def main(argv):
del argv # Unused.
hvd_try_init()
set_env(use_amp=FLAGS.use_amp)
# Check data path
if FLAGS.mode in (
'train', 'train_and_eval') and FLAGS.training_file_pattern is None:
raise RuntimeError(
'You must specify --training_file_pattern for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError('You must specify --validation_file_pattern '
'for evaluation.')
if not FLAGS.val_json_file and not FLAGS.testdev_dir:
raise RuntimeError(
'You must specify --val_json_file or --testdev for evaluation.'
)
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
# The following is for spatial partitioning. `features` has one tensor while
# `labels` had 4 + (`max_level` - `min_level` + 1) * 2 tensors. The input
# partition is performed on `features` and all partitionable tensors of
# `labels`, see the partition logic below.
# In the TPUEstimator context, the meaning of `shard` and `replica` is the
# same; follwing the API, here has mixed use of both.
if FLAGS.use_spatial_partition:
# Checks input_partition_dims agrees with num_cores_per_replica.
if FLAGS.num_cores_per_replica != np.prod(FLAGS.input_partition_dims):
raise RuntimeError(
'--num_cores_per_replica must be a product of array'
'elements in --input_partition_dims.')
labels_partition_dims = {
'mean_num_positives': None,
'source_ids': None,
'groundtruth_data': None,
'image_scales': None,
}
# The Input Partition Logic: We partition only the partition-able tensors.
# Spatial partition requires that the to-be-partitioned tensors must have a
# dimension that is a multiple of `partition_dims`. Depending on the
# `partition_dims` and the `image_size` and the `max_level` in config, some
# high-level anchor labels (i.e., `cls_targets` and `box_targets`) cannot
# be partitioned. For example, when `partition_dims` is [1, 4, 2, 1], image
# size is 1536, `max_level` is 9, `cls_targets_8` has a shape of
# [batch_size, 6, 6, 9], which cannot be partitioned (6 % 4 != 0). In this
# case, the level-8 and level-9 target tensors are not partition-able, and
# the highest partition-able level is 7.
image_size = config.get('image_size')
for level in range(config.get('min_level'),
config.get('max_level') + 1):
def _can_partition(spatial_dim):
partitionable_index = np.where(
spatial_dim % np.array(FLAGS.input_partition_dims) == 0)
return len(partitionable_index[0]) == len(
FLAGS.input_partition_dims)
spatial_dim = image_size // (2**level)
if _can_partition(spatial_dim):
labels_partition_dims['box_targets_%d' %
level] = FLAGS.input_partition_dims
labels_partition_dims['cls_targets_%d' %
level] = FLAGS.input_partition_dims
else:
labels_partition_dims['box_targets_%d' % level] = None
labels_partition_dims['cls_targets_%d' % level] = None
num_cores_per_replica = FLAGS.num_cores_per_replica
input_partition_dims = [
FLAGS.input_partition_dims, labels_partition_dims
]
num_shards = FLAGS.num_cores // num_cores_per_replica
else:
num_cores_per_replica = None
input_partition_dims = None
num_shards = FLAGS.num_cores
if hvd is not None:
num_shards = hvd.size()
params = dict(
config.as_dict(),
model_name=FLAGS.model_name,
num_epochs=FLAGS.num_epochs,
iterations_per_loop=FLAGS.iterations_per_loop,
model_dir=FLAGS.model_dir,
num_shards=num_shards,
num_examples_per_epoch=FLAGS.num_examples_per_epoch,
use_tpu=FLAGS.use_tpu,
backbone_ckpt=FLAGS.backbone_ckpt,
ckpt=FLAGS.ckpt,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
mode=FLAGS.mode,
)
run_config = tf.estimator.RunConfig(
session_config=get_session_config(use_xla=FLAGS.use_xla),
save_checkpoints_steps=600)
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
# TPU Estimator
logging.info(params)
if FLAGS.mode == 'train':
# train_estimator = tf.estimator.tpu.TPUEstimator(
# model_fn=model_fn_instance,
# use_tpu=FLAGS.use_tpu,
# train_batch_size=FLAGS.train_batch_size,
# config=run_config,
# params=params)
params['batch_size'] = FLAGS.train_batch_size
train_estimator = HorovodEstimator(model_fn=model_fn_instance,
model_dir=FLAGS.model_dir,
config=run_config,
params=params)
input_fn = dataloader.InputReader(FLAGS.training_file_pattern,
is_training=True,
params=params,
use_fake_data=FLAGS.use_fake_data)
max_steps = int((FLAGS.num_epochs * FLAGS.num_examples_per_epoch) /
(FLAGS.train_batch_size * num_shards)) + 1
train_estimator.train(input_fn=input_fn, max_steps=max_steps)
