def test_scalar_dot(seed, scalar, is_dynamic):
rng = np.random.RandomState(seed)
a1 = scalar
a2 = rng.randn(3, 4, 5, 6).astype(np.float32)
n = nn.NdArray.from_numpy_array(a2)
v = nn.Variable.from_numpy_array(a2)
ref = F.dot(a1, a2)
ans1 = F.dot(a1, n)
assert_allclose(ans1.data, ref)
out1 = nn.NdArray((3, 4, 5, 6))
F.dot(a1, n, out1)
assert_allclose(out1.data, ref)
with nn.auto_forward(is_dynamic):
ans2 = F.dot(a1, v)
if not is_dynamic:
ans2.forward()
assert_allclose(ans2.d, ref)
out2 = nn.Variable((3, 4, 5, 6))
F.dot(a1, v, out2)
if not is_dynamic:
out2.forward()
assert_allclose(out2.d, ref)
def test_backward_dot_muti_array_out(seed, shape_a, shape_b):
rng = np.random.RandomState(seed)
a = rng.randn(*shape_a).astype(np.float32)
b = rng.randn(*shape_b).astype(np.float32)
inputs = [
a,
b,
]
v_a = nn.Variable.from_numpy_array(a)
v_b = nn.Variable.from_numpy_array(b)
ans = F.dot(v_a, v_b)
out = nn.Variable(ans.shape)
out.data.cast(np.float32)
function_network_tester(rng, F.dot, inputs, func_args=[out], args_out=True)
def test_backward_dot_have_scalar_out(seed, shape_a, shape_b):
rng = np.random.RandomState(seed)
inputs = []
func_args = []
if not shape_a:
a = rng.randn()
func_args += [a]
v_a = nn.Variable()
v_a.data.cast(np.float32)[...] = a
else:
a = rng.randn(*shape_a).astype(np.float32)
inputs += [a]
v_a = nn.Variable.from_numpy_array(a)
if not shape_b:
b = rng.randn()
func_args += [b]
v_b = nn.Variable()
v_b.data.cast(np.float32)[...] = b
else:
b = rng.randn(*shape_b).astype(np.float32)
inputs += [b]
v_b = nn.Variable.from_numpy_array(b)
ans = F.dot(v_a, v_b)
out = nn.Variable(ans.shape)
out.data.cast(np.float32)
func_args += [out]
function_network_tester(rng,
F.dot,
inputs,
func_args=func_args,
have_scalar=True,
args_out=True)
def gcn_layer(A_hat, X, out_features, name, dropout=0.5, activation=F.relu):
'''
GCN layer
Parameters
----------
A_hat: nnabla.Variable
Normalized graph Laplacian
X: nnabla.Variable
Feature matrix
out_features: int
Number of dimensions of output
name: str
Name of parameter scope
dropout: float
Parameter of dropout. If 0, not to use dropout
activaton: nnabla.functons
Activation function
Returns
-------
H: nnabla.Variable
Output of GCN layer
'''
with nn.parameter_scope(name):
if dropout > 0:
X = F.dropout(X, dropout)
H = PF.affine(X, (out_features, ), with_bias=False)
H = F.dot(A_hat, H)
if activation is not None:
H = activation(H)
return H
def test_ndarray_dot(seed, shape, is_dynamic):
rng = np.random.RandomState(seed)
if not shape[0]:
a1 = rng.randn()
n1 = nn.NdArray()
n1.cast(np.float32)[...] = a1
v1 = nn.Variable()
v1.data.cast(np.float32)[...] = a1
else:
a1 = rng.randn(*shape[0]).astype(np.float32)
n1 = nn.NdArray.from_numpy_array(a1)
v1 = nn.Variable.from_numpy_array(a1)
if not shape[1]:
a2 = rng.randn()
n2 = nn.NdArray()
n2.cast(np.float32)[...] = a2
v2 = nn.Variable()
v2.data.cast(np.float32)[...] = a2
else:
a2 = rng.randn(*shape[1]).astype(np.float32)
n2 = nn.NdArray.from_numpy_array(a2)
v2 = nn.Variable.from_numpy_array(a2)
ref = F.dot(a1, a2)
ans1_1 = F.dot(n1, n2)
ans1_2 = F.dot(n1, v2)
ans1_3 = F.dot(v1, n2)
assert_allclose(ans1_1.data, ref, atol=1e-3)
assert_allclose(ans1_2.data, ref, atol=1e-3)
assert_allclose(ans1_3.data, ref, atol=1e-3)
with nn.auto_forward(is_dynamic):
ans1_4 = F.dot(v1, v2)
if is_dynamic:
ans1_4.forward()
assert_allclose(ans1_4.d, ref, atol=1e-3)
out = ref.copy()
F.dot(a1, a2, out)
assert_allclose(out, ref, atol=1e-3)
out1_1 = nn.NdArray(ans1_1.shape)
out1_1.cast(np.float32)
F.dot(n1, n2, out1_1)
assert_allclose(out1_1.data, ref, atol=1e-3)
out1_2 = nn.NdArray(ans1_2.shape)
out1_2.cast(np.float32)
F.dot(n1, v2, out1_2)
assert_allclose(out1_2.data, ref, atol=1e-3)
out1_3 = nn.NdArray(ans1_3.shape)
out1_3.cast(np.float32)
F.dot(v1, n2, out1_3)
assert_allclose(out1_3.data, ref, atol=1e-3)
out1_4 = nn.Variable(ans1_4.shape)
out1_4.data.cast(np.float32)
with nn.auto_forward(is_dynamic):
F.dot(v1, v2, out1_4)
if not is_dynamic:
out1_4.forward()
assert_allclose(out1_4.d, ref, atol=1e-3)
# Ndarray with a wrong dtype
out2_1 = nn.NdArray(ref.shape)
out2_1.cast(int)
out2_2 = nn.Variable(ref.shape)
out2_2.data.cast(int)
# should not exec
with pytest.raises(ValueError) as excinfo:
F.dot(n1, n2, out2_1)
F.dot(n1, v2, out2_1)
F.dot(v1, n2, out2_1)
F.dot(v1, v2, out2_2)