def run(flags_obj):
"""Run ResNet ImageNet training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
Raises:
ValueError: If fp16 is passed as it is not currently supported.
Returns:
Dictionary of training and eval stats.
"""
keras_utils.set_session_config(enable_eager=flags_obj.enable_eager,
enable_xla=flags_obj.enable_xla)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
common.set_gpu_thread_mode_and_count(flags_obj)
if flags_obj.data_delay_prefetch:
common.data_delay_prefetch()
common.set_cudnn_batchnorm_mode()
dtype = flags_core.get_tf_dtype(flags_obj)
if dtype == 'float16':
policy = tf.keras.mixed_precision.experimental.Policy(
'infer_float32_vars')
tf.keras.mixed_precision.experimental.set_policy(policy)
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
# Configures cluster spec for distribution strategy.
num_workers = distribution_utils.configure_cluster(flags_obj.worker_hosts,
flags_obj.task_index)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
num_workers=num_workers,
all_reduce_alg=flags_obj.all_reduce_alg,
num_packs=flags_obj.num_packs)
if strategy:
# flags_obj.enable_get_next_as_optional controls whether enabling
# get_next_as_optional behavior in DistributedIterator. If true, last
# partial batch can be supported.
strategy.extended.experimental_enable_get_next_as_optional = (
flags_obj.enable_get_next_as_optional)
strategy_scope = distribution_utils.get_strategy_scope(strategy)
# pylint: disable=protected-access
if flags_obj.use_synthetic_data:
distribution_utils.set_up_synthetic_data()
input_fn = common.get_synth_input_fn(
height=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
width=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
num_channels=imagenet_preprocessing.NUM_CHANNELS,
num_classes=imagenet_preprocessing.NUM_CLASSES,
dtype=dtype,
drop_remainder=True)
else:
distribution_utils.undo_set_up_synthetic_data()
input_fn = imagenet_preprocessing.input_fn
# When `enable_xla` is True, we always drop the remainder of the batches
# in the dataset, as XLA-GPU doesn't support dynamic shapes.
drop_remainder = flags_obj.enable_xla
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=imagenet_preprocessing.parse_record,
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
dtype=dtype,
drop_remainder=drop_remainder,
tf_data_experimental_slack=flags_obj.tf_data_experimental_slack,
)
eval_input_dataset = None
if not flags_obj.skip_eval:
eval_input_dataset = input_fn(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=imagenet_preprocessing.parse_record,
dtype=dtype,
drop_remainder=drop_remainder)
lr_schedule = 0.1
if flags_obj.use_tensor_lr:
lr_schedule = common.PiecewiseConstantDecayWithWarmup(
batch_size=flags_obj.batch_size,
epoch_size=imagenet_preprocessing.NUM_IMAGES['train'],
warmup_epochs=LR_SCHEDULE[0][1],
boundaries=list(p[1] for p in LR_SCHEDULE[1:]),
multipliers=list(p[0] for p in LR_SCHEDULE),
compute_lr_on_cpu=True)
with strategy_scope:
optimizer = common.get_optimizer(lr_schedule)
if dtype == 'float16':
# TODO(reedwm): Remove manually wrapping optimizer once mixed precision
# can be enabled with a single line of code.
optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer,
loss_scale=flags_core.get_loss_scale(flags_obj,
default_for_fp16=128))
if flags_obj.use_trivial_model:
model = trivial_model.trivial_model(
imagenet_preprocessing.NUM_CLASSES, dtype)
else:
model = resnet_model.resnet50(
num_classes=imagenet_preprocessing.NUM_CLASSES, dtype=dtype)
# TODO(b/138957587): Remove when force_v2_in_keras_compile is on longer
# a valid arg for this model. Also remove as a valid flag.
if flags_obj.force_v2_in_keras_compile is not None:
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=(['sparse_categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly,
experimental_run_tf_function=flags_obj.
