def init_network_from_config(self, config):
self.pretrain = pretrainFromConfig(config)
self.max_seqs = config.int('max_seqs', -1)
self.compression = config.bool('compression', False)
epoch, model_epoch_filename = self.get_epoch_model(config)
assert model_epoch_filename or self.start_epoch
if model_epoch_filename:
print("loading weights from", model_epoch_filename, file=log.v2)
last_model_hdf = h5py.File(model_epoch_filename, "r")
else:
last_model_hdf = None
if config.bool('initialize_from_model', False):
# That's only about the topology, not the params.
print("initializing network topology from model", file=log.v5)
assert last_model_hdf, "last model not specified. use 'load' in config. or don't use 'initialize_from_model'"
network = LayerNetwork.from_hdf_model_topology(last_model_hdf)
else:
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.
network = self.pretrain.get_network_for_epoch(epoch or self.start_epoch)
else:
network = LayerNetwork.from_config_topology(config)
# We have the parameters randomly initialized at this point.
# In training, as an initialization, we can copy over the params of an imported model,
# where our topology might slightly differ from the imported model.
if config.value('import_model_train_epoch1', '') and self.start_epoch == 1:
assert last_model_hdf
old_network = LayerNetwork.from_hdf_model_topology(last_model_hdf)
old_network.load_hdf(last_model_hdf)
last_model_hdf.close()
# Copy params to new network.
from NetworkCopyUtils import intelli_copy_layer
# network.hidden are the input + all hidden layers.
for layer_name, layer in sorted(old_network.hidden.items()):
print("Copy hidden layer %s" % layer_name, file=log.v3)
intelli_copy_layer(layer, network.hidden[layer_name])
for layer_name, layer in sorted(old_network.output.items()):
print("Copy output layer %s" % layer_name, file=log.v3)
intelli_copy_layer(layer, network.output[layer_name])
print("Not copied hidden: %s" % sorted(set(network.hidden.keys()).difference(old_network.hidden.keys())), file=log.v3)
print("Not copied output: %s" % sorted(set(network.output.keys()).difference(old_network.output.keys())), file=log.v3)
# Maybe load existing model parameters.
elif last_model_hdf:
network.load_hdf(last_model_hdf)
last_model_hdf.close()
EngineUtil.maybe_subtract_priors(network, self.train_data, config)
self.network = network
if config.has('dump_json'):
self.network_dump_json(config.value('dump_json', ''))
self.print_network_info()
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 model_epoch_filename:
print >> log.v2, "loading weights from", model_epoch_filename
last_model_hdf = h5py.File(model_epoch_filename, "r")
else:
last_model_hdf = None
if config.bool('initialize_from_model', False):
# That's only about the topology, not the params.
print >> log.v5, "initializing network topology from model"
assert last_model_hdf, "last model not specified. use 'load' in config. or don't use 'initialize_from_model'"
network = LayerNetwork.from_hdf_model_topology(last_model_hdf)
else:
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.
network = self.pretrain.get_network_for_epoch(epoch or self.start_epoch)
else:
network = LayerNetwork.from_config_topology(config)
# We have the parameters randomly initialized at this point.
# In training, as an initialization, we can copy over the params of an imported model,
# where our topology might slightly differ from the imported model.
if config.value('import_model_train_epoch1', '') and self.start_epoch == 1:
assert last_model_hdf
old_network = LayerNetwork.from_hdf_model_topology(last_model_hdf)
old_network.load_hdf(last_model_hdf)
last_model_hdf.close()
# Copy params to new network.
from NetworkCopyUtils import intelli_copy_layer
# network.hidden are the input + all hidden layers.
for layer_name, layer in sorted(old_network.hidden.items()):
print >> log.v3, "Copy hidden layer %s" % layer_name
intelli_copy_layer(layer, network.hidden[layer_name])
for layer_name, layer in sorted(old_network.output.items()):
print >> log.v3, "Copy output layer %s" % layer_name
intelli_copy_layer(layer, network.output[layer_name])
print >> log.v3, "Not copied hidden: %s" % sorted(set(network.hidden.keys()).difference(old_network.hidden.keys()))
print >> log.v3, "Not copied output: %s" % sorted(set(network.output.keys()).difference(old_network.output.keys()))
