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def compute_context_vector(self, prev_state, inputs, precomputed_values=None, mask=None):
"""
Compute the context vector with soft attention.
"""
precomputed_values = precomputed_values if precomputed_values else self.precompute(inputs)
align_weights = self.compute_alignments(pre... |
def train(self, train_set, valid_set=None, test_set=None, train_size=None):
"""
Train the model in multi-GPU environment.
"""
from platoon.channel import Worker
from platoon.param_sync import EASGD, ASGD
server_port = self._port
param_map = self.create_param_map()... |
def concatenate(vars, axis=-1):
"""
A utility function of concatenate.
"""
from deepy.core.neural_var import NeuralVariable
if isinstance(vars[0], NeuralVariable):
concat_var = Concatenate(axis=axis).compute(*vars)
if axis == -1 or axis == vars[0].tensor.ndim - 1:
concat_... |
def var(tensor_type, last_dim=0, test_shape=None):
"""
Wrap a Theano tensor into the variable for defining neural network.
:param last_dim: last dimension of tensor, 0 indicates that the last dimension is flexible
:rtype: deepy.core.neural_var.NeuralVariable
"""
# Create tensor
from deepy.co... |
def _pad(self, side, length):
"""
Pad sequences to given length in the left or right side.
"""
if self._train_set:
self._train_set = pad_dataset(self._train_set, side, length)
if self._valid_set:
self._valid_set = pad_dataset(self._valid_set, side, length)... |
def rmsprop_core(params, gradients, momentum=0.9, learning_rate=0.01):
"""
RMSPROP optimization core.
"""
for param, grad in zip(params, gradients):
rms_ = theano.shared(np.zeros_like(param.get_value()), name=param.name + '_rms')
rms = momentum * rms_ + (1 - momentum) * grad * gr... |
def pad_dataset(subset, side="right", length=-1):
"""
Pad data set to specified length.
Parameters:
length - max length, a just to the max length in the batch if length is -1
"""
assert length == -1 or length > 0
if type(subset[0][0][0]) in [float, int, np.int64, np.int32, np.float32]:
... |
def prepare_epoch(self):
"""
Prepare for one epoch.
Returns:
bool: False if to stop the training.
"""
self.epoch += 1
if self.epoch >= self.epoch_start_halving and ((self.epoch - self.epoch_start_halving) % self._halving_freq == 0):
self._lr *= 0.5... |
def handle_control(self, req, worker_id, req_info):
"""
Handles a control_request received from a worker.
Returns:
string or dict: response
'stop' - the worker should quit
'wait' - wait for 1 second
'eval' - evaluate on valid and test set to start... |
def report(self):
"""
Report elapsed time.
"""
if not self.end_time:
self.end()
print ("Time: {} mins".format((self.end_time - self.start_time )/ 60)) |
def compare(self, cost_map):
"""
Compare to previous records and return whether the given cost is a new best.
:return: True if the given cost is a new best
"""
cri_val = cost_map[self._criteria]
if self._best_criteria is None:
self._best_criteria = cri_val
... |
def run(self, data_x):
"""
Run the model with validation data and return costs.
"""
output_vars = self.compute(*data_x)
return self._extract_costs(output_vars) |
def invoke(self):
"""
This function will be called after each iteration.
"""
self._counter += 1
if self._counter % self._freq == 0:
cnt = 0.
sum_map = defaultdict(float)
for x in self._trainer.get_data(self._data_split):
val_map... |
def _build_loop_vars(self):
"""
Create inner loop variables.
"""
from theano.tensor.var import TensorVariable
from deepy.core.neural_var import NeuralVariable
if not self._loop_vars:
self._ordered_out_keys = self._outputs.keys()
seq_keys = self._se... |
def _scan_step(self, vars):
"""
Internal scan with dummy input variables.
"""
from neural_var import NeuralVariable
if not self._loop_vars:
raise Exception("The loop is not initialized. To initialize the loop, use `with loop as vars`")
replace_map = {}
... |
def get_outputs(self, *args):
"""
Get the outputs of the loop.
Return specific variables by passing the keys to the arguments.
:rtype: MapDict
"""
if args:
output_vars = map(self._scan_outputs.get, args)
if len(output_vars) == 1:
re... |
def momentum_core(params, gradients, momentum=0.9, learning_rate=0.01):
"""
Momentum SGD optimization core.
