def train_and_eval(
params: base_configs.ExperimentConfig,
strategy_override: tf.distribute.Strategy) -> Mapping[str, Any]:
"""Runs the train and eval path using compile/fit."""
logging.info('Running train and eval.')
# Note: for TPUs, strategy and scope should be created before the dataset
strategy = strategy_override or distribution_utils.get_distribution_strategy(
distribution_strategy=params.runtime.distribution_strategy,
all_reduce_alg=params.runtime.all_reduce_alg,
num_gpus=params.runtime.num_gpus,
tpu_address=params.runtime.tpu)
strategy_scope = distribution_utils.get_strategy_scope(strategy)
logging.info('Detected %d devices.',
strategy.num_replicas_in_sync if strategy else 1)
label_smoothing = params.model.loss.label_smoothing
one_hot = label_smoothing and label_smoothing > 0
builders = _get_dataset_builders(params, strategy, one_hot)
datasets = [
builder.build(strategy) if builder else None for builder in builders
]
# Unpack datasets and builders based on train/val/test splits
train_builder, validation_builder = builders # pylint: disable=unbalanced-tuple-unpacking
train_dataset, validation_dataset = datasets
train_epochs = params.train.epochs
train_steps = params.train.steps or train_builder.num_steps
validation_steps = params.evaluation.steps or validation_builder.num_steps
initialize(params, train_builder)
logging.info('Global batch size: %d', train_builder.global_batch_size)
with strategy_scope:
model_params = params.model.model_params.as_dict()
model = get_models()[params.model.name](**model_params)
learning_rate = optimizer_factory.build_learning_rate(
params=params.model.learning_rate,
batch_size=train_builder.global_batch_size,
train_steps=train_steps)
optimizer = optimizer_factory.build_optimizer(
optimizer_name=params.model.optimizer.name,
base_learning_rate=learning_rate,
params=params.model.optimizer.as_dict())
metrics_map = _get_metrics(one_hot)
metrics = [metrics_map[metric] for metric in params.train.metrics]
if one_hot:
loss_obj = tf.keras.losses.CategoricalCrossentropy(
label_smoothing=params.model.loss.label_smoothing)
else:
loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
model.compile(
optimizer=optimizer,
loss=loss_obj,
metrics=metrics,
experimental_steps_per_execution=params.train.steps_per_loop)
initial_epoch = 0
if params.train.resume_checkpoint:
initial_epoch = resume_from_checkpoint(model=model,
model_dir=params.model_dir,
train_steps=train_steps)
callbacks = custom_callbacks.get_callbacks(
model_checkpoint=params.train.callbacks.
enable_checkpoint_and_export,
include_tensorboard=params.train.callbacks.enable_tensorboard,
time_history=params.train.callbacks.enable_time_history,
track_lr=params.train.tensorboard.track_lr,
write_model_weights=params.train.tensorboard.write_model_weights,
initial_step=initial_epoch * train_steps,
batch_size=train_builder.global_batch_size,
log_steps=params.train.time_history.log_steps,
model_dir=params.model_dir)
serialize_config(params=params, model_dir=params.model_dir)
if params.evaluation.skip_eval:
validation_kwargs = {}
else:
validation_kwargs = {
'validation_data': validation_dataset,
'validation_steps': validation_steps,
'validation_freq': params.evaluation.epochs_between_evals,
}
history = model.fit(train_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
initial_epoch=initial_epoch,
callbacks=callbacks,
verbose=2,
**validation_kwargs)
validation_output = None
if not params.evaluation.skip_eval:
validation_output = model.evaluate(validation_dataset,
steps=validation_steps,
verbose=2)
# TODO(dankondratyuk): eval and save final test accuracy
stats = common.build_stats(history, validation_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:
keras_utils.set_gpu_thread_mode_and_count(
per_gpu_thread_count=flags_obj.per_gpu_thread_count,
gpu_thread_mode=flags_obj.tf_gpu_thread_mode,
num_gpus=flags_obj.num_gpus,
datasets_num_private_threads=flags_obj.datasets_num_private_threads
)
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.config.list_physical_devices('GPU') 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,
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:
synthetic_util.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:
synthetic_util.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,
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,
parse_record_fn=cifar_preprocessing.parse_record)
steps_per_epoch = (cifar_preprocessing.NUM_IMAGES['train'] //
flags_obj.batch_size)
lr_schedule = 0.1
if flags_obj.use_tensor_lr:
initial_learning_rate = common.BASE_LEARNING_RATE * flags_obj.batch_size / 128
lr_schedule = tf.keras.optimizers.schedules.PiecewiseConstantDecay(
boundaries=list(p[1] * steps_per_epoch for p in LR_SCHEDULE),
values=[initial_learning_rate] + list(p[0] * initial_learning_rate
for p in LR_SCHEDULE))
with strategy_scope:
optimizer = common.get_optimizer(lr_schedule)
model = resnet_cifar_model.resnet56(
classes=cifar_preprocessing.NUM_CLASSES)
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)
train_epochs = flags_obj.train_epochs
callbacks = common.get_callbacks(steps_per_epoch)
if not flags_obj.use_tensor_lr:
lr_callback = LearningRateBatchScheduler(
schedule=learning_rate_schedule,
batch_size=flags_obj.batch_size,
steps_per_epoch=steps_per_epoch)
callbacks.append(lr_callback)
# 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 = (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=steps_per_epoch,
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_train(flags_obj, datasets_override=None, strategy_override=None):
strategy = strategy_override or distribution_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
tpu_address=flags_obj.tpu)
strategy_scope = distribution_utils.get_strategy_scope(strategy)
train_input_dataset, eval_input_dataset, tr_dataset, te_dataset = setup_datasets(
)
with strategy_scope:
#lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
# 0.1, decay_steps=100000, decay_rate=0.96)
#optimizer = tf.keras.optimizers.SGD(learning_rate=lr_schedule)
model = build_model(mode='normal')
losses = {"logits": "sparse_categorical_crossentropy"}
lossWeights = {"logits": 1.0}
model.compile(optimizer='sgd',
loss=losses,
loss_weights=lossWeights,
metrics=['sparse_categorical_accuracy'])
num_train_examples = tr_dataset.num_examples_per_epoch()
train_steps = num_train_examples // GB_OPTIONS.batch_size
train_epochs = GB_OPTIONS.num_epochs
if not hasattr(tr_dataset, "n_poison"):
n_poison = 0
n_cover = 0
else:
n_poison = tr_dataset.n_poison
n_cover = tr_dataset.n_cover
ckpt_full_path = os.path.join(
GB_OPTIONS.checkpoint_folder,
'model.ckpt-{epoch:04d}-p%d-c%d' % (n_poison, n_cover))
callbacks = [
tf.keras.callbacks.LearningRateScheduler(lr_scheduler),
tf.keras.callbacks.ModelCheckpoint(ckpt_full_path,
save_weights_only=True,
save_best_only=False),
]
num_eval_examples = te_dataset.num_examples_per_epoch()
num_eval_steps = num_eval_examples // GB_OPTIONS.batch_size
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=eval_input_dataset,
validation_freq=flags_obj.epochs_between_evals)
export_path = os.path.join(GB_OPTIONS.checkpoint_folder, 'saved_model')
model.save(export_path, include_optimizer=False)
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
stats = common.build_stats(history, eval_output, callbacks)
logging.info('Run stats:\n%s', stats)
from utils import save_options_to_file
save_options_to_file(GB_OPTIONS,
GB_OPTIONS.checkpoint_folder + 'config.json')
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.
NotImplementedError: If some features are 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:
keras_utils.set_gpu_thread_mode_and_count(
per_gpu_thread_count=flags_obj.per_gpu_thread_count,
gpu_thread_mode=flags_obj.tf_gpu_thread_mode,
num_gpus=flags_obj.num_gpus,
datasets_num_private_threads=flags_obj.datasets_num_private_threads)
common.set_cudnn_batchnorm_mode()
dtype = flags_core.get_tf_dtype(flags_obj)
performance.set_mixed_precision_policy(
flags_core.get_tf_dtype(flags_obj),
flags_core.get_loss_scale(flags_obj, default_for_fp16=128))
data_format = flags_obj.data_format
if data_format is None:
data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
else 'channels_last')
tf.keras.backend.set_image_data_format(data_format)
# Configures cluster spec for distribution strategy.
_ = 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,
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:
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:
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
# Current resnet_model.resnet50 input format is always channel-last.
# We use keras_application mobilenet model which input format is depends on
# the keras beckend image data format.
# This use_keras_image_data_format flags indicates whether image preprocessor
# output format should be same as the keras backend image data format or just
# channel-last format.
use_keras_image_data_format = (flags_obj.model == 'mobilenet')
train_input_dataset = input_fn(
is_training=True,
data_dir=flags_obj.data_dir,
batch_size=flags_obj.batch_size,
parse_record_fn=imagenet_preprocessing.get_parse_record_fn(
use_keras_image_data_format=use_keras_image_data_format),
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,
parse_record_fn=imagenet_preprocessing.get_parse_record_fn(
use_keras_image_data_format=use_keras_image_data_format),
dtype=dtype,
drop_remainder=drop_remainder)
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)
steps_per_epoch = (
imagenet_preprocessing.NUM_IMAGES['train'] // flags_obj.batch_size)
with strategy_scope:
if flags_obj.optimizer == 'resnet50_default':
optimizer = common.get_optimizer(lr_schedule)
elif flags_obj.optimizer == 'mobilenet_default':
initial_learning_rate = \
flags_obj.initial_learning_rate_per_sample * flags_obj.batch_size
optimizer = tf.keras.optimizers.SGD(
learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate,
decay_steps=steps_per_epoch * flags_obj.num_epochs_per_decay,
decay_rate=flags_obj.lr_decay_factor,
staircase=True),
momentum=0.9)
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 = test_utils.trivial_model(imagenet_preprocessing.NUM_CLASSES)
elif flags_obj.model == 'resnet50_v1.5':
model = resnet_model.resnet50(
num_classes=imagenet_preprocessing.NUM_CLASSES)
elif flags_obj.model == 'mobilenet':
# TODO(kimjaehong): Remove layers attribute when minimum TF version
# support 2.0 layers by default.
model = tf.keras.applications.mobilenet.MobileNet(
weights=None,
classes=imagenet_preprocessing.NUM_CLASSES,
layers=tf.keras.layers)
if flags_obj.pretrained_filepath:
model.load_weights(flags_obj.pretrained_filepath)
if flags_obj.pruning_method == 'polynomial_decay':
if dtype != tf.float32:
raise NotImplementedError(
'Pruning is currently only supported on dtype=tf.float32.')
pruning_params = {
'pruning_schedule':
tfmot.sparsity.keras.PolynomialDecay(
initial_sparsity=flags_obj.pruning_initial_sparsity,
final_sparsity=flags_obj.pruning_final_sparsity,
begin_step=flags_obj.pruning_begin_step,
end_step=flags_obj.pruning_end_step,
frequency=flags_obj.pruning_frequency),
}
model = tfmot.sparsity.keras.prune_low_magnitude(model, **pruning_params)
elif flags_obj.pruning_method:
raise NotImplementedError(
'Only polynomial_decay is currently supported.')
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)
train_epochs = flags_obj.train_epochs
callbacks = common.get_callbacks(
pruning_method=flags_obj.pruning_method,
enable_checkpoint_and_export=flags_obj.enable_checkpoint_and_export,
model_dir=flags_obj.model_dir)
# 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)
eval_output = None
if not flags_obj.skip_eval:
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
if flags_obj.pruning_method:
model = tfmot.sparsity.keras.strip_pruning(model)
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)
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_train(flags_obj):
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:
keras_utils.set_gpu_thread_mode_and_count(
per_gpu_thread_count=flags_obj.per_gpu_thread_count,
gpu_thread_mode=flags_obj.tf_gpu_thread_mode,
num_gpus=flags_obj.num_gpus,
datasets_num_private_threads=flags_obj.datasets_num_private_threads)
common.set_cudnn_batchnorm_mode()
performance.set_mixed_precision_policy(
flags_core.get_tf_dtype(flags_obj),
flags_core.get_loss_scale(flags_obj, default_for_fp16=128))
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.
