def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 'divisor'))
type_check.expect(
in_types[0].dtype == in_types[1].dtype,
in_types[0].dtype.kind == 'f',
in_types[1].dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('lhs', 'rhs'))
type_check.expect(
in_types[0].dtype == in_types[1].dtype,
)
type_check.expect_broadcast_shapes(
in_types[0].shape, in_types[1].shape)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a', 'b'))
a_type, b_type = in_types
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
a_type.ndim >= 1,
b_type.ndim >= 1,
)
a_ndim = type_check.eval(a_type.ndim)
b_ndim = type_check.eval(b_type.ndim)
if b_ndim == 1:
a_idx = -2 if self.transa and a_ndim > 1 else -1
type_check.expect(a_type.shape[a_idx] == b_type.shape[0])
elif a_ndim == 1:
b_idx = -1 if self.transb and b_ndim > 1 else -2
type_check.expect(a_type.shape[0] == b_type.shape[b_idx])
else:
a_idx = _get_check_index(self.transa, False,
row_idx=-2, col_idx=-1)
b_idx = _get_check_index(self.transb, True,
row_idx=-2, col_idx=-1)
type_check.expect(a_type.shape[a_idx] == b_type.shape[b_idx])
type_check.expect_broadcast_shapes(
a_type.shape[:-2], b_type.shape[:-2])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('y', 'z'))
y_type, z_type = in_types
type_check.expect(y_type.dtype.kind == 'f',
y_type.dtype == z_type.dtype, y_type.ndim == 2,
z_type.ndim == 2, y_type.shape[0] == z_type.shape[0])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
x_type = in_types[0]
if self.axis >= 0:
type_check.expect(self.axis < x_type.ndim)
else:
type_check.expect(-self.axis <= x_type.ndim)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('sp', 'dn'))
sp_type, dn_type = in_types
# sp_type.shape: ((nb,) ldnz)
# dn_type.shape: ((nb,) _k, _n) when transb is False
sp_k_axis = -1
if self.transa:
sp_k_axis = -2
dn_k_axis = -2
if self.transb:
dn_k_axis = -1
type_check.expect(
sp_type.dtype.kind == 'f',
dn_type.dtype.kind == 'f',
dn_type.ndim >= 2,
dn_type.ndim <= 3,
sp_type.ndim == dn_type.ndim - 1,
sp_type.shape[-1] == self.sp_row.shape[-1],
self.sp_shape[sp_k_axis] == dn_type.shape[dn_k_axis],
)
dn_ndim = type_check.eval(dn_type.ndim)
if dn_ndim == 3:
type_check.expect(
sp_type.shape[0] == self.sp_row.shape[0],
dn_type.shape[0] == self.sp_row.shape[0],
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 'divisor'))
type_check.expect(
in_types[0].dtype == in_types[1].dtype,
in_types[0].dtype.kind == 'f',
in_types[1].dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].ndim > self.axis,
in_types[0].ndim >= -self.axis
)
def check_type_forward(self, in_types):
# Depending on the arguments, pad_width and keywords, the input value
# may be inappropriate. In that case, numpy.pad or cupy.pad will raise
# errors, so that only check the size and the dtype in this function.
type_check.argname(in_types, ('x', ))
x_type = in_types[0]
type_check.expect(x_type.dtype.kind == 'f')
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x1', 'x2'))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == in_types[1].dtype,
in_types[0].shape == in_types[1].shape
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('c_prev1', 'c_prev2', 'x1', 'x2'))
c1_type, c2_type, x1_type, x2_type = in_types
type_check.expect(
c1_type.dtype.kind == 'f',
c2_type.dtype == c1_type.dtype,
x1_type.dtype == c1_type.dtype,
x2_type.dtype == c1_type.dtype,
c1_type.ndim >= 2,
c2_type.ndim >= 2,
x1_type.ndim >= 2,
x2_type.ndim >= 2,
c1_type.ndim == x1_type.ndim,
c1_type.ndim == c2_type.ndim,
c1_type.ndim == x2_type.ndim,
c1_type.shape[0] == x1_type.shape[0],
c1_type.shape[0] == c2_type.shape[0],
c1_type.shape[0] == x2_type.shape[0],
x1_type.shape[1] == 4 * c1_type.shape[1],
x2_type.shape[1] == 4 * c2_type.shape[1],
)
for i in range(2, type_check.eval(c1_type.ndim)):
type_check.expect(x1_type.shape[i] == c1_type.shape[i])
type_check.expect(x2_type.shape[i] == c2_type.shape[i])
type_check.expect(x1_type.shape[i] == x2_type.shape[i])
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(2 <= n_in, n_in <= 3)
x_type, w_type = in_types[:2]
type_check.argname((x_type, w_type), ('x', 'W'))
type_check.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim >= 2,
w_type.ndim == 2,
type_check.prod(x_type.shape[1:]) == w_type.shape[1],
)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.argname((b_type,), ('b',))
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
b_type.shape[0] == w_type.shape[0],
)
if self.mask is not None:
if self.use_batchwise_mask:
type_check.expect(
self.mask.shape[0] == x_type.shape[0],
self.mask.shape[1:] == w_type.shape,
)
else:
type_check.expect(self.mask.shape == w_type.shape)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
x_type, = in_types
if self.axis < 0:
type_check.expect(x_type.ndim >= -self.axis)
else:
type_check.expect(x_type.ndim > self.axis)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('sp', 'dn'))
sp_type, dn_type = in_types