# Maybe load existing model parameters.
elif last_model_hdf:
network.load_hdf(last_model_hdf)
last_model_hdf.close()
EngineUtil.maybe_subtract_priors(network, self.train_data, config)
self.network = network
if config.has('dump_json'):
self.network_dump_json(config.value('dump_json', ''))
self.print_network_info()
def saveCrnnNetwork(epoch, layers):
"""
:type epoch: int
:type layers: list[(numpy.ndarray, numpy.ndarray)]
"""
print("Loading Crnn")
from Network import LayerNetwork
from NetworkHiddenLayer import ForwardLayer
from NetworkOutputLayer import OutputLayer
from Pretrain import pretrain_from_config
from EngineBase import EngineBase
pretrain = pretrain_from_config(config)
is_pretrain_epoch = pretrain and epoch <= pretrain.get_train_num_epochs()
modelFilename = config.value("model", None)
assert modelFilename, "need 'model' in config"
filename = EngineBase.epoch_model_filename(modelFilename, epoch,
is_pretrain_epoch)
assert not os.path.exists(filename), "already exists"
if is_pretrain_epoch:
network = pretrain.get_network_for_epoch(epoch)
else:
network = LayerNetwork.from_config_topology(config)
nHiddenLayers = len(network.hidden)
# print network topology
print("Crnn Network layer topology:")
print("input dim:", network.n_in)
print("hidden layer count:", nHiddenLayers)
print("output dim:", network.n_out["classes"])
print("net weights #:", network.num_params())
print("net params:", network.train_params_vars)
print("net output:", network.output["output"])
assert network.n_in == inputDim
#assert network.n_out == outputDim
assert nHiddenLayers + 1 == layerCount # hidden + output layer
assert len(layers) == layerCount
for i, (layerName, hidden) in enumerate(sorted(network.hidden.items())):
# Some checks whether this is a forward-layer.
assert isinstance(hidden, ForwardLayer)
saveCrnnLayer(hidden, *layers[i])
assert isinstance(network.output["output"], OutputLayer)
saveCrnnLayer(network.output["output"], *layers[len(layers) - 1])
import h5py
print(("Save Crnn model under %s" % filename))
model = h5py.File(filename, "w")
network.save_hdf(model, epoch)
model.close()
def saveCrnnNetwork(epoch, layers):
"""
:type epoch: int
:type layers: list[(numpy.ndarray, numpy.ndarray)]
"""
print("Loading Crnn")
from Network import LayerNetwork
from NetworkHiddenLayer import ForwardLayer
from NetworkOutputLayer import OutputLayer
from Pretrain import pretrain_from_config
from EngineBase import EngineBase
pretrain = pretrain_from_config(config)
is_pretrain_epoch = pretrain and epoch <= pretrain.get_train_num_epochs()
modelFilename = config.value("model", None)
assert modelFilename, "need 'model' in config"
filename = EngineBase.epoch_model_filename(modelFilename, epoch, is_pretrain_epoch)
assert not os.path.exists(filename), "already exists"
if is_pretrain_epoch:
network = pretrain.get_network_for_epoch(epoch)
else:
network = LayerNetwork.from_config_topology(config)
nHiddenLayers = len(network.hidden)
# print network topology
print("Crnn Network layer topology:")
print("input dim:", network.n_in)
print("hidden layer count:", nHiddenLayers)
print("output dim:", network.n_out["classes"])
print("net weights #:", network.num_params())
print("net params:", network.train_params_vars)
print("net output:", network.output["output"])
assert network.n_in == inputDim
#assert network.n_out == outputDim
assert nHiddenLayers + 1 == layerCount # hidden + output layer
assert len(layers) == layerCount
for i, (layerName, hidden) in enumerate(sorted(network.hidden.items())):