force_v2_in_keras_compile)
else:
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=(['sparse_categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly)
callbacks = common.get_callbacks(
learning_rate_schedule, imagenet_preprocessing.NUM_IMAGES['train'])
train_steps = (imagenet_preprocessing.NUM_IMAGES['train'] //
flags_obj.batch_size)
train_epochs = flags_obj.train_epochs
if flags_obj.train_steps:
train_steps = min(flags_obj.train_steps, train_steps)
train_epochs = 1
num_eval_steps = (imagenet_preprocessing.NUM_IMAGES['validation'] //
flags_obj.batch_size)
validation_data = eval_input_dataset
if flags_obj.skip_eval:
# Only build the training graph. This reduces memory usage introduced by
# control flow ops in layers that have different implementations for
# training and inference (e.g., batch norm).
if flags_obj.set_learning_phase_to_train:
# TODO(haoyuzhang): Understand slowdown of setting learning phase when
# not using distribution strategy.
tf.keras.backend.set_learning_phase(1)
num_eval_steps = None
validation_data = None
if not strategy and flags_obj.explicit_gpu_placement:
# TODO(b/135607227): Add device scope automatically in Keras training loop
# when not using distribition strategy.
no_dist_strat_device = tf.device('/device:GPU:0')
no_dist_strat_device.__enter__()
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps // 15,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=validation_data,
validation_freq=flags_obj.epochs_between_evals,
verbose=1)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=1)
if not strategy and flags_obj.explicit_gpu_placement:
no_dist_strat_device.__exit__()
stats = common.build_stats(history, eval_output, callbacks)
return stats
def run(flags_obj):
"""Run ResNet ImageNet training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
Raises:
ValueError: If fp16 is passed as it is not currently supported.
Returns:
Dictionary of training and eval stats.
"""
keras_utils.set_session_config(
enable_eager=flags_obj.enable_eager,
enable_xla=flags_obj.enable_xla)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
common.set_gpu_thread_mode_and_count(flags_obj)
common.set_cudnn_batchnorm_mode()
dtype = flags_core.get_tf_dtype(flags_obj)
if dtype == tf.float16:
loss_scale = flags_core.get_loss_scale(flags_obj, default_for_fp16=128)
policy = tf.compat.v2.keras.mixed_precision.experimental.Policy(
'mixed_float16', loss_scale=loss_scale)
tf.compat.v2.keras.mixed_precision.experimental.set_policy(policy)
if not keras_utils.is_v2_0():
raise ValueError('--dtype=fp16 is not supported in TensorFlow 1.')
elif dtype == tf.bfloat16:
policy = tf.compat.v2.keras.mixed_precision.experimental.Policy(
'mixed_bfloat16')
tf.compat.v2.keras.mixed_precision.experimental.set_policy(policy)
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
# Configures cluster spec for distribution strategy.
num_workers = distribution_utils.configure_cluster(flags_obj.worker_hosts,
flags_obj.task_index)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
num_workers=num_workers,
all_reduce_alg=flags_obj.all_reduce_alg,
num_packs=flags_obj.num_packs,
tpu_address=flags_obj.tpu)
if strategy:
# flags_obj.enable_get_next_as_optional controls whether enabling
# get_next_as_optional behavior in DistributedIterator. If true, last
# partial batch can be supported.
strategy.extended.experimental_enable_get_next_as_optional = (
flags_obj.enable_get_next_as_optional
)
strategy_scope = distribution_utils.get_strategy_scope(strategy)
# pylint: disable=protected-access
if flags_obj.use_synthetic_data:
distribution_utils.set_up_synthetic_data()
input_fn = common.get_synth_input_fn(
height=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
width=imagenet_preprocessing.DEFAULT_IMAGE_SIZE,
num_channels=imagenet_preprocessing.NUM_CHANNELS,
num_classes=imagenet_preprocessing.NUM_CLASSES,
dtype=dtype,
drop_remainder=True)
else:
distribution_utils.undo_set_up_synthetic_data()
