diff --git a/paddleslim/core/registry.py b/paddleslim/core/registry.py
index b746e5089f4c37a9d059d2c05fc7be44fc92a957..8d222cf0199b9d98646b261ad6b79bf3819e525c 100644
--- a/paddleslim/core/registry.py
+++ b/paddleslim/core/registry.py
@@ -25,9 +25,6 @@ class Registry(object):
         return self._module_dict.get(key, None)
 
     def _register_module(self, module_class):
-        if not inspect.isclass(module_class):
-            raise TypeError('module must be a class, but receive {}.'.format(
-                type(module_class)))
         module_name = module_class.__name__
         if module_name in self._module_dict:
             raise KeyError('{} is already registered in {}.'.format(
diff --git a/paddleslim/prune/idx_selector.py b/paddleslim/prune/idx_selector.py
index b17348ea6f3e8866f0a9df50188703d023008668..58cf1111aad35bc5101a6ab5a670c7ef1235360f 100644
--- a/paddleslim/prune/idx_selector.py
+++ b/paddleslim/prune/idx_selector.py
@@ -52,12 +52,11 @@ def default_idx_selector(group, ratio):
        list: pruned indexes
 
     """
-    assert (isinstance(graph, GraphWrapper))
     name, axis, score = group[
         0]  # sort channels by the first convolution's score
     sorted_idx = score.argsort()
 
-    pruned_num = len(sorted_idx) * ratio
+    pruned_num = int(round(len(sorted_idx) * ratio))
     pruned_idx = sorted_idx[:pruned_num]
 
     idxs = []
@@ -94,7 +93,6 @@ def optimal_threshold(group, ratio):
        list: pruned indexes
 
     """
-    assert (isinstance(graph, GraphWrapper))
     name, axis, score = group[
         0]  # sort channels by the first convolution's score
 
diff --git a/paddleslim/prune/prune_walker.py b/paddleslim/prune/prune_walker.py
index 09bb547e6bf81fc16d45988b8a7831abeeaa1e92..6e85be11a3f53926b4d44ad89f641233367f4a4d 100644
--- a/paddleslim/prune/prune_walker.py
+++ b/paddleslim/prune/prune_walker.py
@@ -71,8 +71,11 @@ class PruneWorker(object):
         if visited is not None:
             self.visited = visited
         cls = PRUNE_WORKER.get(op.type())
-        assert cls is not None, "The walker of {} is not registered.".format(
-            op.type())
+        if cls is None:
+            _logger.warn(
+                "{} op will be pruned by default walker to keep the shapes of input and output being same because its walker is not registered.".
+                format(op.type()))
+            cls = PRUNE_WORKER.get("default_walker")
         _logger.debug("\nfrom: {}\nto: {}\npruned_axis: {}; var: {}".format(
             self.op, op, pruned_axis, var.name()))
         walker = cls(op,
@@ -236,6 +239,7 @@ class elementwise_mul(elementwise_op):
         super(elementwise_mul, self).__init__(op, pruned_params, visited)
 
 
+@PRUNE_WORKER.register
 class activation(PruneWorker):
     def __init__(self, op, pruned_params, visited):
         super(activation, self).__init__(op, pruned_params, visited)
@@ -256,6 +260,27 @@ class activation(PruneWorker):
             self._prune_op(op, out_var, pruned_axis, pruned_idx)
 
 
+@PRUNE_WORKER.register
+class default_walker(PruneWorker):
+    def __init__(self, op, pruned_params, visited):
+        super(default_walker, self).__init__(op, pruned_params, visited)
+
+    def _prune(self, var, pruned_axis, pruned_idx):
+        if var in self.op.all_outputs():
+            for in_var in self.op.inputs():
+                if len(in_var.shape()) == len(var.shape()):
+                    pre_ops = in_var.inputs()
+                    for op in pre_ops:
+                        self._prune_op(op, in_var, pruned_axis, pruned_idx)
+
+        for out_var in self.op.all_outputs():
+            if len(out_var.shape()) == len(var.shape()):
+                self._visit(out_var, pruned_axis)
+                next_ops = out_var.outputs()
+                for op in next_ops:
+                    self._prune_op(op, out_var, pruned_axis, pruned_idx)
+
+
 @PRUNE_WORKER.register
 class uniform_random_batch_size_like(activation):
     def __init__(self, op, pruned_params, visited):
diff --git a/paddleslim/prune/pruner.py b/paddleslim/prune/pruner.py
index 317c5a9c914075ad17df240ddf081259a3954872..8169c56ba30cb3ad7c604fce389ef34028a4ae7e 100644
--- a/paddleslim/prune/pruner.py
+++ b/paddleslim/prune/pruner.py
@@ -41,8 +41,6 @@ class Pruner():
     def __init__(self,
                  criterion="l1_norm",
                  idx_selector="default_idx_selector"):
-        self.criterion = criterion
-        self.channel_sortor = channel_sortor
         if isinstance(criterion, str):
             self.criterion = CRITERION.get(criterion)
         else:
@@ -98,7 +96,7 @@ class Pruner():
                 param_v = graph.var(param)
                 pruned_num = int(round(param_v.shape()[0] * ratio))
                 pruned_idx = [0] * pruned_num
-                for name, aixs in group:
+                for name, axis in group:
                     pruned_params.append((name, axis, pruned_idx))
 
             else:
@@ -109,10 +107,10 @@ class Pruner():
                     values = np.array(scope.find_var(name).get_tensor())
                     group_values.append((name, values, axis))
 
-                scores = self.criterion(group_with_values,
+                scores = self.criterion(group_values,
                                         graph)  # [(name, axis, score)]
 
-                pruned_params = self.idx_selector(scores)
+                pruned_params.extend(self.idx_selector(scores, ratio))
 
         merge_pruned_params = {}
         for param, pruned_axis, pruned_idx in pruned_params: