def run(callbacks=None):
keras_utils.set_session_config(enable_xla=FLAGS.enable_xla)
params = config_factory.config_generator(FLAGS.model)
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.override(
{
'strategy_type': FLAGS.strategy_type,
'model_dir': FLAGS.model_dir,
'strategy_config': executor.strategy_flags_dict(),
},
is_strict=False)
params.validate()
params.lock()
pp = pprint.PrettyPrinter()
params_str = pp.pformat(params.as_dict())
logging.info('Model Parameters: {}'.format(params_str))
train_input_fn = None
eval_input_fn = None
training_file_pattern = FLAGS.training_file_pattern or params.train.train_file_pattern
eval_file_pattern = FLAGS.eval_file_pattern or params.eval.eval_file_pattern
if not training_file_pattern and not eval_file_pattern:
raise ValueError(
'Must provide at least one of training_file_pattern and '
'eval_file_pattern.')
if training_file_pattern:
# Use global batch size for single host.
train_input_fn = input_reader.InputFn(
file_pattern=training_file_pattern,
params=params,
mode=input_reader.ModeKeys.TRAIN,
batch_size=params.train.batch_size)
if eval_file_pattern:
eval_input_fn = input_reader.InputFn(
file_pattern=eval_file_pattern,
params=params,
mode=input_reader.ModeKeys.PREDICT_WITH_GT,
batch_size=params.eval.batch_size,
num_examples=params.eval.eval_samples)
return run_executor(params,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
callbacks=callbacks)
def run(callbacks=None):
keras_utils.set_session_config(enable_xla=FLAGS.enable_xla)
params = config_factory.config_generator(FLAGS.model)
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.override(
{
'strategy_type': FLAGS.strategy_type,
'model_dir': FLAGS.model_dir,
'strategy_config': executor.strategy_flags_dict(),
},
is_strict=False)
# Make sure use_tpu and strategy_type are in sync.
params.use_tpu = (params.strategy_type == 'tpu')
if not params.use_tpu:
params.override(
{
'architecture': {
'use_bfloat16': False,
},
'norm_activation': {
'use_sync_bn': False,
},
},
is_strict=True)
params.validate()
params.lock()
pp = pprint.PrettyPrinter()
params_str = pp.pformat(params.as_dict())
logging.info('Model Parameters: %s', params_str)
train_input_fn = None
eval_input_fn = None
training_file_pattern = FLAGS.training_file_pattern or params.train.train_file_pattern
eval_file_pattern = FLAGS.eval_file_pattern or params.eval.eval_file_pattern
if not training_file_pattern and not eval_file_pattern:
raise ValueError(
'Must provide at least one of training_file_pattern and '
'eval_file_pattern.')
if training_file_pattern:
# Use global batch size for single host.
train_input_fn = input_reader.InputFn(
file_pattern=training_file_pattern,
params=params,
mode=input_reader.ModeKeys.TRAIN,
batch_size=params.train.batch_size)
if eval_file_pattern:
eval_input_fn = input_reader.InputFn(
file_pattern=eval_file_pattern,
params=params,
mode=input_reader.ModeKeys.PREDICT_WITH_GT,
batch_size=params.eval.batch_size,
num_examples=params.eval.eval_samples)
if callbacks is None:
callbacks = []
if FLAGS.log_steps:
callbacks.append(
keras_utils.TimeHistory(
batch_size=params.train.batch_size,
log_steps=FLAGS.log_steps,
))
return run_executor(params,
FLAGS.mode,
checkpoint_path=FLAGS.checkpoint_path,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
callbacks=callbacks)