# if FLAGS.eval_after_training:
# # Run evaluation after training finishes.
# eval_params = dict(
# params,
# use_tpu=False,
# input_rand_hflip=False,
# is_training_bn=False,
# use_bfloat16=False,
# )
# eval_estimator = tf.estimator.tpu.TPUEstimator(
# model_fn=model_fn_instance,
# use_tpu=False,
# train_batch_size=FLAGS.train_batch_size,
# eval_batch_size=FLAGS.eval_batch_size,
# config=run_config,
# params=eval_params)
# eval_results = eval_estimator.evaluate(
# input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
# is_training=False),
# steps=FLAGS.eval_samples//FLAGS.eval_batch_size)
# logging.info('Eval results: %s', eval_results)
# ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
# utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
elif FLAGS.mode == 'eval':
config_proto = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
if FLAGS.use_xla and not FLAGS.use_tpu:
config_proto.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
tpu_config = tf.estimator.tpu.TPUConfig(
FLAGS.iterations_per_loop,
num_shards=num_shards,
num_cores_per_replica=num_cores_per_replica,
input_partition_dims=input_partition_dims,
per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig.
PER_HOST_V2)
run_config = tf.estimator.tpu.RunConfig(
cluster=None,
evaluation_master=FLAGS.eval_master,
model_dir=FLAGS.model_dir,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
tpu_config=tpu_config,
)
# Eval only runs on CPU or GPU host with batch_size = 1.
# Override the default options: disable randomization in the input pipeline
# and don't run on the TPU.
# Also, disable use_bfloat16 for eval on CPU/GPU.
eval_params = dict(
params,
use_tpu=False,
input_rand_hflip=False,
is_training_bn=False,
use_bfloat16=False,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=False,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
def terminate_eval():
logging.info('Terminating eval after %d seconds of no checkpoints',
FLAGS.eval_timeout)
return True
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=FLAGS.min_eval_interval,
timeout=FLAGS.eval_timeout,
timeout_fn=terminate_eval):
logging.info('Starting to evaluate.')
try:
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(
FLAGS.validation_file_pattern, is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
logging.info('Eval results: %s', eval_results)
# Terminate eval job when final checkpoint is reached.
try:
current_step = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
logging.info('%s has no global step info: stop!', ckpt)
break
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
total_step = int(
(FLAGS.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size)
if current_step >= total_step:
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.
logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
elif FLAGS.mode == 'train_and_eval':
for cycle in range(FLAGS.num_epochs):
logging.info('Starting training cycle, epoch: %d.', cycle)
train_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
config=run_config,
params=params)
train_estimator.train(input_fn=dataloader.InputReader(
FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data),
steps=int(FLAGS.num_examples_per_epoch /
FLAGS.train_batch_size))
logging.info('Starting evaluation cycle, epoch: %d.', cycle)
# Run evaluation after every epoch.
eval_params = dict(
params,
use_tpu=False,
input_rand_hflip=False,
is_training_bn=False,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=False,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
logging.info('Evaluation results: %s', eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
pass
else:
logging.info('Mode not found.')
def main(_):
if FLAGS.strategy == 'tpu':
tf.disable_eager_execution()
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
tpu_grpc_url = tpu_cluster_resolver.get_master()
tf.Session.reset(tpu_grpc_url)
else:
tpu_cluster_resolver = None
# Check data path
if FLAGS.mode in ('train', 'train_and_eval'):
if FLAGS.training_file_pattern is None:
raise RuntimeError(
'Must specify --training_file_pattern for train.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError(
'Must specify --validation_file_pattern for eval.')
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
if FLAGS.num_epochs: # NOTE: remove this flag after updating all docs.
config.num_epochs = FLAGS.num_epochs
# Parse image size in case it is in string format.
config.image_size = utils.parse_image_size(config.image_size)
# The following is for spatial partitioning. `features` has one tensor while
# `labels` had 4 + (`max_level` - `min_level` + 1) * 2 tensors. The input
# partition is performed on `features` and all partitionable tensors of
# `labels`, see the partition logic below.
# In the TPUEstimator context, the meaning of `shard` and `replica` is the
# same; follwing the API, here has mixed use of both.
if FLAGS.use_spatial_partition:
# Checks input_partition_dims agrees with num_cores_per_replica.
if FLAGS.num_cores_per_replica != np.prod(FLAGS.input_partition_dims):
raise RuntimeError(
'--num_cores_per_replica must be a product of array'
'elements in --input_partition_dims.')