input_fn = imagenet_preprocessing.input_fn
# When `enable_xla` is True, we always drop the remainder of the batches
# in the dataset, as XLA-GPU doesn't support dynamic shapes.
drop_remainder = flags_obj.enable_xla
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=imagenet_preprocessing.parse_record,
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
dtype=dtype,
drop_remainder=drop_remainder,
tf_data_experimental_slack=flags_obj.tf_data_experimental_slack,
training_dataset_cache=flags_obj.training_dataset_cache,
)
eval_input_dataset = None
if not flags_obj.skip_eval:
eval_input_dataset = input_fn(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=imagenet_preprocessing.parse_record,
dtype=dtype,
drop_remainder=drop_remainder)
lr_schedule = 0.1
if flags_obj.use_tensor_lr:
lr_schedule = common.PiecewiseConstantDecayWithWarmup(
batch_size=flags_obj.batch_size,
epoch_size=imagenet_preprocessing.NUM_IMAGES['train'],
warmup_epochs=common.LR_SCHEDULE[0][1],
boundaries=list(p[1] for p in common.LR_SCHEDULE[1:]),
multipliers=list(p[0] for p in common.LR_SCHEDULE),
compute_lr_on_cpu=True)
with strategy_scope:
optimizer = common.get_optimizer(lr_schedule)
if flags_obj.fp16_implementation == 'graph_rewrite':
# Note: when flags_obj.fp16_implementation == "graph_rewrite", dtype as
# determined by flags_core.get_tf_dtype(flags_obj) would be 'float32'
# which will ensure tf.compat.v2.keras.mixed_precision and
# tf.train.experimental.enable_mixed_precision_graph_rewrite do not double
# up.
optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimizer)
# TODO(hongkuny): Remove trivial model usage and move it to benchmark.
if flags_obj.use_trivial_model:
model = trivial_model.trivial_model(
imagenet_preprocessing.NUM_CLASSES)
else:
model = resnet_model.resnet50(
num_classes=imagenet_preprocessing.NUM_CLASSES)
# TODO(b/138957587): Remove when force_v2_in_keras_compile is on longer
# a valid arg for this model. Also remove as a valid flag.
if flags_obj.force_v2_in_keras_compile is not None:
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=(['sparse_categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly,
experimental_run_tf_function=flags_obj.force_v2_in_keras_compile)
else:
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=optimizer,
metrics=(['sparse_categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly)
steps_per_epoch = (
imagenet_preprocessing.NUM_IMAGES['train'] // flags_obj.batch_size)
train_epochs = flags_obj.train_epochs
callbacks = common.get_callbacks(steps_per_epoch,
common.learning_rate_schedule)
if flags_obj.enable_checkpoint_and_export:
ckpt_full_path = os.path.join(flags_obj.model_dir, 'model.ckpt-{epoch:04d}')
callbacks.append(tf.keras.callbacks.ModelCheckpoint(ckpt_full_path,
save_weights_only=True))
# if mutliple epochs, ignore the train_steps flag.
if train_epochs <= 1 and flags_obj.train_steps:
steps_per_epoch = min(flags_obj.train_steps, steps_per_epoch)
train_epochs = 1
num_eval_steps = (
imagenet_preprocessing.NUM_IMAGES['validation'] // flags_obj.batch_size)
validation_data = eval_input_dataset
if flags_obj.skip_eval:
# Only build the training graph. This reduces memory usage introduced by
# control flow ops in layers that have different implementations for
# training and inference (e.g., batch norm).
if flags_obj.set_learning_phase_to_train:
# TODO(haoyuzhang): Understand slowdown of setting learning phase when
# not using distribution strategy.
tf.keras.backend.set_learning_phase(1)
num_eval_steps = None
validation_data = None
if not strategy and flags_obj.explicit_gpu_placement:
# TODO(b/135607227): Add device scope automatically in Keras training loop
# when not using distribition strategy.
no_dist_strat_device = tf.device('/device:GPU:0')
no_dist_strat_device.__enter__()
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=validation_data,
validation_freq=flags_obj.epochs_between_evals,
verbose=2)
if flags_obj.enable_checkpoint_and_export:
if dtype == tf.bfloat16:
logging.warning("Keras model.save does not support bfloat16 dtype.")
else:
# Keras model.save assumes a float32 input designature.
export_path = os.path.join(flags_obj.model_dir, 'saved_model')
model.save(export_path, include_optimizer=False)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
if not strategy and flags_obj.explicit_gpu_placement:
no_dist_strat_device.__exit__()
stats = common.build_stats(history, eval_output, callbacks)
return stats
def run(flags_obj):
"""Run ResNet Cifar-10 training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
Raises:
ValueError: If fp16 is passed as it is not currently supported.
Returns:
Dictionary of training and eval stats.
"""
keras_utils.set_session_config(enable_eager=flags_obj.enable_eager,
enable_xla=flags_obj.enable_xla)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
common.set_gpu_thread_mode_and_count(flags_obj)
common.set_cudnn_batchnorm_mode()
dtype = flags_core.get_tf_dtype(flags_obj)
if dtype == 'fp16':
raise ValueError(
'dtype fp16 is not supported in Keras. Use the default '
'value(fp32).')