"""
free_parameters = []
updates = []
for param, grad in zip(params, gradients):
delta = learning_rate * grad
velocity = theano.shared(np.zeros_like(param.get_value... |
def iftrain(self, then_branch, else_branch):
"""
Execute `then_branch` when training.
"""
return ifelse(self._training_flag, then_branch, else_branch, name="iftrain") |
def switch_training(self, flag):
"""
Switch training mode.
:param flag: switch on training mode when flag is True.
"""
if self._is_training == flag: return
self._is_training = flag
if flag:
self._training_flag.set_value(1)
else:
sel... |
def nag_core(params, J, momentum=0.9, learning_rate=0.01):
"""
Nesterov's Accelerated Gradient (NAG).
See http://www.cs.toronto.edu/~fritz/absps/momentum.pdf .
Still unfinished
"""
# TODO: this requires some refractorings.
for param in params:
step = theano.shared(np.zeros_like(param... |
def skip(self, n_batches, n_epochs=0):
"""
Skip N batches in the training.
"""
logging.info("skip %d epochs and %d batches" % (n_epochs, n_batches))
self._skip_batches = n_batches
self._skip_epochs = n_epochs |
def load_params(self, path, exclude_free_params=False):
"""
Load parameters for the training.
This method can load free parameters and resume the training progress.
"""
self.network.load_params(path, exclude_free_params=exclude_free_params)
self.best_params = self.copy_pa... |
def add_iter_controllers(self, *controllers):
"""
Add iteration callbacks function (receives an argument of the trainer).
:param controllers: can be a `TrainingController` or a function.
:type funcs: list of TrainingContoller
"""
for controller in controllers:
... |
def add_epoch_controllers(self, *controllers):
"""
Add epoch callbacks function.
:param controllers: can be a `TrainingController` or a function.
"""
for controller in controllers:
if isinstance(controller, TrainingController):
controller.bind(self)
... |
def train(self, train_set, valid_set=None, test_set=None, train_size=None):
"""
Train the model and return costs.
"""
self._epoch = 0
while True:
if self._skip_epochs > 0:
logging.info("skipping one epoch ...")
self._skip_epochs -= 1
... |
def _run_train(self, epoch, train_set, train_size=None):
"""
Run one training iteration.
"""
self.network.train_logger.record_epoch(epoch + 1)
costs = self.train_step(train_set, train_size)
if not epoch % self.config.monitor_frequency:
self.report(dict(costs),... |
def _run_valid(self, epoch, valid_set, dry_run=False, save_path=None):
"""
Run one valid iteration, return true if to continue training.
"""
costs = self.valid_step(valid_set)
# this is the same as: (J_i - J_f) / J_i > min improvement
_, J = costs[0]
new_best = Fa... |
def report(self, score_map, type="valid", epoch=-1, new_best=False):
"""
Report the scores and record them in the log.
"""
type_str = type
if len(type_str) < 5:
type_str += " " * (5 - len(type_str))
info = " ".join("%s=%.2f" % el for el in score_map.items())
... |
def get_data(self, data_split="train"):
"""
Get specified split of data.
"""
if data_split == 'train':
return self._current_train_set
elif data_split == 'valid':
return self._current_valid_set
elif data_split == 'test':
return self._cur... |
def run(self, train_set, valid_set=None, test_set=None, train_size=None, epoch_controllers=None):
"""
Run until the end.
:param epoch_controllers: deprecated
"""
epoch_controllers = epoch_controllers if epoch_controllers else []
epoch_controllers += self._epoch_controller... |
def _cut_to_pieces(self, bunch_stack):
"""
:type bunch_stack: list of list of int
"""
stack_len = len(bunch_stack[0])
for i in xrange(0, stack_len, self.fragment_length):
yield np.array(map(lambda stack: stack[i: i + self.fragment_length], bunch_stack)) |
def _pad_zeros(self, bunch_stack):
"""
:type bunch_stack: list of list
"""
min_len = min(map(len, bunch_stack))
for i in range(len(bunch_stack)):
bunch_stack[i] = bunch_stack[i][:min_len] |
def apply(self, func, dim=None):
"""
Apply a function to tensors.
