def pretrainFromConfig(config):
"""
:type config: Config.Config
:rtype: Pretrain | None
"""
pretrainType = config.bool_or_other("pretrain", None)
if pretrainType == "default" or (isinstance(pretrainType, dict)
and pretrainType) or pretrainType is True:
network_init_args = LayerNetwork.init_args_from_config(config)
original_network_json = LayerNetwork.json_from_config(config)
opts = config.get_of_type("pretrain", dict, {})
if config.has("pretrain_copy_output_layer"):
opts.setdefault(
"copy_output_layer",
config.bool_or_other("pretrain_copy_output_layer",
"ifpossible"))
if config.has("pretrain_greedy"):
opts.setdefault("greedy", config.bool("pretrain_greedy", None))
if config.has("pretrain_repetitions"):
if config.is_typed("pretrain_repetitions"):
opts.setdefault("repetitions",
config.typed_value("pretrain_repetitions"))
else:
opts.setdefault("repetitions",
config.int_list("pretrain_repetitions", None))
if config.has("pretrain_construction_algo"):
opts.setdefault("construction_algo",
config.value("pretrain_construction_algo", None))
return Pretrain(original_network_json=original_network_json,
network_init_args=network_init_args,
**opts)
elif not pretrainType:
return None
else:
raise Exception("unknown pretrain type: %s" % pretrainType)
def pretrainFromConfig(config):
"""
:type config: Config.Config
:rtype: Pretrain | None
"""
pretrainType = config.value("pretrain", "")
if pretrainType == "default":
network_init_args = LayerNetwork.init_args_from_config(config)
original_network_json = LayerNetwork.json_from_config(config)
copy_output_layer = config.bool_or_other("pretrain_copy_output_layer", "ifpossible")
greedy = config.bool("pretrain_greedy", None)
if config.is_typed("pretrain_repetitions"):
repetitions = config.typed_value("pretrain_repetitions")
else:
repetitions = config.int_list("pretrain_repetitions", None)
construction_algo = config.value("pretrain_construction_algo", None)
return Pretrain(original_network_json=original_network_json,
network_init_args=network_init_args,
copy_output_layer=copy_output_layer,
greedy=greedy, repetitions=repetitions,
construction_algo=construction_algo)
elif pretrainType == "":
return None
else:
raise Exception("unknown pretrain type: %s" % pretrainType)
def init_network_from_config(self, config):
"""
:param Config.Config config:
"""
self.model_filename = config.value('model', None)
self.pretrain = pretrainFromConfig(config)
self.max_seqs = config.int('max_seqs', -1)
epoch, model_epoch_filename = self.get_epoch_model(config)
assert model_epoch_filename or self.start_epoch
self.epoch = epoch or self.start_epoch
if self.pretrain:
# This would be obsolete if we don't want to load an existing model.
# In self.init_train_epoch(), we initialize a new model.
net_dict = self.pretrain.get_network_json_for_epoch(self.epoch)
else:
net_dict = LayerNetwork.json_from_config(config)
self._init_network(net_desc=net_dict, epoch=self.epoch)
if model_epoch_filename:
print("loading weights from", model_epoch_filename, file=log.v2)
try:
self.network.load_params_from_file(model_epoch_filename, session=self.tf_session)
except tf.errors.NotFoundError:
print("Exiting now because model cannot be loaded.", file=log.v1)
sys.exit(1)
def test_enc_dec1_init(): config = Config() config.load_file(StringIO(config_enc_dec1_json)) network_json = LayerNetwork.json_from_config(config) assert_true(network_json) network = LayerNetwork.from_json_and_config(network_json, config) assert_true(network)
def test_enc_dec1_init():
config = Config()
config.load_file(StringIO(config_enc_dec1_json))
network_json = LayerNetwork.json_from_config(config)
assert_true(network_json)
network = LayerNetwork.from_json_and_config(network_json, config)
assert_true(network)
def init_network_from_config(self, config):
self.pretrain = pretrainFromConfig(config)
self.max_seqs = config.int('max_seqs', -1)
epoch, model_epoch_filename = self.get_epoch_model(config)
assert model_epoch_filename or self.start_epoch
if self.pretrain:
# This would be obsolete if we don't want to load an existing model.
# In self.init_train_epoch(), we initialize a new model.
net_dict = self.pretrain.get_network_json_for_epoch(
epoch or self.start_epoch)
else:
net_dict = LayerNetwork.json_from_config(config)
self._init_network(net_desc=net_dict, epoch=epoch or self.start_epoch)
if model_epoch_filename:
print("loading weights from", model_epoch_filename, file=log.v2)
self.network.load_params_from_file(model_epoch_filename,
session=self.tf_session)
def test_enc_dec1_hdf(): filename = tempfile.mktemp(prefix="crnn-model-test") model = h5py.File(filename, "w") config = Config() config.load_file(StringIO(config_enc_dec1_json)) network_json = LayerNetwork.json_from_config(config) assert_true(network_json) network = LayerNetwork.from_json_and_config(network_json, config) assert_true(network) network.save_hdf(model, epoch=42) model.close() loaded_model = h5py.File(filename, "r") loaded_net = LayerNetwork.from_hdf_model_topology(loaded_model) assert_true(loaded_net) assert_equal(sorted(network.hidden.keys()), sorted(loaded_net.hidden.keys())) assert_equal(sorted(network.y.keys()), sorted(loaded_net.y.keys())) assert_equal(sorted(network.j.keys()), sorted(loaded_net.j.keys())) os.remove(filename)
def pretrainFromConfig(config):
"""
:type config: Config.Config
:rtype: Pretrain | None
"""
pretrainType = config.value("pretrain", "")
if pretrainType == "default":
network_init_args = LayerNetwork.init_args_from_config(config)
original_network_json = LayerNetwork.json_from_config(config)
copy_output_layer = config.bool_or_other("pretrain_copy_output_layer", "ifpossible")
greedy = config.bool("pretrain_greedy", None)
repetitions = config.int_list("pretrain_repetitions", None)
construction_algo = config.value("pretrain_construction_algo", None)
return Pretrain(original_network_json=original_network_json,
network_init_args=network_init_args,
copy_output_layer=copy_output_layer,
greedy=greedy, repetitions=repetitions,
construction_algo=construction_algo)
elif pretrainType == "":
return None
else:
raise Exception("unknown pretrain type: %s" % pretrainType)
def test_enc_dec1_hdf():
filename = tempfile.mktemp(prefix="crnn-model-test")
model = h5py.File(filename, "w")
config = Config()
config.load_file(StringIO(config_enc_dec1_json))
network_json = LayerNetwork.json_from_config(config)
assert_true(network_json)
network = LayerNetwork.from_json_and_config(network_json, config)
assert_true(network)
network.save_hdf(model, epoch=42)
model.close()
loaded_model = h5py.File(filename, "r")
loaded_net = LayerNetwork.from_hdf_model_topology(loaded_model)
assert_true(loaded_net)
assert_equal(sorted(network.hidden.keys()),
sorted(loaded_net.hidden.keys()))
assert_equal(sorted(network.y.keys()), sorted(loaded_net.y.keys()))
assert_equal(sorted(network.j.keys()), sorted(loaded_net.j.keys()))
os.remove(filename)
