def main(argv):
del argv # Unused.
# Configure parameters.
params = params_dict.ParamsDict(mask_rcnn_config.MASK_RCNN_CFG,
mask_rcnn_config.MASK_RCNN_RESTRICTIONS)
params = params_dict.override_params_dict(params,
FLAGS.config_file,
is_strict=True)
params = params_dict.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params = flags_to_params.override_params_from_input_flags(params, FLAGS)
params.validate()
params.lock()
# Check data path
train_input_fn = None
eval_input_fn = None
if (FLAGS.mode in ('train', 'train_and_eval')
and not params.training_file_pattern):
raise RuntimeError(
'You must specify `training_file_pattern` for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if not params.validation_file_pattern:
raise RuntimeError('You must specify `validation_file_pattern` '
'for evaluation.')
if not params.val_json_file and not params.include_groundtruth_in_features:
raise RuntimeError(
'You must specify `val_json_file` or '
'include_groundtruth_in_features=True for evaluation.')
if FLAGS.mode in ('train', 'train_and_eval'):
train_input_fn = dataloader.InputReader(
params.training_file_pattern,
mode=tf.estimator.ModeKeys.TRAIN,
use_fake_data=FLAGS.use_fake_data,
use_instance_mask=params.include_mask)
if (FLAGS.mode in ('eval', 'train_and_eval')
or (FLAGS.mode == 'train' and FLAGS.eval_after_training)):
eval_input_fn = dataloader.InputReader(
params.validation_file_pattern,
mode=tf.estimator.ModeKeys.PREDICT,
num_examples=params.eval_samples,
use_instance_mask=params.include_mask)
run_executer(params, train_input_fn, eval_input_fn)
def main(unused_argv):
params = params_dict.ParamsDict(mnasnet_config.MNASNET_CFG,
mnasnet_config.MNASNET_RESTRICTIONS)
params = params_dict.override_params_dict(params,
FLAGS.config_file,
is_strict=True)
params = params_dict.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params = flags_to_params.override_params_from_input_flags(params, FLAGS)
additional_params = {
'steps_per_epoch': params.num_train_images / params.train_batch_size,
'quantized_training': FLAGS.quantized_training,
}
params = params_dict.override_params_dict(params,
additional_params,
is_strict=False)
params.validate()
params.lock()
if FLAGS.tpu or params.use_tpu:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
else:
tpu_cluster_resolver = None
if params.use_async_checkpointing:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(100, params.iterations_per_loop)
config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=params.iterations_per_loop,
per_host_input_for_training=tf.contrib.tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
# Validates Flags.
if params.precision == 'bfloat16' and params.use_keras:
raise ValueError(
'Keras layers do not have full support to bfloat16 activation training.'
' You have set precision as %s and use_keras as %s' %
(params.precision, params.use_keras))
# Initializes model parameters.
mnasnet_est = tf.contrib.tpu.TPUEstimator(
use_tpu=params.use_tpu,
model_fn=mnasnet_model_fn,
config=config,
train_batch_size=params.train_batch_size,
eval_batch_size=params.eval_batch_size,
export_to_tpu=FLAGS.export_to_tpu,
params=params.as_dict())
if FLAGS.mode == 'export_only':
export(mnasnet_est, FLAGS.export_dir, params, FLAGS.post_quantize)
return
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s',
FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetBigtableInput(
is_training=is_training,
use_bfloat16=False,
transpose_input=params.transpose_input,
selection=selection)
for (is_training,
selection) in [(True, select_train), (False, select_eval)]
]
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput(
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=params.transpose_input,
cache=params.use_cache and is_training,
image_size=params.input_image_size,
num_parallel_calls=params.num_parallel_calls,
use_bfloat16=(params.precision == 'bfloat16'))
for is_training in [True, False]
]
if FLAGS.mode == 'eval':
eval_steps = params.num_eval_images // params.eval_batch_size
# Run evaluation when there's a new checkpoint
for ckpt in evaluation.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time(
) # This time will include compilation time
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d',
eval_results, elapsed_time)
utils.archive_ckpt(eval_results,
eval_results['top_1_accuracy'], ckpt)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= params.train_steps:
tf.logging.info(
'Evaluation finished after training step %d',
current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, params, FLAGS.post_quantize)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
current_step = estimator._load_global_step_from_checkpoint_dir( # pylint: disable=protected-access
FLAGS.model_dir)
tf.logging.info(
'Training for %d steps (%.2f epochs in total). Current'
' step %d.', params.train_steps,
params.train_steps / params.steps_per_epoch, current_step)
start_timestamp = time.time(
) # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if params.use_async_checkpointing:
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(100, params.iterations_per_loop)))
mnasnet_est.train(input_fn=imagenet_train.input_fn,
max_steps=params.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
while current_step < params.train_steps:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
params.train_steps)
mnasnet_est.train(input_fn=imagenet_train.input_fn,
max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
next_checkpoint, int(time.time() - start_timestamp))
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
tf.logging.info('Starting to evaluate.')