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
num_workers=distribution_utils.configure_cluster(),
all_reduce_alg=flags_obj.all_reduce_alg,
num_packs=flags_obj.num_packs)
if strategy:
# flags_obj.enable_get_next_as_optional controls whether enabling
# get_next_as_optional behavior in DistributedIterator. If true, last
# partial batch can be supported.
strategy.extended.experimental_enable_get_next_as_optional = (
flags_obj.enable_get_next_as_optional)
strategy_scope = distribution_utils.get_strategy_scope(strategy)
if flags_obj.use_synthetic_data:
distribution_utils.set_up_synthetic_data()
input_fn = common.get_synth_input_fn(
height=cifar_preprocessing.HEIGHT,
width=cifar_preprocessing.WIDTH,
num_channels=cifar_preprocessing.NUM_CHANNELS,
num_classes=cifar_preprocessing.NUM_CLASSES,
dtype=flags_core.get_tf_dtype(flags_obj),
drop_remainder=True)
else:
distribution_utils.undo_set_up_synthetic_data()
input_fn = cifar_preprocessing.input_fn
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=cifar_preprocessing.parse_record,
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
dtype=dtype,
# Setting drop_remainder to avoid the partial batch logic in normalization
# layer, which triggers tf.where and leads to extra memory copy of input
# sizes between host and GPU.
drop_remainder=(not flags_obj.enable_get_next_as_optional))
eval_input_dataset = None
if not flags_obj.skip_eval:
eval_input_dataset = input_fn(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=cifar_preprocessing.parse_record)
with strategy_scope:
optimizer = common.get_optimizer(learning_rate=0.1 * hvd.size())
# Horovod: add Horovod DistributedOptimizer.
optimizer = hvd.DistributedOptimizer(optimizer)
model = resnet_cifar_model.resnet56(
classes=cifar_preprocessing.NUM_CLASSES)
# TODO(b/138957587): Remove when force_v2_in_keras_compile is on longer
# a valid arg for this model. Also remove as a valid flag.
if flags_obj.force_v2_in_keras_compile is not None:
model.compile(
loss='categorical_crossentropy',
optimizer=optimizer,
metrics=(['categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
#run_eagerly=flags_obj.run_eagerly,
experimental_run_tf_function=False)
else:
model.compile(
loss='categorical_crossentropy',
optimizer=optimizer,
metrics=(['categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
#run_eagerly=flags_obj.run_eagerly,
experimental_run_tf_function=False)
callbacks = common.get_callbacks(learning_rate_schedule,
cifar_preprocessing.NUM_IMAGES['train'])
train_steps = cifar_preprocessing.NUM_IMAGES[
'train'] // flags_obj.batch_size
train_epochs = flags_obj.train_epochs
if flags_obj.train_steps:
train_steps = min(flags_obj.train_steps, train_steps)
train_epochs = 1
num_eval_steps = (cifar_preprocessing.NUM_IMAGES['validation'] //
flags_obj.batch_size)
validation_data = eval_input_dataset
if flags_obj.skip_eval:
if flags_obj.set_learning_phase_to_train:
# TODO(haoyuzhang): Understand slowdown of setting learning phase when
# not using distribution strategy.
tf.keras.backend.set_learning_phase(1)
num_eval_steps = None
validation_data = None
if not strategy and flags_obj.explicit_gpu_placement:
# TODO(b/135607227): Add device scope automatically in Keras training loop
# when not using distribition strategy.
no_dist_strat_device = tf.device('/device:GPU:0')
no_dist_strat_device.__enter__()
callbacks = [
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
# Horovod: average metrics among workers at the end of every epoch.
#
# Note: This callback must be in the list before the ReduceLROnPlateau,
# TensorBoard or other metrics-based callbacks.
hvd.callbacks.MetricAverageCallback(),
# Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
# accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
# the first three epochs. See https://arxiv.org/abs/1706.02677 for details.
hvd.callbacks.LearningRateWarmupCallback(warmup_epochs=3, verbose=1),
]
# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
if hvd.rank() == 0:
callbacks.append(
tf.keras.callbacks.ModelCheckpoint('./checkpoint-{epoch}.h5'))
# Horovod: write logs on worker 0.
verbose = 1 if hvd.rank() == 0 else 0
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=validation_data,
validation_freq=flags_obj.epochs_between_evals,
verbose=verbose)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
if not strategy and flags_obj.explicit_gpu_placement:
no_dist_strat_device.__exit__()
stats = common.build_stats(history, eval_output, callbacks)
return stats
def run(flags_obj):
"""Run ResNet Cifar-10 training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
Raises:
ValueError: If fp16 is passed as it is not currently supported.
