Merge branch 'develop' into pact_clip

paddleslim/core/graph_wrapper.py

@@ -166,9 +166,7 @@ class OpWrapper(object):
         """
         Get all the varibales by the output name.
         """
-        return [
-            self._graph.var(var_name) for var_name in self._op.output(name)
-        ]
+        return [self._graph.var(var_name) for var_name in self._op.output(name)]
 
     def set_attr(self, key, value):
         """
@@ -354,16 +352,6 @@ class GraphWrapper(object):
             ret += np.product(param.shape())
         return ret
 
-    def update_param_shape(self, scope):
-        """
-        Update the shape of parameters in the graph according to tensors in scope.
-        It is used after loading pruned parameters from file.
-        """
-        for param in self.all_parameters():
-            tensor_shape = np.array(
-                scope.find_var(param.name()).get_tensor()).shape
-            param.set_shape(tensor_shape)
-
     def infer_shape(self):
         """
         Update the groups of convolution layer according to current filters.
@@ -375,6 +363,6 @@ class GraphWrapper(object):
 
     def update_groups_of_conv(self):
         for op in self.ops():
-            if op.type() == 'depthwise_conv2d' or op.type(
-            ) == 'depthwise_conv2d_grad':
+            if 'conv2d' in op.type() and op.attr('groups') >= op.inputs(
+                    'Filter')[0].shape()[0]:
                 op.set_attr('groups', op.inputs('Filter')[0].shape()[0])

paddleslim/prune/group_param.py

@@ -58,8 +58,7 @@ def collect_convs(params, graph, visited={}):
         walker = conv2d_walker(
             conv_op, pruned_params=pruned_params, visited=visited)
         walker.prune(param, pruned_axis=0, pruned_idx=[0])
-        if len(pruned_params) > 0:
-            groups.append(pruned_params)
+        groups.append(pruned_params)
     visited = set()
     uniq_groups = []
     for group in groups:

tests/test_group_param.py

@@ -42,6 +42,8 @@ class TestPrune(unittest.TestCase):
             conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
         groups = collect_convs(
             ["conv1_weights", "conv2_weights", "conv3_weights"], main_program)
+        while [] in groups:
+            groups.remove([])
         self.assertTrue(len(groups) == 2)
         self.assertTrue(len(groups[0]) == 18)
         self.assertTrue(len(groups[1]) == 6)