"""
output_dim = dim if dim else self.output_dim
return NeuralVariable(func(self.tensor), output_dim) |
def rprop_core(params, gradients, rprop_increase=1.01, rprop_decrease=0.99, rprop_min_step=0, rprop_max_step=100,
learning_rate=0.01):
"""
Rprop optimizer.
See http://sci2s.ugr.es/keel/pdf/algorithm/articulo/2003-Neuro-Igel-IRprop+.pdf.
"""
for param, grad in zip(params, gradients):
... |
def report(self):
"""
Report usage of training parameters.
"""
if self.logger:
self.logger.info("accessed parameters:")
for key in self.used_parameters:
self.logger.info(" - %s %s" % (key, "(undefined)" if key in self.undefined_parameters else "")) |
def new_block(self, *layers, **kwargs):
"""
Create a parameters block.
:param layers: register some layers in the block
:param name: specify the name of this block
"""
from deepy.layers.block import Block
block = Block(*layers, **kwargs)
return block |
def var(self, tensor_type, last_dim=0, test_shape=None):
"""
An alias of deepy.tensor.var.
"""
from deepy.tensor import var
return var(tensor_type, last_dim=last_dim, test_shape=test_shape) |
def create_vars_from_data(self, dataset, split="train"):
"""
Create vars given a dataset and set test values.
Useful when dataset is already defined.
"""
from deepy.core.neural_var import NeuralVariable
vars = []
if split == "valid":
data_split = datas... |
def scan(self, func, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
A loop function, the usage is identical with the theano one.
:type block: deepy.layers.Block
"""
results, updates = Scanner(func, sequences, outputs, non_sequences, neural_computatio... |
def loop(self, sequences=None, outputs=None, non_sequences=None, block=None, **kwargs):
"""
Start a loop.
Usage:
```
with deepy.graph.loop(sequences={"x": x}, outputs={"o": None}) as vars:
vars.o = vars.x + 1
loop_outputs = deepy.graph.loop_outputs()
r... |
def get_trainer(self, model, method='sgd', config=None, annealer=None, validator=None):
"""
Get a trainer to optimize given model.
:rtype: deepy.trainers.GeneralNeuralTrainer
"""
from deepy.trainers import GeneralNeuralTrainer
return GeneralNeuralTrainer(model, method=me... |
def shared(self, value, name=None):
"""
Create a shared theano scalar value.
"""
if type(value) == int:
final_value = np.array(value, dtype="int32")
elif type(value) == float:
final_value = np.array(value, dtype=env.FLOATX)
else:
final_... |
def fill_parameters(self, path, blocks, exclude_free_params=False, check_parameters=False):
"""
Load parameters from file to fill all blocks sequentially.
:type blocks: list of deepy.layers.Block
"""
if not os.path.exists(path):
raise Exception("model {} does not exis... |
def train_size(self):
"""
Return size of training data. (optional)
:rtype: number
"""
train_set = self.train_set()
if isinstance(train_set, collections.Iterable):
return len(list(train_set))
else:
return None |
def invoke(self):
"""
Run it, return whether to end training.
"""
self._iter += 1
if self._iter - max(self._trainer.best_iter, self._annealed_iter) >= self._patience:
if self._annealed_times >= self._anneal_times:
logging.info("ending")
... |
def invoke(self):
"""
Run it, return whether to end training.
"""
self._iter += 1
logging.info("{} epochs left to run".format(self._patience - self._iter))
if self._iter >= self._patience:
self._trainer.exit() |
def stack_reparameterization_layer(self, layer_size):
"""
Perform reparameterization trick for latent variables.
:param layer_size: the size of latent variable
"""
self.rep_layer = ReparameterizationLayer(layer_size, sample=self.sample)
self.stack_encoders(self.rep_layer) |
def stack_encoders(self, *layers):
"""
Stack encoding layers, this must be done before stacking decoding layers.
"""
self.stack(*layers)
self.encoding_layes.extend(layers) |
def stack_decoders(self, *layers):
"""
Stack decoding layers.
"""
self.stack(*layers)
self.decoding_layers.extend(layers) |
def encode(self, x):
"""
Encode given input.
"""
if not self.encoding_network:
self.encoding_network = NeuralNetwork(self.input_dim, self.input_tensor)
self.encoding_network.input_variables = self.input_variables
for layer in self.encoding_layes:
... |
def decode(self, x):
"""
Decode given representation.
