def conv2d(x_BKRC, f_LKrc, kernelshape, pad=(0, 0), stride=(1, 1)):
col_BmnZ = im2col(x_BKRC, kernelshape, pad, stride)
L, K, r, c = f_LKrc.shape
f_LZ = f_LKrc.reshape([L, K * r * c])
B, m, n, Z = col_BmnZ.shape
col_Bmn_Z = col_BmnZ.reshape([B * m * n, Z])
col_Bmn_L = core.Result(core.Mul22(False, True), [col_Bmn_Z, f_LZ])
return col_Bmn_L.reshape([B, m, n, L]).transpose([0, 3, 1, 2])
def conv2d(x_BKRC, f_LKrc, kernelshape, pad=(0, 0), stride=(1, 1)):
devtype = cgt.get_config()["default_device"].devtype
L, K, r, c = f_LKrc.shape
if devtype == "gpu":
b_1K11 = cgt.zeros((1, L, 1, 1), cgt.floatX)
return core.Result(
cudnn_ops.CudnnConvForward(pad[0], pad[1], stride[0], stride[1]),
[x_BKRC, f_LKrc, b_1K11])
else:
assert devtype == "cpu"
col_BmnZ = im2col(x_BKRC, kernelshape, pad, stride)
f_LZ = f_LKrc.reshape([L, K * r * c])
B, m, n, Z = col_BmnZ.shape
col_Bmn_Z = col_BmnZ.reshape([B * m * n, Z])
col_Bmn_L = core.Result(core.Mul22(False, True), [col_Bmn_Z, f_LZ])
return col_Bmn_L.reshape([B, m, n, L]).transpose([0, 3, 1, 2])
def matmat11a(X, Y):
if isinstance(X, np.ndarray):
return np.dot(X.T, Y.T).sum()
else:
return sum(core.Result(core.Mul22(True, True), [X, Y]))
def matmat00a(X, Y):
if isinstance(X, np.ndarray):
return np.dot(X, Y).sum()
else:
return sum(core.Result(core.Mul22(False, False), [X, Y]))
