def main(argv): del argv # unused arg 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 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_builder = ub.datasets.ImageNetDataset( split=tfds.Split.TRAIN, use_bfloat16=FLAGS.use_bfloat16) train_dataset = train_builder.load(batch_size=batch_size, strategy=strategy) test_builder = ub.datasets.ImageNetDataset(split=tfds.Split.TEST, use_bfloat16=FLAGS.use_bfloat16) clean_test_dataset = test_builder.load(batch_size=batch_size, strategy=strategy) test_datasets = {'clean': clean_test_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.resnet50_het_rank1( input_shape=(224, 224, 3), 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_stddev=FLAGS.prior_stddev, use_tpu=not FLAGS.use_gpu, use_ensemble_bn=FLAGS.use_ensemble_bn, num_factors=FLAGS.num_factors, temperature=FLAGS.temperature, num_mc_samples=FLAGS.num_mc_samples) 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 decay_epochs = [ (FLAGS.train_epochs * 30) // 90, (FLAGS.train_epochs * 60) // 90, (FLAGS.train_epochs * 80) // 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=5) optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=1.0 - FLAGS.one_minus_momentum, 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': rm.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins), 'train/diversity': rm.metrics.AveragePairwiseDiversity(), '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': rm.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins), 'test/diversity': rm.metrics.AveragePairwiseDiversity(), 'test/member_accuracy_mean': (tf.keras.metrics.SparseCategoricalAccuracy()), 'test/member_ece_mean': rm.metrics.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/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)] = ( rm.metrics.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()) 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 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 = inputs['features'] labels = inputs['labels'] 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) 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)) metrics['train/diversity'].add_batch(per_probs) 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) / APPROX_IMAGENET_TRAIN_IMAGES 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 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)) 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, logits) metrics['train/ece'].add_batch(probs, label=labels) for _ in tf.range(tf.cast(steps_per_epoch, tf.int32)): 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'] 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) all_probs = tf.nn.softmax(logits) probs = tf.math.reduce_mean(all_probs, axis=[0, 1]) # marginalize # 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))) l2_loss = compute_l2_loss(model) kl = sum(model.losses) / IMAGENET_VALIDATION_IMAGES elbo = -(negative_log_likelihood + l2_loss + kl) if dataset_name == 'clean': if FLAGS.ensemble_size > 1: per_probs = tf.reduce_mean(all_probs, axis=0) # marginalize samples metrics['test/diversity'].add_batch(per_probs) 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) metrics['test/member_accuracy_mean'].update_state( labels, member_probs) metrics['test/member_ece_mean'].add_batch(member_probs, label=labels) 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'].add_batch(probs, label=labels) 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)].add_batch( probs, label=labels) for _ in tf.range(tf.cast(steps_per_eval, tf.int32)): 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) train_step(train_iterator) current_step = (epoch + 1) * steps_per_epoch 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)) 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(): logging.info('Testing on dataset %s', dataset_name) test_iterator = iter(test_dataset) logging.info('Starting to run eval at epoch: %s', 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, 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_results = { name: metric.result() for name, metric in metrics.items() } total_results.update(corrupt_results) # Results from Robustness Metrics themselves return a dict, so flatten them. total_results = utils.flatten_dictionary(total_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) with summary_writer.as_default(): hp.hparams({ 'base_learning_rate': FLAGS.base_learning_rate, 'one_minus_momentum': FLAGS.one_minus_momentum, 'l2': FLAGS.l2, 'fast_weight_lr_multiplier': FLAGS.fast_weight_lr_multiplier, 'num_eval_samples': FLAGS.num_eval_samples, })
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 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) 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) builder = utils.ImageNetInput(data_dir=FLAGS.data_dir, use_bfloat16=FLAGS.use_bfloat16) train_dataset = builder.as_dataset(split=tfds.Split.TRAIN, batch_size=batch_size) clean_test_dataset = 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.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) 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_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, 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, 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: # Reset covaraince estimator at the begining of a new epoch. model.layers[-1].reset_covariance_matrix() 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), 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) 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) 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 (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 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 test_builder = ub.datasets.ImageNetDataset(split=tfds.Split.TEST, use_bfloat16=FLAGS.use_bfloat16, data_dir=FLAGS.data_dir) clean_test_dataset = test_builder.load(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_sngp( input_shape=(224, 224, 3), batch_size=FLAGS.per_core_batch_size, 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_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, 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()) # 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_member, covmat_member = model(features, training=False) logits_member = ed.layers.utils.mean_field_logits( logits_member, covmat_member, FLAGS.gp_mean_field_factor_ensemble) logits.append(logits_member) 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': rm.metrics.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)] = rm.metrics.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_metric = rm.metrics.EnsembleCrossEntropy() negative_log_likelihood_metric.add_batch(logits, labels=labels) negative_log_likelihood = list( negative_log_likelihood_metric.result().values())[0] per_probs = tf.nn.softmax(logits) probs = tf.reduce_mean(per_probs, axis=0) if name == 'clean': gibbs_ce_metric = rm.metrics.GibbsCrossEntropy() gibbs_ce_metric.add_batch(logits, labels=labels) gibbs_ce = list(gibbs_ce_metric.result().values())[0] 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'].add_batch(probs, label=labels) 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)].add_batch( probs, label=labels) 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()} # Metrics from Robustness Metrics (like ECE) will return a dict with a # single key/value, instead of a scalar. total_results.update(corrupt_results) 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.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 data_dir = FLAGS.data_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_builder = ub.datasets.ImageNetDataset( split=tfds.Split.TRAIN, use_bfloat16=FLAGS.use_bfloat16, data_dir=data_dir) train_dataset = train_builder.load(batch_size=batch_size, strategy=strategy) test_builder = ub.datasets.ImageNetDataset(split=tfds.Split.TEST, use_bfloat16=FLAGS.use_bfloat16, data_dir=data_dir) clean_test_dataset = test_builder.load(batch_size=batch_size, strategy=strategy) test_datasets = { 'clean': clean_test_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: tf.keras.mixed_precision.set_global_policy('mixed_bfloat16') with strategy.scope(): logging.info('Building Keras ResNet-50 model') model = ub.models.resnet50_sngp_be( input_shape=(224, 224, 3), batch_size=batch_size, num_classes=NUM_CLASSES, ensemble_size=FLAGS.ensemble_size, random_sign_init=FLAGS.random_sign_init, use_ensemble_bn=FLAGS.use_ensemble_bn, 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, input_spec_norm=FLAGS.input_spec_norm) 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 decay_epochs = [ (FLAGS.train_epochs * 30) // 90, (FLAGS.train_epochs * 60) // 90, (FLAGS.train_epochs * 80) // 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=5) optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=1.0 - FLAGS.one_minus_momentum, 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': rm.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins), 'test/negative_log_likelihood': tf.keras.metrics.Mean(), 'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(), 'test/ece': rm.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins), 'test/stddev': tf.keras.metrics.Mean(), 'test/member_accuracy_mean': (tf.keras.metrics.SparseCategoricalAccuracy()), 'test/member_ece_mean': rm.metrics.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)] = ( rm.metrics.ExpectedCalibrationError( num_bins=FLAGS.num_bins)) corrupt_metrics['test/stddev_{}'.format(dataset_name)] = ( tf.keras.metrics.Mean()) corrupt_metrics['test/member_acc_mean_{}'.format( dataset_name)] = ( tf.keras.metrics.SparseCategoricalAccuracy()) corrupt_metrics['test/member_ece_mean_{}'.format( dataset_name)] = (rm.metrics.