labels_partition_dims = {
'mean_num_positives': None,
'source_ids': None,
'groundtruth_data': None,
'image_scales': None,
'image_masks': None,
}
# The Input Partition Logic: We partition only the partition-able tensors.
feat_sizes = utils.get_feat_sizes(config.get('image_size'),
config.get('max_level'))
for level in range(config.get('min_level'),
config.get('max_level') + 1):
def _can_partition(spatial_dim):
partitionable_index = np.where(
spatial_dim % np.array(FLAGS.input_partition_dims) == 0)
return len(partitionable_index[0]) == len(
FLAGS.input_partition_dims)
spatial_dim = feat_sizes[level]
if _can_partition(spatial_dim['height']) and _can_partition(
spatial_dim['width']):
labels_partition_dims['box_targets_%d' %
level] = FLAGS.input_partition_dims
labels_partition_dims['cls_targets_%d' %
level] = FLAGS.input_partition_dims
else:
labels_partition_dims['box_targets_%d' % level] = None
labels_partition_dims['cls_targets_%d' % level] = None
num_cores_per_replica = FLAGS.num_cores_per_replica
input_partition_dims = [
FLAGS.input_partition_dims, labels_partition_dims
]
num_shards = FLAGS.num_cores // num_cores_per_replica
else:
num_cores_per_replica = None
input_partition_dims = None
num_shards = FLAGS.num_cores
params = dict(config.as_dict(),
model_name=FLAGS.model_name,
iterations_per_loop=FLAGS.iterations_per_loop,
model_dir=FLAGS.model_dir,
num_shards=num_shards,
num_examples_per_epoch=FLAGS.num_examples_per_epoch,
strategy=FLAGS.strategy,
backbone_ckpt=FLAGS.backbone_ckpt,
ckpt=FLAGS.ckpt,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
mode=FLAGS.mode)
config_proto = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
if FLAGS.strategy != 'tpu':
if FLAGS.use_xla:
config_proto.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
config_proto.gpu_options.allow_growth = True
model_dir = FLAGS.model_dir
strategy = None
if FLAGS.strategy == 'tpu':
tpu_config = tf.estimator.tpu.TPUConfig(
FLAGS.iterations_per_loop if FLAGS.strategy == 'tpu' else 1,
num_cores_per_replica=num_cores_per_replica,
input_partition_dims=input_partition_dims,
per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig.
PER_HOST_V2)
run_config = tf.estimator.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=model_dir,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
tpu_config=tpu_config,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tf_random_seed=FLAGS.tf_random_seed,
)
else:
if FLAGS.strategy == 'gpus':
strategy = tf.distribute.MirroredStrategy()
run_config = tf.estimator.RunConfig(
model_dir=model_dir,
train_distribute=strategy,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tf_random_seed=FLAGS.tf_random_seed,
)
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
max_instances_per_image = config.max_instances_per_image
eval_steps = int(FLAGS.eval_samples // FLAGS.eval_batch_size)
total_examples = int(config.num_epochs * FLAGS.num_examples_per_epoch)
train_steps = total_examples // FLAGS.train_batch_size
logging.info(params)
with tf.io.gfile.GFile(os.path.join(model_dir, 'config.yaml'), 'w') as f:
f.write(str(config))
train_input_fn = dataloader.InputReader(
FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data,
max_instances_per_image=max_instances_per_image)
eval_input_fn = dataloader.InputReader(
FLAGS.validation_file_pattern,
is_training=False,
use_fake_data=FLAGS.use_fake_data,
max_instances_per_image=max_instances_per_image)
if FLAGS.strategy == 'tpu':
estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=params)
else:
params['batch_size'] = (FLAGS.train_batch_size //
getattr(strategy, 'num_replicas_in_sync', 1))
params['num_shards'] = getattr(strategy, 'num_replicas_in_sync', 1)
estimator = tf.estimator.Estimator(model_fn=model_fn_instance,
config=run_config,
params=params)
# start train/eval flow.
if FLAGS.mode == 'train':
estimator.train(input_fn=train_input_fn, max_steps=train_steps)
if FLAGS.eval_after_training:
estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
elif FLAGS.mode == 'eval':
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=FLAGS.min_eval_interval,
timeout=FLAGS.eval_timeout):
logging.info('Starting to evaluate.')
try:
eval_results = estimator.evaluate(eval_input_fn,
steps=eval_steps)
# Terminate eval job when final checkpoint is reached.
try:
current_step = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
logging.info('%s has no global step info: stop!', ckpt)
break
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
if current_step >= train_steps:
logging.info('Eval finished step %d/%d', current_step,
train_steps)
break
except tf.errors.NotFoundError:
# Checkpoint might be not already deleted by the time eval finished.
# We simply skip ssuch case.
logging.info('Checkpoint %s no longer exists, skipping.', ckpt)
elif FLAGS.mode == 'train_and_eval':
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=train_steps)
eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn,
steps=eval_steps,
throttle_secs=600)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
else:
logging.info('Invalid mode: %s', FLAGS.mode)
def main(argv):
del argv # Unused.
# if given an efficentdet ckpt don't use default backbone ckpt
if FLAGS.backbone_ckpt == BACKBONE_CKPT_DEFAULT_DIR and FLAGS.ckpt is not None:
print("Using ckpt flag: {}, ignoring default backbone_ckpt: {}".format(
FLAGS.ckpt, FLAGS.backbone_ckpt))
FLAGS.backbone_ckpt = None
if FLAGS.use_horovod is not None:
if FLAGS.dump_all_ranks:
FLAGS.model_dir += "/worker_" + str(hvd.rank())
if not 'HOROVOD_CYCLE_TIME' in os.environ:
os.environ['HOROVOD_CYCLE_TIME'] = '0.5'
if not 'HABANA_HCCL_COMM_API' in os.environ:
os.environ['HABANA_HCCL_COMM_API'] = '0'
hvd_init()
if not FLAGS.no_hpu:
from habana_frameworks.tensorflow import load_habana_module
load_habana_module()
if FLAGS.use_horovod:
assert (horovod_enabled())
set_env(use_amp=FLAGS.use_amp)
# deterministic setting
if FLAGS.sbs_test or FLAGS.deterministic:
set_deterministic()
# Check data path
if FLAGS.mode in (
'train', 'train_and_eval') and FLAGS.training_file_pattern is None:
raise RuntimeError(
'You must specify --training_file_pattern for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError('You must specify --validation_file_pattern '
'for evaluation.')
if not FLAGS.val_json_file and not FLAGS.testdev_dir:
raise RuntimeError(
'You must specify --val_json_file or --testdev for evaluation.'