# Some checks whether this is a forward-layer.
assert isinstance(hidden, ForwardLayer)
saveCrnnLayer(hidden, *layers[i])
assert isinstance(network.output["output"], OutputLayer)
saveCrnnLayer(network.output["output"], *layers[len(layers) - 1])
import h5py
print(("Save Crnn model under %s" % filename))
model = h5py.File(filename, "w")
network.save_hdf(model, epoch)
model.close()
def test_config1_to_json_network_copy():
config = Config()
config.update(config1_dict)
orig_network = LayerNetwork.from_config_topology(config)
orig_json_content = orig_network.to_json_content()
pprint(orig_json_content)
new_network = LayerNetwork.from_json(orig_json_content, orig_network.n_in, orig_network.n_out)
assert_equal(orig_network.n_in, new_network.n_in)
assert_equal(orig_network.n_out, new_network.n_out)
new_json_content = new_network.to_json_content()
if orig_json_content != new_json_content:
print(dict_diff_str(orig_json_content, new_json_content))
assert_equal(orig_json_content, new_network.to_json_content())
def test_config1_to_json_network_copy():
config = Config()
config.update(config1_dict)
orig_network = LayerNetwork.from_config_topology(config)
orig_json_content = orig_network.to_json_content()
pprint(orig_json_content)
new_network = LayerNetwork.from_json(orig_json_content, orig_network.n_in, orig_network.n_out)
assert_equal(orig_network.n_in, new_network.n_in)
assert_equal(orig_network.n_out, new_network.n_out)
new_json_content = new_network.to_json_content()
if orig_json_content != new_json_content:
print(dict_diff_str(orig_json_content, new_json_content))
assert_equal(orig_json_content, new_network.to_json_content())
def test_network_config1_init():
config = Config()
config.update(config1_dict)
network = LayerNetwork.from_config_topology(config)
assert_in("hidden_0", network.hidden)
assert_in("hidden_1", network.hidden)
assert_equal(len(network.hidden), 2)
assert_is_instance(network.hidden["hidden_0"], ForwardLayer)
assert_equal(network.hidden["hidden_0"].layer_class, "hidden")
assert_false(network.recurrent)
json_content = network.to_json_content()
pprint(json_content)
assert_in("hidden_0", json_content)
assert_equal(json_content["hidden_0"]["class"], "hidden")
assert_in("hidden_1", json_content)
assert_in("output", json_content)
def test_config2_bidirect_lstm():
config = Config()
config.update(config2_dict)
desc = LayerNetworkDescription.from_config(config)
assert_true(desc.bidirectional)
network = LayerNetwork.from_config_topology(config)
net_json = network.to_json_content()
pprint(net_json)
assert_in("output", net_json)
assert_in("hidden_0_fw", net_json)
assert_in("hidden_0_bw", net_json)
assert_in("hidden_1_fw", net_json)
assert_in("hidden_1_bw", net_json)
assert_in("hidden_2_fw", net_json)
assert_in("hidden_2_bw", net_json)
assert_equal(net_json["output"]["from"], ["hidden_2_fw", "hidden_2_bw"])
assert_equal(len(net_json), 7)
def test_network_config1_init():
config = Config()
config.update(config1_dict)
network = LayerNetwork.from_config_topology(config)
assert_in("hidden_0", network.hidden)
assert_in("hidden_1", network.hidden)
assert_equal(len(network.hidden), 2)
assert_is_instance(network.hidden["hidden_0"], ForwardLayer)
assert_equal(network.hidden["hidden_0"].layer_class, "hidden")
assert_false(network.recurrent)
json_content = network.to_json_content()
pprint(json_content)
assert_in("hidden_0", json_content)
assert_equal(json_content["hidden_0"]["class"], "hidden")
assert_in("hidden_1", json_content)
assert_in("output", json_content)
def test_config2_bidirect_lstm():
config = Config()
config.update(config2_dict)
desc = LayerNetworkDescription.from_config(config)
assert_true(desc.bidirectional)
network = LayerNetwork.from_config_topology(config)
net_json = network.to_json_content()
pprint(net_json)
assert_in("output", net_json)
assert_in("hidden_0_fw", net_json)
assert_in("hidden_0_bw", net_json)
assert_in("hidden_1_fw", net_json)
assert_in("hidden_1_bw", net_json)
assert_in("hidden_2_fw", net_json)
assert_in("hidden_2_bw", net_json)
assert_equal(net_json["output"]["from"], ["hidden_2_fw", "hidden_2_bw"])
assert_equal(len(net_json), 7)
def main(argv):
argparser = argparse.ArgumentParser(description='Dump network as JSON.')
argparser.add_argument('crnn_config_file')
argparser.add_argument('--epoch', default=1, type=int)
argparser.add_argument('--out', default="/dev/stdout")
args = argparser.parse_args(argv[1:])
init(configFilename=args.crnn_config_file, commandLineOptions=[])
pretrain = pretrainFromConfig(config)
if pretrain:
network = pretrain.get_network_for_epoch(args.epoch)
else:
network = LayerNetwork.from_config_topology(config)
json_data = network.to_json_content()
f = open(args.out, 'w')
print(json.dumps(json_data, indent=2, sort_keys=True), file=f)
f.close()
rnn.finalize()
def main(argv):
argparser = argparse.ArgumentParser(description='Dump network as JSON.')
argparser.add_argument('crnn_config_file')
argparser.add_argument('--epoch', default=1, type=int)
argparser.add_argument('--out', default="/dev/stdout")
args = argparser.parse_args(argv[1:])
init(configFilename=args.crnn_config_file, commandLineOptions=[])
pretrain = pretrainFromConfig(config)
if pretrain:
network = pretrain.get_network_for_epoch(args.epoch)
else:
network = LayerNetwork.from_config_topology(config)
json_data = network.to_json_content()
f = open(args.out, 'w')
print >> f, json.dumps(json_data, indent=2, sort_keys=True)
f.close()
rnn.finalize()