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()) 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 = inputs['features'] labels = inputs['labels'] 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 isinstance(logits, (list, 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) 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)) probs = tf.nn.softmax(logits) metrics['train/ece'].add_batch(probs, label=labels) metrics['train/loss'].update_state(loss) metrics['train/negative_log_likelihood'].update_state( negative_log_likelihood) metrics['train/accuracy'].update_state(labels, logits) for _ in tf.range(tf.cast(steps_per_epoch, tf.int32)): 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'] logits_list = [] stddev_list = [] for _ in range(FLAGS.ensemble_size): logits = model(images, training=False) if isinstance(logits, (list, 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) member_probs = tf.nn.softmax(logits) 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'].add_batch(member_probs, label=labels) # 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.ensemble_size, tf.shape(labels)[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.ensemble_size))) if dataset_name == 'clean': metrics['test/negative_log_likelihood'].update_state( negative_log_likelihood) metrics['test/accuracy'].update_state(labels, probs) metrics['test/ece'].add_batch(probs, label=labels) 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)].add_batch( probs, label=labels) corrupt_metrics['test/stddev_{}'.format( dataset_name)].update_state(stddev) for _ in tf.range(tf.cast(steps_per_eval, tf.int32)): 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) train_step(train_iterator) current_step = (epoch + 1) * steps_per_epoch 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)) 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) logging.info('Starting to run eval at epoch: %s', 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_results = { name: metric.result() for name, metric in metrics.items() } total_results.update(corrupt_results) # 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() } 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(jereliu): Convert to use SavedModel after fixing the graph-mode # execution bug in SpectralNormalizationConv2D which blocks the model.save() # functionality. final_checkpoint_name = checkpoint.save( os.path.join(FLAGS.output_dir, 'checkpoint')) logging.info('Saved last checkpoint to %s', final_checkpoint_name) with summary_writer.as_default(): hp.hparams({ 'base_learning_rate': FLAGS.base_learning_rate, 'one_minus_momentum': FLAGS.one_minus_momentum, 'l2': FLAGS.l2, 'gp_mean_field_factor': FLAGS.gp_mean_field_factor, 'gp_scale': FLAGS.gp_scale, 'gp_hidden_dim': FLAGS.gp_hidden_dim, 'fast_weight_lr_multiplier': FLAGS.fast_weight_lr_multiplier, 'random_sign_init': FLAGS.random_sign_init, })
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) mixup_params = { 'ensemble_size': 1, 'mixup_alpha': FLAGS.mixup_alpha, 'adaptive_mixup': FLAGS.adaptive_mixup, 'num_classes': NUM_CLASSES, } train_builder = ub.datasets.ImageNetDataset( split=tfds.Split.TRAIN, one_hot=(FLAGS.mixup_alpha > 0), use_bfloat16=FLAGS.use_bfloat16, mixup_params=mixup_params) test_builder = ub.datasets.ImageNetDataset(split=tfds.Split.TEST, use_bfloat16=FLAGS.use_bfloat16) train_dataset = train_builder.load(batch_size=batch_size, strategy=strategy) clean_test_dataset = test_builder.load(batch_size=batch_size, strategy=strategy) test_datasets = { 'clean': clean_test_dataset, } if FLAGS.adaptive_mixup: imagenet_confidence_dataset = ub.datasets.ImageNetDataset( split=tfds.Split.VALIDATION, run_mixup=True, use_bfloat16=FLAGS.use_bfloat16).load(batch_size=batch_size, strategy=strategy) 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) with strategy.scope(): logging.info('Building Keras ResNet-50 model') model = ub.models.resnet50_dropout( input_shape=(224, 224, 3), num_classes=NUM_CLASSES, dropout_rate=FLAGS.dropout_rate, 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()) # Scale learning rate and decay epochs by vanilla settings. base_lr = FLAGS.base_learning_rate * batch_size / 256 decay_epochs = [ (FLAGS.train_epochs * 30) // 90, (FLAGS.train_epochs * 60) // 90, (FLAGS.train_epochs * 80) // 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=5) optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=1.0 - FLAGS.one_minus_momentum, 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': rm.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins), 'test/negative_log_likelihood': tf.keras.metrics.Mean(), 'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(), 'test/ece': rm.metrics.