async def convolvetensor(x, W, b):
logging.info('- - - - - - - - conv2d - - - - - - -')
# 2D convolutions on m*n sized images from X with s*s sized filters from W.
# b is of dimension v
k, r, m, n = dim(x)
x = x.tolist()
v, r, s, s = dim(W)
W = W.tolist()
stype = type(x[0][0][0][0])
await mpc.returnType(stype, k, v, m, n)
x, W = await mpc.gather(x, W)
Y = [[[[b[j]] * m for _ in range(n)] for j in range(v)] for _ in range(k)]
counter = 0
for i in range(k):
for j in range(v):
for l in range(r):
counter += 1
if counter % 500 == 0:
await mpc.barrier()
Y[i][j] = mpc.matrix_add(Y[i][j], inprod2D(x[i][l], W[j][l]))
Y = await mpc.gather(Y)
for i in range(k):
for j in range(v):
for im in range(m):
Y[i][j][im] = mpc._reshare(Y[i][j][im])
Y = await mpc.gather(Y)
if stype.field.frac_length > 0:
l = stype.bit_length
Y = [[[mpc.trunc(y, l=l) for y in _] for _ in _] for _ in Y]
Y = await mpc.gather(Y)
return Y
def truncate(f):
logging.info("- - - - - - - - truncate- - - - - - -")
return np.vectorize(lambda a: mpc.trunc(a + (1<<(f-1)), f))