eval_results = mnasnet_est.evaluate(
input_fn=imagenet_eval.input_fn,
steps=params.num_eval_images // params.eval_batch_size)
tf.logging.info('Eval results at step %d: %s', next_checkpoint,
eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results,
eval_results['top_1_accuracy'], ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info(
'Finished training up to step %d. Elapsed seconds %d.',
params.train_steps, elapsed_time)
if FLAGS.export_dir:
export(mnasnet_est, FLAGS.export_dir, params,
FLAGS.post_quantize)
def main(unused_argv):
params = params_dict.ParamsDict(
resnet_config.RESNET_CFG, resnet_config.RESNET_RESTRICTIONS)
params = params_dict.override_params_dict(
params, FLAGS.config_file, is_strict=True)
params = params_dict.override_params_dict(
params, FLAGS.params_override, is_strict=True)
params = flags_to_params.override_params_from_input_flags(params, FLAGS)
params.validate()
params.lock()
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu if (FLAGS.tpu or params.use_tpu) else '',
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project)
if params.use_async_checkpointing:
save_checkpoints_steps = None
else:
save_checkpoints_steps = max(5000, params.iterations_per_loop)
config = tf.estimator.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
log_step_count_steps=FLAGS.log_step_count_steps,
session_config=tf.ConfigProto(
graph_options=tf.GraphOptions(
rewrite_options=rewriter_config_pb2.RewriterConfig(
disable_meta_optimizer=True))),
tpu_config=tf.estimator.tpu.TPUConfig(
iterations_per_loop=params.iterations_per_loop,
num_shards=params.num_cores,
per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig
.PER_HOST_V2)) # pylint: disable=line-too-long
resnet_classifier = tf.estimator.tpu.TPUEstimator(
use_tpu=params.use_tpu,
model_fn=resnet_model_fn,
config=config,
params=params.as_dict(),
train_batch_size=params.train_batch_size,
eval_batch_size=params.eval_batch_size,
export_to_tpu=FLAGS.export_to_tpu)
assert (params.precision == 'bfloat16' or
params.precision == 'float32'), (
'Invalid value for precision parameter; '
'must be bfloat16 or float32.')
tf.logging.info('Precision: %s', params.precision)
use_bfloat16 = params.precision == 'bfloat16'
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
if FLAGS.bigtable_instance:
tf.logging.info('Using Bigtable dataset, table %s', FLAGS.bigtable_table)
select_train, select_eval = _select_tables_from_flags()
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetBigtableInput( # pylint: disable=g-complex-comprehension
is_training=is_training,
use_bfloat16=use_bfloat16,
transpose_input=params.transpose_input,
selection=selection,
augment_name=FLAGS.augment_name,
randaug_num_layers=FLAGS.randaug_num_layers,
randaug_magnitude=FLAGS.randaug_magnitude)
for (is_training, selection) in [(True,
select_train), (False, select_eval)]
]
else:
if FLAGS.data_dir == FAKE_DATA_DIR:
tf.logging.info('Using fake dataset.')
