add a construct of clip_mech and noise_mech, when noise_mech is adaptive clip_mech must be none

mindarmour/diff_privacy/train/model.py

@@ -143,7 +143,13 @@ class DPModel(Model):
                 LOGGER.error(TAG, msg)
                 raise ValueError(msg)
         self._noise_mech = noise_mech
-        if clip_mech is not None:
+        if noise_mech is not None:
+            if 'Ada' in noise_mech.__class__.__name__ and clip_mech is not None:
+                msg = 'When noise_mech is Adaptive, clip_mech must be None.'
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+
+        if clip_mech is None or isinstance(clip_mech, Cell):
             self._clip_mech = clip_mech
         super(DPModel, self).__init__(**kwargs)
 

tests/ut/python/diff_privacy/test_model_train.py

@@ -134,11 +134,7 @@ def test_dp_model_with_graph_mode_ada_gaussian():
     noise_mech = NoiseMechanismsFactory().create('AdaGaussian',
                                                  norm_bound=norm_clip,
                                                  initial_noise_multiplier=initial_noise_multiplier)
-    clip_mech = ClipMechanismsFactory().create('Gaussian',
-                                               decay_policy='Linear',
-                                               learning_rate=0.01,
-                                               target_unclipped_quantile=0.9,
-                                               fraction_stddev=0.01)
+    clip_mech = None
     net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.1,
                           momentum=0.9)
     model = DPModel(micro_batches=2,