)
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
# The following is for spatial partitioning. `features` has one tensor while
# `labels` had 4 + (`max_level` - `min_level` + 1) * 2 tensors. The input
# partition is performed on `features` and all partitionable tensors of
# `labels`, see the partition logic below.
# In the TPUEstimator context, the meaning of `shard` and `replica` is the
# same; follwing the API, here has mixed use of both.
if FLAGS.use_spatial_partition:
# Checks input_partition_dims agrees with num_cores_per_replica.
if FLAGS.num_cores_per_replica != np.prod(FLAGS.input_partition_dims):
raise RuntimeError(
'--num_cores_per_replica must be a product of array'
'elements in --input_partition_dims.')
labels_partition_dims = {
'mean_num_positives': None,
'source_ids': None,
'groundtruth_data': None,
'image_scales': None,
}
# The Input Partition Logic: We partition only the partition-able tensors.
# Spatial partition requires that the to-be-partitioned tensors must have a
# dimension that is a multiple of `partition_dims`. Depending on the
# `partition_dims` and the `image_size` and the `max_level` in config, some
# high-level anchor labels (i.e., `cls_targets` and `box_targets`) cannot
# be partitioned. For example, when `partition_dims` is [1, 4, 2, 1], image
# size is 1536, `max_level` is 9, `cls_targets_8` has a shape of
# [batch_size, 6, 6, 9], which cannot be partitioned (6 % 4 != 0). In this
# case, the level-8 and level-9 target tensors are not partition-able, and
# the highest partition-able level is 7.
image_size = config.get('image_size')
for level in range(config.get('min_level'),
config.get('max_level') + 1):
def _can_partition(spatial_dim):
partitionable_index = np.where(
spatial_dim % np.array(FLAGS.input_partition_dims) == 0)
return len(partitionable_index[0]) == len(
FLAGS.input_partition_dims)
spatial_dim = image_size // (2**level)
if _can_partition(spatial_dim):
labels_partition_dims['box_targets_%d' %
level] = FLAGS.input_partition_dims
labels_partition_dims['cls_targets_%d' %
level] = FLAGS.input_partition_dims
else:
labels_partition_dims['box_targets_%d' % level] = None
labels_partition_dims['cls_targets_%d' % level] = None
num_cores_per_replica = FLAGS.num_cores_per_replica
input_partition_dims = [
FLAGS.input_partition_dims, labels_partition_dims
]
num_shards = FLAGS.num_cores // num_cores_per_replica
else:
num_cores_per_replica = None
input_partition_dims = None
num_shards = FLAGS.num_cores
if horovod_enabled():
num_shards = hvd.size()
else:
num_shards = 1
params = build_estimator_params('train', config, num_shards)
# disabling input data scaling/flip manipulations.
if FLAGS.sbs_test:
sbs_params = dict(input_rand_hflip=False,
train_scale_min=1,
train_scale_max=1,
dropout_rate=0.0)
params.update(sbs_params)
tf_random_seed = 0 if FLAGS.deterministic else None
run_config = build_estimator_config('train', config, num_shards,
num_cores_per_replica,
input_partition_dims)
write_hparams_v1(FLAGS.model_dir, {
'batch_size': FLAGS.train_batch_size,
**FLAGS.flag_values_dict()
})
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
# TPU Estimator
logging.info(params)
if FLAGS.mode == 'train':
train_estimator = HorovodEstimator(model_fn=model_fn_instance,
model_dir=FLAGS.model_dir,
config=run_config,
params=params)
# for deterministic input, we pass to dataloader False for not manipulating input data
is_training = not FLAGS.deterministic
use_fake_data = FLAGS.use_fake_data or FLAGS.deterministic
input_fn = dataloader.InputReader(FLAGS.training_file_pattern,
is_training=is_training,
params=params,
use_fake_data=use_fake_data,
is_deterministic=FLAGS.deterministic)
max_steps = int((FLAGS.num_epochs * FLAGS.num_examples_per_epoch) /
(FLAGS.train_batch_size * num_shards)) + 1
# for sbs test, train under sbs callbacks
if FLAGS.sbs_test:
from TensorFlow.common.debug import dump_callback
SBS_TEST_CONFIG = os.path.join(
os.environ['TF_TESTS_ROOT'],
"tests/tf_training_tests/side_by_side/topologies/efficientdet/dump_config.json"
)
with dump_callback(SBS_TEST_CONFIG):
train_estimator.train(input_fn=input_fn, max_steps=max_steps)
else:
if FLAGS.ckpt is not None:
train_estimator.train(input_fn=input_fn, steps=max_steps)
else:
train_estimator.train(input_fn=input_fn, max_steps=max_steps)
elif FLAGS.mode == 'eval':
eval_params = build_estimator_params('eval', config, num_shards)
eval_config = build_estimator_config('eval', config, num_shards,
num_cores_per_replica,
input_partition_dims)
