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.expect(in_types.size() == 2)
a_type, b_type = in_types
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
)
a_ndim = type_check.eval(a_type.ndim)
b_ndim = type_check.eval(b_type.ndim)
if a_ndim == 0 or b_ndim == 0:
pass
elif a_ndim == 1 or b_ndim == 1:
type_check.expect(
a_type.ndim == b_type.ndim,
a_type.shape == b_type.shape,
)
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.ndim == b_type.ndim,
a_type.shape[:-2] == b_type.shape[:-2],
a_type.shape[a_idx] == b_type.shape[b_idx],
)
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):
n_in = type_check.eval(in_types.size())
if n_in != 3 and n_in != 5:
raise type_check.InvalidType(
'%s or %s' % (in_types.size() == 3, in_types.size() == 5),
'%s == %s' % (in_types.size(), n_in))
x_type, gamma_type, beta_type = in_types[:3]
M = type_check.eval(gamma_type.ndim)
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= gamma_type.ndim + 1,
x_type.shape[1:1 + M] == gamma_type.shape,
# TODO(tkerola): Check shape
gamma_type.dtype == x_type.dtype,
beta_type.dtype == x_type.dtype,
gamma_type.shape == beta_type.shape,
)
if len(in_types) == 5:
mean_type, var_type = in_types[3:]
type_check.expect(
mean_type.dtype == x_type.dtype,
mean_type.shape == gamma_type.shape,
var_type.dtype == x_type.dtype,
var_type.shape == gamma_type.shape,
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 5)
x_type, gamma_type, beta_type, mean_type, var_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
# TODO(beam2d): Check shape
gamma_type.dtype.kind == 'f',
beta_type.dtype == gamma_type.dtype,
mean_type.dtype == gamma_type.dtype,
var_type.dtype == gamma_type.dtype,
beta_type.shape == gamma_type.shape,
mean_type.shape == gamma_type.shape,
var_type.shape == gamma_type.shape,
)
_x_ndim = type_check.eval(x_type.ndim)
_gamma_ndim = type_check.eval(gamma_type.ndim)
_axis = _compute_axis(_x_ndim, _gamma_ndim, self.axis)
type_check.expect(
x_type.ndim >= len(_axis),
)
_key_axis = _compute_key_axis(_x_ndim, _gamma_ndim, _axis)
type_check.expect(
gamma_type.ndim == len(_key_axis),
)
for i in range(len(_key_axis)):
type_check.expect(
x_type.shape[_key_axis[i]] == gamma_type.shape[i],
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
a_type, b_type = in_types
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
)
a_ndim = type_check.eval(a_type.ndim)
b_ndim = type_check.eval(b_type.ndim)
if a_ndim == 0 or b_ndim == 0:
pass
elif a_ndim == 1 or b_ndim == 1:
type_check.expect(
a_type.ndim == b_type.ndim,
a_type.shape == b_type.shape,
)
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.ndim == b_type.ndim,
a_type.shape[:-2] == b_type.shape[:-2],
a_type.shape[a_idx] == b_type.shape[b_idx],
)
def check_type_forward(self, in_types):
n_in = type_check.eval(in_types.size())
if n_in != 3 and n_in != 5:
raise type_check.InvalidType(
'%s or %s' % (in_types.size() == 3, in_types.size() == 5),
'%s == %s' % (in_types.size(), n_in))
x_type, gamma_type, beta_type = in_types[:3]
M = type_check.eval(gamma_type.ndim)
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= gamma_type.ndim + 1,
x_type.shape[1:1 + M] == gamma_type.shape,
# TODO(beam2d): Check shape
gamma_type.dtype == x_type.dtype,
beta_type.dtype == x_type.dtype,
gamma_type.shape == beta_type.shape,
)
if len(in_types) == 5:
mean_type, var_type = in_types[3:]
type_check.expect(
mean_type.dtype == x_type.dtype,
mean_type.shape == gamma_type.shape,
var_type.dtype == x_type.dtype,
var_type.shape == gamma_type.shape,
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 5)
x_type, gamma_type, beta_type, mean_type, var_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
# TODO(beam2d): Check shape
gamma_type.dtype == x_type.dtype,
beta_type.dtype == x_type.dtype,
gamma_type.shape == beta_type.shape,
mean_type.dtype == x_type.dtype,
mean_type.shape == gamma_type.shape,
var_type.dtype == x_type.dtype,