else:
tf.logging.info('Using dataset: %s', FLAGS.data_dir)
imagenet_train, imagenet_eval = [
imagenet_input.ImageNetInput( # pylint: disable=g-complex-comprehension
is_training=is_training,
data_dir=FLAGS.data_dir,
transpose_input=params.transpose_input,
cache=params.use_cache and is_training,
image_size=params.image_size,
num_parallel_calls=params.num_parallel_calls,
include_background_label=(params.num_label_classes == 1001),
use_bfloat16=use_bfloat16,
augment_name=FLAGS.augment_name,
randaug_num_layers=FLAGS.randaug_num_layers,
randaug_magnitude=FLAGS.randaug_magnitude)
for is_training in [True, False]
]
steps_per_epoch = params.num_train_images // params.train_batch_size
eval_steps = params.num_eval_images // params.eval_batch_size
if FLAGS.mode == 'eval':
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir, timeout=FLAGS.eval_timeout):
tf.logging.info('Starting to evaluate.')
try:
start_timestamp = time.time() # This time will include compilation time
eval_results = resnet_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
checkpoint_path=ckpt)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Eval results: %s. Elapsed seconds: %d',
eval_results, elapsed_time)
# Terminate eval job when final checkpoint is reached
current_step = int(os.path.basename(ckpt).split('-')[1])
if current_step >= params.train_steps:
tf.logging.info(
'Evaluation finished after training step %d', current_step)
break
except tf.errors.NotFoundError:
# Since the coordinator is on a different job than the TPU worker,
# sometimes the TPU worker does not finish initializing until long after
# the CPU job tells it to start evaluating. In this case, the checkpoint
# file could have been deleted already.
tf.logging.info(
'Checkpoint %s no longer exists, skipping checkpoint', ckpt)
else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval'
try:
current_step = tf.train.load_variable(FLAGS.model_dir,
tf.GraphKeys.GLOBAL_STEP)
except (TypeError, ValueError, tf.errors.NotFoundError):
current_step = 0
steps_per_epoch = params.num_train_images // params.train_batch_size
tf.logging.info('Training for %d steps (%.2f epochs in total). Current'
' step %d.',
params.train_steps,
params.train_steps / steps_per_epoch,
current_step)
start_timestamp = time.time() # This time will include compilation time
if FLAGS.mode == 'train':
hooks = []
if params.use_async_checkpointing:
try:
from tensorflow.contrib.tpu.python.tpu import async_checkpoint # pylint: disable=g-import-not-at-top
except ImportError as e:
logging.exception(
'Async checkpointing is not supported in TensorFlow 2.x')
raise e
hooks.append(
async_checkpoint.AsyncCheckpointSaverHook(
checkpoint_dir=FLAGS.model_dir,
save_steps=max(5000, params.iterations_per_loop)))
if FLAGS.profile_every_n_steps > 0:
hooks.append(
tpu_profiler_hook.TPUProfilerHook(
save_steps=FLAGS.profile_every_n_steps,
output_dir=FLAGS.model_dir, tpu=FLAGS.tpu)
)
resnet_classifier.train(
input_fn=imagenet_train.input_fn,
max_steps=params.train_steps,
hooks=hooks)
else:
assert FLAGS.mode == 'train_and_eval'
while current_step < params.train_steps:
# Train for up to steps_per_eval number of steps.
# At the end of training, a checkpoint will be written to --model_dir.
next_checkpoint = min(current_step + FLAGS.steps_per_eval,
params.train_steps)
resnet_classifier.train(
input_fn=imagenet_train.input_fn, max_steps=next_checkpoint)
current_step = next_checkpoint
tf.logging.info('Finished training up to step %d. Elapsed seconds %d.',
next_checkpoint, int(time.time() - start_timestamp))
# Evaluate the model on the most recent model in --model_dir.
# Since evaluation happens in batches of --eval_batch_size, some images
# may be excluded modulo the batch size. As long as the batch size is
# consistent, the evaluated images are also consistent.
tf.logging.info('Starting to evaluate.')