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)] = ( rm.metrics.ExpectedCalibrationError( num_bins=FLAGS.num_bins)) 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 = 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) 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 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) if FLAGS.mixup_alpha > 0: labels = tf.argmax(labels, axis=-1) metrics['train/ece'].add_batch(probs, label=labels) metrics['train/loss'].update_state(loss) metrics['train/negative_log_likelihood'].update_state( negative_log_likelihood) metrics['train/accuracy'].update_state(labels, logits) for _ in tf.range(tf.cast(steps_per_epoch, tf.int32)): 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'] logits_list = [] if dataset_name == 'confidence_validation': num_dropout_samples = 1 else: num_dropout_samples = FLAGS.num_dropout_samples for _ in range(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, [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(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'].add_batch(probs, label=labels) 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)].add_batch( probs, label=labels) if dataset_name == 'confidence_validation': return tf.reshape(probs, [1, -1, NUM_CLASSES]), 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) train_step(train_iterator) current_step = (epoch + 1) * steps_per_epoch 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)) 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) mixup_coeff = tf.where(calibration_per_class > 0, 1.0, FLAGS.mixup_alpha) mixup_coeff = tf.clip_by_value(mixup_coeff, 0, 1) mixup_params['mixup_coeff'] = mixup_coeff builder = ub.datasets.ImageNetDataset( split=tfds.Split.TRAIN, one_hot=(FLAGS.mixup_alpha > 0), use_bfloat16=FLAGS.use_bfloat16, mixup_params=mixup_params) train_dataset = builder.load(batch_size=batch_size, strategy=strategy) train_iterator = iter(train_dataset) if (epoch + 1) % FLAGS.eval_interval == 0: 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) # 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() } 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) with summary_writer.as_default(): hp.hparams({ 'base_learning_rate': FLAGS.base_learning_rate, 'one_minus_momentum': FLAGS.one_minus_momentum, 'l2': FLAGS.l2, 'dropout_rate': FLAGS.dropout_rate, 'num_dropout_samples': FLAGS.num_dropout_samples, })
def main(argv): del argv # unused arg if FLAGS.num_cores > 1: raise ValueError('Only a single accelerator is currently supported.') tf.enable_v2_behavior() tf.random.set_seed(FLAGS.seed) dataset_test = utils.ImageNetInput(is_training=False, data_dir=FLAGS.data_dir, batch_size=FLAGS.per_core_batch_size, use_bfloat16=False).input_fn() test_datasets = {'clean': dataset_test} model = deterministic_model.resnet50(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.output_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) # Collect the logits output for each ensemble member and test data # point. We also collect the labels. logits_test = {'clean': []} labels_test = {'clean': []} 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) logits_test[dataset_name] = [] labels_test[dataset_name] = [] test_datasets[dataset_name] = utils.load_corrupted_test_dataset( name=name, intensity=intensity, batch_size=FLAGS.per_core_batch_size, drop_remainder=True, use_bfloat16=False) for m, ensemble_filename in enumerate(ensemble_filenames): checkpoint.restore(ensemble_filename) logging.info('Working on test data for ensemble member %s', m) for name, test_dataset in test_datasets.items(): logits = [] for features, labels in test_dataset: logits.append(model(features, training=False)) if m == 0: labels_test[name].append(labels) logits = tf.concat(logits, axis=0) logits_test[name].append(logits) if m == 0: labels_test[name] = tf.concat(labels_test[name], axis=0) logging.info('Finished testing on %s', format(name)) metrics = { 'test/ece': ed.metrics.ExpectedCalibrationError(num_classes=NUM_CLASSES, num_bins=15) } corrupt_metrics = {} for name in test_datasets: corrupt_metrics['test/ece_{}'.format( name)] = ed.metrics.ExpectedCalibrationError( num_classes=NUM_CLASSES, num_bins=15) corrupt_metrics['test/nll_{}'.format(name)] = tf.keras.metrics.Mean() corrupt_metrics['test/accuracy_{}'.format( name)] = tf.keras.metrics.Mean() for name, test_dataset in test_datasets.items(): labels = labels_test[name] logits = logits_test[name] nll_test = ensemble_negative_log_likelihood(labels, logits) gibbs_ce_test = gibbs_cross_entropy(labels_test[name], logits_test[name]) labels = tf.cast(labels, tf.int32) logits = tf.convert_to_tensor(logits) per_probs = tf.nn.softmax(logits) probs = tf.reduce_mean(per_probs, axis=0) accuracy = tf.keras.metrics.sparse_categorical_accuracy(labels, probs) if name == 'clean': metrics['test/negative_log_likelihood'] = tf.reduce_mean(nll_test) metrics['test/gibbs_cross_entropy'] = tf.reduce_mean(gibbs_ce_test) metrics['test/accuracy'] = tf.reduce_mean(accuracy) metrics['test/ece'].update_state(labels, probs) else: corrupt_metrics['test/nll_{}'.format(name)].update_state( tf.reduce_mean(nll_test)) corrupt_metrics['test/accuracy_{}'.format(name)].update_state( tf.reduce_mean(accuracy)) corrupt_metrics['test/ece_{}'.format(name)].update_state( labels, probs) corrupt_results = {} corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics, corruption_types, max_intensity) metrics['test/ece'] = metrics['test/ece'].result() total_results = {name: metric for name, metric in metrics.items()} total_results.update(corrupt_results) logging.info('Metrics: %s', total_results)