def test_pending(self):
a = jt.float([1,2,3])
b = jt.float([1,2,3])
c = a.float().float().float() * b.float().float().float()
del a
c.data
assert (c.data==[1,4,9]).all(), c.data
d, = jt.grad(c, [b])
d.data
assert (d.data==[1,2,3]).all(), d.data
def test_int_grad(self):
x = jt.array(2.0)
z = x * x * x * x * x
dx, = jt.grad(z, [x])
self.assertEqual(dx.data, 5 * 2**4)
y1 = jt.int(x)
y2 = jt.float(x)
z = x * x * y1 * y1 * y2
expect_error(lambda: jt.grad(z, [y1]))
dx, = jt.grad(z, [x])
self.assertEqual(dx.data, 48)
def resize_and_crop(x, bbox, interpolation="nearest"):
N, k = bbox.shape
H, W = x.shape
assert k==4
shape = [N,H,W]
# fx x cx
# +------------>
# fy | a dx | b
# | dy
# y | - o -
# |
# cy | c | d
# v
img = x
bb = [ bbox.reindex(shape, ["i0", str(i)]) for i in range(4) ]
hid = jt.index(shape, 1)
wid = jt.index(shape, 2)
one = jt.float(1).broadcast(shape)
x = bb[0]*jt.float(H-1)+hid*(bb[2]-bb[0])
y = bb[1]*jt.float(W-1)+wid*(bb[3]-bb[1])
if interpolation=="nearest":
return img.reindex_var([x.round(), y.round()])
if interpolation=="bilinear":
fx, fy = x.floor(), y.floor()
cx, cy = fx+one, fy+one
dx, dy = x-fx, y-fy
a = img.reindex_var([fx, fy])
b = img.reindex_var([cx, fy])
c = img.reindex_var([fx, cy])
d = img.reindex_var([cx, cy])
dnx, dny = one-dx, one-dy
ab = dx*b + dnx*a
cd = dx*d + dnx*c
o = ab*dny + cd*dy
return o
raise(f"Not support {interpolation}")