diff --git a/paddleslim/prune/group_param.py b/paddleslim/prune/group_param.py
index 52075c9a47d34723d0f90b8c69b982a610aeb2f7..fa9690f35215fad1f54aa246e83af5119ad4c408 100644
--- a/paddleslim/prune/group_param.py
+++ b/paddleslim/prune/group_param.py
@@ -19,7 +19,7 @@ from .prune_walker import conv2d as conv2d_walker
 __all__ = ["collect_convs"]
 
 
-def collect_convs(params, graph):
+def collect_convs(params, graph, visited={}):
     """Collect convolution layers of graph into groups. The layers in the same group is relative on pruning operation.
     A group is a list of tuple with format (param_name, axis) in which `param_name` is the name of parameter and `axis` is the axis to be pruned on.
 
@@ -52,7 +52,6 @@ def collect_convs(params, graph):
         graph = GraphWrapper(graph)
     groups = []
     for param in params:
-        visited = {}
         pruned_params = []
         param = graph.var(param)
         conv_op = param.outputs()[0]
diff --git a/paddleslim/prune/pruner.py b/paddleslim/prune/pruner.py
index 8169c56ba30cb3ad7c604fce389ef34028a4ae7e..90ff075e23cf96e3e0b23e4728297641df44a3c6 100644
--- a/paddleslim/prune/pruner.py
+++ b/paddleslim/prune/pruner.py
@@ -90,7 +90,7 @@ class Pruner():
         visited = {}
         pruned_params = []
         for param, ratio in zip(params, ratios):
-            group = collect_convs([param], graph)[0]  # [(name, axis)]
+            group = collect_convs([param], graph, visited)[0]  # [(name, axis)]
             if only_graph and self.idx_selector.__name__ == "default_idx_selector":
 
                 param_v = graph.var(param)
@@ -126,8 +126,9 @@ class Pruner():
                     pruned_axis])
                 param = graph.var(param_name)
                 if not lazy:
-                    _logger.debug("{}\t{}\t{}".format(param.name(
-                    ), pruned_axis, len(pruned_idx)))
+                    _logger.debug("{}\t{}\t{}\t{}".format(
+                        param.name(), pruned_axis,
+                        param.shape()[pruned_axis], len(pruned_idx)))
                     if param_shape_backup is not None:
                         origin_shape = copy.deepcopy(param.shape())
                         param_shape_backup[param.name()] = origin_shape