def test_grad_refactor_13():
class Net(nn.Cell):
""" Net definition """
def __init__(self):
super(Net, self).__init__()
self.z = Parameter(Tensor(np.ones([2]).astype(np.float32)), name='z')
def construct(self, x, y):
return x * self.z * y
net = Net()
weights = ParameterTuple(net.trainable_params())
C.grad_by_list(net, weights)(Tensor(np.ones([2]).astype(np.float32)), Tensor(np.zeros([2]).astype(np.float32)))
def test_switch_layer_with_single_prim():
class SwitchLayerCell(nn.Cell):
def __init__(self):
super(SwitchLayerCell, self).__init__()
self.layers = (nn.ReLU(), nn.ReLU())
self.z3 = Parameter(
Tensor(np.full([128, 96], 0.6, dtype=np.float32)), name='z3')
def construct(self, index, x):
ret = self.layers[index](x) * self.z3
return ret
index = Tensor(0, dtype=mstype.int32)
net = SwitchLayerCell()
net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
C.grad_by_list(net, ParameterTuple(net.trainable_params()))(index,
Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
C.grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
def test_index_to_switch_layer():
class Layer1(nn.Cell):
def __init__(self):
super(Layer1, self).__init__()
self.z1 = Parameter(Tensor(
np.full([128, 96], 0.6, dtype=np.float32)),
name='z1')
def construct(self, x):
return x * self.z1
class Layer2(nn.Cell):
def __init__(self):
super(Layer2, self).__init__()
self.z2 = Parameter(Tensor(
np.full([128, 96], 0.6, dtype=np.float32)),
name='z2')
def construct(self, x):
return x * self.z2
class SwitchLayerCell(nn.Cell):
def __init__(self):
super(SwitchLayerCell, self).__init__()
self.layers = (Layer1(), Layer2())
self.z3 = Parameter(Tensor(
np.full([128, 96], 0.6, dtype=np.float32)),
name='z3')
def construct(self, index, x):
ret = self.layers[index](x) * self.z3
return ret
index = Tensor(0, dtype=mstype.int32)
net = SwitchLayerCell()
net(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
C.grad_by_list(net, ParameterTuple(net.trainable_params()))(
index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
C.grad_all(net)(index, Tensor(np.full([128, 96], 0.6, dtype=np.float32)))
def construct(self, x, label):
weights = self.weights
return C.grad_by_list(self.network, weights)(x, label)
def construct(self, data, label):
weights = self.weights
grads = C.grad_by_list(self.network, weights)(data, label)
return grads
def construct(self, *inputs):
return C.grad_by_list(self.net, self.weights)(*inputs)
def construct(self, data, label, *args):
weights = self.weights
grads = grad_by_list(self.network, weights)(data, label)
if self.lr_schedule:
self.schedule.update_lr(*args)
return self.optimizer(grads)
def construct(self, a, b, c):
return C.grad_by_list(self.net, self.weights)(a, b, c)
def construct(self, x, y):
return C.grad_by_list(self.net, self.weights)(x, y)
def construct(self):
return C.grad_by_list(self.network, self.weights)()
def construct(self, x, label):
weights = self.weights
grads = C.grad_by_list(self.network, weights)(x, label)
return self.optimizer(grads)
