Fix docs (#827)

docs/zh_cn/api_cn/dygraph/pruners/l1norm_filter_pruner.rst

@@ -79,7 +79,7 @@ L1NormFilterPruner
       from paddleslim import L1NormFilterPruner
       net = mobilenet_v1(pretrained=False) 
       pruner = L1NormFilterPruner(net, [1, 3, 224, 224])
-      plan = pruner.prun_var("conv2d_26.w_0", [0])
+      plan = pruner.prune_var("conv2d_26.w_0", [0])
       print(f"plan: {plan}")
       paddle.summary(net, (1, 3, 224, 224))
    ..
@@ -111,7 +111,7 @@ L1NormFilterPruner
       from paddleslim import L1NormFilterPruner
       net = mobilenet_v1(pretrained=False) 
       pruner = L1NormFilterPruner(net, [1, 3, 224, 224])
-      plan = pruner.prun_vars({"conv2d_26.w_0": 0.5}, [0])
+      plan = pruner.prune_vars({"conv2d_26.w_0": 0.5}, [0])
       print(f"plan: {plan}")
       paddle.summary(net, (1, 3, 224, 224))
    ..

docs/zh_cn/tutorials/pruning/dygraph/self_defined_filter_pruning.md

@@ -69,27 +69,41 @@ import numpy as np
 from paddleslim.dygraph import FilterPruner
 
 class L2NormFilterPruner(FilterPruner):
-
-    def __init__(self, model, input_shape, sen_file=None, opt=None):
+    def __init__(self, model, inputs, sen_file=None, opt=None):
         super(L2NormFilterPruner, self).__init__(
-            model, input_shape, sen_file=sen_file, opt=opt)
-
-    def cal_mask(self, var_name, pruned_ratio, group):
-        value = group[var_name]['value']
-        pruned_dims = group[var_name]['pruned_dims']
-        reduce_dims = [
-                    i for i in range(len(value.shape)) if i not in pruned_dims
-                ]
-
-        # scores = np.mean(np.abs(value), axis=tuple(reduce_dims))
+            model, inputs, sen_file=sen_file, opt=opt)
+
+    def cal_mask(self, pruned_ratio, collection):
+        var_name = collection.master_name
+        pruned_axis = collection.master_axis
+        value = collection.values[var_name]
+        groups = 1
+        for _detail in collection.all_pruning_details():
+            assert (isinstance(_detail.axis, int))
+            if _detail.axis == 1:
+                _groups = _detail.op.attr('groups')
+                if _groups is not None and _groups > 1:
+                    groups = _groups
+                    break
+
+        reduce_dims = [i for i in range(len(value.shape)) if i != pruned_axis]
         scores = np.sqrt(np.sum(np.square(value), axis=tuple(reduce_dims)))
+        if groups > 1:
+            scores = scores.reshape([groups, -1])
+            scores = np.mean(scores, axis=1)
+
         sorted_idx = scores.argsort()
         pruned_num = int(round(len(sorted_idx) * pruned_ratio))
         pruned_idx = sorted_idx[:pruned_num]
-        mask_shape = [value.shape[i] for i in pruned_dims]
+
+        mask_shape = [value.shape[pruned_axis]]
         mask = np.ones(mask_shape, dtype="int32")
+        if groups > 1:
+            mask = mask.reshape([groups, -1])
         mask[pruned_idx] = 0
-        return mask
+        return mask.reshape(mask_shape)
+
+
 ```
 
 如上述代码所示，我们重载了`FilterPruner`基类的`cal_mask`方法，并在`L1NormFilterPruner`代码基础上，修改了计算通道重要性的语句，将其修改为了计算L2Norm的逻辑：
@@ -147,15 +161,22 @@ import numpy as np
 from paddleslim.dygraph import FilterPruner
 
 class FPGMFilterPruner(FilterPruner):
-
-    def __init__(self, model, input_shape, sen_file=None, opt=None):
+    def __init__(self, model, inputs, sen_file=None, opt=None):
         super(FPGMFilterPruner, self).__init__(
-            model, input_shape, sen_file=sen_file, opt=opt)
-
-    def cal_mask(self, var_name, pruned_ratio, group):
-        value = group[var_name]['value']
-        pruned_dims = group[var_name]['pruned_dims']
-        assert(pruned_dims == [0])
+            model, inputs, sen_file=sen_file, opt=opt)
+
+    def cal_mask(self, pruned_ratio, collection):
+        var_name = collection.master_name
+        pruned_axis = collection.master_axis
+        value = collection.values[var_name]
+        groups = 1
+        for _detail in collection.all_pruning_details():
+            assert (isinstance(_detail.axis, int))
+            if _detail.axis == 1:
+                _groups = _detail.op.attr('groups')
+                if _groups is not None and _groups > 1:
+                    groups = _groups
+                    break
 
         dist_sum_list = []
         for out_i in range(value.shape[0]):
@@ -163,13 +184,19 @@ class FPGMFilterPruner(FilterPruner):
             dist_sum_list.append(dist_sum)
         scores = np.array(dist_sum_list)
 
+        if groups > 1:
+            scores = scores.reshape([groups, -1])
+            scores = np.mean(scores, axis=1)
+
         sorted_idx = scores.argsort()
         pruned_num = int(round(len(sorted_idx) * pruned_ratio))
         pruned_idx = sorted_idx[:pruned_num]
-        mask_shape = [value.shape[i] for i in pruned_dims]
+        mask_shape = [value.shape[pruned_axis]]
         mask = np.ones(mask_shape, dtype="int32")
+        if groups > 1:
+            mask = mask.reshape([groups, -1])
         mask[pruned_idx] = 0
-        return mask
+        return mask.reshape(mask_shape)
 
     def get_distance_sum(self, value, out_idx):
         w = value.view()
@@ -210,6 +237,7 @@ optimizer = paddle.optimizer.Momentum(
 
 inputs = [Input([None, 3, 32, 32], 'float32', name='image')]
 labels = [Input([None, 1], 'int64', name='label')]
+net = mobilenet_v1(pretrained=False)
 model = paddle.Model(net, inputs, labels)
 model.prepare(
         optimizer,