def test_inequality(value1, value2):
scalar1 = chainerx.Scalar(value1)
scalar2 = chainerx.Scalar(value2)
assert scalar1 != scalar2
assert scalar2 != scalar1
assert scalar2 != value1
assert value1 != scalar2
assert scalar1 != value2
assert value2 != scalar1
def test_sub_scalar(scalar, device, shape, dtype):
if dtype == 'bool_':
# Boolean subtract is deprecated.
return chainerx.testing.ignore()
x_np = array_utils.create_dummy_ndarray(numpy, shape, dtype)
# Implicit casting in NumPy's multiply depends on the 'casting' argument,
# which is not yet supported (ChainerX always casts).
# Therefore, we explicitly cast the scalar to the dtype of the ndarray
# before the multiplication for NumPy.
expected = x_np - numpy.dtype(dtype).type(scalar)
expected_rev = numpy.dtype(dtype).type(scalar) - x_np
x = chainerx.array(x_np)
scalar_chx = chainerx.Scalar(scalar, dtype)
chainerx.testing.assert_array_equal_ex(x - scalar, expected)
chainerx.testing.assert_array_equal_ex(x - scalar_chx, expected)
chainerx.testing.assert_array_equal_ex(scalar - x, expected_rev)
chainerx.testing.assert_array_equal_ex(scalar_chx - x, expected_rev)
chainerx.testing.assert_array_equal_ex(chainerx.subtract(x, scalar),
expected)
chainerx.testing.assert_array_equal_ex(chainerx.subtract(x, scalar_chx),
expected)
chainerx.testing.assert_array_equal_ex(chainerx.subtract(scalar, x),
expected_rev)
chainerx.testing.assert_array_equal_ex(chainerx.subtract(scalar_chx, x),
expected_rev)
def test_full_with_scalar(shape, dtype, value, device):
scalar = chainerx.Scalar(value, dtype)
a = chainerx.full(shape, scalar)
if scalar.dtype.kind == 'f' and math.isnan(float(scalar)):
assert all([math.isnan(el) for el in a._debug_flat_data])
else:
assert a._debug_flat_data == [scalar.tolist()] * a.size
def test_init_without_dtype(value, dtype):
scalar = chainerx.Scalar(value)
assert scalar.dtype == dtype
if math.isnan(value):
assert math.isnan(scalar.tolist())
else:
assert scalar.tolist() == value
assert isinstance(scalar.tolist(), type(value))
def test_init_casted(value, cast_dtype, expected_value):
scalar = chainerx.Scalar(value, cast_dtype)
assert scalar.dtype == cast_dtype
if math.isnan(expected_value):
assert math.isnan(scalar.tolist())
else:
assert scalar.tolist() == expected_value
assert isinstance(scalar.tolist(), type(expected_value))
def test_equality(value1, value2):
scalar1 = chainerx.Scalar(value1)
scalar2 = chainerx.Scalar(value2)
assert scalar1 == scalar2
assert scalar2 == scalar1
assert scalar1 == value1
assert value1 == scalar1
assert scalar2 == value2
assert value2 == scalar2
assert scalar2 == value1
assert value1 == scalar2
assert scalar1 == value2
assert value2 == scalar1
def test_cast(value):
scalar = chainerx.Scalar(value)
_check_cast_scalar_equals_data(scalar, value)
_check_cast_scalar_equals_data(+scalar, +value)
if isinstance(value, bool):
with pytest.raises(chainerx.DtypeError):
-scalar # should not be able to negate bool
else:
_check_cast_scalar_equals_data(-scalar, -value)
def test_dtype(value):
scalar = chainerx.Scalar(value)
if isinstance(value, bool):
assert scalar.dtype == chainerx.bool_
elif isinstance(value, int):
assert scalar.dtype == chainerx.int64
elif isinstance(value, float):
assert scalar.dtype == chainerx.float64
else:
assert False
def test_truediv_scalar(scalar, device, shape, numeric_dtype):
x_np = array_utils.create_dummy_ndarray(numpy, shape, numeric_dtype)
expected = x_np / scalar
x = chainerx.array(x_np)
scalar_chx = chainerx.Scalar(scalar, numeric_dtype)
chainerx.testing.assert_array_equal_ex(
x / scalar, expected, strides_check=False)
chainerx.testing.assert_array_equal_ex(
x / scalar_chx, expected, strides_check=False)
chainerx.testing.assert_array_equal_ex(
chainerx.divide(x, scalar), expected, strides_check=False)
chainerx.testing.assert_array_equal_ex(
chainerx.divide(x, scalar_chx), expected, strides_check=False)
def test_truediv_scalar(scalar, device, shape, numeric_dtype):
x_np = array_utils.create_dummy_ndarray(numpy, shape, numeric_dtype)
if 'int' in numeric_dtype:
# NumPy does not support itruediv to integer arrays.
expected = (x_np / scalar).astype(numeric_dtype)
else:
expected = x_np / scalar
x = chainerx.array(x_np)
scalar_chx = chainerx.Scalar(scalar, numeric_dtype)
chainerx.testing.assert_array_equal_ex(x / scalar, expected)
chainerx.testing.assert_array_equal_ex(x / scalar_chx, expected)
chainerx.testing.assert_array_equal_ex(chainerx.divide(x, scalar),
expected)
chainerx.testing.assert_array_equal_ex(chainerx.divide(x, scalar_chx),
expected)
def test_add_scalar(scalar, device, shape, dtype):
x_np = array_utils.create_dummy_ndarray(numpy, shape, dtype)
# Implicit casting in NumPy's multiply depends on the 'casting' argument,
# which is not yet supported (ChainerX always casts).
# Therefore, we explicitly cast the scalar to the dtype of the ndarray
# before the multiplication for NumPy.
expected = x_np + numpy.dtype(dtype).type(scalar)
x = chainerx.array(x_np)
scalar_chx = chainerx.Scalar(scalar, dtype)
chainerx.testing.assert_array_equal_ex(x + scalar, expected)
chainerx.testing.assert_array_equal_ex(x + scalar_chx, expected)
chainerx.testing.assert_array_equal_ex(scalar + x, expected)
chainerx.testing.assert_array_equal_ex(scalar_chx + x, expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(x, scalar), expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(x, scalar_chx),
expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(scalar, x), expected)
chainerx.testing.assert_array_equal_ex(chainerx.add(scalar_chx, x),
expected)
def test_fill_with_scalar(xp, device, shape, dtype, value):
a = xp.empty(shape, dtype)
if xp is chainerx:
value = chainerx.Scalar(value, dtype)
a.fill(value)
return a
@pytest.mark.parametrize('shape', [(), (1, ), (2, 3)])
def test_assert_array_equal_fail(shape, dtype):
a, b = _make_onehot_arrays(shape, dtype, 0, 2)
with pytest.raises(AssertionError):
chainerx.testing.assert_array_equal(a, b)
@pytest.mark.parametrize('value1,value2', [
(True, 1),
(True, 1.0),
(False, 0),
(False, 0.0),
(2.0, 2),
(numpy.int32(2), 2.0),
(chainerx.Scalar(2), 2.0),
(chainerx.Scalar(2, chainerx.int64), numpy.float32(2)),
(float('nan'), numpy.float32('nan')),
])
def test_assert_array_equal_scalar(value1, value2):
chainerx.testing.assert_array_equal(value1, value2)
chainerx.testing.assert_array_equal(value2, value1)
@pytest.mark.parametrize('value1,value2', [
(2, 3),
(2.0, 3),
(True, 0),
(True, -1),
(False, 1),
(float('nan'), float('inf')),
def test_init_invalid():
with pytest.raises(TypeError):
chainerx.Scalar("1") # string, which is not a numeric
def test_repr(value):
scalar = chainerx.Scalar(value)
assert repr(scalar) == repr(value)
assert str(scalar) == str(value)
def test_init_with_dtype(value, dtype_spec):
expected_dtype = chainerx.dtype(dtype_spec)
scalar = chainerx.Scalar(value, dtype_spec)
assert scalar.dtype == expected_dtype
assert scalar == chainerx.Scalar(value, expected_dtype)