# sp_type.shape: ((nb,) ldnz)
# dn_type.shape: ((nb,) _k, _n) when transb is False
sp_k_axis = -1
if self.transa:
sp_k_axis = -2
dn_k_axis = -2
if self.transb:
dn_k_axis = -1
type_check.expect(
sp_type.dtype.kind == 'f',
dn_type.dtype.kind == 'f',
dn_type.ndim >= 2,
dn_type.ndim <= 3,
sp_type.ndim == dn_type.ndim - 1,
sp_type.shape[-1] == self.sp_row.shape[-1],
self.sp_shape[sp_k_axis] == dn_type.shape[dn_k_axis],
)
dn_ndim = type_check.eval(dn_type.ndim)
if dn_ndim == 3:
type_check.expect(
sp_type.shape[0] == self.sp_row.shape[0],
dn_type.shape[0] == self.sp_row.shape[0],
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
x_type, = in_types
if self.axis >= 0:
type_check.expect(x_type.ndim >= self.axis)
else:
type_check.expect(x_type.ndim >= -self.axis - 1)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 't'))
x_type, t_type = in_types
type_check.expect(x_type.dtype == numpy.float32,
t_type.dtype.kind == 'i',
x_type.shape == t_type.shape)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 'W'))
x_type, W_type = in_types
type_check.expect(
x_type.dtype.kind == 'f', W_type.dtype == x_type.dtype,
x_type.ndim >= W_type.ndim + 1,
x_type.shape[1:1 + type_check.eval(W_type.ndim)] == W_type.shape)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('gy',))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == self.x.dtype
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
x_type = in_types[0]
if self.axis >= 0:
type_check.expect(self.axis < x_type.ndim)
else:
type_check.expect(-self.axis <= x_type.ndim)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('lhs', 'rhs', 'gy'))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == in_types[1].dtype,
in_types[0].dtype == in_types[2].dtype,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('lhs', 'rhs', 'gy'))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == in_types[1].dtype,
in_types[0].dtype == in_types[2].dtype,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].ndim > self.axis,
in_types[0].ndim >= -self.axis
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a', 'b'))
a_type, b_type = in_types
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
a_type.ndim >= 1,
b_type.ndim >= 1,
)
a_ndim = type_check.eval(a_type.ndim)
b_ndim = type_check.eval(b_type.ndim)
if b_ndim == 1:
a_idx = -2 if self.transa and a_ndim > 1 else -1
type_check.expect(a_type.shape[a_idx] == b_type.shape[0])
elif a_ndim == 1:
b_idx = -1 if self.transb and b_ndim > 1 else -2
type_check.expect(a_type.shape[0] == b_type.shape[b_idx])
else:
a_idx = _get_check_index(self.transa, False,
row_idx=-2, col_idx=-1)
b_idx = _get_check_index(self.transb, True,
row_idx=-2, col_idx=-1)
type_check.expect(a_type.shape[a_idx] == b_type.shape[b_idx])
type_check.expect_broadcast_shapes(
a_type.shape[:-2], b_type.shape[:-2])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x0', 'x1'))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == in_types[1].dtype,
in_types[0].shape == in_types[1].shape
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 'beta'))
x_type, beta_type = in_types
type_check.expect(
x_type.dtype.kind == 'f', beta_type.dtype == x_type.dtype,
beta_type.ndim <= x_type.ndim - 1,
beta_type.shape == x_type.shape[1:1 +
type_check.eval(beta_type.ndim)])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a', ))
a_type, = in_types
type_check.expect(a_type.dtype.kind == 'f')
# Only 2D array shapes allowed
type_check.expect(a_type.ndim == 2)
# Matrix inversion only allowed for square matrices
type_check.expect(a_type.shape[0] == a_type.shape[1])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
x_type, = in_types
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= 2,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
x_type, = in_types
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= 2,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a', 'b'))
a_type, b_type = in_types
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
x_type, = in_types
type_check.expect(
x_type.dtype == numpy.float32,
x_type.ndim >= 2,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
type_check.expect(in_types[0].dtype.kind == 'f')
if self.axis is not None:
if self.axis >= 0:
type_check.expect(self.axis < in_types[0].ndim)
else:
type_check.expect(-self.axis - 1 < in_types[0].ndim)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('n', 'x'))
n_type, x_type = in_types
type_check.expect(
n_type.dtype.kind == 'i',
x_type.dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
type_check.expect(in_types[0].dtype.kind == 'f')
if self.axis is not None:
if self.axis >= 0:
type_check.expect(self.axis < in_types[0].ndim)
else:
type_check.expect(-self.axis - 1 < in_types[0].ndim)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('n', 'x'))
n_type, x_type = in_types
type_check.expect(
n_type.dtype.kind == 'i',
x_type.dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('h', ))
h_type, = in_types
