def _get_variable_or_create(self, v, callback, current_scope):
if v.variable is not None:
return v.variable
v = callback._apply_generate_variable(v)
if v.variable is not None:
return v.variable
pvar = v.proto
name = pvar.name
shape = list(pvar.shape.dim)
if shape[0] < 0:
shape[0] = self.batch_size
shape = tuple(shape)
assert np.all(np.array(shape) > 0
), "Shape must be positive. Given {}.".format(shape)
if pvar.type != 'Parameter':
# Create a new variable and returns.
var = nn.Variable(shape)
v.variable = var
var.name = name
return var
# Trying to load the parameter from .nnp file.
callback.verbose('Loading parameter `{}` from .nnp.'.format(name))
try:
param = get_parameter(name)
if param is None:
logger.info(
'Parameter `{}` is not found. Initializing.'.format(name))
tmp = _create_variable(pvar, name, shape, self.rng)
param = tmp.variable_instance
set_parameter(name, param)
# Always copy param to current scope even if it already exists.
with nn.parameter_scope('', current_scope):
set_parameter(name, param)
except:
import sys
import traceback
raise ValueError(
'An error occurs during creation of a variable `{}` as a'
' parameter variable. The error was:\n----\n{}\n----\n'
'The parameters registered was {}'.format(
name, traceback.format_exc(), '\n'.join(
list(nn.get_parameters(grad_only=False).keys()))))
assert shape == param.shape
param = param.get_unlinked_variable(need_grad=v.need_grad)
v.variable = param
param.name = name
return param
def generate_parameters_data(self, var_list, batch_size):
from nnabla.utils.load import _create_variable
import numpy as np
rng = np.random.RandomState(0)
for var in var_list:
shape = tuple([d if d >= 1 else batch_size for d in var.shape.dim])
variable = _create_variable(var, var.name, shape, rng)
p = self._nnp.parameter.add()
p.variable_name = variable.name
p.shape.dim.extend(variable.shape)
p.data.extend(variable.variable_instance.d.flatten())
