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resnet_cifar_main.py
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resnet_cifar_main.py
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Runs a ResNet model on the Cifar-10 dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import app as absl_app
from absl import flags
import tensorflow as tf
from official.utils.flags import core as flags_core
from official.utils.logs import logger
from official.utils.misc import distribution_utils
from official.utils.misc import keras_utils
from official.vision.image_classification import cifar_preprocessing
from official.vision.image_classification import common
from official.vision.image_classification import resnet_cifar_model
LR_SCHEDULE = [ # (multiplier, epoch to start) tuples
(0.1, 91), (0.01, 136), (0.001, 182)
]
def learning_rate_schedule(current_epoch,
current_batch,
batches_per_epoch,
batch_size):
"""Handles linear scaling rule and LR decay.
Scale learning rate at epoch boundaries provided in LR_SCHEDULE by the
provided scaling factor.
Args:
current_epoch: integer, current epoch indexed from 0.
current_batch: integer, current batch in the current epoch, indexed from 0.
batches_per_epoch: integer, number of steps in an epoch.
batch_size: integer, total batch sized.
Returns:
Adjusted learning rate.
"""
del current_batch, batches_per_epoch # not used
initial_learning_rate = common.BASE_LEARNING_RATE * batch_size / 128
learning_rate = initial_learning_rate
for mult, start_epoch in LR_SCHEDULE:
if current_epoch >= start_epoch:
learning_rate = initial_learning_rate * mult
else:
break
return learning_rate
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 define_cifar_flags():
common.define_keras_flags(dynamic_loss_scale=False)
flags_core.set_defaults(data_dir='/tmp/cifar10_data/cifar-10-batches-bin',
model_dir='/tmp/cifar10_model',
train_epochs=182,
epochs_between_evals=10,
batch_size=128)
def main(_):
with logger.benchmark_context(flags.FLAGS):
return run(flags.FLAGS)
if __name__ == '__main__':
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
define_cifar_flags()
absl_app.run(main)