type_check.expect(
h_type.dtype.kind == 'f',
h_type.ndim == 2,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a',))
a_type = in_types[0]
type_check.expect(
a_type.dtype.kind == 'f',
a_type.ndim >= 1
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('h',))
h_type, = in_types
type_check.expect(
h_type.dtype.kind == 'f',
h_type.ndim == 2,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a',))
a_type, = in_types
type_check.expect(a_type.dtype == numpy.float32)
# Only 2D array shapes allowed
type_check.expect(a_type.ndim == 2)
# Matrix inversion only allowed for square matrices
type_check.expect(a_type.shape[0] == a_type.shape[1])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('lhs', 'rhs'))
type_check.expect(
in_types[0].dtype.kind == 'f',
in_types[0].dtype == in_types[1].dtype,
)
type_check.expect_broadcast_shapes(in_types[0].shape,
in_types[1].shape)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a', 'b'))
a_type, b_type = in_types
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
x_type, = in_types
type_check.expect(
x_type.dtype.kind == 'f',
-x_type.ndim <= self.axis < x_type.ndim,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('gy',))
gy_type = in_types[0]
type_check.expect(
gy_type.dtype.char == 'f',
gy_type.ndim == 4
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('pred', 'true'))
pred_type, true_type = in_types
type_check.expect(pred_type.dtype.kind == 'f',
true_type.dtype.kind == 'f')
type_check.expect(pred_type.shape == true_type.shape, )
def check_type_forward(self, in_types):
type_check.argname(in_types, ('a',))
a_type = in_types[0]
type_check.expect(
a_type.dtype.kind == 'f',
a_type.ndim >= 2
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 'W'))
x_type, W_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
W_type.dtype == x_type.dtype,
x_type.ndim >= W_type.ndim + 1,
x_type.shape[1:1 + type_check.eval(W_type.ndim)] == W_type.shape
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 't'))
x_type, t_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
t_type.dtype.kind == 'i',
x_type.shape == t_type.shape
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('mean', 'ln_var'))
m_type, v_type = in_types
type_check.expect(
m_type.dtype.kind == 'f',
m_type.dtype == v_type.dtype,
m_type.shape == v_type.shape,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x0', 'x1', 'y'))
x0_type, x1_type, y_type = in_types
type_check.expect(
x0_type.dtype.kind == 'f', x0_type.dtype == x1_type.dtype,
y_type.dtype.kind == 'i', x0_type.shape == x1_type.shape,
x1_type.shape[0] == y_type.shape[0], x1_type.shape[0] > 0,
x0_type.ndim == 2, x1_type.ndim == 2, y_type.ndim == 1)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 't'))
x_type, t_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
t_type.dtype.kind == 'i',
t_type.shape == x_type.shape,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
a_type, = in_types
type_check.expect(a_type.dtype.kind == 'f')
# Only a minibatch of 2D array shapes allowed.
type_check.expect(a_type.ndim == 3)
# Matrix inversion only allowed for square matrices
# so assert the last two dimensions are equal.
type_check.expect(a_type.shape[-1] == a_type.shape[-2])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('real', 'imag'))
r_type, i_type = in_types
type_check.expect(
r_type.dtype.kind == 'f',
r_type.ndim > 0,
r_type.shape == i_type.shape,
r_type.dtype == i_type.dtype,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x',))
x_type = in_types[0]
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim == 4,
x_type.shape[1] >= self.kh * self.kw,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', ))
a_type, = in_types
type_check.expect(a_type.dtype.kind == 'f')
# Only a minibatch of 2D array shapes allowed.
type_check.expect(a_type.ndim == 3)
# Matrix inversion only allowed for square matrices
# so assert the last two dimensions are equal.
type_check.expect(a_type.shape[-1] == a_type.shape[-2])
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 't'))
x_type, t_type = in_types
type_check.expect(
t_type.dtype.kind == 'i',
x_type.ndim == 2,
t_type.ndim == 1,
x_type.shape[0] == t_type.shape[0],
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('x', 'beta'))
x_type, beta_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
beta_type.dtype == x_type.dtype,
beta_type.ndim <= x_type.ndim - 1,
beta_type.shape == x_type.shape[1:1 +
type_check.eval(beta_type.ndim)]
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('theta',))
theta_type = in_types[0]
type_check.expect(
theta_type.dtype.kind == 'f',
theta_type.ndim == 3,
theta_type.shape[1] == 2,
theta_type.shape[2] == 3,
)
def check_type_forward(self, in_types):
type_check.argname(in_types, ('y', 'z'))
y_type, z_type = in_types
type_check.expect(
y_type.dtype.kind == 'f',
y_type.dtype == z_type.dtype,
y_type.ndim == 2,
z_type.ndim == 2,
y_type.shape[0] == z_type.shape[0]
)