.format(dataset_name)].update_state(
ood_labels, ood_probs)
step_fn(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 epoch % FLAGS.evaluation_interval == 0:
for dataset_name, test_dataset in test_datasets.items():
test_iterator = iter(test_dataset)
logging.info('Testing on dataset %s', dataset_name)
for step in range(steps_per_eval[dataset_name]):
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)
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()
}
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)
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)
# Initialize distribution strategy on flag-specified accelerator
strategy = utils.init_distribution_strategy(FLAGS.force_use_cpu,
FLAGS.use_gpu, FLAGS.tpu)
train_batch_size = FLAGS.train_batch_size * FLAGS.num_cores
eval_batch_size = FLAGS.eval_batch_size * FLAGS.num_cores
# As per the Kaggle challenge, we have split sizes:
# train: 35,126
# validation: 10,906 (currently unused)
# test: 42,670
ds_info = tfds.builder('diabetic_retinopathy_detection').info
steps_per_epoch = ds_info.splits['train'].num_examples // train_batch_size
steps_per_eval = ds_info.splits['test'].num_examples // eval_batch_size
dataset_train_builder = ub.datasets.get('diabetic_retinopathy_detection',
split='train',
data_dir=FLAGS.data_dir)
dataset_train = dataset_train_builder.load(batch_size=train_batch_size)
dataset_train = strategy.experimental_distribute_dataset(dataset_train)
dataset_test_builder = ub.datasets.get('diabetic_retinopathy_detection',
split='test',
data_dir=FLAGS.data_dir)
dataset_test = dataset_test_builder.load(batch_size=eval_batch_size)
dataset_test = strategy.experimental_distribute_dataset(dataset_test)
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-50 deterministic model.')
model = ub.models.resnet50_deterministic(
input_shape=utils.load_input_shape(dataset_train),
num_classes=1) # binary classification task
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) / DEFAULT_TRAIN_BATCH_SIZE
lr_decay_epochs = [
(int(start_epoch_str) * FLAGS.train_epochs) // DEFAULT_NUM_EPOCHS
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.BinaryAccuracy(),
'train/loss': tf.keras.metrics.Mean(), # NLL + L2
'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/negative_log_likelihood': tf.keras.metrics.Mean(),
'test/accuracy': tf.keras.metrics.BinaryAccuracy(),
'test/ece': 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
# Finally, define OOD metrics outside the accelerator scope for CPU eval.
metrics.update({
'train/auc': tf.keras.metrics.AUC(),
'test/auc': tf.keras.metrics.AUC()
})
@tf.function
def train_step(iterator):
"""Training step function."""
def step_fn(inputs):
"""Per-replica step function."""
images = inputs['features']
labels = inputs['labels']
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.binary_crossentropy(y_true=tf.expand_dims(
labels, axis=-1),
y_pred=logits,
from_logits=True))
l2_loss = sum(model.losses)
loss = negative_log_likelihood + (FLAGS.l2 * 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.squeeze(tf.nn.sigmoid(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, probs)
metrics['train/auc'].update_state(labels, probs)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def test_step(iterator):
"""Evaluation step function."""
def step_fn(inputs):
"""Per-replica step function."""
images = inputs['features']
labels = inputs['labels']
logits = model(images, training=False)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
negative_log_likelihood = tf.reduce_mean(
tf.keras.losses.binary_crossentropy(y_true=tf.expand_dims(
labels, axis=-1),
y_pred=logits,
from_logits=True))
probs = tf.squeeze(tf.nn.sigmoid(logits))
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/accuracy'].update_state(labels, probs)
metrics['test/auc'].update_state(labels, probs)
metrics['test/ece'].update_state(labels, probs)
strategy.run(step_fn, args=(next(iterator), ))
metrics.update({'test/ms_per_example': tf.keras.metrics.Mean()})
start_time = time.time()
for epoch in range(initial_epoch, FLAGS.train_epochs):
train_iterator = iter(dataset_train)
test_iterator = iter(dataset_test)
logging.info('Starting to run epoch: %s', epoch + 1)
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)
for step in range(steps_per_eval):
if step % 20 == 0:
logging.info('Starting to run eval step %s of epoch: %s', step,
epoch + 1)
test_start_time = time.time()
test_step(test_iterator)
ms_per_example = (time.time() -
test_start_time) * 1e6 / eval_batch_size
metrics['test/ms_per_example'].update_state(ms_per_example)
logging.info(
'Train Loss (NLL+L2): %.4f, Accuracy: %.2f%%, AUC: %.2f%%, ECE: %.2f%%',
metrics['train/loss'].result(),
metrics['train/accuracy'].result() * 100,
metrics['train/auc'].result() * 100,
metrics['train/ece'].result() * 100)
logging.info(
'Test NLL: %.4f, Accuracy: %.2f%%, AUC: %.2f%%, ECE: %.2f%%',
metrics['test/negative_log_likelihood'].result(),
metrics['test/accuracy'].result() * 100,
metrics['test/auc'].result() * 100,
metrics['test/ece'].result() * 100)
total_results = {
name: metric.result()
for name, metric in metrics.items()
}
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)
# TODO(nband): debug checkpointing
# Also save Keras model, due to checkpoint.save issue
keras_model_name = os.path.join(FLAGS.output_dir,
f'keras_model_{epoch + 1}')
model.save(keras_model_name)
logging.info('Saved keras model to %s', keras_model_name)
final_checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved last checkpoint to %s', final_checkpoint_name)
keras_model_name = os.path.join(FLAGS.output_dir,
f'keras_model_{FLAGS.train_epochs}')
model.save(keras_model_name)
logging.info('Saved keras model to %s', keras_model_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 //
FLAGS.num_dropout_samples_training)
test_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 // test_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=test_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=test_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 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 = 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),
}
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
images = tf.tile(images,
[FLAGS.num_dropout_samples_training, 1, 1, 1])
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)
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)
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
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)
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 / 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('Model checkpoint will be saved 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
test_batch_size = batch_size
data_buffer_size = batch_size * 10
train_dataset_builder = ds.WikipediaToxicityDataset(
split='train',
data_dir=FLAGS.in_dataset_dir,
shuffle_buffer_size=data_buffer_size)
ind_dataset_builder = ds.WikipediaToxicityDataset(
split='test',
data_dir=FLAGS.in_dataset_dir,
shuffle_buffer_size=data_buffer_size)
ood_dataset_builder = ds.CivilCommentsDataset(
split='test',
data_dir=FLAGS.ood_dataset_dir,
shuffle_buffer_size=data_buffer_size)
ood_identity_dataset_builder = ds.CivilCommentsIdentitiesDataset(
split='test',
data_dir=FLAGS.identity_dataset_dir,
shuffle_buffer_size=data_buffer_size)
train_dataset_builders = {
'wikipedia_toxicity_subtypes': train_dataset_builder
}
test_dataset_builders = {
'ind': ind_dataset_builder,
'ood': ood_dataset_builder,
'ood_identity': ood_identity_dataset_builder,
}
class_weight = utils.create_class_weight(train_dataset_builders,
test_dataset_builders)
logging.info('class_weight: %s', str(class_weight))
ds_info = train_dataset_builder.tfds_info
# Positive and negative classes.
num_classes = ds_info.metadata['num_classes']
train_datasets = {}
dataset_steps_per_epoch = {}
total_steps_per_epoch = 0
for dataset_name, dataset_builder in train_dataset_builders.items():
train_datasets[dataset_name] = dataset_builder.load(
batch_size=batch_size)