"""
if not self.rep_dim:
raise Exception("rep_dim must be set to decode.")
if not self.decoding_network:
self.decoding_network = NeuralNetwork(self.rep_dim)
for layer in self.decoding_layers... |
def create_2d_gaussian(dim, sigma):
"""
This function creates a 2d gaussian kernel with the standard deviation
denoted by sigma
:param dim: integer denoting a side (1-d) of gaussian kernel
:param sigma: floating point indicating the standard deviation
:returns: a numpy 2d array
"""
# ... |
def elastic_distortion(image, kernel_dim=21, sigma=6, alpha=30, negated=True):
"""
This method performs elastic transformations on an image by convolving
with a gaussian kernel.
:param image: a numpy nd array
:kernel_dim: dimension(1-D) of the gaussian kernel
:param sigma: standard deviation of ... |
def stack_layer(self, layer, no_setup=False):
"""
Stack a neural layer.
:type layer: NeuralLayer
:param no_setup: whether the layer is already initialized
"""
if layer.name:
layer.name += "%d" % (len(self.layers) + 1)
if not self.layers:
la... |
def register_layer(self, layer):
"""
Register the layer so that it's param will be trained.
But the output of the layer will not be stacked.
"""
if type(layer) == Block:
layer.fix()
self.parameter_count += layer.parameter_count
self.parameters.extend(l... |
def monitor_layer_outputs(self):
"""
Monitoring the outputs of each layer.
Useful for troubleshooting convergence problems.
"""
for layer, hidden in zip(self.layers, self._hidden_outputs):
self.training_monitors.append(('mean(%s)' % (layer.name), abs(hidden).mean())) |
def all_parameters(self):
"""
Return all parameters.
"""
params = []
params.extend(self.parameters)
params.extend(self.free_parameters)
return params |
def setup_variables(self):
"""
Set up variables.
"""
if self.input_tensor:
if type(self.input_tensor) == int:
x = dim_to_var(self.input_tensor, name="x")
else:
x = self.input_tensor
else:
x = T.matrix('x')
... |
def compute(self, *x):
"""
Return network output.
"""
self._compile()
outs = self._compute(*x)
if self._output_keys:
return MapDict(dict(zip(self._output_keys, outs)))
else:
return outs |
def save_params(self, path, new_thread=False):
"""
Save parameters to file.
"""
save_logger.info(path)
param_variables = self.all_parameters
params = [p.get_value().copy() for p in param_variables]
if new_thread:
thread = Thread(target=save_network_par... |
def load_params(self, path, exclude_free_params=False):
"""
Load parameters from file.
"""
if not os.path.exists(path): return;
logging.info("loading parameters from %s" % path)
# Decide which parameters to load
if exclude_free_params:
params_to_load =... |
def report(self):
"""
Print network statistics.
"""
logging.info("network inputs: %s", " ".join(map(str, self.input_variables)))
logging.info("network targets: %s", " ".join(map(str, self.target_variables)))
logging.info("network parameters: %s", " ".join(map(str, self.al... |
def init(self, input_dim=0, input_dims=None, no_prepare=False):
"""
Initialize the layer.
:param no_prepare: avoid calling preparation function
"""
if self.initialized:
return
# configure input dimensions
if input_dims:
self.input_dims = in... |
def compute(self, *inputs, **kwargs):
"""
Compute based on NeuralVariable.
:type inputs: list of NeuralVariable
:return: NeuralVariable
"""
from deepy.core.neural_var import NeuralVariable
from deepy.core.graph import graph
if type(inputs[0]) != NeuralVar... |
def belongs_to(self, block):
"""
Let the given block or network manage the parameters of this layer.
:param block: Block or NeuralNetwork
:return: NeuralLayer
"""
if self._linked_block:
raise SystemError("The layer {} has already blonged to {}".format(self.nam... |
def register_parameters(self, *parameters):
"""
Register parameters.
"""
for param in parameters:
self.parameter_count += np.prod(param.get_value().shape)
self.parameters.extend(parameters) |
def register_updates(self, *updates):
"""
Register updates that will be executed in each iteration.
"""
for key, node in updates:
if key not in self._registered_updates:
self.updates.append((key, node))
self._registered_updates.add(key) |
def register_training_updates(self, *updates):
"""
Register updates that will only be executed in training phase.