var_type.shape == gamma_type.shape,
)
_x_ndim = type_check.eval(x_type.ndim)
_gamma_ndim = type_check.eval(gamma_type.ndim)
_axis = _compute_axis(_x_ndim, _gamma_ndim, self.axis)
type_check.expect(
x_type.ndim >= len(_axis),
)
_key_axis = _compute_key_axis(_x_ndim, _gamma_ndim, _axis)
type_check.expect(
gamma_type.ndim == len(_key_axis),
)
for i in range(len(_key_axis)):
type_check.expect(
x_type.shape[_key_axis[i]] == gamma_type.shape[i],
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
x_type, t_type = in_types
type_check.expect(x_type.dtype.kind == 'f', t_type.dtype == np.int32)
t_ndim = type_check.eval(t_type.ndim)
type_check.expect(x_type.ndim >= t_type.ndim,
x_type.shape[0] == t_type.shape[0],
x_type.shape[2:t_ndim + 1] == t_type.shape[1:])
for i in moves.range(t_ndim + 1, type_check.eval(x_type.ndim)):
type_check.expect(x_type.shape[i] == 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_ndim = type_check.eval(t_type.ndim)
type_check.expect(x_type.ndim >= t_type.ndim,
x_type.shape[0] == t_type.shape[0],
x_type.shape[2:t_ndim + 1] == t_type.shape[1:])
for i in six.moves.range(t_ndim + 1, type_check.eval(x_type.ndim)):
type_check.expect(x_type.shape[i] == 1)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
ndim = type_check.eval(in_types[0].ndim)
for i in six.moves.range(1, type_check.eval(in_types.size())):
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
if ndim <= 1:
type_check.expect(in_types[0].shape == in_types[i].shape)
continue
for d in six.moves.range(1, ndim):
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
def check_type_forward(self, in_types):
n_in = type_check.eval(in_types.size())
if n_in != 3:
raise type_check.InvalidType('%s == %s' % (in_types.size(), n_in))
x_type, gamma_type, beta_type = in_types[:3]
M = type_check.eval(gamma_type.ndim)
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= gamma_type.ndim + 1,
x_type.shape[1:1 + M] == gamma_type.shape,
gamma_type.dtype == x_type.dtype,
beta_type.dtype == x_type.dtype,
gamma_type.shape == beta_type.shape,
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
ndim = type_check.eval(in_types[0].ndim)
for i in six.moves.range(1, type_check.eval(in_types.size())):
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
if ndim <= 1:
type_check.expect(in_types[0].shape == in_types[i].shape)
continue
for d in six.moves.range(1, ndim):
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
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): n_in = in_types.size() type_check.expect(3 <= n_in, n_in <= 4) x_type = in_types[0] v_type = in_types[1] g_type = in_types[2] type_check.expect( x_type.dtype.kind == "f", v_type.dtype.kind == "f", g_type.dtype.kind == "f", x_type.ndim == 4, v_type.ndim == 4, g_type.ndim == 4, x_type.shape[1] == v_type.shape[0] ) if self.outh is not None: type_check.expect( x_type.shape[2] == conv.get_conv_outsize(self.outh, v_type.shape[2],self.sy, self.ph), ) if self.outw is not None: type_check.expect( x_type.shape[3] == conv.get_conv_outsize(self.outw, v_type.shape[3], self.sx, self.pw), ) if type_check.eval(n_in) == 4: b_type = in_types[3] type_check.expect( b_type.dtype == x_type.dtype, b_type.ndim == 1, b_type.shape[0] == v_type.shape[1] )
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(4 <= n_in, n_in <= 5)
x_type = in_types[0]
w_type = in_types[1]
b_type = in_types[2]
ct_type = in_types[3]
type_check.expect(
x_type.dtype.kind == "f",
w_type.dtype.kind == "f",
b_type.dtype.kind == "f",
x_type.ndim == 3,
w_type.ndim == 2,
b_type.ndim == 1,
b_type.shape[0] * 3 == w_type.shape[0] * 2,
ct_type.dtype == x_type.dtype,
ct_type.ndim == 2,
ct_type.shape[1] == x_type.shape[1],
)
if type_check.eval(n_in) == 5:
mask_x_type = in_types[4]
type_check.expect(
mask_x_type.dtype == x_type.dtype,
mask_x_type.ndim == 2,
mask_x_type.shape[1] == x_type.shape[1],
)
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.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == 4,
