def main(argv):
arg_parser = argparse.ArgumentParser(description='Dump search scores and other info to HDF file.')
arg_parser.add_argument('config', help="filename to config-file")
arg_parser.add_argument("--dataset", default="config:train")
arg_parser.add_argument("--epoch", type=int, default=-1, help="-1 for last epoch")
arg_parser.add_argument("--output_file", help='hdf', required=True)
arg_parser.add_argument("--rec_layer_name", default="output")
arg_parser.add_argument("--cheating", action="store_true", help="add ground truth to the beam")
arg_parser.add_argument("--att_weights", action="store_true", help="dump all softmax_over_spatial layers")
arg_parser.add_argument("--verbosity", default=4, type=int, help="5 for all seqs (default: 4)")
arg_parser.add_argument("--seq_list", nargs="+", help="use only these seqs")
args, remaining_args = arg_parser.parse_known_args(argv[1:])
init(config_filename=args.config, log_verbosity=args.verbosity, remaining_args=remaining_args)
dataset = init_dataset(args.dataset)
print("Dataset:")
pprint(dataset)
if args.seq_list:
dataset.seq_tags_filter = set(args.seq_list)
dataset.partition_epoch = 1 # reset
if isinstance(dataset, MetaDataset):
for sub_dataset in dataset.datasets.values():
dataset.seq_tags_filter = set(args.seq_list)
sub_dataset.partition_epoch = 1
dataset.finish_epoch() # enforce reset
if dataset.seq_tags_filter is not None:
print("Using sequences:")
pprint(dataset.seq_tags_filter)
if args.epoch >= 1:
config.set("load_epoch", args.epoch)
def net_dict_post_proc(net_dict):
"""
:param dict[str] net_dict:
:return: net_dict
:rtype: dict[str]
"""
prepare_compile(
rec_layer_name=args.rec_layer_name, net_dict=net_dict,
cheating=args.cheating, dump_att_weights=args.att_weights,
hdf_filename=args.output_file, possible_labels=dataset.labels)
return net_dict
engine = Engine(config=config)
engine.use_search_flag = True
engine.init_network_from_config(config, net_dict_post_proc=net_dict_post_proc)
engine.search(
dataset,
do_eval=config.bool("search_do_eval", True),
output_layer_names=args.rec_layer_name)
engine.finalize()
print("Search finished.")
assert os.path.exists(args.output_file), "hdf file not dumped?"
def benchmark(lstm_unit, use_gpu):
"""
:param str lstm_unit: e.g. "LSTMBlock", one of LstmCellTypes
:param bool use_gpu:
:return: runtime in seconds of the training itself, excluding initialization
:rtype: float
"""
device = {True: "GPU", False: "CPU"}[use_gpu]
key = "%s:%s" % (device, lstm_unit)
print(">>> Start benchmark for %s." % key)
config = Config()
config.update(make_config_dict(lstm_unit=lstm_unit, use_gpu=use_gpu))
dataset_kwargs = config.typed_value("train")
Dataset.kwargs_update_from_config(config, dataset_kwargs)
dataset = init_dataset(dataset_kwargs)
engine = Engine(config=config)
engine.init_train_from_config(config=config, train_data=dataset)
print(">>> Start training now for %s." % key)
start_time = time.time()
engine.train()
runtime = time.time() - start_time
print(">>> Runtime of %s: %s" % (key, hms_fraction(runtime)))
engine.finalize()
return runtime
def benchmark(lstm_unit, use_gpu):
"""
:param str lstm_unit: e.g. "LSTMBlock", one of LstmCellTypes
:param bool use_gpu:
:return: runtime in seconds of the training itself, excluding initialization
:rtype: float
"""
device = {True: "GPU", False: "CPU"}[use_gpu]
key = "%s:%s" % (device, lstm_unit)
print(">>> Start benchmark for %s." % key)
config = Config()
config.update(make_config_dict(lstm_unit=lstm_unit, use_gpu=use_gpu))
dataset_kwargs = config.typed_value("train")
Dataset.kwargs_update_from_config(config, dataset_kwargs)
dataset = init_dataset(dataset_kwargs)
engine = Engine(config=config)
engine.init_train_from_config(config=config, train_data=dataset)
print(">>> Start training now for %s." % key)
start_time = time.time()
engine.train()
runtime = time.time() - start_time
print(">>> Runtime of %s: %s" % (key, hms_fraction(runtime)))
engine.finalize()
return runtime