def get_model(args, scope):
with tf.variable_scope(scope):
args, base_config, base_model, config_module = get_base_config(args)
checkpoint = check_logdir(args, base_config)
model = create_model(
args, base_config, config_module, base_model, None)
return model, checkpoint
def main():
# Parse args and create config
args, base_config, base_model, config_module = get_base_config(
sys.argv[1:])
if args.mode == "interactive_infer":
raise ValueError(
"Interactive infer is meant to be run from an IPython",
"notebook not from run.py.")
# Initilize Horovod
if base_config['use_horovod']:
import horovod.tensorflow as hvd
hvd.init()
if hvd.rank() == 0:
deco_print("Using horovod")
else:
hvd = None
restore_best_checkpoint = base_config.get('restore_best_checkpoint', False)
# Check logdir and create it if necessary
checkpoint = check_logdir(args, base_config, restore_best_checkpoint)
if args.enable_logs:
if hvd is None or hvd.rank() == 0:
old_stdout, old_stderr, stdout_log, stderr_log = create_logdir(
args, base_config)
base_config['logdir'] = os.path.join(base_config['logdir'], 'logs')
if args.mode == 'train' or args.mode == 'train_eval' or args.benchmark:
if hvd is None or hvd.rank() == 0:
if checkpoint is None or args.benchmark:
deco_print("Starting training from scratch")
else:
deco_print(
"Restored checkpoint from {}. Resuming training".format(
checkpoint), )
elif args.mode == 'eval' or args.mode == 'infer':
if hvd is None or hvd.rank() == 0:
deco_print("Loading model from {}".format(checkpoint))
# Create model and train/eval/infer
with tf.Graph().as_default():
model = create_model(args, base_config, config_module, base_model, hvd)
if args.mode == "train_eval":
train(model[0], model[1], debug_port=args.debug_port)
elif args.mode == "train":
train(model, None, debug_port=args.debug_port)
elif args.mode == "eval":
evaluate(model, checkpoint)
elif args.mode == "infer":
infer(model, checkpoint, args.infer_output_file, args.use_trt)
if args.enable_logs and (hvd is None or hvd.rank() == 0):
sys.stdout = old_stdout
sys.stderr = old_stderr
stdout_log.close()
stderr_log.close()
def _get_model(args, scope):
'''
A simpler version of what run.py does. It returns the created model and its saved checkpoint
'''
with tf.variable_scope(scope):
args, base_config, base_model, config_module = get_base_config(args)
checkpoint = check_logdir(args, base_config)
model = create_model(args, base_config, config_module, base_model, None)
return model, checkpoint
def get_model(args):
args, base_config, base_model, config_module = get_base_config(args)
checkpoint = check_logdir(args, base_config)
# infer_config = copy.deepcopy(base_config)
if args.mode == "interactive_infer":
nested_update(base_config,
copy.deepcopy(config_module['interactive_infer_params']))
model = base_model(params=base_config, mode=args.mode, hvd=None)
model.compile()
return model, checkpoint, base_config["data_layer_params"]
def run():
"""This function executes a saved checkpoint for
50 LibriSpeech dev clean files whose alignments are stored in
calibration/target.json
This function saves a pickle file with logits after running
through the model as calibration/sample.pkl
:return: None
"""
args, base_config, base_model, config_module = get_calibration_config(
sys.argv[1:])
config_module["infer_params"]["data_layer_params"]["dataset_files"] = \
["calibration/sample.csv"]
config_module["base_params"]["decoder_params"][
"infer_logits_to_pickle"] = True
load_model = base_config.get('load_model', None)
restore_best_checkpoint = base_config.get('restore_best_checkpoint', False)
base_ckpt_dir = check_base_model_logdir(load_model, args,
restore_best_checkpoint)
base_config['load_model'] = base_ckpt_dir
# Check logdir and create it if necessary
checkpoint = check_logdir(args, base_config, restore_best_checkpoint)
# Initilize Horovod
if base_config['use_horovod']:
import horovod.tensorflow as hvd
hvd.init()
if hvd.rank() == 0:
deco_print("Using horovod")
from mpi4py import MPI
MPI.COMM_WORLD.Barrier()
else:
hvd = None
if args.enable_logs:
if hvd is None or hvd.rank() == 0:
old_stdout, old_stderr, stdout_log, stderr_log = create_logdir(
args, base_config)
base_config['logdir'] = os.path.join(base_config['logdir'], 'logs')
if args.mode == 'infer':
if hvd is None or hvd.rank() == 0:
deco_print("Loading model from {}".format(checkpoint))
else:
print("Run in infer mode only")
sys.exit()
with tf.Graph().as_default():
model = create_model(args, base_config, config_module, base_model, hvd,
checkpoint)
infer(model, checkpoint, args.infer_output_file)
return args.calibration_out
def get_model(args):
args, base_config, base_model, config_module = get_base_config(args)
checkpoint = check_logdir(args, base_config)
model = create_model(args, base_config, config_module, base_model,
None)
return model, checkpoint