dataset_steps_per_epoch[dataset_name] = (
dataset_builder.num_examples // batch_size)
total_steps_per_epoch += dataset_steps_per_epoch[dataset_name]
test_datasets = {}
steps_per_eval = {}
for dataset_name, dataset_builder in test_dataset_builders.items():
test_datasets[dataset_name] = dataset_builder.load(
batch_size=test_batch_size)
steps_per_eval[dataset_name] = (dataset_builder.num_examples //
test_batch_size)
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 %s model', FLAGS.model_family)
bert_config_dir, bert_ckpt_dir = utils.resolve_bert_ckpt_and_config_dir(
FLAGS.bert_model_type, FLAGS.bert_dir, FLAGS.bert_config_dir,
FLAGS.bert_ckpt_dir)
bert_config = utils.create_config(bert_config_dir)
model, bert_encoder = ub.models.DropoutBertBuilder(
num_classes=num_classes,
bert_config=bert_config,
use_mc_dropout_mha=FLAGS.use_mc_dropout_mha,
use_mc_dropout_att=FLAGS.use_mc_dropout_att,
use_mc_dropout_ffn=FLAGS.use_mc_dropout_ffn,
use_mc_dropout_output=FLAGS.use_mc_dropout_output,
channel_wise_dropout_mha=FLAGS.channel_wise_dropout_mha,
channel_wise_dropout_att=FLAGS.channel_wise_dropout_att,
channel_wise_dropout_ffn=FLAGS.channel_wise_dropout_ffn)
optimizer = utils.create_optimizer(
FLAGS.base_learning_rate,
steps_per_epoch=total_steps_per_epoch,
epochs=FLAGS.train_epochs,
warmup_proportion=FLAGS.warmup_proportion)
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())
metrics = {
'train/negative_log_likelihood':
tf.keras.metrics.Mean(),
'train/accuracy':
tf.keras.metrics.Accuracy(),
'train/accuracy_weighted':
tf.keras.metrics.Accuracy(),
'train/auroc':
tf.keras.metrics.AUC(),
'train/loss':
tf.keras.metrics.Mean(),
'train/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'train/precision':
tf.keras.metrics.Precision(),
'train/recall':
tf.keras.metrics.Recall(),
'train/f1':
tfa_metrics.F1Score(num_classes=num_classes,
average='micro',
threshold=FLAGS.ece_label_threshold),
}
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
if FLAGS.prediction_mode:
latest_checkpoint = tf.train.latest_checkpoint(
FLAGS.eval_checkpoint_dir)
else:
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(
) // total_steps_per_epoch
elif FLAGS.model_family.lower() == 'bert':
# load BERT from initial checkpoint
bert_checkpoint = tf.train.Checkpoint(model=bert_encoder)
bert_checkpoint.restore(
bert_ckpt_dir).assert_existing_objects_matched()
logging.info('Loaded BERT checkpoint %s', bert_ckpt_dir)
metrics.update({
'test/negative_log_likelihood':
tf.keras.metrics.Mean(),
'test/auroc':
tf.keras.metrics.AUC(curve='ROC'),
'test/aupr':
tf.keras.metrics.AUC(curve='PR'),
'test/brier':
tf.keras.metrics.MeanSquaredError(),
'test/brier_weighted':
tf.keras.metrics.MeanSquaredError(),
'test/ece':
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/acc':
tf.keras.metrics.Accuracy(),
'test/acc_weighted':
tf.keras.metrics.Accuracy(),
'test/eval_time':
tf.keras.metrics.Mean(),
'test/precision':
tf.keras.metrics.Precision(),
'test/recall':
tf.keras.metrics.Recall(),
'test/f1':
tfa_metrics.F1Score(num_classes=num_classes,
average='micro',
threshold=FLAGS.ece_label_threshold),
})
for fraction in FLAGS.fractions:
metrics.update({
'test_collab_acc/collab_acc_{}'.format(fraction):
um.OracleCollaborativeAccuracy(fraction=float(fraction),
num_bins=FLAGS.num_bins)
})
for dataset_name, test_dataset in test_datasets.items():
if dataset_name != 'ind':
metrics.update({
'test/nll_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
'test/auroc_{}'.format(dataset_name):
tf.keras.metrics.AUC(curve='ROC'),
'test/aupr_{}'.format(dataset_name):
tf.keras.metrics.AUC(curve='PR'),
'test/brier_{}'.format(dataset_name):
tf.keras.metrics.MeanSquaredError(),
'test/brier_weighted_{}'.format(dataset_name):
tf.keras.metrics.MeanSquaredError(),
'test/ece_{}'.format(dataset_name):
um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
'test/acc_{}'.format(dataset_name):
tf.keras.metrics.Accuracy(),
'test/acc_weighted_{}'.format(dataset_name):
tf.keras.metrics.Accuracy(),
'test/eval_time_{}'.format(dataset_name):
tf.keras.metrics.Mean(),
'test/precision_{}'.format(dataset_name):
tf.keras.metrics.Precision(),
'test/recall_{}'.format(dataset_name):
tf.keras.metrics.Recall(),
'test/f1_{}'.format(dataset_name):
tfa_metrics.F1Score(num_classes=num_classes,
average='micro',
threshold=FLAGS.ece_label_threshold),
})
for fraction in FLAGS.fractions:
metrics.update({
'test_collab_acc/collab_acc_{}_{}'.format(
fraction, dataset_name):
um.OracleCollaborativeAccuracy(
fraction=float(fraction), num_bins=FLAGS.num_bins)
})
@tf.function
def generate_sample_weight(labels, class_weight, label_threshold=0.7):
"""Generate sample weight for weighted accuracy calculation."""
if label_threshold != 0.7:
logging.warning(
'The class weight was based on `label_threshold` = 0.7, '
'and weighted accuracy/brier will be meaningless if '
'`label_threshold` is not equal to this value, which is '
'recommended by Jigsaw Conversation AI team.')
labels_int = tf.cast(labels > label_threshold, tf.int32)
sample_weight = tf.gather(class_weight, labels_int)
return sample_weight
@tf.function
def train_step(iterator, dataset_name):
"""Training StepFn."""
def step_fn(inputs):
"""Per-Replica StepFn."""
features, labels, _ = utils.create_feature_and_label(inputs)
with tf.GradientTape() as tape:
logits = model(features, training=True)
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
loss_logits = tf.squeeze(logits, axis=1)
if FLAGS.loss_type == 'cross_entropy':
logging.info('Using cross entropy loss')
negative_log_likelihood = tf.nn.sigmoid_cross_entropy_with_logits(
labels, loss_logits)
elif FLAGS.loss_type == 'focal_cross_entropy':
logging.info('Using focal cross entropy loss')
negative_log_likelihood = tfa_losses.sigmoid_focal_crossentropy(
labels,
loss_logits,
alpha=FLAGS.focal_loss_alpha,
gamma=FLAGS.focal_loss_gamma,
from_logits=True)
elif FLAGS.loss_type == 'mse':
logging.info('Using mean squared error loss')
loss_probs = tf.nn.sigmoid(loss_logits)
negative_log_likelihood = tf.keras.losses.mean_squared_error(
labels, loss_probs)
elif FLAGS.loss_type == 'mae':
logging.info('Using mean absolute error loss')
loss_probs = tf.nn.sigmoid(loss_logits)
negative_log_likelihood = tf.keras.losses.mean_absolute_error(
labels, loss_probs)
negative_log_likelihood = tf.reduce_mean(
negative_log_likelihood)
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.sigmoid(logits)
# Cast labels to discrete for ECE computation.
ece_labels = tf.cast(labels > FLAGS.ece_label_threshold,
tf.float32)
one_hot_labels = tf.one_hot(tf.cast(ece_labels, tf.int32),
depth=num_classes)
ece_probs = tf.concat([1. - probs, probs], axis=1)
auc_probs = tf.squeeze(probs, axis=1)
pred_labels = tf.math.argmax(ece_probs, axis=-1)
sample_weight = generate_sample_weight(
labels, class_weight['train/{}'.format(dataset_name)],
FLAGS.ece_label_threshold)
metrics['train/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['train/accuracy'].update_state(labels, pred_labels)
metrics['train/accuracy_weighted'].update_state(
ece_labels, pred_labels, sample_weight=sample_weight)
metrics['train/auroc'].update_state(labels, auc_probs)
metrics['train/loss'].update_state(loss)
metrics['train/ece'].update_state(ece_labels, ece_probs)
metrics['train/precision'].update_state(ece_labels, pred_labels)
metrics['train/recall'].update_state(ece_labels, pred_labels)
metrics['train/f1'].update_state(one_hot_labels, ece_probs)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def test_step(iterator, dataset_name):
"""Evaluation StepFn to log metrics."""
def step_fn(inputs):
"""Per-Replica StepFn."""
features, labels, _ = utils.create_feature_and_label(inputs)
eval_start_time = time.time()
logits = model(features, training=False)
eval_time = (time.time() -
eval_start_time) / FLAGS.per_core_batch_size
if FLAGS.use_bfloat16:
logits = tf.cast(logits, tf.float32)
probs = tf.nn.sigmoid(logits)
