def test_training_property():
import nnabla.parametric_functions as PF
class ConvBn(nn.Module):
def call(self, x):
h = PF.convolution(x, 3, (3, 2))
return h
model = ConvBn()
x = nn.Variable((64, 3, 32, 32))
y = model(x)
model.training = True
assert model.training == True
def call(self, x1, x2):
y1 = self.conv_bn_1(x1)
y2 = self.conv_bn_2(x2)
y = F.concatenate(y1, y2, axis=1)
# ConvBn() will be destroyed when leave this scope.
# Thus, the parameters owned by `cb` object will be released too.
cb = ConvBn(1)
y = F.concatenate(y, cb(x1), axis=1)
return y
def __init__(self):
self.conv_bn_1 = ConvBn(1)
self.conv_bn_2 = ConvBn(1)
def __init__(self):
self.conv_bn = ConvBn(3)
def __init__(self):
self.cb = ConvBn(2)
self.cb2 = ConvBn(2)
self.shared1 = Shared(self.cb2)
self.shared2 = Shared(self.cb2)