Returns:
Dictionary of training and eval stats.
"""
keras_utils.set_session_config(
enable_eager=flags_obj.enable_eager,
enable_xla=flags_obj.enable_xla)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
common.set_gpu_thread_mode_and_count(flags_obj)
common.set_cudnn_batchnorm_mode()
dtype = flags_core.get_tf_dtype(flags_obj)
if dtype == 'fp16':
raise ValueError('dtype fp16 is not supported in Keras. Use the default '
'value(fp32).')
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
strategy = distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
num_workers=distribution_utils.configure_cluster(),
all_reduce_alg=flags_obj.all_reduce_alg,
num_packs=flags_obj.num_packs)
if strategy:
# flags_obj.enable_get_next_as_optional controls whether enabling
# get_next_as_optional behavior in DistributedIterator. If true, last
# partial batch can be supported.
strategy.extended.experimental_enable_get_next_as_optional = (
flags_obj.enable_get_next_as_optional
)
strategy_scope = distribution_utils.get_strategy_scope(strategy)
if flags_obj.use_synthetic_data:
distribution_utils.set_up_synthetic_data()
input_fn = common.get_synth_input_fn(
height=cifar_preprocessing.HEIGHT,
width=cifar_preprocessing.WIDTH,
num_channels=cifar_preprocessing.NUM_CHANNELS,
num_classes=cifar_preprocessing.NUM_CLASSES,
dtype=flags_core.get_tf_dtype(flags_obj),
drop_remainder=True)
else:
distribution_utils.undo_set_up_synthetic_data()
input_fn = cifar_preprocessing.input_fn
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=cifar_preprocessing.parse_record,
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
dtype=dtype,
# Setting drop_remainder to avoid the partial batch logic in normalization
# layer, which triggers tf.where and leads to extra memory copy of input
# sizes between host and GPU.
drop_remainder=(not flags_obj.enable_get_next_as_optional))
eval_input_dataset = None
if not flags_obj.skip_eval:
eval_input_dataset = input_fn(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=cifar_preprocessing.parse_record)
with strategy_scope:
optimizer = common.get_optimizer()
model = resnet_cifar_model.resnet56(classes=cifar_preprocessing.NUM_CLASSES)
# TODO(b/138957587): Remove when force_v2_in_keras_compile is on longer
# a valid arg for this model. Also remove as a valid flag.
if flags_obj.force_v2_in_keras_compile is not None:
model.compile(
loss='categorical_crossentropy',
optimizer=optimizer,
metrics=(['categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly,
experimental_run_tf_function=flags_obj.force_v2_in_keras_compile)
else:
model.compile(
loss='categorical_crossentropy',
optimizer=optimizer,
metrics=(['categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly)
callbacks = common.get_callbacks(
learning_rate_schedule, cifar_preprocessing.NUM_IMAGES['train'])
train_steps = cifar_preprocessing.NUM_IMAGES['train'] // flags_obj.batch_size
train_epochs = flags_obj.train_epochs
if flags_obj.train_steps:
train_steps = min(flags_obj.train_steps, train_steps)
train_epochs = 1
num_eval_steps = (cifar_preprocessing.NUM_IMAGES['validation'] //
flags_obj.batch_size)
validation_data = eval_input_dataset
if flags_obj.skip_eval:
if flags_obj.set_learning_phase_to_train:
# TODO(haoyuzhang): Understand slowdown of setting learning phase when
# not using distribution strategy.
tf.keras.backend.set_learning_phase(1)
num_eval_steps = None
validation_data = None
if not strategy and flags_obj.explicit_gpu_placement:
# TODO(b/135607227): Add device scope automatically in Keras training loop
# when not using distribition strategy.
no_dist_strat_device = tf.device('/device:GPU:0')
no_dist_strat_device.__enter__()
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=validation_data,
validation_freq=flags_obj.epochs_between_evals,
verbose=2)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
if not strategy and flags_obj.explicit_gpu_placement:
no_dist_strat_device.__exit__()
stats = common.build_stats(history, eval_output, callbacks)
return stats
def run(flags_obj):
"""Run ResNet Cifar-10 training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
Raises:
ValueError: If fp16 is passed as it is not currently supported.