w_type.ndim == 4,
x_type.shape[1] == w_type.shape[0]
)
if self.outh is not None:
type_check.expect(
x_type.shape[2] ==
conv.get_conv_outsize(self.outh, w_type.shape[2],
self.sy, self.ph),
)
if self.outw is not None:
type_check.expect(
x_type.shape[3] ==
conv.get_conv_outsize(self.outw, w_type.shape[3],
self.sx, self.pw),
)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
b_type.shape[0] == w_type.shape[1]
)
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.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.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.expect(in_types.size() == 2)
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.expect(in_types.size() == 2)
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.expect(in_types.size() == 4)
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):
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):
n_in = in_types.size()
type_check.expect(2 <= n_in, n_in <= 3)
x_type, w_type = in_types[:2]
type_check.expect(x_type.dtype.kind == 'f', w_type.dtype.kind == 'f',
x_type.ndim == self.ndim + 2,
w_type.ndim == self.ndim + 2,
x_type.shape[1] == w_type.shape[0])
if self.outs is not None:
for i, (out, s, p, di) in enumerate(
zip(self.outs, self.stride, self.pad, self.dilate)):
lower_bound = conv.get_conv_outsize(out,
w_type.shape[i + 2],
s,
p,
d=di)
upper_bound = conv.get_conv_outsize(out,
w_type.shape[i + 2],
s,
p,
cover_all=True,
d=di)
type_check.expect(lower_bound <= x_type.shape[i + 2],
x_type.shape[i + 2] <= upper_bound)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
# Need to consider the case that group count > 1.
# b_type.shape[0] == w_type.shape[1]
)
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.expect(x_type.dtype.kind == 'f', w_type.dtype.kind == 'f',
x_type.ndim == 4, w_type.ndim == 4,
x_type.shape[1] == w_type.shape[0])
if self.outh is not None:
lower_bound = conv.get_conv_outsize(self.outh, w_type.shape[2],
self.sy, self.ph)
upper_bound = conv.get_conv_outsize(self.outh,
w_type.shape[2],
self.sy,
self.ph,
cover_all=True)
type_check.expect(lower_bound <= x_type.shape[2],
x_type.shape[2] <= upper_bound)
if self.outw is not None:
lower_bound = conv.get_conv_outsize(self.outw, w_type.shape[3],
self.sx, self.pw)
upper_bound = conv.get_conv_outsize(self.outw,
w_type.shape[3],
self.sx,
self.pw,
cover_all=True)
type_check.expect(lower_bound <= x_type.shape[3],
x_type.shape[3] <= upper_bound)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(b_type.dtype == x_type.dtype, b_type.ndim == 1,
b_type.shape[0] == w_type.shape[1])
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(3 <= n_in, n_in <= 4)
x_type = in_types[0]
v_type = in_types[1]
g_type = in_types[2]
type_check.expect(x_type.dtype.kind == "f", v_type.dtype.kind == "f",
g_type.dtype.kind == "f", x_type.ndim == 4,
v_type.ndim == 4, g_type.ndim == 4,
x_type.shape[1] == v_type.shape[0])
if self.outh is not None:
type_check.expect(
x_type.shape[2] == conv.get_conv_outsize(
self.outh, v_type.shape[2], self.sy, self.ph), )
if self.outw is not None:
type_check.expect(
x_type.shape[3] == conv.get_conv_outsize(
self.outw, v_type.shape[3], self.sx, self.pw), )
if type_check.eval(n_in) == 4:
b_type = in_types[3]
type_check.expect(b_type.dtype == x_type.dtype, b_type.ndim == 1,
b_type.shape[0] == v_type.shape[1])
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.expect(in_types.size() == 4)
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.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == self.ndim + 2,
w_type.ndim == self.ndim + 2,
x_type.shape[1] == w_type.shape[0]
)
if self.outs is not None:
for i, (out, s, p) in enumerate(zip(
self.outs, self.stride, self.pad)):
type_check.expect(
x_type.shape[i + 2] ==
conv.get_conv_outsize(out, w_type.shape[i + 2], s, p)
)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
b_type.shape[0] == w_type.shape[1]