# Eval only runs on CPU or GPU host with batch_size = 1.
# Override the default options: disable randomization in the input pipeline
# and don't run on the TPU.
# Also, disable use_bfloat16 for eval on CPU/GPU.
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=False,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=eval_config,
params=eval_params)
def terminate_eval():
logging.info('Terminating eval after %d seconds of no checkpoints',
FLAGS.eval_timeout)
return True
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=FLAGS.min_eval_interval,
timeout=FLAGS.eval_timeout,
timeout_fn=terminate_eval):
logging.info('Starting to evaluate.')
try:
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(
FLAGS.validation_file_pattern, is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
logging.info('Eval results: %s', eval_results)
# Terminate eval job when final checkpoint is reached.
try:
current_step = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
logging.info('%s has no global step info: stop!', ckpt)
break
write_summary(eval_results, ckpt, current_step)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
total_step = int(
(FLAGS.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size)
if current_step >= total_step:
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.
logging.info(
'Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
elif FLAGS.mode == 'train_and_eval':
train_params = build_estimator_params('train', config, num_shards)
train_config = build_estimator_config('train', config, num_shards,
num_cores_per_replica,
input_partition_dims)
train_estimator = HorovodEstimator(model_fn=model_fn_instance,
model_dir=FLAGS.model_dir,
config=train_config,
params=train_params)
eval_estimator = None
for cycle in range(FLAGS.num_epochs):
logging.info('Starting training cycle, epoch: %d.', cycle)
train_estimator.train(
input_fn=dataloader.InputReader(
FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data),
max_steps=(cycle + 1) *
int(FLAGS.num_examples_per_epoch / FLAGS.train_batch_size))
# synchronization point for all ranks
if horovod_enabled():
hvd.allreduce(tf.constant(0))
logging.info('Starting evaluation cycle, epoch: %d.', cycle)
# Run evaluation after every epoch.
if eval_estimator is None:
eval_params = build_estimator_params('eval', config,
num_shards)
eval_config = build_estimator_config('eval', config,
num_shards,
num_cores_per_replica,
input_partition_dims)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=False,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=eval_config,
params=eval_params)
if is_rank0():
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(
FLAGS.validation_file_pattern, is_training=False),
steps=FLAGS.eval_samples // FLAGS.eval_batch_size)
checkpoint_path = Path(FLAGS.model_dir)
last_ckpt = tf.train.latest_checkpoint(str(checkpoint_path),
latest_filename=None)
current_step = int(os.path.basename(last_ckpt).split('-')[1])
write_summary(eval_results, FLAGS.model_dir, current_step)
logging.info('Evaluation results: %s', eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
pass
else:
logging.info('Mode not found.')
def main(_):
if FLAGS.strategy == 'horovod':
import horovod.tensorflow as hvd # pylint: disable=g-import-not-at-top
logging.info('Use horovod with multi gpus')
hvd.init()
os.environ['CUDA_VISIBLE_DEVICES'] = str(hvd.local_rank())
import tensorflow.compat.v1 as tf # pylint: disable=g-import-not-at-top
tf.enable_v2_tensorshape()
tf.disable_eager_execution()
if FLAGS.strategy == 'tpu':
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
tpu_grpc_url = tpu_cluster_resolver.get_master()
tf.Session.reset(tpu_grpc_url)
else:
tpu_cluster_resolver = None
# Check data path
if FLAGS.mode in (
'train', 'train_and_eval') and FLAGS.training_file_pattern is None:
raise RuntimeError(
'You must specify --training_file_pattern for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError('You must specify --validation_file_pattern '
'for evaluation.')
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
if FLAGS.num_epochs: # NOTE: remove this flag after updating all docs.
config.num_epochs = FLAGS.num_epochs
# Parse image size in case it is in string format.
config.image_size = utils.parse_image_size(config.image_size)
# The following is for spatial partitioning. `features` has one tensor while
# `labels` had 4 + (`max_level` - `min_level` + 1) * 2 tensors. The input
# partition is performed on `features` and all partitionable tensors of
# `labels`, see the partition logic below.
# In the TPUEstimator context, the meaning of `shard` and `replica` is the
# same; follwing the API, here has mixed use of both.
if FLAGS.use_spatial_partition:
# Checks input_partition_dims agrees with num_cores_per_replica.
if FLAGS.num_cores_per_replica != np.prod(FLAGS.input_partition_dims):
raise RuntimeError(
'--num_cores_per_replica must be a product of array'
'elements in --input_partition_dims.')
labels_partition_dims = {
'mean_num_positives': None,
'source_ids': None,
'groundtruth_data': None,
'image_scales': None,
}
# The Input Partition Logic: We partition only the partition-able tensors.
# Spatial partition requires that the to-be-partitioned tensors must have a
# dimension that is a multiple of `partition_dims`. Depending on the
# `partition_dims` and the `image_size` and the `max_level` in config, some
# high-level anchor labels (i.e., `cls_targets` and `box_targets`) cannot
# be partitioned. For example, when `partition_dims` is [1, 4, 2, 1], image
# size is 1536, `max_level` is 9, `cls_targets_8` has a shape of
# [batch_size, 6, 6, 9], which cannot be partitioned (6 % 4 != 0). In this
# case, the level-8 and level-9 target tensors are not partition-able, and
# the highest partition-able level is 7.
feat_sizes = utils.get_feat_sizes(config.get('image_size'),
config.get('max_level'))
for level in range(config.get('min_level'),
config.get('max_level') + 1):
def _can_partition(spatial_dim):
partitionable_index = np.where(
spatial_dim % np.array(FLAGS.input_partition_dims) == 0)
return len(partitionable_index[0]) == len(
FLAGS.input_partition_dims)
spatial_dim = feat_sizes[level]
if _can_partition(spatial_dim['height']) and _can_partition(
spatial_dim['width']):
labels_partition_dims['box_targets_%d' %
level] = FLAGS.input_partition_dims
labels_partition_dims['cls_targets_%d' %
level] = FLAGS.input_partition_dims
else:
labels_partition_dims['box_targets_%d' % level] = None
labels_partition_dims['cls_targets_%d' % level] = None
num_cores_per_replica = FLAGS.num_cores_per_replica
input_partition_dims = [
FLAGS.input_partition_dims, labels_partition_dims
]
num_shards = FLAGS.num_cores // num_cores_per_replica
else:
num_cores_per_replica = None
input_partition_dims = None
num_shards = FLAGS.num_cores
params = dict(config.as_dict(),
model_name=FLAGS.model_name,
iterations_per_loop=FLAGS.iterations_per_loop,
model_dir=FLAGS.model_dir,
num_shards=num_shards,
num_examples_per_epoch=FLAGS.num_examples_per_epoch,
strategy=FLAGS.strategy,
backbone_ckpt=FLAGS.backbone_ckpt,
ckpt=FLAGS.ckpt,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
mode=FLAGS.mode)
config_proto = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
if FLAGS.strategy != 'tpu':
if FLAGS.use_xla:
config_proto.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
config_proto.gpu_options.allow_growth = True
tpu_config = tf.estimator.tpu.TPUConfig(
FLAGS.iterations_per_loop if FLAGS.strategy == 'tpu' else 1,
num_cores_per_replica=num_cores_per_replica,
input_partition_dims=input_partition_dims,
per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig.
PER_HOST_V2)
if FLAGS.strategy == 'horovod':
model_dir = FLAGS.model_dir if hvd.rank() == 0 else None
else:
model_dir = FLAGS.model_dir
run_config = tf.estimator.tpu.RunConfig(
cluster=tpu_cluster_resolver,
model_dir=model_dir,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
tpu_config=tpu_config,
tf_random_seed=FLAGS.tf_random_seed,
)
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
max_instances_per_image = config.max_instances_per_image
eval_steps = int(FLAGS.eval_samples // FLAGS.eval_batch_size)
use_tpu = (FLAGS.strategy == 'tpu')
logging.info(params)
def _train(steps):
"""Build train estimator and run training if steps > 0."""
train_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=use_tpu,
train_batch_size=FLAGS.train_batch_size,
config=run_config,
params=params)
train_estimator.train(input_fn=dataloader.InputReader(
FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data,
max_instances_per_image=max_instances_per_image),
max_steps=steps)
def _eval(steps):
"""Build estimator and eval the latest checkpoint if steps > 0."""
eval_params = dict(
params,
strategy=FLAGS.strategy,
input_rand_hflip=False,
is_training_bn=False,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
eval_results = eval_estimator.evaluate(input_fn=dataloader.InputReader(
FLAGS.validation_file_pattern,
is_training=False,
max_instances_per_image=max_instances_per_image),
steps=steps,
name=FLAGS.eval_name)
logging.info('Evaluation results: %s', eval_results)
return eval_results
# start train/eval flow.
if FLAGS.mode == 'train':
total_examples = int(config.num_epochs * FLAGS.num_examples_per_epoch)
_train(total_examples // FLAGS.train_batch_size)
if FLAGS.eval_after_training:
_eval(eval_steps)
elif FLAGS.mode == 'eval':
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=FLAGS.min_eval_interval,
timeout=FLAGS.eval_timeout):
logging.info('Starting to evaluate.')
try:
eval_results = _eval(eval_steps)
# Terminate eval job when final checkpoint is reached.
try:
current_step = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
logging.info('%s has no global step info: stop!', ckpt)
break
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
total_step = int(
(config.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size)
if current_step >= total_step:
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.
