i0 = i - d0
return Lamda[i:n0](Sum[sum_limit((r, ), w, n0, di,
i0)](x[i0 + di * r] @ w[di]))
if batch_size:
batch_size = batch_size[0]
k = Symbol.k(integer=True)
return Lamda[k:batch_size](conv1d(x[k], w))
else:
return conv1d(x, w)
conv1d = Function.conv1d(real=True,
nargs=(2, ),
eval=conv1d,
shape=property(shape))
def conv2d(x, w, *limits):
if limits:
(r, ), *_ = limits
else:
r = (1, 1)
l0, l1, in_channels, out_channels = w.shape
*batch_size, n0, n1, _in_channels = x.shape
assert in_channels == _in_channels
def conv2d(x, w):
