def test_parameter_update_int32_and_tensor():
""" test_parameter_update """
net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = Momentum(net.get_parameters(), Tensor(np.array([0.1, 0.01, 0.001]), mstype.float32), 0.001)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
# compile train graph
train_network.set_train()
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
_executor.compile(train_network, inputs, label)
# test tensor
param_lr = train_network.parameters_dict()['learning_rate']
update_network = ParameterUpdate(param_lr)
update_network.phase = 'update_param'
input_lr = Tensor(np.array([0.2, 0.02, 0.002]), mstype.float32)
_executor.compile(update_network, input_lr)
# test int32
param_step = train_network.parameters_dict()['global_step']
update_global_step = ParameterUpdate(param_step)
input_step = Tensor(np.array([1000]), mstype.int32)
_executor.compile(update_global_step, input_step)
def test_parameter_update_error():
""" test_parameter_update """
input_np = np.array([1])
input_parameter = Parameter(np.array([1]), 'input_parameter')
with pytest.raises(TypeError):
ParameterUpdate(input_np)
with pytest.raises(TypeError):
ParameterUpdate(input_parameter)
def __init__(self, in_channel, x):
super().__init__()
#self._save_graphs(save_graph_flag=True, save_graph_path=".")
self.biasadd = P.BiasAdd()
self.equal = P.Equal()
self.addn = P.AddN()
self.conv = Conv2d(in_channels=in_channel,
out_channels=in_channel,
kernel_size=1,
stride=1,
has_bias=False,
weight_init='ones',
pad_mode='same')
self.bn = BatchNorm2d(num_features=in_channel)
self.assignadd = P.AssignAdd()
self.assign = P.Assign()
self.relu = ReLU()
self.mean = P.ReduceMean(keep_dims=False)
self.bias = Parameter(Tensor(
np.random.randint(2, size=(3, )).astype((np.float32))),
name="bias")
self.bias2 = Parameter(Tensor(np.ones([3]).astype(np.float32)),
name="bias2")
self.parameterupdate = ParameterUpdate(self.bias)
self.value = Tensor(np.random.randn(*(3, )), ms.float32)
self.x = x
def test_parameter_update_float32():
""" test_parameter_update """
net = Net()
loss = nn.SoftmaxCrossEntropyWithLogits()
optimizer = Momentum(net.get_parameters(), 0.01, 0.001)
net_with_loss = WithLossCell(net, loss)
train_network = TrainOneStepCell(net_with_loss, optimizer)
# compile train graph
train_network.set_train()
inputs = Tensor(np.ones([1, 64]).astype(np.float32))
label = Tensor(np.zeros([1, 10]).astype(np.float32))
_executor.compile(train_network, inputs, label)
# construct and compile update graph
param_lr = train_network.parameters_dict()['learning_rate']
update_network = ParameterUpdate(param_lr)
update_network.phase = 'update_param'
input_lr = Tensor(0.0001, mstype.float32)
_executor.compile(update_network, input_lr)