logging.info('Checkpoint %s no longer exists, skipping.', ckpt)
elif FLAGS.mode == 'train_and_eval':
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
try:
step = int(os.path.basename(ckpt).split("-")[1])
current_epoch = (step * FLAGS.train_batch_size //
FLAGS.num_examples_per_epoch)
logging.info('found ckpt at step %d (epoch %d)', step,
current_epoch)
except (IndexError, TypeError):
logging.info("Folder has no ckpt with valid step.",
FLAGS.model_dir)
current_epoch = 0
epochs_per_cycle = 1 # higher number has less graph construction overhead.
for e in range(current_epoch + 1, config.num_epochs + 1,
epochs_per_cycle):
print('-----------------------------------------------------\n'
'=====> Starting training, epoch: %d.' % e)
_train(e * FLAGS.num_examples_per_epoch // FLAGS.train_batch_size)
print('-----------------------------------------------------\n'
'=====> Starting evaluation, epoch: %d.' % e)
eval_results = _eval(eval_steps)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
else:
logging.info('Invalid mode: %s', FLAGS.mode)
def main(argv):
assert len(argv) >= 1
if len(argv) > 1: # Do not accept unknown args.
raise ValueError('Received unknown arguments: {}'.format(argv[1:]))
if FLAGS.use_tpu:
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu,
zone=FLAGS.tpu_zone,
project=FLAGS.gcp_project)
tpu_grpc_url = tpu_cluster_resolver.get_master()
tf.Session.reset(tpu_grpc_url)
else:
tpu_cluster_resolver = None
# Check data path
if FLAGS.mode in ('train',
'train_and_eval') and FLAGS.training_file_pattern is None:
raise RuntimeError('You must specify --training_file_pattern for training.')
if FLAGS.mode in ('eval', 'train_and_eval'):
if FLAGS.validation_file_pattern is None:
raise RuntimeError('You must specify --validation_file_pattern '
'for evaluation.')
# Parse and override hparams
config = hparams_config.get_detection_config(FLAGS.model_name)
config.override(FLAGS.hparams)
if FLAGS.num_epochs: # NOTE: remove this flag after updating all docs.
config.num_epochs = FLAGS.num_epochs
# Parse image size in case it is in string format.
config.image_size = utils.parse_image_size(config.image_size)
# The following is for spatial partitioning. `features` has one tensor while
# `labels` had 4 + (`max_level` - `min_level` + 1) * 2 tensors. The input
# partition is performed on `features` and all partitionable tensors of
# `labels`, see the partition logic below.
# In the TPUEstimator context, the meaning of `shard` and `replica` is the
# same; follwing the API, here has mixed use of both.
if FLAGS.use_spatial_partition:
# Checks input_partition_dims agrees with num_cores_per_replica.
if FLAGS.num_cores_per_replica != np.prod(FLAGS.input_partition_dims):
raise RuntimeError('--num_cores_per_replica must be a product of array'
'elements in --input_partition_dims.')
