Pre Merge pull request !335 from ZhidanLiu/ut

tests/ut/python/privacy/sup_privacy/test_model_train.py

@@ -85,3 +85,51 @@ def test_suppress_model_with_pynative_mode():
 
     model_instance.train(epochs, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
                          dataset_sink_mode=False)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_card
+@pytest.mark.component_mindarmour
+def test_suppress_model_with_graph_mode():
+    """
+    Feature: suppress model
+    Description:graph mode
+    Expectation: none.
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    networks_l5 = LeNet5()
+    epochs = 5
+    batch_num = 10
+    mask_times = 10
+    lr = 0.01
+    masklayers_lenet5 = []
+    masklayers_lenet5.append(MaskLayerDes("conv1.weight", 0, False, False, -1))
+    suppress_ctrl_instance = SuppressPrivacyFactory().create(networks_l5,
+                                                             masklayers_lenet5,
+                                                             policy="local_train",
+                                                             end_epoch=epochs,
+                                                             batch_num=batch_num,
+                                                             start_epoch=1,
+                                                             mask_times=mask_times,
+                                                             lr=lr,
+                                                             sparse_end=0.50,
+                                                             sparse_start=0.0)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
+    net_opt = nn.SGD(networks_l5.trainable_params(), lr)
+    model_instance = SuppressModel(
+        network=networks_l5,
+        loss_fn=net_loss,
+        optimizer=net_opt,
+        metrics={"Accuracy": Accuracy()})
+    model_instance.link_suppress_ctrl(suppress_ctrl_instance)
+    suppress_masker = SuppressMasker(model=model_instance, suppress_ctrl=suppress_ctrl_instance)
+    config_ck = CheckpointConfig(save_checkpoint_steps=batch_num, keep_checkpoint_max=10)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
+                                 directory="./trained_ckpt_file/",
+                                 config=config_ck)
+    ds_train = ds.GeneratorDataset(dataset_generator, ['data', 'label'])
+
+    model_instance.train(epochs, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
+                         dataset_sink_mode=False)