Returns:
Dictionary of training and eval stats.
"""
keras_utils.set_session_config(enable_eager=flags_obj.enable_eager,
enable_xla=flags_obj.enable_xla)
# Execute flag override logic for better model performance
if flags_obj.tf_gpu_thread_mode:
common.set_gpu_thread_mode_and_count(flags_obj)
common.set_cudnn_batchnorm_mode()
dtype = flags_core.get_tf_dtype(flags_obj)
if dtype == 'fp16':
raise ValueError(
'dtype fp16 is not supported in Keras. Use the default '
'value(fp32).')
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
if flags_obj.use_synthetic_data:
distribution_utils.set_up_synthetic_data()
input_fn = common.get_synth_input_fn(
height=cifar_preprocessing.HEIGHT,
width=cifar_preprocessing.WIDTH,
num_channels=cifar_preprocessing.NUM_CHANNELS,
num_classes=cifar_preprocessing.NUM_CLASSES,
dtype=flags_core.get_tf_dtype(flags_obj),
drop_remainder=True)
else:
distribution_utils.undo_set_up_synthetic_data()
input_fn = cifar_preprocessing.input_fn
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=cifar_preprocessing.parse_record,
datasets_num_private_threads=flags_obj.datasets_num_private_threads,
dtype=dtype,
# Setting drop_remainder to avoid the partial batch logic in normalization
# layer, which triggers tf.where and leads to extra memory copy of input
# sizes between host and GPU.
drop_remainder=(not flags_obj.enable_get_next_as_optional))
eval_input_dataset = None
if not flags_obj.skip_eval:
eval_input_dataset = input_fn(
is_training=False,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
num_epochs=flags_obj.train_epochs,
parse_record_fn=cifar_preprocessing.parse_record)
# with strategy_scope:
optimizer = common.get_optimizer()
model = resnet_cifar_model.resnet56(
classes=cifar_preprocessing.NUM_CLASSES)
# TODO(b/138957587): Remove when force_v2_in_keras_compile is on longer
# a valid arg for this model. Also remove as a valid flag.
if flags_obj.force_v2_in_keras_compile is not None:
model.compile(
loss='categorical_crossentropy',
optimizer=optimizer,
metrics=(['categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly,
experimental_run_tf_function=flags_obj.force_v2_in_keras_compile)
else:
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=(['categorical_accuracy']
if flags_obj.report_accuracy_metrics else None),
run_eagerly=flags_obj.run_eagerly)
callbacks = common.get_callbacks(learning_rate_schedule,
cifar_preprocessing.NUM_IMAGES['train'])
train_steps = cifar_preprocessing.NUM_IMAGES[
'train'] // flags_obj.batch_size
train_epochs = flags_obj.train_epochs
if flags_obj.train_steps:
train_steps = min(flags_obj.train_steps, train_steps)
train_epochs = 1
num_eval_steps = (cifar_preprocessing.NUM_IMAGES['validation'] //
flags_obj.batch_size)
validation_data = eval_input_dataset
if flags_obj.skip_eval:
if flags_obj.set_learning_phase_to_train:
# TODO(haoyuzhang): Understand slowdown of setting learning phase when
# not using distribution strategy.
tf.keras.backend.set_learning_phase(1)
num_eval_steps = None
validation_data = None
# _callback = tf.keras.callbacks.ModelCheckpoint(filepath=flags_obj.model_dir,
# save_weights_only=True,
# verbose=1)
# callbacks.append(cp_callback)
model.load_weights(flags_obj.model_dir)
'''
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=validation_data,
validation_freq=flags_obj.epochs_between_evals,
verbose=2)
'''
# status = checkpoint.restore(tf.train.latest_checkpoint("/calc/stoeckl/binary_populations/checkpoints/"))
# status.expect_partial()
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
# stats = common.build_stats(history, eval_output, callbacks)
print("Result")
print(f"loss: {eval_output[0]}, acc: {eval_output[1]}")
stats = None
return stats