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
a_type, b_type = in_types
# a_type.shape: ((nb,) _m, _k) when transa is False
# b_type.shape: ((nb,) _k, _n) when transb is False
a_m_axis, a_k_axis = -2, -1
b_k_axis, b_n_axis = -2, -1
sp_m_axis, sp_n_axis = -2, -1
if self.transa:
a_m_axis, a_k_axis = -1, -2
if self.transb:
b_k_axis, b_n_axis = -1, -2
if self.transc:
sp_m_axis, sp_n_axis = -1, -2
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
a_type.ndim >= 2,
a_type.ndim <= 3,
a_type.ndim == b_type.ndim,
a_type.shape[a_m_axis] == self.sp_shape[sp_m_axis],
b_type.shape[b_n_axis] == self.sp_shape[sp_n_axis],
a_type.shape[a_k_axis] == b_type.shape[b_k_axis],
)
a_ndim = type_check.eval(a_type.ndim)
if a_ndim == 3:
type_check.expect(
a_type.shape[0] == self.sp_row.shape[0],
b_type.shape[0] == self.sp_row.shape[0],
)
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.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == self.ndim + 2,
w_type.ndim == self.ndim + 2,
x_type.shape[1] == w_type.shape[0]
)
if self.outs is not None:
for i, (out, s, p, di) in enumerate(zip(
self.outs, self.stride, self.pad, self.dilate)):
lower_bound = conv.get_conv_outsize(
out, w_type.shape[i + 2], s, p, d=di)
upper_bound = conv.get_conv_outsize(
out, w_type.shape[i + 2], s, p, cover_all=True, d=di)
type_check.expect(
lower_bound <= x_type.shape[i + 2],
x_type.shape[i + 2] <= upper_bound)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
# Need to consider the case that group count > 1.
# b_type.shape[0] == w_type.shape[1]
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
a_type, b_type = in_types
# a_type.shape: ((nb,) _m, _k) when transa is False
# b_type.shape: ((nb,) _k, _n) when transb is False
a_m_axis, a_k_axis = -2, -1
b_k_axis, b_n_axis = -2, -1
sp_m_axis, sp_n_axis = -2, -1
if self.transa:
a_m_axis, a_k_axis = -1, -2
if self.transb:
b_k_axis, b_n_axis = -1, -2
if self.transc:
sp_m_axis, sp_n_axis = -1, -2
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
a_type.ndim >= 2,
a_type.ndim <= 3,
a_type.ndim == b_type.ndim,
a_type.shape[a_m_axis] == self.sp_shape[sp_m_axis],
b_type.shape[b_n_axis] == self.sp_shape[sp_n_axis],
a_type.shape[a_k_axis] == b_type.shape[b_k_axis],
)
a_ndim = type_check.eval(a_type.ndim)
if a_ndim == 3:
type_check.expect(
a_type.shape[0] == self.sp_row.shape[0],
b_type.shape[0] == self.sp_row.shape[0],
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
a_type = self.value
b_type = in_types[0]
type_check.expect(
a_type.dtype.kind == 'f',
b_type.dtype.kind == 'f',
a_type.ndim >= 1,
a_type.ndim == b_type.ndim,
)
ndim = type_check.eval(a_type.ndim)
if ndim == 1:
type_check.expect(a_type.shape == b_type.shape)
else:
a_idx = _matmul._get_check_index(False,
False,
row_idx=-2,
col_idx=-1)
b_idx = _matmul._get_check_index(False,
True,
row_idx=-2,
col_idx=-1)
type_check.expect(
a_type.shape[:-2] == b_type.shape[:-2],
a_type.shape[a_idx] == b_type.shape[b_idx],
)
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):
type_check.expect(in_types.size() == 2)
x_type, t_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
t_type.dtype == numpy.int32
)
t_ndim = type_check.eval(t_type.ndim)
type_check.expect(
x_type.ndim >= t_type.ndim,
x_type.shape[0] == t_type.shape[0],
x_type.shape[2: t_ndim + 1] == t_type.shape[1:]
)
for i in six.moves.range(t_ndim + 1, type_check.eval(x_type.ndim)):
type_check.expect(x_type.shape[i] == 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_ndim = type_check.eval(t_type.ndim)
type_check.expect(
x_type.ndim >= t_type.ndim,
x_type.shape[0] == t_type.shape[0],
x_type.shape[2: t_ndim + 1] == t_type.shape[1:]
)
for i in six.moves.range(t_ndim + 1, type_check.eval(x_type.ndim)):
type_check.expect(x_type.shape[i] == 1)
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.expect(in_types.size() > 0)
type_check._argname((in_types[0], ), ('x0', ))