"""
for key, node in updates:
if key not in self._registered_training_updates:
self.training_updates.append((key, node))
self._reg... |
def register_monitors(self, *monitors):
"""
Register monitors they should be tuple of name and Theano variable.
"""
for key, node in monitors:
if key not in self._registered_monitors:
node *= 1.0 # Avoid CudaNdarray
self.training_monitors.appen... |
def multiple_l2_norm(tensors):
"""
Get the L2 norm of multiple tensors.
This function is taken from blocks.
"""
# Another way for doing this, I don't know which one is fast
# return T.sqrt(sum(T.sum(t ** 2) for t in tensors))
flattened = [T.as_tensor_variable(t).flatten() for t in tensors]
... |
def dump_one(elt_to_pickle, file_obj):
"""
dumps one element to file_obj, a file opened in write mode
"""
pickled_elt_str = dumps(elt_to_pickle)
file_obj.write(pickled_elt_str)
# record separator is a blank line
# (since pickled_elt_str might contain its own newli... |
def load(file_obj):
"""
load contents from file_obj, returning a generator that yields one
element at a time
"""
cur_elt = []
for line in file_obj:
cur_elt.append(line)
if line == '\n':
pickled_elt_str = ''.join(cur_elt)
cur_el... |
def fix(self):
"""
Fix the block, register all the parameters of sub layers.
:return:
"""
if not self.fixed:
for layer in self.layers:
if not layer.initialized:
raise Exception("All sub layers in a block must be initialized when fix... |
def register_layer(self, layer):
"""
Register one connected layer.
:type layer: NeuralLayer
"""
if self.fixed:
raise Exception("After a block is fixed, no more layers can be registered.")
self.layers.append(layer) |
def load_params(self, path, exclude_free_params=False):
from deepy.core import graph
"""
Load parameters to the block.
"""
from deepy.core.comp_graph import ComputationalGraph
model = graph.compile(blocks=[self])
model.load_params(path, exclude_free_params=exclude... |
def compute_step(self, state, lstm_cell=None, input=None, additional_inputs=None):
"""
Compute one step in the RNN.
:return: one variable for RNN and GRU, multiple variables for LSTM
"""
if not self.initialized:
input_dim = None
if input and hasattr(input.... |
def get_initial_states(self, input_var, init_state=None):
"""
:type input_var: T.var
:rtype: dict
"""
initial_states = {}
for state in self.state_names:
if state != "state" or not init_state:
if self._input_type == 'sequence' and input_var.ndim... |
def get_step_inputs(self, input_var, states=None, mask=None, additional_inputs=None):
"""
:type input_var: T.var
:rtype: dict
"""
step_inputs = {}
if self._input_type == "sequence":
if not additional_inputs:
additional_inputs = []
i... |
def convert_to_theano_var(obj):
"""
Convert neural vars to theano vars.
:param obj: NeuralVariable or list or dict or tuple
:return: theano var, test var, tensor found, neural var found
"""
from deepy.core.neural_var import NeuralVariable
if type(obj) == tuple:
return tuple(convert_t... |
def convert_to_neural_var(obj):
"""
Convert object and a test object into neural var.
:param obj: tensor or list or dict or tuple
:param test_obj: NeuralVar or list or dict or tuple
:return:
"""
from theano.tensor.var import TensorVariable
from deepy.core.neural_var import NeuralVariable... |
def neural_computation(original_func, prefer_tensor=False):
"""
An annotation to enable theano-based fucntions to be called with NeuralVar.
:param original_func:
:param prefer_tensor: a switch to return tensors when no inputs
:return:
"""
def wrapper(*args, **kwargs):
normal_args, t... |
def onehot_tensor(i_matrix, vocab_size):
"""
# batch x time
"""
dim0, dim1 = i_matrix.shape
i_vector = i_matrix.reshape((-1,))
hot_matrix = T.extra_ops.to_one_hot(i_vector, vocab_size).reshape((dim0, dim1, vocab_size))
return hot_matrix |
def create_request_elements(
cls, request_type, credentials, url, method='GET', params=None,
headers=None, body='', secret=None, redirect_uri='', scope='',
csrf='', user_state=''
):
"""
Creates |oauth2| request elements.
