Fix sensitive API. (#30)

paddleslim/prune/pruner.py

@@ -81,7 +81,8 @@ class Pruner():
                 pruned_idx = self._cal_pruned_idx(param_t, ratio, axis=0)
             param = graph.var(param)
             conv_op = param.outputs()[0]
-            walker = conv2d_walker(conv_op,pruned_params=pruned_params, visited=visited)
+            walker = conv2d_walker(
+                conv_op, pruned_params=pruned_params, visited=visited)
             walker.prune(param, pruned_axis=0, pruned_idx=pruned_idx)
 
         merge_pruned_params = {}
@@ -94,9 +95,12 @@ class Pruner():
 
         for param_name in merge_pruned_params:
             for pruned_axis in merge_pruned_params[param_name]:
-                pruned_idx = np.concatenate(merge_pruned_params[param_name][pruned_axis])
+                pruned_idx = np.concatenate(merge_pruned_params[param_name][
+                    pruned_axis])
                 param = graph.var(param_name)
-                _logger.debug("{}\t{}\t{}".format(param.name(), pruned_axis, len(pruned_idx)))
+                if not lazy:
+                    _logger.debug("{}\t{}\t{}".format(param.name(
+                    ), 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
@@ -105,8 +109,10 @@ class Pruner():
                     param.set_shape(new_shape)
                 if not only_graph:
                     param_t = scope.find_var(param.name()).get_tensor()
-                    if param_backup is not None and (param.name() not in param_backup):
-                         param_backup[param.name()] = copy.deepcopy(np.array(param_t))
+                    if param_backup is not None and (
+                            param.name() not in param_backup):
+                        param_backup[param.name()] = copy.deepcopy(
+                            np.array(param_t))
                     try:
                         pruned_param = self._prune_tensor(
                             np.array(param_t),
@@ -114,11 +120,11 @@ class Pruner():
                             pruned_axis=pruned_axis,
                             lazy=lazy)
                     except IndexError as e:
-                        _logger.error("Pruning {}, but get [{}]".format(param.name(
-                        ), e))
+                        _logger.error("Pruning {}, but get [{}]".format(
+                            param.name(), e))
 
                     param_t.set(pruned_param, place)
-    
+        graph.update_groups_of_conv()
         return graph.program, param_backup, param_shape_backup
 
     def _cal_pruned_idx(self, param, ratio, axis):