labels_partition_dims = {
'mean_num_positives': None,
'source_ids': None,
'groundtruth_data': None,
'image_scales': None,
}
# The Input Partition Logic: We partition only the partition-able tensors.
# Spatial partition requires that the to-be-partitioned tensors must have a
# dimension that is a multiple of `partition_dims`. Depending on the
# `partition_dims` and the `image_size` and the `max_level` in config, some
# high-level anchor labels (i.e., `cls_targets` and `box_targets`) cannot
# be partitioned. For example, when `partition_dims` is [1, 4, 2, 1], image
# size is 1536, `max_level` is 9, `cls_targets_8` has a shape of
# [batch_size, 6, 6, 9], which cannot be partitioned (6 % 4 != 0). In this
# case, the level-8 and level-9 target tensors are not partition-able, and
# the highest partition-able level is 7.
feat_sizes = utils.get_feat_sizes(
config.get('image_size'), config.get('max_level'))
for level in range(config.get('min_level'), config.get('max_level') + 1):
def _can_partition(spatial_dim):
partitionable_index = np.where(
spatial_dim % np.array(FLAGS.input_partition_dims) == 0)
return len(partitionable_index[0]) == len(FLAGS.input_partition_dims)
spatial_dim = feat_sizes[level]
if _can_partition(spatial_dim['height']) and _can_partition(
spatial_dim['width']):
labels_partition_dims['box_targets_%d' %
level] = FLAGS.input_partition_dims
labels_partition_dims['cls_targets_%d' %
level] = FLAGS.input_partition_dims
else:
labels_partition_dims['box_targets_%d' % level] = None
labels_partition_dims['cls_targets_%d' % level] = None
num_cores_per_replica = FLAGS.num_cores_per_replica
input_partition_dims = [
FLAGS.input_partition_dims, labels_partition_dims]
num_shards = FLAGS.num_cores // num_cores_per_replica
else:
num_cores_per_replica = None
input_partition_dims = None
num_shards = FLAGS.num_cores
params = dict(
config.as_dict(),
model_name=FLAGS.model_name,
iterations_per_loop=FLAGS.iterations_per_loop,
model_dir=FLAGS.model_dir,
num_shards=num_shards,
num_examples_per_epoch=FLAGS.num_examples_per_epoch,
use_tpu=FLAGS.use_tpu,
backbone_ckpt=FLAGS.backbone_ckpt,
ckpt=FLAGS.ckpt,
val_json_file=FLAGS.val_json_file,
testdev_dir=FLAGS.testdev_dir,
mode=FLAGS.mode,
)
config_proto = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)
if FLAGS.use_xla and not FLAGS.use_tpu:
config_proto.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
tpu_config = tf.estimator.tpu.TPUConfig(
FLAGS.iterations_per_loop,
num_shards=num_shards,
num_cores_per_replica=num_cores_per_replica,
input_partition_dims=input_partition_dims,
per_host_input_for_training=tf.estimator.tpu.InputPipelineConfig
.PER_HOST_V2)
run_config = tf.estimator.tpu.RunConfig(
cluster=tpu_cluster_resolver,
evaluation_master=FLAGS.eval_master,
model_dir=FLAGS.model_dir,
log_step_count_steps=FLAGS.iterations_per_loop,
session_config=config_proto,
tpu_config=tpu_config,
tf_random_seed=FLAGS.tf_random_seed,
)
model_fn_instance = det_model_fn.get_model_fn(FLAGS.model_name)
# TPU Estimator
logging.info(params)
if FLAGS.mode == 'train':
train_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
config=run_config,
params=params)
train_estimator.train(
input_fn=dataloader.InputReader(FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data),
max_steps=int((config.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size))
if FLAGS.eval_after_training:
# Run evaluation after training finishes.
eval_params = dict(
params,
use_tpu=FLAGS.use_tpu,
input_rand_hflip=False,
is_training_bn=False,
precision=None,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
is_training=False),
steps=FLAGS.eval_samples//FLAGS.eval_batch_size)
logging.info('Eval results: %s', eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
elif FLAGS.mode == 'eval':
# Eval only runs on CPU or GPU host with batch_size = 1.
# Override the default options: disable randomization in the input pipeline
# and don't run on the TPU.
eval_params = dict(
params,
use_tpu=FLAGS.use_tpu,
input_rand_hflip=False,
is_training_bn=False,
precision=None,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
def terminate_eval():
logging.info('Terminating eval after %d seconds of no checkpoints',
FLAGS.eval_timeout)
return True
# Run evaluation when there's a new checkpoint
for ckpt in tf.train.checkpoints_iterator(
FLAGS.model_dir,
min_interval_secs=FLAGS.min_eval_interval,
timeout=FLAGS.eval_timeout,
timeout_fn=terminate_eval):
logging.info('Starting to evaluate.')
try:
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
is_training=False),
steps=FLAGS.eval_samples//FLAGS.eval_batch_size)
logging.info('Eval results: %s', eval_results)
# Terminate eval job when final checkpoint is reached.
try:
current_step = int(os.path.basename(ckpt).split('-')[1])
except IndexError:
logging.info('%s has no global step info: stop!', ckpt)
break
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
total_step = int((config.num_epochs * FLAGS.num_examples_per_epoch) /
FLAGS.train_batch_size)
if current_step >= total_step:
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.
logging.info('Checkpoint %s no longer exists, skipping checkpoint',
ckpt)
elif FLAGS.mode == 'train_and_eval':
# resumeした場合、global_stepにstep数が入っていて、cycleは関係がない
for cycle in range(config.num_epochs):
logging.info('Starting training cycle, epoch: %d.', cycle)
train_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
config=run_config,
params=params)
train_estimator.train(
input_fn=dataloader.InputReader(FLAGS.training_file_pattern,
is_training=True,
use_fake_data=FLAGS.use_fake_data),
steps=int(FLAGS.num_examples_per_epoch / FLAGS.train_batch_size))
logging.info('Starting evaluation cycle, epoch: %d.', cycle)
# Run evaluation after every epoch.
eval_params = dict(
params,
use_tpu=FLAGS.use_tpu,
input_rand_hflip=False,
is_training_bn=False,
)
eval_estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn_instance,
use_tpu=FLAGS.use_tpu,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
config=run_config,
params=eval_params)
eval_results = eval_estimator.evaluate(
input_fn=dataloader.InputReader(FLAGS.validation_file_pattern,
is_training=False),
steps=FLAGS.eval_samples//FLAGS.eval_batch_size)
logging.info('Evaluation results: %s', eval_results)
ckpt = tf.train.latest_checkpoint(FLAGS.model_dir)
logging.info(f'save_checkpoint_start for epoch: {cycle}')
now = datetime.now().strftime('%Y%m%d%H%M%S')
with open('/tmp/main_inner.log', 'a') as f:
f.write(f'{now}: save_checkpoint_start {cycle}\n')
utils.archive_ckpt(eval_results, eval_results['AP'], ckpt)
with open('/tmp/main_inner.log', 'a') as f:
f.write(f'{now}: save_checkpoint_end {cycle}\n')
logging.info(f'save_checkpoint_end for epoch: {cycle}')
else:
logging.info('Mode not found.')