"""
headers = headers or {... |
def decode_state(cls, state, param='user_state'):
"""
Decode state and return param.
:param str state:
state parameter passed through by provider
:param str param:
key to query from decoded state variable. Options include 'csrf'
and 'user_state'.
... |
def refresh_credentials(self, credentials):
"""
Refreshes :class:`.Credentials` if it gives sense.
:param credentials:
:class:`.Credentials` to be refreshed.
:returns:
:class:`.Response`.
"""
if not self._x_refresh_credentials_if(credentials):
... |
def _x_credentials_parser(credentials, data):
"""
We need to override this method to fix Facebooks naming deviation.
"""
# Facebook returns "expires" instead of "expires_in".
credentials.expire_in = data.get('expires')
if data.get('token_type') == 'bearer':
... |
def _x_request_elements_filter(cls, request_type, request_elements,
credentials):
"""
Google doesn't accept client ID and secret to be at the same time in
request parameters and in the basic authorization header in the access
token request.
"""
... |
def login(provider_name):
"""
Login handler, must accept both GET and POST to be able to use OpenID.
"""
# We need response object for the WerkzeugAdapter.
response = make_response()
# Log the user in, pass it the adapter and the provider name.
result = authomatic.login(
WerkzeugAd... |
def normalize_dict(dict_):
"""
Replaces all values that are single-item iterables with the value of its
index 0.
:param dict dict_:
Dictionary to normalize.
:returns:
Normalized dictionary.
"""
return dict([(k, v[0] if not isinstance(v, str) and len(v) == 1 else v)
... |
def items_to_dict(items):
"""
Converts list of tuples to dictionary with duplicate keys converted to
lists.
:param list items:
List of tuples.
:returns:
:class:`dict`
"""
res = collections.defaultdict(list)
for k, v in items:
res[k].append(v)
return norm... |
def json_qs_parser(body):
"""
Parses response body from JSON, XML or query string.
:param body:
string
:returns:
:class:`dict`, :class:`list` if input is JSON or query string,
:class:`xml.etree.ElementTree.Element` if XML.
"""
try:
# Try JSON first.
ret... |
def resolve_provider_class(class_):
"""
Returns a provider class.
:param class_name: :class:`string` or
:class:`authomatic.providers.BaseProvider` subclass.
"""
if isinstance(class_, str):
# prepare path for authomatic.providers package
path = '.'.join([__package__, 'providers... |
def id_to_name(config, short_name):
"""
Returns the provider :doc:`config` key based on it's ``id`` value.
:param dict config:
:doc:`config`.
:param id:
Value of the id parameter in the :ref:`config` to search for.
"""
for k, v in list(config.items()):
if v.get('id') =... |
def create_cookie(self, delete=None):
"""
Creates the value for ``Set-Cookie`` HTTP header.
:param bool delete:
If ``True`` the cookie value will be ``deleted`` and the
Expires value will be ``Thu, 01-Jan-1970 00:00:01 GMT``.
"""
value = 'deleted' if del... |
def save(self):
"""
Adds the session cookie to headers.
"""
if self.data:
cookie = self.create_cookie()
cookie_len = len(cookie)
if cookie_len > 4093:
raise SessionError('Cookie too long! The cookie size {0} '
... |
def _get_data(self):
"""
Extracts the session data from cookie.
"""
cookie = self.adapter.cookies.get(self.name)
return self._deserialize(cookie) if cookie else {} |
def data(self):
"""
Gets session data lazily.
"""
if not self._data:
self._data = self._get_data()
# Always return a dict, even if deserialization returned nothing
if self._data is None:
self._data = {}
return self._data |
def _signature(self, *parts):
"""
Creates signature for the session.
"""
signature = hmac.new(six.b(self.secret), digestmod=hashlib.sha1)
signature.update(six.b('|'.join(parts)))
return signature.hexdigest() |
def _serialize(self, value):
"""
Converts the value to a signed string with timestamp.
:param value:
Object to be serialized.
:returns:
Serialized value.
"""
# data = copy.deepcopy(value)
data = value
# 1. Serialize
ser... |
def _deserialize(self, value):
"""
Deserializes and verifies the value created by :meth:`._serialize`.
:param str value:
The serialized value.
:returns:
Deserialized object.
"""
# 3. Split
encoded, timestamp, signature = value.split('|'... |
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