def one_test_dot(self, size, n_template, template_size, stride, padding):
rng = xp.random.RandomState(0)
d = rng.randn(n_template, template_size)
dmat = template._temp2mat(d, size, stride, padding, xp=xp)
assert dmat.shape[1] == template._coef_size(d.shape[-1], size, stride,
padding)
x = rng.randn(dmat.shape[0], dmat.shape[1])
xmat = template._coef2mat(x, size, d.shape[-1], stride, padding, xp=xp)
print(xmat.shape)
assert allclose(xp.tensordot(x, dmat, 2),
xp.tensordot(d, xmat, 2))
# matrix dot expression
fit = xp.tensordot(x, dmat, 2)
assert allclose(xp.dot(x.reshape(-1),
dmat.reshape(-1, dmat.shape[-1])), fit)
assert allclose(xp.dot(d.reshape(-1),
xmat.reshape(-1, xmat.shape[-1])), fit)
# with batch x
batch_size = 4
x = rng.randn(batch_size, dmat.shape[0], dmat.shape[1])
xmat = template._coef2mat(x, size, d.shape[-1], stride, padding, xp=xp)
assert allclose(xp.tensordot(x, dmat, 2),
xp.tensordot(d, xmat, ((0, 1), (-3, -2))))
def error(self, x, D, alpha, mask=None):
mask = xp.ones(self.y.shape, dtype=float) if mask is None else mask
alpha = alpha * xp.sum(mask, axis=-1, keepdims=True)
D = normalize.l2(D, axis=-1, xp=xp)
loss = xp.sum(0.5 / alpha *
xp.square(xp.abs(self.y - xp.tensordot(x, D, axes=1))) *
mask)
return loss + xp.sum(xp.abs(x))
def test_same(self):
# stride 1
# n_template: 2, template_size: 3
D = xp.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
x = xp.array([[1, 2, 3, 4, 5, 6, 7, 8, 9],
[10, 20, 30, 40, 50, 60, 70, 80, 90]], dtype=np.float32)
size, stride, padding = 7, 1, 'SAME'
dmat = template._temp2mat(D, size, stride, padding, xp=xp)
assert dmat.shape[1] == template._coef_size(D.shape[-1], size, stride,
padding)
expected = xp.array([[[3, 0, 0, 0, 0, 0, 0],
[2, 3, 0, 0, 0, 0, 0],
[1, 2, 3, 0, 0, 0, 0],
[0, 1, 2, 3, 0, 0, 0],
[0, 0, 1, 2, 3, 0, 0],
[0, 0, 0, 1, 2, 3, 0],
[0, 0, 0, 0, 1, 2, 3],
[0, 0, 0, 0, 0, 1, 2],
[0, 0, 0, 0, 0, 0, 1]],
[[6, 0, 0, 0, 0, 0, 0],
[5, 6, 0, 0, 0, 0, 0],
[4, 5, 6, 0, 0, 0, 0],
[0, 4, 5, 6, 0, 0, 0],
[0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 4, 5, 6, 0],
[0, 0, 0, 0, 4, 5, 6],
[0, 0, 0, 0, 0, 4, 5],
[0, 0, 0, 0, 0, 0, 4]]], dtype=xp.float32)
assert allclose(dmat, expected)
xmat = template._coef2mat(x, size, D.shape[-1], stride, padding,
xp=xp)
expected = xp.array([[[3, 4, 5, 6, 7, 8, 9],
[2, 3, 4, 5, 6, 7, 8],
[1, 2, 3, 4, 5, 6, 7]],
[[30, 40, 50, 60, 70, 80, 90],
[20, 30, 40, 50, 60, 70, 80],
[10, 20, 30, 40, 50, 60, 70]]], dtype=xp.float32)
assert allclose(xmat, expected)
assert allclose(xp.tensordot(x, dmat, 2),
xp.tensordot(D, xmat, 2))
# stride 2
size, stride, padding = 7, 2, 'SAME'
dmat = template._temp2mat(D, size, stride, padding, xp=xp)
assert dmat.shape[1] == template._coef_size(D.shape[-1], size, stride,
padding)
expected = xp.array([[[3, 0, 0, 0, 0, 0, 0],
[1, 2, 3, 0, 0, 0, 0],
[0, 0, 1, 2, 3, 0, 0],
[0, 0, 0, 0, 1, 2, 3],
[0, 0, 0, 0, 0, 0, 1]],
[[6, 0, 0, 0, 0, 0, 0],
[4, 5, 6, 0, 0, 0, 0],
[0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 0, 4, 5, 6],
[0, 0, 0, 0, 0, 0, 4]]], dtype=xp.float32)
assert allclose(dmat, expected)
x = xp.array([[1, 2, 3, 4, 5],
[10, 20, 30, 40, 50]], dtype=np.float32)
xmat = template._coef2mat(x, size, D.shape[-1], stride, padding,
xp=xp)
expected = xp.array([[[2, 0, 3, 0, 4, 0, 5],
[0, 2, 0, 3, 0, 4, 0],
[1, 0, 2, 0, 3, 0, 4]],
[[20, 0, 30, 0, 40, 0, 50],
[0, 20, 0, 30, 0, 40, 0],
[10, 0, 20, 0, 30, 0, 40]]], dtype=xp.float32)
assert allclose(xmat, expected)
assert allclose(xp.tensordot(x, dmat, 2), xp.tensordot(D, xmat, 2))
# stride 2
size, stride, padding = 6, 2, 'SAME'
dmat = template._temp2mat(D, size, stride, padding, xp=xp)
print(template._coef_size(D.shape[-1], size, stride,
padding))
assert dmat.shape[1] == template._coef_size(D.shape[-1], size, stride,
padding)
expected = xp.array([[[2, 3, 0, 0, 0, 0],
[0, 1, 2, 3, 0, 0],
[0, 0, 0, 1, 2, 3],
[0, 0, 0, 0, 0, 1]],
[[5, 6, 0, 0, 0, 0],
[0, 4, 5, 6, 0, 0],
[0, 0, 0, 4, 5, 6],
[0, 0, 0, 0, 0, 4]]], dtype=xp.float32)
assert allclose(dmat, expected)
x = xp.array([[1, 2, 3, 4],
[10, 20, 30, 40]], dtype=np.float32)
xmat = template._coef2mat(x, size, D.shape[-1], stride, padding, xp=xp)
expected = xp.array([[[2, 0, 3, 0, 4, 0],
[0, 2, 0, 3, 0, 4],
[1, 0, 2, 0, 3, 0]],
[[20, 0, 30, 0, 40, 0],
[0, 20, 0, 30, 0, 40],
[10, 0, 20, 0, 30, 0]]], dtype=xp.float32)
#assert allclose(xmat, expected)
assert allclose(xp.tensordot(x, dmat, 2), xp.tensordot(D, xmat, 2))
def test_valid(self):
# stride 1
# n_template: 2, template_size: 3
D = xp.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
x = xp.array([[1, 2, 3, 4, 5], [10, 20, 30, 40, 50]], dtype=np.float32)
size, stride, padding = 7, 1, 'VALID'
dmat = template._temp2mat(D, size, stride, padding, xp=xp)
assert dmat.shape[1] == template._coef_size(D.shape[-1], size, stride,
padding)
expected = xp.array([[[1, 2, 3, 0, 0, 0, 0],
[0, 1, 2, 3, 0, 0, 0],
[0, 0, 1, 2, 3, 0, 0],
[0, 0, 0, 1, 2, 3, 0],
[0, 0, 0, 0, 1, 2, 3]],
[[4, 5, 6, 0, 0, 0, 0],
[0, 4, 5, 6, 0, 0, 0],
[0, 0, 4, 5, 6, 0, 0],
[0, 0, 0, 4, 5, 6, 0],
[0, 0, 0, 0, 4, 5, 6]]], dtype=xp.float32)
assert allclose(dmat, expected)
xmat = template._coef2mat(x, size, D.shape[-1], stride, padding,
xp=xp)
expected = xp.array([[[1, 2, 3, 4, 5, 0, 0],
[0, 1, 2, 3, 4, 5, 0],
[0, 0, 1, 2, 3, 4, 5]],
[[10, 20, 30, 40, 50, 0, 0],
[0, 10, 20, 30, 40, 50, 0],
[0, 0, 10, 20, 30, 40, 50]]], dtype=xp.float32)
assert allclose(xmat, expected)
assert allclose(xp.tensordot(x, dmat, 2), xp.tensordot(D, xmat, 2))
# stride 2
size, stride, padding = 7, 2, 'VALID'
dmat = template._temp2mat(D, size, stride, padding, xp=xp)
assert dmat.shape[1] == template._coef_size(D.shape[-1], size, stride,
padding)
x = xp.array([[1, 2, 3], [10, 20, 30]], dtype=np.float32)
xmat = template._coef2mat(x, size, D.shape[-1], stride, padding, xp=xp)
expected = xp.array([[[1, 0, 2, 0, 3, 0, 0],
[0, 1, 0, 2, 0, 3, 0],
[0, 0, 1, 0, 2, 0, 3]],
[[10, 0, 20, 0, 30, 0, 0],
[0, 10, 0, 20, 0, 30, 0],
[0, 0, 10, 0, 20, 0, 30]]], dtype=xp.float32)
assert allclose(xmat, expected)
assert allclose(xp.tensordot(x, dmat, 2), xp.tensordot(D, xmat, 2))
# stride 2
size, stride, padding = 6, 2, 'VALID'
dmat = template._temp2mat(D, size, stride, padding, xp=xp)
assert dmat.shape[1] == template._coef_size(D.shape[-1], size, stride,
padding)
expected = xp.array([[[0, 1, 2, 3, 0, 0],
[0, 0, 0, 1, 2, 3]],
[[0, 4, 5, 6, 0, 0],
[0, 0, 0, 4, 5, 6]]], dtype=xp.float32)
assert allclose(dmat, expected)
x = xp.array([[1, 2], [10, 20]], dtype=np.float32)
xmat = template._coef2mat(x, size, D.shape[-1], stride, padding,
xp=xp)
expected = xp.array([[[0, 1, 0, 2, 0, 0],
[0, 0, 1, 0, 2, 0],
[0, 0, 0, 1, 0, 2]],
[[0, 10, 0, 20, 0, 0],
[0, 0, 10, 0, 20, 0],
[0, 0, 0, 10, 0, 20]]], dtype=xp.float32)
assert allclose(xmat, expected)
assert allclose(xp.tensordot(x, dmat, 2), xp.tensordot(D, xmat, 2))