ndim = type_check.eval(in_types[0].ndim)
for i in six.moves.range(1, type_check.eval(in_types.size())):
type_check._argname((in_types[i], ), ('x{}'.format(i), ))
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
if ndim <= 1:
continue
for d in six.moves.range(0, ndim):
if d == 1:
continue
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
type_check.argname((in_types[0],), ('x0',))
ndim = type_check.eval(in_types[0].ndim)
for i in six.moves.range(1, type_check.eval(in_types.size())):
type_check.argname((in_types[i],), ('x{}'.format(i),))
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
if ndim <= 1:
continue
for d in six.moves.range(0, ndim):
if d == 1:
continue
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
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.expect(in_types.size() > 0)
type_check.expect(
in_types[0].ndim > type_check.make_variable(self.axis, 'axis'))
type_check.expect(-in_types[0].ndim <= self.axis,
self.axis < in_types[0].ndim)
ndim = type_check.eval(in_types[0].ndim)
axis = self.axis % ndim
for i in six.moves.range(1, type_check.eval(in_types.size())):
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
for d in six.moves.range(0, ndim):
if d == axis:
continue
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
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', '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.expect(in_types.size() == 2)
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.expect(in_types.size() > 0)
type_check.expect(in_types[0].ndim >
type_check.make_variable(self.axis, 'axis'))
type_check.expect(
-in_types[0].ndim <= self.axis,
self.axis < in_types[0].ndim
)
ndim = type_check.eval(in_types[0].ndim)
axis = self.axis % ndim
for i in six.moves.range(1, type_check.eval(in_types.size())):
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].ndim == in_types[i].ndim,
)
for d in six.moves.range(0, ndim):
if d == axis:
continue
type_check.expect(in_types[0].shape[d] == in_types[i].shape[d])
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
shapes = [type_check.eval(t).shape for t in in_types]
r_shapes = [s[::-1] for s in shapes]
r_filled = six.moves.zip_longest(*r_shapes, fillvalue=1)
for ss in r_filled:
d = max(ss)
if not all(s == d or s == 1 for s in ss):
expect = 'each dimension has the same size or is 1'
actual = 'shapes: ' + ', '.join(map(str, shapes))
raise type_check.InvalidType(expect, actual)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
shapes = [type_check.eval(t).shape for t in in_types]
r_shapes = [s[::-1] for s in shapes]
r_filled = six.moves.zip_longest(*r_shapes, fillvalue=1)
for ss in r_filled:
d = max(ss)
if not all(s == d or s == 1 for s in ss):
expect = 'each dimension has the same size or is 1'
actual = 'shapes: ' + ', '.join(map(str, shapes))
raise type_check.InvalidType(expect, actual)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 3)
x_type, gamma_type, beta_type = in_types
M = type_check.eval(gamma_type.ndim)
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= gamma_type.ndim + 1,
x_type.shape[1:1 + M] == gamma_type.shape,
# TODO(tkerola): Check shape
gamma_type.dtype == x_type.dtype,
beta_type.dtype == x_type.dtype,
gamma_type.shape == beta_type.shape,
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() > 0)
type_check.expect(
-in_types[0].ndim - 1 <= self.axis,
self.axis <= in_types[0].ndim
)
# XXX: modified
for i in range(1, type_check.eval(in_types.size())):
type_check.expect(
in_types[0].dtype == in_types[i].dtype,
in_types[0].shape == in_types[i].shape,
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
c_type, x_type = in_types
type_check.expect(
c_type.dtype.kind == 'f',
x_type.dtype == c_type.dtype,
c_type.ndim >= 2,
x_type.ndim >= 2,
c_type.ndim == x_type.ndim,
x_type.shape[0] <= c_type.shape[0],
x_type.shape[1] == 4 * c_type.shape[1],
)
for i in six.moves.range(2, type_check.eval(c_type.ndim)):
