Python chainer.utils.type_check 模块,expect() 实例源码
我们从Python开源项目中,提取了以下50个代码示例,用于说明如何使用chainer.utils.type_check.expect()。
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 n_in.eval() == 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, w_type = in_types[:2]
type_check.expect(
x_type.dtype == numpy.float32,
w_type.dtype == numpy.float32,
)
if n_in.eval() == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == numpy.float32,
)
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(n_in == 1)
x_type = in_types[0]
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim == 4,
x_type.shape == self.indexes.shape,
)
if self.outh is not None:
expected_h = conv.get_conv_outsize(
self.outh, self.kh, self.sy, self.ph, cover_all=self.cover_all)
type_check.expect(x_type.shape[2] == expected_h)
if self.outw is not None:
expected_w = conv.get_conv_outsize(
self.outw, self.kw, self.sx, self.pw, cover_all=self.cover_all)
type_check.expect(x_type.shape[3] == expected_w)
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",
v_type.ndim == 4,
g_type.ndim == 4,
x_type.shape[1] == v_type.shape[1],
)
if type_check.eval(n_in) == 4:
b_type = in_types[3]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.shape[0] == v_type.shape[0],
)
def check_type_forward(self, n_in <= 4)
x_type = in_types[0]
v_type = in_types[1]
g_type = in_types[1]
type_check.expect(
x_type.dtype.kind == "f",
x_type.ndim == self.ndim + 2,
v_type.ndim == self.ndim + 2,
g_type.ndim == self.ndim + 2,
)
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 = t_type.ndim.eval()
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, x_type.ndim.eval()):
type_check.expect(x_type.shape[i] == 1)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
ndim = type_check.Variable(len(self._shape), 'len(shape)')
type_check.expect(in_types[0].ndim <= ndim)
shape = in_types[0].shape.eval()
# 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.expect(
in_types.size() == 1,
)
x_type, = in_types
cnt = _count_unkNown_dims(self.shape)
if cnt == 0:
type_check.expect(
type_check.prod(x_type.shape) == type_check.prod(self.shape))
else:
kNown_size = 1
for s in self.shape:
if s > 0:
kNown_size *= s
size_var = type_check.Variable(kNown_size,
'kNown_size(=%d)' % kNown_size)
type_check.expect(
type_check.prod(x_type.shape) % size_var == 0)
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,
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 range(2, c_type.ndim.eval()):
type_check.expect(x_type.shape[i] == c_type.shape[i])
def check_type_forward(self, in_types):
n_in = in_types.size().eval()
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]
type_check.expect(
x_type.dtype.kind == 'f',
x_type.ndim >= gamma_type.ndim + 1,
# 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, n_in <= 3)
x_type = in_types[0]
w_type = in_types[1]
type_check.expect(
x_type.dtype == numpy.float32,
w_type.ndim == 4,
x_type.shape[1] == w_type.shape[1],
)
if n_in.eval() == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == numpy.float32,
)
def check_type_forward(self,
in_types[0].dtype == numpy.float32
)
if self.axis is not None:
for axis in self.axis:
if axis >= 0:
type_check.expect(
axis < in_types[0].ndim,
)
else:
type_check.expect(
-axis - 1 < in_types[0].ndim,
)
def check_type_forward(self,
in_types[0].dtype.kind == 'f'
)
if self.axis is not None:
for axis in self.axis:
if axis >= 0:
type_check.expect(
axis < in_types[0].ndim,
)
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 == numpy.float32,
b_type.dtype == numpy.float32
)
_check_ndim(a_type)
_check_ndim(b_type)
a_type = _convert_type(a_type)
b_type = _convert_type(b_type)
a_idx = _get_check_index(self.transa, False)
b_idx = _get_check_index(self.transb, True)
type_check.expect(
a_type.shape[a_idx] == b_type.shape[b_idx]
)
def check_type_forward(self,
b_type.dtype == numpy.float32
)
_check_ndim(a_type, lower=2, upper=3)
_check_ndim(b_type, upper=3)
a_type = _convert_type(a_type, vector_ndim=2)
b_type = _convert_type(b_type, vector_ndim=2)
a_idx = _get_check_index(self.transa, False, row_idx=1, col_idx=2)
b_idx = _get_check_index(self.transb, True, col_idx=2)
type_check.expect(
a_type.shape[a_idx] == b_type.shape[b_idx]
)
def check_type_forward(self, n_in))
x_type, beta_type = in_types[:3]
M = gamma_type.ndim.eval()
type_check.expect(
x_type.dtype.kind == 'f',
x_type.shape[1:1 + M] == gamma_type.shape,
)
if len(in_types) == 5:
mean_type,
)
def check_type_forward(self, n_in <= 3)
x_type = in_types[0]
w_type = in_types[1]
type_check.expect(
x_type.dtype.kind == 'f',
)
if n_in.eval() == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
)
def check_type_forward(self,
)
if n_in.eval() == 4:
b_type = in_types[3]
type_check.expect(
b_type.dtype == x_type.dtype,
)
def check_type_forward(self, n_in <= 4)
x_type, w_type, g_type = in_types[:3]
type_check.expect(
x_type.dtype.kind == "f",
w_type.dtype.kind == "f",
g_type.ndim == 2,
)
def check_type_forward(self,
x_type.ndim == 3,
w_type.ndim == 3,
)
def check_type_forward(self, in_types):
n_in = in_types.size()
type_check.expect(2 <= n_in, n_in <= 3)
x_type = in_types[0]
w_type = in_types[1]
type_check.expect(
x_type.dtype.kind == 'f',
w_type.dtype.kind == 'f',
x_type.ndim == 4,
w_type.ndim == 5,
x_type.shape[1] == w_type.shape[0] * w_type.shape[2],
)
if type_check.eval(n_in) == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == x_type.dtype,
b_type.ndim == 2,
b_type.shape[0] == w_type.shape[0],
b_type.shape[1] == w_type.shape[1],
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 3)
x_type, t_type, w_type = in_types
type_check.expect(
x_type.dtype.kind == 'f',
t_type.dtype == numpy.int32,
w_type.dtype == 'f',
t_type.ndim == x_type.ndim - 1,
w_type.ndim == x_type.ndim - 1,
x_type.shape[0] == t_type.shape[0],
x_type.shape[0] == w_type.shape[0],
x_type.shape[2:] == t_type.shape[1:],
x_type.shape[2:] == w_type.shape[1:],
)
def check_type_forward(self, w_type = in_types[:2]
type_check.expect(
x_type.dtype == np.float32,
w_type.dtype == np.float32,
)
if n_in.eval() == 3:
b_type = in_types[2]
type_check.expect(
b_type.dtype == np.float32,
)
def check_type_forward(self,
in_types[0].dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
x_type, = in_types
type_check.expect(
x_type.dtype == numpy.float32,
)
def check_type_forward(self,
in_types[0].dtype == numpy.float32
)
def check_type_forward(self, in_types):
type_check.expect(
in_types[0].dtype.kind == 'f',
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1,)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2,)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
x_type, roi_type = in_types
type_check.expect(
x_type.dtype == numpy.float32,
roi_type.dtype == numpy.float32,
roi_type.ndim == 2,
roi_type.shape[1] == 5,
)
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 2)
type_check.expect(
in_types[0].dtype == numpy.float32,
in_types[1].dtype == numpy.float32,
in_types[0].shape == in_types[1].shape
)