# Cast labels to discrete for ECE computation.
ece_labels = tf.cast(labels > FLAGS.ece_label_threshold,
tf.float32)
one_hot_labels = tf.one_hot(tf.cast(ece_labels, tf.int32),
depth=num_classes)
ece_probs = tf.concat([1. - probs, probs], axis=1)
pred_labels = tf.math.argmax(ece_probs, axis=-1)
auc_probs = tf.squeeze(probs, axis=1)
loss_logits = tf.squeeze(logits, axis=1)
negative_log_likelihood = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels, loss_logits))
sample_weight = generate_sample_weight(
labels, class_weight['test/{}'.format(dataset_name)],
FLAGS.ece_label_threshold)
if dataset_name == 'ind':
metrics['test/negative_log_likelihood'].update_state(
negative_log_likelihood)
metrics['test/auroc'].update_state(labels, auc_probs)
metrics['test/aupr'].update_state(labels, auc_probs)
metrics['test/brier'].update_state(labels, auc_probs)
metrics['test/brier_weighted'].update_state(
tf.expand_dims(labels, -1),
probs,
sample_weight=sample_weight)
metrics['test/ece'].update_state(ece_labels, ece_probs)
metrics['test/acc'].update_state(ece_labels, pred_labels)
metrics['test/acc_weighted'].update_state(
ece_labels, pred_labels, sample_weight=sample_weight)
metrics['test/eval_time'].update_state(eval_time)
metrics['test/precision'].update_state(ece_labels, pred_labels)
metrics['test/recall'].update_state(ece_labels, pred_labels)
metrics['test/f1'].update_state(one_hot_labels, ece_probs)
for fraction in FLAGS.fractions:
metrics['test_collab_acc/collab_acc_{}'.format(
fraction)].update_state(ece_labels, ece_probs)
else:
metrics['test/nll_{}'.format(dataset_name)].update_state(
negative_log_likelihood)
metrics['test/auroc_{}'.format(dataset_name)].update_state(
labels, auc_probs)
metrics['test/aupr_{}'.format(dataset_name)].update_state(
labels, auc_probs)
metrics['test/brier_{}'.format(dataset_name)].update_state(
labels, auc_probs)
metrics['test/brier_weighted_{}'.format(
dataset_name)].update_state(tf.expand_dims(labels, -1),
probs,
sample_weight=sample_weight)
metrics['test/ece_{}'.format(dataset_name)].update_state(
ece_labels, ece_probs)
metrics['test/acc_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels)
metrics['test/acc_weighted_{}'.format(
dataset_name)].update_state(ece_labels,
pred_labels,
sample_weight=sample_weight)
metrics['test/eval_time_{}'.format(dataset_name)].update_state(
eval_time)
metrics['test/precision_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels)
metrics['test/recall_{}'.format(dataset_name)].update_state(
ece_labels, pred_labels)
metrics['test/f1_{}'.format(dataset_name)].update_state(
one_hot_labels, ece_probs)
for fraction in FLAGS.fractions:
metrics['test_collab_acc/collab_acc_{}_{}'.format(
fraction,
dataset_name)].update_state(ece_labels, ece_probs)
strategy.run(step_fn, args=(next(iterator), ))
@tf.function
def final_eval_step(iterator):
"""Final Evaluation StepFn to save prediction to directory."""
def step_fn(inputs):
bert_features, labels, additional_labels = utils.create_feature_and_label(
inputs)
logits = model(bert_features, training=False)
features = inputs['input_ids']
return features, logits, labels, additional_labels
(per_replica_texts, per_replica_logits, per_replica_labels,
per_replica_additional_labels) = (strategy.run(
step_fn, args=(next(iterator), )))
if strategy.num_replicas_in_sync > 1:
texts_list = tf.concat(per_replica_texts.values, axis=0)
logits_list = tf.concat(per_replica_logits.values, axis=0)
labels_list = tf.concat(per_replica_labels.values, axis=0)
additional_labels_dict = {}
for additional_label in utils.IDENTITY_LABELS:
if additional_label in per_replica_additional_labels:
additional_labels_dict[additional_label] = tf.concat(
per_replica_additional_labels[additional_label],
axis=0)
else:
texts_list = per_replica_texts
logits_list = per_replica_logits
labels_list = per_replica_labels
additional_labels_dict = {}
for additional_label in utils.IDENTITY_LABELS:
if additional_label in per_replica_additional_labels:
additional_labels_dict[
additional_label] = per_replica_additional_labels[
additional_label]
return texts_list, logits_list, labels_list, additional_labels_dict
if FLAGS.prediction_mode:
# Prediction and exit.
for dataset_name, test_dataset in test_datasets.items():
test_iterator = iter(test_dataset) # pytype: disable=wrong-arg-types
message = 'Final eval on dataset {}'.format(dataset_name)
logging.info(message)
texts_all = []
logits_all = []
labels_all = []
additional_labels_all_dict = {}
if 'identity' in dataset_name:
for identity_label_name in utils.IDENTITY_LABELS:
additional_labels_all_dict[identity_label_name] = []
try:
with tf.experimental.async_scope():
for step in range(steps_per_eval[dataset_name]):
if step % 20 == 0:
message = 'Starting to run eval step {}/{} of dataset: {}'.format(
step, steps_per_eval[dataset_name],
dataset_name)
logging.info(message)
(text_step, logits_step, labels_step,
additional_labels_dict_step
) = final_eval_step(test_iterator)
texts_all.append(text_step)
logits_all.append(logits_step)
labels_all.append(labels_step)
if 'identity' in dataset_name:
for identity_label_name in utils.IDENTITY_LABELS:
additional_labels_all_dict[
identity_label_name].append(
additional_labels_dict_step[
identity_label_name])
except (StopIteration, tf.errors.OutOfRangeError):
tf.experimental.async_clear_error()
logging.info('Done with eval on %s', dataset_name)
texts_all = tf.concat(texts_all, axis=0)
logits_all = tf.concat(logits_all, axis=0)
labels_all = tf.concat(labels_all, axis=0)
additional_labels_all = []
if additional_labels_all_dict:
for identity_label_name in utils.IDENTITY_LABELS:
additional_labels_all.append(
tf.concat(
additional_labels_all_dict[identity_label_name],
axis=0))
additional_labels_all = tf.convert_to_tensor(additional_labels_all)
utils.save_prediction(texts_all.numpy(),
path=os.path.join(
FLAGS.output_dir,
'texts_{}'.format(dataset_name)))
utils.save_prediction(labels_all.numpy(),
path=os.path.join(
FLAGS.output_dir,
'labels_{}'.format(dataset_name)))
utils.save_prediction(logits_all.numpy(),
path=os.path.join(
FLAGS.output_dir,
'logits_{}'.format(dataset_name)))
if 'identity' in dataset_name:
utils.save_prediction(
additional_labels_all.numpy(),
path=os.path.join(
FLAGS.output_dir,
'additional_labels_{}'.format(dataset_name)))
logging.info('Done with testing on %s', dataset_name)
else:
# Execute train / eval loop.
start_time = time.time()
train_iterators = {}
for dataset_name, train_dataset in train_datasets.items():
train_iterators[dataset_name] = iter(train_dataset)
for epoch in range(initial_epoch, FLAGS.train_epochs):
logging.info('Starting to run epoch: %s', epoch)
current_step = epoch * total_steps_per_epoch
for dataset_name, train_iterator in train_iterators.items():
for step in range(dataset_steps_per_epoch[dataset_name]):
train_step(train_iterator, dataset_name)
current_step += 1
max_steps = total_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 epoch % FLAGS.evaluation_interval == 0:
for dataset_name, test_dataset in test_datasets.items():
test_iterator = iter(test_dataset) # pytype: disable=wrong-arg-types
logging.info('Testing on dataset %s', dataset_name)
try:
with tf.experimental.async_scope():
for step in range(steps_per_eval[dataset_name]):
if step % 20 == 0:
logging.info(
'Starting to run eval step %s/%s of epoch: %s',
step, steps_per_eval[dataset_name],
epoch)
test_step(test_iterator, dataset_name)
except (StopIteration, tf.errors.OutOfRangeError):
tf.experimental.async_clear_error()
logging.info('Done with testing on %s', dataset_name)
logging.info('Train Loss: %.4f, AUROC: %.4f',
metrics['train/loss'].result(),
metrics['train/auroc'].result())
logging.info('Test NLL: %.4f, AUROC: %.4f',
metrics['test/negative_log_likelihood'].result(),
metrics['test/auroc'].result())
# record results
total_results = {}
for name, metric in metrics.items():
total_results[name] = metric.result()
with summary_writer.as_default():
for name, result in total_results.items():
tf.summary.scalar(name, result, step=epoch + 1)
for name, metric in metrics.items():
metric.reset_states()
checkpoint_interval = min(FLAGS.checkpoint_interval,
FLAGS.train_epochs)
if checkpoint_interval > 0 and (epoch +
1) % checkpoint_interval == 0:
checkpoint_name = checkpoint.save(
os.path.join(FLAGS.output_dir, 'checkpoint'))
logging.info('Saved checkpoint to %s', checkpoint_name)
# Save model in SavedModel format on exit.
final_save_name = os.path.join(FLAGS.output_dir, 'model')
model.save(final_save_name)
logging.info('Saved model to %s', final_save_name)
