def check_forward(self, e1_data, e2_data, W_data, V1_data, V2_data,
b_data):
e1 = chainer.Variable(e1_data)
e2 = chainer.Variable(e2_data)
W = chainer.Variable(W_data)
e1_data = e1_data.reshape(e1_data.shape[0], -1)
e2_data = e2_data.reshape(e2_data.shape[0], -1)
xp = cuda.get_array_module(e1)
y_expect = xp.einsum('ij,ik,jkl->il', e1_data, e2_data, W_data)
flags = V1_data is None, V2_data is None, b_data is None
if any(flags):
if not all(flags):
raise ValueError(
'Test either all or none of the optional parameters.')
y = functions.bilinear(e1, e2, W)
else:
V1 = chainer.Variable(V1_data)
V2 = chainer.Variable(V2_data)
b = chainer.Variable(b_data)
y = functions.bilinear(e1, e2, W, V1, V2, b)
y_expect = xp.einsum('ij,ik,jkl->il', e1_data, e2_data, W_data)
y_expect += e1_data.dot(V1_data)
y_expect += e2_data.dot(V2_data)
y_expect += b_data
testing.assert_allclose(y_expect, cuda.to_cpu(y.data))
assert y.data.dtype == e1_data.dtype
def check_forward(self, e1_data, e2_data, W_data, V1_data, V2_data,
b_data):
e1 = chainer.Variable(e1_data)
e2 = chainer.Variable(e2_data)
W = chainer.Variable(W_data)
e1_data = e1_data.reshape(e1_data.shape[0], -1)
e2_data = e2_data.reshape(e2_data.shape[0], -1)
xp = cuda.get_array_module(e1)
y_expect = xp.einsum('ij,ik,jkl->il', e1_data, e2_data, W_data)
flags = V1_data is None, V2_data is None, b_data is None
if any(flags):
if not all(flags):
raise ValueError(
'Test either all or none of the optional parameters.')
y = functions.bilinear(e1, e2, W)
else:
V1 = chainer.Variable(V1_data)
V2 = chainer.Variable(V2_data)
b = chainer.Variable(b_data)
y = functions.bilinear(e1, e2, W, V1, V2, b)
y_expect = xp.einsum('ij,ik,jkl->il', e1_data, e2_data, W_data)
y_expect += e1_data.dot(V1_data)
y_expect += e2_data.dot(V2_data)
y_expect += b_data
testing.assert_allclose(y_expect, cuda.to_cpu(y.data))
assert y.data.dtype == e1_data.dtype
def forward(self, inputs, device):
if self.test_partial:
e1, e2, W = inputs
V1 = None
V2 = None
b = None
else:
e1, e2, W, V1, V2, b = inputs
flags = V1 is None, V2 is None, b is None
if any(flags):
if not all(flags):
raise ValueError(
'Test either all or none of the optional parameters.')
y = functions.bilinear(e1, e2, W)
else:
y = functions.bilinear(e1, e2, W, V1, V2, b)
return y,
def forward(self, inputs, device):
if self.test_partial:
e1, e2, W = inputs
V1 = None
V2 = None
b = None
else:
e1, e2, W, V1, V2, b = inputs
flags = V1 is None, V2 is None, b is None
if any(flags):
if not all(flags):
raise ValueError(
'Test either all or none of the optional parameters.')
y = functions.bilinear(e1, e2, W)
else:
y = functions.bilinear(e1, e2, W, V1, V2, b)
return y,
def test_invalid_full_partial_ambiguous(self):
with self.assertRaises(ValueError):
functions.bilinear(self.e1, self.e2, self.W, self.V1)
def test_invalid_full_partial_ambiguous(self):
with self.assertRaises(ValueError):
functions.bilinear(self.e1, self.e2, self.W, self.V1)
def f(*inputs):
y = functions.bilinear(*inputs)
return y * y
def f(*inputs):
y = functions.bilinear(*inputs)
return y * y