diff --git a/mmrazor/models/algorithms/quantization/mm_architecture.py b/mmrazor/models/algorithms/quantization/mm_architecture.py
index c14aae08c..33cd84b1d 100644
--- a/mmrazor/models/algorithms/quantization/mm_architecture.py
+++ b/mmrazor/models/algorithms/quantization/mm_architecture.py
@@ -7,8 +7,9 @@
 from mmengine.runner import load_checkpoint
 from mmengine.structures import BaseDataElement
 from torch import nn
-from torch.ao.quantization import FakeQuantizeBase
 
+from mmrazor.models.fake_quants import BaseFakeQuantize
+from mmrazor.models.observers import BaseObserver
 from mmrazor.models.task_modules import build_graphmodule
 from mmrazor.registry import MODEL_WRAPPERS, MODELS
 from ..base import BaseAlgorithm
@@ -62,8 +63,14 @@ def __init__(self,
         self.forward_modes = forward_modes
 
         self.qmodels = self._build_qmodels(self.architecture)
-
         self.sync_qparams('predict')
+        self.reset_min_max_vals(self.qmodels)
+
+    def reset_min_max_vals(self, model):
+        for module in model.modules():
+            if isinstance(module, BaseObserver):
+                assert hasattr(module, 'reset_min_max_vals')
+                module.reset_min_max_vals()
 
     def sync_qparams(self, src_mode):
 
@@ -72,7 +79,7 @@ def traverse(module, prefix):
                 if module is None:
                     continue
                 child_name = f'{prefix}{name}'
-                if isinstance(child, FakeQuantizeBase):
+                if isinstance(child, BaseFakeQuantize):
                     for name, param in child.named_parameters():
                         param_name = f'{child_name}.{name}'
                         src_param = src_state_dict[param_name]
@@ -114,10 +121,15 @@ def _build_qmodels(self, model):
             graph_mopdule = build_graphmodule(model, traced_graph)
             observed_module = self.quantizer.prepare(model, graph_mopdule)
             qmodels[mode] = observed_module
-        # import pdb
-        # pdb.set_trace()
-        # dummy_input = torch.randn(self.input_shapes)
-        # qmodels['predict'](dummy_input, None, 'predict')
+
+        is_training = qmodels['predict'].training
+        # Avoid random input changing bn's statistics
+        qmodels['predict'].eval()
+        # Input a random tensor to modify the shape of scale and zero_point
+        # in fakequant in per-channel mode.
+        dummy_input = torch.randn(self.input_shapes)
+        qmodels['predict'](dummy_input, None, 'predict')
+        qmodels['predict'].train(mode=is_training)
 
         return qmodels