type_check.expect(x_type.shape[i] == c_type.shape[i])
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
ndim = type_check.make_variable(len(self._shape), 'len(shape)')
type_check.expect(in_types[0].ndim <= ndim)
shape = type_check.eval(in_types[0].shape)
# check the shape in inverse order
for i in six.moves.range(-1, -len(shape) - 1, -1):
if shape[i] == self._shape[i] or shape[i] == 1:
continue
expect = 'in_type[0].shape[%d] == %d' % (i, self._shape[i])
if self._shape[i] != 1:
expect += ' or in_type[0].shape[%d] == 1' % i
actual = 'in_type[0].shape: %s' % str(shape)
raise type_check.InvalidType(expect, actual)
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x', ))
ndim = type_check.make_variable(len(self._shape), 'len(shape)')
type_check.expect(in_types[0].ndim <= ndim)
shape = type_check.eval(in_types[0].shape)
# check the shape in inverse order
for i in six.moves.range(-1, -len(shape) - 1, -1):
if shape[i] == self._shape[i] or shape[i] == 1:
continue
expect = 'in_type[0].shape[%d] == %d' % (i, self._shape[i])
if self._shape[i] != 1:
expect += ' or in_type[0].shape[%d] == 1' % i
actual = 'in_type[0].shape: %s' % str(shape)
raise type_check.InvalidType(expect, actual)
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.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == 4,
w_type.ndim == 6,
x_type.shape[1] == w_type.shape[3],
)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(b_type.dtype == x_type.dtype, b_type.ndim == 3,
b_type.shape == w_type.shape[:3])
def check_type_forward(self, in_types):
type_check._argname(in_types, ('c', 'x'))
c_type, x_type = in_types
type_check.expect(
c_type.dtype.kind == 'f',
x_type.dtype == c_type.dtype,
c_type.ndim >= 2,
x_type.ndim >= 2,
c_type.ndim == x_type.ndim,
x_type.shape[0] <= c_type.shape[0],
x_type.shape[1] == 4 * c_type.shape[1],
)
for i in six.moves.range(2, type_check.eval(c_type.ndim)):
type_check.expect(x_type.shape[i] == c_type.shape[i])
def check_type_forward(self, in_types):
type_check.expect(in_types.size() >= 2)
c_types = in_types[:-1]
x_type = in_types[-1]
n_ary = len(c_types)
type_check.expect(x_type.ndim >= 2)
for i in six.moves.range(len(c_types)):
type_check.expect(
c_types[i].dtype.kind == 'f',
x_type.dtype == c_types[i].dtype,
c_types[i].ndim >= 2,
c_types[i].ndim == x_type.ndim,
x_type.shape[0] == c_types[i].shape[0],
x_type.shape[1] == (3 + n_ary) * c_types[i].shape[1],
)
for j in six.moves.range(2, type_check.eval(c_types[i].ndim)):
type_check.expect(x_type.shape[i] == c_types[i].shape[j])
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.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.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
b_type.shape[0] == w_type.shape[0],
)
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.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == 4,
w_type.ndim == 6,
x_type.shape[1] == w_type.shape[3],
)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 3,
b_type.shape == w_type.shape[:3]
)
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.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == 4,
w_type.ndim == 4,
x_type.shape[1] == w_type.shape[0]
)
if self.outh is not None:
lower_bound = conv.get_conv_outsize(
self.outh, w_type.shape[2], self.sy, self.ph,
d=self.dy)
upper_bound = conv.get_conv_outsize(
self.outh, w_type.shape[2], self.sy, self.ph, cover_all=True,
d=self.dy)
type_check.expect(
lower_bound <= x_type.shape[2],
x_type.shape[2] <= upper_bound)
if self.outw is not None:
lower_bound = conv.get_conv_outsize(
self.outw, w_type.shape[3], self.sx, self.pw,
d=self.dx)
upper_bound = conv.get_conv_outsize(
self.outw, w_type.shape[3], self.sx, self.pw, cover_all=True,
d=self.dx)
type_check.expect(
lower_bound <= x_type.shape[3],
x_type.shape[3] <= upper_bound)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 1,
# Need to consider the case that group count > 1.
# b_type.shape[0] == w_type.shape[1],
)