eval_results = resnet_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=params.num_eval_images // params.eval_batch_size)
tf.logging.info('Eval results at step %d: %s',
next_checkpoint, eval_results)
elapsed_time = int(time.time() - start_timestamp)
tf.logging.info('Finished training up to step %d. Elapsed seconds %d.',
params.train_steps, elapsed_time)
if FLAGS.export_dir is not None:
# The guide to serve a exported TensorFlow model is at:
# https://www.tensorflow.org/serving/serving_basic
tf.logging.info('Starting to export model.')
export_path = resnet_classifier.export_saved_model(
export_dir_base=FLAGS.export_dir,
serving_input_receiver_fn=imagenet_input.image_serving_input_fn)
if FLAGS.add_warmup_requests:
inference_warmup.write_warmup_requests(
export_path,
FLAGS.model_name,
params.image_size,
batch_sizes=FLAGS.inference_batch_sizes,
image_format='JPEG')
def main(unused_argv):
del unused_argv # Unused
params = params_dict.ParamsDict({},
mobilenet_config.MOBILENET_RESTRICTIONS)
params = flags_to_params.override_params_from_input_flags(params, FLAGS)
params = params_dict.override_params_dict(params,
mobilenet_config.MOBILENET_CFG,
is_strict=False)
params = params_dict.override_params_dict(params,
FLAGS.config_file,
is_strict=True)
params = params_dict.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
input_perm = [0, 1, 2, 3]
output_perm = [0, 1, 2, 3]
batch_axis = 0
batch_size_per_shard = params.train_batch_size // params.num_cores
if params.transpose_enabled:
if batch_size_per_shard >= 64:
input_perm = [3, 0, 1, 2]
output_perm = [1, 2, 3, 0]
batch_axis = 3
else:
input_perm = [2, 0, 1, 3]
output_perm = [1, 2, 0, 3]
batch_axis = 2
additional_params = {
'input_perm': input_perm,
'output_perm': output_perm,
}
params = params_dict.override_params_dict(params,
additional_params,
is_strict=False)
params.validate()
params.lock()
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu if (FLAGS.tpu or params.use_tpu) else '',
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project)
if params.eval_total_size > 0:
eval_size = params.eval_total_size
else:
eval_size = params.num_eval_images
eval_steps = eval_size // params.eval_batch_size
iterations = (eval_steps
if FLAGS.mode == 'eval' else params.iterations_per_loop)
eval_batch_size = (None
if FLAGS.mode == 'train' else params.eval_batch_size)
per_host_input_for_training = (params.num_cores <= 8
if FLAGS.mode == 'train' else True)
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=FLAGS.model_dir,
save_checkpoints_secs=FLAGS.save_checkpoints_secs,
save_summary_steps=FLAGS.save_summary_steps,
session_config=tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement),
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=iterations,
per_host_input_for_training=per_host_input_for_training))
inception_classifier = tf.contrib.tpu.TPUEstimator(
model_fn=model_fn,
use_tpu=params.use_tpu,
config=run_config,
params=params.as_dict(),
train_batch_size=params.train_batch_size,
eval_batch_size=eval_batch_size,
batch_axis=(batch_axis, 0))
# Input pipelines are slightly different (with regards to shuffling and
# preprocessing) between training and evaluation.
imagenet_train = supervised_images.InputPipeline(is_training=True,
data_dir=FLAGS.data_dir)
imagenet_eval = supervised_images.InputPipeline(is_training=False,
data_dir=FLAGS.data_dir)
if params.moving_average:
eval_hooks = [LoadEMAHook(FLAGS.model_dir)]
else:
eval_hooks = []
if FLAGS.mode == 'eval':
def terminate_eval():
tf.logging.info('%d seconds without new checkpoints have elapsed '
'... terminating eval' % FLAGS.eval_timeout)
return True
def get_next_checkpoint():
return evaluation.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=params.min_eval_interval,
timeout=FLAGS.eval_timeout,
timeout_fn=terminate_eval)
for checkpoint in get_next_checkpoint():
tf.logging.info('Starting to evaluate.')
try:
eval_results = inception_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
hooks=eval_hooks,
checkpoint_path=checkpoint)
tf.logging.info('Evaluation results: %s' % eval_results)
except tf.errors.NotFoundError:
# skip checkpoint if it gets deleted prior to evaluation
tf.logging.info('Checkpoint %s no longer exists ... skipping')
elif FLAGS.mode == 'train_and_eval':
for cycle in range(params.train_steps // params.train_steps_per_eval):
tf.logging.info('Starting training cycle %d.' % cycle)
inception_classifier.train(input_fn=imagenet_train.input_fn,
steps=params.train_steps_per_eval)
tf.logging.info('Starting evaluation cycle %d .' % cycle)
eval_results = inception_classifier.evaluate(
input_fn=imagenet_eval.input_fn,
steps=eval_steps,
hooks=eval_hooks)
tf.logging.info('Evaluation results: %s' % eval_results)
else:
tf.logging.info('Starting training ...')
inception_classifier.train(input_fn=imagenet_train.input_fn,
steps=params.train_steps)
if FLAGS.export_dir:
tf.logging.info('Starting to export model with image input.')
inception_classifier.export_saved_model(
export_dir_base=FLAGS.export_dir,
serving_input_receiver_fn=image_serving_input_fn)
if FLAGS.tflite_export_dir:
tf.logging.info('Starting to export default TensorFlow model.')
savedmodel_dir = inception_classifier.export_saved_model(
export_dir_base=FLAGS.tflite_export_dir,
serving_input_receiver_fn=functools.partial(tensor_serving_input_fn, params)) # pylint: disable=line-too-long
tf.logging.info('Starting to export TFLite.')
converter = tf.lite.TFLiteConverter.from_saved_model(
savedmodel_dir, output_arrays=['softmax_tensor'])
tflite_file_name = 'mobilenet.tflite'
if params.post_quantize:
converter.post_training_quantize = True
tflite_file_name = 'quantized_' + tflite_file_name
tflite_file = os.path.join(savedmodel_dir, tflite_file_name)
tflite_model = converter.convert()
tf.gfile.GFile(tflite_file, 'wb').write(tflite_model)
def main(unused_argv):
params = params_dict.ParamsDict(squeezenet_config.SQUEEZENET_CFG,
squeezenet_config.SQUEEZENET_RESTRICTIONS)
params = params_dict.override_params_dict(params,
FLAGS.config_file,
is_strict=True)
params = params_dict.override_params_dict(params,
FLAGS.params_override,
is_strict=True)
params = flags_to_params.override_params_from_input_flags(params, FLAGS)
total_steps = (
(params.train.num_epochs * params.train.num_examples_per_epoch) //
params.train.train_batch_size)
params.override(
{
"train": {
"total_steps": total_steps
},
"eval": {
"num_steps_per_eval": (total_steps // params.eval.num_evals)
},
},
is_strict=False)
params.validate()
params.lock()
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
if not params.use_async_checkpointing:
save_checkpoints_steps = max(5000, params.train.iterations_per_loop)
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=params.model_dir,
save_checkpoints_steps=save_checkpoints_steps,
session_config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False),
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=params.train.iterations_per_loop,
num_shards=params.train.num_cores_per_replica,
),
)
estimator = contrib_tpu.TPUEstimator(
model_fn=squeezenet_model.model_fn,
use_tpu=params.use_tpu,
config=run_config,
train_batch_size=params.train.train_batch_size,
eval_batch_size=params.eval.eval_batch_size,
params=params.as_dict(),
)
for eval_cycle in range(params.eval.num_evals):
current_cycle_last_train_step = ((eval_cycle + 1) *
params.eval.num_steps_per_eval)
estimator.train(input_fn=data_pipeline.InputReader(FLAGS.data_dir,
is_training=True),
steps=current_cycle_last_train_step)
tf.logging.info("Running evaluation")
tf.logging.info(
"%s",
estimator.evaluate(input_fn=data_pipeline.InputReader(
FLAGS.data_dir, is_training=False),
steps=(params.eval.num_eval_examples //
params.eval.eval_batch_size)))
