def __call__(self, x, x_len):
if self.use_mask:
mask = self.xp.arange(x.shape[2]) < x_len[0]
x *= mask
x_len = (x_len + 2 * self.pad0 - self.dilate[0] * (self.ksize[0] - 1) - 1) // self.stride0 + 1
x = F.convolution_1d(
x=x,
W=self.W,
b=self.b,
stride=self.stride,
pad=self.pad,
dilate=self.dilate,
groups=self.groups)
return x, x_len
def __call__(self, x, x_len):
if self.use_mask:
mask = F.broadcast_to(self.xp.arange(x.shape[2]), x.shape).array <\
F.expand_dims(F.expand_dims(x_len, -1), -1).array
x *= mask
x_len = (x_len + 2 * self.pad0 - self.dilate[0] *
(self.ksize[0] - 1) - 1) // self.stride0 + 1
x = F.convolution_1d(x=x,
W=self.W,
b=self.b,
stride=self.stride,
pad=self.pad,
dilate=self.dilate,
groups=self.groups)
return x, x_len
def test_conv1d_invalid(self):
(x, W, b) = self._get_data(2)
with self.assertRaises(ValueError):
F.convolution_1d(x, W, b)
def test_conv1d(self):
(x, W, b) = self._get_data(1)
testing.assert_allclose(
F.convolution_nd(x, W, b).data, F.convolution_1d(x, W, b).data)
def reverse(self, x):
return F.convolution_1d(x, self.invW)
def __call__(self, x):
return F.convolution_1d(x, self.W), \
x.shape[0] * x.shape[-1] * F.log(F.det(self.W[..., 0]))
def test_conv1d_invalid(self):
(x, W, b) = self._get_data(2)
with self.assertRaises(ValueError):
F.convolution_1d(x, W, b)
def test_conv1d(self):
(x, W, b) = self._get_data(1)
testing.assert_allclose(
F.convolution_nd(x, W, b).data, F.convolution_1d(x, W, b).data)