_ = 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,
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)
distribution_utils.undo_set_up_synthetic_data()
train_input_dataset, eval_input_dataset, tr_dataset, te_dataset = setup_datasets(flags_obj)
lr_schedule = common.PiecewiseConstantDecayWithWarmup(
batch_size=GB_OPTIONS.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)
steps_per_epoch = (imagenet_preprocessing.NUM_IMAGES['train'] // GB_OPTIONS.batch_size)
with strategy_scope:
optimizer = common.get_optimizer(lr_schedule)
model = build_model(imagenet_preprocessing.NUM_CLASSES, mode='resnet50')
if GB_OPTIONS.pretrained_filepath is not None:
latest = tf.train.latest_checkpoint(GB_OPTIONS.pretrained_filepath)
print(latest)
model.load_weights(latest)
#losses = ["sparse_categorical_crossentropy"]
#lossWeights = [1.0]
model.compile(
optimizer=optimizer,
loss="sparse_categorical_crossentropy",
#loss_weights=lossWeights,
metrics=['sparse_categorical_accuracy'])
train_epochs = GB_OPTIONS.num_epochs
if not hasattr(tr_dataset, "n_poison"):
n_poison=0
n_cover=0
else:
n_poison = tr_dataset.n_poison
n_cover = tr_dataset.n_cover
callbacks = common.get_callbacks(
steps_per_epoch=steps_per_epoch,
pruning_method=flags_obj.pruning_method,
enable_checkpoint_and_export=False,
model_dir=GB_OPTIONS.checkpoint_folder
)
ckpt_full_path = os.path.join(GB_OPTIONS.checkpoint_folder, 'model.ckpt-{epoch:04d}-p%d-c%d'%(n_poison,n_cover))
callbacks.append(tf.keras.callbacks.ModelCheckpoint(ckpt_full_path, save_weights_only=True, save_best_only=True))
num_eval_steps = imagenet_preprocessing.NUM_IMAGES['validation'] // GB_OPTIONS.batch_size
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
eval_input_dataset = None
history = model.fit(
train_input_dataset,
epochs=train_epochs,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=eval_input_dataset,
validation_freq=flags_obj.epochs_between_evals
)
export_path = os.path.join(GB_OPTIONS.checkpoint_folder, 'saved_model')
model.save(export_path, include_optimizer=False)
eval_output = model.evaluate(
eval_input_dataset, steps=num_eval_steps, verbose=2
)
stats = common.build_stats(history, eval_output, callbacks)
cmmd = 'cp config.py '+GB_OPTIONS.checkpoint_folder
os.system(cmmd)
return stats
def run(flags_obj, datasets_override=None, strategy_override=None):
"""Run MNIST model training and eval loop using native Keras APIs.
Args:
flags_obj: An object containing parsed flag values.
datasets_override: A pair of `tf.data.Dataset` objects to train the model,
representing the train and test sets.
strategy_override: A `tf.distribute.Strategy` object to use for model.
Returns:
Dictionary of training and eval stats.
"""
# Start TF profiler server.
tf.profiler.experimental.server.start(flags_obj.profiler_port)
strategy = strategy_override or distribute_utils.get_distribution_strategy(
distribution_strategy=flags_obj.distribution_strategy,
num_gpus=flags_obj.num_gpus,
tpu_address=flags_obj.tpu)
strategy_scope = distribute_utils.get_strategy_scope(strategy)
mnist = tfds.builder('mnist', data_dir=flags_obj.data_dir)
if flags_obj.download:
mnist.download_and_prepare()
mnist_train, mnist_test = datasets_override or mnist.as_dataset(
split=['train', 'test'],
decoders={'image': decode_image()}, # pylint: disable=no-value-for-parameter
as_supervised=True)
train_input_dataset = mnist_train.cache().repeat().shuffle(
buffer_size=50000).batch(flags_obj.batch_size)
eval_input_dataset = mnist_test.cache().repeat().batch(
flags_obj.batch_size)
with strategy_scope:
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
0.05, decay_steps=100000, decay_rate=0.96)
optimizer = tf.keras.optimizers.SGD(learning_rate=lr_schedule)
model = build_model()
model.compile(optimizer=optimizer,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
num_train_examples = mnist.info.splits['train'].num_examples
train_steps = num_train_examples // flags_obj.batch_size
train_epochs = flags_obj.train_epochs
ckpt_full_path = os.path.join(flags_obj.model_dir,
'model.ckpt-{epoch:04d}')
callbacks = [
tf.keras.callbacks.ModelCheckpoint(ckpt_full_path,
save_weights_only=True),
tf.keras.callbacks.TensorBoard(log_dir=flags_obj.model_dir),
]
num_eval_examples = mnist.info.splits['test'].num_examples
num_eval_steps = num_eval_examples // flags_obj.batch_size
history = model.fit(train_input_dataset,
epochs=train_epochs,
steps_per_epoch=train_steps,
callbacks=callbacks,
validation_steps=num_eval_steps,
validation_data=eval_input_dataset,
validation_freq=flags_obj.epochs_between_evals)
export_path = os.path.join(flags_obj.model_dir, 'saved_model')
model.save(export_path, include_optimizer=False)
eval_output = model.evaluate(eval_input_dataset,
steps=num_eval_steps,
verbose=2)
stats = common.build_stats(history, eval_output, callbacks)
return stats